Abstract
Recent literature reviews of bioassessment methods raise questions about use of least-impacted reference sites to characterize natural conditions that no longer exist within contemporary landscapes. We explore an alternate approach for bioassessment that uses species site occupancy data from museum archives as input for species distribution models (SDMs) stacked to predict species assemblages of freshwater fishes in Texas. When data for estimating reference conditions are lacking, deviation between richness of contemporary versus modeled species assemblages could provide a means to infer relative biological integrity at appropriate spatial scales. We constructed SDMs for 100 freshwater fish species to compare predicted species assemblages to data on contemporary assemblages acquired by four independent surveys that sampled 269 sites. We then compared site-specific observed/predicted ratios of the number of species at sites to scores from a multimetric index of biotic integrity (IBI). Predicted numbers of species were moderately to strongly correlated with the numbers observed by the four surveys. We found significant, though weak, relationships between observed/predicted ratios and IBI scores. SDM-based assessments identified patterns of local assemblage change that were congruent with IBI inferences; however, modeling artifacts that likely contributed to over-prediction of species presence may restrict the stand-alone use of SDM-derived patterns for bioassessment and therefore warrant examination. Our results suggest that when extensive standardized survey data that include reference sites are lacking, as is commonly the case, SDMs derived from generally much more readily available species site occupancy data could be used to provide a complementary tool for bioassessment.
Similar content being viewed by others
References
Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:1677–1688
Araújo MB, Luoto M (2007) The importance of biotic interactions for modelling species distributions under climate change. Glob Ecol Biogeogr 16:743–753
Austin M (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157:101–118
Baselga A, Araujo MB (2010) Do community-level models describe community variation effectively? J Biogeogr 37:1842–1850
Bateman BL, VanDerWal J, Williams SE, Johnson CN (2012) Biotic interactions influence the projected distribution of a specialist mammal under climate change. Divers Distrib 18:861–872
Bean PT, Bonner TH, Littrell BM (2007) Spatial and temporal patterns in the fish assemblage of the Blanco River, Texas. Tex J Sci 59:179
Bowman M, Somers K (2005) Considerations when using the reference condition approach for bioassessment of freshwater ecosystems. Water Qual Res J Can 40:347–360
Brooker RW, Travis JMJ, Clark EJ, Dytham C (2007) Modelling species’ range shifts in a changing climate: the impacts of biotic interactions, dispersal distance and the rate of climate change. J Theor Biol 245:59–65
Calabrese JM, Certain G, Kraan C, Dormann CF (2013) Stacking species distribution models and adjusting bias by linking them to macroecological models. Glob Ecol Biogeogr 23(1):99–112
Cao Y, Hawkins CP (2011) The comparability of bioassessments: a review of conceptual and methodological issues. J N Am Benthol Soc 30:680–701
Cao Y, DeWalt RE, Robinson JL, Tweddale T, Hinz L, Pessino M (2013) Using Maxent to model the historic distributions of stonefly species in Illinois streams: the effects of regularization and threshold selections. Ecol Model 259:30–39
Chessman BC (2006) Prediction of riverine fish assemblages through the concept of environmental filters. Mar Freshw Res 57:601–609
Chessman BC, Royal MJ (2004) Bioassessment without reference sites: use of environmental filters to predict natural assemblages of river macroinvertebrates. J N Am Benthol Soc 23:599–615
Chessman B, Muschal M, Royal M (2008) Comparing apples with apples: use of limiting environmental differences to match reference and stressor-exposure sites for bioassessment of streams. River Res Appl 24:103–117
Costa GC, Nogueira C, Machado RB, Colli GR (2009) Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot. Biodivers Conserv 19:883–899
Dolédec S, Statzner B (2010) Responses of freshwater biota to human disturbances: contribution of J-NABS to developments in ecological integrity assessments. J N Am Benthol Soc 29:286–311
Dutton AR (1989) Hydrogeochemical processes involved in salt-dissolution zones, Texas panhandle, U.S.A. Hydrol Process 3:75–89
Dziock F, Henle K, Foeckler F, Follner K, Scholz M (2006) Biological indicator systems in floodplains: a review. Int Rev Hydrobiol 91(4):271–291
Elith J, Graham CH, Anderson RP, Dudık M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17(1):43–57
Fausch KD, Karr JR, Yant PR (1984) Regional application of an index of biotic integrity based on stream fish communities. Trans Am Fish Soc 113:39–55
Ferrier S, Guisan A (2006) Spatial modelling of biodiversity at the community level. J Appl Ecol 43:393–404
Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49
Gelfand AE, Schmidt AM, Wu S, Silander JA Jr, Latimer A, Rebelo AG (2005) Modelling species diversity through species level hierarchical modelling. J R Stat Soc 54:1–20
Gioia P, Pigott JP (2000) Biodiversity assessment: a case study in predicting richness from the potential distributions of plant species in the forests of south-western Australia. J Biogeogr 27:1065–1078
Graham CH, Hijmans RJ (2006) A comparison of methods for mapping species ranges and species richness. Glob Ecol Biogeogr 15:578–587
Growns I, Rourke M, Gilligan D (2013) Toward river health assessment using species distributional modeling. Ecol Indic 29:138–144
Guisan A, Rahbek C (2011) SESAM–a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. J Biogeogr 38:1433–1444
Guisan A, Theurillat JP (2000) Equilibrium modeling of alpine plant distribution: how far can we go? Phytocoenologia 30:353–384
Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009
Guralnick R, Van Cleve J (2005) Strengths and weaknesses of museum and national survey data sets for predicting regional species richness: comparative and combined approaches. Divers Distrib 11:349–359
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29
Hawkins CP, Olson JR, Hill RA (2010) The reference condition: predicting benchmarks for ecological and water-quality assessments. J N Am Benthol Soc 29:312–343
Heard T, Perkin JS, Bonner TH (2012) Intra-annual variation in fish communities and habitat associations in a Chihuahua Desert reach of the Rio Grande/Rio Bravo Del Norte. West N Am Nat 72:1–15
Heikkinen RK, Luoto M, Virkkala R, Pearson RG, Körber JH (2007) Biotic interactions improve prediction of boreal bird distributions at macro-scales. Glob Ecol Biogeogr 16:754–763
Hendrickson DA, Cohen AE (2012) Fishes of Texas project and online database. Published by Texas Natural History Collection, a division of the Department of Integrative Biology, University of Texas at Austin
Herlihy AT, Paulsen SG, Sickle JV, Stoddard JL, Hawkins CP, Yuan LL (2008) Striving for consistency in a national assessment: the challenges of applying a reference-condition approach at a continental scale. J N Am Benthol Soc 27:860–877
Hitt NP, Angermeier PL (2008) Evidence for fish dispersal from spatial analysis of stream network topology. J N Am Benthol Soc 27:304–320
Hitt NP, Angermeier PL (2011) Fish community and bioassessment responses to stream network position. J N Am Benthol Soc J 30:296–309
Hubbs C (1957) Distributional patterns of Texas fresh-water fishes. Southwest Nat 2:89–104
Hubbs C, Edwards RJ, Garrett GP (2008) An annotated checklist of the freshwater fishes of Texas, with keys to identification of species. Tex J Sci 43:1–87
Humphries P, Winemiller KO (2009) Historical impacts on river Fauna, shifting baselines, and challenges for restoration. Bioscience 59:673–684
Karr JR (1981) Assessment of biotic integrity using fish communities. Fisheries 6:21–27
Karr JR, Chu EW (1999) Restoring life in running waters: better biological monitoring. Island Press, Washington, D.C.
King RS, KO Winemiller, JM Taylor, JA Back, A Pease (2009) Development of biological indicators of nutrient enrichment for application in texas streams. Final report to Texas Commission on Environmental Quality, §106 Water Pollution Control Grant #98665304
Kleinsasser LJ, Jurgensen T, Bowles D, Boles S, Aziz K, Saunders K, Linam G, Trungale J, Mayes K, Rector J et al (2004) Status of biotic integrity, water quality, and physical habitat in wadeable east Texas streams. Resources Protection Division, Texas Parks and Wildlife Department River Studies Report 19
Kollaus KA, Bonner TH (2012) Habitat associations of a semi-arid fish community in a karst spring-fed stream. J Arid Environ 76:72–79
Kuemmerle T, Hickler T, Olofsson J, Schurgers G, Radeloff VC (2012) Reconstructing range dynamics and range fragmentation of European bison for the last 8000 years. Divers Distrib 18:47–59
Labay B (2010) The influence of land use, zoogeographic history, and physical habitat on fish community diversity in the lower Brazos watershed. Theses and Dissertations-Biology 27, Texas State University, San Marcos
Labay B, Cohen AE, Sissel B, Hendrickson DA, Martin FD, Sarkar S (2011) Assessing historical fish community composition using surveys, historical collection data, and species distribution models. PLoS One 6:e25145
Leathwick JR, Rowe D, Richardson J, Elith J, Hastie T (2005) Using multivariate adaptive regression splines to predict the distributions of New Zealand’s freshwater diadromous fish. Freshw Biol 50:2034–2052
Lehmann A, Leathwick J, Overton JMC (2002) Assessing New Zealand fern diversity from spatial predictions of species assemblages. Biodivers Conserv 11:2217–2238
Linam GW, Kleinsasser LJ, Mayes KB (2002) Regionalization of the index of biotic integrity for Texas streams. Resources Protection Division, Texas Parks and Wildlife Department River Studies Report 17
Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385–393
Lobo JM (2008) More complex distribution models or more representative data? Biodivers Inform 5:14–19
Mateo RG, Felicísimo ÁM, Pottier J, Guisan A, Muñoz J (2012) Do stacked species distribution models reflect altitudinal diversity patterns? PLoS One 7:e32586
Norris RH, Hawkins CP (2000) Monitoring river health. Hydrobiologia 435:5–17
Olden JD (2003) A species-specific approach to modeling biological communities and its potential for conservation. Conserv Biol 17:854–863
Omernik JM (1987) Ecoregions of the conterminous United States. Ann Assoc Am Geogr 77:118–125
Ostrand KG, Wilde GR (2002) Seasonal and spatial variation in a prairie stream-fish assemblage. Ecol Freshw Fish 11:137–149
Pauly D (1995) Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol Evol 10:430
Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117
Pease AA, Taylor JM, Winemiller KO, King RS (2011) Multiscale environmental influences on fish assemblage structure in central Texas streams. Trans Am Fish Soc 140:1409–1427
Pellissier L, Pradervand JN, Pottier J, Dubuis A, Maiorano L, Guisan A (2012) Climate-based empirical models show biased predictions of butterfly communities along environmental gradients. Ecography 35:684–692
Peppler-Lisbach C, Schröder B (2004) Predicting the species composition of Nardus stricta communities by logistic regression modelling. J Veg Sci 15:623–634
Peterson AT, Soberon J, Pearson RG, Anderson RP, Martinez-Meyer E, Nakamura M, Araujo M (2011) Ecological niches and geographic distributions. Princeton University Press, New Jersey
Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259
Pineda E, Lobo JM (2009) Assessing the accuracy of species distribution models to predict amphibian species richness patterns. J Anim Ecol 78:182–190
Piñeiro G, Perelman S, Guerschman JP, Paruelo JM (2008) How to evaluate models: observed vs. predicted or predicted vs. observed? Ecol Model 216:316–322
Pinnegar JK, Engelhard GH (2008) The “shifting baseline” phenomenon: a global perspective. Rev Fish Biol Fish 18:1–16
R Development Core Team (2012) A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Raxworthy CJ, Ingram CM, Rabibisoa N, Pearson RG (2007) Applications of ecological niche modeling for species delimitation: a review and empirical evaluation using day geckos (Phelsuma) from Madagascar. Syst Biol 56:907–923
Renner IW, Warton DI (2013) Equivalence of MAXENT and poisson point process models for species distribution modeling in ecology. Biometrics 69:274–281
Scott JM (2002) Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, D.C.
Seegert G (2000) The development, use, and misuse of biocriteria with an emphasis on the index of biotic integrity. Environ Sci Policy 3:51–58
Smith EP, Rose KA (1995) Model goodness-of-fit analysis using regression and related techniques. Ecol Model 77:49–64
Soberón J (2007) Grinnellian and Eltonian niches and geographic distributions of species. Ecol Lett 10(12):1115–1123
Soberón J, Nakamura M (2009) Niches and distributional areas: concepts, methods, and assumptions. Proc Natl Acad Sci 106(Supplement 2):19644–19650
Speight MCD, Castella E (2001) An approach to interpretation of lists of insects using digitised biological information about the species. J Insect Conserv 5:131–139
Stoddard JL, Larsen DP, Hawkins CP, Johnson RK, Norris RH (2006) Setting expectations for the ecological condition of streams: the concept of reference condition. Ecol Appl 16:1267–1276
Stranko SA, Hurd MK, Klauda RJ (2005) Applying a large, statewide database to the assessment, stressor diagnosis, and restoration of stream fish communities. Environ Monit Assess 108:99–121
Suter GW II (1993) A critique of ecosystem health concepts and indexes. Environ Toxicol Chem 12:1533–1539
Turak E, Flack LK, Norris RH, Simpson J, Waddell N (1999) Assessment of river condition at a large spatial scale using predictive models. Freshw Biol 41:283–298
Turner W, Spector S, Gardiner N, Fladeland M, Sterling E, Steininger M (2003) Remote sensing for biodiversity science and conservation. Trends Ecol Evol 18(6):306–314
VanDerWal J, Shoo LP, Johnson CN, Williams SE (2009) Abundance and the environmental niche: environmental suitability estimated from niche models predicts the upper limit of local abundance. Am Nat 174:282–291
Vasconcelos TS, Rodríguez MÁ, Hawkins BA (2012) Species distribution modelling as a macroecological tool: a case study using New World amphibians. Ecography 35:539–548
Warren D, Seifert S (2010) Environmental niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335–342
Whittier TR, Stoddard JL, Larsen DP, Herlihy AT (2007) Selecting reference sites for stream biological assessments: best professional judgment or objective criteria. J N Am Benthol Soc 26:349–360
Wilde GR (2011) Reproductive ecology and population dynamics of fishes in the upper Brazos river. Texas Parks and Wildlife State Wildlife Grant annual report
Winemiller KO, King RS, Taylor J, Pease A (2009) Refinement and validation of habitat quality indices (HQI) and aquatic life use (ALU) indices for application to assessment and monitoring of texas surface waters. final project report, Texas Commission on Environmental Quality Contract 582-6-80304, 81 pp
Wright JF, Moss D, Armitage PD, Furse MT (1984) A preliminary classification of running-water sites in Great Britain based on macro-invertebrate species and the prediction of community type using environmental data. Freshw Biol 14:221–256
Acknowledgments
This study was supported by The University of Texas at Austin, Texas Parks and Wildlife Department State Wildlife Grant (Data standardization and georeferencing of the Fishes of Texas database, F06AF00007) made available through the United States Fish and Wildlife Service’s State Wildlife Grant program (T-106), and Texas Commission on Environmental Quality (Digital Fish Atlas grant contract No. 582-11-99736). Thanks to the thousands of collectors who contributed to the Fishes of Texas database. We also thank Gary Garrett, Tim Birdsong, Kevin Mayes, and Josh Perkin for early discussions and concept reviews, 6 anonymous reviewers, and Meagan Bean, Preston Bean, Steven Curtis, Thom Heard, Douglas Knabe, Kristy Kollaus, Doug Martin, Allison Pease, Zach Shattuck, Jason Taylor, and Gene Wilde for help in compiling portions of the survey databases used in our analyses.
Conflict of interest
The authors declare that they have no conflicts of interest.
Author information
Authors and Affiliations
Corresponding author
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Labay, B.J., Hendrickson, D.A., Cohen, A.E. et al. Can Species Distribution Models Aid Bioassessment when Reference Sites are Lacking? Tests Based on Freshwater Fishes. Environmental Management 56, 835–846 (2015). https://doi.org/10.1007/s00267-015-0567-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00267-015-0567-0