Landscape Ecology

, Volume 31, Issue 6, pp 1369–1382 | Cite as

Integrated models that unite local and regional data reveal larger-scale environmental relationships and improve predictions of species distributions

  • Robert J. FletcherJr.
  • Robert A. McCleery
  • Daniel U. Greene
  • Courtney A. Tye
Research Article



The scale of environmental relationships is often inferred through the use of species distribution models. Yet such models are frequently developed at two distinct scales. Coarse-scale models typically use information-poor (e.g., presence-only) data to predict relative distributions across geographic ranges, whereas fine-scale models often use richer information (e.g., presence–absence data) to predict distributions at local to landscape scales.


We unite presence–absence and presence-only data to predict occurrence of species, what we refer to as integrated distribution models. We determine if integrated models improve predictions of species distributions and identification of characteristic spatial scales of environmental relationships relative to presence–absence modeling and ensemble modeling that averages predictions from separate presence-only and presence–absence models.


We apply recent advances in integrated distribution models to predict Sherman’s fox squirrel (Sciurus niger shermani) distribution in north-central Florida. Presence-only data were collected through a citizen-science program across its geographic range, while presence–absence data were collected using camera trapping surveys across 40 landscapes.


Integrated models estimated environmental relationships with greater precision and identified larger characteristic scales for environmental relationships than using presence–absence data alone. In addition, integrated models tended to have greater predictive performance, which was more robust to the amount of presence–absence and presence-only data used in modeling, than presence–absence and ensemble models.


Integrated distribution models hold much potential for improving our understanding of environmental relationships, the scales at which environmental relationships operate, and providing more accurate predictions of species distributions. Many avenues exist for further advancement of these modeling approaches.


Citizen science Ensemble model Habitat suitability Integrated distribution model Multi-scale analysis Point process Presence-only Sciurus niger 



B. Reichert and three anonymous reviewers provided thoughtful reviews on previous versions of this manuscript, which greatly clarified the ideas presented here. R. Dorazio provided useful insight. We thank the University of Florida, the Florida Fish and Wildlife Conservation Commission, the private landowners, National Forest Service, Florida Forest Service, Florida Park Service, The Florida National Guard’s Camp Blanding Joint Training Center, and the University of Florida’s Ordway-Swisher Biological Station, Plant Science Research & Education Unit, and Austin Cary Forest for providing site access and logistical support. We also thank the U.S. Department of Agriculture, USDA-NIFA Initiative Grant No. 2012-67009-20090 for support. Finally, we thank the technicians and many volunteers for assistance in the field, and the voluntary participants for submitting their sightings to our online survey.

Supplementary material

10980_2015_327_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (DOCX 1169 kb)


  1. Abadi F, Gimenez O, Arlettaz R, Schaub M (2010) An assessment of integrated population models: bias, accuracy, and violation of the assumption of independence. Ecology 91:7–14CrossRefPubMedGoogle Scholar
  2. Albright TP, Pidgeon AM, Rittenhouse CD, Clayton MK, Flather CH, Culbert PD, Wardlow BD, Radeloff VC (2010) Effects of drought on avian community structure. Glob Change Biol 16:2158–2170CrossRefGoogle Scholar
  3. Araujo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–47CrossRefPubMedGoogle Scholar
  4. Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157:101–118CrossRefGoogle Scholar
  5. Beale CM, Lennon JJ, Yearsley JM, Brewer MJ, Elston DA (2010) Regression analysis of spatial data. Ecol Lett 13:246–264CrossRefPubMedGoogle Scholar
  6. Brotons L, Thuiller W, Araujo MB, Hirzel AH (2004) Presence–absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27:437–448CrossRefGoogle Scholar
  7. Cushman SA, McGarigal K (2002) Hierarchical, multi-scale decomposition of species-environment relationships. Landscape Ecol 17:637–646CrossRefGoogle Scholar
  8. de Knegt HJ, van Langevelde F, Coughenour MB, Skidmore AK, de Boer WF, Heitkonig IMA, Knox NM, Slotow R, van der Waal C, Prins HHT (2010) Spatial autocorrelation and the scaling of species–environment relationships. Ecology 91:2455–2465CrossRefPubMedGoogle Scholar
  9. Dickinson JL, Zuckerberg B, Bonter DN (2010) Citizen science as an ecological research tool: challenges and benefits. In: Futuyma DJ, Shafer HB, Simberloff D (eds) Annual review of ecology, evolution, and systematics, vol 41, pp 149–172Google Scholar
  10. Dorazio RM (2012) Predicting the geographic distribution of a species from presence-only data subject to detection errors. Biometrics 68:1303–1312CrossRefPubMedGoogle Scholar
  11. Dorazio RM (2014) Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Glob Ecol Biogeogr 23:1472–1484CrossRefGoogle Scholar
  12. Elith J, Graham CH (2009) Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography 32:66–77CrossRefGoogle Scholar
  13. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annual review of ecology evolution and systematics, pp 677–697Google Scholar
  14. Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberon J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151CrossRefGoogle Scholar
  15. Evans JM, Fletcher RJ Jr, Alavalapati J (2010) Using species distribution models to identify suitable areas for biofuel feedstock production. Glob Chang Biol Bioenergy 2:63–78CrossRefGoogle Scholar
  16. 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
  17. Fithian W, Elith J, Hastie T, Keith DA (2015) Bias correction in species distribution models: pooling survey and collection data for multiple species. Methods Ecol Evol 6:424–438CrossRefGoogle Scholar
  18. Fletcher RJ Jr, Hutto RL (2008) Partitioning the multi-scale effects of human activity on the occurrence of riparian forest birds. Landscape Ecol 23:727–739CrossRefGoogle Scholar
  19. Fletcher RJ Jr, Revell A, Reichert BE, Kitchens WM, Dixon JD, Austin JD (2013) Network modularity reveals critical scales for connectivity in ecology and evolution. Nat Commun 4:2572PubMedGoogle Scholar
  20. Fourcade Y, Engler JO, Roedder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9:e97122CrossRefPubMedPubMedCentralGoogle Scholar
  21. Franklin J (2009) Mapping species distributions: spatial inference and prediction. Cambridge University Press, CambridgeGoogle Scholar
  22. Getis A, Franklin J (1987) Second-order neighborhood analysis of mapped point patterns. Ecology 68:473–477CrossRefGoogle Scholar
  23. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009CrossRefGoogle Scholar
  24. Hastie T, Fithian W (2013) Inference from presence-only data; the ongoing controversy. Ecography 36:864–867CrossRefPubMedPubMedCentralGoogle Scholar
  25. Holland JD, Bert DG, Fahrig L (2004) Determining the spatial scale of species’ response to habitat. Bioscience 54:227–233CrossRefGoogle Scholar
  26. Horne JK, Schneider DC (1995) Spatial variance in ecology. Oikos 74:18–26CrossRefGoogle Scholar
  27. Inventory FNA (2010) Guide to the natural communities of Florida: 2010. TallahasseeGoogle Scholar
  28. Jackson HB, Fahrig L (2015) Are ecologists conducting research at the optimal scale? Glob Ecol Biogeogr 24:52–63CrossRefGoogle Scholar
  29. Jackson ND, Fahrig L (2014) Landscape context affects genetic diversity at a much larger spatial extent than population abundance. Ecology 95:871–881CrossRefPubMedGoogle Scholar
  30. Jones CC, Acker SA, Halpern CB (2010) Combining local- and large-scale models to predict the distributions of invasive plant species. Ecol Appl 20:311–326CrossRefPubMedGoogle Scholar
  31. Kadmon R, Farber O, Danin A (2004) Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecol Appl 14:401–413CrossRefGoogle Scholar
  32. Kantola AT, Humphrey SR (1990) Habitat use by Sherman’s fox squirrel (Sciurus niger shermani) in Florida. J Mammal 71:411–419CrossRefGoogle Scholar
  33. Keil P, Wilson AM, Jetz W (2014) Uncertainty, priors, autocorrelation and disparate data in downscaling of species distributions. Divers Distrib 20:797–812CrossRefGoogle Scholar
  34. Kellner KF, Swihart RK (2014) Accounting for imperfect detection in ecology: a quantitative review. PLoS ONE 9:e111436CrossRefPubMedPubMedCentralGoogle Scholar
  35. Kery M, Royle JA, Schmid H (2005) Modeling avian abundance from replicated counts using binomial mixture models. Ecol Appl 15:1450–1461CrossRefGoogle Scholar
  36. Koprowski JL (1994) Sciurus niger. Mamm Species 479:1–9Google Scholar
  37. Lahoz-Monfort JJ, Guillera-Arroita G, Wintle BA (2014) Imperfect detection impacts the performance of species distribution models. Glob Ecol Biogeogr 23:504–515CrossRefGoogle Scholar
  38. Lawler JJ, Shafer SL, Blaustein AR (2010) Projected climate impacts for the amphibians of the western hemisphere. Conserv Biol 24:38–50CrossRefPubMedGoogle Scholar
  39. Lawson CR, Hodgson JA, Wilson RJ, Richards SA (2014) Prevalence, thresholds and the performance of presence–absence models. Methods Ecol Evol 5:54–64CrossRefGoogle Scholar
  40. Liu C, White M, Newell G (2011) Measuring and comparing the accuracy of species distribution models with presence–absence data. Ecography 34:232–243CrossRefGoogle Scholar
  41. Liu C, White M, Newell G (2013) Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40:778–789CrossRefGoogle Scholar
  42. Loeb SC, Moncrief ND (1993) The biology of fox squirrels in the Southeast: a review. In: Moncrief ND, Edwards JW, Tappe PA (eds) Fox squirrels, Sciurus niger, Proceedings of the 2nd symposium of Southeast, pp 1–20Google Scholar
  43. Loiselle BA, Howell CA, Graham CH, Goerck JM, Brooks T, Smith KG, Williams PH (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conserv Biol 17:1591–1600CrossRefGoogle Scholar
  44. Loiselle BA, Jorgensen PM, Consiglio T, Jimenez I, Blake JG, Lohmann LG, Montiel OM (2008) Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? J Biogeogr 35:105–116Google Scholar
  45. MacKenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248–2255CrossRefGoogle Scholar
  46. Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W (2009) Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15:59–69CrossRefGoogle Scholar
  47. McCarthy KP, Fletcher RJ, Rota CT, Hutto RL (2012) Predicting species distributions from samples collected along roadsides. Conserv Biol 26:68–77CrossRefPubMedGoogle Scholar
  48. Miguet P, Jackson HB, Jackson ND, Martin AE, Fahrig L. What determines the spatial extent of landscape effects on species? Landscape Ecol (in press)Google Scholar
  49. Miller JR, Turner MG, Smithwick EAH, Dent CL, Stanley EH (2004) Spatial extrapolation: the science of predicting ecological patterns and processes. Bioscience 54:310–320CrossRefGoogle Scholar
  50. Moore JC (1956) Variation in the fox squirrel in Florida. Amer Midland Nat 55:41–65CrossRefGoogle Scholar
  51. Moore JC (1957) The natural history of the fox squirrel, Sciurus niger shermani. Bull Am Mus Nat Hist 113:1–72Google Scholar
  52. Norris K (2004) Managing threatened species: the ecological toolbox, evolutionary theory and declining-population paradigm. J Appl Ecol 41:413–426CrossRefGoogle Scholar
  53. Oneill RV, Hunsaker CT, Timmins SP, Jackson BL, Jones KB, Riitters KH, Wickham JD (1996) Scale problems in reporting landscape pattern at the regional scale. Landscape Ecol 11:169–180CrossRefGoogle Scholar
  54. Pearce JL, Boyce MS (2006) Modelling distribution and abundance with presence-only data. J Appl Ecol 43:405–412CrossRefGoogle Scholar
  55. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  56. Phillips SJ, Dudik M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19:181–197CrossRefPubMedGoogle Scholar
  57. Renner IW, Elith J, Baddeley A, Fithian W, Hastie T, Phillips SJ, Popovic G, Warton DI (2015) Point process models for presence-only analysis. Methods Ecol Evol 6:366–379CrossRefGoogle Scholar
  58. Renner IW, Warton DI (2013) Equivalence of MAXENT and poisson point process models for species distribution modeling in ecology. Biometrics 69:274–281CrossRefPubMedGoogle Scholar
  59. Ries L, Fletcher RJ, Battin J, Sisk TD (2004) Ecological responses to habitat edges: mechanisms, models, and variability explained. Annu Rev Ecol Evol Syst 35:491–522CrossRefGoogle Scholar
  60. Rota CT, Fletcher RJ Jr, Evans JM, Hutto RL (2011) Does accounting for detectability improve species distribution models? Ecography 34:659–670CrossRefGoogle Scholar
  61. Royle JA (2004) N-mixture models for estimating population size from spatially replicated counts. Biometrics 60:108–115CrossRefPubMedGoogle Scholar
  62. Royle JA, Chandler RB, Yackulic C, Nichols JD (2012) Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions. Methods Ecol Evol 3:545–554CrossRefGoogle Scholar
  63. Royle JA, Dorazio RM (2008) Hierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations, and communities. Academic Press, New YorkGoogle Scholar
  64. Schaub M, Gimenez O, Sierro A, Arlettaz R (2007) Use of integrated modeling to enhance estimates of population dynamics obtained from limited data. Conserv Biol 21:945–955CrossRefPubMedGoogle Scholar
  65. Smith AC, Fahrig L, Francis CM (2011) Landscape size affects the relative importance of habitat amount, habitat fragmentation, and matrix quality on forest birds. Ecography 34:103–113CrossRefGoogle Scholar
  66. Soberon J (2007) Grinnellian and Eltonian niches and geographic distributions of species. Ecol Lett 10:1115–1123CrossRefPubMedGoogle Scholar
  67. Thompson CM, McGarigal K (2002) The influence of research scale on bald eagle habitat selection along the lower Hudson River, New York (USA). Landscape Ecol 17:569–586CrossRefGoogle Scholar
  68. Thompson HR (1955) Spatial point processes, with applications to ecology. Biometrika 42:102–115CrossRefGoogle Scholar
  69. Thornton DH, Branch LC, Sunquist ME (2011) The influence of landscape, patch, and within-patch factors on species presence and abundance: a review of focal patch studies. Landscape Ecol 26:7–18CrossRefGoogle Scholar
  70. Thornton DH, Fletcher RJ Jr (2014) Body size and spatial scales in avian response to landscapes: a meta-analysis. Ecography 37:454–463Google Scholar
  71. Urban DL, Oneill RV, Shugart HH (1987) Landscape ecology. Bioscience 37:119–127CrossRefGoogle Scholar
  72. Warton DI, Renner IW, Ramp D (2013) Model-based control of observer bias for the analysis of presence-only data in ecology. PLoS ONE 8:e79168CrossRefPubMedPubMedCentralGoogle Scholar
  73. Warton DI, Shepherd LC (2010) Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology. Ann Appl Stat 4:1383–1402CrossRefGoogle Scholar
  74. Weigl PD, Steele MA, Sherman LJ, Ha JC, Sharpe TL (1989) The ecology of the fox squirrel Sciurus niger in North Carolina: implications for survival in the Southeast. Bull Tall Timbers Res Stn 24(I-XII):1–93Google Scholar
  75. Wenger SJ, Olden JD (2012) Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods Ecol Evol 3:260–267CrossRefGoogle Scholar
  76. Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397CrossRefGoogle Scholar
  77. Zuckerberg B, Desrochers A, Hochachka WM, Fink D, Koenig WD, Dickinson JL (2012) Overlapping landscapes: a persistent, but misdirected concern when collecting and analyzing ecological data. J Wildl Manag 76:1072–1080CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Robert J. FletcherJr.
    • 1
  • Robert A. McCleery
    • 1
  • Daniel U. Greene
    • 1
  • Courtney A. Tye
    • 1
  1. 1.Department of Wildlife Ecology and ConservationUniversity of FloridaGainesvilleUSA

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