Additions of landscape metrics improve predictions of occurrence of species distribution models

Abstract

Species distribution models are used to aid our understanding of the processes driving the spatial patterns of species’ habitats. This approach has received criticism, however, largely because it neglects landscape metrics. We examined the relative impacts of landscape predictors on the accuracy of habitat models by constructing distribution models at regional scales incorporating environmental variables (climate, topography, vegetation, and soil types) and secondary species occurrence data, and using them to predict the occurrence of 36 species in 15 forest fragments where we conducted rapid surveys. We then selected six landscape predictors at the landscape scale and ran general linear models of species presence/absence with either a single scale predictor (the probabilities of occurrence of the distribution models or landscape variables) or multiple scale predictors (distribution models + one landscape variable). Our results indicated that distribution models alone had poor predictive abilities but were improved when landscape predictors were added; the species responses were not, however, similar to the multiple scale predictors. Our study thus highlights the importance of considering landscape metrics to generate more accurate habitat suitability models.

Keywords

Ecological niche model / Generalized linear models / Habitat suitability / Landscape structure / Maxent

References

[1] Andrén H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos, 1994, 71: 355-366.

[2] APG. [Angiosperm Phylogeny Group] III. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Bot J Linnean Soc, 2009, 161: 105-121.

[3] Ashcroft MB, French KO, Chisholm L. A simple post hoc method to add spatial context to predictive species distribution models. Ecol Model, 2012, 228: 17-26.

[4] Blake JG. Trophic structure of bird communities in forest patches in East-Central Illinois. Wilson Bull, 1983, 95(3): 416-430.

[5] Boscolo D, Metzger JP, Vielliard JME. Efficiency of playback for assuring the occurrence of five birds species in Brazilian Atlantic Forest fragments. An Acad Bras Ciênc, 2006, 78: 629-644.

[6] Brotons L, Thuiller W, Araujo MB, Hirzel AH. Presence absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 2004, 27: 437-448.

[7] Brown JH, Kodric-brown A. Turnover rates in insular biogeography: effect of immigration on extinction. Ecology, 1977, 58: 445-449.

[8] Burnham KP, Anderson DR. Model selection and inference, 1998, New York: Springer

[9] Cabeza M, Arponen A, JaattelA L, Kujala H, Van-Teeffelen H, Hansk I. Conservation planning with insects at three different spatial scales. Ecography, 2010, 33: 54-63.

[10] Cottam G, Curtis JT. The use of distance measures in phytosociological sampling. Ecology, 1956, 37: 451-460.

[11] Crump ML, Scott JRNJ Heyer WR Standard techniques for inventory and monitoring: visual encounter surveys. Measuring and monitoring biological diversity: standard methods for amphibians, 1994, Washington: Smithsonian Institution Press 84 92

[12] Denslow JS. Gap partitioning among tropical rainforest trees. Biotropica, 1980, 12: 47-55.

[13] Drummond GM, Martins CS, Machado ABM, Sebaio FA, Antonini Y. Biodiversidade em Minas Gerais: um atlas para sua conservação, 2005 2 Belo Horizonte: Fundação Biodiversitas 222

[14] Elith J, Leathwick JR. Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst, 2009, 40: 677-697.

[15] 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, Nakasawa Y, Overton JMCCM, Peterson AT, Phillips SJ, Richardon K, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 2006, 29: 129-151.

[16] Engler R, Guisan A, Rechsteiner L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J Appl Ecol, 2004, 41: 263-274.

[17] Ewers RM, Didham RK. Confounding factors in the detection of species responses to habitat fragmentation. Biol Rev, 2006, 81: 117-142.

[18] Fahrig L. Effects of habitat fragmentation on biodiversity. Ann Rev Ecol Evol Syst, 2003, 34: 487-515.

[19] Falls JB. Mapping territories with playback: an accurate census method for songbirds. Stud Avian Biol, 1981, 6: 86-91.

[20] Ferrier S, Watson G (1997) An evaluation of the effectiveness of environmental surrogates and modelling techniques in predicting the distribution of biological diversity. Consult. Rep. NSW Natl. Parks Wildl. Serv. Dep. Environ., Sport Territ., Environ. Aust., Canberra. http://www.deh.gov.au/biodiversity/publications/technical/surrogates/

[21] Filz KJ, Schmitt T, Engler JO. How fine is fine-scale? Questioning the use of fine-scale bioclimatic data in species distribution models used for forecasting abundance patterns in butterflies. Eur J Entomol, 2013, 110: 311-317.

[22] Fleishman E, Murphy DD, Brussard PF. A new method for selection of umbrella species for conservation planning. Ecol Appl, 2000, 10: 569-579.

[23] Foltête JC, Clauzel C, Vuidel G, Toumant P. Integrating graph-based connectivity metrics into species distribution models. Landsc Ecol, 2012, 27: 557-569.

[24] Guisan A, Thuiller W. Predictive habitat distribution models in ecology. Ecol Model, 2000, 135: 147-186.

[25] Guisan A, Thuiller W. Predicting species distribution: offering more than simple habitat models. Ecol Lett, 2005, 8: 993-1009.

[26] Guisan A, Zimmermann NE. Predictive habitat distribution models in ecology. Ecol Model, 2000, 135: 147-186.

[27] Guisan A, Lehmann A, Ferrier S, Austin M, Overton JMCC, Aspinall R, Hastie T. Making better biogeographical predictions of species’ distributions. J Appl Ecol, 2006, 43: 386-392.

[28] Guisan A, Zimmermann NE, Elith J, Graham CH, Phillips S, Peterson AT. What matters for predicting the occurrences of trees: techniques, data, or species’ characteristics?. Ecol Monogr, 2007, 77: 615-630.

[29] Hanski I. Metapopulation dynamics. Nature, 1998, 396: 41-50.

[30] Henle K, Davies KF, Kleyer M, Margules C, Settele J. Predictors of species sensitivity to fragmentation. Biodivers Conserv, 2004, 13: 207-251.

[31] Hijmans RJ. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology, 2012, 93: 679-688.

[32] Hirzel AH, Le LAYG. Habitat suitability modelling and niche theory. J Appl Ecol, 2008, 45: 1372-1381.

[33] Holdridge LR, Grenke WC, Hatheway WH, Liang T, Tosi JAJR. Forest environments in tropical life zones: a pilot study, 1971, New York: Pergamon Press.

[34] Hopkins RL. Use of landscape pattern metrics and multiscale data in aquatic species distribution models: a case study of a freshwater mussel. Landsc Ecol, 2009, 24: 943-955.

[35] Lawton JH. Population abundances, geographic ranges and conservation: 1994 Witherby Lecture. Bird Stud, 1996, 43: 3-19.

[36] Loiselle BA, Jorgensen PM, Consiglio T, Jimenez I, Blake JG, Lohmann LG, Montiel OM. Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcome?. J Biogeogr, 2008, 35: 105-116.

[37] Margules CR, Pressey RL. Systematic conservation planning. Nature, 2000, 405: 243-253.

[38] Martensen AC, Pimentel RG, Metzger JP. Relative effects of fragment size and connectivity on bird community in the Atlantic Rain Forest: implications for conservation. Biol Conserv, 2008, 141: 2184-2192.

[39] Mcgarigal K, Cushman SA, Neel MC, Ene E (2002) FRAGSTATS v3: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html. Accessed 30 Mar 2015

[40] Meyer CB, Thuiller W. Accuracy of resource selection functions across spatial scales. Divers Distrib, 2006, 12: 288-297.

[41] Pearce J, Ferrier S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model, 2000, 133: 225-245.

[42] Pearman PB, Guisan A, Broennimann O, Randin CF. Niche dynamics in space and time. Trends Ecol Evol, 2008, 23: 149-158.

[43] Peterson AT. Uses and requirements of ecological niche models and related distributional models. Biodivers Inform, 2006, 3: 59-72.

[44] Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Model, 2006, 190: 231-259.

[45] Pulliam HR. On the relationship between niche and distribution. Ecol Lett, 2000, 3: 349-361.

[46] R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. www.R-project.org. Accessed 10 April 2012

[47] Robinson GR, Quinn JF. Extinction, turnover and species diversity in an experimentally fragmented California annual grassland. Oecologia, 1988, 76: 71-82.

[48] Rosales-Meda MM. Caracterización de la población del mono aullador (Alouatta palliata palliata) em el Refugio Nacional de Vida Silvestre Isla San Lucas, Costa Rica. Neotrop Primates, 2007, 14(3): 122-127.

[49] Rosenzweig M. Species diversity in space and time, 1995, Cambridge: Cambridge University Press

[50] Sayre R, Roca E, Sedagatkish G, Young B, Keel S, Roca R, Sheppard S. Nature in focus: rapid ecological assessment, 2000, Washington: Island Press.

[51] SOS Mata Atlântica-INPE (2008) Atlas da evolução dos remanescentes florestais da Mata Atlântica no período de 2000-2005. Fundação SOS Mata Atlântica, São Paulo. www.sosma.org.br e www.inpe.br

[52] Temple SA, Cary JR. Modeling dynamics of forest interior bird populations in fragmented landscapes. Conserv Biol, 1988, 2: 340-347.

[53] Thuiller W, Brotons L, Araújo MB, Lavorel S. Effects of restricting environmental range of data to project current and future species distributions. Ecography, 2004, 27: 165-172.

[54] Titeux N, Dufrene M, Radoux J, Hirzel AH, Defourny P. Fitness-related parameters improve presence-only distribution modelling for conservation practice: the case of the red-backed shrike. Biol Conserv, 2007, 138: 207-223.

[55] Warren DL. In defense of ‘niche modeling’. Trends Ecol Evol, 2012, 27: 497-500.