Using spatial data to determine the influence of pollutants on the occurrence of species in the Amazon
Abstract
The hydrological and energy cycles in the Amazon Basin region changed in the last decades, due to the influence of anthropic action. However, the effects of these on the local fauna have not yet been deeply analyzed. In this context, this work sought to develop an experiment of Species Distribution Modeling of birds, based on meteorological and aerosol data collected in the region of interest during the GoAmazon 2014/15 project, through the application of the Maximum Entropy Model, in order to determine the influence of pollutants on the occurrence of species.
Keywords:
GoAmazon, Species Distribution Modelling, Maximum Entropy
References
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Hernandez, P. A., Graham, C. H., Master, L. L., and Albert, D. L. (2006). The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29(5):773–785.
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Mateo, R. G., Vanderpoorten, A., Munoz, J., Laenen, B., and Désamoré, A. (2013). Modeling species distributions from heterogeneous data for the biogeographic regionalization of the european bryophyte flora. PLoS One, 8(2):e55648.
Miyaji, R. O., Bauer, L. O., Ferrari, V. M., Almeida, F. V., Correa, 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 a Gestão do Meio Ambiente e Recursos Naturais. SBC.
Phillips, S. J. (2005). Maximum entropy modeling of species geographic distribution. Ecological Modelling, 190:231–259.
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.
Phillips, S. J., Dudik, M., and Schapire, R. E. A. (2004). Maximum entropy approach to species distribution modelling. In Proceedings of the Twenty-First International Conference on Machine Learning, pages 655–662.
Pinaya, J. and Correa, 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.
XGBoost Developers (2020). XGBoost Documentation. https://xgboost.readthedocs.io/en/latest/. Acesso em: 01/07/2021.
Bertsimas, D., Pawlowski, C., and Zhuo, Y. D. (2018). From predictive methods to missing data imputation: An optimization approach. Journal of Machine Learning Research, 18:1–39.
Carneiro, L. R. d. A., Lima, A. P., Machado, R. B., and Magnusson, W. E. (2016). Limitations to the use of species-distribution models for environmental-impact assessments in the amazon. PLoS One, 11(1):e0146543.
Golini, N. (2011). Bayesian Modelling of Presence-only Data. PhD thesis, Spienza Universidade de Roma
Hegel, T. M., Cushman, S. A., Evans, J., and Huettmann, F. (2010). Current State of the Art for Statistical Modelling of Species Distributions, pages 273–311. Springer Japan, Tokyo.
Hernandez, P. A., Graham, C. H., Master, L. L., and Albert, D. L. (2006). The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29(5):773–785.
Hutchinson, G. E. (1991). Population studies: Animal ecology and demography. Bulletin of Mathematical Biology, 53(1-2):193–213.
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, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F. d., Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H., Fan, J., et al.(2016). Introduction: observations and modeling of the green ocean amazon (goamazon2014/5). Atmospheric Chemistry and Physics, 16(8):4785–4797.
Mateo, R. G., Vanderpoorten, A., Munoz, J., Laenen, B., and Désamoré, A. (2013). Modeling species distributions from heterogeneous data for the biogeographic regionalization of the european bryophyte flora. PLoS One, 8(2):e55648.
Miyaji, R. O., Bauer, L. O., Ferrari, V. M., Almeida, F. V., Correa, 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 a Gestão do Meio Ambiente e Recursos Naturais. SBC.
Phillips, S. J. (2005). Maximum entropy modeling of species geographic distribution. Ecological Modelling, 190:231–259.
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.
Phillips, S. J., Dudik, M., and Schapire, R. E. A. (2004). Maximum entropy approach to species distribution modelling. In Proceedings of the Twenty-First International Conference on Machine Learning, pages 655–662.
Pinaya, J. and Correa, 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.
XGBoost Developers (2020). XGBoost Documentation. https://xgboost.readthedocs.io/en/latest/. Acesso em: 01/07/2021.
Published
2021-10-04
How to Cite
MIYAJI, Renato O.; CORRÊA, Pedro L. P.; RIZZO, Luciana V..
Using spatial data to determine the influence of pollutants on the occurrence of species in the Amazon. In: WORKSHOP ON UNDERGRADUATE STUDENT WORK (WTAG) - BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 36. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 15-21.
DOI: https://doi.org/10.5753/sbbd_estendido.2021.18157.
