Handling uncertainty through Bayesian inference for Species Distribution Modelling in the Amazon Basin region
Abstract
Species Distribution Modelling (SDM) is used for biodiversity preservation and wildlife management. In order to apply it, a large and reliable dataset about the species occurrence is required. However, in some cases, this can be difficult when there are only a few occurrence records. In this context, uncertainty handling techniques can be applied. Thus, Bayesian inference was used in this study to perform SDM in the Amazon Basin region near Manaus (AM) with data collected by the GoAmazon 2014/15 project. The results were compared with those obtained in statistical models and were similar.
References
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Di Lorenzo, B., Farcomeni, A., and Golini, N. (2011). A bayesian model for presenceonly semicontinuous data, with application to prediction of abundance of taxus baccata in two italian regions. Journal of Agriculture Biological and Environmental Statistics, 16:339–356.
Ellison, A. M. (2004). Bayesian inference in ecology. Ecology Letters, 7:509–520.
Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4):457–511.
Gelman, A. and Rubin, D. B. (1995). Avoiding model selection in bayesian social research. Sociological Methodology, 25:165–174.
Golini, N. (2011). Bayesian Modelling of Presence-only Data. PhD thesis, Spienza Universidade de Roma.
Hegel, T. M., Cushman, A., Evans, J., and Huetmann, F. (2010). Spatial Complexity, Informatics and Wildlife Conservation, chapter Current State of the Art for Statistical Modelling of Species Distributions. Springer.
Hutchinson, G. E. (1991). Population studies: Animal ecology and demography. Bulletin of Mathematical Biology, 53(1-2):193–213.
ICMBio (2021). Portal da biodiversidade do instituto chico mendes de conservação da biodiversidade. https://portaldabiodiversidade.icmbio.gov.br/portal/. Acesso em: 202104-21.
Johnson, R., Chawla, N., and Hellmann, J. (2012). Species distribution modeling and prediction: A class imbalance problem. pages 9–16.
Kunhert, P. M., Martin, T. G., and P, G. S. (2010). A guide to eliciting and using expert knowledge in bayesian ecological models. Ecology Letters, 13:900–914.
Low-Choy, S. L., O’Leary, R., and Mergensen, K. (2009). Elicitation by design in ecology: using expert opinion to inform priors for bayesian statistical models. Ecology, 90(1):265–277.
Martin, S. T., Artaxo, P., Machado, L., Manzi, A. O., Souza, R. A. F. d., Schumacher, C., Wang, J., Biscaro, T., Brito, J., Calheiros, A., et al. (2017). The green ocean amazon experiment (goamazon2014/5) observes pollution affecting gases, aerosols, clouds, and rainfall over the rain forest. Bulletin of the American Meteorological Society, 98(5):981–997.
Martin, T. G., Kuhnert, P. M., Mengersen, K., and Possingham, H. P. (2005). The power of expert opinion in ecological models using bayesian methods: Impact of grazing on birds. Ecological Applications, 15:266–280.
Mateo, R. G., Vanderpoorten, A., Muñoz, J., Laenen, B., and Désamoré, A. (2013). Modeling species distributions from heterogeneous data for the biogeographic regionalization of the european bryophyte ora. PLoS One, 8(2):e55648.
Miyaji, R. O., Bauer, L. O., Ferrari, V. M., Almeida, F. V., Corrêa, P. L. P., and Rizzo, L. V. (2021). Interpolação espacial de variáveis ambientais e aerossóis na região da bacia amazônica próxima a manaus-am. In Anais do XII Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais. SBC.
Niamir, A. (2019). Incorporating knowledge uncertainty into species distribution modelling. Biodiversity and Conservation, 28:571–588.
Phillips, S. J. (2005). Maximum entropy modeling of species geographic distribution. Ecological Modelling, 190:231–259.
Pinaya, J. (2019). Processo de modelagem paleoclimática de distribuição de espécies com enfoque nas mudanças climáticas. Tese apresentada à Escola Politécnica da Universidade de São Paulo.
Pinaya, J. and Corrêa, P. (2014). Metodologia para definição das atividades do processo de modelagem de distribuição de espécies. In Anais do V Workshop de Computação Aplicada a Gestão do Meio Ambiente e Recursos Naturais, pages 45–54, Porto Alegre, RS, Brasil. SBC.
Salvatier, J., Wieck, T. V., and Fonnesbeck, C. (2016). Probabilistic programming in python using pymc3. Peer J Computer Science.
Published
2021-11-29
How to Cite
MIYAJI, Renato O.; CORRÊA, Pedro L. P..
Handling uncertainty through Bayesian inference for Species Distribution Modelling in the Amazon Basin region. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 18. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 83-94.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2021.18243.
