Integrated models that unite local and regional data reveal larger-scale environmental relationships and improve predictions of species distributions
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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.
KeywordsCitizen 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.
- 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
- 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
- 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
- Franklin J (2009) Mapping species distributions: spatial inference and prediction. Cambridge University Press, CambridgeGoogle Scholar
- Inventory FNA (2010) Guide to the natural communities of Florida: 2010. TallahasseeGoogle Scholar
- Koprowski JL (1994) Sciurus niger. Mamm Species 479:1–9Google Scholar
- 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
- 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
- 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
- Moore JC (1957) The natural history of the fox squirrel, Sciurus niger shermani. Bull Am Mus Nat Hist 113:1–72Google Scholar
- 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
- Thornton DH, Fletcher RJ Jr (2014) Body size and spatial scales in avian response to landscapes: a meta-analysis. Ecography 37:454–463Google Scholar
- 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