High-resolution species-distribution model based on systematic sampling and indirect observations
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Species distribution models (SDMs) are often limited by the use of coarse-resolution environmental variables and by the number of observations required for their calibration. This is particularly true in the case of elusive animals. Here, we developed a SDM by combining three elements: a database of explanatory variables, mapped at a fine resolution; a systematic sampling scheme; and an intensive survey of indirect observations. Using MaxEnt, we developed the SDM for the population of the Asiatic wild ass (Equus hemionus), a rare and elusive species, at three spatial scales: 10, 100, and 1000 m per pixel. We used indirect observations of feces mounds. We constructed 14 layers of explanatory variables, in five categories: water, topography, biotic conditions, climatic variables and anthropogenic variables. Woody vegetation cover and slopes were found to have the strongest effect on the distribution of wild ass and were included as the main predictors in the SDM. Model validation revealed that an intensive survey of feces mounds and high-resolution predictor layers resulted in a highly accurate and informative SDM. Fine-grain (10 and 100 m) SDMs can be utilized to: (1) characterize the variables influencing species distribution at high resolution and local scale, including anthropogenic effects and geomorphologic features; (2) detect potential population activity centers; (3) locate potential corridors of movement and possible isolated habitat patches. Such information may be useful for the conservation efforts of the Asiatic wild ass. This approach could be applied to other elusive species, particularly large mammals.
KeywordsEquus hemionus Wild Ass Habitat preferences Feces MAXENT Species distribution model
We would like to thank David Saltz, Alan R. Templeton, and Amos Bouskila for their contributions to this study; and Itamar Giladi for providing insightful comments that greatly improved the manuscript. This research was supported by the United States-Israel Binational Science Foundation Grant 2011384 awarded to S. B-D, A. R. Templeton and A. Bouskila. GIS layers were provided by the GIS Department of the Israel Nature and Parks Authority. This is publication <918> of the Mitrani Department of Desert Ecology.
- Beier P, Penrod K, Luke C, Spencer W, Cabañero C (2006) South coast missing linkages: Restoring connectivity to wildlands in the largest metropolitan area in the united states. In: Crooks KR, Sanjayan M (eds) Connectivity conservation. Cambridge University Press, Cambridge, pp 555–586CrossRefGoogle Scholar
- Groves C (1986) The taxonomy, distribution, and adaptations of recent equids. In: Meadow RH, Uerpmann HP (eds) Equids in the ancient world. Ludwig Reichert Verlag, WiesbadenGoogle Scholar
- Moehlman P, Shah N, Feh C (2008) Equus hemionus. IUCN. http://www.iucnredlist.org/details/full/7951/0. Accessed Aug 2016
- Norris D (2014) Model thresholds are more important than presence location type: understanding the distribution of lowland tapir (Tapirus terrestris) in a continuous Atlantic forest of southeast Brazil tropical conservation. Science 7:529–547Google Scholar
- Peterson AT (2011) Ecological niches and geographic distributions (MPB-49), vol 49. Princeton University Press, PrincetonGoogle Scholar
- Phillips S (2006) A brief tutorial on Maxent. AT & T Research. http://www.cs.princeton.edu/~schapire/maxent/tutorial/tutorial.doc
- Stauffer D, Best L (1986) Nest-site characteristics of open-nesting birds in riparian habitats in iowa. Wilson Bull 98(2):231–242Google Scholar
- Stern E, Gardus Y, Meir A, Krakover S, Tzoar H (1986) Atlas of the Negev. Keter Publishing House, JerusalemGoogle Scholar