Agriculture, Ecosystems and Environment 132 (2009) 135–142

Climate and land-use influences on avifauna in central Argentina: Broad-scalepatterns and implications of agricultural conversion for biodiversity

Anne M. Schrag a,*, Marıa Elena Zaccagnini a, Noelia Calamari b, Sonia Canavelli b

a Instituto Nacional de Tecnologıa Agropecuaria (INTA), Instituto de Recursos Biologicos, De los Reseros y las Cabanas S/N, 1712 Castelar, Argentinab Instituto Nacional de Tecnologıa Agropecuaria (INTA), Estacion Experimental Agropecuaria Parana, Ruta 11 Km 12.5, 3101 Oro Verde, Entre Rıos, Argentina

A R T I C L E I N F O

Article history:

Received 15 September 2008

Received in revised form 18 March 2009

Accepted 22 March 2009

Available online 16 April 2009

Keywords:

Climate

Land use

Birds

Richness

Argentina

Monitoring

A B S T R A C T

Climate and land use drive biodiversity patterns at large scales. Changes in these variables, either

through increased atmospheric carbon dioxide concentrations or conversion of land to agriculture, will

lead to broad-scale changes in species patterns at regional scales. In countries with developing

economies, conversion of land to monocultures of highly valuable crops, such as soybeans, is already

occurring and is expected to increase in the future. Monitoring this change, as well as its impacts on

important indicator species, such as birds, will provide insight into the rate and direction of these

important changes. We used data from the first four years of a long-term monitoring program to relate

broad-scale patterns in climate and land-use change to bird species richness and composition across

land-use gradients in central Argentina. Results suggest that species richness is positively correlated

with cover of native vegetation and negatively correlated with agricultural use. Predictor variables

(including climate and land-use variables) explained 71% of the variation in species richness across the

region. Bird species richness follows a gradient of temperature and precipitation across the region, with

the hottest and wettest areas having the highest predicted species richness. Canonical correspondence

analysis results show that raptors are found mainly in areas of agricultural use and, therefore, may be

more vulnerable to agricultural practices and pesticide use. The results of this study suggest that

continued conversion of native ecosystems to annual crops may lead to decreased overall richness of

avifauna and possible important changes in species composition.

� 2009 Elsevier B.V. All rights reserved.

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Agriculture, Ecosystems and Environment

journal homepage: www.e lsev ier .com/ locate /agee

1. Introduction

The conservation of biodiversity is critically dependent uponsufficient knowledge of the spatial distribution and abundance ofspecies (Margules and Pressey, 2000; Ferrier et al., 2004). Toeffectively manage species as a part of conservation efforts, it isoften necessary to improve current understanding of theirabundance and distribution, as well as factors influencing thesemeasures, thus providing a baseline from which to begin long-termmonitoring for changes in status and trends (Noss, 1990). Whilethis type of information is becoming more readily available inplaces such as the United States and Europe, regional-scalemonitoring of species distributions and abundances throughoutLatin America has, for the most part, just begun (Blanco andCarbonell, 2001; Young et al., 2001).

* Corresponding author. Current address: Northern Great Plains Program, World

Wildlife Fund, 202 S. Black, Ste. 3, Bozeman, Montana 59715, USA.

Tel.: +1 406 585 3486; fax: +1 406 582 7640.

E-mail address: anne.schrag@wwfus.org (A.M. Schrag).

0167-8809/$ – see front matter � 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.agee.2009.03.009

Large-scale processes, such as climate, land-use and land-coverchange, are believed to be drivers of population dynamics atlandscape to regional scales. Increasing demand for food andenergy due to worldwide population growth in epicenters such asChina and India has led to cascading impacts on native andagricultural ecosystems in countries with developing economiesthat hold vast expanses of potentially productive agricultural land,such as Brazil and Argentina (Donald, 2004). The central portion ofArgentina, which includes the provinces of Buenos Aires, Cordoba,Santa Fe, Entre Rıos and La Pampa, represents one such area ofsignificant agricultural production (Leon et al., 1984). As in manyareas worldwide, the conversion of native vegetation to agricul-tural landscapes in this part of Argentina is progressing at a rapidrate (Molinari, 1987; Filloy and Bellocq, 2007). This process isexacerbated by the growing demand for soy-based products forbiofuel production, which has led to a staggering, 100-fold increasein the area of agricultural land dominated by monocultures ofsoybeans in Argentina since 1961 (Fearnside, 2001; Donald, 2004).In addition to the conversion of native vegetation to annual crops,land previously used for other agricultural purposes (e.g., pasture-land or fallow fields) is also being converted to intensive, non-

A.M. Schrag et al. / Agriculture, Ecosystems and Environment 132 (2009) 135–142136

rotational crops, such as soybeans, in an effort to increase profits(Fearnside, 2001; Donald, 2004).

Determining the effects of this change on biodiversity willrequire long-term ecosystem monitoring. However, examplesfrom ecosystems that have incurred similar changes suggest thatthese processes can be detrimental to indicator species such aslandbirds (Chamberlain et al., 2000; Donald, 2004). In most cases,such changes lead to decreases in the diversity and structure ofvegetation, which affect habitat quality and availability and, thus,impact species distribution, abundance and composition acrossagricultural land-use gradients (Heikkinen et al., 2004; Filloy andBellocq, 2007).

In this study, we focus on the first four years of a long-termmonitoring program to assess the impacts of agricultural practiceson bird species richness and relative abundance in central

Fig. 1. Map of study area. Dots indicate transect locations

Argentina. Landbirds are often chosen as focal species inbiodiversity monitoring because of their migratory behavior,sensitivity to environmental change and place in the trophiccascade (Hutto, 1998; Hausner et al., 2003). These behavioralcharacteristics allow them to serve as important indicators ofenvironmental change, whereby monitoring a group of bird speciesis often used as an indicator of overall ecosystem health. Due to theimpact of large-scale drivers, such as land use and climate, on birdspecies distribution and abundance, and the projected change insuch factors during the coming decades, it is imperative toestablish a current, baseline relationship between these driversand bird populations against which future changes may becompared.

Information from this monitoring program eventually willprovide ideal data for assessing long-term trends in bird

within portions of five provinces, which are labeled.

Table 2The average value and standard deviation for bird richness and the five land-use

classes across years (2003–2006) and across transects.

Variable name Average value Standard deviation

Bird species richness 41.85 �5.47

% Cover annual crops 60.58 �8.29

% Cover fields 25.16 �6.50

% Cover native vegetation 13.74 �3.83

% Cover pastures 8.65 �4.04

% Cover other uses 2.42 �2.65

The standard deviation was calculated within transect and across years and then

averaged across all transects to provide one value for each of the following

variables: (1) bird species richness; (2) % cover annual crops; (3) % cover fields; (4) %

cover native vegetation; (5) % cover pastures; and (6) % cover other uses.

A.M. Schrag et al. / Agriculture, Ecosystems and Environment 132 (2009) 135–142 137

populations in this agro-ecosystem. However, with the datacurrently available, we focused on establishing a model thatexplains the relationship between large-scale drivers and aviancommunity dynamics. We hypothesized that landscape- toregional-scale patterns in landbird species richness are partiallyexplained by the influence of large-scale drivers (such as climate,land use and primary productivity) that impact habitat availabilityin this ecosystem. Thus, our first objective was to relate climate,land use and the normalized difference vegetation index (NDVI—aproxy for primary productivity and vegetation biomass) to birdspecies richness in central Argentina. We also hypothesized thatland-use change in particular influences bird species compositionand, thus, species composition varies across the land-use gradient,with species selecting different land-use types as optimal habitats,leading to a differentiation in species composition across land-usegradients. Our second objective, therefore, was to determine themanner in which species composition varies across land-usegradients in this ecosystem using multivariate statistical analysis.

2. Methods

2.1. Bird species richness and relative abundance

Surveys of bird species richness and relative abundance wereconducted along transects in central Argentina annually from 2003to 2006 (Fig. 1). The study region contains five provinces, includingBuenos Aires, Cordoba, Santa Fe, Entre Rıos and La Pampa.Transects were established using a stratified design. A30 km � 30 km grid was placed over the entire area and eightstrata were defined based on predominant agro-economicactivities and provincial boundaries (see Table 1 for descriptionsof strata). Within each stratum, grid cells to be sampled weresystematically chosen with a random start and, within each cell,transects were randomly selected to coincide with secondary andtertiary roads (Canavelli et al., 2003; Calamari et al. (2005);Canavelli, pers. comm.).

Each transect was 30 km in length, and permanently markedsurvey points were located every 1 km along the transect. Surveyswere conducted during the austral breeding season (January andFebruary) each year throughout the four-year period from 2003 to2006. Bird surveys were performed at each point by teams of twoindividuals (three to four teams total, depending on the year)following the point-count method (Bibby et al., 2000). A total of 47transects were surveyed in 2003 and 2004, and an additional 17transects (for a total of 64) were added in 2005 and 2006. Speciesrichness was estimated at each point as the number of species seenor heard during a 5-min period. Relative abundance was estimatedas an index based on presence/absence data for each species ateach point. The transect was selected as the sample unit for thisstudy and, therefore, an average of all point values was taken to

Table 1Definition of eight strata, based on provincial boundaries and agro-economical

zones.

Stratum Province Agro-economical zone

1 Cordoba Dairy/cattle and livestock production

2 Cordoba Agriculture (crops)

3 Santa Fe Dairy/cattle and livestock production

4 Santa Fe Agriculture (crops)

5 Entre Rıos Mix: agriculture (primary)

and livestock production (secondary)

6 Entre Rıos Mix: livestock production (primary)

and agriculture (secondary)

7 Santa Fe and

Cordoba

Mix: livestock production (primary)

and agriculture (secondary)

8 Cordoba and

La Pampa

Mix: livestock production (primary)

and agriculture (secondary)

yield one value per transect for each of the response variables.These calculations were then averaged over the four years toproduce the following variables: (1) bird species richness for eachtransect, as an average value for the 2003–2006 period (see Table 2for average value and standard deviation of richness) and (2)relative abundance of each species in each transect, as an averagevalue for the 2003–2006 period. We chose to use an average acrossthe four-year study period in order to incorporate annual variationand provide a reasonable baseline against which future trends maybe analyzed.

2.2. Land-use data

Surveys of bird populations and land use were conductedsimultaneously along each of the transects. Percent cover of each offive land-use classes was estimated in a circle with a 200 m radiusthat was centered on each point along the transect. Land-useclasses included: (1) annual crops (e.g., soybeans, sorghum, wheat,corn); (2) pastures (of both annual and perennial species); (3)plowed and unplowed fields; (4) native woodland (characteristicspecies include: Prosopis affinis, Prosopis nigra, Acacia caven,Geoffroea decorticans); and (5) other uses (including, but notlimited to, aquatic habitats). ‘‘Pastures’’ refers to cultivatedpastures – not native grasslands – for the purposes of this study.Plowed fields are those that have recently been plowed and, thus,have high soil exposure, while unplowed fields include weedy andfallow fields (e.g., fields with vegetation but no standing crops).Annual crops are fields that have standing crops. The percent coverfor all points was averaged to give one value for each transect, andthen the values for each transect and each year were averaged togive one overall value for each transect for the 2003–2006 period(see Table 2 for average value and standard deviation for each ofthe cover types). Again, we chose to average values across the yearsof the study in order to provide an appropriate baseline againstwhich future trends may be assessed.

2.3. Climate data

Climate data were provided by the Instituto de Clima y Agua atthe Instituto Nacional de Tecnologıa Agropecuaria and werederived from 20-year averages (1981–2000) for maximumtemperature, minimum temperature and total annual precipita-tion from 39 climate stations that were located within thebounding box delineated by the following latitude and longitude:(�58.34, �30.82) and (�64.81, �36.07). This area encompasses allof the bird survey transects. Only stations that had a continuousrecord during the 20-year time period were used in the analysis.

To associate broad-scale climatic gradients that may affectlandbird habitat with bird survey transect locations, spatialinterpolation of the point-based climate station data was performedusing the Universal Kriging tool in ArcGIS 9.1 (ESRI, 2005). This

Fig. 2. Scatter plots of bird species richness versus predictor variables. Bird species richness is shown on the x-axis and each of the predictor variables is shown on the y-axis.

Corresponding Pearson product–momentum correlation coefficients are shown in Table 4.

A.M. Schrag et al. / Agriculture, Ecosystems and Environment 132 (2009) 135–142138

method allowed us to sample interpolated climate values for thelocation of the midpoint of each bird monitoring transect. Whileerror is inherent in the kriging process, the lack of strong elevationalgradients across the study area and the numerical agreementbetween the sampled value and the measured value of the variableat the nearest climate station allowed us to feel confident in thismethod as a way to associate point-based climate data with point-based bird and land-use surveys.

2.4. NDVI data

To determine whether species richness depends upon broad-scale variation in vegetation measures such as productivity, whichmay act as surrogates for habitat quality, we sampled the meanNDVI value for the location of each transect midpoint. NDVI ishighly correlated with absorbed photosynthetically active radia-tion and is often used as a measure of primary productivity andtotal vegetative biomass (Brown et al., 2004).

We used NDVI data from the Advanced Very High ResolutionRadiometer (AVHRR), a part of the NASA Global Inventory,Monitoring and Modeling Project. We calculated NDVI for eachtransect midpoint by sampling a 3 � 3 pixel area (each pixel is1 km2) centered on the midpoint location and averaging the valuesof the nine resulting cells. We used images beginning in August2002 to July 2006 in order to encompass the austral summer andthe accompanying previous spring. Seasons were defined asfollows: (1) spring = August, September, October; (2) summer = -November, December, January; (3) fall = February, March, April;

and (4) winter = May, June, July. We then averaged the NDVI valuesby season and across the study period to produce one value foreach transect for each season.

2.5. Data analysis

All statistical analyses were performed using R 2.4.1 (R CoreDevelopment Team, 2007). To investigate the influence of climate,land use and vegetation biomass and productivity on bird speciesrichness we used generalized linear models (GLMs). The success ofthe GLMs was determined by using Akaike’s Information Criterion(AIC; Burnham and Anderson, 2002) and R2 values.

Because GLMs are influenced by the order in which predictorvariables are added to the model, we added variables to the modelaccording to the following underlying ecological principles.Climate variables should be added to the model first becausethey act as large-scale drivers of vegetation patterns acrosslandscapes and regions. Land-use variables should be added to themodel second because changes in land use impact the speciescomposition and structure of vegetation communities. NDVIvariables should be added to the model third (last) because theyserve as an index of current vegetation by serving as proxies forprimary productivity and vegetative biomass, thus encompassingthe other two sets of variables (climate and land use).

The predictor variables used in this model are inherentlyintercorrelated and autocorrelated. For example, all predictorvariables used in the model are interrelated and influential indetermining patterns of suitable bird habitat. Spatial data, such as

Table 3Significance of climate, land-use and vegetation variables as predictors of species

richness in the generalized linear model.

Variable type Variable name p < 0.05 p < 0.1

Land use % Cover annual crops *

% Cover pasture

% Cover plowed and unplowed fields *

% Cover native vegetation

% Cover other uses (including aquatic)

Climate 20-Year average maximum temperature

20-Year average minimum temperature

20-Year average total annual precipitation

Productivity Spring NDVI (Aug, Sept, Oct)

Summer NDVI (Nov, Dec, Jan)

Fall NDVI (Feb, Mar, Apr)

Winter NDVI (May, Jun, Jul)

Significance level (p < 0.05 or p < 0.01) is indicated by an asterisk (*).

Table 4Pearson product–momentum correlation coefficient (values range from �1.0 to

+1.0) for each of the predictor variables in relation to bird species richness.

Variable type Variable name Corr. coefficient

Land use % Cover annual crops �0.61

% Cover pasture 0.08

% Cover plowed and unplowed fields 0.10

% Cover native vegetation 0.77

% Cover other uses (including aquatic) �0.26

Climate 20-Year average maximum temperature 0.50

20-Year average minimum temperature 0.56

20-Year average total annual precipitation 0.54

Productivity Spring NDVI (Aug, Sept, Oct) 0.57

Summer NDVI (Nov, Dec, Jan) 0.51

Fall NDVI (Feb, Mar, Apr) 0.39

Winter NDVI (May, Jun, Jul) 0.28

A.M. Schrag et al. / Agriculture, Ecosystems and Environment 132 (2009) 135–142 139

climate and remotely sensed (e.g., NDVI) data, are inherentlyautocorrelated. To address this autocorrelation, we examined theresiduals from the correlations with Moran’s I to ensure that theerror terms did not violate the assumption of independence.

To examine bird species composition across land-usegradients, we used canonical correspondence analysis (CCA).CCA is a constrained ordination technique that is often used inecological studies to examine the effects of environmentalgradients on species distributions (Legendre and Legendre,1998). Because CCA is a constrained ordination technique, itonly displays the variation explained by the constrainingvariables: in this case, the land-use gradients. Thus, thistechnique is best suited to those questions with specifichypotheses, as opposed to exploratory analysis, which is mostappropriately accomplished with unconstrained ordinationtechniques, such as non-metric multidimensional scaling (Blair,1996). In this study, we developed an a priori hypothesis thatbird species composition is determined by and varies with land-use type. Thus, for the CCA, we used the relative abundance ofbird species as the species data and the percent cover of the fiveland-use types as the environmental data. We used a Monte

Fig. 3. Maps showing the relationship between climatic variables and predicted bird s

precipitation (mm, left), minimum temperature (8C, middle) and maximum temperatur

worldwide climate data set (Hijmans et al., 2005). Triangles represent predicted bird spe

richness and large triangles representing hot spots for species richness in this study ar

Carlo technique with 100 permutations to determine thesignificance of the analysis (p < 0.01).

3. Results

We had good success in predicting bird species richness usingclimate, land-use and primary productivity variables. We chose themodel with the lowest AIC value and highest R2 value to representthe relationship between bird species richness and large-scaleecosystem drivers (Table 3). The final model had an AIC of 376.75and an R2 = 0.71 (df = 49). This model included all of the land-usevariables, the four seasonal NDVI measurements and the threebasic climate variables (precipitation, minimum temperature andmaximum temperature). The percent cover of annual crops wassignificant at the p < 0.05 level; the percent cover of plowed andunplowed fields was significant at the p < 0.1 level.

We associated bird species richness with each of the predictorvariables using Pearson’s product–momentum correlation coeffi-cient (Table 4; Fig. 2). We found a strong positive correlationbetween species richness and the percent cover of nativevegetation (r = 0.77), and we found a negative correlation betweenspecies richness and the percent cover of annual crops (r = �0.61).A weaker positive correlation existed between bird species

pecies richness. Climatic variables represent 1950–2000 normals for total annual

e (8C, right). Climatic data were derived from the WorldClim spatially interpolated

cies richness at each transect, with small triangles representing the lowest species

ea.

Fig. 4. Ordination diagram for canonical correspondence analysis. Dots represent

transect midpoints and letters represent four-letter species codes (comprised of the

first two letters of the genus and species). Arrow length indicates correlation

between the environmental variable and the ordination axes, thus describing the

relative contribution of the variable to species composition.

A.M. Schrag et al. / Agriculture, Ecosystems and Environment 132 (2009) 135–142140

richness and minimum temperature (r = 0.56), maximum tem-perature (r = 0.50) and annual precipitation (r = 0.54). We createdmaps of the spatial relationship between climatic predictorvariables and predicted bird species richness (Fig. 3). Areas thatreceive more annual precipitation and have higher minimum andmaximum temperatures appear to support higher overall birdspecies richness, which is likely due to the close link betweenclimatic variables and primary productivity. Thus, there exists agradient of species richness from northeast (wettest and hottestareas) to southwest (driest and coolest areas) in this portion ofArgentina.

Results of the canonical correspondence analysis suggest thatbird species respond to land-use categories and that speciesdifferentiate themselves along a land-use gradient (Table 5, Fig. 4).The first two axes of the CCA explained 32.36% and 39.88% of thevariance in the species data, respectively, with canonical eigen-values of 0.1480 and 0.03439. The sum of all unconstrainedeigenvalues was 0.2431. Environmental axes (land-use arrows)explained 46.85% of the variance in the species data. The five land-use categories, and the bird species that grouped along each one,are shown in Table 5. These results imply that the relativeabundance of each of these species is positively correlated with theland-use type under which it is classified. To ensure that land-usevariables are directly influencing species composition and are notserving as surrogates for climate variables, we also ran the CCAwith the climate variables. Results of these analyses confirmed that

Table 5Species most closely associated (out of a total 172) with each of the land-use types

in the CCA.

Habitat Scientific name Common name

Annual crops Ammodramus humeralis Grassland sparrow

Colaptes campestris Field flicker

Columba picazuro Picazuro pigeon

Falco sparverius American kestrel

Guira guira Guira cuckoo

Nothura maculosa Spotted nothura

Passer domesticus House sparrow

Plegadis chihi White-faced ibis

Sicalis flaveola Saffron finch

Athene (Speotyto) cunicularia Burrowing owl

Sturnella supercilliaris White-browed blackbird

Troglodytes aedon House wren

Plowed and

unplowed fields

Bubulcus ibis Cattle egret

Anumbius annumbi Firewood gatherer

Embernagra platensis Great pampa finch

Molothrus bonariensis Shiny cowbird

Upucerthia certhioides Chaco earthcreeper

Native vegetation Columbina picui Picui ground dove

Paroaria coronate Red-crested cardinal

Phaeoprogne tapera Brown-chested martin

Saltator aurantiirostris Golden-billed saltator

Furnarius rufus Rufous hornero

Myopsitta monachus Monk parakeet

Phacellodomus striaticollis Freckle-breasted thornbird

Pitangus sulphuratus Great kiskadee

Schoeniophylax phryganophila Chotoy spinetail

Vanellus chilensis Southern lapwing

Zenaida auriculata Eared dove

Pastures Columba maculosa Spot-winged pigeon

Caracara (Polyborus) plancus Crested caracara

Molothrus badius Bay-winged cowbird

Buteo swainsoni Swainson’s hawk

Other uses Milvago chimango Chimango

Sicalis luteola Grassland yellow finch

Sporophila caerulecens Double-collared seedeater

Synallaxis albescens Pale-breasted spinetail

Tyrannus savanna Fork-tailed flycatcher

Zonotrichia capensis Rufous-collared sparrow

bird species are responding directly to land-use variables, whileclimate variables are influencing land-use variables but onlyindirectly influencing bird species composition. Native vegetationdistribution is closely associated with climatic variables, whereascultivated and pasture lands are less associated with climaticvariables. These results support the heavy influence of humanintervention, such as irrigation and fertilizer application, inagricultural and pastoral lands.

4. Discussion

This study demonstrates the complex relationship amongclimate, land-use and primary productivity variables in driving therichness and species composition of avian communities inArgentine grasslands. Climate is a large-scale ecosystem driverthat influences land-use practices and productivity gradients.These variables, in turn, more directly drive the species richnessand composition of birds across the landscape. Annual crops werehighlighted in this analysis as being highly influential to therichness and composition of bird communities in the Argentinepampas. The results of the GLM suggest that the presence of annualcrops is the land-use variable most likely to influence bird speciesrichness across the landscape. Therefore, this model suggests that ashift in land use from native vegetation to monocultures of annualcrops will lead to changes in species richness at landscape toregional scales, with increasing area planted in annual cropsleading to decreased species richness. Given the growing demandfor agriculture-based food and fuel products, the conversion toannual crops is likely to increase, thus having increasingly negativeimpacts on the richness of the native bird communities of theregion (Fearnside, 2001; Donald, 2004).

Using the results of the CCA model, we can hypothesizepotential impacts of shifting land use on bird species compositionin the region. We decided to focus this analysis on land-usevariables as a way to better understand which bird species may bemost impacted by agricultural land uses. We acknowledge thatclimate, as a large-scale driver of productivity and land use, mayinfluence these land-use variables, but we suggest that under-standing the relationship between land use and species composi-tion may be more directly useful for driving future land-use

A.M. Schrag et al. / Agriculture, Ecosystems and Environment 132 (2009) 135–142 141

planning in this region. While it is somewhat difficult tocompletely separate the species into clean community groupsdue to the overlapping position of the species in relation to theland-use classifications that were used, it is interesting to notesome patterns that emerge along the land-use gradient. Forexample, raptor species, such as American kestrel (Falco sparver-

ius), burrowing owl (Athene (Speotyto) cunicularia), crestedcaracara (Caracara (Polyborus) plancus) and Swainson’s hawk(Buteo swainsoni), are associated with agricultural land uses, suchas annual crops or pastures. This association makes these speciesvulnerable to potential impacts from agricultural practices, such aspesticide use. In studies from 1996 to 2000 on the impacts ofpesticide use on bird deaths, mortalities of these four species dueto pesticides were recorded (Zaccagnini, 2001; Hooper et al., 2002).These results suggest that a continuing shift in land use throughoutthe region may lead to long-term changes in the composition of theavifaunal community and overall decreases in species richness.

In addition to examining the effects of land-use change on avianbiodiversity, we also examined the impacts of large-scale climaticpatterns on the avifauna of the region. The spatial relationshipbetween climatic variables and species richness (Fig. 3) suggeststhe existence of climatic and productivity gradients across theregion that influence avian species richness at larger spatial scales.The significance of temperature to overall species richnesssuggests the possible existence of temperature thresholds foravifauna in the region. Temperature dictates the annual movementpatterns of migrating birds, as well as influencing the availability offood sources. This study suggests that bird species in this area maybe seasonally dependent upon the vegetation of the region for foodand nesting purposes.

Examining the impacts of this time series of climatic factors willprove to be an important aspect of understanding biodiversitygradients across the region. With concern rising over the potentialimpacts of climate change on biodiversity worldwide, it isimportant to note that an increase in maximum and minimumtemperatures in the region may have a substantial impact on avianspecies richness and community composition. Previous studieshave shown that temperature increases may lead to the followingimpacts on avian species: earlier arrival dates for migratory speciesand associated match–mismatch issues with food sources (Inouyeet al., 2000); range shifts (Peterson et al., 2002; Araujo et al., 2005);and even possible extinction (Thomas et al., 2004). The effects ofclimate change in the region are likely to be exacerbated by land-use change, thus leading to combined effects that may be moredetrimental to bird communities than land-use change alone(Parmesan and Yohe, 2003; Thomas et al., 2004).

It is commonly believed that the best way to build ecosystemsthat are resistant and resilient to climate change is to decreasecompeting threats to biodiversity. In central Argentina, world-wide economic forces and population growth are workingtogether to provide incentives for conversion from nativevegetation to monocultures of annual crops that provide littlein the way of habitat for bird species (Molinari, 1987; Filloy andBellocq, 2007). Given the prediction of an average global increasein temperature of 2 8C over the next century (IPCC, 2007), it islikely that habitat, food availability and migratory corridors forthe avifauna of central Argentina will be affected, even withouttaking into account the impacts of competing pressures, such asland-use change. It is, thus, imperative that long-term monitoringprograms, such as the one presented in this paper, are developedto track the impacts of such changes on biodiversity in countrieswhere relatively few resources are spent on conservation. Usingthe results of such programs, it is then possible to suggestsustainable management of agricultural lands for both economicand conservation benefits and to decrease competing pressures onbiodiversity.

Acknowledgements

Funding for this research was provided by INTA Project AERN2622, USFWS Grant # 98210-1-G958 and Neotropical MigratoryBirds Conservation Act Grant # 2534. The INTA-CIRN-Instituto deRecursos Biologicos provided facilities to Anne Schrag during thecompletion of this research. We thank two anonymous reviewersfor their helpful and thoughtful comments on the manuscript. Wegratefully acknowledge the contributions of Pablo Mercuri, CarlosDi Bella and Nicolas Mari of the Instituto de Clima y Agua at INTAfor providing the raw weather station and NDVI data. Rob Bennettsand Josh Gage provided technical advice and support. AndreaGoijman provided assistance with defining preferred habitats foreach of the bird species in this study.

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