OIKOS 89: 387–391. Copenhagen 2000

Explicit links among physical stress, habitat heterogeneity andbiodiversity

Thomas W. Therriault and Jurek Kolasa

Therriault, T. W. and Kolasa, J. 2000. Explicit links among physical stress, habitatheterogeneity and biodiversity. – Oikos 89: 387–391.

We tested the links among biodiversity, habitat heterogeneity and physical stress in asystem of artificial rock pools on the north coast of Jamaica that mimic naturalaquatic invertebrate communities. The experimental design consisted of three tiers ofsmall plastic pools arranged at increasing distances from the shore. As a result ofcommunity development over six months (January to June 1997), we observedconsiderable differentiation of physical conditions among replicate habitats at thebenign end of the physical gradient, with a concurrent increase in biodiversity (speciesrichness per habitat unit). The most probable explanation for this observed gradientis self-generated habitat heterogeneity that, in turn, promotes biodiversity, likelythrough species interactions. Using additional analyses, including randomizationtechniques, we excluded the effects of sample size and external factors as sources forthe observed increase in biodiversity in the third tier (furthest from the sea). Weinterpret this result as evidence for the complex causal relationship among physicalstress, habitat heterogeneity and biodiversity.

T. W. Therriault and J. Kolasa, Dept of Biology, McMaster Uni6., 1280 Main StreetWest, Hamilton, ON, Canada L8S 4K1 (therritw@mcmaster.ca).

Stressful environments often have low species diversity.Stress has negative effects on biodiversity by reducingproductivity, individual survival and colonization (slowand difficult for species without special adaptations)(Colinvaux 1986). Specific causes of stress differ amonghabitats and taxa, but the impacts of stress tend tointerfere with species performance and may result fromnutrient deficiency, excessive or inadequate physicalconditions, and intensive grazing or predation. Stresshas been correlated with both the decline of diversitywith elevation (Yoda 1967, Whittaker 1977) and thelatitudinal gradient from the tropics to the poles (Wal-lace 1878, Fischer 1960). Various explanations, includ-ing different levels of heterogeneity, have been cited inecology textbooks to account for diversity gradients.

Both spatial and temporal habitat heterogeneity af-fect the structure and dynamics of ecological communi-ties (Kolasa and Pickett 1991, Tilman 1994) byincreasing species diversity in terrestrial and aquaticsystems (MacArthur and MacArthur 1961, Downing

1991, Huston 1994). This increase, known as the rescueeffect (Gotelli 1991) or the mass effect (Shmida andWilson 1985), arises through various mechanisms suchas differential use of microhabitats in space and timeand inter-habitat migration (Jobbagy et al. 1996). Thescale and degree of heterogeneity within a landscape areperceived differently by different organisms (Harmon etal. 1986, Wiens 1994, Knaapen et al. 1996). Hetero-geneity promotes diversity at different scales (Arita1997, Vivian-Smith 1997) and rare species appear tobenefit more than common species (Vivian-Smith 1997).How local diversity is related to biogeographic gradi-ents remains unanswered (Davidovitz and Rosenzweig1998).

Recently, biodiversity has received much attentionfrom both conservationists and ecologists and has beenaddressed by microcosm studies (McGrady-Steed et al.1997). Globally, habitat heterogeneity decreases asphysical stress increases due to human impacts (Bots-ford et al. 1997, Chapin et al. 1997, Dobson et al. 1997,

Accepted 22 October 1999

Copyright © OIKOS 2000ISSN 0030-1299Printed in Ireland – all rights reserved

OIKOS 89:2 (2000) 387

Matson et al. 1997, Noble and Dirzo 1997, Vitousek etal. 1997). Both habitat heterogeneity and physical stressare implicated in species biodiversity and we need togain a better understanding of how diversity is pro-moted, how habitat heterogeneity and physical stressare related, and how biodiversity will be affected if theirdirection and/or intensity change. Interactions betweenthe two may help assess the effects of habitat change onbiodiversity.

Methods and materials

We evaluated links among biodiversity (as species rich-ness, S), habitat heterogeneity and physical stress using48 experimental, artificial rock pools arranged in threetiers (or blocks) located near the Discovery Bay MarineLaboratory on the north coast of Jamaica. This micro-cosm system accumulated species via natural coloniza-tion over a period of six months (January 1997 to June1997) and produced a habitat heterogeneity gradientthat negatively correlated with the initial physical stressgradient. The most stressful environment was near theocean and the least stressful environment was in thecoastal scrub forest.

Faunal samples were collected in one day by obtain-ing 250 ml of water and sediments from the pool(slightly stirred to dislodge organisms from the poolwalls and to homogenize their distribution) and passedthrough a 63-mm net. Organisms were caught in acollecting container and immediately preserved in 60%ethanol. Invertebrates sampled included worms, crus-taceans, and aquatic insect larvae and pupae. A total of13 species were identified in the 48 pools. Physicalvariables describing pool conditions including tempera-ture, salinity, dissolved oxygen, and pH were measuredat the time of biotic sampling. Measurements for allpools were completed within one hour.

The experimental design consisted of three tiers (3 m,11 m and 21 m from the ocean respectively) and eachtier consisted of two rows with eight pools (3 L volume)per row, totaling 48 identical artificial plastic poolsembedded in concrete. Pool biodiversity represented theend point of community development from January toJune of 1997.

We adopted a proxy measure of stress that was theinverse of the mean combined species abundance ineach pool pair (see below) on the assumption that sucha combined abundance realistically reflects the overallquality of conditions within each pool. Given thataquatic organisms generally respond positively (i.e. in-creasing abundance) when physical stress is low, speciesabundances allow for a quantification of the level ofstress in this system. This measure coincided with thegradient of physical conditions from the ocean’s edgeinland.

Habitat heterogeneity was quantified as the variabil-ity in Principal component analysis (PCA) scores ob-tained from data on pool temperature, salinity, pH, anddissolved oxygen. Again, this variability was calculatedpairwise for adjacent pools in both directions, withineach tier. Therefore, an extreme measurement for anysingle pool would not bias the results.

Since the communities were allowed to develop natu-rally (pools were dry at the start of the experiment), theinitial habitat heterogeneity data were collected duringan earlier study where communities of different inverte-brates (from the same species pool) were randomlydistributed over the 48 artificial rock pools. Half of theintroduced communities were an identical mixture ofspecies and half were transplants from natural pools.The habitat data collected at the conclusion of thisstudy illustrate the degree to which the biological com-munities modify their habitat and increase poolheterogeneity.

Statistical analyses were completed using Statisticaand SAS at a significance level of a=0.05.

Results

The tiers differed significantly in levels of physical stress(df=2, 43, F=5.93, p=0.0053) and species richness orbiodiversity (df=2, 43, F=17.45, pB0.0001). As ex-pected, species diversity was affected by both physicalstress and habitat heterogeneity (Fig. 1). Species diver-sity increased (df=2, 43, F=17.45, pB0.0001) withdecreasing levels of stress and with increasing habitatheterogeneity. Furthermore, no gradient existed within

Fig. 1. Biodiversity as a function of physical stress and habitatheterogeneity in a system of artificial rock pools. Biodiversitywas measured as species richness, physical stress was repre-sented by the logarithm of abundance and habitat heterogene-ity as the variability in PCA scores of pool conditions.

388 OIKOS 89:2 (2000)

Fig. 2. The rarefaction curves for randomized pool communi-ties across the entire habitat gradient (broken line) and ran-domized within a tier (continuous line). Data were randomized30 times for each case (total N=1380 for each curve). Finedotted lines show the 95% confidence limits around the rar-efaction lines.

observed increase in biodiversity in the third tier wasdue to differences in factors other than abundance.Differences in habitat heterogeneity is the only factor,of which we are aware, that increases biodiversity.

It is important to note that habitat heterogeneity maybe generated by both external factors and by the resi-dent biological community, or their interactions. Dataobtained during a previous study using the same exper-imental set of pools indicated no difference in habitatheterogeneity among tiers at the beginning of an exper-iment (df=2, 9, F=0.59, p=0.58; Fig. 3). However,the data collected during this study showed a significantdifference in habitat heterogeneity among tiers (df=2,9, F=11.92, p=0.0030; Fig. 3) with the greatest habi-tat heterogeneity observed in the third tier. Further-more, we have replicated the experiment in 1998 andcollected organic matter on a 1-mm mesh sieve todetermine if allochthonous inputs could have producedthe heterogeneity differences among pools. We foundthat the amount of organic matter, primarily leaves,was not significantly greater in Tier 3 compared to Tier2 (Tier 1 had no leaves and was excluded). While themean standard deviation among pairs of adjacent poolswas larger in the third tier, the relative pool-to-pooldifferences were smaller (recall that the third tier hadthe greatest habitat heterogeneity). We conclude thatexposure differences due to location alone do not pro-duce significant small-scale heterogeneity. Thus, suchheterogeneity must have been generated by the bioticactivity in adjacent pools. There were also significantdifferences in biodiversity between the first two tiersand the third tier (Tiers 1 and 3, p=0.0001; Tiers 2 and3, p=0.0001). However, there were no significant dif-ferences between Tiers 1 and 2 for either habitat hetero-geneity (p=0.91) or biodiversity (p=0.99).

a tier for species abundance or for species richnessindicating that the only significant cline of conditionswas perpendicular to the shore.

The negative relationship between physical stress andbiodiversity may be explained by a positive relationshipcommonly observed between the number of individualsin a sample and species richness, the rarefaction curve.In order to control for the sample size effect, we createda rarefaction curve by randomizing the biotic commu-nity data independently of pool location. This was doneby randomizing the biotic data among pools andamong species, which allowed us to determine ‘‘new’’species abundances and species richness values for eachpool. We also randomized the data within each tier(block randomization) to retain a dependence on loca-tion: the same randomization procedure (i.e. amongpools and among species) was followed except bioticdata were randomized among the 16 pools locatedwithin each tier independently. Thus pool location wascritical in determining this rarefaction curve. Random-izations were carried out 30 times for both independentand dependent pool communities. By comparing thetwo curves, we were able to determine if the higherobserved biodiversity in the third tier was a function ofgreater total abundance per pool within this tier. If thehigher diversity in the third tier were simply a functionof the sampling design, the two curves would be thesame. However, we found that the rarefaction curve fordata randomized within a tier (different habitat zones)produced more species given the same abundance thanthe curve for the entire habitat gradient (homogeneityof slopes, transformed as ln [abundance]; df=1, 2758,F=337.1, pB0.0001; Fig. 2). This indicated that the

Fig. 3. Spatial heterogeneity within groups of pools increasedduring the course of study (filled bars) and developed agradient from the ocean’s edge inland. Note that initial habitatheterogeneity was constant across tiers (open bars). Again,habitat heterogeneity was measured as the variability in PCAscores of pool conditions.

OIKOS 89:2 (2000) 389

Discussion

Biodiversity has a significant influence on the level ofhabitat heterogeneity. Tiers 2 and 3 are susceptible tosimilar exogenous factors (i.e. differential shade, wind,and plant debris inputs) and, if the increase in hetero-geneity were due to those factors alone, one wouldexpect to find similar levels of habitat heterogeneitybetween these two tiers. However, the only knowndifference between Tiers 2 and 3 is biodiversity (S),hence we suggest it had a significant influence on thelevel of habitat heterogeneity. If this is the case, thebiological community contributes significantly to thedevelopment of habitat heterogeneity that, in turn,boosts biodiversity. We, therefore, postulate a linkamong biodiversity, habitat heterogeneity and physicalstress as an important factor to consider in assessmentof environmental processes promoting or demotingbiodiversity.

One plausible interpretation of the experimental re-sults is that communities at the benign end of thegradient developed over progressively divergent trajec-tories (cf. Samuels and Drake 1997). This developmen-tal divergence appears to have had a strong effect onphysical conditions in individual pools and may havefurther enhanced differential settlement and persistenceof new colonizers. Thus, a low-variation, low-stressenvironment led to self-generated biodiversity mediatedby autogenic spatial heterogeneity. We tested this ideafurther by examining the individual and combined ef-fects of physical stress and habitat heterogeneity onbiodiversity. The interaction effect between stress andheterogeneity on biodiversity was not significant (SASGLM, df=1, 40, F=1.83, p=0.18) but individually,both low physical stress and high habitat heterogeneitycontributed significantly to biodiversity (pB0.0001 andp=0.0011, respectively). It is possible that higher pro-ductivity at the benign end of the stress gradient (onaverage higher observed chlorophyll concentrations)also may have contributed to higher biodiversity byremoving energetic constraints and permitting longerfood chains (Pimm 1982). However, it is not clear howhigher productivity alone could explain higher spatialheterogeneity. Another mechanism likely to contributeto the differentiation of pools might be cascadingtrophic relationships. We have often observed, althoughnot quantified, that pools with dragonfly larvae havefew midges and high chlorophyll concentrations. How-ever, pools without dragonfly larvae often supportmany midges and have low chlorophyll concentrations(clear water).

While we identify a probable direct link among lowphysical stress, self-generated heterogeneity, and biodi-versity, we acknowledge that other factors also may beimportant. Their relative contributions should be evalu-ated further through field experimentation. It is proba-ble that disturbance, stress, heterogeneity, productivity,

and rescue effect all play roles in enhancing and main-taining high species richness, even in a single ecosystem.

Our study adds one more direct and potentiallyimportant mechanism through which human-producedenvironmental stress may reduce biodiversity. If thereverse process is true, then stress-related reduction ofheterogeneity might be responsible for species extinc-tions. Species extinctions may additionally reduce het-erogeneity and encourage a cascade of steps leading tofurther extinctions.

Acknowledgements – Funding for this project was provided byNSERC and OGS scholarships to T.W.T. and an NSERCoperating grant to J.K. We thank the staff of the D.B.M.L. fortheir assistance. This manuscript was improved by commentsfrom R. Bailey and P. Marquet and editorial suggestions fromJ. Therriault. This is contribution No. 617 from the DBML.

ReferencesArita, H. T. 1997. The non-volant mammal fauna of Mexico-

species richness in a megadiverse country. – Biodiv. Con-serv. 6: 787–795.

Botsford, L. W., Castilla, J. C. and Peterson, C. H. 1997. Themanagement of fisheries and marine ecosystems. – Science277: 509–515.

Chapin, F. S. I., Walker, B. H., Hobbs, R. J. et al. 1997. Bioticcontrol over the functioning of ecosystems. – Science 277:500–504.

Colinvaux, P. 1986. Ecology. – John Wiley and Sons.Davidovitz, G. and Rosenzweig, M. L. 1998. The latitudinal

gradient of species diversity among North Americangrasshoppers (Acrididae) within a single habitat: a test ofspatial heterogeneity hypothesis. – J. Biogeogr. 25: 553–560.

Dobson, A. P., Bradshaw, A. D. and Baker, A. J. M. 1997.Hopes for the future: restoration ecology and conservationbiology. – Science 277: 515–522.

Downing, J. A. 1991. Biological heterogeneity in aquaticecosystems. – In: Kolasa, J. and Pickett, S. T. A. (eds),Ecological heterogeneity. Springer-Verlag, pp. 160–180.

Fischer, A. G. 1960. Latitudinal variation in organic diversity.– Evolution 14: 64–81.

Gotelli, N. J. 1991. Metapopulation models: the rescue effect,the propagule rain, and the core-satellite hypothesis. –Am. Nat. 138: 768–776.

Harmon, M. E., Franklin, J. R., Swanson, F. J. et al. 1986.Ecology of coarse woody debris in temperate ecosystems. –Adv. Ecol. Res. 15: 133–302.

Huston, M. 1994. Biological diversity: the coexistence of spe-cies on changing landscapes. – Cambridge Univ. Press.

Jobbagy, E. G., Paruelo, J. M. and Leon, R. J. C. 1996.Vegetation heterogeneity and diversity in flat and moun-tain landscapes of Patagonia (Argentina). – J. Veg. Sci. 7:599–608.

Knaapen, J. P., Schefer, P. M. and Harms, W. R. 1996.Estimating habitat isolation in landscape planning. –Landscape Urban Plann. 23: 1–16.

Kolasa, J. and Pickett, S. T. A. (eds) 1991. Ecological hetero-geneity. – Springer-Verlag.

MacArthur, R. H. and MacArthur, J. 1961. On bird speciesdiversity. – Ecology 42: 594–598.

Matson, P. A., Parton, W. J., Power, A. G. and Swift, M. J.1997. Agricultural intensification and ecosystem properties.– Science 277: 504–509.

McGrady-Steed, J., Harris, P. M. and Morin, P. J. 1997.Biodiversity regulates ecosystem predictability. – Nature390: 162–165.

390 OIKOS 89:2 (2000)

Noble, I. R. and Dirzo, R. 1997. Forests as human-dominatedecosystems. – Science 277: 522–525.

Pimm, S. L. 1982. Food webs – population and communitybiology. – Chapman and Hall.

Samuels, C. L. and Drake, J. A. 1997. Divergent perspectiveson community convergence. – Trends Ecol. Evol. 12:427–432.

Shmida, A. and Wilson, M. V. 1985. Biological determinantsof species diversity. – J. Biogeogr. 12: 1–20.

Tilman, D. 1994. Competition and biodiversity in spatiallystructured habitat. – Ecology 75: 2–16.

Vitousek, P. M., Mooney, H. A., Lubchenco, J. and Melillo, J.M. 1997. Human domination of earth’s ecosystems. –Science 277: 494–499.

Vivian-Smith, G. 1997. Microtopographic heterogeneity andfloristic diversity in experimental wetland communites. – J.Ecol. 85: 71–82.

Wallace, A. R. 1878. Tropical nature and other essays. –Macmillan.

Whittaker, R. H. 1977. Evolution of species diversity in landcommunities. – Evol. Biol. 10: 1–66.

Wiens, J. A. 1994. Using fractal analysis to assess how speciesperceive landscape structure. – Landsc. Ecol. 9: 25–36.

Yoda, K. 1967. A preliminary survey of the forest vegetationof eastern Nepal. II. General description, structure andfloristic composition of the same plots chosen from differ-ent vegetation zones. – J. Col. Arts Sci. Chiba Univ. Nat.Sci. Ser. 5: 99–140.

OIKOS 89:2 (2000) 391