Severe Loss of Suitable Climatic Conditions for Marsupial Species in Brazil: Challenges and Opportunities for Conservation

PLoS ONE

Volumn 7, Issue 9, 2012, Pages

  Loyola, Rafael D ^a   Lemes, Priscila ^a   Faleiro, Frederico V ^a   Trindade Filho, Joaquim ^a   Machado, Ricardo B ^b  

^a FEDERAL UNIVERSITY OF GOIÁS   (Brazil)

^b UNIVERSITY OF BRASILIA   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTICAL PARAMETERS; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOME; BRAZIL; CLIMATE CHANGE; CLIMATE SPACE; ENVIRONMENTAL IMPACT ASSESSMENT; FORECASTING; GENERALIZED ADDITIVE MODEL; GENERALIZED BOOSTING REGRESSION MODEL; GENERALIZED LINEAR MODEL; GREENHOUSE EFFECT; LAND USE; MARSUPIAL; MODEL PROJECTION; MULTIVARIATE ADAPTIVE REGRESSION MODEL; NONHUMAN; PAMPAS; PREDICTIVE VALUE; RANDOM FOREST; SPECIES CONSERVATION; SPECIES DISTRIBUTION; SPECIES EXTINCTION; SPECIES RICHNESS; STATISTICAL MODEL; TURNOVER TIME;

ANIMALS; BIODIVERSITY; BRAZIL; CLIMATE; CLIMATE CHANGE; CONSERVATION OF NATURAL RESOURCES; ENDANGERED SPECIES; MARSUPIALIA; MODELS, BIOLOGICAL; MULTIVARIATE ANALYSIS; PHYLOGEOGRAPHY; RISK; SPECIES SPECIFICITY;

METATHERIA;

EID: 84866997217     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0046257     Document Type: Article

References (36)

1. Araújo MB, Rahbek C, (2006) Ecology. How does climate change affect biodiversity? Science 313: 1396-1397 doi:10.1126/science.1131758.
2. Kerr JT, Kharouba HM, Currie DJ, (2007) The macroecological contribution to global change solutions. Science 316: 1581-1584 doi:10.1126/science.1133267.
3. Pearson RG, Dawson TP, (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography 12: 361-371 doi:10.1046/j.1466-822X.2003.00042.x.
4. Franklin J (2009) Mapping species distributions: spatial inference and prediction. Cambridge: Cambridge University Press.
5. Lawler JJ, Wiersma YF, Huettmann F (2011) Designing predictive models for increased utility: using species distribution models for conservation planning and ecological forecasting. In: Drew A, Wiersma YF, Huettmann F, editors. Predictive Modeling in Landscape Ecology. New York: Springer. pp. 271-290.
6. Pearson RG, Thuiller W, Araújo MB, Martinez-Meyer E, Brotons L, et al. (2006) Model-based uncertainty in species range prediction. Journal of Biogeography 33: 1704-1711 doi:10.1111/j.1365-2699.2006.01460.x.
7. Araújo MB, New M, (2007) Ensemble forecasting of species distributions. Trends in Ecology and Evolution 22: 42-47.
8. Thuiller W, Lafourcade B, Engler R, Araújo MB, (2009) BIOMOD- a platform for ensemble forecasting of species distributions. Ecography 32: 369-373 doi:10.1111/j.1600-0587.2008.05742.x.
9. Diniz-Filho JAF, Bini LM, Fernando Rangel T, Loyola RD, Hof C, et al. (2009) Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32: 897-906 doi:10.1111/j.1600-0587.2009.06196.x.
10. Lewinsohn TM, Prado PI, (2005) How Many Species Are There in Brazil? Conservation Biology 19: 619-624 doi:10.1111/j.1523-1739.2005.00680.x.
11. Cáceres NC, Monteiro-Filho ELA (2006) Os marsupiais do Brasil: biologia, ecologia e evolução. 1a ed. Campo Grande: Editora da UFSM.
12. Fernandez FAS, Pires AS (2006) Perspectivas para a sobrevivência dos marsupiais brasileiros em fragmentos florestais: o que sabemos e o que ainda precisamos aprender? In: Cáceres NC, Monteiro-Filho ELA, editors. Os marsupiais do Brasil: biologia, ecologia e evolução. Campo Grande: Editora UFMS. pp. 191-201.
13. Rossi RV, Bianconi GV, Pedro WA (2006) Ordem Didelphimorphia. In: Reis NR, Perecchi AL, Pedro WA, Lima IP, editors. Mamíferos do Brasil. Londrina: Edifurb. pp. 27-66.
14. Gardner AL (2008) Mammals of South America: marsupials, xenarthrans, shrews, and bats. V. 1. Chicago: University of Chicago Press.
15. Loyola RD, Carvalho RA, Faleiro FV, Trindade-Filho J, Lemes P, et al. (2012) Conservação da diversidade filogenética e funcional de mamíferos do Brasi. In: Freitas TRO, Vieira EM, editors. Mamíferos do Brasil: Genética, Sistemática, Ecologia e Conservação- vol II. Sociedade de Mastozoologia do Brasil. p. in press.
16. IPCC (2007) Climate Change 2007- Impacts, Adaptation and Vulnerability: Working Group II contribution to the Fourth Assessment Report of the IPCC. Parry ML, Canziani OF, Palutikof JP, Van Der Linden PJ, Hanson CE, editors Cambridge University Press.
17. Guisan A, Edwards TC, Hastie T, (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157: 89-100 doi:10.1016/S0304-3800(02)00204-1.
18. Hastie T, Tibshirani R, (1986) Generalized Additive Models. Statistical Science 1: 297-310.
19. Friedman JH, (1991) Multivariate adaptive regression splines. Annals of Statistics 19: 1-141.
20. Breiman L, (2001) Random forest. Machine Learning 45: 5-32 doi:10.1023/A.
21. Manel S, Dias JM, Buckton ST, Ormerod SJ, (1999) Alternative methods for predicting species distribution: an illustration with Himalayan river birds. Journal of Applied Ecology 36: 734-747 doi:10.1046/j.1365-2664.1999.00440.x.
22. Friedman JH, (2001) Greedy function approximation: a gradient boosting machine. Annals of Statistics 29: 1189-1232.
23. Allouche O, Tsoar A, Kadmon R, (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43: 1223-1232 doi:10.1111/j.1365-2664.2006.01214.x.
24. Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W, (2009) Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions 15: 59-69 doi:10.1111/j.1472-4642.2008.00491.x.
25. Diniz-Filho JAF, Nabout JC, Bini LM, Loyola RD, Rangel TF, et al. (2010) Ensemble forecasting shifts in climatically suitable areas for Tropidacris cristata (Orthoptera: Acridoidea: Romaleidae). Insect Conservation and Diversity doi:10.1111/j.1752-4598.2010.00090.x.
26. Garcia RA, Burgess ND, Cabeza M, Rahbek C, Araújo MB, (2011) Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates. Global Change Biology pp. n/a-n/a doi:10.1111/j.1365-2486.2011.02605.x.
27. Eastman JR (2009) IDRISI Kilimanjaro Guide to GIS and Image Processing. Worcester, Massachusetts: Clark University.
28. Lawler JJ, Shafer SL, White D, Kareiva P, Maurer EP, et al. (2009) Projected climate-induced faunal change in the Western Hemisphere. Ecology 90: 588-597.
29. Beaumont LJ, Pitman A, Perkins S, Zimmermann NE, Yoccoz NG, et al. (2011) Impacts of climate change on the world's most exceptional ecoregions. Proceedings of the National Academy of Sciences of the United States of America 108: 2306-2311 doi:10.1073/pnas.1007217108.
30. Diniz-filho JAF, de Oliveira G, Bini LM, Loyola RD, Nabout JC, et al. (2009) Conservation biogeography and climate change in the brazilian cerrado. Natureza & Conservação 7: 100-112.
31. Prevedello JA, Vieira MV, (2009) Does the type of matrix matter? A quantitative review of the evidence. Biodiversity and Conservation 19: 1205-1223 doi:10.1007/s10531-009-9750-z.
32. Prevedello JA, Forero-Medina G, Vieira MV, (2011) Does land use affect perceptual range? Evidence from two marsupials of the Atlantic Forest. Journal of Zoology 284: 53-59 doi:10.1111/j.1469-7998.2010.00783.x.
33. Salazar LF, Nobre CA, Oyama MD, (2007) Climate change consequences on the biome distribution in tropical South America. Geophysical Research Letters 34: L09708 doi:10.1029/2007GL029695.
34. Püttker T, Pardini R, Meyer-Lucht Y, Sommer S, (2008) Responses of five small mammal species to micro-scale variations in vegetation structure in secondary Atlantic Forest remnants, Brazil. BMC ecology 8: 9 doi:10.1186/1472-6785-8-9.
35. Pimm SL, Jenkins CN, Joppa LN, Roberts DL, Russell GJ, (2010) How Many Endangered Species Remain to be Discovered in Brazil? Natureza & Conservação 08: 71-77 doi:10.4322/natcon.00801011.
36. Bini LM, Diniz-Filho JAF, Rangel TFLVB, Bastos RP, Pinto MP, (2006) Challenging Wallacean and Linnean shortfalls: knowledge gradients and conservation planning in a biodiversity hotspot. Diversity and Distributions 12: 475-482 doi:10.1111/j.1366-9516.2006.00286.x.