TROPICAL FISHES AS BIOLOGICAL BULLDOZERS: DENSITY …€¦ · q 2004 by the Ecological Society of America TROPICAL FISHES AS BIOLOGICAL BULLDOZERS: DENSITY EFFECTS ON RESOURCE HETEROGENEITY

2267

Ecology, 85(8), 2004, pp. 2267–2278q 2004 by the Ecological Society of America

TROPICAL FISHES AS BIOLOGICAL BULLDOZERS: DENSITY EFFECTSON RESOURCE HETEROGENEITY AND SPECIES DIVERSITY

ALEXANDER S. FLECKER1,2 AND BRAD W. TAYLOR1,3

1Department of Ecology and Evolutionary Biology, Corson Hall, Cornell University, Ithaca, New York 14853 USA

Abstract. We examined whether grazing fishes exert density-dependent effects on thespatial heterogeneity of resources and benthic species diversity in a tropical Andean stream.We hypothesized that bulldozer grazers (i.e., all or nothing grazing) can be important sourcesof spatial heterogeneity and that a unimodal relationship should exist between grazer densityand resource heterogeneity. We reasoned that organismally generated heterogeneity wouldbe minimal at low densities if grazers are ineffective, and at high densities if grazersthoroughly remove resources. In contrast, spatial heterogeneity should be highest at inter-mediate densities as bulldozer grazers maintain a dynamic mosaic of resource states. Weposited that a corresponding unimodal relationship should exist between grazer density andbenthic diversity, if resource heterogeneity is an important mechanism maintaining speciesdiversity.

We carried out observational and experimental studies to test our hypotheses. First, wequantified natural spatial patterns of benthic resources across transects using fish feedingscars as surrogates. We then analyzed data using a set of landscape indices that capturedifferent components of heterogeneity. A large degree of spatial heterogeneity of algal andsediment resources was apparent both within and among pools. Second, we manipulateddensities of the common grazing fish Parodon apolinari (Parodontidae) in 8-m2 pens, andquantified spatial heterogeneity and corresponding benthic diversity within enclosures. Par-odon exerted strong density-dependent effects on the spatial heterogeneity of resources.Initially (at 7 d) we observed a unimodal relationship between grazer density and spatialheterogeneity along our density gradient (1.25–10.0 Parodon/m2), as predicted. However,at 14 d, our lowest fish density (1.25 Parodon/m2) was sufficient to generate substantialspatial heterogeneity. Although grazers were major sources of heterogeneity, we found littlesupport for the hypothesis that organismally generated heterogeneity is an important mech-anism for maintaining species diversity. Interestingly, the greatest invertebrate richnessoccurred in high density treatments, which had the most spatially uniform resource distri-butions. These results suggest that at the within-pool scale, grazers can have importanteffects on both spatial heterogeneity and benthic diversity, yet their generation of resourcepatchiness does not appear to be the underlying mechanism driving strong fish effects onspecies diversity.

Key words: algae; Andes; ecosystem engineer; fish; geostatistics; grazer; habitat modification;landscape; patch dynamics; resource heterogeneity; spatial ecology; tropics.

INTRODUCTION

Ecological heterogeneity is a theme that has bothintrigued and frustrated ecologists, who have oftenviewed heterogeneity as a noisy irritant that obscuresour capacity to decipher pattern and impart generalityto the real world. Nevertheless, heterogeneity can haveimportant implications for population dynamics, com-munity structure, and ecosystem processes (e.g., Pick-ett and White 1985, Pringle et al. 1988, Kolasa andPickett 1991, Naeem and Colwell 1991, Levin 1992,Palmer and Poff 1997, Hutchings et al. 2000, Wiens2000, Cardinale et al. 2002, and many others). For

Manuscript received 21 March 2003; revised 19 October 2003;accepted 3 December 2003; final version received 12 January2003. Corresponding Editor: J. R. Bence.

2 E-mail: [email protected] Present address: Department of Zoology and Physiology,

University of Wyoming, Laramie, Wyoming 82071 USA.

example, species coexistence may depend, in part, onspatial and temporal mosaics of resources (e.g., Hornand MacArthur 1972, Levins 1979, Huston 1994, Ro-senzweig 1995).

Although considerable attention has been paid to un-derstanding the consequences of heterogeneity for or-ganisms, much less effort has been devoted to quan-tifying the role of organisms in generating spatial het-erogeneity of their physical environment. Organismsthat modify habitat, or ecosystem engineers, representone likely set of biotic sources of spatial heterogeneity(Jones et al. 1994, 1997, Lawton 2000, Pickett et al.2000). Engineering organisms are widespread in natureand have great potential to be strong interactors in nat-ural communities due to their direct impact on habitatstructure and the availability of resources to other or-ganisms (Jones et al. 1994, 1997). Surprisingly, therehave been few attempts to experimentally manipulatesuch organisms and explicitly quantify their influence

2268 ALEXANDER S. FLECKER AND BRAD W. TAYLOR Ecology, Vol. 85, No. 8

PLATE 1. Parodon apolinari (center) grazing on stonesurface, leaving a large feeding scar. To the right and belowthe feeding scar is a heavy accumulation of sediment. Photocredit: B. W. Taylor.

on the spatial heterogeneity of their environment. Suchan approach should prove useful for ultimately under-standing the role of organisms in influencing habitatheterogeneity and the consequences of this heteroge-neity for community and ecosystem processes.

Tropical streams are particularly amenable for de-ciphering the effects of organisms on resource hetero-geneity. Tropical streams often have large numbers ofepibenthic feeders, many of which are ‘‘bulldozer’’grazers (i.e., ‘‘all-or-nothing grazers’’ sensu Sommer1999, 2000) that can create a spatial mosaic of algaland organic sediment resources by consuming or oth-erwise moving substantial quantities of benthic mate-rial (Power 1983, 1990, Pringle et al. 1993, Pringle andBlake 1994, Pringle 1996, Flecker 1992, 1996, 1997).Here we examine the influence of fishes in generatingspatial heterogeneity of resources and their effects onspecies diversity in a tropical Andean stream. We ex-plored these themes by manipulating densities of Par-odon apolinari, an abundant epibenthic feeder of theAndean piedmont region. Parodon is a ‘‘bulldozer’’grazer (sensu Sommer 2000) that leaves readily distin-guishable feeding scars on epilithic substrates. Al-though feeding scars do not tell us the precise com-position of resources, they serve as surrogates of re-source states and are convenient tools for assessingspatial heterogeneity because a large quantity of spatialdata can be generated from relatively little samplingeffort (e.g., Matthews et al. 1986). We viewed our ex-perimental arenas as ‘‘microlandscapes’’ (see Wiensand Milne 1989, Crist et al. 1992, Wiens 2000) andadopted a series of measures developed in the land-scape ecology literature to quantify spatial patterns asa function of fish density. We used observational stud-ies combined with an experimental manipulation to ad-dress two questions: (1) Does spatial heterogeneity

vary as a consequence of grazing-fish density? and (2)Are differences in spatial heterogeneity manifested inbenthic community diversity? We found that bulldozergrazers such as Parodon indeed generate substantialspatial heterogeneity and that Parodon may be effec-tive in maintaining species diversity of benthic inver-tebrates. Nevertheless, spatial heterogeneity may notbe the important mechanistic link ultimately explainingpatterns of species diversity.

DESCRIPTION OF THE STUDY SITE

The study was conducted in Rio Las Marias (98109N, 698449 W), a midsized stream located in the Ve-nezuelan Andean piedmont (see Flecker 1996, Fleckeret al. 2002). During the dry season (generally betweenDecember and April), when stream flows are reduced,Rio Las Marias is transparent and there is substantialsettling of sediments onto stony-bottom substrates.These sediments are relatively rich in organic content(ø10–60% ash-free dry mass [AFDM]) and appear tobe largely derived from decomposing periphyton andfish feces (Flecker 1992, 1996). The field experimentreported here was conducted in March 1998 in a longpool (ø10–12 m wide) with minimal flow (,2 cm/s).

Rio Las Marias has a diverse assemblage of fisheswith more than 80 fish species known from the site.Fishes and tadpoles that consume large quantities ofbenthic organic matter and algae are abundant. In thisstudy, we used one of the most common grazing fishes,the characoid Parodon apolinari (Parodontidae), as ourfocal species (see Plate 1). Parodon apolinari is re-stricted to the Andean piedmont and montane streams(Taphorn 1992), where they often aggregate in large,mobile schools. Densities of Parodon vary consider-ably both spatially (i.e., within and among pools) andamong years (range: 1998 within-pool plots, 0–10.0Parodon/m2; 1998 among pools, 0–2.6 Parodon/m2;1999 within-pool plots, 0–18.1 Parodon/m2; 1999among pools, 0–3.7 Parodon/m2). Their diet is com-posed largely of periphyton, and they clear accruedsediments as a consequence of their foraging activity.Parodon, like other epibenthic feeders in the Andeanpiedmont, leave characteristic feeding scars, often gen-erating a mosaic of algal and detrital resources on thestream bottom (Flecker 1992, 1996, 1997).

METHODS

Feeding scar heterogeneity

Our premise is that fish introduce considerable spa-tial heterogeneity of resources by consuming or dis-turbing sediments and algae on benthic substrates. Todocument the extent to which fish generate heteroge-neity by clearing sediments in the natural stream set-ting, we quantified the distribution of fresh feedingscars, and different scar ‘‘healing’’ states from organicmatter deposition. We laid out transects in each of 10pools during the first and last weeks of January 1998

August 2004 2269GRAZER EFFECTS ON SPATIAL HETEROGENEITY

in order to quantify patterns of feeding scars by epi-benthic fishes. A feeding scar ‘‘resource state’’ wasqualitatively scored at points along each transect sep-arated at 5-cm intervals. At each point, one of fourstates (0, 1, 2, 3) was assigned, representing (0) freshfeeding scars; (1) distinct feeding scars in early stagesof ‘‘healing’’, that is, covered with a light sedimentlayer; (2) distinct feeding scars in later stages of ‘‘heal-ing’’, that is, with a moderate to heavy sediment layer;and (3) heavy sediment displaying no evidence of afeeding scar. To quantify heterogeneity and assesswhether within-pool spatial patterns varied amongpools, we used a series of indices (evenness, patchiness,and contagion) that capture different components oflandscape heterogeneity (see methods below for grazerdensity experiment).

In February and March 2000, we sampled sedimentsand algae from 44 stones in order to assess whetherour qualitative scores of feeding scars effectively rep-resented different resource states. Algae and benthicorganic matter were sampled from 11 stones for eachof the four categories of feeding scars. Stones wereselected haphazardly from two pools and we only in-cluded those in which the entire sampled surface couldbe assigned to a single qualitative score. Loose (epi-pelic) and attached (epilithic) algae were separated foreach stone and a subsample was collected for analysisof chlorophyll a, algal assemblage composition, andbenthic organic matter. Loose fractions were collectedwithin a 6.15-cm2 circular template by lightly squirtingwater on the stone surface and collecting any loosematerial that was readily dislodged. The attached frac-tion was the remaining algae within the template re-moved with a toothbrush. Methods for algal and sed-iment analyses were identical to those described belowfor the grazer density experiment.

Grazer density experiment

Rationale.—In March 1998, we manipulated densi-ties of the epibenthic fish, Parodon apolinari, to ex-plore the effects of grazer density on spatial hetero-geneity of resources. We posited that a nonlinear re-lationship should exist between Parodon density andthe spatial heterogeneity of organic-rich sediment andalgal resources producing a hump-shaped curve (seePoff and Nelson-Baker 1997). We reasoned that at bothhigh and low Parodon densities, low resource hetero-geneity should be generated for contrasting reasons;fish at low densities should be ineffective at controllingresource distributions, whereas at high densities theyshould strongly limit resource abundance. Maximumheterogeneity therefore should be observed at inter-mediate densities at which fish can maintain a dynamicmosaic of resource patches. Our prediction was anal-ogous to the suggestion that greatest heterogeneity isfound at levels of intermediate disturbance, which gen-erate a similar unimodal relationship (see Kolasa andRollo 1991).

In addition, we predicted that species diversity ofbenthic algal and invertebrate assemblages should behighest in Parodon treatments with the greatest re-source heterogeneity. A large literature exists that hasexplored links between structural heterogeneity andspecies diversity, and such a relationship has virtuallybecome an axiom in ecology. Thus, we expected that(1) Parodon would be effective at generating resourceheterogeneity, (2) the greatest heterogeneity would beobserved at intermediate fish densities, and (3) speciesdiversity in intermediate Parodon treatments would behigh due to the maintenance of a dynamic resourcemosaic.

Experimental design.—We set up an enclosure ex-periment that allowed us to control fish density andquantify patterns of resource heterogeneity and com-munity structure. Cages were constructed of 6-mmplastic mesh supported by rebar and enclosed an areaof 8.06 m2 (3.30 3 2.45 m). This mesh size excludedmost benthic fishes except the smallest size classes.Cages were built without floors so the bottom was nat-ural stream. Natural bottom substrates enclosed by eachcage were supplemented with cobbles collected fromthe stream in order to standardize substrate surface areaacross all enclosures. Four density treatments were es-tablished in a randomized complete block design: (1)low Parodon density (10 fish per cage), (2) medium-low density (20 fish), (3) medium-high density (40fish), and (4) high density (80 fish). These correspondedto Parodon densities of ;1.25, 2.5, 5, and 10 fish/m2,respectively, and were within the natural range of den-sities for the total grazing fish assemblage observed atthe site. In addition, an open reference plot accessibleto the natural fish assemblage was established in eachblock. We paid particular attention to minimizing dif-ferences in flow and depth among all treatments withina block. Three randomized complete blocks were setup totaling 15 experimental units (four treatments andone reference 3 three blocks).

The experiment ran over a 14-d period. We chose torun this experiment for a relatively short duration be-cause sediment feeding by fishes is a dynamic processwhereby fish influence resource accrual within a timescale of hours to days (Flecker 1992, 1996). Moreover,colonization rates and growth rates of insects and algaeare extremely rapid in piedmont streams (Flecker 1992,Flecker and Feifarek 1994, Flecker et al. 2002; A.Flecker, unpublished data). All stones lacked feedingscars at the onset of the experiment before fish wereintroduced to enclosures. On two dates, we quantifiedresource heterogeneity in each enclosure and at the endof the experiment we collected benthic algal, insect,and sediment samples. Spatial heterogeneity of re-sources was quantified using feeding scars as a sur-rogate of resource states (i.e., organic matter and al-gae). To score a cage, we placed a large quadrat insidethat was divided into 300 grid cells, each 15 3 15 cm.The quadrat was perched above the stream bottom and


a portable, suspended walkway allowed us to scorecages from overhead without disturbing the substrate.For each grid cell, we recorded the percentage of freshfeeding scars using a scoring system of eight discretecategories varying from 0 to 100%. These scores werethen mapped allowing for analysis of differences inspatial structure of resources among the density treat-ments.

We sampled algae and invertebrates at the end of theexperiment (i.e., day 14) from four randomly selectedstones from each enclosure. We only sampled stonesthat were large enough to occupy most of a 15 3 15cm grid cell. Unlike feeding scars that could be quan-tified in all grid cells, invertebrate and algal sampleswere necessarily limited due to the logistical con-straints of transporting and quantifying large numbersof benthic samples. Invertebrates were sampled usinga small hand net (202-mm Nitex mesh), preserved in95% ethanol, and sorted to the lowest possible taxon.Algal samples were analyzed for chlorophyll a andspecies composition. Attached algal cells were re-moved with a toothbrush from a 6.15-cm2 circle delin-eated with an acetate template. Algal samples wereplaced immediately on ice and chlorophyll was ex-tracted in 90% ethanol (Nusch 1980) for 24 h and sub-sequently analyzed with a fluorometer (Model 10-AU,Turner Designs, Sunnyvale, California, USA) at thefield site. Samples for algal cell counts were preservedin ;3% formalin and later enumerated.

Quantifying heterogeneity.—Spatial heterogeneityhas many different attributes and no single metric in-corporates all of them simultaneously (see Li and Reyn-olds 1994, 1995, Forman 1995, Cooper et al. 1997,Wiens 2000). We chose to quantify spatial heteroge-neity using three landscape indices that capture dif-ferent elements of heterogeneity: (1) contagion(O’Neill et al. 1988, Li and Reynolds 1994) as a mea-sure of the extent that patch types are aggregated, i.e.,spatial arrangement; (2) evenness (Romme 1982, Liand Reynolds 1994) as a measure of the relative pro-portion of different patch types; and (3) patchiness(Romme 1982, Li and Reynolds 1994) as a measure ofthe contrast between neighboring patch types. Conta-gion (C), evenness (E), and patchiness (P) indices allvary from 0 to 1; a low contagion value (i.e., ap-proaching 0) represents a heterogeneous landscape,whereas, for evenness and patchiness, high values (i.e.,approaching 1) represent heterogeneous landscapes.

In addition to landscape indices, we generated var-iate maps of feeding scars as visual tools for depictingspatial patterns of feeding scars among the differentexperimental treatments. Variate maps were producedusing the geostatistical procedure of block kriging(Isaaks and Srivastava 1989), with a block size of 15cm across each enclosure, using the geostatistical soft-ware GS1, version 3.1.7 (Robertson 1998).

Statistical analyses.—To assess the relationship be-tween response variables (e.g., sediment heterogeneity,

algal and invertebrate richness) and Parodon density,we used simple linear regression analysis and selectedthe best linear or curvilinear model using PROCRSREG in SAS Software (SAS Institute 1989). Weused multivariate regression analysis in PROC GLM(SAS Institute 1989) when relating multiple dependentvariables (i.e., contagion, evenness, and patchiness) tofish density. Both dependent (i.e., contagion, evenness,and patchiness) and independent (fish density) vari-ables were treated as continuous. The response of dif-ferent algal taxa to the Parodon density treatments gen-erally did not display linear trends. As a result, thesedata were analyzed using a randomized block MAN-OVA with block as a random effect. Subsequently, in-dividual algal groups were analyzed using ANOVAs,or by simple regression if a linear trend was evident.Cell count data were log transformed and analyzed us-ing the PROC GLM procedure of SAS statistical soft-ware (SAS Institute 1989). For all randomized blockdesign analyses, we left the blocking term in the modelonly when it was significant. Landscape indices werearcsine transformed to normalize the data and to equal-ize the variance among treatments.

Species richness was expressed for data rarified tothe least abundant sample among the experimentaltreatments (invertebrates, 42 individuals; algae, 1331individuals). Rarefaction generates the expected av-erage number of species in a small collection of in-dividuals by repeated random resampling of a largepool of individuals (Gotelli and Colwell 2001). Werarified species richness because large differences inthe numbers of individuals were found among treat-ments and rarefaction allowed us to compare speciesrichness by scaling to an equal number of individualsdrawn per sample (McCabe and Gotelli 2000, Gotelliand Colwell 2001). Rarefaction is especially desirablefor estimating species richness in situations where thenumber of individuals varies greatly among experi-mental treatments so as not to confound treatment ef-fects on abundance with those on species richness (seeGotelli and Colwell 2001). Rarified species richnesswas estimated by Monte Carlo simulation usingEcoSim simulation software, version 6.11 (Gotelli andEntsminger 2001), and all randomizations were re-peated 1000 times.

RESULTS

Feeding scar heterogeneity: natural patterns

Pool surveys.—Surveys of natural patterns of feed-ing scars revealed that fish produce substantial resourceheterogeneity both within pools and among pools (Fig.1). Most pools displayed at least some evidence of fishgenerating a mosaic of resource states. Nevertheless,some pools (e.g., pools 4, 6, 7) were dominated byrelatively recent feeding scars (scores 0 and 1), whereasrecent feeding scars were sparse in other pools (pools5, 9, 10). These differences in feeding scars among


FIG. 1. Cross-sectional transects of fish-feeding scars scored for 10 pools in January 1998, and corresponding landscapeindices (evenness, relative contagion, and relative patchiness index). Scars were recorded at 5-cm intervals and assigned aqualitative score of 0–3, based on a gradient of recent feeding (i.e., fresh grazing scar, score 0) to no evidence of grazing(i.e., heavy sediment layer, score 3).

TABLE 1. Mean 6 1 SE sediment dry mass and chlorophyll a for stones assigned to differentscores (0–3) of resource states.

Score

Sediment

Organic mass(g/m2)

Inorganic mass(g/m2)

Percentageorganic

Algae (as chlorophyll a)

Epilithic(mg/m2)

Epipelic(mg/m2)

0123

1.21a 6 0.111.60a 6 0.102.35b 6 0.194.52b 6 0.58

1.42a 6 0.543.55b 6 1.13

12.73c 6 2.6625.24d 6 4.31

68.1a 6 9.746.1a 6 8.918.5b 6 2.216.6b 6 1.0

3.25a 6 0.875.32a 6 1.855.72a 6 1.643.94a 6 1.33

0.55a 6 0.290.35a 6 0.121.52b 6 0.105.65c 6 1.29

Notes: Organic and inorganic fractions increase whereas percentage organic content decreaseswith score. Chlorophyll a is reported for both epilithic (attached) and epipelic (loose) fractions.For each column, letters denote significant differences between scores based on Tukey’s posthoc multiple-comparison tests.

pools were reflected in a wide range of values observedfor our heterogeneity indices. For example, evennessvaried among pools from 0.06 to 0.95 during our earlyJanuary 1998 survey (Fig. 1). Landscape indices weretightly correlated to one another (C vs. E, r 5 0.99; Cvs. P, r 5 0.93; E vs. P, r 5 0.90); thus, all indicesdisplayed similar patterns even though they captureddifferent aspects of heterogeneity.

Feeding scars as resource surrogates.—Our quali-tative scoring system of feeding scars appeared to bean effective surrogate of resource states, as sedimentand chlorophyll differed significantly among our fourqualitative categories (Table 1). Dry mass of sedimentsincreased progressively with our scoring system(Wilks’ lambda, F6,66 5 12.13, P , 0.0001; AFDM,F3,34 5 24.52, P , 0.0001; inorganic sediments, F3,34


→

FIG. 2. Kriged contour maps showing spatial patterns for different Parodon density treatments for percentages of freshgrazing scars within 15 3 15 cm cells. A single block is shown for day 7 to illustrate visual differences observed amongtreatments. The open reference plot was accessible to all grazing fishes.

5 19.39, P , 0.0001), and differences were highlysignificant for both organic and inorganic matter (Table1). Likewise, algal biomass, as measured by chloro-phyll a, increased along our scoring gradient (Wilks’lambda, F6,78 5 13.66, P , 0.0001). This increase wasdriven by epipelic (i.e., loose overstory) algae, whichdisplayed highly significant differences among quali-tative scores (F3,40 5 15.25, P , 0.0001). In contrast,no differences were observed among the different scor-ing categories for epilithic (i.e., attached) algae (F3,40

5 0.11, P , 0.74).

Grazer density experiment

Feeding scar heterogeneity.—Parodon rapidlycleared substrates leaving readily visible feeding scarsresulting in striking differences among density treat-ments. Consistent with our expectations, a greater pro-portion of substrate was cleared as grazer density in-creased (day 7, r2 5 0.93, F1,9 5 120.07, P , 0.0001;day 14, r2 5 0.75, F1,9 5 49.27, P , 0.0001), andsubstrates were virtually grazed in their entirety (i.e.,100% feeding scars) in the highest density treatment(i.e., 10 Parodon/m2). Highly significant differenceswere observed among Parodon density treatments inthe spatial heterogeneity of resources, that is sediment-rich patches vs. feeding scars (Fig. 2). After seven days,a hump-shaped curve was apparent in the relationshipbetween grazer density and spatial heterogeneity of re-sources; i.e., greater heterogeneity was observed forintermediate densities of Parodon (2.5 and 5 fish/m2)compared to either the low-density (1.25 fish/m2) orhigh-density (10 fish/m2) treatments (Fig. 3). This uni-modal curve was apparent for each of our landscapemeasures, and fits to a quadratic model after seven dayswere highly significant (Wilks’ lambda, F3,7 5 9.01, P, 0.0084; evenness, r2 5 0.60, F1,9 5 33.42, P ,0.0003; contagion, r2 5 0.50, F1,9 5 27.56, P , 0.0005;and patchiness, r2 5 0.35, F1,9 5 24.06, P , 0.0008)(Fig. 3). Interestingly, the shape of the relationshipbetween grazer density and resource heterogeneitychanged considerably by 14 days, from a unimodal toa negative curvilinear relationship (Wilks’ lambda, F3,7

5 5.83, P , 0.0256; evenness, r2 5 0.64, F1,9 5 20.15,P , 0.0015; contagion, r2 5 0.77, F1,9 5 41.57, P ,0.0001; and patchiness, r2 5 0.80, F1,9 5 41.13, P ,0.0001) and the presence of a single fish per squaremeter was sufficient to produce comparatively highamounts of spatial heterogeneity (Fig. 3). Thus, by 14days, the greatest spatial heterogeneity was observedat the two lowest grazer densities, and the highest den-sity treatment was a homogenous grazed surface devoidof sediments and associated epipelic algae.

Invertebrate and algal diversity and abundance.—We observed a highly significant effect of Parodondensity on invertebrate species richness. Species rich-ness was estimated by rarefaction (see Methods) be-cause more than twice the number of invertebrates wasfound on substrates in low density enclosures comparedto the other Parodon density treatments (Table 2; r2 50.56, F1,9 5 13.00, P , 0.005). Estimated invertebraterichness (i.e., rarified richness) increased with Parodondensity despite a decrease in raw richness (r2 5 0.51,F1,9 5 12.02, P , 0.007; Fig. 4). Thus, at high grazerdensity, the few individuals present represented a broaddiversity of invertebrate taxa. In contrast, large num-bers of individuals were found at low grazer densities,however, assemblages were largely dominated by fewinvertebrate taxa.

Likewise, total algal biovolumes in low Parodondensity treatments were nearly twice those of any othertreatment (Table 2). Although algal biovolumes dif-fered among treatments, specific patterns varied amongtaxa (MANOVA, Wilks’ lambda, F12,8 5 3.54, P ,0.044). For example, biovolumes were 15–88 timesgreater at low Parodon density (i.e., 1.25 fish/m2) com-pared to all other treatments for diatoms (one-way AN-OVA, F3,6 5 9.79, P , 0.01) and filamentous greenalgae (one-way ANOVA, F3,6 5 12.19, P , 0.006). Incontrast, grazers appeared to facilitate cyanobacteria,which displayed the greatest biovolume in the highParodon density treatment (one-way ANOVA, F3,6 54.72, P , 0.0587). The response of cyanobacteria waslargely driven by the nitrogen-fixing cyanobacteriumCalothrix, as progressively greater biovolumes wereobserved with increasing Parodon density (simple lin-ear regression, r2 5 0.69, F1,9 5 5.01, P , 0.0599).Algal taxon richness exhibited a pattern similar to bio-volume, with considerably greater expected richness inthe low Parodon density treatment (mean 6 1 SE 526.8 6 0.9 taxa) compared to the other treatments (12.16 0.9–14.7 6 2.5 taxa; one-way ANOVA, F3,6 5 21.49,P , 0.002; Fig. 4b). There was a weak negative trendbetween expected algal richness and Parodon density,however, this relationship was not highly significant (r2

5 0.56, F1,9 5 4.03, P , 0.085).Despite the strong treatment effects of Parodon den-

sity, there were no significant effects of spatial hetero-geneity per se on species richness of either inverte-brates or algae. This absence of any relationship be-tween heterogeneity and richness was observed re-gardless of the heterogeneity index employed (simplelinear regression; invertebrates evenness, P , 0.8470;algae evenness, P , 0.8075; invertebrates contagion,



FIG. 3. Landscape indices for different Parodon density treatments (solid circles) and open reference plots (open squares)based on quantification of fresh feeding scars. Heterogeneous landscapes are represented by low values for the contagionindex and high values for evenness and patchiness indices. All density treatments and open reference plot replicates areshown for each of the two dates (day 7 and day 14).

P , 0.6503; algae contagion, P , 0.8334; invertebratespatchiness, P , 0.5566; algae patchiness, P , 0.4892).

DISCUSSION

Our field observations and experimental findingssuggest that grazing fishes generate spatial heteroge-neity in tropical Andean streams. Using three landscapemeasures, we observed high concordance in the rela-tionship between fish density and spatial heterogeneitydespite the fact that indices captured different aspectsof heterogeneity (e.g., relative proportion of patchtypes, contrast among patches) (Li and Reynolds 1994,Forman 1995). Moreover, the range of index values forspatial heterogeneity measurements at 5-cm intervals

in our pool surveys (Fig. 1) was comparable to thosefor measurements at 15-cm intervals in our experi-mental study (Fig. 3).

Although previous studies have established thatgrazers can act as sources of environmental hetero-geneity (e.g., Gelwick and Matthews 1997, Knapp etal. 1999, Gelwick 2000, Adler et al. 2001, Augustineand Frank 2001), relatively little attention has beenpaid to the relationship between grazer density andspatial heterogeneity of resources (but see Sarnelle etal. 1993, Sommer 1999, 2000, Adler et al. 2001). Wefound that creating a heterogeneous microlandscape re-quires few individuals, but the degree of heterogeneityis at least in part a function of grazer density. We sug-


TABLE 2. Mean densities (no./m2) for total invertebrates and biovolumes (mm3/m2) of major algal groups from differentParodon density treatments.

Invertebratesand algae

Parodon density treatment (no./m2)

1.25 2.5 5 10 Open

Total invertebrates 34 851.3(6819.1)

20 071.3(11 140.0)

7766.3(733.2)

4006.3(1212.9)

1751(309.8)

AlgaeCalothrix

Total cyanobacteria

Desmids

Filamentous greens

Diatoms

19 591.0(6187.1)

22 902.9(6683.3)1661.5(498.0)

36 839.5(22 261.7)

5557.1(3315.5)

26 482.8(5487.2)

26 813.5(5533.9)

106.6(65.6)

1639.5(1109.9)

339.5(130.8)

30 061.4(6756.0)

30 231.1(6736.5)

84.1(50.7)

2360.4(444.0)366.0(90.2)

33 653.8(6856.4)

33 843.5(6738.5)

163.9(96.6)474.0

(222.3)157.4(38.0)

21 946.8(2814.2)

22 191.5(2745.6)

45.1(23.6)

1162.9(409.2)

61.7(42.0)

Note: Numbers in parentheses are standard errors.

FIG. 4. Expected taxon richness of algae (top panel) andinvertebrates (bottom panel) for the different Parodon densitytreatments (solid circles) and open reference plots (opensquares). Expected richness is taxon richness standardizedfor differences in abundance among treatments and was cal-culated using rarefaction.

gest that a unimodal curve may often describe the re-lationship between consumer density and spatial het-erogeneity of resources; however, a suite of factors willmodify the nature of the density–heterogeneity rela-tionship. It is likely that variation in the relationshipwill be context dependent and influenced by consumer

feeding mode and the pre-existing spatial pattern ofbackground environmental structure (Adler et al.2001), as well as the scale of measurement and theinterplay between rates of resource removal (via directconsumption or nontrophic processes) and resource re-newal (Abrams 2000).

Our findings partially support the notion of a hump-shaped density–heterogeneity relationship. After sevendays, we observed a unimodal curve similar to ourpredictions. However, by 14 days, fewer individualswere needed to generate a level of heterogeneity com-parable to higher fish densities the week before. Wesurmise that the curve shifted such that only the de-scending limb of the putative unimodal density-het-erogeneity curve was apparent by day 14. With moretime, we might have seen a further shift in this curvewhereby even fewer individuals would be able to gen-erate highly significant amounts of spatial heteroge-neity. Similarly, simulations by Poff and Nelson-Baker(1997) to model effects of grazing snails suggested atemporal shift towards fewer individuals needed to gen-erate high spatial heterogeneity. Corresponding to ourfindings, Poff and Nelson-Baker observed that, at oneweek, intermediate densities (100 snails/m2) producedhigh spatial heterogeneity, as measured by landscapepatchiness; yet, by two weeks, much lower densities(20 snails/m2) could generate comparable levels of het-erogeneity in their simulations.

Does spatial heterogeneity explain patternsof species richness?

We hypothesized that the generation of higher spatialheterogeneity of resources at intermediate consumerdensities would be an important mechanism for main-taining species richness. The relationship betweengrazer density and plant diversity is often predicted tobe unimodal (e.g., Harper 1969, Paine 1977, Lubchen-co 1978, Milchunas et al. 1988), and the intermediatedisturbance hypothesis (IDH; Connell 1978) is one


conceptual framework that is used to explain this pat-tern (see Steinman 1996, Collins and Glenn 1997). Therationale for a unimodal relationship is that few grazer-resistant taxa can persist under intense grazing pres-sure, whereas competitive exclusion occurs in the ab-sence of grazing thus resulting in decreased diversity.In contrast to the IDH, the notion that organismallygenerated heterogeneity maintains richness at the land-scape scale (i.e., across patch types) requires no specialrole for competitive interactions and assumes that spe-cies abundance may be more related to the distributionof resources rather than the ways that organisms sub-divide resources (Huston 1994, Jones et al. 1997). Theimportant requisites are that (1) organisms generate andmaintain microhabitat heterogeneity via trophic or non-trophic mechanisms, and (2) at least some subset ofthe pool of colonizing species specializes on the dif-ferent patch types (Wright et al. 2002).

In our experiment, taxonomic richness of inverte-brates and algae was strongly influenced by grazingfish density; nonetheless we found little support for theimportance of organismally generated heterogeneity inexplaining patterns of diversity. Despite the strong ef-fects of Parodon on spatial heterogeneity, invertebrateand algal richness was not positively correlated withheterogeneity. One possibility is that disparities in sam-ple sizes used to measure spatial heterogeneity andspecies richness limited our ability to detect such arelationship. However, this alone is an unsatisfying ex-planation given that rarified invertebrate richness wasgreatest in enclosures with the highest fish densitieswhere heterogeneity was virtually undetectable by anymeasure. Thus fish appear to strongly influence inver-tebrate species richness, although the mechanism ex-plaining high species richness with increasing Parodondensity is puzzling. Our results contrast with Sommer’s(1999, 2000) studies in aquaria, in which grazing snailsenhanced algal diversity by increasing the spatial het-erogeneity of benthic biofilms.

The hypothesis that organismally generated hetero-geneity enhances species richness assumes that sometaxa specialize on distinct patch types. For example,beavers generate habitat heterogeneity through theirdam-building activities, which in turn results in in-creased plant species richness across the landscape(Wright et al. 2002). Beaver increase species richnessin part because a host of plant species found in beavermeadows do not occur in unengineered riparian zones.In the case of grazing fishes in tropical Andean streams,we found little evidence that most algae and inverte-brate taxa were specialized to any given patch type;instead we often observed broad overlap of speciesoccurring in both recently grazed and ungrazed patches.Thus, among assemblages of generalists capable of col-onizing and surviving in a broad assortment of patchtypes, other factors such as resource availability, foodquality, or predators may be more important in ulti-mately determining patterns of diversity.

Patterns of diversity in Andean piedmont streamsmay be highly dependent on the interplay between ratesof patch generation by grazing fishes and patch colo-nization by insects and algae. Grazing fish may notenhance diversity if invertebrates and algae respond attemporal scales that are much slower than the fre-quency with which fish graze benthic substrates. Mod-els of patch dynamics suggest that time scales of patchgeneration and colonization can strongly affect the re-sulting diversity, and depending on the relative ratesof these processes can lead to either species enrich-ment, impoverishment, or little effect (Caswell and Co-hen 1991). For example, Huston’s (1994) dynamicequilibrium model (DEM) of species diversity empha-sizes how fundamentally different relationships be-tween disturbance frequency or intensity and speciesdiversity can result, depending on rates of populationgrowth and competitive displacement. Thus, a givenlevel of disturbance can result in either high, low, orintermediate species diversity depending on the inter-play between rates of different processes.

In summary, our findings suggest that organisms canbe important sources of environmental heterogeneityin tropical Andean streams. Nonetheless, we found lit-tle evidence that increased microhabitat heterogeneitysupported more diverse assemblages of organisms. Theconsequences of organismally generated heterogeneitymay be more important in affecting the spatial distri-bution of specific functionally important species (e.g.,Calothrix) rather than community structural propertiessuch as diversity. We focused on heterogeneity in ben-thic sediments because our previous work (Flecker1992, 1996, 1997) and work by others (Power 1990,Pringle et al. 1993, 1999, Pringle and Blake 1994) hasshown that this resource is important for algal and in-vertebrate assemblages in tropical streams. However,we conclude that organismally generated heterogeneityin benthic sediments does not necessarily promote spe-cies diversity due to the absence of patch differentiationor resource specialization by algae and invertebratescoupled with high turnover rates of benthic sedimentpatches. For example, we have found that benthic or-ganic matter turnover rate in Rio Las Marias can be asrapid as one to three days (B. Taylor and A. Flecker,unpublished data). Undoubtedly, the relationship be-tween spatial heterogeneity and diversity is complexand will remain a fundamental challenge for ecologiststo disentangle.

ACKNOWLEDGMENTS

We thank Naomi Altman, Ken Gerow, and Pat Sullivan forstatistical advice. S. Alexander, B. Daley, G. Galbreath, J.Figueredo, J. Hood, A. Seitz, and A. Taylor assisted withfieldwork. We appreciate the support of Bernardo and CayeyaPerez and the Figueredo family in Venezuela. Dr. Don Ta-phorn and colleagues at the UNELLEZ in Guanare greatlyfacilitated our research. Comments by Bryon Daley, GretchenGettel, Becky Irwin, Angus McIntosh, Pete McIntyre, BobbiPeckarsky, Brian Roberts, and Justin Wright improved the


manuscript. This work was supported by NSF grant DEB-9615349 to A. S. Flecker.

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