Abstract
Consider again Lena’s wind farm study (Exercise 14.3). She would like to predict what fish occur where.
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References
Christin, S., Hervet, É., & Lecomte, N. (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution, 10, 1632–1644.
Christin, S., Hervet, É., & Lecomte, N. (2021). Going further with model verification and deep learning. Methods in Ecology and Evolution, 12, 130–134.
Dunstan, P. K., Foster, S. D., & Darnell, R. (2011). Model based grouping of species across environmental gradients. Ecological Modelling, 222, 955–963.
Elith, J., Graham, C., Anderson, R., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R., Huettmann, F., Leathwick, J., Lehmann, A., Li, J., Lohmann, L., Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J., Peterson, A., Phillips, S., Richardson, K., Scachetti-Pereira, R., Schapire, R., Soberon, J., Williams, S., Wisz, M., & Zimmermann, N. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151.
Elith, J., & Leathwick, J. (2007). Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Diversity and Distributions, 13, 265–275.
Friedman, J. (1991). Multivariate adaptive regression splines. Annals of Statistics, 19, 1–67.
Harris, D. J. (2015). Generating realistic assemblages with a joint species distribution model. Methods in Ecology and Evolution, 6, 465–473.
Hui, F. K. C., Warton, D. I., Foster, S., & Dunstan, P. (2013). To mix or not to mix: Comparing the predictive performance of mixture models versus separate species distribution models. Ecology, 94, 1913–1919.
Norberg, A., Abrego, N., Blanchet, F. G., Adler, F. R., Anderson, B. J., Anttila, J., Araújo, M. B., Dallas, T., Dunson, D., Elith, J., Foster, S. D., Fox, R., Franklin, J., Godsoe, W., Guisan, A., O’Hara, B., Hill, N. A., Holt, R. D., Hui, F. K. C., Husby, M., Kålås, J. A., Lehikoinen, A., Luoto, M., Mod, H. K., Newell, G., Renner, I., Roslin, T., Soininen, J., Thuiller, W., Vanhatalo, J., Warton, D., White, M., Zimmermann, N. E., Gravel, D., & Ovaskainen, O. (2019). A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs, 89, e01370.
Olden, J., Lawler, J., & Poff, N. (2008). Machine learning methods without tears: A primer for ecologists. The Quarterly Review of Biology, 83, 171–193.
Ovaskainen, O., & Abrego, N. (2020). Joint species distribution modelling: With applications in R. Cambridge: Cambridge University Press.
Ovaskainen, O., & Soininen, J. (2011). Making more out of sparse data: Hierarchical modeling of species communities. Ecology, 92, 289–295.
Stoklosa, J., & Warton, D. I. (2018). A generalized estimating equation approach to multivariate adaptive regression splines. Journal of Computational and Graphical Statistics, 27, 245–253.
Stone, M. (1977). An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion. Journal of the Royal Statistical Society: Series B (Methodological), 39, 44–47.
ter Braak, C. J. F. (1986). Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology, 67, 1167–1179.
ter Braak, C. J. F., & Prentice, I. C. (1988). A theory of gradient analysis. Advances in Ecological Research, 18, 271–317.
Yee, T. (2006). Constrained additive ordination. Ecology, 87, 203–213.
Yee, T. W. (2010). The VGAM package for categorical data analysis. Journal of Statistical Software, 32, 1–34.
Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68, 49–67.
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Warton, D.I. (2022). Predicting Multivariate Abundances. In: Eco-Stats: Data Analysis in Ecology. Methods in Statistical Ecology. Springer, Cham. https://doi.org/10.1007/978-3-030-88443-7_15
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DOI: https://doi.org/10.1007/978-3-030-88443-7_15
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