Endemic Species Distribution

Endemic species distributions

on the east slope of the Andes

in Peru and Bolivia

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ENDEMIC SPECIES DISTRIBUTIONS on the east slope of the Andes in Peru and Bolivia

This publication has been financed by The Gordon and Betty Moore

Foundationwww.moore.org

Published by

Edited by Bruce E. Young

Endemic species distributions on the east slope of the Andes in Peru and Bolivia

Colección Boliviana de Fauna

Museo Nacional de Historia Natural

Bolivia

Museo de Historia Natural

Universidad Mayor de San Marcos

Cover Photo:© J.P. O’Neil/VIREO (Iridosornis jelskii)

Back Cover Photo:NatureServe (Centropogon sp.)

Editorial CoordinationCristiane Nascimento

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PrinterGráfica Biblos

© NatureServe 2007ISBN: 0-9711053-6-7

Total or partial use of text permitted with proper citation. Recommended citation:Young, B. E. 2007. Endemic species distributions on the east slope of the Andes in Peru and Bolivia. NatureServe, Arlington, Virginia, USA.

Recommended citation for an individual chapter:Beck, S. G., P. A. Hernandez, P. M. Jørgensen, L. Paniagua, M. E. Timaná, and B. E. Young. 2007. Vascular plants. Pp. 18-34 in B. E. Young (editor), Endemic species distributions on the east slope of the Andes in Peru and Bolivia. NatureServe, Arlington, Virginia, USA.

This publication was financed by

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I. Summary

II. Introduction (Bruce E. Young)

III. Study Area (Bruce E. Young)

IV. Distribution Modeling Methods (Pilar A. Hernandez)

V. Vascular Plants (Stephan G. Beck, Pilar A. Hernandez, Peter M. Jørgensen, Lily Paniagua, Martín E. Timaná, and Bruce E. Young)

VI. Amphibians (César Aguilar, Lourdes Arangüena, Jesús H. Córdova, Dirk Embert, Pilar A. Hernandez, Lily Paniagua, Carolina Tovar, and Bruce E. Young)

VII. Mammals (Víctor Pacheco, Heidi L. Quintana, Pilar A. Hernandez, Lily Paniagua, Julieta Vargas, and Bruce E. Young)

VIII. Birds (Irma Franke, Pilar A. Hernandez, Sebastian K. Herzog, Lily Paniagua, Aldo Soto, Carolina Tovar, Thomas Valqui, and Bruce E. Young)

IX. Synthesis (Pilar A. Hernandez and Bruce E. Young)

X. Using the Data

XI. Acknowledgements

XII. Author Addresses

XIII. Literature Cited

Appendix 1. Sources of locality dataAppendix 2. List of focal species included in the studyAppendix 3. Reviewers of locality data and draft distribution maps

Table of Contents

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I. Summary

To provide comprehensive input to conservation planning, we mapped the distributions of endemic plants and animals in a study area encompassing roughly the Amazonian slope below tree line in Peru and Bolivia. We used Maxent as an inductive method of predictive distribution modeling where possible and deductive methods of the remaining species. These distribution models facilitated the prediction of distributions even in areas where field surveys have not taken place, avoiding to some extent a bias caused by the uneven distribution of collecting effort. The environmental data that formed the base of the models included climate variables, elevation and topographical data, and vegetation indices derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. Data from over 7,150 unique localities contributed to producing distribution maps for 782 species endemic to the study area. The species included all members of 15 vascular plant families or genera and all amphibians, mammals, and birds endemic to the study area. We selected the plant groups based on the evenness and currency of taxonomic understanding and the diversity of life forms, elevation, and habitats represented.

The results show distinct areas of endemism for each of the eighteen taxonomic groups studied. Three plant groups (Chrysobalanaceae, Inga, and Malpighiaceae) showed endemism in the lowlands, amphibians and Acanthaceae showed peaks of endemism at mid elevations, and birds, mammals, and nine plant groups had endemism peaks at high elevations above 2,000 m. Two plant families, Anacardiaceae and Cyatheaceae, did not have significant overlap of endemic species. The plant groups varied significantly in the geographical location of endemic peaks from the northern to the southern limits of the study area. Amphibians showed a major diversity peak in central Cochabamba Department, Bolivia. However, summed irreplaceability analysis revealed the existence of equally important areas in Amazonas and San Martín Departments in northern Peru where large numbers of microendemic species occurred. Richness of endemic species of mammals was highest in long band at high-elevations in the Andes from Cusco, Peru, to Cochabamba, Bolivia. Summed irreplaceability analysis also highlighted the importance of the region of the La Libertad-San Martín departmental border in the

Cordillera Central as being important for narrow-ranging endemics. Bird endemism peaked in six areas ranging from the Carpish Hills region of Huanuco Department, Peru, to the Cordillera de Cocapata-Tiraque in Cochabamba, Bolivia. Although birds have been the subject of numerous previous analyses of endemism in the Andes, our predictive modeling methods identified two previously unrecognized areas—the western Cordillera de Vilcabamba and the region along the Río Mapacho-Yavero east of Cusco, both in Peru. Although these two localities have been poorly explored ornithologically, the models predicted that many endemic species occur in both places.

Taken together, the target taxonomic groups displayed 12 areas of endemism where at least one group exhibited a peak. The cordilleras near La Paz, Bolivia, had the greatest cross-group endemism. Eight plant groups as well as birds and mammals all have concentrations of endemic species there. National protected areas covered at least portions of nine of the 12 areas of endemism. Nevertheless, large segments of the areas of endemism identified in our analysis are currently unprotected at the national level.

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Importance of Endemics to ConservationMore than ever before, conservationists use data on the geography of biodiversity to set priorities for locating protected areas (Brooks et al. 2006). Key input to these analyses includes data on endangered and endemic species. By definition, endangered species require action or they will be lost forever. Endemic species also require attention because of their often limited distributions and consequent susceptibility to endangerment. If their habitat needs are not fulfilled where they occur, they will decline and disappear. To help prevent biodiversity loss, we therefore must protect the habitats of both endangered and endemic species.

The existence of endemism in many parts of the world, especially the montane tropics, is a beguiling factor for conservationists. In these regions, even a strategy of creating large reserves protecting entire ecosystems may not sufficiently protect all endemics because some of these species may be restricted to mountaintops or valleys that lie between the large reserves. The endemic amphibians in Mexico are a good example. Although Mexico has numerous large biosphere reserves, a recent analysis showed that just 33% of the threatened amphibian species in that country occur in at least one protected area (Young et al. 2004). A focus on large reserves is critical to maintaining functioning ecosystems, but in areas with many endemic species, conservationists need to consider additional measures to ensure protection of all elements of biodiversity.

Recognizing the importance of endemism to conservation, a number of conservationists has analyzed distributions of endemic species to provide guidance on where a small investment in conservation can yield important results in terms of numbers of species saved from extinction. For example, Myers (1988, 1990) examined endemic plant species worldwide and showed that protecting 746,400 km2, an area representing 0.5% of the Earth’s land surface, in 18 sites worldwide would conserve 50,000 species of endemic plants (20% of all known plant species). This study introduced the ‘hotspots’ concept that is still in use today as a guiding principle to conservation (Mittermeier et al. 2000, Mittermeier et al. 2005, but see Ceballos and Ehrlich 2006). A similar analysis on birds

has focused attention on the places where the world’s endemic birds need protection (Stattersfield et al. 1998). A comprehensive conservation strategy for a region requires these sorts of analyses for a wide range of taxa, ideally including groups that have different habitat affinities (Young et al. 2002). This report describes just such a study.

Definitions of EndemismAlthough most people feel they have an intuitive sense of the definition of the term endemic, historical confusion over its application in conservation biology suggests that a brief discussion of the word is useful (Anderson 1994). An endemic species is one that is restricted to a particular geographic area. The geographical area can be defined by political boundaries, such as country or department endemics, or by ecological boundaries such as a species endemic to Polylepis forest. Geographical features also serve as points of reference, so a species can be endemic to South America or to Isabela Island in the Galapagos. Context is important when discussing endemic species because of the ability of the concept to expand and contract. Simply calling a species “endemic” therefore does not prove to be very illuminating. Endemic to what?

A related concept is that of a range-restricted species, or one with a small range. The author must define a threshold range size, below which a species is considered to be range restricted. BirdLife, for example, assigns a cut-off of 50,000 km2 to define range restricted birds (Stattersfeld et al. 1998). Endemic species are therefore not necessarily the same as range-restricted species, although there can be considerable overlap. For example, the Dark-winged Trumpeter (Psophia viridis) is endemic to Brazil south of the main trunk of the Amazon River. But with an estimated range size of 1.4 million km2 (calculated from Ridgely et al. 2005), this bird would not generally qualify as a range-restricted species. A converse example is the tree frog Duellmanohyla lythrodes. Its range covers just 1,340 km2 in southern Central America (IUCN et al. 2006), but includes parts of two countries. Of course, many species are both national or ecoregion endemics and have restricted ranges. The point is that the terms are not synonymous.

II. IntroductionBy Bruce E. Young

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In this report, we treat species restricted to our study area on the east slope of the Andes in Peru and Bolivia as endemic species (see Study Area below for a full description). Although many species have restricted ranges using almost any realistic threshold, others are widely distributed along 1,500 km of the Andean cordillera within this area.

By combining information about the distributions of many endemic species from a taxonomic group, we can identify areas of endemism. Traditionally, ecologists have overlayed maps of endemic species and called the areas where many ranges overlap areas of endemism (e.g., Cracraft 1985, Ridgely and Tudor 1989). This is essentially the approach we follow here, although we explore different ways to overlap ranges and weight differently species with larger or smaller ranges. Recent research in biogeography has taken advantage of the increasing availability of phylogenetic trees to combine geographical analyses of distributions with evolutionary relationships among species to develop hypotheses about the centers of origins of groups of species (e.g., Barker et al. 2004). Because of a lack of phylogenetic information about many of the species in our study, we do not address origins of species groups other than to comment on hypotheses in the literature.

Modeling DistributionsConserving species first requires knowing where they live. For hundreds of years biologists have conducted field inventories to map the distribution of plants and animals. Yet our understanding of the distribution of most species, especially in remote regions, is still incomplete. Field work can be time-intensive, costly, and even hazardous. Good inventories tell us where particular species have been found, but not where else they are likely to occur.

By combining reliable locational data with technological and analytical tools, however, we can learn more about species distributions. The development of high-speed computers and geographic mapping software now allows us to model the distribution of a particular species by analyzing the environmental characteristics of its known localities (Guisan and Zimmermann 2000, Elith and Burgman 2003, Guisan and Thuiller 2005). These mathematically defined models can then be combined with known constraints based on the species’ life history to predict where else on the landscape the species might occur. A variety of environmental data are used as the basis for these mathematical models, some of which have only recently become widely available. These include digital elevation models (and other descriptions of topography such as terrain, slope, and aspect that can

be derived from these data), current vegetation cover based on analysis of satellite imagery, and digital data layers providing estimates of precipitation, temperature, and other climatic conditions.

Species distribution models generated in this quantitative fashion are much more detailed than the familiar polygon depictions of species’ ranges found in field guides. Another benefit is that they control somewhat for the bias that most collectors work near cities or along roads and rivers (c.f. Nelson et al. 1990). If one simply examined localities where a particular plant has been collected, you might believe that it is restricted to roadsides (where collectors have easy access). Species distribution models identify remote natural areas where a species is likely to occur because of shared characteristics with sites where collectors have worked. Through the use of these models, we hope to improve our knowledge of the distributions of plant and animal species endemic to our study area. Analyses of these data help pinpoint areas of endemism for different kinds of organisms as well as identify concentrations of endemic species that occur outside of the existing protected areas system.

Study Objectives and Significance We carried out this study as part of a larger project aimed at filling knowledge gaps in support of conservation planning on the east slope of the Andes in Peru and Bolivia. Although conservation priority-setting exercises have taken place in the region for some time, the lack of comprehensive information on species distribution has led to reliance on vegetation maps and expert opinion rather than quantitative analysis of distribution patterns of varying taxonomic groups (Rodríguez and Young 2000, Ibisch and Mérida. 2004, Müller et al. 2004). By incorporating data we provide here, future priority-setting will be better informed and provide even more useful details about where conservation action is most needed.

The goals of this study are three-fold:• To produce, in a digital format, accurate distribution

maps of the birds, mammals, amphibians, and plants endemic to the east slope of the central Andes for use in conservation planning,

• to identify concentrations of endemic species in the study area, and

• to make the distributional information widely available to academic scientists, conservationists, and government planners.

We chose the three vertebrate classes because these were the only ones with comprehensive taxonomic

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and digital distribution information available to allow for an efficient selection of endemic species (Patterson et al. 2005, Ridgely et al. 2005, IUCN et al. 2006). Limiting the analysis to these three classes still allows us to cover terrestrial (birds and mammals) and aquatic (amphibians) habitats. Because the diversity of vascular plants is high in the study area, we limited our analysis to 12 families and three genera of plants that represent the diversity of taxa containing endemic species. For the focal vertebrate groups, we examined all species from these families that are endemic to the study area.

This study is one of the first extensive uses of predictive distribution modeling techniques for conservation planning purposes in South America. Although many studies have modeled species distributions to aid in their conservation in various parts of the world (Chen and Peterson 2002, Engler et al. 2004, Loiselle et al. 2003, Raxworthy et al. 2003), most have not produced fine-scale maps for multiple species that can be used individually and/or collectively for regional conservation. Building on previous research refining modeling algorithms and comparing model performance under different conditions (Elith et al. 2006, Hernandez et al. 2006, and Phillips et al. 2006), we were able to model the ranges of over 700 species.

Running these models required the compilation of a data set of over 6,400 museum and observation records of the target species. We acquired these data by contacting curators at 61 herbaria and 19 natural history museums (Appendix 1). We then geo-referenced records that did not come with geographical coordinates and subjected the data to extensive review to ensure a high level of accuracy of the data that entered the models. This effort therefore represents a substantial collaboration between the conservation and museum communities, both of which are benefiting from the resulting data and analyses.

In addition to using standard elevation and climate data, we incorporated Moderate Resolution Imaging Spectroradiometer (MODIS) data into the models. These data depict current vegetation cover and thus help ensure that predictions show where a species is likely to occur today, taking into account recent deforestation. We also employ novel techniques to accommodate models that combine high precision satellite imagery with lower precision locality data, some of which are derived from collections made 50-100 years ago.

The resulting range maps provide much greater spatial resolution than those available previously. Previous compilations of distributions of South American

vertebrates (e.g., Ridgely et al. 2005, Patterson et al. 2005, IUCN et al. 2006) are based on polygons drawn, often by eye, on maps with scales often in excess of 1:1,000,000. While these polygon maps represent a tremendous advance and provide the basis for important global and hemispheric analyses (Rodrigues et al. 2004, Stuart et al. 2004, Young et al. 2004, Orme et al. 2005, Brooks et al. 2006), they nevertheless do not provide the precision necessary for regional conservation within large countries. The refined distribution maps produced by this study, and the analyses based on them, provide heretofore unavailable fine-scale input into conservation planning at this level.

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Geographical LimitsThis study focuses on the Yungas and associated ecosystems downslope to and including the Amazonian lowlands in the central Andes of Peru and northern Bolivia (Figure 1). The Yungas represent a belt of humid tropical montane forest that occurs along most of the eastern slope of the Andes from Peru to Argentina. In Peru and Bolivia, the Yungas range from about 500-1,200 m to 2,800-3,500 m above sea level. Above the Yungas is a grassland ecosystem called puna, which varies from wet in the north (where it transitions to the even wetter páramo in Ecuador and further north) to dry in the south. The study area encompasses all of the Yungas in Peru and Bolivia, humid lowland forest downslope of the Yungas, and the Beni savannahs in northern Bolivia. The northern and eastern limits of the study area are delimited by the national boundaries of these countries. The southern limit of the study area is set at the division between the northern and southern Yungas. The southern Yungas are strongly influenced by cold fronts sweeping north from Patagonia; the central Yungas, much less so. The zone of transition between these two regions corresponds roughly with where the main cordillera of the Andes “bends” from a northwest-southeast orientation to a north-south direction in Bolivia. We exclude the northern dry forests of the Maranon River valley in Peru because they represent the edge of a phytogeographic area that extends north and west outside of our area of focus into Ecuador. Overall, the study area extends from 5o 23’ to 18o 15’ S latitude and from 60o 23’ to 79o 26’ W longitude, covering 1,249,282 km2.

Major Ecoregions and HabitatsThe study area extends across all or portions of seven ecoregions as defined by Olson et al. (2001): Peruvian Yungas, Bolivian Yungas, Napo Moist Forests, Ucayali Moist Forests, Southwest Amazon Moist Forests, Beni Savanna, and Iquitos Varzeá (Figure 2). The two Yungas ecoregions have similar environmental conditions and forest structure but differ somewhat in species composition. The three Amazonian moist forest ecoregions also share physical characteristics but are differentiated based on species groups that are unique to each. The following is a description of these four major kinds of ecoregions, based mostly

on the information in Olson et al. (2001) and at the World Wildlife Fund-US ecoregion website (http://worldwildlife.org/science/ecoregions.cfm). Details on further finer scale ecological systems within these ecoregions is available elsewhere (Josse et al. 2007).

Yungas. The two Yungas ecoregions stretch along the eastern slope of the Andes covering the montane forests that occur above lowland forests and below the treeless páramo and puna habitats. The climate for this topographically complex area ranges from dry (500 mm of annual precipitation) in a few valleys to wet (up to 6,000 mm) across most of the area (Killeen et al. 2007). The Yungas ecoregions include cloud forests, where a significant portion of the annual precipitation comes in the form of wind-blown mist that intersects the vegetation and drips to the ground, causing constant conditions of high humidity. Cloud forests occur at different elevational bands depending on local topographical features and the prevailing winds. Temperatures in the Yungas range from 6-12o C in the north to 8-22o C in the south. Except for occasional dry valleys where many trees are deciduous, most of the trees are evergreen. Forest height decreases with increasing elevation and, especially in cloud forests, trees are characteristically covered with mosses and other epiphytic plants. Tree diversity is highest at lower elevations, and decreases as elevation rises. Bamboo (Chusquea spp.) and tree ferns (Cyathea spp.) are conspicuous at higher elevations.

The upper limit of the Yungas ecoregions is often made up of a forest dominated by short trees in the genus Polylepis (Rosaceae). Polylepis forests can also be found in isolated patches surrounded by puna far higher than the current treeline. This pattern may be the result of millennia of anthropogenic disturbances, such as fires and overgrazing, that have converted much original Polylepis cover into puna grasslands (Ellenberg 1979).

Amazonian Moist Forests. Amazonian moist forests occur below the Yungas from northern Bolivia through the northern limit of the study area. The topography is relatively flat with a gentle slope from the lower limits of Yungas forest down to 100 m elevation in the east.

III. Study AreaBy Bruce E. Young

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Annual precipitation is heavy in the north, ranging from 2,500-3,000 mm in the east to 4,000 mm at the base of the Andean foothills, and moderate in the south (1,500-2,100 mm). Temperatures are generally hot except during the cooler parts of the year in the more seasonal southern sections. Monthly mean temperatures range from 12 to 38o C. The forests are evergreen and characterized by high diversity and high crowns (canopies reaching 40 m high, with even higher emergents). The region includes terra firme forests

above flood levels, some várzea forests subject to seasonal or permanent flooding by whitewater rivers, and igapó forests subject to seasonal or permanent flooding by blackwater rivers. Small segments of várzea forest occur along rivers throughout this ecoregion. A large expanse of várzea in the northeastern portion of the study area is classified as a distinct ecoregion, Iquitos Várzea. Although the diversity of Amazonian moist forests is generally among the highest of any forest type in the world, monodominant stands develop

Figure 1. Map of the study area showing major political and geographic features.

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in some areas where either large bamboo (Gadua spp.) or palms (Mauritia flexuosa or Jessenia bataua) succeed in suppressing growth of other species.

A distinctive feature of some sections of the lower portion of the study area is the white-sand forests that occur on poor soils. These forests are distinctive by their lower tree heights, lower density of trees, and sparser understory. The white-sand forests in our study area are similar floristically and faunistically to the extensive white-sand forests in the Guianan Shield (Stotz et al. 1996). Whereas most parts of the Amazonian moist forests host species with wide distributions in the Amazon Basin, the white-sand forests contain a number of species, some still being discovered, that are restricted to these habitats (Alvarez and Whitney 2003, Whitney et al. 2004).

Iquitos Várzea. The Iquitos Várzea encompasses a large area of forests subject to seasonal flooding from whitewater rivers. The region is centered on the confluence of the rivers Ucayali and Maranon near the Peruvian city of Iquitos at elevations from 75-150 m. The seasonal deposition of sediments causes the soils to have higher nutrient contents than is typical in the Amazonian lowlands. Precipitation varies from 2,400-3,000 mm annually, and temperatures average 26o C with little seasonal variation. Várzea forests typically exhibit a similar stature as terra firme forests but have

lower tree diversity. Mature trees often have extensive buttresses and support large epiphyte communities (Prance 1989). Because of the continually changing courses of the rivers that abound in this ecoregion, the forests are often in a stage of succession.

Beni Savanna. In contrast to most of the rest of the study area, the Beni Savanna is a grassland consisting of seasonal savannas and wetlands. Forests develop only along rivers and in isolated pockets. The Beni Savanna occurs in northern Bolivia and the Pampas del Heath region of southern Peru on a large plain that ranges from 130-235 m in elevation. Precipitation varies from 2,000 mm in the west to 1,300 mm in the east. Seasonal flooding of vast areas is caused more by runoff from the Andes overflowing river banks than by local rainfall. The annual mean temperature is about 25o C, although daily highs can reach the upper 30s. The vegetation is dominated by sedges and grasses. Forest islands can form wherever the soil becomes elevated due to human activity, animal activity (e.g., termites), or natural events (e.g., river bank formation). Dispersal of tree species by mobile vertebrates explains how forests become established far from seed sources.

Significance for ConservationBy almost any measure, the study area harbors some of the most important biodiversity of anywhere on Earth. Whether measured by species richness or the density of endemics, the study area warrants significant attention by conservationists. For example, the study area includes three of the World Wildlife Fund’s “Global 200” ecoregions suggested to be the most important for preserving representative examples of the Earth’s biodiversity (Olson and Dinerstein 2002). The Yungas portion of the study area lies within the Tropical Andes hotspot, one of the 34 highest-priority regions proposed by Mittermeier et al. (2005) for global biodiversity conservation. This same area has been identified has having the highest level of terrestrial vertebrate endemism anywhere (Lamoreux et al. 2005).

Examining data for individual taxa helps explain why this region has global importance for conservation. The greatest diversity of amphibians occurs in the upper Amazonian forests of the study area (Young et al. 2004). A global analysis of bird distributions highlighted the study area as being among the richest in the world for diversity and endemism (Orme et al. 2005). The Peruvian portion of the distribution also scores as among the highest in the world for mammal diversity (Ceballos and Ehrlich 2006). For vascular plants, the pattern is the same. The tropical Andes, which include the Yungas portion of the study area,

Figure 2. Map of the study area showing the locations of the seven WWF ecoregions.

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is one of only five areas of the world that attain a species richness of more than 5,000 species per 10,000 km2 (Barthlott et al. 2005, Mutke and Barthlott 2005). Comprehensive, global-scale analyses have not yet been performed for terrestrial invertebrate groups, but the plant and vertebrate data suggest that the study area will also prove to support significant levels of invertebrate diversity as well.

Significant portions of the region remain relatively undisturbed and some are legally protected in large national parks. However, large areas have already been deforested and others are threatened by a number of factors. Expanding agricultural frontiers for cash crops such as citrus and coffee as well as subsistence farming continue to degrade habitats. The lower Yungas are cleared for coca (Erythroxylum coca and Erythroxylum novogranatense) plantations for both local consumption and the international drug trade, and higher elevation forests are increasingly cleared for marijuana (Cannabis sativa) and opium (Papaver somniferum) production (Fjeldså et al. 2005). Major international infrastructure projects, such as gas pipelines and transportation networks promoted by the Initiative for the Infrastructure Integration of South America (IIRSA, initials from Spanish) and others also result in ongoing habitat degradation and provide corridors for colonist expansion.

In Peru, 38% of the Yungas in San Martín and Amazonas Departments, 25% of the Yungas in Pasco and Junín Departments, and 15% of Cusco Department have already been deforested (CDC-UNALM and TNC 2006). The rate of deforestation has accelerated in eastern Bolivia to ~2,900 km2 yr-1, with the rate on the rise even within putative protected areas (Killeen et al. 2007). These threats underscore the urgency for regional conservation planning and implementing effective strategies to preserve this tremendous wealth of biodiversity into the future.

Identifying Species that are Endemicto the Study AreaWe defined the focal species for this study as those that are endemic to our study area. Because ecological boundaries are rarely as sharp as depicted on an ecoregion map, we maintained some flexibility in our criteria for inclusion. For example, species associated with montane closed-canopy forest may occur in isolated Polylepis woodlands substantial distances from the currently recognized treeline and therefore outside of a strict definition of our study area. Many of these species are otherwise restricted to the study area. Similarly, we wanted to avoid excluding species

with small ranges in the Amazon lowlands within the study area that have one or two records from adjacent (and ecologically identical) areas in Brazil or Ecuador. Because of the ecological affinity the species have with the ecoregions included in the study, we devised a set of criteria that would allow inclusion of these species.

The criteria were:1. All species from the focal groups (birds, mammals, amphibians, and 12 families plus three genera of plants) with ranges entirely within our study area buffered by 100 km in all directions. 2. From the resulting list of species, we eliminated all of those that were restricted to the buffer area and therefore did not occur in the study area at all.

3. For the species occurring in both the buffer area and the study area, we eliminated all of those that were restricted to habitat types such as puna that did not occur in substantial amounts within the study area. Additionally, for species of humid forests on the northern and eastern boundaries of the study area, we eliminated species for which the majority of known localities lie outside of the study area.

4. For plants, we did not include those species for which current taxonomists recognize one or more infra-specific categories (e.g. subspecies, varieties), some of which are reported outside the boundaries of our project’s study area. For this reason, we did not include species such as Cavendishia nobilis (Ericaceae) or Justicia kuntzei (Acanthaceae), among others, in our study.

We also eliminated species for which the taxonomic status is unclear such that the known localities may refer to more than one biological species (e.g., the mouse opossum Marmosa quichua, family Didelphidae). We also had no choice but to eliminate valid species endemic to the study area for which we know of no discrete locality where the species is confirmed present. For example, the hummingbird Discosura letitiae (Trochilidae) is known from two localities in Bolivia, but the collections were made well before the era of providing precise location information on specimen tags. Without knowing more details about where this species was found, we cannot even predict what its distribution might be.

In practice, for the three vertebrate groups we developed a Geographical Information System (GIS) algorithm to select species whose distributions met the inclusion criteria. Range maps in GIS format for these groups are available at NatureServe’s website: (http://

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www.natureserve.org/getData/animalData.jsp). The algorithm compared these maps with the buffered study area to develop a list of candidate species. We refined this list by examining habitat affinities of species in borderline cases and by consulting taxonomic specialists to add recently-described species or to eliminate those with questionable taxonomic status.

Selecting endemic plant species was more difficult because of the lack of comprehensive, geospatially explicit distribution data for any of the focal groups. We therefore relied on draft lists of national endemics and input from taxonomic specialists. In cases in which we were unsure of the distribution of a species, we compiled localities from herbarium records and plotted them on a map of the study area. For species that occurred in both the study area and the buffer zone, we again relied on habitat information to determine whether to include the species. The resulting lists of focal species included 115 birds, 55 mammals, 177 amphibians, and 435 plants.

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Predictive distribution modeling (PDM) is increasingly being used in ecology, biogeography, evolution, and conservation biology to investigate the processes driving the distribution patterns of species and to predict where species might occur in areas previously not surveyed (Guisan and Thuiller 2005). These models are valuable tools for conservation: they can direct biological surveys towards places where species are likely to be found, provide a baseline for predicting a species’ response to landscape alterations and/or climate change, and identify high-priority sites for conservation.

PDM relies on a description of the species’ relationship with its environment to depict areas within a region of interest where the species is likely to occur. The species-environment relationship can either be defined by a biologist familiar with the species, as in deductive PDM, or developed inductively. Inductive methods use the environmental conditions at points of known occurrence in a statistical analysis to construct a definition of the species’ relationship with its environment. GIS data layers provide the description of the environmental conditions at known localities and are used by both deductive and inductive PDM to predict the species’ distribution pattern across the relevant region. Inductive approaches are often more practical than deductive methods because they can be developed at any spatial scale and can be used to model species whose habitat requirements are poorly understood. They are limited only by the availability of environmental data and species locality data. Thus, when confronted with the task of mapping the distributions of hundreds of plant and animal species endemic to the eastern slope of the Andes and lowland areas in the Amazon Basin of Peru and Bolivia, we chose to use inductive PDM as much as possible.

Species Locality DataWe obtained locality records for endemic species from natural history museums, herbaria, published literature and reliable observational data (for birds and mammals only). When specific geographic coordinates were not provided for a locality, we used digital maps and gazetteers to assign geographical coordinates to these records. Following our initial quality check to

fix obvious errors, scientists familiar with the species reviewed the locality data to identify and correct errors in geo-referencing as well as omission and commission errors. See the taxonomic sections below for more details on the methods used for each group.

Environmental Data We used environmental GIS layers describing climatic, topographic and vegetation cover conditions within our study area to develop species distribution models. These environmental data were sourced from four freely available data providers and developed further for our PDM purposes. Each layer was converted to the study’s geographic projection (a customized Lambert Azimuthal Equal Area), resampled to 1 km resolution (if provided at a finer resolution) and clipped to the study area buffered by 100 km, ensuring that geographic coordinates of the pixel boundaries were identical between layers. Even though a number of environmental datasets were available at a finer resolution, a 1 km pixel was selected for PDM because the spatial precision of the species locality data in the majority of cases is low and therefore better matched to environmental data depicted at a coarser (i.e. 1 km) pixel resolution. The environmental layers obtained and/or derived from the four data providers are described below. All preparations of these data layers were performed using ESRI ArcInfo Workstation (9.1) unless indicated differently.

Hole-filled seamless Shuttle Radar Topographic Mission (SRTM) �0 m digital elevation data Version 2. We derived three topographic layers from the STRM dataset. Data tiles covering the PDM study area were obtained from CGIAR (http://srtm.csi.cgiar.org, version 3 currently available), merged into a single raster layer and resampled to a 1 km pixel resolution. We obtained slope data from this elevation layer by calculating the degree of slope (i.e. maximum rate of change in elevation from each pixel to its neighbors) using the ArcInfo Workstation GRID command SLOPE. The third topographic layer called topographic exposure expresses the relative position of each pixel on a hillslope (e.g. valley bottom, toe slope, slope, and ridge). It is calculated by determining the difference

IV. Distribution Modeling MethodsBy Pilar A. Hernandez

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between the mean elevation within a neighborhood of pixels and the center pixel. The difference is determined over a number of neighborhood windows and averaged in a hierarchical fashion (more weight given to the smallest window) to produce a standardized measure of topographic exposure. We calculated topographic exposure using an ArcInfo Workstation application by Zimmerman (2000) on the digital elevation data using three neighborhood windows of 3x3, 6x6 and 9x9.

Worldclim bioclimatic database (http://www.worldclim.org). Worldclim provides 19 summary climatic variables of precipitation and temperature for the 1950-2000 time period (Hijmans 2005). It is inadvisable to use all of these variables because colinearity in PDM predictor layers can have adverse effects on model performance. In an effort to identify and remove redundant information in our PDM environmental layer database we performed a correlation analysis to identify a subset of climatic variables that were not correlated with each other and also not correlated with elevation. This analysis was performed separately for the montane region (elevation greater than 800 meters) and lowland region of our study area to derive a list of uncorrelated variables for the two regions for PDM input (Table 1).

Moderate Resolution Imaging Spectroradiometer (MODIS) �00m Global Vegetation Continuous Fields (Hansen et al. 2003, http://glcf.umiacs.umd.edu/data/modis/vcf/data.shtml). We used the percent tree cover layer for South America, in geographic projection.

MODIS/Terra Vegetation Indices 1�-Day L3 Global 1km (NASA EOS data gateway: http://edcimswww.cr.usgs.gov/pub/imswelcome). We obtained data tiles covering the study area for the years 2001-2003. We chose the Enhanced Vegetation Index (EVI) instead of the traditional vegetation index

NDVI (also available in this dataset) because EVI has proven to be less prone to saturation in humid forested areas (Huete et al. 2002) and therefore more sensitive to canopy variation than NDVI. The EVI data tiles were projected, merged, and exported to geotif images using the MODIS Reprojection Tool (3.2a, available at http://edcdaac.usgs.gov/landdaac/tools/modis/index.asp) creating a single image for each 16-day time period. These EVI geotif images were entered into a standardized principle components analysis (PCA) utilizing a correlation matrix. We used the remote sensing software ENVI (4.2) for this analysis. PCA is a commonly used data reduction technique of multi-temporal remotely sensed imagery (Hirosawa et al. 1996). We utilized the first two axes of the PCA for PDM, as they can be interpreted to represent vegetation structure and temporal dynamics respectively. We created six additional environmental predictor layers by summarizing the three MODIS data layers within moving windows of 2 km or 5 km using the ArcInfo Workstation GRID command FOCALMEAN. A spatial mismatch between the low precision of the species locality data and high precision of the MODIS satellite data may reduce the utility of the MODIS data products for predicting the distribution of our endemic species. Summarizing each MODIS layer within a spatial moving window was an attempt to compensate for this mismatch. Also, summarizing vegetation cover data in this way may be more ecologically relevant because factors influencing habitat selection are not restricted to the site of a species occurrence but also include the conditions of the surrounding landscape.

Inductive PDM Numerous inductive PDM methods are available and more continue to be developed (Guisan and Thuiller 2005, Elith et al. 2006). Our study required mapping the distributions of all amphibians, mammals, birds, and plants (12 families plus three genera) endemic to the

Table 1. Climatic and topographic variables selected for montane and lowland regions.

MontaneMean Diurnal RangeIsothermalityPrecipitation of Wettest MonthPrecipitation of Driest MonthPrecipitation SeasonalityElevation Slope Topographic Position Index

LowlandsAnnual Mean TemperatureTemperature SeasonalityMax Temperature of Warmest MonthPrecipitation of Wettest MonthPrecipitation SeasonalityElevationSlopeTopographic Position Index

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Amazon watershed below treeline in Peru and Bolivia. The large number of species inhibited our ability to select more than one modeling method. We required a method that performs consistently well with a wide range of species and with less than perfect locality data. We compiled locality data for these species in an ad hoc fashion from many different sources. Most records were obtained before the widespread use of global positioning systems (GPS) and therefore cannot be geo-referenced with high levels of spatial precision. Because the species to be modeled are endemics, they all have relatively limited spatial distributions. Their restricted ranges and the general paucity of collection and observation efforts throughout the study area result in the availability of few points of known occurrence for PDM modeling.

The statistical mechanics approach Maxent was an obvious candidate because previous comparative studies demonstrated that it performs well even with small sample sizes (Hernandez et al. 2006, Elith et al. 2006, Phillips et al. 2006). Also the freely available application facilitates modeling many species at one time. To ensure that Maxent was best suited to modeling distributions of Andean species, we compared the success of Maxent and two new promising methods: Mahalanobis Typicalities (a method adopted from remote sensing analyses), and Random Forests (a model averaging approach to classification and regression trees). We tested each method at predicting ranges of eight bird and eight mammal species using locality and environmental data gathered for our study. We found that Maxent performed very well, producing results that were more consistent across species with widely varying conditions (Hernandez et al., unpublished manuscript). Results of this comparative analysis supported our decision to select Maxent as the inductive PDM method for our study.

Inductive PDM models were developed using Maxent for all species with two or more unique localities. Maxent is based on a statistical mechanics approach called maximum entropy, meant for making predictions from incomplete information. It estimates the most uniform distribution (maximum entropy) across the study area given the constraint that the expected value of each environmental predictor variable under this estimated distribution matches its empirical average (average values for the set of presence-only occurrence data). Detailed descriptions of Maxent’s methods can be found in Phillips et al. (2004 and 2006). The algorithm is implemented in a stand-alone, freely available application (http://www.cs.princeton.edu/~schapire/maxent/). We considered only linear and quadratic features

because of the low numbers of localities available for our study species. If two or more localities occur in the same analysis pixel, Maxent considers them as one unique record. Maxent’s predictions are ‘cumulative values’, representing as a percentage the probability value for the current analysis pixel and all other pixels with equal or lower probability values. The pixel with a value of 100 is the most suitable, while pixels closer to 0 are the least suitable within the study area. Four Maxent models were developed for each species using all the available locality data but varying the input environmental layers. MODIS data products have not been extensively used in PDM to date. Therefore, we created four models for each species to test the utility of incorporating MODIS data products as PDM predictors and to determine the best way to use these data. Model 1 consisted of either the climatic and topographic variables selected for montane or lowlands regions (Table 1) depending on where the species was primarily distributed. The remaining models included the same climatic and topography layers as model 1. In addition, model 2 included the MODIS layers not summarized, model 3 included MODIS layers summarized within a 2 km moving window, and model 4 included MODIS layers summarized within a 5 km moving window.

We did not attempt to partition the data into records used for training the model and those set aside for a statistical model evaluation because of the scarcity and low spatial precision of available locality data. In the absence of an independent evaluation dataset with a sufficient number of highly accurate occurrence records, we believe that expert review is the only way to determine which modeling procedures produces the most realistic predicted distribution map. We sought external review by specialists familiar with the endemic species to produce presence-absence maps of the species. We did this by asking each reviewer to (1) select the best Maxent model generated with the four different ways of incorporating MODIS data, (2) choose a threshold (prediction value above which model predictions are to be considered positive) for the selected model to best represent the distribution of the species, and finally (3) identify predicted areas that should be removed because the species is known not to occur there. This was achieved mostly by drawing a polygon around predicted areas where the experts believed it was likely for the species to occur. We then clipped out all other areas from the predicted distribution. In a few cases we used the elevation layer to remove areas with elevations above or below what would be expected for the species.

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During the review of the predictive distribution maps it became evident that Maxent occasionally did not predict occurrence at some localities for which there were records. Further investigation revealed in most cases that these records had errors in geo-referencing or represented misidentifications. Maxent highlighted these probable errors allowing us to correct them and rerun the models with the updated locality data.

At times none of the four Maxent models produced a realistic distribution map for the species. For a few species a new environmental variable dataset was hand-selected and resulted in an improved model for that species. In other instances the reviewers felt that Maxent had erroneously excluded from its predictions a valid locality, so we converted the absent prediction to present in an area delineated by the expert reviewers. If all attempts to refine the Maxent model failed to produce an adequate prediction distribution map, we followed a deductive PDM approach to determine the distribution of the species.

Deductive PDM We relied on deductive PDM approaches when the species is known from only one locality or when the inductive Maxent approach did not produce a realistic distribution model. Often very little is known about the habitat requirements of these species besides the elevation at which the specimens were collected. For species that occur within regions of high topographic variation, under the specialist’s direction we created presence-absence maps by defining the maximum and minimum elevations at which the species is expected to occur. Then the specialists indicated the areas that should be removed from the predicted distribution as was done for the inductive models. Elevation ranges were often defined by buffering recorded elevations of known localities by 100 to 200 meters. For species that occur in regions with low topographic variation (mainly lowland areas) or those for which reliable elevation information was unavailable, the specialists drew a polygon to delineate the predicted distribution. This was achieved mostly by buffering the known localities by one to 10 km (most often 5 km) depending on the dispersal ability of the kind of plant or animal being modeled, or by drawing a polygon to represent the expected distribution region (e.g., riparian areas in a given drainage). For two bird species, we used the ecological systems layer developed for the project as input into a deductive distribution model by either delineating polygons to be included in the prediction or identifying areas to clip out of a model based on elevational range.

Approaches to identifying concentrations of endemic speciesNumerous indices have been proposed to quantify and map patterns of endemism (Crisp et al. 2001, Tribsch 2004). Each provides insight into the patterns of endemism but may be subject to the effects of spatial bias if survey intensity varies across the area in question. Patterns of total species richness and richness of range-restricted species are often but not always correlated. Some regions have higher numbers of endemics than would be expected by total species richness (Crisp et al. 2001, Jetz and Rahbek 2002), and these are often the areas of most interest to conservation and biogeographical studies. It was beyond the scope of our study to estimate total species richness and therefore we cannot calculate indices of diversity that attempt to factor out its influence on patterns of endemism. Although some may consider this to be a weakness in our study design, we note that accurate total species richness estimates are hard to obtain at the fine spatial scale of our study because widespread and common species are generally not documented as well as rare/range-restricted species. Measurement error in total species richness will have an unknown influence on indices of endemism corrected by species richness. We feel that our approach of considering only data for species restricted to our study area is more transparent and facilitates interpretation of the resulting patterns of endemism.

We calculated the following three indices utilizing the predicted distribution data to identify areas of endemism:

1. Endemic species richness. The number of species considered endemic to the project area that are predicted to occur in each analysis pixel. This overlay technique was suggested over three decades ago by Müller (1973). We defined areas of endemism as those pixels in which there occurred at least two-thirds of the maximum number of overlapping endemic species anywhere. For example, the greatest number of overlapping endemic mammals was 24, so mammalian areas of endemism were those with 17-24 overlapping species.

2. Summed irreplaceability. The likelihood that an analysis pixel must be protected to achieve a specified conservation target for the study area (Ferrier et al. 2000). We used 10 km2 analysis pixels and set as a conservation target 25 of these pixels for each species. If a species occurs in less than 25 of the 10 km2 pixels, we set the target as the number of pixels in which the species occurs. For each species, irreplaceability for

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each pixel ranges from 0 to 1. Low numbers indicate that a species occurs in many pixels, whereas values close to one reflect the existence of species with very restricted ranges. Summed irreplaceability sums the irreplaceability values for all species occurring at each pixel, drawing attention to the sites (pixels) with the most unique, narrow-ranged species. Summed irreplaceability incorporates the concept that the species with the smallest ranges offer the fewest options for conservation, just as weighted endemism (the sum of the inverse of each species’ range that overlaps each pixel, also known as ‘range-size rarity’; Knapp 2002) does, but additionally incorporates the complementarity of sites for protecting suites of species.

3. Richness of range-restricted species. Because of the large size of the study area, some species with relatively large ranges will be included as endemics. To focus exclusively on species with small ranges, we also calculated richness of restricted range species. We defined restricted range species as those falling in the first quartile of range size of resident, non-marine South American species for each group, calculated from the range maps available from NatureServe (Patterson et al. 2005, Ridgely et al. 2005, IUCN et al. 2006). The cut-off range sizes calculated in this manner are 48,222 km2

for mammals, 76,096 km2 for birds, and just 280 km2 for amphibians. Results using these criteria are more comparable to other studies (e.g., Fjeldså et al. 1999) than using simply the richness of all species endemic to the study area. Because comprehensive range size data for South American vascular plants are not available, we could not perform this analysis on plants.

In an attempt to identify important areas for future field surveys of endemics, we recalculated endemic species richness excluding predicted distributions that are less than 50 km from known localities. This analysis identifies areas that are expected to hold many endemic species but where biological surveys have so far not yet taken place or have not been carried out in the appropriate season or using appropriate methods to detect the species in question.

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Introduction The Andes, with their tremendous variety of ecological formations across nearly 8000 km of landscape, offer a fascinating geographical setting for the study and analysis of patterns of vascular plant distribution. The combined effects of climatic changes, past geotectonic events, and modern ecological interactions have produced a flora of complex evolutionary history and biogeographical affinities. Ever since Humboldt’s essay on the phytogeography of the Andes (Barthlott et al. 2005), this mountain system has provided biologists with a unique setting for the study of vegetation history and the nature of biogeographical processes (Gentry 1982, Simpson 1983, Young et al. 2002). From a biogeographic viewpoint, the Andean mountain ranges may either serve as pathways for, or as barriers to, plant migration (Janzen 1967, Ghalambor 2006). Conversely, isolated peaks and mountain masses separated from each other by many kilometers of lowlands often serve as mainland islands and exhibit many of the biogeographic characteristics of oceanic islands (Vuilleumier 1970, Carlquist 1974). We now recognize that the Andean vegetation was dramatically affected by orogenic and climatic processes, such as Plio-Pleistocene uplift and glaciations (Simpson 1983, van der Hammen and Cleef 1983, Markgraf 1993). Evidence suggests that habitat fragmentation over geological time may have led to allopatric speciation (Gentry 1982, Young 1995, Young et al. 2002). In addition, elevation, humidity, and topographic position may have also promoted the development of clines and subsequent parapatric speciation (Young 1995).

This combination of past processes and present-day habitat heterogeneity has led to the formation of a biome rich in species of plants. The tropical Andes have been recognized at the top of several biodiversity hotspots on Earth (Myers et al. 2000, Barthlott et al. 2005). The central Andes also have large numbers of species not found anywhere else in the world (Gentry 1986, Knapp 2002, Young et al. 2002), with an estimated 20,000 endemic plant species (Myers et al. 2000). In the last three decades, the high degree of endemism has been interpreted and analyzed from approaches

including Pleistocene processes (Prance 1973, Prance 1982, Knapp and Mallet 2003, Bonaccorso 2006) and edaphic specialization (Gentry 1986, Tuomisto et al. 1995, Clarke and Funk 2005).

The Pleistocene refugia theory proposes that climatic changes during the Pleistocene and Holocene caused large changes in vegetation cover and species distribution (Prance 1982). Fluctuating climate change caused rainforest areas to alternate between scattered refugia during dry periods and vast expanses during humid periods, promoting allopatric speciation of plants and animals (Simpson and Haffer 1978). Support for the model depended on identifying centers of endemism. For plants, researchers tested the model on Andean Polylepis (Vuilleumier 1971, Simpson 1975), Rubiaceae (Simpson 1972), ferns (Tyron 1972), Hymenaea (Fabaceae, Langenheim et al. 1973), and palms (Arecaceae, Moore 1973). G. T. Prance examined the distribution patterns of the tree families Caryocaraceae, Chrysobalanaceae, Dichapetalaceae, and Lecythidaceae, eventually proposing 26 Pleistocene refugia (Prance 1973, 1981a, 1981b, 1982).

The Pleistocene refugia model has been challenged repeatedly (reviewed by Bush 1994; see also Knapp and Mallet 2003, Bush and De Oliveira, 2006). One of the most convincing criticisms came from a study of Brazilian herbarium data that showed that many of the proposed centers of endemism might be artifacts that better reflect collecting intensity near cities than biological patterns (Nelson et al. 1990). Regardless, the search for evidence to test the refugia model contributed to the compilation of vast amounts of plant distribution data and inspired further assessments of patterns of plant endemism in the Amazon basin.

An alternative model of speciation and endemism in the Amazon basin relates to edaphic specialization (Gentry 1986). A study of the Bignoniaceae suggested that up to 65 percent of the locally endemic species in Latin America were habitat specialists, being restricted to terra firme forests, white-sand savannas, and

V. Vascular PlantsBy Stephan G. Beck, Pilar A. Hernandez, Peter M. Jørgensen, Lily Paniagua, Martín E. Timaná, and Bruce E. Young

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riverine forests, among others (Gentry 1986). Similar specialization was reported for species of Passiflora in the Iquitos area (Gentry 1986). Based on extensive field studies, the analysis of satellite images, and the distribution of pteridophytes and Melastomataceae, landscape heterogeneity in Peruvian lowland rainforests appears to be related to variations in soil types (Tuomiso et al. 1995). Gentry (1992) indicated that the greatest concentration of local endemics can be found in four situations: 1) isolated patches of unusual habitat, especially in Amazonia; 2) cloud forest ridges, especially along the lower slopes, of the Andes and in adjacent southern Central America; 3) isolated dry inter-Andean valleys, and similarly dissected central Mexican valleys; 4) the two largest islands of the Greater Antilles, Cuba and Hispaniola. More recently, Clark and Funk (2005) employed standardized plant checklist and collection data from the Guiana shield and the Manaus area to support the contention that distribution patterns are determined in part by the presence of sandstone or white sand substrates.

Recently, studies of plant biogeography have re-emphasized the importance of intrinsic factors, such as life form, dispersal ability, and physiological limitation, and extrinsic factors such as elevation gradient and degree of human disturbance (Kessler 2000a, 2000b, 2001a, 2001b, 2001c, 2002a, 2002b; Kessler et al. 2001; Krömer et al. 2005; Krömer et al. 2006). Kessler (2000a) analyzed the distribution of six plant families (Acanthaceae, Araceae, Bromeliaceae, Cactaceae, Melastomataceae, and Pteridophyta) in 62 study plots in the Tucumano-Boliviano biogeographic zone of the Andes and concluded that endemism increased with elevation, reaching a peak at 1700 m for the terrestrial Pteridophyta and empiphytic Bromeliaceae and Cactaceae. Based on an analysis of the Ecuadorian flora, Kessler (2002a) concluded that patterns of endemism seemed to be influenced by intrinsic taxon-specific traits, such as life form, reproduction, dispersal, demography, and population structure, as well as by environmental factors such as topographical fragmentation, and warned that a similar set of ecological and biogeographical conditions will not always lead to similar patterns of endemism among different taxa. Van der Werff and Consiglio (2004) also recommended not using a single life form (e.g. trees) to determine patterns of endemism. Instead, biological inventories should include all life forms in order to provide more accurate assessments of endemism. Finally, Kessler (2001b) examined four plant families along a gradient of increasing anthropogenic forest disturbance and found that endemism was higher in disturbed than

mature forest, but declined in more heavily disturbed habitats. He proposed that endemic species are competitively inferior to other co-occurring taxa and depend on colonizing disturbed habitats.

The study of endemism has benefited from advances in GIS technology and the development of PDM methods (Guisan and Zimmermann 2000). One study taking advantage of these technologies to analyze Ecuadorian Araceae showed that humidity and elevation are strongly correlated with diversity but weakly correlated with endemism (Leimbeck et al. 2004). The authors suggest that historical factors may be more important for explaining patterns of endemism. Another study used models predicting the distributions of 83 species of Anthurium to identify three potential hotspots of endemism in Ecuador (Vargas et al. 2004).

Past studies that covered our study area have identified various peaks of diversity and endemism in the eastern Andes. The highest levels of range-size rarity (a measure of range restrictiveness or endemicity) of species of Solanum (Solanaceae) in our study area peaked in the northern Andes and near La Paz (Knapp 2002). Species of Loasaceae have exceptional diversity of restricted-range species in the Amotape-Huancabamba region at the northwest corner of the study area and extending into southern Ecuador (Weigend 2002). Much of this diversity, however, is restricted to dry habitats not included in our study area. Examination of Neotropical Ericaceae revealed a floristic unit of the tropical Andes biogeographic region in our study area, extending from south-central Peru to northern Bolivia (Luteyn 2002). This unit includes five endemic genera, Demosthenesia, Siphonandra, Pellegrinni, Rusbya, and Polyclita, and numerous species. In Peru, the ferns (Pteridophytes) are most diverse and have the greatest number of national endemic species at elevations of 500-1,500 m on the eastern slope of the Andes (León and Young 1996). A comprehensive look at Peruvian flowering plants found the highest density of national endemics, expressed as either numbers of species or density of species per unit area, at elevations of 2,500-3,000 m (van der Werff and Consiglio 2004). Plant form has a large influence on the elevation at which endemics are most common. Tree and liana endemism is highest below 500 m; herb, shrub, and vine endemism peaks at 2,000-3,000 m; and epiphyte endemism reaches its maximum at 1,500-3,000 m (van der Werff and Consiglio 2004). These summaries do not distinguish between the east and west slope of the Andes, but due to the much higher plant diversity of the east slope, the results for just the east slope probably do not differ significantly. These studies

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examine endemism for different taxa and at different scales, but provide useful benchmarks with which to compare our results. Here we describe how we use the modeling techniques explained previously to help identify specific areas where concentrations of endemic plant species occur on the east slope of the central Andes.

MethodsCriteria for selecting target plant groups. The overwhelmingly high diversity of vascular plants throughout the upper Amazonian watershed precluded the possibility of including all species in this analysis. Instead, we selected a large sample of plant groups to represent the flora of the study area. By group we refer to a taxonomic family or a species-rich genus. We used the following criteria for selecting plant groups to address in this study:

1. Taxonomic knowledge. Selected plant groups must be well known at the species level in both Peru and Bolivia. We therefore restricted the list to groups with recent monographs describing the characteristics and distribution of each species. The publication of a monograph generally meant that the specimens of the group housed in major herbaria were fairly accurately identified. The knowledge needed to be even for species occurring in both countries to avoid biasing results toward either country. For this reason, for example, we did not treat orchids (Orchidaceae) because although they are well known in Bolivia (e.g., Müller et al. 2003), their taxonomy and distribution are less well understood in Peru.

2. General Distribution. Selected groups must show examples of endemism in the study area. For obvious reasons, we did not include families that have few or no species endemic to the study area.

3. Available distribution information. The groups we selected were known to have readily available locality data in herbaria or the literature for use in distribution models. Because of this factor we eliminated groups such as the cacti (Cactaceae) for which many species have been described based on vague locality information. In general, groups treated in recent monographs satisfied this condition.

�. Diversity of life forms. Among the candidate groups, we selected a suite that together represents the range of plant life forms in the study area, including herbs, vines, lianas, shrubs, treelets, and trees. The list includes species that root in the ground as well as those that live as epiphytes or hemiepiphytes.

�. Diversity of elevation. The suite of groups that we selected has endemic species that tend to occur across the range of elevations represented in the study area.

�. Diversity of habitats. The groups we chose also have species with habitat affinities that include all major habitats that occur in the study area. Thus the list includes groups that occur in the mountain forest of the Yungas, lowland moist forests, savannas, and dry valleys.

�. Economic uses. We included groups with species of economic value to help make the results more relevant for the general public and because species with economic uses can become threatened due to overexploitation.

Based on these criteria, we developed a list of twelve focal families plus three focal genera from two families to include in the study (Table 2). Besides not addressing obvious candidate families such as the Orchidaceae and Cactaceae as explained above, we did not focus on Pteridophyta other than the Cyatheaceae because a revision of the Bolivian species is not yet complete, Araceae because too many species have yet to be described, Amaryllidaceae because they have received relatively little attention from collectors in our study area, and the Aristolochiaceae because its center of distribution is south of the study area. In sum, the list includes 435 species (complete species list in Appendix 2).

Compilation of herbarium records. We gathered herbarium records for the focal species by systematically searching the collections at major herbaria within the study area and the TROPICOS database of the Missouri Botanical Garden (see Appendix 1 for complete list of contributing herbaria). We augmented the sample by including records from herbaria where specialists on particular families worked and by searching the literature. Because most specimen labels did not include coordinates for the collecting locations, we geo-referenced the localities using digital gazetteers and maps. To check the accuracy of the geo-referencing, we then sent for review maps of the localities recorded for each species to taxonomic specialists for each group (see Appendix 3 for list of specialists consulted).

Model runs and review. After incorporating reviewers’ comments in the data set, we ran Maxent models for each species as described above in Distribution Modeling Methods with the exception of those species known only from single localities. We then convened a group of eight Peruvian and nine Bolivian botanists

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Table 2. Characteristics of the focal groups of vascular plants.

Number Elevation of spp endemic where to project diversityGroup area † Life forms peaks Habitats Acanthaceae 157 Herbs, shrubs mid Savannahs, Yungas, lowland forestAnacardiaceae 5 Herbs, shrubs low Lowland forest, dry valleysAquifoliaceae 14 Herbs, shrubs high Yungas, lowland forestBruneliaceae 10 Trees high YungasCampanulaceae 45 Shrubs, vines high YungasChrysobalanaceae 13 Trees low Lowland forestCyathaceae 5 Tree ferns high YungasEricaceae 47 Shrubs, vines, epiphytes low YungasFabaceae-Inga 16 Herbs, shrubs low Lowland forestFabaceae- Mimosa 7 Shrubs low Dry valleysLoasaceae 19 Herbs, shrubs mid, high YungasMalpighiaceae 25 Shrubs, vines mid, high Dry valleys, YungasMarcgraviaceae 7 Shrubs, hemiepiphytes mid Lowland forestOnagraceae–Fuchsia 33 Shrubs, vines, treelets high YungasPassifloraceae 32 Lianas high Yungas

† Does not include species of uncertain taxonomic validity or species not known from at least one specific locality.

who were familiar with the species and the study area to review the resulting modeled distributions (see Appendix 3 for list of reviewers). With but one exception, these reviewers were different from the taxonomic specialists who reviewed the locality data. All but two of the reviewers did not participate in any other aspect of the study and were therefore unlikely to be biased in their appraisal of the models. For each species, the botanists selected which, if any, of the four Maxent models reflected a realistic depiction of the distribution. In the cases in which a Maxent model was reasonable, the botanists then selected a cut-off threshold to convert the continuous Maxent predictions to presence-absence maps. Again, this selection was based on the botanists’ experience with the species in the field. Finally, the botanists then eliminated from the Maxent prediction areas such as isolated mountain ranges where the species is known not to occur.

For cases in which no Maxent model produced a logical representation of a species’ range, the botanists suggested criteria for producing a deductive model. In most cases, the criteria included elevational distributions in the area of the known collections although in others we used the limits of ecological systems where the species occurred. For some wide-ranging species, the

Maxent model produced satisfactory results for most but not all of the distribution. For these species, we produced hybrid models using part of the Maxent prediction in one portion of the range and a deductive model for the remaining part.

For species known only from single localities, the botanists again selected an elevational range for the species in the vicinity of the locality. In cases of lowland species or those without a known elevational distribution, we simply drew a circle around the locality with a radius appropriate to the dispersal distance of the plant form involved. For example, we typically used a 5 km radius for trees and 2 km for herbs.

ResultsDistribution Modeling. We compiled a total of 3,040 unique locality records for the 435 endemic plant species in the 15 groups treated (Figure 3, Table 3). The data set ranged from a minimum of 1 (N = 125 species) to a maximum of 84 records (for Fuchsia sanctae-rosae) per species, with a mean of 7.0 records per species. The group with the most records was Mimosa (mean = 16.0 records per species) whereas Anacardiaceae and Malpighiaceae had the least (mean = 3.4 for each group). The distributions of the endemic species ranged from

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2 km2 (Ilex trachyphylla and Centropogon bangii) to 237,600 km2 for Inga steinbachii with an overall mean of 17,484 km2. The Loasaceae had the smallest average range size (5,357 km2) while the Aquifoliaceae was the group with the largest distributions (mean = 46,131 km2).

Maxent produced suitable results for 264 species (61%). We used hybrid models for 18 species (4%) and deductive models for 153 of the species (35%), including all species known from single localities. Focusing only on the species known from more than one locality, Maxent yielded a complete or hybrid model for 85% of species. There were no detectable differences among families for the utility of Maxent. Reviewers thought the inductive modeling procedure produced satisfactory results for at least two-thirds of

the species in each family. For species with at least two unique localities, the utility of Maxent was no different for lowland (those with the majority of localities below 800 m) versus highland species (X2 = 0.009, d.f. = 2, P > 0.90). Most (79%) Maxent models used for plants were either model 3 (with MODIS satellite data generalized to 2 km) or model 4 (with MODIS data generalized to 5 km), although some (16%) used ungeneralized MODIS data and a few (6%) worked better without MODIS data.

Endemic Species Richness. Locations of endemic species in each of the focal groups are recorded in Figures 4-18. The numbers and degree of overlap of endemic species varied significantly among families resulting in endemism “peaks” that ranged from 2-20 species.

Figure 3. Distribution of plant collecting localities in the study area.

23

Table 3. Summary of locality data, modeling methods used, and predicted range sizes.

Group

Vascular Plants

Acanthaceae 157 814 82 49 5.2 78 10 70 13 204,251 16,969

Anacardiaceae 5 17 5 2 3.4 2 0 3 82 105,975 25,216

Aquifoliaceae 14 104 18 1 7.4 13 0 1 77 143,572 46,131

Brunelliaceae 10 69 29 6 6.9 3 0 7 34 23,186 5,896

Campanulaceae 45 377 44 4 8.4 38 1 6 2 117,044 14,925

Chrysobalanaceae 13 45 12 6 3.5 5 1 7 9 76,618 12,146

Cyatheaceae 5 34 24 2 6.8 3 0 2 49 25,155 6,366

Ericaceae 47 457 59 14 9.7 31 0 16 22 71,814 11,839

Fabaceae (Inga) 16 121 28 4 7.6 9 2 5 78 237,600 37,291

Fabaceae (Mimosa) 7 112 54 1 16.0 6 0 1 4 34,667 11,294

Loasaceae 19 85 38 9 4.5 10 0 9 28 24,174 5,357

Malpighiaceae 25 84 19 10 3.4 15 0 10 14 71,171 7,727

Marcgraviaceae 7 68 20 0 9.7 6 1 0 8,072 77,737 38,949

Onagraceae (Fuchsia) 33 477 84 3 14.5 29 1 3 25 72,659 15,469

Passiflora 32 176 32 12 5.5 16 2 14 19 34,353 6,687

Vascular Plant

subtotal �3� 3,0�0 �� 123 �.0 2�� 1� 1�3 2 23�,�00 1�.��

Vertebrates

Amphibians 177 1,060 64 65 6.0 85 8 84 13 690,992 18,935

Mammals 55 618 70 4 11.2 47 0 8 33 344,920 47,800

Birds 115 2.436 94 3 21.2 99 6 10 78 309,168 33,544

Total ��2 �,1�� 1�� .1 �� 32 2�� 2 ��0,��2 20,1�1

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Anacardiaceae. Endemic species of this family occur in the lowlands and showed no overlap

(Figure 5).

Figure �. Richness of endemic species of Anacardiaceae.

Figure �. Richness of endemic species of Acanthaceae.

Acanthaceae. Endemic species are concentrated narrowly

in the Carpish Hills and Tingo Maria region of the department

of Huanuco, Peru, and broadly in the Andes of the department of La Paz, Bolivia (Figure 4). Lesser

numbers of endemics occur in the lower foothills of the border

between the Departments of Cusco and Madre de Dios, Peru. Endemic areas, defined as those

places with at least two-thirds of the maximum number of

overlapping species for the group, have at least 13 co-occurring

species and cover 18,095 km2 at an average elevation of 1,080 m.

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Figure �. Richness of endemic species of Aquifoliaceae.

Aquifoliaceae. Endemic species richness is highest in western San Martin on the lower slopes of the Cordillera Central and the Carpish Hills in Huanuco, continuing south into adjacent portions of the department of Pasco (Figure 6). Minor concentrations occur along the Paucartambo river on the border between the Departments of Cusco and Madre de Dios and near La Paz in Bolivia. Endemic areas have at least five co-occurring species and cover 15,632 km2 at an average elevation of 2,340 m.

Figure �. Richness of endemic species of Brunelliaceae.

Brunelliaceae. Richness of endemics is highest in the cordilleras near La Paz and the Cordillera de Cocapata-Tiraque in the Department of Cochabamba where up to three species are endemic (Figure 7). Endemic areas have at least two co-occurring species and cover 15,470 km2 at an average elevation of 2,372 m.

2�


Figure �. Richness of endemic species of Campanulaceae.

Campanulaceae. Richness of endemic species in this family reaches up to 18 co-occurring

species in the one area of endemism evident for this family

on the east slope of the cordillera in the department of La Paz

(Figure 8). Endemic areas have at least twelve co-occurring species

and cover 2,458 km2 at an average elevation of 2,539 m.

Figure �. Richness of endemic species of Chrysobalanaceae.

Chrysobalanaceae. The highest density of endemic

Chrysobalanaceae occurs near Iquitos, Loreto Department, Peru (Figure 9). Endemic areas have at

least three co-occurring species and cover 3,199 km2 at an average

elevation of 105 m.

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Figure 10. Richness of endemic species of Cyatheaceae.

Cyatheaceae. Just five tree ferns are endemic to the study area. Areas of overlap between two species occur in Cusco and La Paz Departments in Peru and Bolivia, respectively (Figure 10).

Figure 11. Richness of endemic species of Ericaceae.

Ericaceae. Endemic areas for this group are similar to those for Campanulaceae, running the length of the east slope of the cordillera in the department of La Paz (Figure 11). Smaller concentrations of endemics occur in Cusco, Puno, and Cochabamba. Endemic areas have at least ten co-occurring species and cover 5,606 km2 at an average elevation of 2,621 m.

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Figure 12. Richness of endemic species of Inga.

Inga. Although the study included 16 species of Inga, no more

than five co-occur. The highest concentrations of these species are near Tarapoto, Iquitos, and

Cocha Cashu, Peru (Figure 12). Endemic areas have at least three

co-occurring species and cover 4,491 km2 at an average elevation

of 287 m.

Figure 13. Richness of endemic species of Mimosa.

Mimosa. Seven species of Mimosa are endemic to the study area. Up to four of these are sympatric in

the cordilleras in southeastern La Paz Department, Bolivia (Figure 13). Endemic areas have at least three co-occurring species and cover 4,290 km2 at an average

elevation of 2,278 m.

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Figure 1�. Richness of endemic species of Loasaceae.

Loasaceae. The only significant concentrations of species of Loasaceae occurred in southern Amazonas Department, Peru, near Chachapoyas (Figure 14). Endemic areas have at least three co-occurring species and cover 375 km2 at an average elevation of 2,643 m.

Figure 1�. Richness of endemic species of Malpighiaceae.

Malpighiaceae. The study included 25 species of Malpighiaceae, but these are widely scattered through the study area. The concentrations of endemics occur near Tarapoto and near Iquitos in northern Peru (Figure 15). Endemic areas have at least three co-occurring species and cover 2,529 km2 at an average elevation of 348 m.

30


Figure 1�. Richness of endemic species of Marcgraviaceae.

Marcgraviaceae. Up to four endemic species of

Marcgraviaceae occur in the cordilleras around La Paz (Figure

16). Endemic areas have at least three co-occurring species and cover 19,067 km2 at an average

elevation of 2,005 m.

Figure 1�. Richness of endemic species of Fuchsia.

Fuchsia. Highest concentrations of endemic species occur on the

slopes of the mountains between Paucartambo and Marcapata in

Cusco, with lesser numbers in the Carpish Hills-central Pasco and

Cochabamba regions (Figure 17). Endemic areas have at least eight

co-occurring species and cover 6,011 km2 at an average elevation

of 2,861 m.

31

Figure 18. Richness of endemic species of Passifloraceae.

Passifloraceae. Densities of endemic species in this family are highest on the slopes of the Andes near La Paz (Figure 18). Endemic areas have at least five co-occurring species and cover 658 km2 at an average elevation of 2,373 m.

32


Acanthaceae, Campanulaceae, and Ericaceae with higher elevation endemics.The varying locations of concentrations of endemic species may reflect different evolutionary histories of the focal groups. Taxa with large concentrations of species in small geographic areas may have differentiated in these places fairly recently in geological history and have not yet dispersed far from the places where they originated. These recently-evolved species with restricted ranges are called neoendemics (Young et al. 2002). Candidate groups that may contain many neoendemics include the Acanthaceae, Campanulacee, Fuchsia, and Passifloraceae. The clumping of many endemic species in small geographic areas is suggestive of multiple speciation events coupled with low dispersal. Conversely, restricted range species that occur far from related restricted-range species may represent relict populations of old species that were once more widely distributed. Possible examples from our study of these “paleoendemics” include members of the Anacardiaceae. Detailed phylogenetic analyses of the species making up these groups are necessary to confirm these hypotheses, despite the suggestive biogeographical data we present. Both paleoendemics and neoendemics from closely related lineages sometimes co-occur, as has been shown in birds (Fjeldså et al. 1999), further emphasizing the need for phylogenetic study.

Could the results shown here be the result of collecting bias, as demonstrated for reputed centers of endemism of Amazonian plants (Nelson et al. 1990)? On one level, concentrations of endemic species near the Carpish Hills, Cusco, and La Paz surely reflect to some degree the amount of effort that field botanists have invested at these favored collecting localities. Use of distribution models helps somewhat to control for bias by predicting distributions away from places where species were collected. The tremendous variation in the patterns shown for the different focal groups, however, suggests that the major patterns may be real. For example, the steep Andean slopes around La Paz have been subject to countless collecting expeditions, but the area does not register as harboring many endemic species of Fuchsia (Figure 17). Fuchsia is usually a conspicuous plant that would be hard for the legions of plant collectors fanning out from La Paz to overlook. Similarly, the east slope of the Andes in the northern Peruvian department of San Martin has received much attention by field biologists. Being closer to the equator, we might expect a large diversity of endemics. We do find many endemic Aquifoliaceae (Figure 16), but few endemics from any of the other taxa studied here, again suggesting that Aquifoliaceae is truly more diverse in northern Peru

Summed Irreplaceability. Because many of the focal groups had small numbers of endemic species, we calculated summed irreplaceability for the entire sample of vascular plants. Species known from single localities were common and thus received heavy weighting for their tiny ranges. The result is a map that shows small peaks of irreplaceability near Iquitos, Peru, and throughout the length of the upper portion of the study area in the Andes (Figure 19). Peaks at Iquitos reflect the occurrence there of a few species of Chrysobalanaceae restricted to the white-sand forests.

Understudied Areas of Endemism. Again, we combined data from all plant species for this analysis. The areas that are predicted to have endemic species but are far from where the species have been collected include northern Cochabamba, the cordilleras north of La Paz, the Paucartambo region on the Cusco-Madre de Dios border, and a few other localities scattered to the north (Figure 20).

DiscussionOur results add substantial details to the previously-recognized pattern of high levels of endemism on the east slope of the central Andes. Endemic species are in no way distributed evenly along this corridor. Localized concentrations occur throughout, but the taxonomic affinities of the concentrations vary. The Aquifoliaceae, Chrysobalanaceae, Inga, Loasaceae, and Malpighiaceae are richest in the north, the Brunelliaceae, Campanulaceae, Ericaceae, Marcgraviaceae, Mimosa, and Passifloraceae are richest in the south, Fuchsia is richest in the center of the study area, and the Acanthaceae has concentrations of endemic species in both the north and south. Previous analyses, often conducted over much larger geographical areas, typically do not provide information at a finer scale than the 1° × 1° Flora Neotropica grid (e.g., Knapp 2002, Luteyn 2002).

These findings support the few previous studies of endemism in some of the target groups. We found the highest diversity of endemic Loasaceae in the northwestern corner of the study area where concentrations of species of this family were previously reported (Weigend 2002). Endemic Ericaceae were more common in southern Peru and northern Bolivia, as suggested in the past (Luteyn 2002). Our limited sample further confirms the widely-reported pattern of endemism peaks at 2,000-3,000 m elevation (Gentry 1986, Kessler 2000a, van der Werff and Consiglio 2004). The groups we purposefully selected because they are known to have lowland endemics (Anacardiaceae, Chrysobalanaceae, Inga, Mimosa) had far fewer endemic species than did groups such as

33

Figure 1�. Summed irreplaceability of all endemic species of vascular plants in this study.

Figure 20. Understudied areas of plant endemism (places where endemic species are predicted to occur but are farther than 50 km from the nearest confirmed locality).

3�


than elsewhere in our study area, and that other taxa are less diverse in northern Peru than further south.Maxent proved to be a versatile algorithm for modeling our sample of plant species from the central Andes. Although the high levels of diversity, low collecting effort relative to the expanse of territory, difficult access, and microendemism all contribute to large numbers of species being known from single or few localities, we still were able to successfully model the distributions of nearly two-thirds of the species. The modeled distributions in many cases produced satisfactory results, as judged by external reviewers, even when a species was known from as few as two localities. Distributions of many montane plants may be limited by physiological adaptation to narrow climatic conditions (Kessler 2000a, 2001c). The environmental data we used in the models may have successfully captured these conditions, resulting in their accurate depiction of the species’ ranges.

The MODIS vegetation data significantly improved Maxent models in 94% of the species modeled inductively. For most of these, the MODIS data worked best when generalized over 2-5 km radii from each 1-km2 analysis cell. Generalizing the MODIS data apparently was effective at controlling for the approximate nature of the majority of the coordinates assigned to collecting localities.

3�

IntroductionUntil very recently, amphibians were one of the most poorly-understood classes of vertebrates in South America. Concerted attention to amphibian taxonomy, distribution, and natural history on the continent over the past two decades has substantially improved our knowledge, but much remains to be learned. For example, the rate of description of new species is currently at the highest ever in scientific history (Duellman 1999). Some areas, such as Bolivia, have only recently received significant attention. For example, a review of South American amphibian diversity published in 1988 and based on 40 well-know field sites did not list any in Bolivia (Duellman 1988). The situation improved shortly thereafter, and in just ten years of focused field study by a handful of herpetologists, the list of amphibians known from that country jumped from 112 to 186 (De la Riva et al. 2000). This number remains far from complete, because an additional 67 species are known from neighboring countries close to the Bolivian border but have yet to be found in the country, not to mention the unknown number of undiscovered species lurking in Bolivian forests and swamps. The incompleteness of the record makes any analysis of biodiversity preliminary. The pressing need for conservation planning nevertheless demands that we derive as many lessons as possible from the data available (e.g., Ibisch and Mérida 2004).

Because of the developing nature of our knowledge, the literature describing patterns of endemism in amphibians within the study area is scant. One study of amphibian diversity in the Brazilian portion of the Amazon basin found that, although some species are endemic to the basin, very few have restricted ranges (Azevedo-Ramos and Galatti 2002). An examination of Amazonian species of the genus Eleutherodactylus, the most species-rich genera of amphibians, showed higher diversity in the Loreto-lower Amazonas/San Martin region of Peru (Lynch 1980). Another study determining relationships among lowland tropical anuran faunas described two distinct faunas from Peru,

one from the Iquitos region which was most closely aligned with Amazonian Ecuador, and another from southern Peru and adjacent Bolivia (Ron 2000). Surveys in the lowlands of southern Peru have highlighted the extraordinary diversity of herpetofauna there (Doan and Arizábal 2002). Yet another study divided South America into twelve biogeographic regions and examined species richness and endemism in each (Duellman 1999). The Andes made up one of these regions and contained both the greatest number of species and the greatest number of endemics. Other regions that cover sections of our study area include Amazonia-Guiana, which had slightly less than half of both the species and endemics as the Andes, and Pampean-Monte (encompassing the Beni savannahs as well as extensive grassland areas in Argentina), which had about one tenth as many species and endemics as the Andes. Taken together, these results suggest that, like the other taxa covered in our study, we should find most endemic species in the montane regions.

MethodsSelection of species. We followed the guidelines described in Chapter III: Study Area to select the 177 amphibian species endemic to the study area (complete species list in Appendix 2). The list comprises 172 anurans (frogs and toads), one salamander, and four caecilians. We did not include the toad Bufo poeppigii because it possibly occurs outside of the study area in Ecuador. Confusion over the distinction between this species and Bufo marinus clouds our understanding of its distributional limits (Córdova and Descailleaux 1996). Although Colostethus melanolaemus has been reported as endemic to Amazonian Peru, it has recently been found outside the study area in Brazil (V. Morales, personal communication). Dendrobates amazonicus is also reputed to be endemic to our study area near Iquitos, but we do not include it in our study because it was described from a photograph, not a specimen, limiting scientists’ ability to ascertain whether other specimens pertain to this species. For nomenclature, we follow IUCN et al. (2006). This scheme adopts the changes suggested

VI. AmphibiansBy César Aguilar, Lourdes Arangüena, Jesús H. Córdova, Dirk Embert, Pilar A. Hernandez, Lily Paniagua, Carolina Tovar, and Bruce E. Young

3�


recently for hylid frogs (Faivovich et al. 2005) but not controversial higher-level changes (Frost et al. 2006).

Compilation of locality records. We obtained specimen locality information for the list of 177 target species from the major collections in Peru and Bolivia as well as all North American museums with significant holdings of specimens from South America (see Appendix 1 for list of contributors). We geo-referenced all localities for which the specimen labels did not include geographical coordinates using the sources described in Chapter V: Vascular Plants. We supplemented this data set with additional information from the literature by searching the Zoological Record for the target species and their known synonyms. Once we compiled a draft database of locality records, L. Rodríguez and D. Embert, herpetologists with broad knowledge of the amphibians of Peru and Bolivia, respectively, reviewed the data to ensure accuracy and completeness.

Model runs and reviews. We ran Maxent models on all species with at least two localities. In addition to variables mentioned in addition to variables mentioned in Chapter IV: Distribution Modeling Methods, we used, for highland species (those occurring principally above 800 m), a variable that reflected the range of elevations across each 1 km2 pixel. A group of nine herpetologists from Peru and Bolivia reviewed the models and suggested criteria for deriving deductive models for both the species known from single localities and those known from multiple localities for which Maxent did not produce a meaningful model (see Appendix 3 for list of specialists consulted). Criteria used for deductive models included elevation, watershed boundaries, location of land with natural habitat, or a fixed distance related to the dispersal capability of the species involved. In the cases for which Maxent produced useful models, the reviewers selected one of the four models (i.e., using MODIS vegetation data in different manners, see Chapter IV: Distribution Modeling Methods) that best depicted their understanding of the range, and then identified the most appropriate threshold for developing a predicted presence-absence distribution map.

ResultsDistribution Modeling. We compiled 1,060 unique locality records for the 177 focal amphibian species (Figure 21, Table 3). Although the species averaged 6.0 localities each, the median was just two. The low median reflects the fact that 65 amphibians (37% of the total) in the study area are known from single localities. The most widely collected species was Eleutherodactylus platydactylus, known from 64 localities from San Martin, Peru, to Santa Cruz, Bolivia.

For distribution models, reviewers selected Maxent for 85 species (48% of the total), hybrid Maxent-deductive models for eight species (5%) and deductive models for the remaining 84 species (47%). Considering just the sample of species for which there were at least two unique records, Maxent provided an acceptable whole or partial distribution model for 83% of species. Among the four Maxent models, reviewers preferred model 1 (no MODIS data) for 3% of species, model 2 (Modis data ungeneralized) for 18% of species, model 3 (MODIS data generalized 2 km) for 35% of species and model 4 (MODIS data generalized 5 km) for 43% of species. The species with the largest range was Colostethus trilineatus, occurring over an estimated 690,992 km2 in lowland southern Peru and northern Bolivia. The most restricted species was Eleutherodactylus lucida, known from an estimated area of 13 km2 at over 3,500 m north of Mahuayura, Ayacucho Department, Peru. The average range size for all endemic amphibians was 18,935 km2, but due to the large number of species known from single localities the median range size was 399 km2.

Endemic Species Richness. Concentrations of endemic amphibians were much greater in central Cochabamba department, where up to 29 species co-occur, than elsewhere in the study area (Figure 22). Other places with overlapping ranges of many endemic species (although with fewer than the 21 required by our definition to be

Figure 21. Distribution of amphibian collecting localities in the study area.

3�

considered areas of endemism) include the cordilleras below La Paz, including some of the outlying ridges such as the Serranía del Eslabon and the Serranía Chiru Choricha, the Paucartambo region in Cusco Department, the area north of Ayacucho in Ayacucho Department, and the Tarapoto region of San Martin Department. The amphibian area of endemism totals 2,798 km2 and occurs at a mean elevation of 1,440 m.

Summed Irreplaceability. The analysis of summed irreplaceability reflects the large numbers of amphibians with very small ranges, especially in the north (Figure 23). Isolated peaks occur in many parts of the study area. Several are clustered in northern San Martin and adjacent Amazonas Departments, with additional peaks in Ayacucho, Cusco, and Cochabamba Departments.

Richness of Range-restricted Species. Eighty-six target species had ranges small enough to be considered to have restricted ranges. Their ranges were so small and their distributions so scattered geographically that there was little overlap across species. The locations of these species are mostly in the northern half of the study area. The areas highlighted by the summed irreplaceability analysis cover most of the places where range-restricted amphibians occur.

Understudied Areas of Endemism. This analysis highlighted areas in southern Peru and Bolivia as being especially understudied (Figure 24). These places are the Río Inambari drainage in northwestern Puno, the Peru-Bolivia border area on the lower slopes of the Cordillera de Apolobamba, the cordilleras north of La Paz, and the cordilleras on the east slope of the Andes along the borders of La Paz and Cochabamba Departments.

DiscussionEndemic amphibians from the eastern slope of the central Andes are characterized by their exceptionally small ranges. Half of the species endemic to our study area had ranges that we estimate to be less than 400 km2. These species tend to occur at mid elevations in the Andes, as suggested previously (Duellman 1999, Reichle 2004). Few endemic species occurred in the lowlands where overall diversity is known to be very high (De la Riva et al. 1990, Reichle 2004, Young et al. 2004). The largest concentrations of endemic species are in the south near Cochabamba, but significant numbers of microendemic species occur throughout the study area at mid elevations, especially in the north (Figures 22, 23).

Could the patterns we see be the result of incomplete knowledge about amphibian diversity and distribution

Figure 22. Endemic richness of amphibian species.

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in the study area? Additional collecting efforts in previously unstudied areas will certainly extend the ranges of some species and reveal the existence of new species, as it did during the decade of the 1990s in Bolivia (De la Riva et al. 1990). However, many species may truly be microendemic. Many well-studied amphibians elsewhere in the Neotropics are clearly microendemic. For example, the Golden Toad (Bufo periglenes) occurred along approximately 10 km of ridge-top cloud forest in Costa Rica (Savage 2002). The area was intensely surveyed by herpetologists over three decades and the species did not appear elsewhere. Similarly, the treefrog Tepuihyla talbergae appears to be restricted to the immediate vicinity of Kaieteur Falls, Guyana (Duellman and Yoshpa 1996), and there are many other examples of microendemic amphibians from the New World tropics. Moreover, ecological theory predicts that species occurring in northern Peru, with its greater proximity to the equator, should be smaller than those farther from the equator in Bolivia (Rappaport 1982, Stephens 1989).

Alternatively, the recent surge of collecting effort in many parts of Bolivia may have led to better knowledge of the Bolivian fauna and thus broader ranges recognized for species there than in Peru.

Supporting this contention is the observation that comparing just the national endemic species from our sample, Bolivian endemics have been recorded at an average of nearly two more unique localities than Peruvian endemics, a statistically significant difference (t-test with unequal variances, d.f. = 155, P = 0.02). On the other hand, Peru has been subject to a much longer tradition of herpetological fieldwork and is unlikely to be under collected relative to Bolivia (see, for example, studies listed in Duellman 1988). Collecting continues apace, as demonstrated for example by the work of herpetologists from the San Marcos and Dresden Natural History Museums (Lehr and Aguilar 2002, Lehr et al. 2001, 2002, 2004a, 2004b, 2005). Even large field collections made as long ago as the 1970s are still being worked up (Duellman 2004). Perhaps the vast size of the country results in the distances between collecting localities being larger than in Bolivia, resulting in less overlap in species composition and therefore the appearance of many species being restricted to single localities. Resolution of this question awaits further, comprehensive survey and inventory work throughout the study area.

Our analysis suggests that priorities for future field studies might most be profitably focused on southern

Figure 23. Summed irreplaceability of endemic amphibians.

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Figure 2�. Understudied areas of amphibian endemism.

Peru and Bolivia. As described in Chapter IV: Distribution Modeling Methods, we used predicted areas that were more than 50 km from the nearest locality as an indication of where distributional information is most needed. Because so many of the ranges were small, especially for northern Peruvian species, there were few sections of ranges farther than 50 km from confirmed localities. The results are therefore heavily influenced by the distribution of a limited number of widespread species that are endemic to the study area. Although many species occur as microendemics in northern Peru as indicated by the summed irreplaceability analysis, our method for identifying understudied areas did not highlight northern Peru. Nevertheless, as pointed out in the discussion of whether the areas of endemism analysis is influenced by collecting bias, additional field work in northern Peru is also urgently needed to clarify the distributions of species endemic to that region (Duellman 2004).

The distribution patterns of endemic amphibians in the study area present a conservation challenge akin to the problem described for Mexico. Endemic species occur in isolated pockets over a large geographic area. No system of large biosphere-type reserves would ensure the survival of all species unless nearly all of

the mid-elevation Andes were included in one reserve or another. Large reserves are clearly necessary to maintain ecosystem functioning. However, a wider net of smaller reserves, perhaps at the departmental or municipal level, are needed to maintain habitats for the many microendemic species that occur between the big reserves. We hope that the data presented here and made available freely to the public (see Chapter X: Using the Data) will help inspire the establishment of more of these badly-needed protected areas.

�0


Introduction Although mammalogists have debated the origin of South America’s diversity for decades, few have attempted to identify the locations of areas of endemism on the continent. Species and even genera continue to be discovered at a remarkable rate and significant range extensions for known species are still common (Anderson 1997, Patterson 2000, Salazar-Bravo et al. 2002, Salazar-Bravo et al. 2003, Pacheco and Hocking 2006, Villalpando et al. 2006). Considering this unstable state of taxonomy and less-than-satisfactory knowledge about distributions, it is not surprising that few scientists have attempted comprehensive analyses of mammalian endemism on the continent.

Hershkovitz (1972) described the biogeography of South American mammals, but did not venture to specify areas of endemism. He suggested that the origins of the fauna occurring on the east slopes of the central Andes are the lowland species from the adjacent Amazon basin. In terms of diversity, he described a general pattern of decreasing species richness along the slopes of the Andes south of Ecuador. He did not suggest that any particular area supported substantially more endemic species than any other. At the time that he wrote, the fauna was still poorly known. Hershkovitz was aware of 810 South American species in 1972 whereas we now recognize 1,355 species (NatureServe 2004). Knowledge of the distributions of these species has similarly improved, but is far from complete.

In the 1980s, Reig (1986) provided an updated view of the biogeography of high Andean rodents. Although his analysis is restricted to species that occur in páramo and puna, some conclusions are relevant because related species inhabit our study area on the east slope of the Andes in Peru and Bolivia (Figure 1). For example, Reig (1986) proposed that the akodontines originated from North American cricetids, dispersed to high Andean Bolivia, and then diversified and spread out from there. This process suggests that we should see the greatest concentration of akodontine rodents in Bolivia and lesser numbers elsewhere. In contrast, the echimyid and abrocomid rodents diversified in Amazonia and the

Argentine pampas, respectively, and then dispersed into the highlands. If this is the case, we would not expect to see concentrations of these groups within the study area.

A more recent study used parsimony analysis of endemicity, a statistical technique that examines the similarity of the faunas in different areas (Rosen and Smith 1988), to search for areas of endemism in South American marsupials, primates, and rodents (Costa et al. 2000). The analysis was based on species occurring in 69 grid cells, each measuring 550 km x 550 km, placed over the continent. The authors found no indication of a grouping of similar species anywhere within our study area in the eastern Andes of Peru and Bolivia. However, one can argue that the ranges of endemic species are small compared to the size of the analysis grid cells. A single grid cell could include habitats as distinct as Amazonian moist forest and high-elevation puna. Considering the differences in mammal communities in these disparate habitats, it is not surprising that the authors found no similarities in adjoining grid cells.

The most recent effort to map mammalian endemism analyzed, among other factors, the distribution of restricted-range mammal species worldwide (Ceballos and Ehrlich 2006). The authors defined restricted-range mammals as those with distributions of less than 250,000 km2. Their unit of analysis was 100 x 100 km grid cells. The results show a relative concentration of these species along the east side of the Andes in Peru and into northern Bolivia. The global scale of their analysis and consequent large size of grid cells provide a valuable intercontinental comparison but are not appropriate for identifying locations of areas of endemism on a more local scale. The analysis we provide here of the distributions of mammals endemic to the east slope of the Andes in Peru and Bolivia is therefore the first of its kind to show fine-scale patterns of richness for this highly diverse region.

MethodsSelection of species. We followed the guidelines described in Chapter III: Study Area to select the 55

VII. MammalsBy Víctor Pacheco, Heidi L. Quintana, Pilar A. Hernandez, Lily Paniagua, Julieta Vargas, Bruce E. Young

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mammal species endemic to the study area (complete species list in Appendix 2). Other species considered but rejected include the Bolivian Chinchilla Rat (Abrocoma boliviensis) because it occurs just outside the study area, the Quechuan Mouse Opossum (Marmosa quichua) and the Junin Slender Opossum (Marmosops juninensis) because of taxonomic uncertainties about the specimens attributed to these species, the Matses Big-eared Bat (Micronycteris matses) because further survey work will almost certainly show that this volant species occurs outside the study area in nearby Brazil, the Bolivian Spiny Rat (Proechimys bolivianus) because it is now considered a synonym of P. brevicauda (Wilson and Reeder 2005), the water rat Nectomys garleppii because it is now considered a synonym of N. apicalis (Patton et al. 2000), and the Long-nosed Scolomys (Scolomys ucayalensis) because it now includes the form juruaense and therefore is not endemic to the study area (Gómez-Laverde et al. 2004). For nomenclature and taxonomic status, we follow Wilson and Reeder (2005).

Compilation of locality records. We requested specimen locality information for the target species from the major local natural history museums as well as all North American museums with significant holdings of specimens from South America (see Appendix 1 for list of contributors). We geo-referenced all localities for which the specimen labels did not include geographical coordinates using the sources described in Chapter V: Vascular Plants.

We supplemented this data set with additional information from the literature by searching the Zoological Record for the target species and their synonyms. Larger mammals are rare in collections because of both the time and cost involved in collecting, preparing, and storing specimens and the difficulty in securing collecting permits. We therefore supplemented our sample for large mammals with field observations or reliably identified tracks. These observations are part of the database maintained at the Centro de Datos para la Conservación at the Universidad Nacional Agraria La Molina in Lima, Peru. V. Pacheco and J. Vargas, two mammalogists with extensive field and museum experience in Peru and Bolivia, respectively, reviewed map displays of the locality database to ensure accuracy before running models. In addition, V. Pacheco visited the Colección Boliviana de Fauna in La Paz, Bolivia, to ensure consistency in the identification of small rodents.

Model runs and review. We ran Maxent models using the revised locality data for all species with at least two distinct localities. V. Pacheco reviewed the results for

Peru and J. Vargas reviewed the results for Bolivia. These reviewers determined whether any of the Maxent models produced reasonable results and if they did, identified the model and threshold that produced the most reasonable map for the species according to our present understanding of its distribution and the habitat available. The reviewers also identified predicted areas of distribution where the species is known not to occur. In cases in which the Maxent model did not produce a useful prediction, we used deductive models. In these cases for montane species with only one record such as Thomasomys rosalinda, we used a buffer of 100 m of elevation above and below the known record, reflecting conservatively the likely home range area of the species. For lowland species, we buffered known localities by distances reflecting the probable area over which the particular type of species might disperse. For example, we buffered one locality for the primate Callicebus ollalae by 10 km.

ResultsDistribution Modeling. In sum, we compiled 618 unique localities for the 55 endemic mammal species (Figure 25, Table 3). Four species (7% of the total) are known from single localities: Cuscomys ashaninka, Rhipidomys ochrogaster, Thomasomys onkiro, and Thomasomys rosalinda. The most widely collected species was the Yungas Akodont (Akodon aerosus), known from 70 localities. On average species were known from 11.2 localities. Twenty species (36%) are known from at least 10 localities and just seven (13%) are known from 25 or more localities.

We used Maxent models for 47 species (85%) and deductive models for the remaining species. The fraction modeled using Maxent increases to 92% when considering only species known from more than one distinct locality. Reviewers selected model 4 (incorporating MODIS data generalized to 5 km) for 42 species (89% of inductively modeled species) and model 3 (MODIS data generalized to 2 km) for the remaining species.

Range sizes for mammals averaged large compared to the other vertebrates examined in this study. The average size was 47,800 km2. The Buff-bellied Rhipidomys (Rhipidomys ochrogaster), known from a single locality in the department of Puno, Peru, had the smallest range of the target species, just 33 km2. The Inca Hocicudo (Oxymycterus inca) had the largest range, 344,920 km2, from extreme southern Ecuador to central Bolivia.

Endemic Species Richness. Combining the ranges of all focal mammals reveals a narrow band of relatively

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high numbers of endemic species occurring just below treeline and extending from the Department of Cusco in Peru to the Department of La Paz, Bolivia (Figure 26). Similar densities of endemics occur disjunctly further south in the Cordillera de Tunari-Tiraque, department of Cochabamba. Throughout these areas, endemic species richness ranged from 17 to 20 species. In central Cusco and northwestern Puno, localized areas harbored up to 25 species, or nearly half of all endemic species examined in this study. These were the only two restricted areas having significantly more endemics than elsewhere. North of Cusco, peaks in endemism, again located just below treeline, averaged about 10 sympatric species. Relatively few endemics at the level of our study area occur in the Amazon lowlands. All areas with at least 17 species of endemic mammals cover a total of 12,538 km2 at an average elevation of 2,809 m.

Summed Irreplaceability. Weighting the analysis to highlight areas where the smallest-ranged species occur produced a slightly different result (Figure 27). The narrow band from Cusco to La Paz and in Cochabamba disappears. However, five areas stand out as particularly important:

1. The La Libertad-San Martin border in the Cordillera Central. Location of the Abiseo River National Park and home to endemic species such as Thomasomys apeco and T. macrotis.

2. West side of the Apurimac river in Ayacucho east to southeastern Madre de Dios. The area upslope from the river including the localities Huanhuachallo and Santa Rosa river, which are the only places where the bats Sturnira nana and Mimon koepckeae are known to occur, extending east to just into Madre de Dios.

3. Northern tip of the Cordillera de Vilcabamaba. An isolated ridge in Otishi National Park, Junín Department, Peru, where the microendemic Ashaninka Arboreal Chinchilla Rat (Cuscomys ashaninka) and Ashaninka Thomasomys (Thomasomys onkiro) were discovered (Emmons 1999, Luna and Pacheco 2002).

4. Western Beni Department, Bolivia. A small area near the border with the department of La Paz where the microendemic Ollala Brothers’ Titi (Callicebus olallae) and Río Beni Titi (Callicebus modestus) occur.

5. Cordilleras near La Paz, Bolivia. This small area supports many endemics, including several with relatively large ranges and a few more restricted species, such as the arboreal mouse Rhagomys longilingua.

Richness of Range-restricted Species. Thirty-eight of the mammal species are considered to have restricted ranges. The distribution of this subset mirrors the distribution of all endemic mammals with the exception of an additional peak on the Cordillera de Colan in Amazonas Department, Peru.

Understudied Areas of Endemism. Analysis of areas where species are predicted to occur but are far from current records identifies five areas as worthy of field mammalian surveys (Figure 28).

1. Northwestern Huanuco. On the lower slopes of the Cordillera de Turco.

2. Western Cordillera de Vilcabamba. The area between the Apurimac river and Cerro Pumasillo in the department of Cusco.

3. Cordillera de Paucartambo. The area along both sides of the Cordillera, which forms the boundary between the departments of Cusco and Madre de Dios.

4. Foothills above the Inambari river. A remote area in northwestern Puno Department.

5. Central Bolivian Yungas. Eastern La Paz Department near the border with Cochabamba.

Figure 2�. Distribution of mammal collecting localities in the study area.

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Figure 2�. Endemic richness of mammal species.

Figure 2�. Summed irreplaceability of endemic mammals.

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Figure 2�. Understudied areas of mammal endemism.

DiscussionLike other animal and plant taxa in the study area, endemism in mammals is largely a phenomenon of mid to upper elevations on the east slope of the Andes (Pacheco 2002). The highest concentrations of endemic species are in a narrow band just below treeline in Yungas forests, in a pattern also reported for Andean birds (Graves 1988). A noteworthy pattern is the overall lack of isolated areas with very high levels of endemism. Instead, we found a uniformly high density of endemics all the way from Cusco to Cochabamba. This observation resulted from the many species with distributions that spread over significant portions of this area and a lack of microendemic species. Consequently, average range size was larger for mammals than for other taxa (Table 3). However, the description of Marmosops creightoni (Voss et al. 2004) and the discovery of several undescribed taxa of Thomasomys (Pacheco 2003) highlight the possibility that some of the species treated here may represent complexes of species. The pattern of range sizes that we observed might change when the species complexes receive proper systematic revision. The summed irreplaceability analysis highlighted areas that have many endemics to the study area but also microendemics such as Cuscomys ashaninka, Mimon

koepckeae, and Thomasomys macrotis that contributed heavily to the weighting.

A few endemic species occur in the lowlands, but in densities that are an order of magnitude lower than at higher elevations (Figure 26), as observed previously (Pacheco 2002). This observation matches the pattern observed for other taxa that Amazonian lowland species tend to be more widespread than montane species (Stotz et al. 1996, Duellman 1999). Further survey work in the lowlands may even reduce the densities of endemics farther by demonstrating that some species now considered endemic are, in fact, more widely distributed.

Across the 13 degrees of latitude covered in this study, we found a greater richness of endemic mammals in the south than in the north (Figure 26). Although we did not include widespread species, the results of our sample do not correspond to the general pattern of decreasing diversity with distance from the equator (Pagel et al. 1991). Our results also do not agree with Hershkovitz’s (1972) prediction of decreasing Andean mammal diversity south from the equator. “Rappaport’s Rule,” stating that range sizes increase with increasing

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distance from the equator, has been invoked to explain latitudinal gradients in diversity (Rappaport 1982, Stephens 1989). Our finding of a greater diversity of endemics (which have relatively small ranges) in the southern two-thirds of the study area does not support this rule. Reig’s (1986) hypothesis about the diversification of the akodontine rodents may help explain why our results contradict these biogeographical rules of thumb. The species that make up the Cusco to Cochabamba peak of endemism are dominated by akodontines. Reig predicted that we should record more of these species in Bolivia and southern Peru than in northern Peru, and that is precisely the pattern we found. Thus our results support more a center of origin model than a latitudinal gradient model of species diversification.

The relative uniformity and large extent of the areas of peak endemism suggest that the results are not heavily influenced by collection biases. Although one of the two greatest concentrations of endemic species is in the well-collected Cusco area (including the Manu Biosphere Reserve, Solari et al. 2006), the other northwestern Puno area is poorly studied—our database records only two records for Akodon aerosus there. Additionally, no other peaks are evident near cities throughout the study area. Inventories at the five locations identified as understudied areas of endemism will be useful to determine if the pattern of areas of endemism described here hold or if substantial numbers of microendemic species are discovered.

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Introduction Because of their mostly diurnal habits, ease of identification, and perhaps esthetic appeal, birds have received a tremendous amount of attention from taxonomists and biogeographers. Regular discoveries of new species continue to surprise ornithologists (e.g., Valqui and Fjeldså 1999, O’Neill et al. 2000), but the rate of description of new species is far less than for other groups because there are simply many fewer bird species left to find. Field researchers today focus more on providing detailed distributional and ecological information than on taxonomy. Consequently we know more about the patterns of avian distribution than for virtually any other class of organism.

Biogeographers have been analyzing the South American avifauna for some time (e.g., Hellmayr 1912, Mayr 1964). Birds in particular were used to support the hypothesis that changing climates in the past led to Pleistocene refugia where humid forest birds became restricted during drying periods and then diversified and dispersed as climates became wetter (Haffer 1969). An early, comprehensive attempt to map areas of endemism in South America appeared in the mid 1980s (Cracraft 1985), just after a study of range-restricted birds in the Andes of Colombia and Ecuador (Terborgh and Winter 1983). Cracraft (1985) defined areas of endemism as places where species’ ranges are congruent and identified 33 such areas in South America. He defined the Peruvian Andean Center as a region running the length of the Andes in Peru and into northern Bolivia. The area is separated from the North Andean Center by the Huancabamba gap in the dry valley of the Maranon river in northern Peru and to the south by the dry valleys between the Bolivian cities of Sucre and Cochabamba. The Peruvian Andean Center includes the entire upper portion of the current study. At the time that Cracraft wrote, distributional information was too fragmentary to describe finer-scale divisions other than to show that the Apurimac river in Peru divides the fauna of the eastern Andes. A few years later, Ridgely and Tudor (1989) also mapped areas of

endemism for South American birds and described a Peruvian Andean center with essentially the same circumscription as identified by Cracraft (1985).

A subsequent attempt defined ‘endemic bird areas’ as places that encompass the complete ranges of at least two species with restricted ranges, defined as those less than 50,000 km2 (Stattersfield et al. 1998). The resulting endemic areas are delineated at a finer scale than previous analyses. Our study area includes five of these endemic bird areas: Andean ridge-top forests, North-east Peruvian cordilleras, Peruvian East Andean foothills, Bolivian and Peruvian lower Yungas, Bolivian and Peruvian upper Yungas as well as parts of six others. Stattersfield et al. (1998) collated locality records for range-restricted birds from the literature and from a network of field workers and then used the extent of relevant habitats or geographical features such as elevational contours, rivers, or coastlines to develop distribution maps for each species.

Fjeldså and colleagues (1999, 2005) followed a different approach to map the distributions of Andean birds. Using published data and the results of extensive field work, they built a database scoring the presence of each species in each cell of a quarter degree grid placed over the Andes. Scoring the numbers of species in each cell provides a picture of the richness of either all species or the species with the smallest ranges, defined as those in the lower quartile of ranked range sizes. This method allows a finer-scale view of the locations of concentrations of species with small ranges. Fjeldså et al. (1999, 2005) found the highest densities of restricted-range species near the Maranon River in northern Peru, on the east slope of the cordillera in Huanuco in central Peru, in the Cusco region, and along the Andes near La Paz.

The distribution modeling approach we follow provides more detailed maps than previously available that in turn allow a more detailed analysis of the locations of areas of endemism. The scale of the results is

VIII. BirdsBy Irma Franke, Pilar A. Hernandez, Sebastian K. Herzog, Lily Paniagua, Aldo Soto, Carolina Tovar, Thomas Valqui,and Bruce E. Young

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therefore more useful for regional conservation planning than previous continental-scale analyses. Instead of highlighting large areas covering tens of thousands of square kilometers as needing protection, we can pinpoint specific places as alternatives for establishing national- or department-level protected areas. We can also compare our results with those of Stattersfield et al. (1998) and Fjeldså et al. (1999, 2005) to determine if previous analyses completely covered all areas with high levels of endemism.

MethodsSelection of species. We followed the guidelines described in Chapter III: Study Area to select the 115 species of birds treated in this study (complete species list in Appendix 2). We eliminated several candidate species, including Aglaeactis aliciae, Cranioleuca albicapilla, Asthenes ottonis, Grallaria andicolus, and Incaspiza laeta, that occurred close to the study area boundaries, but are restricted to either dry forest or grassland habitats that are largely outside of the study area. For nomenclature and the designation of species status, we followed the recommendations of the South American Classification Committee of the American Ornithologists’ Union (Remsen et al. 2006). The one exception is Hemispingus auricularis, which we treat as distinct from Hemispingus atropileus following the recommendation of García-Moreno et al. (2001).

Compilation of locality records We requested specimen locality information for the target species from the major local natural history museums as well as all North American museums with significant holdings of specimens from South America (see Appendix 1 for list of contributors). We geo-referenced all localities for which the specimen labels did not include geographical coordinates using the sources described in the Vascular Plants section. We supplemented this data set with additional information from the literature by searching the Zoological Record for the target species and their known synonyms. Additionally, we searched for records in the reports of Conservation International’s Rapid Assessment Program (RAP), the Field Museum’s Rapid Biological Inventory (RBI), and the Smithsonian Institution’s Monitoring and Assessment of Biodiversity (SI/MAB) program. Because most species of birds can be identified by sight or sound in the field by experienced observers, we included observational records compiled by Asociación Armonía (BirdLife International in Bolivia) and largely representing observations made in Bolivia by S. K. Herzog and A. B. Hennessey. During the review process we added additional unpublished records provided by M. Anciães, J. Fjeldså, D. F. Lane,

J. P. O’Neil, and T. Valqui. To ensure the accuracy of the coordinates assigned to each locality, two ornithologists with extensive experience in Peru (D. F. Lane and J. P. O’Neil) reviewed maps displaying the localities for each species that occurs in Peru. The staff of Asociación Armonía performed the same function for the Bolivian data.

Model runs and review. We ran Maxent models using the revised locality data for all species with at least two distinct localities. Three reviewers familiar with the study area and species reviewed the output (see Appendix 3 for list of specialists consulted). These reviewers determined whether any of the Maxent models produced reasonable results, and if they did, identified the model and threshold that produced the most reasonable map for the species according to our present understanding of its distribution and the habitat available. The reviewers also identified predicted areas of distribution where the species is known not to occur.

For most cases in which the Maxent model produced unusable results, we used deductive models based on elevational ranges to depict distributions. In two cases, we reran Maxent with a reduced number of environmental variables to improve the predictions. Only three species, the Scarlet-banded Barbet (Capito wallacei), Vilcabamba Tapaculo (Scytalopus urubambae), and Sira Tanager (Tangara phillipsi), are known from a single locality. We modeled their distribution using the elevational range where the species have been recorded.

ResultsDistribution Modeling. We compiled a total of 2,437 unique locality records for the 115 endemic bird species (Figure 29, Table 3). Sample sizes of localities ranged from one (for Capito wallacei) to 94 (Atlapetes rufinucha), averaging 21.2. Thirty-seven species (32% of the total) had at least 25 unique localities and 76 species (66%) had at least 10 unique localities. Reviewers selected Maxent models for 99 (86%) species, hybrid models for six (5%) species, and deductive models for ten (9%) species. Reviewers preferred Maxent results using model 4 (incorporating MODIS data generalized to 5 km) most frequently (74 or 70%), followed by model 3 (MODIS data generalized to 2 km) for 17% of the inductively modeled species. The other models, either not using MODIS data or using ungeneralized MODIS data, were chosen for just 10% of these species. The range sizes of the resulting distribution maps varied from 78 km2 (Scytalopus urubambae) to 309,168 km2

(Grallaria eludens) with an average of 33,544 km2.There was no obvious taxonomic signature to the

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results. Among the families with several target species, there were examples in each of relatively wide-ranging and narrow-ranging species. For example, the tanagers (Thraupidae) included both broadly-distributed (Conirostrum ferrugineiventre, 82,401 km2) and narrow endemic (Tangara meyerdeschauenseei, 2,734 km2) species. The same pattern holds for other families with at least five target species, including the hummingbirds (Trochilidae), ovenbirds (Furnariidae), antbirds (Thamnophilidae), antpittas (Formicariidae), tapaculos (Rhinocryptidae), tyrant flycatchers (Tyrannidae), and sparrows (Emberizidae).

Endemic Species Richness. Superimposing the ranges of all species treated in this study reveals four major and two minor areas with concentrations of endemic bird species (Figure 30).

• Major areas (with 31-3� species per 1 km2 grid cell)

1. Southern Huanuco. The portion of the east slope of the Peruvian Andes in the southern half of the department of Huanuco including the Carpish Hills area along the road to Tingo Maria and extending south to the northwest corner of the Yanachaga-Chemillén National Park in Pasco Department.

2. Central Cusco. The region extending from the right bank of the Apurimac river east to the

Yungas of Megantoni National Sanctuary and Manú National Park, including the southwestern Cordillera de Vilcabamba, Machu Picchu, and the valleys of Ocobamba, Yanatile, and Mapacho-Yavero.

3. Cordillera de Apolobamba. The portion of the east slope of the Andes in Bolivia just south of the Peruvian border in Madidi National Park.

4. Cordillera de Cocapata-Tiraque. A section of the Andes near the city of Cochabamba.

• Minor areas (with 2�-30 species per 1 km2 grid cell)

1. Cordillera de La Paz. Also known as the Cordillera Real, near the Bolivian capital city of La Paz.

2. Cordillera de Tres Cruces. Near departmental border between La Paz and Cochabamba.

These six regions cover a total of 24,788 km2. All occur along the upper elevational limit of the study area on the east slope of the Andes at an average elevation of 2,540 m. Very few, typically less than five, endemic species occurred anywhere in the study area below 1,500 m elevation.

Summed Irreplaceability. Summed irreplaceability, weighted to give greater emphasis to species with smaller ranges, highlights the Cordillera de Colan and the Alto Mayo region, two upland areas at the northernmost extension of the eastern Andes in Peru before the precipitous slope down to the Maranon river (Figure 31). This is the only place where the endangered Long-whiskered Owlet (Xenoglaux loweryi) occurs.

Richness of Range-restricted Species. The peaks in endemism exhibited by the 103 species of birds with restricted ranges were virtually identical to those for the entire set of endemic species shown in Figure 30.

Understudied Areas of Endemism. The map showing areas where endemic bird species are predicted to occur but have not been recorded within 50 km identifies a long section of the Andes from the central Peruvian department of Huanuco south to the Bolivian border (Figure 32). Bolivia appears to be better studied with just isolated regions where species are predicted but have not yet been recorded. The major understudied areas are:

1. Northwestern Huanuco. On the lower slopes of the Cordillera de Turco in the valleys of the Chontayacu and Huanuco rivers.

Figure 2�. Distribution of avian collecting localities in the study area.

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Figure 30. Endemic richness of bird species.

Figure 31. Summed irreplaceability of endemic birds.

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2. San Fernando-Rio Mantaro river valley. A remote area in southern Junín and northern Ayacucho departments along the last section of the Mantaro river before it empties into the Apurímac river.

3. East side of the Ene river. An area upslope of the town of Quempiri, east of the Ene river in southern Junin Department encompassing the northern portion of the Cordillera de Vilcabamba and parts of the Ashaninka Comunal Reserve and Otishi National Park.

4. Western Cordillera de Vilcabamba. The area between the Apurimac river and Cerro Pumasillo east of Machu Picchu in the department of Cusco.

5. Cordillera de Paucartambo. The area along both sides of the Cordillera, which forms the boundary between the departments of Cusco and Madre de Dios and includes the western limits of Manú National Park.

6. Foothills above the Inambari river. From the San Gaba to Pararani river valleys in northwestern Puno Department.

7. Rivers Mapiri-Tupuani. The upper tributaries, including the lower Consata and Mapiri rivers and Tipuani, in the Department of La Paz.

8. Cordilleras west of Yungas de Cochabamba. Eastern La Paz Department near the border with Cochabamba.

DiscussionMost of the bird species endemic to the study area had characteristics that allowed for successful inductive distribution modeling. Maxent produced favorable results for 91% of the species studied. The adequate sample of localities available for most species and the fact that most of the target bird species tend to distribute themselves in elevational bands may have contributed to this result. Inductive PDM methods tend to work better for specialist than generalist species (Elith et al. 2006), which may explain why the models worked so well for our focal endemic species, most of which are specialists.

Our experience modeling the Cloudforest Screech-Owl (Megascops marshalli) demonstrates the power of species distribution models for predicting species’ occurrences. When we began the study, this species

Figure 32. Understudied areas of bird endemism.

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was known from three localities in Pasco and northern Cusco, Peru. The Maxent model using these points predicted the species to occur not only in the vicinity of the known localities, but also in widely disjunct locations in the Bolivian Andes in northern La Paz and northern Cochabamba departments. Later we learned of tape recordings and one specimen of this species from three areas in Bolivia (S. K. Herzog, unpubl. data) precisely where Maxent predicted it to occur.

Rare lowland species such as the Selva Cacique (Cacicus koepckeae) were especially difficult to model with the environmental data layers available. This species is known from just six scattered localities and may be restricted to microhabitat types such as canebreaks that are not reflected in the remotely-sensed MODIS vegetation data. The Blue-throated Macaw (Ara glaucogularis) was another lowland species that was difficult to model. The Maxent model showed a much wider distribution than is currently known for this critically endangered species that is subject to intensive monitoring. The area of over-prediction may indicate potential habitat for the species, but persecution for the pet trade and other threats may have extirpated the species from these areas. Alternatively, the palms

(Attalea phalerata, Acrocomia totai, and Scheelia princeps) which produce fruit important to the macaw’s diet (Juniper and Parr 1998), may be rare or absent from the areas of over-prediction. Because of one or both of these reasons, models for lowland species produced with the methods employed here required judicious clipping by ornithologists familiar with the species to eliminate areas of over-prediction.

The endemic areas maps for birds probably suffer fewer biases than for the other taxa treated in this study. We obtained sufficient sample sizes for most species and the average range size was larger than for many plant groups and amphibians, suggesting that the models depicted most of the species’ actual ranges. Birds do not show the patterns of microendemism seen in other taxa. Therefore there is less of a possibility of the data showing large concentrations of endemics near access points for collectors and few species elsewhere. Nonetheless, two of the major areas of endemism occur near the accessible regions of Cusco and Cochabamba. Intense study in the cordilleras in these areas may lead to a bias toward recording more endemic species there than elsewhere. Conversely, the other major area of

Figure 33. Endemic bird areas (Stattersfield et al. 1��) superimposed on Figure 30.

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endemism for birds occurs in northern Bolivia in an area that is just now the subject of bird surveys. The lack of locality information for these areas compared to others suggests that the distribution models are working as they were intended—serving as proxies for more intensive survey effort.

The Stattersfield et al. (1998) analysis of endemic bird areas showed most of the upper portion of the study area to occur in an endemic bird area (Figure 33). Thus, in a broad sense, our results, produced by a more detailed process to identify species’ ranges, support the conclusions of the Stattersfield et al. (1998) report. Because of the fine resolution of the results we present, we can suggest which parts of a particular endemic bird area harbor the most endemic species. For example, the portion of the Bolivian and Peruvian lower Yungas area has many more endemic species in Cochabamba than in La Paz (Figure 30). One of the major endemic areas that we identified, the central Cusco region, is not completely covered by designated endemic bird areas. Our results predict that the western Cordillera de Vilcabamba, between the Apurímac river and Cerro Pumasillo, forms part of the ranges of many endemic species and therefore

is an important candidate for protection. This region has received little attention by ornithologists as shown in Figure 32, suggesting that it may have been overlooked previously. Similarly, the region along the Mapacho-Yavero river east of Cusco was also not included in an endemic bird area but predicted to have substantial numbers of endemic species (Figure 30). Again, this is a poorly explored area (Figure 32) and therefore may have been ignored in the past.

Stattersfield et al. (1998) reported two lowland endemic bird areas in the study area, the Upper Amazon-Napo lowlands and the South-east Peruvian lowlands. Neither of these areas is highlighted in our study because the higher elevation sites have many more endemics. Also, the two lowland endemic bird areas include species that range into Amazonian Ecuador, Colombia, and Brazil and are not treated here.

Our results are also in general agreement with the patterns of endemic species richness described by Fjeldså et al. (2005). The major areas of endemism described in the two analyses coincide (Figure 34). The differences are in emphasis. Fjeldså et al. (2005) highlight the La Paz region more and the Cordillera

Figure 3�. Endemic bird areas (Fjeldså et al. 200�) superimposed on Figure 30.

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de Apolobamba less than this study. In the Cusco region, the Fjeldså et al. (2005) results are similar to those of the endemic bird areas, showing the greatest abundance of endemic species in central Cusco near Machu Picchu. Our results suggest that regions both east and west of there may have nearly as many of these species. The differences are likely due to our use of predicted ranges in an attempt to control for the collecting bias that has occurred at places such as the Machu Picchu and La Paz regions.

Our predictions of these previously under-recognized areas of endemism are valuable for the practice of conservation. Land around cities tends to be more valuable due to its development potential. Creating protected areas in these places is often complicated by the many competing economic interests. By identifying remote areas that have similar biodiversity value, conservationists can site protected areas there resulting in reduced costs and fewer conflicts.

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This report so far has detailed our findings for each of the fifteen plant and three vertebrate groups addressed. Conservationists interested in preserving entire biotas require information on the distribution of all elements of biodiversity before making effective decisions about where to focus protection efforts. Although no study can realistically address all levels of biodiversity, here we combine results from the target groups to provide a comprehensive picture of the efficacy of modeling, the congruence of areas of endemism for different groups, and prospects for conservation.

Use of Modeled DistributionsAs demonstrated in our pilot study (Hernandez et al., unpublished manuscript), Maxent proved remarkably flexible as an inductive modeling algorithm in a wide variety of contexts for species as distinct as herbaceous plants and montane deer. Our results are based on a total of 525 inductive models for 782 species of plants and animals. Considering only the species known from at least two distinct localities, Maxent provided useful models for 89% of the 587 species in this category.

Maxent worked best for birds and mammals, less well for amphibians, and about average for plants (Table 3). It may be no coincidence that the groups with the largest samples of localities, birds and mammals, were also the groups with the best model performance. Even for amphibians, Maxent still worked for three quarters of the species with multiple localities. Many species of amphibians rely on small water bodies for all or portions of their annual cycles. These geographical features occur in most landscapes at scales that are too small to register in the environmental data currently available for modeling. Also, watershed boundaries may represent important barriers to dispersal for some amphibian species. Without being able to reflect these features in environmental data layers, no inductive modeling procedure could be more successful at predicting distributions. As noted previously, our knowledge about amphibian taxonomy and systematics in South America is still incomplete. Confusion about the taxonomic status of specimens in collections can further hamper modeling efforts.

A remarkable accomplishment of Maxent was to be able to model ranges of species with very few

distinct localities. Previously Maxent has been tested successfully with samples as small as five localities (Hernandez et al. 2006). Here we show that it is worth attempting Maxent models for species with as few as even two localities, at least in the conditions of the eastern Andes with their dramatic local differences in elevation and climate. Our sample included 90 species known from two localities. The specialist reviewers agreed that the Maxent model was useful for 67% of these species. Working with such small samples clearly requires the close collaboration of knowledgeable specialists. The results are valuable to predict places where the specialists can visit to look for additional records of these scarce and poorly-known species.

Congruence of Areas of EndemismWe were able to identify areas of endemism for amphibians, mammals, birds, and 13 of the 15 focal plant groups. Nowhere do the Anacardiaceae and Cyatheaceae have more than two co-occurring endemics, so we do not include these families in this synthesis of areas of endemism. How much do the areas of endemism identified in this study overlap across groups? Table 4 summarizes the 12 regions that support at least one area of endemism, and Figure 35 depicts these areas geographically. One area (Cocha Cashu) is important only for Inga, but the remaining eleven areas are important for multiple groups. Two areas stand out as supporting large numbers of endemic species for plants, mammals, and birds: the Paucartambo-Marcapata region and the Cordilleras near La Paz. Birds and mammals showed the most overlap among any combination of groups, with all five of the areas of high mammalian endemism also being important for birds. The 12 regions listed in Table 4 are areas of endemism for an average of 3.4 species groups each.

Most areas of endemism are distributed along mid to upper elevations on the slope of the Andes (Figure 35). Nine of the 12 areas of endemism identified consist of high elevation slopes that extend uphill to tree line. We identified two lowland areas, Iquitos and Cocha Cashu, as a result of consciously choosing plant groups such as Chrysobalanaceae, Inga, and Malpighiaceae that contain primarily lowland species. One mid-

IX. SynthesisBy Pilar A. Hernandez and Bruce E. Young

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Figure 3�. A composite view of the areas of endemism for birds, amphibians, mammals, and 13 plant groups (all except Anacardiaceae and Cyatheaceae, two families that do not have more than two overlapping endemic species). The darker colors indicate areas where more focal groups have overlapping areas of endemism.

elevation site, Tarapoto, was important for Inga and Malpighiaceae. Summed irreplaceability analysis, which gives heavier weight to species with smaller ranges and to areas with rarer combinations of species, also highlights some lowland areas, including the Iquitos and Yurimaguas areas in Loreto, Peru, for plants, and a small area of Beni, Bolivia, for mammals (Figures 19, 27). Although the lowlands do not contain peaks of endemism that are as species-rich as higher elevations, they nevertheless have at least a few areas that contain unique assemblages of endemic species.

For most groups, the highest number of endemic species occurs in montane forest just below tree line, as has been described previously (Graves 1988, Pacheco 2002, van der Werff and Consiglio 2004). This pattern, however, is by no means universal. Areas with high diversity of endemic amphibians occur at elevations averaging 1,531 m, more than 1,000 m lower than for either birds or mammals. Our effort to select plant taxa with a range of elevations at which endemics are concentrated was successful. Focal groups have the highest concentrations of endemics at elevations

ranging from 106-2,861 m. Three plant groups peak in endemic diversity below 1,000 m, one peaks between 1,000-2,000 m, and nine peak over 2,000 m.

Prospects for ConservationOur results highlight the importance of incorporating the distributions of species from a variety of taxonomic groups when performing conservation priority-setting exercises. Although budget and time limitations prevented us from working with taxonomic groups such as fish, invertebrates, and many plant families, we nevertheless worked with enough groups to show that no single group can serve as a surrogate for all biodiversity. Areas of endemism for amphibians occur, in most cases, far from areas of endemism for other vertebrate groups and at lower elevations. Plant families vary widely in the elevations where endemism is highest, and the specific locations of concentrations of endemics vary widely within large countries such as Peru and Bolivia. Other studies have confirmed the lack of congruence among the distribution of different taxonomic groups at a global scale, but few have done so at the fine scale used in this study (Prendergast et

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Table 4. Summary of areas of endemism identified in this project, in order from north to south.

Area Department Vascular Examples of Protected Plants Endemic Species Areas2

Iquitos Loreto Peru Chrysbalanaceae, Hirtella revillae, – Inga, Inga gereauana, Malpighiaceae Heteropterys actinoctenia

Tarapoto San Martín Peru Inga, Inga cynometrifolia, – Malpighiaceae Hiraea christianeae

Cordillera Central Amazonas, Peru Loasaceae, Nasa formosissima, Cordillera de Colán en Amazonas y San Martín Aquifoliaceae Ilex tarapotina Reserved Zone, San Martín Abiseo river N. Park

Cordillera Huanuco Peru Acanthaceae, Aphelandra mucronata, –Carpish Aquifoliaceae, Ilex aggregate, Fuchsia Fuchsia ceracea, Nephelornis oneilli

Cocha Cashu M. de Dios Peru Inga Inga megalobotrys Manu National Park

Cord. de Cusco Peru Fuchsia XX XX Fuchsia tunariensis, Machu Pichu Vilcabamba Mormopterus phrudus, Historical Sanctuary Atlapetes canigenis

Paucartambo- Cusco Peru Aquifoliaceae, X XX Ilex crassifolioides, Manu National Park, Marcapata (and marginally Fuchsia Fuchsia vargasiana, Megantoni in M. de Dios) Rhagomys longilingua, National Iridosornis reinhardti Sanctuary

Northwestern Puno Peru XX X Delothraupis –Puno castaneoventris, Akodon aerosus,

Cord. de La Paz Bolivia Brunelliaceae, XX Brunellia boliviana, Madidi N. Park, Apolobamba Ericaceae, Thibaudia axillaries, Apolobamba Natural Marcgraviaceae Sarcopera oxystilis, Integrated Thomasomys ladewi, Management Tangara Area meyerdeschauenseei

Cordilleras La Paz Bolivia Acanthaceae, X X Justicia albadenia, Cotapata N. Park near La Paz Aquifoliaceae, Ilex pseudoebenacea, and Integrated Brunelliaceae, Brunellia coroicoana, Management Area Campanulaceae, Centropogon bangii, Madidi N. Park Ericaceae, Sphyrospermum Marcgraviaceae, sessiliflorum, Mimosa, Souroubea stichadenia, Passifloraceae Oryzomys levipes, Atlapetes rufinucha

Cord. de Cochabamba Bolivia Brunelliaceae, X XX Brunellia rhoides, Tunari N. ParkCocapata-Tiraque Fuchsia Fuchsia garleppiana, Akodon siberiae, Hemitriccus spodiops

Central Cochabamba Bolivia XX X Simoxenops striatus Carrasco N. ParkCochabamba

Cou

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Bird

s1

Mam

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1 XX = Major area of endemism; X = minor area of endemism2 Only national-level protected areas are listed. Additional local or private reserves may also occur.

Am

phib

ians

1

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al. 1993, Dobson et al. 1997, van Jaarsveld et al. 1998, Lamoreux et al. 2006).

Some of the focal groups exhibited patterns of microendemism, in which numerous species are known from just one or a few localities that differ among species. Overall richness of endemic amphibians peaked in central Bolivia, but the summed irreplaceability analysis showed the importance of San Martin and Amazonas provinces in northern Peru because of the microendemics there. Although the map of endemic species richness for Acanthaceae shows a small number of well-defined areas of endemism (Figure 4), many microendemic species are scattered along the lower slopes of the Andes from Amazonas to Junin Departments. Overall, 197 of the 782 species mapped in this study are known from single localities and therefore represent microendemic species. Because of the large number of microendemics in the study area, conservationists should follow a strategy in which they site large ecosystem-level reserves in the major areas of endemism and promote the establishment of smaller regional reserves to coincide with the places where microendemic species occur. The species distribution maps generated in this project provide a valuable scientific basis for determining where these microendemic species occur as part of regional and local conservation planning exercises.

How well does the system of protected areas in Peru and Bolivia overlap with the areas of endemism identified in this study? Figures 36-39 shows that, although some national-level protected areas in the two countries protect areas of endemism, most of the areas of endemism fall outside of existing parks and reserves. Nine of the 12 areas of endemism have at least one national protected area covering a portion of the area. In some cases, however, the area covered is minimal. For example, the Cordillera de Vilcabamba area, important for birds, mammals, and Fuchsia, is protected only by the tiny Machu Picchu Historical Sanctuary. As a historical sanctuary, the management objectives are more directed at preserving cultural artifacts than biodiversity. The greatest overlap of areas of endemism in the entire study area occurs in the Cordilleras near La Paz (Figure 35). Only one small national park (Carrasco) protects the upper slopes of this extensive region (Figure 39). On the other hand, the Cocha Cashu area is entirely protected by Manu National Park and the Apolobamba area is largely contained within Madidi National Park. These results reveal that conservationists have achieved important accomplishments in protecting some key areas for endemism, but that additional work remains before they can proclaim that endemism in the region is well-protected.

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Figure 3�. National protected areas overlaid on the areas of endemism shown in Figure 3�.

Figure 3�. National protected areas and areas of endemism in northern Peru.

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Figure 3�. National protected areas and areas of endemism in southern Peru.

Figure 3�. National protected areas and areas of endemism in Bolivia.

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We encourage the reader to use the distributional data generated in this study for independent analyses of biogeography, diversity, and conservation planning. The individual range maps and richness analyses are available for download on the Internet at http://www.natureserve.org/AndesAmazon. The distributional data are available both as map images to copy and paste into documents and as grids that are compatible with ESRI Arc software. The data are freely available for non-commercial uses. Reposting the data on other websites is not allowed without prior written permission from NatureServe.

XI. AcknowledgementsWe are deeply indebted to the Gordon and Betty Moore Foundation for financial support and technical guidance throughout this project. For their leadership, ideas, and constructive criticism, all of which substantially improved this work over the life of the project, we thank D. Grossman, L. Master, B. Stein, and J. Swenson. We thank the curators of the museums listed in Appendix 1 for kindly making their specimen data available to us. We especially recognize the Field Museum of Natural History, the Missouri Botanical Garden, the University of California at Berkeley, the University of Kansas Museum of Natural History, the University of Michigan Museum of Zoology, the University of New Mexico Museum of Southwestern Biology, and the Yale Peabody Museum for making their collection data available over the Internet. We are grateful to C. Antezana, S. Arrázola, M. Atahuachi, N. de la Barra, P. Flores, D. Ibáñez, I. Jiménez, R. I. Meneses, M. Mercado, M. Moraes R., T. Ortuño, A. Palabral, R. Seidel, E. Valenzuela Celis, C. Zambrana y M. Zeballos M.O. for compiling plant locality databases. For technical assistance geo-referencing specimen localities, we thank B. Bassuner, J. C. Lozano, R. I. Sprem, J. Suárez, and K. S. Ting. Especially deserving of our gratitude for sharing their scientific expertise are the many reviewers of draft data sets and distribution models listed in Appendix 3. We acknowledge useful advice on modeling species distributions provided by D. Armenteras, R. Caballero, R. Eastman, C. Graham, K. Naoki, J. Parra, S. Phillips, and F. Sangermano. For making the BirdLife Endemic Bird Area data available to us, we thank D. Díaz. For providing baseline geographical data, we thank N. Araujo of FAN-Bolivia. R. Mobarec artfully coordinated logistics for many of the workshops we held with collaborators and reviewers. D. Steere, C. Shaw, M. Rosen, and their wonderful colleagues at the Smithsonian Natural History Library cheerfully facilitated our many visits to access the literature. We thank J. Dyson, C. Nascimento, H. Prado, and R. Riordan for their efforts and creative talent in the editing, design, and production phases of this publication, C. Josse for improving a draft of the chapter describing the study area, C. Klimovsky for translating the text into Spanish, and T. Howe for posting the data on the Internet.

X. Using the Data

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XII. Author AddressesCésar AguilarMuseo de Historia NaturalUniversidad Nacional Mayor de San MarcosApartado 140434Lima-14, Peru

Lourdes ArangüenaCentro de Datos para la ConservaciónDpto. Manejo ForestalFacultad de Ciencias ForestalesUniversidad Nacional Agraria La MolinaAptdo. 456, Lima 100, Peru

Stephan BeckHerbario Nacional de BoliviaCalle 27, CotacotaLa Paz, Bolivia

Jesús H. CórdovaMuseo de Historia NaturalUniversidad Nacional Mayor de San MarcosApartado 140434Lima-14, Peru

Dirk EmbertFundación Amigos de la NaturalezaKm. � y 1/2 carretera antigua a CochabambaSanta Cruz de la Sierra, Bolivia

Irma FrankeMuseo de Historia NaturalUniversidad Nacional Mayor de San MarcosApartado 140434Lima-14, Peru

Pilar A. HernandezNatureServe2 Parr StreetToronto, ON, M6J 2E3Canada

Sebastian K. HerzogAsociación Armonía - BirdLife InternationalAv. Lomas de Arena 400Casilla 3566Santa Cruz de la Sierra, Bolivia

Peter M. JørgensenMissouri Botanical Garden P.O. Box 299 Saint Louis, Missouri 63166-0299, USA

Víctor PachecoMuseo de Historia NaturalUniversidad Nacional Mayor de San MarcosApartado 140434Lima-14, Peru

Lily PaniaguaNatureServeApdo. 358-1260Plaza Colonial, EscazúCosta Rica

Heidi L. QuintanaMuseo de Historia NaturalUniversidad Nacional Mayor de San MarcosApartado 140434Lima-14, Peru

Aldo SotoCentro de Datos parala ConservaciónDpto. Manejo ForestalFacultad de Ciencias ForestalesUniversidad Nacional Agraria La MolinaAptdo. 456, Lima 100, Peru

Martín E. TimanáNatureServeJr. Carlos Arrieta 14191er PisoSanta BeatrizLima 1, Peru

Carolina TovarCentro de Datos para la ConservaciónDpto. Manejo ForestalFacultad de Ciencias ForestalesUniversidad Nacional Agraria La MolinaAptdo. 456, Lima 100, Peru

Thomas ValquiMuseum of Natural ScienceLouisiana State University 119 Foster HallBaton Rouge, LA 70803, USA

CORBIDICalle Sta. Rita 117Urb. Huetares de San AntonioSurcoLima, Peru

Julieta VargasColección Boliviana de FaunaMuseo Nacional de Historia Natural Calle 26 Cota Cota s/nCasilla 8706La Paz, Bolivia

Bruce E. YoungNatureServeApdo. 358-1260Plaza Colonial, EscazúCosta Rica

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Appendices

Appendix 1. Sources of locality data. Appendix 1A: Natural history museums that contributed animal locality data. Collection Institution Birds Mammals Amphibians

American Natural History Museum x x xAsociación Armonía x Carnegie Museum of Natural History x x Centro de Biodiversidad y Genética (Cochabamba, Bolivia) x Centro de Datos para la Conservación (CDC) de la Universidad Nacional Agraria La Molina x Colección Boliviana de Fauna - Museo Nacional de Historia Natural x x Delaware Museum of Natural History x Field Museum x x xLouisiana State University, Museum of Natural Science x x xMuseo de Historia Natural Noel Kempff Mercado (Santa Cruz, Bolivia) x xMuseo de Historia Natural, Universidad Nacional Mayor de San Marcos x x xThe Academy of Natural Sciences x United States National Museum of Natural History x x xUniversity of California, Berkeley, Museum of Vertebrate Zoology x x xUniversity of Kansas, Museum of Natural History xUniversity of Michigan, Museum of Zoology x x xUniversity of New Mexico, Museum of Southwestern Biology x x Yale Peabody Museum x

Appendix 1B: Herbaria that contributed plant locality data. Albion R. Hodgdon Herbarium. University of New Hampshire. Durham, New Hampshire. Bailey Hortorium Herbarium, Cornell University. Ithaca, New York. Botanical Museum. Lund, Sweden. Botanischer Garten und Botanisches Museum Berlin-Dahlem. Berlin, Germany Conservatoire et Jardin botaniques de la Ville de Genève. Geneve, Switzerland. Daubeny Herbarium. University of Oxford. Oxford,England, U.K. Dudley Herbarium. California Academy of Sciences. San Francisco, California. Field Museum of Natural History. Chicago, Illinois. Fielding-Druce Herbarium. University of Oxford. Oxford, England, U.K. Harvard University Herbaria. Cambridge, Massachusetts. Herbario Amazonense. Universidad Nacional de la Amazónia Peruana. Iquitos, Peru Herbario del Oriente Boliviano. Museo de Historia Natural Noel Kempff Mercado. Universidad Autónoma Gabriel René Moreno. Santa Cruz, Bolivia. Herbario Nacional Colombiano. Universidad Nacional de Colombia. Bogota, Colombia. Herbario Nacional de Bolivia. La Paz, Bolivia. Herbario Nacional del Ecuador. Museo Ecuatoriano de Ciencias Naturales. Quito, Ecuador. Herbario Nacional Forestal Martín Cárdenas. Cochabamba, Bolivia. Herbario Nacional. Universidad Nacional Autónoma de México. Mexico City, Mexico. Herbario Selva Central. Oxapampa, Peru.

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Herbario Vargas. Universidad Nacional San Antonio Abad del Cusco. Cusco, Peru. Herbario. Museo de Historia Natural, Universidad Nacional Mayor de San Marcos. Lima, Peru. Herbario. Fundación Miguel Lillo. Tucuman, Argentina. Herbario. Instituto de Botánica Darwinion. Buenos Aires, Argentina. Herbario. Instituto de Botánica del Nordeste. Corrientes, Argentina. Herbario. Instituto Nacional de Tecnología Agropecuaria. Buenos Aires, Argentina Herbario. Proyecto BOLFOR. Santa Cruz, Bolivia. Herbario. Real Jardín Botánico. Madrid, Spain. Herbario. Universidad Nacional Agraria La Molina. Lima, Peru. Herbario. Universidad Nacional de Cajamarca. Cajamarca, Peru. Herbario. Universidad Nacional de Córdoba. Cordoba, Argentina. Herbário. Universidade de São Paulo. São Paulo, Brazil. Herbarium of the University of Arizona. Tucson, Arizona. Herbarium Senckenbergianum. Forschungsinstitut Senckenberg. Frankfurt, Germany. Herbarium Truxillense. Universidad Nacional de La Libertad-Trujillo. Trujillo, Peru. Herbarium. Naturhistorisches Museum Wien. Vienna, Austria. Herbarium. Pomona College. Claremont, California. Herbarium. Royal Botanic Gardens, Kew. Richmond, England, U.K. Herbarium. University of Michigan. Ann Arbor, Michigan. Herbarium. Uppsala University. Uppsala, Sweden. Herbarium. Academy of Natural Sciences. Philadelphia, Pennsylvania. Herbarium. Albrecht-von-Haller-Institut für Pflanzenwissenschaften. Universität Göttingen. Göttingen, Germany. Herbarium. California Academy of Sciences. San Francisco, California. Herbarium. Heidelberger Institut für Pflanzenwissenschaften. Universität Heidelberg. Heidelberg, Germany. Herbarium. Institut für Systematische Botanik. Universität Zürich. Zürich. Switzerland. Herbarium. Marie Selby Botanical Gardens. Sarasota, Florida. Herbarium. Missouri Botanical Garden. St. Louis, Missouri. Herbarium. National Museum in Prague. Prague, Czech Republic. Herbarium. Natural History Museum of Los Angeles County. Los Angeles, California. Herbarium. Rancho Santa Ana Botanic Garden. Claremont, California. Herbarium. Royal Botanic Garden. Edinburgh, Scotland, U.K. Herbarium. Swedish Museum of Natural History. Stockholm, Sweden. Herbarium. The Natural History Museum. London,England, U.K. Herbarium. United States National Arboretum. Washington D.C. Herbarium. University of California. Los Angeles, California. Herbarium. University of Copenhagen. Copenhagen, Denmark. Herbarium. University of Texas at Austin. Austin, Texas. Herbarium. V. L. Komarov Botanical Institute. Saint Petersburg, Russia. Herbier National de Paris. Muséum National d’Histoire Naturelle. Paris, France. Nationaal Herbarium Nederland, Leiden University branch. Leiden, Netherlands United States National Herbarium. Smithsonian Institution. Washington D.C. University Herbarium. University of California. Berkeley, California. University of Aarhus Herbarium Jutlandicum. Aarhus, Denmark. William and Lynda Steere Herbarium. New York Botanical Garden. Bronx, New York. Appendix 1C. Literature Sources of Locality Data. See http://www.natureserve.org/AndesAmazon for a list of references used for additional locality data.

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Appendix 2. List of focal species included in the study.

Group Order Family Species Number of Model* Range Unique Size Localities Km2

Plants Filicales Cyatheaceae Cnemidaria alatissima 1 Deductive 49 Cyathea arnecornelii 6 m3 360 Cyathea bettinae 1 Deductive 388 Cyathea boliviana 24 m3 25,155 Cyathea multisegmenta 2 m3 5,879 Celastrales Aquifoliaceae Ilex aggregata 18 m4 143,572 Ilex anonoides 12 m4 120,156 Ilex crassifolioides 7 m3 34,296 Ilex gotardensis 5 m4 15,822 Ilex herzogii 6 m4 3,035 Ilex hippocrateoides 17 m4 71,456 Ilex imbricata 2 m4 499 Ilex loretoica 1 Deductive 77 Ilex mandonii 10 m3 26,401 Ilex microsticta 6 m2 47,527 Ilex pseudoebenacea 3 m4 723 Ilex tarapotina 6 m3 104,227 Ilex trichoclada 3 m3 8,352 Ilex villosula 8 m3 69,690 Campanulales Campanulaceae Centropogon bangii 1 Deductive 2 Centropogon brittonianus 17 m4 1,650 Centropogon eilersii 3 m3/Partial 1,320 Centropogon gloriversus 8 m3 2,539 Centropogon incanus 18 m2 17,450 Centropogon isabellinus 7 m3 1,805 Centropogon magnificus 2 m4 30 Centropogon mandonis 25 m3 20,980 Centropogon perlongus 3 m3 6,297 Centropogon reflexus 10 m2 643 Centropogon roseus 11 m4 117,044 Centropogon sciaphilus 11 m4 63,517 Centropogon umbrosus 20 m3 35,512 Centropogon unduavensis 6 m3 883 Centropogon varicus 4 m4 3,913 Centropogon vitifolius 1 Deductive 78 Centropogon yungasensis 13 m3 75,152 Siphocampylus actinothrix 8 m3 6,638 Siphocampylus andinus 21 m3 10,667 Siphocampylus angustiflorus 5 m2 15,550

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Siphocampylus arachnes 3 m3 8,180 Siphocampylus ayersiae 6 m3 22,588 Siphocampylus bilabiatus 11 m4 31,074 Siphocampylus boliviensis 44 m4 30,895 Siphocampylus comosus 7 m4 3,201 Siphocampylus correoides 5 m3 4,394 Siphocampylus dubius 6 m3 1,374 Siphocampylus flagelliformis 13 m3 32,567 Siphocampylus kuntzeanus 1 Deductive 78 Siphocampylus longior 5 m4 24,510 Siphocampylus membranaceus 4 m4 11,407 Siphocampylus neurotrichus 4 m4 1,643 Siphocampylus oblongifolius 2 m3 865 Siphocampylus plegmatocaulis 3 m3 1,178 Siphocampylus radiatus 9 m3 4,142 Siphocampylus reflexus 3 m4 1,941 Siphocampylus rosmarinifolius 8 m2 2,677 Siphocampylus rusbyanus 19 m4 58,915 Siphocampylus sparsipilus 2 Deductive 507 Siphocampylus spruceanus 1 Deductive 80 Siphocampylus subcordatus 2 Deductive 157 Siphocampylus tunarensis 6 m4 7,874 Siphocampylus tunicatus 9 m4 2,579 Siphocampylus vatkeanus 2 m3 55 Siphocampylus werdermannii 8 m3 37,077 Ericales Ericaceae Bejaria infundibula 12 m3 9,692 Cavendishia martii 59 m2 71,814 Cavendishia punctata 10 m3 13,002 Ceratostema ferreyrae 1 Deductive 623 Demosthenesia buxifolia 4 m4 17,759 Demosthenesia cordifolia 1 Deductive 1,077 Demosthenesia dudleyi 1 Deductive 1,271 Demosthenesia mandonii 54 m3 32,211 Demosthenesia oppositifolia 4 m2 4,260 Demosthenesia pearcei 8 m1 15,519 Demosthenesia spectabilis 18 m4 13,176 Demosthenesia vilcabambensis 2 m2 5,693 Demosthenesia weberbauerii 1 Deductive 1,288 Diogenesia boliviana 11 m4 10,150 Diogenesia laxa 1 Deductive 1,288 Diogenesia racemosa 3 m4 4,845 Diogenesia vargasiana 6 m2 2,101 Disterigma ovatum 10 m4 16,288 Disterigma pallidum 10 m3 5,329 Disterigma pernettyoides 35 m3 43,495 Polyclita turbinata 30 m4 3,611 Psammisia globosa 2 Deductive 945 Satyria boliviana 8 m4 16,378


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Satyria neglecta 1 Deductive 87 Satyria polyantha 2 m4 3,584 Satyria vargasii 2 m3 10,303 Siphonandra boliviana 1 Deductive 76 Siphonandra elliptica 32 m2 69,075 Sphyrospermum buesii 1 Deductive 307 Sphyrospermum sessiliflorum 2 Deductive 158 Themistoclesia peruviana 8 m2 45,897 Themistoclesia unduavensis 5 m2 5,327 Thibaudia axillaris 1 Deductive 22 Thibaudia biflora 1 Deductive 13,830 Thibaudia cardiophylla 1 Deductive 51 Thibaudia crenulata 49 m4 63,715 Thibaudia croatii 3 m3 1,240 Thibaudia densiflora 7 m2 5,196 Thibaudia dudleyi 6 m1 3,034 Thibaudia herrerae 1 Deductive 659 Thibaudia macrocalyx 21 m4 5,195 Thibaudia rauhii 2 m2 1,017 Thibaudia regularis 4 m4 14,828 Thibaudia uniflora 1 Deductive 75 Vaccinium elvirae 7 m1 16,890 Vaccinium mathewsii 7 m4 3,543 Vaccinium sphyrospermoides 1 Deductive 520 Fabales Fabaceae Inga amboroensis 1 Deductive 703 Inga approximata 16 m3 29,864 Inga augustii 1 Deductive 78 Inga cynometrifolia 2 m4 739 Inga expansa 21 m4/Partial 46,174 Inga fosteriana 1 Deductive 79 Inga gereauana 3 m4 8,540 Inga killipiana 5 m4 35,587 Inga lineata 14 m3 83,091 Inga longipes 9 m2 39,581 Inga maynensis 10 m4 17,710 Inga megalobotrys 1 Deductive 1,251 Inga pluricarpellata 2 m3 9,629 Inga porcata 5 m4 85,533 Inga steinbachii 28 m4/Partial 237,600 Inga tarapotensis 2 Deductive 501 Mimosa boliviana 54 m4 34,667 Mimosa cuzcoana 5 m4 2,846 Mimosa pectinatipinna 6 m4 7,218 Mimosa revoluta 38 m4 27,240 Mimosa rusbyana 4 m4 3,646 Mimosa williamsii 1 Deductive 4 Mimosa woodii 4 m4 3,440 Myrtales Onagraceae


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Fuchsia abrupta 22 m3 15,121 Fuchsia apetala 79 m4 63,311 Fuchsia austromontana 24 m3/Partial 27,433 Fuchsia ceracea 1 Deductive 79 Fuchsia chloroloba 11 m4 24,355 Fuchsia cochabambana 9 m3 5,600 Fuchsia confertifolia 2 m2 732 Fuchsia coriacifolia 1 Deductive 43 Fuchsia decussata 17 m3 42,288 Fuchsia ferreyrae 7 m4 20,988 Fuchsia fontinalis 12 m2 9,615 Fuchsia furfuracea 23 m4 23,115 Fuchsia garleppiana 7 m4 8,103 Fuchsia huanucoensis 3 m4 3,894 Fuchsia inflata 10 m3 11,893 Fuchsia juntasensis 13 m4 26,313 Fuchsia llewelynii 2 m2 613 Fuchsia macropetala 7 m4 5,696 Fuchsia macrophylla 37 m4 72,659 Fuchsia mathewsii 18 m3 5,027 Fuchsia mezae 1 Deductive 25 Fuchsia nana 9 m4 7,342 Fuchsia ovalis 6 m2 9,576 Fuchsia pilosa 7 m3 2,587 Fuchsia rivularis 15 m4 6,629 Fuchsia salicifolia 9 m4 12,095 Fuchsia sanctae-rosae 84 m3 62,713 Fuchsia sanmartina 4 m4 3,594 Fuchsia simplicicaulis 10 m3 26,549 Fuchsia tincta 9 m4 1,251 Fuchsia tunariensis 8 m4 8,473 Fuchsia vargasiana 6 m3 2,073 Fuchsia wurdackii 4 m3 676 Polygalales Malpighiaceae Adelphia macrophylla 3 m4 13,830 Adelphia mirabilis 1 Deductive 77 Amorimia camporum 7 m3 5,866 Bunchosia berlinii 7 m3 12,242 Bunchosia bonplandiana 1 Deductive 14 Diplopterys schunkei 1 Deductive 78 Diplopterys woytkowskii 3 m4 37,558 Excentradenia boliviana 1 Deductive 77 Heteropterys actinoctenia 1 Deductive 79 Heteropterys andina 3 m4 553 Heteropterys fulva 1 Deductive 80 Heteropterys magnifica 1 Deductive 78 Heteropterys oxenderi 2 m4 355 Hiraea christianeae 1 Deductive 77 Lophopterys peruviana 3 m4 5,167


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Mascagnia boliviana 1 Deductive 76 Mezia russellii 2 m4 2,078 Stigmaphyllon aberrans 4 m3 4,415 Stigmaphyllon argenteum 19 m3 71,171 Stigmaphyllon coloratum 5 m4 30,698 Stigmaphyllon cuzcanum 4 m4 1,265 Stigmaphyllon peruvianum 7 m3 3,221 Stigmaphyllon tarapotense 3 m4 2,569 Stigmaphyllon yungasense 2 m3 1,474 Tetrapterys stipulacea 1 Deductive 79 Rosales Brunelliaceae Brunellia boliviana 29 m3 17,914 Brunellia briquetii 1 Deductive 80 Brunellia brunnea 3 m3 9,937 Brunellia coroicoana 10 Deductive 4,460 Brunellia cuzcoensis 1 Deductive 1,395 Brunellia dichapetaloides 1 Deductive 58 Brunellia dulcis 1 Deductive 34 Brunellia hexasepala 1 Deductive 61 Brunellia rhoides 21 m1 23,186 Brunellia weberbaueri 1 Deductive 1,834 Chrysobalanaceae Hirtella aramangensis 1 Deductive 62 Hirtella beckii 1 Deductive 9 Hirtella lightioides 12 Deductive 8,186 Hirtella revillae 8 m3 21,891 Hirtella standleyi 1 Deductive 65 Licania boliviensis 3 m4/Partial 7,019 Licania bullata 1 Deductive 323 Licania cecidiophora 2 m2 2,780 Licania filomenoi 2 m4 3,460 Licania klugii 8 m2 76,618 Licania tambopatensis 4 m4 37,330 Licania trigonioides 1 Deductive 76 Licania vasquezii 1 Deductive 80 Sapindales Anacardiaceae Mauria boliviana 1 Deductive 82 Mauria denticulata 1 Deductive 329 Mauria killipii 5 m4 17,345 Schinopsis peruviana 5 m4 2,351 Thyrsodium herrerense 5 Deductive 105,975 Scrophulariales Acanthaceae Aphelandra campii 2 Deductive 112 Aphelandra castanifolia 24 m1 70,712 Aphelandra cuscoensis 3 m4 30,513 Aphelandra dasyantha 3 m3 593 Aphelandra eurystoma 3 m4 14,871


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Aphelandra ferreyrae 5 m3 6,946 Aphelandra goodspeedii 17 m4 121,788 Aphelandra hapala 1 Deductive 2,334 Aphelandra inaequalis 3 Deductive 6,288 Aphelandra jacobinoides 4 m1 1,987 Aphelandra juninensis 1 Deductive 56 Aphelandra kolobantha 4 m4 2,588 Aphelandra latibracteata 9 m3 9,727 Aphelandra limbatifolia 3 m4 4,993 Aphelandra luyensis 1 Deductive 30 Aphelandra macrosiphon 6 m1/Partial 16,092 Aphelandra modesta 1 Deductive 79 Aphelandra montis-scalaris 1 Deductive 352 Aphelandra mucronata 4 m3 1,463 Aphelandra neillii 5 m3 18,877 Aphelandra pepe-parodii 2 Deductive 6,359 Aphelandra peruviana 2 Deductive 3,613 Aphelandra rubra 32 m2 44,965 Aphelandra rusbyi 8 m3 79,719 Aphelandra tillettii 1 Deductive 1,261 Aphelandra weberbaueri 1 Deductive 406 Aphelandra wurdackii 2 m2 1,374 Dicliptera palmariensis 2 Deductive 183 Dicliptera purpurascens 9 m4/Partial 10,472 Justicia albadenia 11 m3 53,467 Justicia alpina 5 m1 2,977 Justicia aphelandroides 2 Deductive 207 Justicia arcuata 10 m4/Partial 45,522 Justicia beckii 5 m2 28,300 Justicia boliviensis 41 m2 53,592 Justicia chapareensis 2 Deductive 141 Justicia concavibracteata 4 m1 204,251 Justicia cuspidulata 1 Deductive 79 Justicia cuzcoensis 3 Deductive 13,235 Justicia dryadum 6 m4 45,775 Justicia elegantissima 1 Deductive 79 Justicia hylophila 3 m3 26,785 Justicia iochila 1 Deductive 79 Justicia israelvargasii 2 m4 6,695 Justicia kessleri 10 m4 4,827 Justicia lancifolia 1 Deductive 2,318 Justicia longiacuminata 2 m3 27 Justicia loretensis 1 Deductive 352 Justicia manserichensis 6 m2 20,772 Justicia megalantha 2 m3 5,229 Justicia mendax 3 m3 7,026 Justicia miguelii 10 m4 13,969 Justicia monopleurantha 9 m2 29,864 Justicia pluriformis 6 m1 5,115 Justicia pozuzoensis 1 Deductive 224 Justicia pyrrhostachya 2 Deductive 159


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Justicia rauhii 5 m3 15,442 Justicia ruiziana 2 Deductive 156 Justicia rusbyana 5 m4 5,011 Justicia soukupii 2 Deductive 4,128 Justicia steinbachiorum 2 m4 1,211 Justicia tarapotensis 1 Deductive 78 Justicia tremulifolia 1 Deductive 77 Justicia umbricola 6 m1 4,791 Justicia weberbaueri 2 Deductive 1,258 Justicia yungensis 4 m2 7,770 Justicia yuyoeensis 2 m1 1,871 Mendoncia aurea 1 Deductive 76 Mendoncia gigas 2 Deductive 158 Mendoncia killipii 3 Deductive 236 Mendoncia klugii 2 Deductive 422 Mendoncia peruviana 1 Deductive 79 Mendoncia robusta 3 m4 14,208 Mendoncia smithii 4 m4 1,339 Mendoncia tarapotana 7 m2 21,617 Odontonema hookerianum 2 m4 835 Oplonia grandiflora 1 Deductive 78 Orophochilus stipulaceus 2 m3 520 Pachystachys badiospica 5 m4 118,156 Pachystachys fosteri 1 Deductive 78 Pachystachys incarnata 1 Deductive 78 Pachystachys killipii 1 Deductive 76 Pachystachys longibracteata 4 m4 2,059 Pachystachys ossolae 14 m2 58,594 Pachystachys puberula 14 m2 5,714 Pachystachys rosea 2 m4 1,671 Pachystachys schunkei 1 Deductive 227 Pseuderanthemum weberbaueri 1 Deductive 466 Ruellia antiquorum 7 m4 2,448 Ruellia beckii 1 Deductive 286 Ruellia dolichosiphon 1 Deductive 51,039 Ruellia gracilis 6 m4 16,291 Ruellia haenkeana 82 m3/Partial 113,061 Ruellia pearcei 26 m2/Partial 110,927 Ruellia phyllocalyx 1 Deductive 77 Ruellia rauhii 1 Deductive 77 Ruellia ruiziana 17 m1 104,702 Ruellia tarapotana 10 m4 168,354 Ruellia tessmannii 1 Deductive 79 Ruellia yurimaguensis 15 m3/Partial 64,908 Sanchezia arborea 1 Deductive 77 Sanchezia aurantiaca 1 Deductive 77 Sanchezia aurea 1 Deductive 107 Sanchezia bicolor 1 Deductive 70 Sanchezia capitata 1 Deductive 74 Sanchezia conferta 2 Deductive 801 Sanchezia cyathibractea 1 Deductive 78


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Sanchezia dasia 1 Deductive 76 Sanchezia decora 1 Deductive 77 Sanchezia ferreyrae 1 Deductive 76 Sanchezia filamentosa 4 m2 30,232 Sanchezia flava 2 m4 516 Sanchezia killipii 1 Deductive 78 Sanchezia klugii 3 m3 10,095 Sanchezia lasia 2 Deductive 153 Sanchezia lispa 1 Deductive 77 Sanchezia loranthifolia 2 m4 4,635 Sanchezia megalia 7 m2 30,333 Sanchezia ovata 6 m2 115,458 Sanchezia oxysepala 5 m2 11,006 Sanchezia pedicellata 1 Deductive 76 Sanchezia pulchra 3 m2 151 Sanchezia punicea 2 m4 3,948 Sanchezia rhodochroa 1 Deductive 78 Sanchezia rosea 1 Deductive 79 Sanchezia rubriflora 4 m3 3,464 Sanchezia sanmartininensis 1 Deductive 77 Sanchezia scandens 9 m3 69,165 Sanchezia sprucei 3 m4 13,215 Sanchezia stenantha 8 m4 23,858 Sanchezia stenomacra 3 m4 13,971 Sanchezia sylvestris 1 Deductive 65 Sanchezia tarapotensis 3 m3 10,893 Sanchezia tigrina 3 Deductive 229 Sanchezia villosa 1 Deductive 55 Sanchezia williamsii 1 Deductive 140 Sanchezia woytkowskii 1 Deductive 13 Sanchezia wurdackii 3 m4 4,346 Sanchezia xantha 1 Deductive 39 Stenostephanus cochabambensis 4 m4 641 Stenostephanus crenulatus 32 m4 19,722 Stenostephanus davidsonii 4 m4/Partial 9,309 Stenostephanus krukoffii 4 m1 21,464 Stenostephanus longistaminus 11 m2/Partial 42,355 Stenostephanus lyman-smithii 16 m1 23,684 Stenostephanus pyramidalis 8 m3 5,855 Stenostephanus spicatus 3 m3 498 Stenostephanus sprucei 4 m4 44,693 Stenostephanus tenellus 4 m2 25,016 Streblacanthus amoenus 10 m3 72,855 Suessenguthia barthleniana 12 m4/Partial 24,652 Suessenguthia koessleri 5 m3 2,954 Suessenguthia vargasii 14 m2/Partial 69,356 Suessenguthia wenzelii 2 Deductive 18,117 Tetramerium surcubambense 1 Deductive 57 Tetramerium zeta 2 Deductive 800 Trichosanchezia chrysothrix 3 m4 3,517 Theales


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Marcgraviaceae Marcgravia longifolia 8 m3 44,822 Marcgravia weberbaueri 16 m4 77,737 Sarcopera oxystilis 20 m4 32,314 Schwartzia magnifica 10 m4 67,625 Souroubea fragilis 6 m4/Partial 26,414 Souroubea peruviana 3 m3 8,072 Souroubea stichadenia 5 m4 15,661 Violales Loasaceae Caiophora canarinoides 38 m2 24,174 Caiophora madrequisa 9 m3 20,137 Caiophora vargasii 1 Deductive 276 Mentzelia heterosepala 4 m4 10,371 Nasa aspiazui 1 Deductive 80 Nasa callacallensis 1 Deductive 78 Nasa colanii 1 Deductive 54 Nasa driesslei 2 m4 3,145 Nasa ferruginea 7 m4 22,551 Nasa formosissima 3 m3 481 Nasa herzogii 3 m4 1,867 Nasa kuelapensis 1 Deductive 57 Nasa limata 2 m3 6,444 Nasa nubicolorum 1 Deductive 28 Nasa pascoensis 1 Deductive 67 Nasa stuebeliana 2 m4 655 Nasa tingomariensis 6 m4 11,241 Nasa umbraculifera 1 Deductive 29 Nasa victorii 1 Deductive 53 Violales Passifloraceae Passiflora amazonica 3 m3 6,611 Passiflora aristulata 10 m3 13,968 Passiflora buchtienii 7 m2 715 Passiflora callacallensis 1 Deductive 19 Passiflora chaparensis 3 m4 1,451 Passiflora cirrhipes 1 Deductive 77 Passiflora cuzcoensis 1 Deductive 78 Passiflora dalechampioides 4 m2 17,761 Passiflora fernandezii 2 Deductive 156 Passiflora ferruginea 5 m3 34,353 Passiflora frutescens 2 m3 482 Passiflora guenteri 1 Deductive 79 Passiflora hastifolia 1 Deductive 32 Passiflora heterohelix 1 Deductive 76 Passiflora inca 4 m4 1,061 Passiflora insignis 11 m3 705 Passiflora leptoclada 3 m1 32,781 Passiflora macropoda 12 m4 2,321 Passiflora mandonii 32 m3/Partial 14,424 Passiflora nephrodes 13 m3 4,711


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Passiflora parvifolia 10 m3/Partial 25,597 Passiflora pascoensis 5 m3 4,612 Passiflora poeppigii 1 Deductive 79 Passiflora quadriflora 1 Deductive 78 Passiflora runa 1 Deductive 78 Passiflora skiantha 1 Deductive 80 Passiflora solomonii 7 m4 5,904 Passiflora tarapotina 12 m3 23,475 Passiflora tatei 15 m3 21,810 Passiflora venosa 4 Deductive 316 Passiflora weberbaueri 1 Deductive 24 Passiflora weigendii 1 Deductive 59Amphibians Anura Bufonidae Atelopus andinus 3 m2 4,938 Atelopus dimorphus 2 Deductive 98 Atelopus epikeisthos 1 Deductive 80 Atelopus erythropus 2 m3 2,810 Atelopus pulcher 9 m3 34,999 Atelopus pyrodactylus 1 Deductive 79 Atelopus reticulatus 1 Deductive 78 Atelopus seminiferus 1 Deductive 77 Atelopus siranus 1 Deductive 79 Atelopus tricolor 20 m4 60,435 Bufo amboroensis 2 m4 134 Bufo arborescandens 2 m4 1,336 Bufo chavin 3 m4 3,302 Bufo fissipes 7 m4 63,171 Bufo inca 8 m4 26,413 Bufo justinianoi 6 m2 7,056 Bufo multiverrucosus 2 m3 428 Bufo nesiotes 1 Deductive 79 Bufo quechua 5 m4 6,695 Bufo stanlaii 16 m4 29,192 Truebella tothastes 1 Deductive 79 Centrolenidae Centrolene azulae 1 Deductive 78 Centrolene fernandoi 1 Deductive 77 Centrolene lemniscatum 1 Deductive 78 Centrolene mariae 1 Deductive 79 Centrolene muelleri 1 Deductive 78 Cochranella chancas 1 Deductive 77 Cochranella croceopodes 2 m4 1,121 Cochranella ocellata 3 m4 9,989 Cochranella phenax 1 Deductive 77 Cochranella pluvialis 3 m4 28,562 Cochranella saxiscandens 1 Deductive 79 Cochranella spiculata 10 m2 7,974 Cochranella tangarana 1 Deductive 77 Cochranella truebae 5 m3 212


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Hyalinobatrachium bergeri 20 m3 86,887 Hyalinobatrachium lemur 1 Deductive 77 Dendrobatidae Colostethus aeruginosus 1 Deductive 78 Colostethus alessandroi 3 Deductive 116 Colostethus argyrogaster 5 m4 14,505 Colostethus craspedoceps 1 Deductive 76 Colostethus eleutherodactylus 1 Deductive 78 Colostethus idiomelas 6 Deductive 1,633 Colostethus insulatus 1 Deductive 106 Colostethus leucophaeus 1 Deductive 79 Colostethus mcdiarmidi 4 m4 28,067 Colostethus mittermeieri 2 m1 420 Colostethus ornatus 2 Deductive 329 Colostethus patitae 1 Deductive 79 Colostethus poecilonotus 1 Deductive 79 Colostethus sordidatus 1 Deductive 76 Colostethus spilotogaster 1 Deductive 77 Colostethus trilineatus 43 m3/Partial 690,992 Colostethus utcubambensis 1 Deductive 79 Cryptophyllobates azureiventris 3 m3 9,706 Dendrobates biolat 10 m4 58,003 Dendrobates captivus 1 Deductive 78 Dendrobates fantasticus 5 Deductive 275 Dendrobates imitator 14 Deductive 1,688 Dendrobates lamasi 5 m4 20,861 Dendrobates mysteriversus 1 Deductive 77 Dendrobates reticulatus 6 Deductive 38,400 Dendrobates sirensis 1 Deductive 79 Dendrobates variabilis 4 m4 6,446 Epipedobates bassleri 13 m3 14,220 Epipedobates bolivianus 5 m2 14,790 Epipedobates cainarachi 4 m4 4,171 Epipedobates planipaleae 1 Deductive 79 Epipedobates pongoensis 1 Deductive 80 Epipedobates rubriventris 1 Deductive 79 Epipedobates silverstonei 5 Deductive 941 Epipedobates simulans 4 m2 5,745 Epipedobates smaragdinus 6 m3 22,513 Hylidae Dendropsophus allenorum 5 m4 3,783 Dendropsophus aperomeus 6 m3 34,931 Dendropsophus delarivai 9 m4/Partial 84,968 Dendropsophus joannae 2 m2 29,716 Hyla chlorostea 1 Deductive 51 Hyloscirtus antoniiochoai 2 m3 63 Hyloscirtus armatus 37 m2 70,957 Hyloscirtus charazani 1 Deductive 112 Hypsiboas balzani 24 m2 51,568 Hypsiboas callipleura 8 m4/Partial 12,092 Hypsiboas melanopleura 1 Deductive 79


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Hypsiboas palaestes 3 m3 399 Phyllomedusa baltea 3 m3 1,700 Phyllomedusa duellmani 2 m2 1,684 Osteocephalus elkejungingerae 5 m4 33,802 Osteocephalus leoniae 3 Deductive 1,518 Scinax oreites 12 m3/Partial 10,151 Scinax pedromedinae 12 m4 44,897 Leptodactylidae Edalorhina nasuta 2 m4 202 Eleutherodactylus araiodactylus 1 Deductive 77 Eleutherodactylus ardalonychus 3 m3 4,752 Eleutherodactylus ashkapara 3 m3 95 Eleutherodactylus bearsei 5 m3 11,872 Eleutherodactylus bisignatus 3 m4 414 Eleutherodactylus caliginosus 1 Deductive 78 Eleutherodactylus citriogaster 5 Deductive 243 Eleutherodactylus condor 9 m4 24,190 Eleutherodactylus corrugatus 3 Deductive 744 Eleutherodactylus cruralis 59 m2/Partial 227,134 Eleutherodactylus cuneirostris 1 Deductive 77 Eleutherodactylus danae 56 m3/Partial 143,926 Eleutherodactylus delius 1 Deductive 77 Eleutherodactylus fraudator 10 m3 2,520 Eleutherodactylus imitatrix 6 m4 20,284 Eleutherodactylus infraguttatus 1 Deductive 79 Eleutherodactylus lindae 3 m4 36 Eleutherodactylus lirellus 6 m4 21,103 Eleutherodactylus llojsintuta 5 m4 4,797 Eleutherodactylus lucida 2 m4 13 Eleutherodactylus luscombei 3 m3 12,382 Eleutherodactylus melanogaster 2 Deductive 88 Eleutherodactylus mendax 14 m2 45,591 Eleutherodactylus mercedesae 6 m4 28,399 Eleutherodactylus metabates 1 Deductive 79 Eleutherodactylus olivaceus 14 m3/Partial 9,157 Eleutherodactylus pataikos 1 Deductive 76 Eleutherodactylus percnopterus 6 m3 17,703 Eleutherodactylus platydactylus 64 m2/Partial 91,964 Eleutherodactylus pluvicanorus 15 m4 5,731 Eleutherodactylus rhabdolaemus 49 m2 115,157 Eleutherodactylus rufioculis 3 Deductive 171 Eleutherodactylus sagittulus 2 m3 184 Eleutherodactylus salaputium 7 Deductive 236 Eleutherodactylus scitulus 1 Deductive 79 Eleutherodactylus stictoboubonis 1 Deductive 78 Eleutherodactylus toftae 45 m2 159,554 Eleutherodactylus zongoensis 1 Deductive 77 Gastrotheca abdita 1 Deductive 78 Gastrotheca atympana 1 Deductive 28 Gastrotheca excubitor 13 m4 9,818 Gastrotheca lauzuricae 1 Deductive 59


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Gastrotheca ochoai 4 m3 1,960 Gastrotheca ossilaginis 2 m4 109 Gastrotheca phalarosa 1 Deductive 77 Gastrotheca rebeccae 4 Deductive 1,337 Gastrotheca splendens 1 Deductive 418 Gastrotheca stictopleura 5 m3 124 Gastrotheca testudinea 13 m2 130,743 Gastrotheca zeugocystis 1 Deductive 78 Ischnocnema sanctaecrucis 6 m2 8,107 Ischnocnema saxatilis 11 m3 5,011 Leptodactylus didymus 18 Deductive 286,791 Leptodactylus griseigularis 30 m1 100,237 Leptodactylus pascoensis 2 m3 27,058 Leptodactylus rhodostima 1 Deductive 77 Phrynopus adenopleurus 1 Deductive 117 Phrynopus bagrecitoi 2 Deductive 96 Phrynopus bracki 2 Deductive 90 Phrynopus carpish 2 m3 11,956 Phrynopus fallaciosus 1 Deductive 40 Phrynopus iatamasi 2 m4 97 Phrynopus kauneorum 4 m3 102 Phrynopus kempffi 2 m3 155 Phrynopus laplacai 6 m3 115 Phrynopus pinguis 1 Deductive 326 Phyllonastes carrascoicola 3 m3 1,068 Phyllonastes duellmani 1 Deductive 78 Phyllonastes lynchi 1 Deductive 79 Phyllonastes ritarasquinae 2 m1 603 Telmatobius atahualpai 4 m4 3,674 Telmatobius bolivianus 8 m4 18,046 Telmatobius colanensis 1 Deductive 77 Telmatobius edaphonastes 1 Deductive 79 Telmatobius necopinus 1 Deductive 79 Telmatobius sibiricus 5 m4 600 Telmatobius timens 2 m4 1,971 Telmatobius truebae 6 m4 3,117 Telmatobius yuracare 7 m3 4,049 Microhylidae Altigius alios 2 Deductive 624 Caudata Plethodontidae Bolitoglossa digitigrada 1 Deductive 78 Gymnophiona Caeciliidae Caecilia inca 1 Deductive 79 Caecilia marcusi 6 m2 82,543 Oscaecilia koepckeorum 1 Deductive 78 Rhinatrematidae Epicrionops peruvianus 1 Deductive 78Mammals Didelphimorphia


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Didelphidae Gracilinanus aceramarcae 12 m4 59,084 Marmosa andersoni 3 Deductive 121,856 Marmosops creightoni 3 m4 6,337 Paucituberculata Caenolestidae Lestoros inca 25 m4 30,108 Cingulata Dasypodidae Dasypus pilosus 13 m4 24,156 Primates Aotidae Aotus miconax 5 m4 29,385 Pitheciidae Callicebus aureipalatii 2 m4 336 Callicebus modestus 2 m4 3,890 Callicebus oenanthe 8 m4 15,979 Callicebus olallae 2 Deductive 2,047 Atelidae Oreonax flavicauda 10 m4 41,669 Rodentia Sciuridae Sciurus pyrrhinus 17 m4 220,762 Sciurus sanborni 8 m4 57,815 Cricetidae Akodon aerosus 70 m4 155,455 Akodon kofordi 8 m4 38,198 Akodon mimus 32 m3 14,723 Akodon orophilus 22 m4 47,267 Akodon siberiae 6 m4 9,698 Akodon surdus 5 m4 11,508 Akodon torques 38 m4 31,515 Amphinectomys savamis 2 m4 1,111 Lenoxus apicalis 11 m4 33,723 Neusticomys peruviensis 3 Deductive 113,629 Oecomys phaeotis 8 m4 228,897 Oryzomys keaysi 46 m4 96,882 Oryzomys levipes 53 m4 56,203 Oryzomys polius 4 m3 34,961 Oxymycterus hiska 7 m4 123,701 Oxymycterus hucucha 4 m4 5,258 Oxymycterus inca 16 m4 344,920 Rhagomys longilingua 3 Deductive 8,861 Rhipidomys ochrogaster 1 Deductive 33 Thomasomys apeco 3 m4 1,294 Thomasomys daphne 19 m4 17,417 Thomasomys eleusis 3 m4 1,650 Thomasomys gracilis 7 m4 5,939 Thomasomys incanus 10 m4 23,772 Thomasomys ischyurus 3 m4 17,341 Thomasomys kalinowskii 13 m4 51,442


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Thomasomys ladewi 3 m4 19,889 Thomasomys macrotis 2 m4 464 Thomasomys notatus 12 m4 37,911 Thomasomys onkiro 1 Deductive 60 Thomasomys oreas 18 m4 62,719 Thomasomys rosalinda 1 Deductive 35 Dasyproctidae Dasyprocta kalinowskii 11 m4 91,425 Abrocomidae Cuscomys ashaninka 1 Deductive 60 Echimyidae Dactylomys peruanus 5 m4 46,372 Makalata rhipidura 8 m3 109,091 Mesomys leniceps 2 m4 8,874 Chiroptera Molossidae Mormopterus phrudus 2 m3 3,678 Phyllostomidae Carollia manu 7 m4 95,677 Mimon koepckeae 2 m3 316 Sturnira nana 2 m4 1,150 Artiodactyla Cervidae Mazama chunyi 34 m4 62,430Birds Tinamiformes Tinamidae Nothocercus nigrocapillus 26 m1 78,903 Galliformes Cracidae Pauxi unicornis 16 m3/m4/Partial** 14,073 Odontophoridae Odontophorus balliviani 26 m2 46,443 Psittaciformes Psittacidae Ara glaucogularis 23 m4 8,914 Nannopsittaca dachilleae 15 Partial/Hand 101,517 Hapalopsittaca melanotis 35 m4 20,210 Strigiformes Strigidae Megascops marshalli 6 m4 12,709 Xenoglaux loweryi 5 m4 3,016 Apodiformes Trochilidae Phaethornis koepckeae 23 m1 42,113 Phaethornis stuarti 31 m4/Partial 52,064 Leucippus viridicauda 10 m4 27,297 Phlogophilus harterti 14 m4 18,036 Heliodoxa branickii 15 m3 86,582 Aglaeactis castelnaudii 18 m4 9,102 Metallura theresiae 16 m4 20,699


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Metallura eupogon 12 m3 14,306 Metallura aeneocauda 41 m4 28,690 Loddigesia mirabilis 6 m4 3,054 Piciformes Capitonidae Capito wallacei 1 Deductive 139 Eubucco versicolor 88 m1 170,959 Ramphastidae Aulacorhynchus huallagae 3 m4 4,810 Andigena cucullata 38 m3 31,107 Picidae Picumnus steindachneri 7 m2 5,616 Picumnus castelnau 8 m4 21,837 Picumnus subtilis 8 Deductive 18,026 Passeriformes Furnariidae Cinclodes aricomae 14 Deductive 5,641 Leptasthenura xenothorax 10 m4 3,948 Schizoeaca helleri 8 m3 24,629 Schizoeaca harterti 26 m3 11,312 Synallaxis cabanisi 50 m4 77,703 Cranioleuca marcapatae 15 m4 13,291 Cranioleuca albiceps 33 m4 28,748 Cranioleuca henricae 12 m4 2,239 Thripophaga berlepschi 7 m4 5,779 Asthenes urubambensis 29 m4 35,023 Asthenes berlepschi 10 Deductive 473 Simoxenops striatus 22 m4 37,130 Thripadectes scrutator 25 m4 69,519 Thamnophilidae Thamnophilus aroyae 34 m4 24,723 Myrmotherula grisea 21 m1 29,755 Herpsilochmus motacilloides 10 m4 48,064 Herpsilochmus parkeri 2 m3 4,971 Herpsilochmus gentryi 11 Deductive 2,208 Terenura sharpei 5 m4 26,064 Myrmoborus melanurus 8 m4 59,279 Percnostola arenarum 11 m4 11,045 Pithys castaneus 3 m2/Partial 1,894 Formicariidae Formicarius rufifrons 6 m4 83,184 Grallaria eludens 5 m4 309,168 Grallaria carrikeri 5 m4 19,418 Grallaria przewalskii 12 m4 6,376 Grallaria capitalis 8 m4 6,439 Grallaria erythroleuca 11 m4 17,462 Grallaria blakei 7 m4 3,832 Grallaria erythrotis 45 m3 22,754 Hylopezus auricularis 5 m3 1,098 Grallaricula ochraceifrons 3 m3 2,086 Rhinocryptidae


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Scytalopus parvirostris 63 m3 78,423 Scytalopus macropus 12 m4 10,129 Scytalopus femoralis 21 m4 49,171 Scytalopus altirostris 5 m2 10,044 Scytalopus acutirostris 2 Deductive 6,218 Scytalopus urubambae 1 Deductive 1 Scytalopus schulenbergi 19 m4 12,922 Tyrannidae Leptopogon taczanowskii 29 m4 38,206 Anairetes agraphia 16 m4 38,401 Anairetes alpinus 17 m4 50,394 Phylloscartes parkeri 9 m4 38,579 Phyllomyias sp. nov. 9 m4 3,012 Zimmerius bolivianus 54 m4 56,428 Zimmerius villarejoi 3 m4/Partial 2,416 Zimmerius viridiflavus 27 m4 41,361 Pseudotriccus simplex 17 m4 43,356 Myiornis albiventris 43 m4 67,211 Hemitriccus spodiops 33 m4 41,720 Poecilotriccus luluae 8 m4 3,950 Poecilotriccus albifacies 16 Deductive 2,924 Poecilotriccus pulchellus 5 Deductive 3,506 Myiophobus inornatus 15 m3 21,416 Myiotheretes fuscorufus 28 m3 45,346 Cotingidae Doliornis sclateri 12 m4 19,922 Pipreola intermedia 52 m4 52,226 Pipreola pulchra 15 m1 41,514 Lipaugus uropygialis 16 m4 15,436 Pipridae Lepidothrix coeruleocapilla 36 m3 95,078 Corvidae Cyanolyca viridicyanus 62 m4 133,484 Troglodytidae Thryothorus eisenmanni 8 m2 3,010 Cinnycerthia peruana 33 m2 45,002 Cinnycerthia fulva 32 m4 59,861 Turdidae Entomodestes leucotis 73 m3 121,073 Thraupidae Creurgops dentatus 18 m4 46,287 Hemispingus auricularis 37 m4 49,342 Hemispingus calophrys 19 m4 13,246 Hemispingus parodii 4 m4 8,201 Hemispingus rufosuperciliaris 13 m4 23,017 Hemispingus xanthophthalmus 36 m4 55,312 Hemispingus trifasciatus 35 m4 41,490 Nephelornis oneilli 12 m4 15,913 Ramphocelus melanogaster 19 m4 41,659 Buthraupis aureodorsalis 4 m4 2,737 Delothraupis castaneoventris 83 m3 66,053


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Iridosornis jelskii 40 m4 68,438 Iridosornis reinhardti 31 m4 34,933 Tangara meyerdeschauenseei 5 m3 2,734 Tangara phillipsi 1 Deductive 117 Conirostrum ferrugineiventre 49 m4 82,401 Emberizidae Atlapetes rufinucha 94 m4 23,426 Atlapetes forbesi 8 m4 10,083 Atlapetes canigenis 7 m3 19,164 Atlapetes melanopsis 4 m4 4,170 Atlapetes melanolaemus 21 m4/Partial 19,619 Atlapetes terborghi 4 m4 1,581 Icteridae Cacicus koepckeae 6 Hand 21,075 Cacicus chrysonotus 43 m4 58,121 Psarocolius atrovirens 88 m4 71,706

* m1 = model 1 (no MODIS data); m2 = model 2 (with MODIS data); m3 = (with MODIS data generalized to 2 km); m4 = model 4 (with MODIS data generalized to 5 km); Hand = variable set hand-picked; Partial = part inductive using model indicated and part deductive. See Chapter IV: Distribution Modeling Methods for more details. ****m3 was used for the Peruvian portion of the distribution and m4 for the Bolivian portion of the distribution.


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Appendix 3. Reviewers of locality data and draft distribution maps.

PlantsS. AltamiranoW. AndersonC. AndersonC. AntezanaS. ArrazolaS. BeckG. BarrieraP. BerryN. de la BarraS. DresslerW. GalianoC. HughesD. IbañezS. LeivaB. LeónP.-A. LoizeauJ. LuteynR. MenesesJ. MitchellB. MostacedoG. NavarroP. NuñezT. PenningtonG. PranceV. QuipuscoaE. RodríguezA. SagasteguiI. SánchezB. SteinA. TupayachiJ. WoodC. ZambranaM. Zapata

AmphibiansC. AguilarW. ArizábalJ. C. ChaparroJ. CórdovaD. EmbertV. MoralesD. NeiraS. ReichleL. RodríguezP. Venegas

MammalsV. PachecoH. QuintanaR. TimmJ. Vargas

BirdsI. FrankeS. HerzogD. LaneJ. O’NeillT. Valqui

Endemic species distributions

on the east slope of the Andes

in Peru and BoliviaEn

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ENDEMIC SPECIES DISTRIBUTIONS on the east slope of the Andes in Peru and Bolivia

This publication has been financed by The Gordon and Betty Moore

Foundationwww.moore.org

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Endemic Species Distribution