Skip to main content

Advertisement

Log in

High-resolution species-distribution model based on systematic sampling and indirect observations

  • Original Paper
  • Published:
Biodiversity and Conservation Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Bar-David S, Saltz D, Dayan T, Shkedy Y (2008) Using spatially expanding populations as a tool for evaluating landscape planning: the reintroduced persian fallow deer as a case study. J Nat Conserv 16:164

    Article  Google Scholar 

  • Barry S, Elith J (2006) Error and uncertainty in habitat models. J Appl Ecol 43:413–423

    Article  Google Scholar 

  • 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 555–586

    Chapter  Google Scholar 

  • Bellamy C, Scott C, Altringham J (2013) Multiscale presence-only habitat suitability models: fine-resolution maps for eight bat species. J Appl Ecol 50:892–901

    Article  Google Scholar 

  • Belsky A, Mwonga S, Amundson R, Duxbury J, Ali A (1993) Comparative effects of isolated trees on their undercanopy environments in high- and low-rainfall savannas. J Appl Ecol 30:143–155

    Article  Google Scholar 

  • Blank L, Carmel Y (2012) Woody vegetation patch types affect herbaceous species richness and composition in a mediterranean ecosystem. Community Ecol 13:72–81

    Article  Google Scholar 

  • Carmel Y, Stoller-Cavari L (2006) Comparing environmental and biological surrogates for biodiversity at a local scale. Isr J Ecol Evol 52:11–27

    Article  Google Scholar 

  • Colbert T et al (2001) High-throughput screening for induced point mutations. Plant Physiol 126:480–484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Crawley M, Harral J (2001) Scale dependence in plant biodiversity. Science 291:864–868

    Article  CAS  PubMed  Google Scholar 

  • Danin A (1999) Desert rocks as plant refugia in the near east. Bot Rev 65:93–170

    Article  Google Scholar 

  • Davidson A, Carmel Y, Bar-David S (2013) Characterizing wild ass pathways using a non-invasive approach: applying least-cost path modelling to guide field surveys and a model selection analysis. Landsc Ecol 28:1465

    Article  Google Scholar 

  • Duff A, Morrell T (2007) Predictive occurrence models for bat species in california. J Wildl Manag 71:693–700

    Article  Google Scholar 

  • Elith J et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151

    Article  Google Scholar 

  • Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers distrib 17:43–57

    Article  Google Scholar 

  • Fernandez N, Delibes M, Palomares F (2006) Landscape evaluation in conservation: molecular sampling and habitat modeling for the Iberian lynx. Ecol Appl 16:1037–1049

    Article  PubMed  Google Scholar 

  • Gallant D, Vasseur L, Berube C (2007) Unveiling the limitations of scat surveys to monitor social species: a case study on river otters. J Wildl Manag 71:258–265

    Article  Google Scholar 

  • Giotto N, Gerard JF, Ziv A, Bouskila A, Bar-David S (2015) Space-use patterns of the Asiatic Wild Ass (Equus hemionus): complementary insights from displacement, Recursion movement and habitat selection analyses. PLoS ONE 10(12):e0143279

    Article  PubMed  PubMed Central  Google Scholar 

  • Graham C, Ferrier S, Huettman F, Moritz C, Peterson A (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol Evol 19:497–503

    Article  PubMed  Google 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, Wiesbaden

    Google Scholar 

  • Gueta T, Carmel Y (2016) Quantifying the value of user-level data cleaning for big data: a case study using mammal distribution models. Ecol Inform. doi:10.1111/1365-2664.12701

    Google Scholar 

  • Gueta T, Templeton A, Bar-David S (2014) Development of genetic structure in a heterogeneous landscape over a short time frame: the reintroduced asiatic wild ass. Conserv Genet 15:1231

    Article  Google Scholar 

  • Guisan A, Zimmermann N (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186

    Article  Google Scholar 

  • Guisan A, Graham C, Elith J, Huettmann F (2007) Sensitivity of predictive species distribution models to change in grain size. Divers Distrib 13:332–340

    Article  Google Scholar 

  • Guisan A et al (2013) Predicting species distributions for conservation decisions. Ecol Lett 16:1424–1435

    Article  PubMed  PubMed Central  Google Scholar 

  • Henley S, Ward D (2006) An evaluation of diet quality in two desert ungulates exposed to hyper-arid conditions. Afr J Range Forage Sci 23:185–190

    Article  Google Scholar 

  • Henley S, Ward D, Schmidt I (2007) Habitat selection by two desert-adapted ungulates. J Arid Environ 70:39–48

    Article  Google Scholar 

  • Hernandez P, Graham C, Master L, Albert D (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785

    Article  Google Scholar 

  • Hess GR, Bartel RA, Leidner AK, Rosenfeld KM, Rubino MJ, Snider SB, Ricketts TH (2006) Effectiveness of biodiversity indicators varies with extent, grain, and region. Biol Conserv 132:448–457

    Article  Google Scholar 

  • Hughes A, Inouye B, Johnson M, Underwood N, Vellend M (2008) Ecological consequences of genetic diversity. Ecol Lett 11:609

    Article  PubMed  Google Scholar 

  • Jeschke J, Strayer D (2008) Usefulness of bioclimatic models for studying climate change and invasive species. Ann N Y Acad Sci 1134:1–24

    Article  PubMed  Google Scholar 

  • Jiménez-Valverde A, Acevedo P, Barbosa AM, Lobo JM, Real R (2013) Discrimination capacity in species distribution models depends on the representativeness of the environmental domain. Glob Ecol Biogeogr 22:508–516

    Article  Google Scholar 

  • Kays R, Gompper M, Ray J (2008) Landscape ecology of eastern coyotes based on large-scale estimates of abundance. Ecol Appl 18:1014–1027

    Article  PubMed  Google Scholar 

  • Kent R, Bar-Massada A, Carmel Y (2011) Multiscale analyses of mammal species composition-environment relationship in the contiguous USA. PloS ONE 6:e25440

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kramer-Schadt S et al (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379. doi:10.1111/ddi.12096

    Article  Google Scholar 

  • Kumar S, Spaulding S, Stohlgren T, Hermann K, Schmidt T, Bahls L (2009) Potential habitat distribution for the freshwater diatom didymosphenia geminata in the continental US. Front Ecol Environ 7:415–420

    Article  Google Scholar 

  • Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17:145–151

    Article  Google Scholar 

  • Manel S, Dias J, Buckton S, Ormerod S (1999) Alternative methods for predicting species distribution: an illustration with himalayan river birds. J Appl Ecol 36:734–747

    Article  Google Scholar 

  • Manel S, Williams H, Ormerod S (2001) Evaluating presence-absence models in ecology: the need to account for prevalence. J Appl Ecol 38:921–931

    Article  Google Scholar 

  • Marmion M, Parviainen M, Luoto M, Heikkinen R, Thuiller W (2009) Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15:59–69

    Article  Google Scholar 

  • Moehlman P, Shah N, Feh C (2008) Equus hemionus. IUCN. http://www.iucnredlist.org/details/full/7951/0. Accessed Aug 2016

  • Moran PA (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23

    Article  CAS  PubMed  Google Scholar 

  • 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 7:529–547

    Google Scholar 

  • Pearce J, Boyce M (2006) Modelling distribution and abundance with presence-only data. J Appl Ecol 43:405–412

    Article  Google Scholar 

  • Perinchery A, Jathanna D, Kumar A (2011) Factors determining occupancy and habitat use by Asian small-clawed otters in the Western Ghats India. J Mamm 92:796–802

    Article  Google Scholar 

  • Peterson AT (2011) Ecological niches and geographic distributions (MPB-49), vol 49. Princeton University Press, Princeton

    Google Scholar 

  • Phillips S (2006) A brief tutorial on Maxent. AT & T Research. http://www.cs.princeton.edu/~schapire/maxent/tutorial/tutorial.doc

  • Phillips S, Dudik M (2008) Modeling of species distributions with maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175

    Article  Google Scholar 

  • Phillips SJ, Elith J (2013) On estimating probability of presence from use-availability or presence-background data. Ecology 94:1409–1419

    Article  PubMed  Google Scholar 

  • Phillips S, Anderson R, Schapire R (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259

    Article  Google Scholar 

  • Polak T, Gutterman Y, Hoffman I, Saltz D (2014) Redundancy in seed dispersal by three sympatric ungulates: a reintroduction perspective. Anim Conserv 17:565

    Article  Google Scholar 

  • Radosavljevic A, Anderson RP (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. J Biogeogr 41:629–643. doi:10.1111/jbi.12227

    Article  Google Scholar 

  • Renan S, Greenbaum G, Shahar N, Templeton A, Bouskila A, Bar-David S (2015) Stochastic modelling of shifts in allele frequencies reveals a strongly polygynous mating system in the re-introduced asiatic wild ass. Mol Ecol 24:1433

    Article  PubMed  Google Scholar 

  • Saccheri I, Kuussaari M, Kankare M, Vikman P, Fortelius W, Hanski I (1998) Inbreeding and extinction in a butterfly metapopulation. Nature 392:491–494

    Article  CAS  Google Scholar 

  • Saltz D, Rubenstein D (1995) Population-dynamics of a reintroduced asiatic wild ass Equus hemionus herd. Ecol Appl 5:327–335

    Article  Google Scholar 

  • Saltz D, Schmidt H, Rowen M, Karnieli A, Ward D, Schmidt I (1999) Assessing grazing impacts by remote sensing in hyper-arid environments. J Range Manag 52:500–507

    Article  Google Scholar 

  • Saltz D, Rowen M, Rubenstein D (2000) The effect of space-use patterns of reintroduced asiatic wild ass on effective population size. Conserv Biol 14:1852–1861

    Article  Google Scholar 

  • Schulz E, Kaiser TM (2013) Historical distribution, habitat requirements and feeding ecology of the genus Equus (Perissodactyla). Mamm Review 43:111–123. doi:10.1111/j.1365-2907.2012.00210.x

    Article  Google Scholar 

  • Stauffer D, Best L (1986) Nest-site characteristics of open-nesting birds in riparian habitats in iowa. Wilson Bull 98(2):231–242

    Google Scholar 

  • Stern E, Gardus Y, Meir A, Krakover S, Tzoar H (1986) Atlas of the Negev. Keter Publishing House, Jerusalem

    Google Scholar 

  • St-Louis A, Côté SD (2014) Resource selection in a high-altitude rangeland equid, the kiang (Equus kiang): influence of forage abundance and quality at multiple spatial scales. Can J Zool 92:239–249. doi:10.1139/cjz-2013-0191

    Article  Google Scholar 

  • Tsoar A, Allouche O, Steinitz O, Rotem D, Kadmon R (2007) A comparative evaluation of presence-only methods for modelling species distribution. Divers Distrib 13:397–405

    Article  Google Scholar 

  • Valverde AJ, Lobo J, Hortal J (2008) Not as good as they seem: the importance of concepts in species distribution modelling. Divers Distrib 14:885–890

    Article  Google Scholar 

  • Vina A, Tuanmu M, Xu W, Li Y, Ouyang Z, DeFries R, Liu J (2010) Range-wide analysis of wildlife habitat: implications for conservation. Biol Conserv 143:1960–1969

    Article  Google Scholar 

  • Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD, Campbell Grant EH, Veran S (2013) Presence-only modelling using MAXENT: when can we trust the inferences? Methods Ecol Evol 4:236–243

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yohay Carmel.

Additional information

Communicated by Dirk Sven Schmeller.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 1470 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nezer, O., Bar-David, S., Gueta, T. et al. High-resolution species-distribution model based on systematic sampling and indirect observations. Biodivers Conserv 26, 421–437 (2017). https://doi.org/10.1007/s10531-016-1251-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10531-016-1251-2

Keywords

Navigation