Environmental Management

, Volume 56, Issue 4, pp 835–846 | Cite as

Can Species Distribution Models Aid Bioassessment when Reference Sites are Lacking? Tests Based on Freshwater Fishes

  • Ben J. Labay
  • Dean A. Hendrickson
  • Adam E. Cohen
  • Timothy H. Bonner
  • Ryan S. King
  • Leroy J. Kleinsasser
  • Gordon W. Linam
  • Kirk O. Winemiller


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.


Bioassessment Community modeling Conservation Fish biodiversity Species distribution modeling Reference sites 



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.

Supplementary material

267_2015_567_MOESM1_ESM.docx (27 kb)
Supplementary material 1 (DOCX 26 kb)


  1. Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:1677–1688CrossRefGoogle Scholar
  2. Araújo MB, Luoto M (2007) The importance of biotic interactions for modelling species distributions under climate change. Glob Ecol Biogeogr 16:743–753CrossRefGoogle Scholar
  3. Austin M (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157:101–118CrossRefGoogle Scholar
  4. Baselga A, Araujo MB (2010) Do community-level models describe community variation effectively? J Biogeogr 37:1842–1850Google Scholar
  5. Bateman BL, VanDerWal J, Williams SE, Johnson CN (2012) Biotic interactions influence the projected distribution of a specialist mammal under climate change. Divers Distrib 18:861–872CrossRefGoogle Scholar
  6. Bean PT, Bonner TH, Littrell BM (2007) Spatial and temporal patterns in the fish assemblage of the Blanco River, Texas. Tex J Sci 59:179Google Scholar
  7. Bowman M, Somers K (2005) Considerations when using the reference condition approach for bioassessment of freshwater ecosystems. Water Qual Res J Can 40:347–360Google Scholar
  8. 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 245:59–65CrossRefGoogle Scholar
  9. 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):99–112CrossRefGoogle Scholar
  10. Cao Y, Hawkins CP (2011) The comparability of bioassessments: a review of conceptual and methodological issues. J N Am Benthol Soc 30:680–701CrossRefGoogle Scholar
  11. 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 259:30–39CrossRefGoogle Scholar
  12. Chessman BC (2006) Prediction of riverine fish assemblages through the concept of environmental filters. Mar Freshw Res 57:601–609CrossRefGoogle Scholar
  13. 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 23:599–615CrossRefGoogle Scholar
  14. 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 24:103–117CrossRefGoogle Scholar
  15. 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 19:883–899CrossRefGoogle Scholar
  16. 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 29:286–311CrossRefGoogle Scholar
  17. Dutton AR (1989) Hydrogeochemical processes involved in salt-dissolution zones, Texas panhandle, U.S.A. Hydrol Process 3:75–89CrossRefGoogle Scholar
  18. Dziock F, Henle K, Foeckler F, Follner K, Scholz M (2006) Biological indicator systems in floodplains: a review. Int Rev Hydrobiol 91(4):271–291CrossRefGoogle Scholar
  19. 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 29:129–151CrossRefGoogle Scholar
  20. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17(1):43–57CrossRefGoogle Scholar
  21. Fausch KD, Karr JR, Yant PR (1984) Regional application of an index of biotic integrity based on stream fish communities. Trans Am Fish Soc 113:39–55CrossRefGoogle Scholar
  22. Ferrier S, Guisan A (2006) Spatial modelling of biodiversity at the community level. J Appl Ecol 43:393–404CrossRefGoogle Scholar
  23. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49CrossRefGoogle Scholar
  24. 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 54:1–20CrossRefGoogle Scholar
  25. 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 27:1065–1078CrossRefGoogle Scholar
  26. Graham CH, Hijmans RJ (2006) A comparison of methods for mapping species ranges and species richness. Glob Ecol Biogeogr 15:578–587CrossRefGoogle Scholar
  27. Growns I, Rourke M, Gilligan D (2013) Toward river health assessment using species distributional modeling. Ecol Indic 29:138–144CrossRefGoogle Scholar
  28. 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 38:1433–1444CrossRefGoogle Scholar
  29. Guisan A, Theurillat JP (2000) Equilibrium modeling of alpine plant distribution: how far can we go? Phytocoenologia 30:353–384CrossRefGoogle Scholar
  30. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009CrossRefGoogle Scholar
  31. 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 11:349–359CrossRefGoogle Scholar
  32. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29CrossRefGoogle Scholar
  33. Hawkins CP, Olson JR, Hill RA (2010) The reference condition: predicting benchmarks for ecological and water-quality assessments. J N Am Benthol Soc 29:312–343CrossRefGoogle Scholar
  34. 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 72:1–15CrossRefGoogle Scholar
  35. 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 16:754–763CrossRefGoogle Scholar
  36. 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 AustinGoogle Scholar
  37. 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 27:860–877CrossRefGoogle Scholar
  38. Hitt NP, Angermeier PL (2008) Evidence for fish dispersal from spatial analysis of stream network topology. J N Am Benthol Soc 27:304–320CrossRefGoogle Scholar
  39. Hitt NP, Angermeier PL (2011) Fish community and bioassessment responses to stream network position. J N Am Benthol Soc J 30:296–309CrossRefGoogle Scholar
  40. Hubbs C (1957) Distributional patterns of Texas fresh-water fishes. Southwest Nat 2:89–104CrossRefGoogle Scholar
  41. 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 43:1–87Google Scholar
  42. Humphries P, Winemiller KO (2009) Historical impacts on river Fauna, shifting baselines, and challenges for restoration. Bioscience 59:673–684CrossRefGoogle Scholar
  43. Karr JR (1981) Assessment of biotic integrity using fish communities. Fisheries 6:21–27CrossRefGoogle Scholar
  44. Karr JR, Chu EW (1999) Restoring life in running waters: better biological monitoring. Island Press, Washington, D.C.Google Scholar
  45. 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 #98665304Google Scholar
  46. 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 19Google Scholar
  47. Kollaus KA, Bonner TH (2012) Habitat associations of a semi-arid fish community in a karst spring-fed stream. J Arid Environ 76:72–79CrossRefGoogle Scholar
  48. 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 18:47–59CrossRefGoogle Scholar
  49. 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 MarcosGoogle Scholar
  50. 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:e25145CrossRefGoogle Scholar
  51. 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 50:2034–2052CrossRefGoogle Scholar
  52. Lehmann A, Leathwick J, Overton JMC (2002) Assessing New Zealand fern diversity from spatial predictions of species assemblages. Biodivers Conserv 11:2217–2238CrossRefGoogle Scholar
  53. 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 17Google Scholar
  54. Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385–393CrossRefGoogle Scholar
  55. Lobo JM (2008) More complex distribution models or more representative data? Biodivers Inform 5:14–19CrossRefGoogle Scholar
  56. Mateo RG, Felicísimo ÁM, Pottier J, Guisan A, Muñoz J (2012) Do stacked species distribution models reflect altitudinal diversity patterns? PLoS One 7:e32586CrossRefGoogle Scholar
  57. Norris RH, Hawkins CP (2000) Monitoring river health. Hydrobiologia 435:5–17CrossRefGoogle Scholar
  58. Olden JD (2003) A species-specific approach to modeling biological communities and its potential for conservation. Conserv Biol 17:854–863CrossRefGoogle Scholar
  59. Omernik JM (1987) Ecoregions of the conterminous United States. Ann Assoc Am Geogr 77:118–125CrossRefGoogle Scholar
  60. Ostrand KG, Wilde GR (2002) Seasonal and spatial variation in a prairie stream-fish assemblage. Ecol Freshw Fish 11:137–149CrossRefGoogle Scholar
  61. Pauly D (1995) Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol Evol 10:430CrossRefGoogle Scholar
  62. 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 34:102–117CrossRefGoogle Scholar
  63. Pease AA, Taylor JM, Winemiller KO, King RS (2011) Multiscale environmental influences on fish assemblage structure in central Texas streams. Trans Am Fish Soc 140:1409–1427CrossRefGoogle Scholar
  64. 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 35:684–692CrossRefGoogle Scholar
  65. Peppler-Lisbach C, Schröder B (2004) Predicting the species composition of Nardus stricta communities by logistic regression modelling. J Veg Sci 15:623–634Google Scholar
  66. 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 JerseyGoogle Scholar
  67. Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175CrossRefGoogle Scholar
  68. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  69. Pineda E, Lobo JM (2009) Assessing the accuracy of species distribution models to predict amphibian species richness patterns. J Anim Ecol 78:182–190CrossRefGoogle Scholar
  70. Piñeiro G, Perelman S, Guerschman JP, Paruelo JM (2008) How to evaluate models: observed vs. predicted or predicted vs. observed? Ecol Model 216:316–322CrossRefGoogle Scholar
  71. Pinnegar JK, Engelhard GH (2008) The “shifting baseline” phenomenon: a global perspective. Rev Fish Biol Fish 18:1–16CrossRefGoogle Scholar
  72. R Development Core Team (2012) A language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  73. 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 56:907–923CrossRefGoogle Scholar
  74. Renner IW, Warton DI (2013) Equivalence of MAXENT and poisson point process models for species distribution modeling in ecology. Biometrics 69:274–281CrossRefGoogle Scholar
  75. Scott JM (2002) Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, D.C.Google Scholar
  76. Seegert G (2000) The development, use, and misuse of biocriteria with an emphasis on the index of biotic integrity. Environ Sci Policy 3:51–58CrossRefGoogle Scholar
  77. Smith EP, Rose KA (1995) Model goodness-of-fit analysis using regression and related techniques. Ecol Model 77:49–64CrossRefGoogle Scholar
  78. Soberón J (2007) Grinnellian and Eltonian niches and geographic distributions of species. Ecol Lett 10(12):1115–1123CrossRefGoogle Scholar
  79. Soberón J, Nakamura M (2009) Niches and distributional areas: concepts, methods, and assumptions. Proc Natl Acad Sci 106(Supplement 2):19644–19650CrossRefGoogle Scholar
  80. Speight MCD, Castella E (2001) An approach to interpretation of lists of insects using digitised biological information about the species. J Insect Conserv 5:131–139CrossRefGoogle Scholar
  81. 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 16:1267–1276CrossRefGoogle Scholar
  82. 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 108:99–121CrossRefGoogle Scholar
  83. Suter GW II (1993) A critique of ecosystem health concepts and indexes. Environ Toxicol Chem 12:1533–1539CrossRefGoogle Scholar
  84. 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 41:283–298CrossRefGoogle Scholar
  85. 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):306–314CrossRefGoogle Scholar
  86. 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 174:282–291CrossRefGoogle Scholar
  87. Vasconcelos TS, Rodríguez MÁ, Hawkins BA (2012) Species distribution modelling as a macroecological tool: a case study using New World amphibians. Ecography 35:539–548CrossRefGoogle Scholar
  88. Warren D, Seifert S (2010) Environmental niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335–342CrossRefGoogle Scholar
  89. 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 26:349–360CrossRefGoogle Scholar
  90. Wilde GR (2011) Reproductive ecology and population dynamics of fishes in the upper Brazos river. Texas Parks and Wildlife State Wildlife Grant annual reportGoogle Scholar
  91. 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 ppGoogle Scholar
  92. 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 14:221–256CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Ben J. Labay
    • 1
  • Dean A. Hendrickson
    • 1
  • Adam E. Cohen
    • 1
  • Timothy H. Bonner
    • 2
  • Ryan S. King
    • 3
  • Leroy J. Kleinsasser
    • 4
  • Gordon W. Linam
    • 4
  • Kirk O. Winemiller
    • 5
  1. 1.Department of Integrative Biology, Biodiversity CollectionsUniversity of TexasAustinUSA
  2. 2.Department of Biology/Aquatic StationTexas State UniversitySan MarcosUSA
  3. 3.Department of BiologyBaylor UniversityWacoUSA
  4. 4.River Studies Program, Inland FisheriesTexas Parks and Wildlife DepartmentSan MarcosUSA
  5. 5.Department of Wildlife and FisheriesTexas A&M UniversityCollege StationUSA

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