Species Distribution Model: MaxEntThere is a piece of code to run to check that the necessary files to use the MaxEnt SDM are installed correctly on your computer. See the dismo vignette for more information. With your type of model, (a) run 1 full model (all predictors), (b) then run a reduced model with just bio1, bio5, bio12. See the vignette for how to apply a model and generate a prediction across space. Full model with all 9 predictor variables.
Reduced model with only 3 predictors.
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Species Distribution Model: Generalized Linear ModelsAndrew DennhardtWednesday, December 2, 2015Model Category: Regression. Model Description: This model can be applied in univariate and multivariate applications, and it is used to estimate an ecological response as a linear combination of independent predictor variables. Model Assumptions: This model assumes that the response data are drawn from some statistical distribution other than that of the continuous Gaussian (Normal). Model errors require a specific statistical distribution that is paired with a link function that relates the linear function of predictors to some function of the response variable. For instance, the Poisson distribution requires a log link function to model discretely-distributed response variables (e.g., counts). Model Response Data: Presence-absences and other binary data as well as counts, proportions, and ordinal data. Model Explanatory Data: Discrete, categorical, and continuous predictor variables are each feasible to apply in these models. Model Links and Use with R: To fit these models, you can use one of the following functions-
Furthermore, an introductory lecture about generalized linear models is also available at http://statmath.wu.ac.at/courses/heather_turner/glmCourse_001.pdf. Example Papers: To learn more about how these models can be used to estimate species’ distributions, you can consult the following examples from the literature-
Example with R:
Species Distribution Model: RANDOM FORESTSModel Category: Machine Learning Model Description: Random Forests (RF) is an ensemble technique that uses bootstrap aggregation (bagging) and classification or regression trees. Bootstrap aggregation takes uniform samples from an original dataset of predictor and response to create a subset of data that is allowed to have duplicated samples (replace=T). Then, each sample is used to create a tree. Trees break datasets up into subsets based on measures of variance. The breaks are applied on the grounds of creating two subsets with the minimum possible total intrasubset variance. Each split is normally done by considering all the points in the set that is going to be split. However, in random forests only a select number of randomly selected points in the set is used to split the points into two subsets. In this way, the RF technique takes n number of bagged subsets from an original dataset and creates n number of trees that are grown by randomly sampling i number of points for splitting at each node. Once all the trees are grown, and a new value needs to be predicted, the values calculated by all the regression trees are averaged, or, in the case of classifications trees, each tree casts a vote. This is how RF acts as an ensemble technique. If probabilities of species occurrence are desired, the votes (binary) from classification trees are used to create the probabilities. Model Assumptions: Random forests has the common assumption that samples are representative of the species being modeled and that the samples are independent. There are no assumptions about the distribution of the data. Model Response Data: The model can use presence/absence, pseudo-absence, and abundance. Presence/absence data would use a classification tree, while abundance data will use a regression tree. Model Explanatory Data: The model can handle categorical and continuous predictors. Model Links and Use with R: CRAN website: https://cran.r-project.org/web/packages/randomForest/index.html Helpful videos: Youtube user mathematicalmonk’s: https://www.youtube.com/watch?v=o7iDkcpOr_g Youtube user edureka!’s: https://www.youtube.com/watch?v=IJgR7n-VqSo Helpful presentation slides by Dr. Adele Cutler of Utah State University: http://www.math.usu.edu/adele/RandomForests/Ovronnaz.pdf Example Papers: Elith, J., & Graham, C. H. (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. http://doi.org/10.1111/j.1600-0587.2008.05505.x Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, 9(2), 181-199. http://doi.org/10.1007/s10021-005-0054-1 Example with R:
Bring in the presence and background data.
Load the full presence dataset.
Predict presence probabilities using Random Forests model using all the predictors except biome, and then only three predictors.
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