Journal of Forestry Research

, Volume 28, Issue 5, pp 963–974 | Cite as

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

  • Érica Hasui
  • Vinícius X. Silva
  • Rogério G. T. Cunha
  • Flavio N. Ramos
  • Milton C. Ribeiro
  • Mario Sacramento
  • Marco T. P. Coelho
  • Diego G. S. Pereira
  • Bruno R. Ribeiro
Original Paper

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 

Notes

Acknowledgements

We thank Mainara Xavier Jordani and Diogo Borges Provete for their assistance in the field; the anonymous reviewers for critically reading the text; the Instituto Chico Mendes (ICMBio) for issuing the capture and transportation licenses (No. 10704-1; 22020-1); the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) for its financial support through the Biota Minas Program (Proc. No. APQ 03549-09); and Roy Richard Funch who revised the English translation.

References

  1. Andrén H (1994) Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71:355–366CrossRefGoogle Scholar
  2. APG. [Angiosperm Phylogeny Group] III (2009) An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Bot J Linnean Soc 161:105–121CrossRefGoogle Scholar
  3. Ashcroft MB, French KO, Chisholm L (2012) A simple post hoc method to add spatial context to predictive species distribution models. Ecol Model 228:17–26CrossRefGoogle Scholar
  4. Blake JG (1983) Trophic structure of bird communities in forest patches in East-Central Illinois. Wilson Bull 95(3):416–430Google Scholar
  5. Boscolo D, Metzger JP, Vielliard JME (2006) Efficiency of playback for assuring the occurrence of five birds species in Brazilian Atlantic Forest fragments. An Acad Bras Ciênc 78:629–644CrossRefPubMedGoogle Scholar
  6. Brotons L, Thuiller W, Araujo MB, Hirzel AH (2004) Presence absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27:437–448CrossRefGoogle Scholar
  7. Brown JH, Kodric-brown A (1977) Turnover rates in insular biogeography: effect of immigration on extinction. Ecology 58:445–449CrossRefGoogle Scholar
  8. Burnham KP, Anderson DR (1998) Model selection and inference. Springer, New YorkCrossRefGoogle Scholar
  9. Cabeza M, Arponen A, JaattelA L, Kujala H, Van-Teeffelen H, Hansk I (2010) Conservation planning with insects at three different spatial scales. Ecography 33:54–63CrossRefGoogle Scholar
  10. Cottam G, Curtis JT (1956) The use of distance measures in phytosociological sampling. Ecology 37:451–460CrossRefGoogle Scholar
  11. Crump ML, Scott JRNJ (1994) Standard techniques for inventory and monitoring: visual encounter surveys. In: Heyer WR et al (eds) Measuring and monitoring biological diversity: standard methods for amphibians. Smithsonian Institution Press, Washington, pp 84–92Google Scholar
  12. Denslow JS (1980) Gap partitioning among tropical rainforest trees. Biotropica 12:47–55CrossRefGoogle Scholar
  13. Drummond GM, Martins CS, Machado ABM, Sebaio FA, Antonini Y (2005) Biodiversidade em Minas Gerais: um atlas para sua conservação, 2nd edn. Fundação Biodiversitas, Belo Horizonte, p 222Google Scholar
  14. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697CrossRefGoogle Scholar
  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 (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151CrossRefGoogle Scholar
  16. Engler R, Guisan A, Rechsteiner L (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J Appl Ecol 41:263–274CrossRefGoogle Scholar
  17. Ewers RM, Didham RK (2006) Confounding factors in the detection of species responses to habitat fragmentation. Biol Rev 81:117–142CrossRefPubMedGoogle Scholar
  18. Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Ann Rev Ecol Evol Syst 34:487–515CrossRefGoogle Scholar
  19. Falls JB (1981) Mapping territories with playback: an accurate census method for songbirds. Stud Avian Biol 6:86–91Google Scholar
  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 (2013) 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 110:311–317CrossRefGoogle Scholar
  22. Fleishman E, Murphy DD, Brussard PF (2000) A new method for selection of umbrella species for conservation planning. Ecol Appl 10:569–579CrossRefGoogle Scholar
  23. Foltête JC, Clauzel C, Vuidel G, Toumant P (2012) Integrating graph-based connectivity metrics into species distribution models. Landsc Ecol 27:557–569CrossRefGoogle Scholar
  24. Guisan A, Thuiller W (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  25. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009CrossRefGoogle Scholar
  26. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  27. Guisan A, Lehmann A, Ferrier S, Austin M, Overton JMCC, Aspinall R, Hastie T (2006) Making better biogeographical predictions of species’ distributions. J Appl Ecol 43:386–392CrossRefGoogle Scholar
  28. Guisan A, Zimmermann NE, Elith J, Graham CH, Phillips S, Peterson AT (2007) What matters for predicting the occurrences of trees: techniques, data, or species’ characteristics? Ecol Monogr 77:615–630CrossRefGoogle Scholar
  29. Hanski I (1998) Metapopulation dynamics. Nature 396:41–50CrossRefGoogle Scholar
  30. Henle K, Davies KF, Kleyer M, Margules C, Settele J (2004) Predictors of species sensitivity to fragmentation. Biodivers Conserv 13:207–251CrossRefGoogle Scholar
  31. Hijmans RJ (2012) Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology 93:679–688CrossRefPubMedGoogle Scholar
  32. Hirzel AH, Le LAYG (2008) Habitat suitability modelling and niche theory. J Appl Ecol 45:1372–1381CrossRefGoogle Scholar
  33. Holdridge LR, Grenke WC, Hatheway WH, Liang T, Tosi JAJR (1971) Forest environments in tropical life zones: a pilot study. Pergamon Press, New YorkGoogle Scholar
  34. Hopkins RL (2009) Use of landscape pattern metrics and multiscale data in aquatic species distribution models: a case study of a freshwater mussel. Landsc Ecol 24:943–955CrossRefGoogle Scholar
  35. Lawton JH (1996) Population abundances, geographic ranges and conservation: 1994 Witherby Lecture. Bird Stud 43:3–19CrossRefGoogle Scholar
  36. Loiselle BA, Jorgensen PM, Consiglio T, Jimenez I, Blake JG, Lohmann LG, Montiel OM (2008) Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcome? J Biogeogr 35:105–116Google Scholar
  37. Margules CR, Pressey RL (2000) Systematic conservation planning. Nature 405:243–253CrossRefPubMedGoogle Scholar
  38. Martensen AC, Pimentel RG, Metzger JP (2008) Relative effects of fragment size and connectivity on bird community in the Atlantic Rain Forest: implications for conservation. Biol Conserv 141:2184–2192CrossRefGoogle Scholar
  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 (2006) Accuracy of resource selection functions across spatial scales. Divers Distrib 12:288–297CrossRefGoogle Scholar
  41. Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133:225–245CrossRefGoogle Scholar
  42. Pearman PB, Guisan A, Broennimann O, Randin CF (2008) Niche dynamics in space and time. Trends Ecol Evol 23:149–158CrossRefPubMedGoogle Scholar
  43. Peterson AT (2006) Uses and requirements of ecological niche models and related distributional models. Biodivers Inform 3:59–72CrossRefGoogle Scholar
  44. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  45. Pulliam HR (2000) On the relationship between niche and distribution. Ecol Lett 3:349–361CrossRefGoogle Scholar
  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 (1988) Extinction, turnover and species diversity in an experimentally fragmented California annual grassland. Oecologia 76:71–82CrossRefPubMedGoogle Scholar
  48. Rosales-Meda MM (2007) Caracterización de la población del mono aullador (Alouatta palliata palliata) em el Refugio Nacional de Vida Silvestre Isla San Lucas, Costa Rica. Neotrop Primates 14(3):122–127Google Scholar
  49. Rosenzweig M (1995) Species diversity in space and time. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  50. Sayre R, Roca E, Sedagatkish G, Young B, Keel S, Roca R, Sheppard S (2000) Nature in focus: rapid ecological assessment. Island Press, WashingtonGoogle Scholar
  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 (1988) Modeling dynamics of forest interior bird populations in fragmented landscapes. Conserv Biol 2:340–347CrossRefGoogle Scholar
  53. Thuiller W, Brotons L, Araújo MB, Lavorel S (2004) Effects of restricting environmental range of data to project current and future species distributions. Ecography 27:165–172CrossRefGoogle Scholar
  54. Titeux N, Dufrene M, Radoux J, Hirzel AH, Defourny P (2007) Fitness-related parameters improve presence-only distribution modelling for conservation practice: the case of the red-backed shrike. Biol Conserv 138:207–223CrossRefGoogle Scholar
  55. Warren DL (2012) In defense of ‘niche modeling’. Trends Ecol Evol 27:497–500CrossRefPubMedGoogle Scholar

Copyright information

© Northeast Forestry University and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Érica Hasui
    • 1
  • Vinícius X. Silva
    • 1
  • Rogério G. T. Cunha
    • 1
  • Flavio N. Ramos
    • 1
  • Milton C. Ribeiro
    • 2
  • Mario Sacramento
    • 1
    • 5
  • Marco T. P. Coelho
    • 1
    • 3
  • Diego G. S. Pereira
    • 4
  • Bruno R. Ribeiro
    • 1
    • 3
  1. 1.Laboratório de Ecologia de Fragmentos Florestais (ECOFRAG), Instituto de Ciência da NaturezaUniversidade Federal de AlfenasAlfenasBrazil
  2. 2.Laboratório de Ecologia Espacial e Conservação (LEEC), Departamento de EcologiaUNESPRio ClaroBrazil
  3. 3.Programa de Pós-Graduação em Ecologia e Evolução da Universidade Federal de GoiásUniversidade Federal de GoiásGoiâniaBrazil
  4. 4.Departamento de Ciências FlorestaisUniversidade Federal de LavrasLavrasBrazil
  5. 5.Estação de Hidrobiologia e Piscicultura de Furnas – EHPFSão José da BarraBrazil

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