Dear Lara,
Several correlative species distribution models exist (and are used
currently), including machine learning approaches as well, such as
artificial neural network (ANN), boosted regression trees (RGB / GBM),
genetic algorithm (GA), and random forests (RF).
You may find the papers comparing or grouping them interesting:
- Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, García
Marquéz JR, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C,
Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S
(2013): Collinearity: a review of methods to deal with it and a
simulation study evaluating their performance. Ecography 36(1): 27–46.
- Elith J, Graham CH (2009)? Do they? How do they? Why do they differ?
On finding reasons for differing performances of species distribution
models. Ecography 32(1) 66–77.
- Heikkinen RK, Luoto M, Araújo MB, Virkkala R, Thuiller W, Sykes MT
(2006): Methods and uncertainties in bioclimatic envelope modelling
under climate change. Progress in Physical Geography 30(6): 751–777.
- Hernandez PA, Graham CH, Master LL, Albert DL (2006): The effect of
sample size and species characteristics on performance of different
species distribution modeling methods. Ecography 29(5): 773–785.
- Pearson RG, Thuiller W, Araújo MB, Martinez-Meyer E, Brotons L,
McClean C, Miles L, Segurado P, Dawson TE, Lees DC (2006): Model-based
uncertainty in species’ range prediction. Journal of Biogeography
33(10): 1704–1711.
- Thuiller W (2004): Patterns and uncertainties of species’ range shifts
under climate change. Global Change Biology 10(12): 2020–2027.
Also see package 'sdm' by Naimi and Araújo.
HTH,
Ákos Bede-Fazekas
Hungarian Academy of Sciences
2019.06.07. 15:45 keltezéssel, Lara Silva írta:
Hello everyone!
I am new in modelling species (plants). However, I used some approaches
like ENFA (Biomapper), Maxent, generalized linear and additive models (in
R) and Biomod2.
I would like to improve my knowledge in this area.Currently, what methods
have been used?
Any suggestions?
Regards,
Lara
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