Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-10T05:48:52.922Z Has data issue: false hasContentIssue false
  • English
  • Français

Distribution and habitat use of the Madagascar Peregrine Falcon: first estimates for area of habitat and population size

Published online by Cambridge University Press:  13 June 2022

LUKE J. SUTTON Open the ORCID record for LUKE J. SUTTON [Opens in a new window] ,
LILY-ARISON RENE DE ROLAND ,
RUSSELL THORSTROM  and
CHRISTOPHER J. W. MCCLURE Open the ORCID record for CHRISTOPHER J. W. MCCLURE [Opens in a new window]
LUKE J. SUTTON*
Affiliation:
The Peregrine Fund, 5668 West Flying Hawk Lane, Boise, Idaho 83709 USA.
LILY-ARISON RENE DE ROLAND
Affiliation:
The Peregrine Fund’s Madagascar Project, BP4113 Antananarivo (101), Madagascar.
RUSSELL THORSTROM
Affiliation:
The Peregrine Fund, 5668 West Flying Hawk Lane, Boise, Idaho 83709 USA.
CHRISTOPHER J. W. MCCLURE
Affiliation:
The Peregrine Fund, 5668 West Flying Hawk Lane, Boise, Idaho 83709 USA.
*
*Author for correspondence; email: lsutton@peregrinefund.org
Get access Rights & Permissions [Opens in a new window]

Summary

Accurately demarcating distributions of biological taxa has long been at the core of ecology. Yet our understanding of the factors defining species range limits is incomplete, especially for tropical species in the Global South. Human-driven threats to the survival of many taxa are increasing, particularly habitat loss and climate change. Identifying distributional range limits of at-risk and data-limited species using Species Distribution Models (SDMs) can thus inform spatial conservation planning to mitigate these threats. The Madagascar Peregrine Falcon Falco peregrinus radama is the resident sub-species of the Peregrine Falcon complex distributed across Madagascar, Mayotte, and the Comoros Islands. There are currently significant knowledge gaps regarding its distribution, habitat preferences, and population size. Here, we use penalized logistic regression and environmental ordination to identify Madagascar Peregrine Falcon habitat in both geographic and environmental space and propose a population size estimate based on inferred habitat. From the penalized logistic regression model, the core habitat area of the Madagascar Peregrine Falcon extends across the central and northern upland plateau of Madagascar with patchier habitat across coastal and low-elevation areas. Range-wide habitat use in both geographic and environmental space indicated positive associations with high elevation and aridity, coupled with high vegetation heterogeneity and >95% herbaceous landcover, but general avoidance of areas >30% cultivated land and >10% mosaic forest. Based on inferred high-class habitat from the penalized logistic regression model, we estimate this habitat area could potentially support a population size ranging between 150 and 300 pairs. Following IUCN Red List guidelines, this subspecies would be classed as ‘Vulnerable’ due to its small population size. Despite its potentially large range, the Madagascar Peregrine Falcon has specialised habitat requirements and would benefit from targeted conservation measures based on spatial models to maintain viable populations.

Keywords

conservation planninghabitat usepopulation densityrange mapsresource selection functionsspecies distribution models
Type
Research Article
Information
Bird Conservation International , Volume 32 , Issue 4 , December 2022 , pp. 624 - 640
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of BirdLife International

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

Aiello‐Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. and Anderson, R. P. (2015) spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38: 541–545.Google Scholar
Akaike, H. (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control. AC-19: 716–723.CrossRefGoogle Scholar
Amatulli, G., Domisch, S., Tuanmu, M. N., Parmentier, B., Ranipeta, A., Malczyk, J. and Jetz, W. (2018) A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5: 180040.CrossRefGoogle ScholarPubMed
Anctil, A., Franke, A. and Bêty, J. (2014) Heavy rainfall increases nestling mortality of an arctic top predator: experimental evidence and long-term trend in peregrine falcons. Oecologia 174: 1033–1043.CrossRefGoogle ScholarPubMed
Anderson, R. P., Peterson, A. T. and Gómez‐Laverde, M. (2002) Using niche‐based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 98: 3–16.CrossRefGoogle Scholar
Barbet‐Massin, M., Jiguet, F., Albert, C. H., and Thuiller, W. (2012) Selecting pseudo‐absences for species distribution models: how, where and how many? Meth. Ecol. Evol. 3: 327–338.CrossRefGoogle Scholar
Barve, N. and Barve, V. (2013) ENMGadgets: tools for pre and post processing in ENM workflows. https://github.com/narayanibarve/ENMGadgets.Google Scholar
Basille, M., Calenge, C., Marboutin, E., Andersen, R. and Gaillard, J. M. (2008) Assessing habitat selection using multivariate statistics: Some refinements of the ecological-niche factor analysis. Ecol. Modell. 211: 233–240.CrossRefGoogle Scholar
Beck, J., Böller, M., Erhardt, A. and Schwanghart, W. (2014) Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecol. Informatics 19: 10–15.CrossRefGoogle Scholar
BirdLife International (2019) Falco peregrinus (amended version of 2016 assessment). The IUCN Red List of Threatened Species 2019: e.T45354964A155500538. https://doi.org/10.2305/IUCN.UK.2016-3.RLTS.T45354964A155500538.en. Downloaded on 14 May 2021.CrossRefGoogle Scholar
Bivand, R. and Rundel, C. (2019) rgeos: Interface to geometry engine - open source (’GEOS’). R package version 0.4-3. https://CRAN.R-project.org/package=rgeos.Google Scholar
Bivand, R., Keitt, T. and Rowlingson, B. (2019) rgdal: Bindings for the ’Geospatial’ Data Abstraction Library. R package version 1.4-3. https://CRAN.R-project.org/package=rgdal.Google Scholar
Bivand, R., Pebesma, E. and Gomez-Rubio, V. (2013) Applied spatial data analysis with R. 2nd Edition. NY, USA: Springer.Google Scholar
Bocedi, G., Palmer, S. C., Pe’er, G., Heikkinen, R. K., Matsinos, Y. G., Watts, K. and Travis, J.M. (2014) Range Shifter: a platform for modelling spatial eco‐evolutionary dynamics and species’ responses to environmental changes. Meth. Ecol. Evol. 5: 388–396.CrossRefGoogle Scholar
Boyce, M. S., Vernier, P. R., Nielsen, S. E. and Schmiegelow, F. K. (2002) Evaluating resource selection functions. Ecol. Modell. 157: 281–300.CrossRefGoogle Scholar
Bradley, M., Johnstone, R., Court, G. and Duncan, T. (1997) Influence of weather on breeding success of peregrine falcons in the Arctic.  The Auk. 114: 786–791.CrossRefGoogle Scholar
Bradter, U., Mair, L., Jönsson, M., Knape, J., Singer, A. and Snäll, T. (2018) Can opportunistically collected Citizen Science data fill a data gap for habitat suitability models of less common species? Meth. Ecol. Evol. 9: 1667–1678.CrossRefGoogle Scholar
Breiner, F. T., Guisan, A., Nobis, M. P., and Bergamini, A. (2017) Including environmental niche information to improve IUCN Red List assessments. Divers. Distrib. 23: 484–495.CrossRefGoogle Scholar
Brooks, T. M., Pimm, S. L., Akçakaya, H. R., Buchanan, G. M., Butchart, S. H., Foden, W., Hilton-Taylor, C., Hoffmann, M., Jenkins, C.N., Joppa, L. and Li, B. V. (2019) Measuring terrestrial area of habitat (AOH) and its utility for the IUCN Red List. Trends Ecol. Evol. 34: 977–986.Google ScholarPubMed
Buechley, E. R., Santangeli, A., Girardello, M., Neate‐Clegg, M. H., Oleyar, D., McClure, C. J. and Şekercioğlu, Ç. H. (2019) Global raptor research and conservation priorities: Tropical raptors fall prey to knowledge gaps. Divers. Distrib. 25: 856–869.CrossRefGoogle Scholar
Burnham, K. and Anderson, D. (2004) Model selection and multi-model inference. Second Edition. NY, USA: Springer-Verlag.CrossRefGoogle Scholar
Da Silva, F. P., Fernandes-Ferreira, H., Montes, M. A. and da Silva, L. G. (2020) Distribution modeling applied to deficient data species assessment: A case study with Pithecopus nordestinus (Anura, Phyllomedusidae).  Neotrop. Biol. Conserv.  15: 165–175.CrossRefGoogle Scholar
Danielson, J. J. and Gesch, D. B. (2011) Global multi-resolution terrain elevation data 2010 (GMTED2010) (p. 26). US Department of the Interior, US Geological Survey.Google Scholar
Davies, R. A. G., Virani, M., Ogada, D., Botha, A., Buij, R., Brouwer, J., Barlow, C., Azafzaf, H., Kendall, C., Monadjem, A., McClure, C., Rayner, A., Wroblewski, T., Trice, S., Sutton, L. J., Baker, N. and Baker, L. (2020) AFRICAN RAPTOR DATABANK: a secure, live data observatory for the distribution and movements of African raptors. Habitat Info Ltd, Solva, UK. http://www.habitatinfo.com/ardb_observatory/Google Scholar
Elith, J. and Leathwick, J. R. (2009) The contribution of species distribution modelling to conservation prioritization. Pp. 70–93 in Moilanen, A., Wilson, K. A. and Possingham, H. P., eds. Spatial conservation prioritization: Quantitative methods and computational tools. Oxford, UK: Oxford University Press.Google Scholar
Engler, J. O., Stiels, D., Schidelko, K., Strubbe, D., Quillfeldt, P. and Brambilla, M. (2017) Avian SDMs: current state, challenges, and opportunities.  J. Avian Biol.  48: 1483–1504.Google Scholar
Ferguson-Lees, J. and Christie, D. A. (2005) Raptors of the world. London, UK: Christopher Helm.Google Scholar
Ferraz, K. M. P. M. D. B., Morato, R. G., Bovo, A. A. A., da Costa, C. O. R., Ribeiro, Y. G. G., de Paula, R. C., Desbiez, A. L. J., Angelieri, C. S. C. and Traylor‐Holzer, K. (2020) Bridging the gap between researchers, conservation planners, and decision makers to improve species conservation decision‐making.  Conserv. Sci. Pract. e330.Google Scholar
Fithian, W. and Hastie, T. (2013) Finite-sample equivalence in statistical models for presence-only data. Ann. Appl. Stat. 7: 1917–1939.CrossRefGoogle ScholarPubMed
Fourcade, Y., Besnard, A. G. and Secondi, J. (2017) Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics.  Glob. Ecol. Biogeogr.  27: 245–256.CrossRefGoogle Scholar
Franklin, J. (2010) Moving beyond static species distribution models in support of conservation biogeography.  Divers. Distrib.  16: 321–330.Google Scholar
Friedman, J., Hastie, T. and Tibshirani, R. (2010) Regularization paths for generalized linear models via coordinate descent. J. Statist. Softw. 33: 1–22.CrossRefGoogle ScholarPubMed
Garshelis, D. L. (2000) Delusions in habitat evaluation: measuring use, selection, and importance. In: Boitani, L. and Fuller, T. K., eds. Research techniques in animal ecology: controversies and consequences. New York, USA: Columbia University Press.Google Scholar
Gastón, A. and García-Viñas, J. I. (2011) Modelling species distributions with penalised logistic regressions: A comparison with maximum entropy models.  Ecol. Modell.  222: 2037–2041.CrossRefGoogle Scholar
GBIF (2019a) Global Biodiversity Information Facility Occurrence Download. https://doi.org/10.15468/dl.hcsgkjCrossRefGoogle Scholar
GBIF (2019b) Global Biodiversity Information Facility Occurrence Download. https://doi.org/10.15468/dl.o4bdwvCrossRefGoogle Scholar
Goodman, S. M., Pidgeon, M., Hawkins, A. F. A. and Schulenberg, T. S. (1997) The birds of south-eastern Madagascar. Fieldiana: Zool. 87: 1–132.Google Scholar
Guevara, L., Gerstner, B. E., Kass, J. M. and Anderson, R. P. (2018) Toward ecologically realistic predictions of species distributions: A cross‐time example from tropical montane cloud forests. Glob. Change Biol. 24: 1511–1522.CrossRefGoogle ScholarPubMed
Guisan, A., Thuiller, W. and Zimmermann, N. E. (2017) Habitat suitability and distribution models: with applications in R. Cambridge, UK: Cambridge University Press.Google Scholar
Hefley, T. J. and Hooten, M. B. (2015) On the existence of maximum likelihood estimates for presence‐only data.  Meth. Ecol. Evol.  6: 648–655.CrossRefGoogle Scholar
Herkt, K. M. B., Skidmore, A. K. and Fahr, J. (2017) Macroecological conclusions based on IUCN expert maps: A call for caution. Glob. Ecol. Biogeogr. 26: 930–941.CrossRefGoogle Scholar
Hijmans, R. J. (2017) raster: Geographic data analysis and modeling. R package version 2.6-7. https://CRAN.R-project.org/package=raster.Google Scholar
Hijmans, R. J., Phillips, S., Leathwick, J. and Elith, J. (2017) dismo: Species distribution modeling. R package version 1.1-4. https://CRAN.R-project.org/package=dismo.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: 2027–2036.CrossRefGoogle Scholar
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. and Guisan, A. (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol. Modell. 199: 142–152.CrossRefGoogle Scholar
Hobi, M. L., Dubinin, M., Graham, C. H., Coops, N. C., Clayton, M. K., Pidgeon, A. M. and Radeloff, V. C. (2017) A comparison of Dynamic Habitat Indices derived from different MODIS products as predictors of avian species richness. Remote Sens. Environ. 195: 142–152.CrossRefGoogle Scholar
Hurvich, C. M. and Tsai, C. L. (1989) Regression and time-series model selection in small sample sizes. Biometrika 76: 297–307.Google Scholar
IUCN Standards and Petitions Committee (2019) Guidelines for using the IUCN Red List Categories and Criteria. Version 14. Prepared by the Standards and Petitions Committee. http://www.iucnredlist.org/documents/RedListGuidelines.pdf.Google Scholar
Isaac, N. J., Jarzyna, M. A., Keil, P., Dambly, L. I., Boersch-Supan, P. H., Browning, E., Freeman, S. N., Golding, N., Guillera-Arroita, G., Henrys, P. A., Jarvis, S., Lahoz-Monfort, J., Pagel, J., Pescott, O. L., Schmucki, R., Simmonds, E. G. and O’Hara, R. B. (2019) Data integration for large-scale models of species distributions. Trends Ecol. Evol. 35: 56–67.CrossRefGoogle ScholarPubMed
Jenkins, A. R. and Hockey, P. A. (2001) Prey availability influences habitat tolerance: an explanation for the rarity of Peregrine Falcons in the tropics. Ecography 24: 359–365.Google Scholar
Jolly, A., Oberlé, P. and Albignac, R. Eds. (2016) Key environments: Madagascar. New York: Elsevier.Google Scholar
Ladle, R. and Whittaker, R. J. (2011) Conservation biogeography. London, UK: John Wiley and Sons.CrossRefGoogle Scholar
Langrand, O. (1990) Guide to the birds of Madagascar. New Haven, CT: Yale University Press.Google Scholar
Lawler, J. J., Wiersma, Y. F. and Huettman, F. (2011) Using species distribution models for conservation planning and ecological forecasting. Pp. 271–290 in Drew, C. A., Wiersma, Y. F. and Huettmann, F., eds. Predictive species and habitat modeling in landscape ecology. New York, USA: Springer.CrossRefGoogle Scholar
Levin, S. A. (1992) The problem of pattern and scale in ecology. Ecology 73: 1943–1967.CrossRefGoogle Scholar
Matthiopoulos, J., Fieberg, J. and Aarts, G. (2020) Species-habitat associations: Spatial data, predictive models, and ecological insights. University of Minnesota Libraries Publishing. Retrieved from the University of Minnesota Digital Conservancy. http://hdl.handle.net/11299/217469.CrossRefGoogle Scholar
McClure, C. J. W., Anderson, D. L., Buij, R., Dunn, L., Henderson, M. T., McCabe, J., … and Tavares, J. (2021) Commentary: The past, present, and future of the Global Raptor Impact Network. J. Raptor Res. DOI: 10.3356/JRR-21-13.CrossRefGoogle Scholar
McGarigal, K., Cushman, S. A., Neel, M. C. and Ene, E. (2002) FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst.Google Scholar
Mearns, R. and Newton, I. (1988) Factors affecting breeding success of peregrines in south Scotland.  J. Anim. Ecol. 57: 903–916.CrossRefGoogle Scholar
Meyer, C., Kreft, H., Guralnick, R. and Jetz, W. (2015) Global priorities for an effective information basis of biodiversity distributions.  Nature Commun. 6: 1–8.CrossRefGoogle ScholarPubMed
Millspaugh, J. J., Rota, C. T., Gitzen, R. A., Montgomery, R. A., Bonnot, T. W., Belant, J. L., Ayers, C. R., Kesler, D. C., Eads, D. A. and Jachowski, C. M. B. (2020) Analysis of resource selection by animals. Pp. 333–357 in Population ecology in practice. London, UK: Wiley Blackwell.Google Scholar
Muscarella, R., Galante, P. J., Soley‐Guardia, M., Boria, R.A., Kass, J. M., Uriarte, M. and Anderson, R. P. (2014) ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Meth. Ecol. Evol. 5: 1198–1205.CrossRefGoogle Scholar
Neate-Clegg, M. H., Horns, J. J., Adler, F. R., Aytekin, M. Ç. K. and Şekercioğlu, Ç. H. (2020) Monitoring the world’s bird populations with community science data.  Biol. Conserv.  248: 108653.Google Scholar
Newbold, T. (2010) Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models.  Progr. Phys. Geogr.  34: 3–22.Google Scholar
Oliver, T. H., Gillings, S., Girardello, M., Rapacciuolo, G., Brereton, T. M., Siriwardena, G. M., Roy, D. B., Pywell, R. and Fuller, R. J. (2012) Population density but not stability can be predicted from species distribution models.  J. Appl. Ecol.  49: 581–590.Google Scholar
Pearce, J. L. and Boyce, M. S. (2006) Modelling distribution and abundance with presence-only data. J. Appl. Ecol. 43: 405–412.Google Scholar
Peterson, A. T., Papeş, M. and Soberón, J. (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Modell. 213: 63–72.CrossRefGoogle Scholar
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., and Blair, M. E. (2017) Opening the black box: an open‐source release of Maxent. Ecography 40: 887–893.CrossRefGoogle Scholar
R Core Team (2018) R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.Google Scholar
Radeloff, V. C., Dubinin, M., Coops, N. C., Allen, A. M., Brooks, T. M., Clayton, M. K., Costa, G. C., Graham, C. H., Helmers, D. P., Ives, A. R., Kolesov, D., Pidgeon, A. M., Rapacciuolo, G., Razenkova, E., Suttidate, N., Young, B. E. Zhu, L. and Hobi, M. L. (2019) The dynamic habitat indices (DHIs) from MODIS and global biodiversity.  Remote Sens. Environ. 222: 204–214.CrossRefGoogle Scholar
Ram, K. and Wickham, H. (2018) wesanderson: A Wes Anderson palette generator. R package version 0.3. 6.Google Scholar
Ramesh, V., Gopalakrishna, T., Barve, S. and Melnick, D. J. (2017) IUCN greatly underestimates threat levels of endemic birds in the Western Ghats. Biol. Conserv. 210: 205–221.CrossRefGoogle Scholar
Ratcliffe, D. A. (1993) The Peregrine Falcon. 2nd edition. London, UK: T. and A.D. Poyser.Google Scholar
Razafimanjato, G., de Roland, L. A. R., Rabearivony, J. and Thorstrom, R. (2007) Nesting biology and food habits of the Peregrine Falcon Falco peregrinus radama in the south-west and central plateau of Madagascar. Ostrich 78: 7–12.CrossRefGoogle Scholar
Rhoden, C. M., Peterman, W. E. and Taylor, C. A. (2017) Maxent-directed field surveys identify new populations of narrowly endemic habitat specialists. PeerJ. 5: e3632.CrossRefGoogle ScholarPubMed
Riddle, B. R., Ladle, R. J., Lourie, S. A. and Whittaker, R. J. (2011) Basic biogeography: estimating biodiversity and mapping nature. Pp. 45–92 in Ladle, R. J. and Whittaker, R. J., eds. Conservation biogeography. Chichester, UK: John Wiley and Sons Ltd.CrossRefGoogle Scholar
Rinnan, D. S. (2018) CENFA: Climate and ecological niche factor analysis. R package version 1.0.0. https://CRAN.R-project.org/package=CENFAGoogle Scholar
Rinnan, D. S. and Lawler, J. (2019) Climate‐niche factor analysis: a spatial approach to quantifying species vulnerability to climate change. Ecography 42: 1494–1503.CrossRefGoogle Scholar
Scott, J. M., Heglund, P. J., Morrison, M. L., Haufler, J. B., Raphael, M. G., Wall, W. A. and Samson, F.B. Eds. (2002) Predicting species occurrences: Issues of accuracy and scale. Covelo, CA, USA: Island Press.Google Scholar
Sinclair, I. and Langrand, O. (2013) Birds of the Indian Ocean islands. South Africa: Penguin Random House.Google Scholar
Smith, A. B. (2019) enmSdm: Tools for modeling niches and distributions of species. R package v0.3.4.6. https://github.com/adamlilith/enmSdm/Google Scholar
Sullivan, B. L., Wood, C. L., Iliff, M. J., Bonney, R. E., Fink, D. and Kelling, S. (2009) eBird: A citizen-based bird observation network in the biological sciences. Biol. Conserv. 142: 2282–2292.CrossRefGoogle Scholar
Sutton, L. J., Anderson, D. L., Franco, M., M.Clure, C. J. W., M.randa, E. B., Vargas, F. H., Vargas González, J. de J. and Puschendorf, R. (2021a) Geographic range estimates and environmental requirements for the harpy eagle derived from spatial models of current and past distribution. Ecol. Evol. 11: 481–497.CrossRefGoogle Scholar
Sutton, L. J., Anderson, D. L., Franco, M., McClure, C. J. W., Miranda, E. B., Vargas, F. H., Vargas González, J. de J. and Puschendorf, R. (2021b) Range-wide habitat use and Key Biodiversity Area coverage for a lowland tropical forest raptor across an increasingly deforested landscape. bioRxiv. DOI: https://doi.org/10.1101/2021.08.18.456651.CrossRefGoogle Scholar
Sutton, L. J. and Puschendorf, R. (2020) Climatic niche of the Saker Falcon Falco cherrug: predicted new areas to direct population surveys in Central Asia. Ibis 162: 27–41.Google Scholar
Thornthwaite, C. W. (1948) An approach toward a rational classification of climate.  Geogr. Rev.  38: 55–94.CrossRefGoogle Scholar
Title, P. O. and Bemmels, J. B. (2018) ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41: 291–307.CrossRefGoogle Scholar
Tuanmu, M. N. and Jetz, W. (2014) A global 1‐km consensus land‐cover product for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 23: 1031–1045.CrossRefGoogle Scholar
Tuanmu, M. N. and Jetz, W. (2015) A global, remote sensing‐based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 24: 1329–1339.Google Scholar
VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L., Storlie, C. and VanDerWal, M. J. (2014) SDMTools: Species Distribution Modelling Tools: Tools for processing data associated with species distribution modelling exercises. R package version 1.1-221. https://CRAN.R-project.org/package=SDMToolsGoogle Scholar
van Proosdij, A. S., Sosef, M. S., Wieringa, J. J. and Raes, N. (2016) Minimum required number of specimen records to develop accurate species distribution models. Ecography 39: 542–552.CrossRefGoogle Scholar
Warren, D. L. and Seifert, S. N. (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications. 21: 335–342.CrossRefGoogle ScholarPubMed
Warton, D. I. and Shepherd, L. C. (2010) Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology.  Ann. Appl. Statist. 4: 1383–1402.Google Scholar
White, C. M., Cade, T. J. and Enderson, J. H. (2013) Peregrine Falcons of the world. Barcelona, Spain: Lynx Edicions.Google Scholar
Wilson, K. A., Auerbach, N. A., Sam, K., Magini, A. G., Moss, A. S. L., Langhans, S. D., Budiharta, S., Terzano, D. and Meijaard, E. (2016) Conservation research is not happening where it is most needed.  PLoS Biology.  14: p.e1002413.CrossRefGoogle Scholar
Zou, H. and Hastie, T. (2005) Regularization and variable selection via the elastic net. J. Roy. Statist. Soc: ser. B (statistical methodology) 67: 301–320.CrossRefGoogle Scholar
Supplementary material: File

Sutton et al. supplementary material

Sutton et al. supplementary material

Download Sutton et al. supplementary material(File)
File 17.1 KB