Species distribution modeling experiment based on environmental and aerosol variables in the region near Manaus (AM)
Abstract
The Amazon Rainforest region is considered to be the one that concentrates the greatest biodiversity in the world. Seen by many as a unique study laboratory, there are numerous possibilities for applications of computational models to extract value from data collected in the region. This work presents an experiment of modeling the distribution of species located in a region close to Manaus (AM). This modeling was based on environmental and aerosol variables, whose raw data were obtained from the GOAmazon project repository. The integration of environmental data with species occurrence data of Brazilian fauna allowed the models described to predict the probability of a species being present in the studied area.
Keywords:
Goamazon, Species Distribution, Ecological Niche
References
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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.
Cirino, G., Brito, J., Barbosa, H. M., Rizzo, L. V., Tunved, P., de Sá, S. S., Jimenez, J. L., Palm, B. B., Carbone, S., Lavric, J. V., et al. (2018). Observations of manaus urban plume evolution and interaction with biogenic emissions in goamazon 2014/5. Atmospheric Environment, 191:513–524.
Effrosynidis, D., Tsikliras, A., Arampatzis, A., and Sylaios, G. (2020). Species distribution modelling via feature engineering and machine learning for pelagic fishes in the mediterranean sea. Applied Sciences, 10(24).
Elith, J. and Leathwick, J. R. (2009). Species distribution models: Ecological explanation and prediction across space and time. The Annual Review of Ecology, Evolution and Systematics, 40:677–697.
Friedman, J. H. (2002). Stochastic gradient boosting. 38(4):367–378.
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, 53:193–213.
Johnson, R., Chawla, N., and Hellmann, J. (2012). Species distribution modeling and prediction: A class imbalance problem. pages 9–16.
Leidenberger, S., Obst, M., Kulawik, R., Stelzer, K., Heyer, K., Hardisty, A., and Bourlat, S. J. (2015). Evaluating the potential of ecological niche modelling as a component in marine non-indigenous species risk assessments. Marine Pollution Bulletin, 97(1):470–487.
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., Muñoz, 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., Almeida, F. V., Bueno,W., and Corêa, P. L. P. (2021). Dados bioclimáticos para modelagem de distribuição de espécies baseada em variáveis ambientais e aerossóis na região próxima a manaus (am). Zenodo. DOI: 10.5281/zenodo.4824365.
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
Radosavljevic, A. and Anderson, R. P. (2014). Making better maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41(4):629–643.
Scikit-learn Developers (2020). Scikit-learn: User guide. https://scikit-learn.org/stable/user_guide.html. Acesso em: 13/03/2021.
The Imbalanced-learn Developers (2021). Imbalanced-learn documentation. https://imbalanced-learn.org/stable/. Acesso em: 18/03/2021.
Wei, D., Fuentes, J. D., Gerken, T., Trowbridge, A. M., Stoy, P. C., and Chamecki, M. (2019). Influences of nitrogen oxides and isoprene on ozone-temperature relationships in the amazon rain forest. Atmospheric Environment, 206:280–292.
XGBoost Developers (2020). XGBoost Documentation. https://xgboost.readthedocs.io/en/latest/. Acesso em: 20/03/2021.
Published
2021-07-18
How to Cite
DE ALMEIDA, Felipe V.; BUENO, Weslley M.; MIYAJI, Renato O.; CORRÊA, Pedro L. P..
Species distribution modeling experiment based on environmental and aerosol variables in the region near Manaus (AM). In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 12. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 87-96.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2021.15740.
