Skip to main content Accessibility help
×
Hostname: page-component-788cddb947-55tpx Total loading time: 0 Render date: 2024-10-13T22:57:12.899Z Has data issue: false hasContentIssue false

15 - Modeling Niches and Mapping Distributions

Progress and Promise of Ecological Niche Models for Primate Research

from Part III - GIS Analysis in Broad-Scale Space

Published online by Cambridge University Press:  29 January 2021

Francine L. Dolins
Affiliation:
University of Michigan, Dearborn
Christopher A. Shaffer
Affiliation:
Grand Valley State University, Michigan
Leila M. Porter
Affiliation:
Northern Illinois University
Jena R. Hickey
Affiliation:
University of Georgia
Nathan P. Nibbelink
Affiliation:
University of Georgia
Get access

Summary

In this chapter we briefly review the burgeoning field of ecological niche modeling and explore its relevance to studies of primate ecology, evolution, and conservation. Recent years have witnessed an explosion of interest in ecological niche models, spurred on by the increasing availability of occurrence data and spatially explicit environmental data, as well as GIS tools and technologies appropriate for processing the increasingly high-resolution and multidimensional data typical of this field.

Type
Chapter
Information
Spatial Analysis in Field Primatology
Applying GIS at Varying Scales
, pp. 315 - 348
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aguilera, P. A., Fernández, A., Reche, F., and Rumí, R. 2010. Hybrid Bayesian network classifiers: application to species distribution models. Environmental Modelling and Software 25: 16301639.Google Scholar
Allouche, O., Tsoar, A., and Kadmon, R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43: 12231232.Google Scholar
Anderson, R. P. and Raza, A. 2010. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. Journal of Biogeography 37: 13781393.Google Scholar
Anderson, R. P., Lew, D., and Peterson, A. T. 2003. Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecological Modelling 162: 211232.Google Scholar
Araújo, M. B. and New, M. 2007. Ensemble forecasting of species distributions. Trends in Ecology & Evolution 22: 4247.Google Scholar
Araújo, M. B., Pearson, R. G., Thuiller, W., and Erhard, M. 2005. Validation of species–climate impact models under climate change. Global Change Biology 11: 15041513.Google Scholar
Barry, S. and Elith, J. 2006. Error and uncertainty in habitat models. Journal of Applied Ecology 43: 413423.Google Scholar
Beck, J., Ballesteros-Mejia, L., Nagel, P., and Kitching, I. J. 2013. Online solutions and the “Wallacean shortfall”: what does GBIF contribute to our knowledge of species’ ranges? Diversity and Distributions 19: 10431050.Google Scholar
Beeton, T. A., Glantz, M. M., Trainer, A. K., Temirbekov, S. S., and Reich, R. M. 2013. The fundamental hominin niche in late Pleistocene Central Asia: a preliminary refugium model. Journal of Biogeography 41: 95110.Google Scholar
Bett, N. N., Blair, M. E., and Sterling, E. J. 2012. Ecological niche conservatism in doucs (genus Pygathrix). International Journal of Primatology 33: 972988.CrossRefGoogle Scholar
Bjork, A., Liu, W., Wertheim, J. O., Hahn, B. H., and Worobey, M. 2011. Evolutionary history of chimpanzees inferred from complete mitochondrial genomes. Molecular Biology and Evolution 28: 615623.Google Scholar
Blair, M. E. and Melnick, D. J. 2012a. Genetic evidence for dispersal by both sexes in the Central American squirrel monkey, Saimiri oerstedii citrinellus. American Journal of Primatology 74: 3747.Google Scholar
Blair, M. E. and Melnick, D. J. 2012b. Scale-dependent effects of a heterogeneous landscape on genetic differentiation in the Central American squirrel monkey (Saimiri oerstedii). PLoS ONE 7: e43027.Google Scholar
Blair, M. E., Rose, R. A., Ersts, P. J., et al. 2012. Incorporating climate change into conservation planning: identifying priority areas across a species’ range. Frontiers of Biogeography 4: 157167.Google Scholar
Blair, M. E., Gutiérrez-Espeleta, G. A., and Melnick, D. J. 2013a. Subspecies of the Central American squirrel monkey (Saimiri oerstedii) as units for conservation. International Journal of Primatology 34: 8698.Google Scholar
Blair, M. E., Sterling, E. J., Dusch, M., Raxworthy, C. J., and Pearson, R. G. 2013b. Ecological divergence and speciation between lemur (Eulemur) sister species in Madagascar. Journal of Evolutionary Biology 26: 17901801.Google Scholar
Booth, T. H., Nix, H. A., Busby, J. R., and Hutchinson, M. F. 2014. BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Diversity and Distributions 20: 19.Google Scholar
Boria, R. A., Olson, L. E., Goodman, S. M., and Anderson, R. P. 2014. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling 275: 7377.Google Scholar
Boubli, J. P., and de Lima, M. G. 2009. Modeling the geographical distribution and fundamental niches of Cacajao spp. and Chiropotes israelita in northwestern Amazonia via a maximum entropy algorithm. International Journal of Primatology 30: 217228.CrossRefGoogle Scholar
Braunisch, V., Coppes, J., Arlettaz, R., et al. 2013. Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change. Ecography 36: 971983.Google Scholar
Breiman, L. 2001. Statistical modeling: the two cultures. Statistical Science 16: 199215.Google Scholar
Brown, J. L., and Yoder, A. D. 2015. Shifting ranges and conservation challenges for lemurs in the face of climate change. Ecology and Evolution 5: 11311142.CrossRefGoogle ScholarPubMed
Busby, J. R. 1991. BIOCLIM: a bioclimate analysis and prediction system. Plant Protection Quarterly 6: 89.Google Scholar
Calvignac-Spencer, S., Merkel, K., Kutzner, N., et al. 2013. Carrion fly-derived DNA as a tool for comprehensive and cost-effective assessment of mammalian biodiversity. Molecular Ecology 22: 915924.Google Scholar
Campos, F. A. and Jack, K. M. 2013. A potential distribution model and conservation plan for the critically endangered Ecuadorian capuchin, Cebus albifrons aequatorialis. International Journal of Primatology 34: 899916.Google Scholar
Carpenter, G., Gillison, A. N., and Winter, J. 1993. DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation 2: 667680.CrossRefGoogle Scholar
Chapman, C. A., Chapman, L. J., and Gillespie, T. R. 2002. Scale issues in the study of primate foraging: red colobus of Kibale National Park. American Journal of Physical Anthropology 117: 349363.CrossRefGoogle Scholar
Chatterjee, H. J., Tse, J. S. Y., and Turvey, S. T. 2012. Using ecological niche modelling to predict spatial and temporal distribution patterns in Chinese gibbons: lessons from the present and the past. Folia Primatologica 83: 8599.CrossRefGoogle ScholarPubMed
Chiou, K. L., Pozzi, L., Lynch Alfaro, J. W., and Di Fiore, A. 2011. Pleistocene diversification of living squirrel monkeys (Saimiri spp.) inferred from complete mitochondrial genome sequences. Molecular Phylogenetics and Evolution 59: 736745.Google Scholar
Conroy, G. C., Emerson, C. W., Anemone, R. L., and Townsend, K. E. B. 2012. Let your fingers do the walking: a simple spectral signature model for “remote” fossil prospecting. Journal of Human Evolution 63: 7984.Google Scholar
Coudrat, C. and Nekaris, K. A.-I. 2013. Modelling niche differentiation of co-existing, elusive and morphologically similar species: a case study of four macaque species in Nakai-Nam Theun National Protected Area, Laos. Animals 3: 4562.CrossRefGoogle ScholarPubMed
Cutler, D. R., Edwards, T. C. Jr, Beard, K. H., et al. 2007. Random forests for classification in ecology. Ecology 88: 27832792.Google Scholar
Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C., and Mace, G. M. 2011. Beyond predictions: biodiversity conservation in a changing climate. Science 332: 5358.Google Scholar
De’ath, G. and Fabricius, K. E. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81: 31783192.Google Scholar
Dormann, C. F., McPherson, J. M., Araújo, M. B., et al. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30: 609628.Google Scholar
Dormann, C. F., Elith, J., Bacher, S., et al. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36: 2746.Google Scholar
Drake, J. M., Randin, C., and Guisan, A. 2006. Modelling ecological niches with support vector machines. Journal of Applied Ecology 43: 424432.CrossRefGoogle Scholar
Elith, J. and Graham, C. H. 2009. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography 32: 6677.CrossRefGoogle Scholar
Elith, J., Graham, C. H., Anderson, R. P., et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129151.Google Scholar
Elith, J., Leathwick, J. R., and Hastie, T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology 77: 802813.Google Scholar
Elith, J., Phillips, S. J., Hastie, T., et al. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17: 4357.Google Scholar
Engler, J. O., Rödder, D., Elle, O., Hochkirch, A., and Secondi, J. 2013. Species distribution models contribute to determine the effect of climate and interspecific interactions in moving hybrid zones. Journal of Evolutionary Biology 26: 24872496.Google Scholar
Farber, O. and Kadmon, R. 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecological Modelling 160: 115130.Google Scholar
Ferrier, S., Drielsma, M., Manion, G., and Watson, G. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales: II. Community-level modelling. Biodiversity and Conservation 11: 23092338.CrossRefGoogle Scholar
Ford, S. M. 2006. The biogeographic history of Mesoamerican primates. Pages 81114 in New Perspectives in the Study of Mesoamerican Primates. Estrada, A., Garber, P. A., Pavelka, M., and Luecke, L. (Eds.). Springer, New York.Google Scholar
Franklin, J. 2009. Mapping Species Distributions: Spatial Inference and Prediction. Cambridge University Press, Cambridge.Google Scholar
Friedman, J. H. 1991. Multivariate adaptive regression splines. The Annals of Statistics 19: 167.Google Scholar
Friedman, N., Geiger, D., and Goldszmidt, M. 1997. Bayesian network classifiers. Machine Learning 29: 131163.Google Scholar
Georges, D. and Thuiller, W. 2013. An example of species distribution modeling with biomod2. R CRAN Project. Tutorial supplied with the “biomod2” package.Google Scholar
Guisan, A. and Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters 8: 9931009.Google Scholar
Guisan, A., Edwards, T. C. Jr., and Hastie, T. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157: 89100.CrossRefGoogle Scholar
Guo, Q. and Liu, Y. 2010. ModEco: an integrated software package for ecological niche modeling. Ecography 33: 637642.Google Scholar
Guo, Q., Kelly, M. and Graham, C. H. 2005. Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modelling 182: 7590.Google Scholar
Heikkinen, R. K., Luoto, M., Virkkala, R., Pearson, R. G., and Körber, J.-H. 2007. Biotic interactions improve prediction of boreal bird distributions at macro-scales. Global Ecology and Biogeography 16: 754763.Google Scholar
Hershkovitz, P. 1984. Taxonomy of squirrel monkeys genus Saimiri (Cebidae, Platyrrhini): a preliminary report with description of a hitherto unnamed form. American Journal of Primatology 7: 155210.Google Scholar
Hickey, J. R., Nackoney, J., Nibbelink, N. P., et al. 2013. Human proximity and habitat fragmentation are key drivers of the rangewide bonobo distribution. Biodiversity and Conservation 22: 30853104.Google Scholar
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 19651978.CrossRefGoogle Scholar
Hijmans, R. J. and Elith, J. 2013. Species distribution modeling with R. R CRAN Project. Tutorial supplied with the “dismo” package.Google Scholar
Hirzel, A. H. and Le Lay, G. 2008. Habitat suitability modelling and niche theory. Journal of Applied Ecology 45: 13721381.Google Scholar
Hirzel, A. H., Hausser, J., Chessel, D., and Perrin, N. 2002. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83: 20272036.CrossRefGoogle Scholar
Hochachka, W. M., Fink, D., Hutchinson, R. A., et al. 2012. Data-intensive science applied to broad-scale citizen science. Trends in Ecology & Evolution 27: 130137.CrossRefGoogle ScholarPubMed
Holzmann, I., Agostini, I., DeMatteo, K., et al. 2015. Using species distribution modeling to assess factors that determine the distribution of two parapatric howlers (Alouatta spp.) in South America. International Journal of Primatology 36: 1832.Google Scholar
Junker, J., Blake, S., Boesch, C., et al. 2012. Recent decline in suitable environmental conditions for African great apes. Diversity and Distributions 18: 10771091.Google Scholar
Kadmon, R., Farber, O., and Danin, A. 2003. A systematic analysis of factors affecting the performance of climatic envelope models. Ecological Applications 13: 853867.Google Scholar
Kearney, M. and Porter, W. 2009. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecology Letters 12: 334350.Google Scholar
Keitt, T. H., Bjornstad, O. N., Dixon, P. M., and Citron-Pousty, S. 2002. Accounting for spatial pattern when modeling organism–environment interactions. Ecography 25: 616625.Google Scholar
Kéry, M., Gardner, B., and Monnerat, C. 2010. Predicting species distributions from checklist data using site-occupancy models. Journal of Biogeography 37: 18511862.Google Scholar
Kramer-Schadt, S., Niedballa, J., Pilgrim, J. D., et al. 2013. The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions 19: 13661379.Google Scholar
Kumara, H. N., Irfan-Ullah, M., and Kumar, S. 2009. Mapping potential distribution of slender loris subspecies in peninsular India. Endangered Species Research 7: 2938.Google Scholar
Leathwick, J. R., Elith, J., and Hastie, T. 2006a. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecological Modelling 199: 188196.Google Scholar
Leathwick, J. R., Elith, J., Francis, M. P., Hastie, T., and Taylor, P. 2006b. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Marine Ecology Progress Series 321: 267281.Google Scholar
Lehmann, J., Korstjens, A. H., and Dunbar, R. I. M. 2010. Apes in a changing world: the effects of global warming on the behaviour and distribution of African apes. Journal of Biogeography 37: 22172231.Google Scholar
Levi, T., Silvius, K. M., Oliveira, L. F. B., Cummings, A. R., and Fragoso, J. M. V. 2013. Competition and facilitation in the capuchin–squirrel monkey relationship. Biotropica 45: 636643.Google Scholar
Li, J. and Hilbert, D. W. 2008. LIVES: a new habitat modelling technique for predicting the distribution of species’ occurrences using presence-only data based on limiting factor theory. Biodiversity and Conservation 17: 30793095.Google Scholar
Liu, C., Berry, P. M., Dawson, T. P., and Pearson, R. G. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28: 385393.Google Scholar
Loiselle, B. A., Howell, C. A., Graham, C. H., et al. 2003. Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology 17: 15911600.Google Scholar
Losos, J. B. 2008. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecology Letters 11: 9951003.Google Scholar
Lozier, J. D., Aniello, P., and Hickerson, M. J. 2009. Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling. Journal of Biogeography 36: 16231627.Google Scholar
Luo, Z., Zhou, S., Yu, W., et al. 2014. Impacts of climate change on the distribution of Sichuan snub-nosed monkeys (Rhinopithecus roxellana) in Shennongjia area, China. American Journal of Primatology 77: 135151.Google Scholar
Lynch Alfaro, J. W., Boubli, J. P., Paim, F. P., et al. 2015. Biogeography of squirrel monkeys (genus Saimiri): south-central Amazon origin and pan-Amazonian diversification of a lowland primate. Molecular Phylogenetics and Evolution 82B: 436454.Google Scholar
MacColl, A. D. C. 2011. The ecological causes of evolution. Trends in Ecology & Evolution 26: 514522.CrossRefGoogle ScholarPubMed
Manel, S., Dias, J.-M., and Ormerod, S. J. 1999. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological Modelling 120: 337347.Google Scholar
McPherson, J. M., Jetz, W., and Rogers, D. J. 2004. The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? Journal of Applied Ecology 41: 811823.Google Scholar
Meyer, A. L. S., Pie, M. R., and Passos, F. C. 2014. Assessing the exposure of lion tamarins (Leontopithecus spp.) to future climate change. American Journal of Primatology 76: 551562.Google Scholar
Mitchell, M. W., Locatelli, S., Sesink Clee, P. R., Thomassen, H. A., and Gonder, M. 2015. Environmental variation and rivers govern the structure of chimpanzee genetic diversity in a biodiversity hotspot. BMC Evolutionary Biology 15: 1.Google Scholar
Moisen, G. G. and Frescino, T. S. 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling 157: 209225.Google Scholar
Morales-Jimenez, A. L., Nekaris, A., Lee, J., and Thompson, S. 2005. Modeling distributions for Colombian spider monkeys (Ateles spp.) to find priorities for conservation. American Journal of Primatology 66 (Suppl. 1): 131.Google Scholar
Muscarella, R., Galante, P. J., Soley-Guardia, M., et al. 2014. ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution 5: 11981205.Google Scholar
Newbold, T. 2010. Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models. Progress in Physical Geography 34: 322.Google Scholar
Nix, H. A. 1986. A biogeographic analysis of Australian elapid snakes. Pages 415 in Atlas of Elapid Snakes of Australia. Longmore, R. (Ed.). Australian Government Publishing Service, Canberra.Google Scholar
Nores, M. 1999. An alternative hypothesis for the origin of Amazonian bird diversity. Journal of Biogeography 26: 475485.Google Scholar
Oliveira, G. de, Rangel, T. F., Lima-Ribeiro, M. S., Terribile, L. C., and Diniz-Filho, J. A. F. 2014. Evaluating, partitioning, and mapping the spatial autocorrelation component in ecological niche modeling: a new approach based on environmentally equidistant records. Ecography 37: 637647.Google Scholar
Pawlak, Z. 1991. Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer, Dordrecht.Google Scholar
Pearson, R. G. and Dawson, T. P. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography 12: 361371.CrossRefGoogle Scholar
Pearson, R. G., Dawson, T. P., Berry, P. M., and Harrison, P. A. 2002. SPECIES: a spatial evaluation of climate impact on the envelope of species. Ecological Modelling 154: 289300.Google Scholar
Pearson, R. G., Thuiller, W., Araújo, M. B., et al. 2006. Model-based uncertainty in species range prediction. Journal of Biogeography 33: 17041711.Google Scholar
Pearson, R. G., Raxworthy, C. J., Nakamura, M., and Peterson, A. T. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography 34: 102117.Google Scholar
Peck, M., Thorn, J., Mariscal, A., et al. 2011. Focusing conservation efforts for the critically endangered brown-headed spider monkey (Ateles fusciceps) using remote sensing, modeling, and playback survey methods. International Journal of Primatology 32: 134148.Google Scholar
Peterson, A. T. 2003. Predicting the geography of species’ invasions via ecological niche modeling. Quarterly Review of Biology 78: 419433.CrossRefGoogle ScholarPubMed
Peterson, A. T. and Nakazawa, Y. 2008. Environmental data sets matter in ecological niche modelling: an example with Solenopsis invicta and Solenopsis richteri. Global Ecology and Biogeography 17: 135144.Google Scholar
Peterson, A. T., Papes, M., and Eaton, M. 2007. Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30: 550560.Google Scholar
Peterson, A. T., Soberón, J., Pearson, R. G., et al. 2011. Ecological Niches and Geographic Distributions. Princeton University Press, Princeton, NJ.Google Scholar
Phillips, S. J. 2009. A brief tutorial on MaxEnt. Available at: https://biodiversityinformatics.amnh.org/open_source/maxent/Maxent_tutorial2017.pdf.Google Scholar
Phillips, S. J. and Dudík, M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161175.Google Scholar
Phillips, S. J., Anderson, R. P., and Schapire, R. E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231259.Google Scholar
Pintea, L., Jantz, S., Nackoney, J. R., and Hansen, M. C. 2014. The first high resolution maps of chimpanzee habitat health in Africa. In IUCN World Parks Congress, November 12–19, 2014. Sydney, Australia.Google Scholar
Potts, J. M. and Elith, J. 2006. Comparing species abundance models. Ecological Modelling 199: 153163.Google Scholar
R Core Team. 2013. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.Google Scholar
Radosavljevic, A. and Anderson, R. P. 2014. Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography 41: 629643.Google Scholar
Raxworthy, C. J., Ingram, C. M., Rabibisoa, N., and Pearson, R. G. 2007. Applications of ecological niche modeling for species delimitation: a review and empirical evaluation using day geckos (Phelsuma) from Madagascar. Systematic Biology 56: 907923.Google Scholar
Renner, I. W. and Warton, D. I. 2013. Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics 69: 274281.Google Scholar
Renner, I. W., Elith, J., Baddeley, A., et al. 2015. Point process models for presence-only analysis. Methods in Ecology and Evolution 6: 366379.Google Scholar
Rode, E. J., Stengel, C. J., and Nekaris, K. A.-I. 2013. Habitat assessment and species niche modeling. Pages 79102 in Primate Ecology and Conservation: A Handbook of Techniques. Sterling, E. J., Bynum, N., and Blair, M. E. (Eds.). Oxford University Press, Oxford.Google Scholar
Rodríguez-Vargas, A. R. 2003. Analysis of the hypothetical population structure of the squirrel monkey (Saimiri oerstedii) in Panamá. Pages 5362 in Primates in Fragments: Ecology and Conservation. Marsh, L.K. (Ed.). Kluwer, New York.Google Scholar
Rotenberry, J. T., Preston, K. L., and Knick, S. T. 2006. GIS-based niche modeling for mapping species’ habitat. Ecology 87: 14581464.Google Scholar
Rouget, M., Richardson, D. M., Lavorel, S., et al. 2001. Determinants of distribution of six Pinus species in Catalonia, Spain. Journal of Vegetation Science 12: 491502.Google Scholar
Rylands, A. B. and Mittermeier, R. A. 2009. The diversity of the New World primates (Platyrrhini): an annotated taxonomy. Pages 2354 in South American Primates. Garber, P. A., Estrada, A., Bicca-Marques, J. C., Heymann, E. W., and Strier, K. B. (Eds.). Springer, New York.Google Scholar
Scachetti-Pereira, R. 2002. DesktopGarp v1.1.6. Available at www.nhm.ku.edu/desktopgarp.Google Scholar
Scott, J. M., Csuti, B., Jacobi, J. D., and Estes, J. E. 1987. Species richness: a geographic approach to protecting future biological diversity. BioScience 37: 782788.Google Scholar
Segurado, P. and Araújo, M. B. 2004. An evaluation of methods for modelling species distributions. Journal of Biogeography 31: 15551568.Google Scholar
Sesink Clee, P. R., Abwe, E. E., Ambahe, R. D., et al. 2015. Chimpanzee population structure in Cameroon and Nigeria is associated with habitat variation that may be lost under climate change. BMC Evolutionary Biology 15: 2.Google Scholar
Soberón, J. and Peterson, A. T. 2005. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiversity Informatics 2: 110.Google Scholar
Solano Rojas, D. 2007. Evaluación del hábitat, el paisaje y la población del mono tití (Cebidae, Platyrrhini: Saimiri oerstedii oerstedii) en la Península de Osa, Costa Rica. Master’s Thesis. Universidad Nacional de Costa Rica.Google Scholar
Sterling, E. J., Bynum, N., and Blair, M. E. 2013. Conclusion: the future of studying primates in a changing world. Pages 346350 in Primate Ecology and Conservation: A Handbook of Techniques. Sterling, E. J., Bynum, N., and Blair, M. E.. (Eds.). Oxford University Press, Oxford.Google Scholar
Stockwell, D. and Peters, D. 1999. The GARP modeling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science 13: 143158.Google Scholar
Stockwell, D. R. B. and Peterson, A. T. 2002. Effects of sample size on accuracy of species distribution models. Ecological Modelling 148: 113.Google Scholar
Struebig, M. J., Fischer, M., Gaveau, D. L. A., et al. 2015. Anticipated climate and land‐cover changes reveal refuge areas for Borneo’s orang‐utans. Global Change Biology 21: 28912904.Google Scholar
Thorn, J. S., Nijman, V., Smith, D., and Nekaris, K. A.-I. 2009. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus). Diversity and Distributions 15: 289298.Google Scholar
Thuiller, W. 2003. BIOMOD: optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology 9: 13531362.Google Scholar
Thuiller, W., Lafourcade, B., Engler, R., and Araújo, M. B. 2009. BIOMOD: a platform for ensemble forecasting of species distributions. Ecography 32: 369373.Google Scholar
Tsoar, A., Allouche, O., Steinitz, O., Rotem, D., and Kadmon, R. 2007. A comparative evaluation of presence-only methods for modelling species distribution. Diversity and Distributions 13: 397405.Google Scholar
Václavík, T., Kupfer, J. A., and Meentemeyer, R. K. 2011. Accounting for multi-scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM). Journal of Biogeography 39: 4255.Google Scholar
VanDerWal, J., Shoo, L. P., Graham, C., and Williams, S. E. 2009. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecological Modelling 220: 589594.Google Scholar
Vidal-García, F. and Serio-Silva, J. C. 2011. Potential distribution of Mexican primates: modeling the ecological niche with the maximum entropy algorithm. Primates 52: 261270.Google Scholar
Voskamp, A., Rode, E. J., Coudrat, C. N. Z., et al. 2014. Modelling the habitat use and distribution of the threatened Javan slow loris Nycticebus javanicus. Endangered Species Research 23: 277286.CrossRefGoogle Scholar
Vu, M. V., Thach, H. M., and Pham, V. T. 2010. Using environmental niche model to study the distribution of Tonkin snub-nosed monkey (Rhinopithecus avunculus) in Northeastern Vietnam under some climate change scenarios. Pages 156–164 in Proceedings of the 24th International Conference on Informatics for Environmental Protection, Cologne/Bonn, Germany.Google Scholar
Vu, M. V., Thach, H. M., Le, M. T. T., and Pham, V. T. 2011. Study on the using of environmental niche model Bioclim to estimate the distribution of Francois’s Langur (Trachypithecus francoisi) in Northern of Vietnam under climate change of IPCC scenario A2. VNU Journal of Science: Natural Sciences & Technology 27: 7076.Google Scholar
Walker, P. A. and Cocks, K. D. 1991. HABITAT: a procedure for modelling a disjoint environmental envelope for a plant or animal species. Global Ecology and Biogeography Letters 1: 108118.Google Scholar
Warren, D. L., Glor, R. E., and Turelli, M. 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62: 28682883.Google Scholar
Warren, D. L., Glor, R. E. and Turelli, M. 2010. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33: 607611.Google Scholar
Wiens, J. J. 2004. Speciation and ecology revisited: phylogenetic niche conservatism and the origin of species. Evolution 58: 193197.Google Scholar
Willems, E. P. and Hill, R. A. 2009. A critical assessment of two species distribution models: a case study of the vervet monkey (Cercopithecus aethiops). Journal of Biogeography 36: 23002312.Google Scholar
Wisz, M. S., Hijmans, R. J., Li, J., et al. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions 14: 763773.Google Scholar
Wong, M. H. G., Li, R., Xu, M., and Long, Y. 2013. An integrative approach to assessing the potential impacts of climate change on the Yunnan snub-nosed monkey. Biological Conservation 158: 401409.Google Scholar
Yackulic, C. B., Chandler, R., Zipkin, E. F., et al. 2013. Presence-only modelling using MAXENT: when can we trust the inferences? Methods in Ecology and Evolution 4: 236243.Google Scholar
Yesson, C., Brewer, P. W., Sutton, T., et al. 2007. How global is the Global Biodiversity Information Facility? PLoS ONE 2: e1124.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×