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modEvA is an R package for species distribution model evaluation and analysis.
It includes functions for variation partitioning; calculating several measures of model discrimination, classification, explanatory power, and calibration; optimizing prediction thresholds based on a number of criteria; performing multivariate environmental similarity surface (MESS) analysis; computing variable importance for different modelling methods; and displaying various analytical plots. It includes also a sample data set with some species distribution models.
To install the latest CRAN version of modEvA, paste the following command in the R console (while connected to the internet):
install.packages("modEvA")To install the development version (with new features, possibly also new bugs, but also new bug fixes) of modEvA from R-Forge, paste the following command in the R console (while connected to the internet):
install.packages("modEvA", repos="http://R-Forge.R-project.org")This should work if you have the latest version of R; otherwise, it may either fail (producing a message like "package 'modEvA' is not available for your R version") or install an older version of modEvA. To check which modEvA version you have actually installed, type citation(package="modEvA"). To install the latest modEvA version, you can either upgrade R or download the compressed modEvA package source files to your disk (.tar.gz for Linux/Mac or .zip for Windows, available here) and then install the package from there, e.g. with R menu "Packages - Install packages from local zip files" (Windows), or "Packages & Data - Package installer, Packages repository - Local source package" (Mac), or "Tools - Install packages - Install from: Package Archive File" (RStudio).
You only need to install (each version of) the package once, but then every time you re-open R you need to load the package by typing:
library(modEvA)You can then check out the package help files and try some of the provided examples:
help("modEvA")Barbosa A.M., Real R., Muñoz A.R. & Brown J.A. (2013) New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity and Distributions 19: 1333-1338 (DOI: 10.1111/ddi.12100)
Areias-Guerreiro J., Mira A. & Barbosa A.M. (2016) How well can models predict changes in species distributions? A 13-year-old otter model revisited. Hystrix – Italian Journal of Mammalogy, 27(1). DOI: https://doi.org/10.4404/hystrix-27.1-11867
Barbosa A.M., Brown J.A., Acevedo P., Lobo J.M. & Real R. (in prep.) The ABC of model evaluation: a visual method for a clearer assessment of model accuracy
Coelho L., Romero D., Queirolo D. & Guerrero J.C. (2018) Understanding factors affecting the distribution of the maned wolf (Chrysocyon brachyurus) in South America: Spatial dynamics and environmental drivers. Mammalian Biology, 92: 54-61. https://doi.org/10.1016/j.mambio.2018.04.006
da Silva B.A., Guerrero J.C., Bidegaray-Batista L. & Simo M. (2020) Description of Latica, a new monotypic spider genus from Uruguay and Argentina (Araneae, Herpyllinae, Gnaphosidae): An integrative approach. Zoologischer Anzeiger, 288: 84-96. DOI: 10.1016/j.jcz.2020.07.006
De Araujo C.B., Marcondes-Machado L.O. & Costa G.C. (2013) The importance of biotic interactions in species distribution models: a test of the Eltonian noise hypothesis using parrots. Journal of Biogeography, 41: 513-523
Dias A., Palma L., Carvalho F., Neto D., Real J., Beja P. (2017) The role of conservative versus innovative nesting behavior on the 25‐year population expansion of an avian predator. Ecology and Evolution 7: 4241–4253. https://doi.org/10.1002/ece3.3007
Horn, J., Becher, M. A., Kennedy, P. J., Osborne, J. L. & Grimm, V. (2016) Multiple stressors: using the honeybee model BEEHAVE to explore how spatial and temporal forage stress affects colony resilience. Oikos, 125: 1001-1016. DOI:10.1111/oik.02636
Mata-Nicolas E., Montero-Pau J., Gimeno-Paez E., Garcia-Perez A., Ziarsolo P., Blanca J., van der Knaap E., Diez M.J., Canizares J. (2021) Discovery of a Major QTL Controlling Trichome IV Density in Tomato Using K-Seq Genotyping. Genes, 12(2): 243. https://doi.org/10.3390/genes12020243
Moreno‐Zarate L., Estrada A., Peach W. & Arroyo B. (2020) Spatial heterogeneity in population change of the globally threatened European turtle dove in Spain: The role of environmental favourability and land use. Diversity & Distributions, https://doi.org/10.1111/ddi.13067
Naman S.M., Rosenfeld J.S., Kiffney P.M., Richardson J.S. (2018) The energetic consequences of habitat structure for forest stream salmonids. Journal of Animal Ecology, DOI: 10.1111/1365-2656.12845
Romero D., Olivero J., Real R. & Guerrero J.C. (2019) Applying fuzzy logic to assess the biogeographical risk of dengue in South America. Parasites & Vectors, 12: 428. DOI: 10.1186/s13071-019-3691-5
Waterhouse M., Baxter C., Duarte Romero B., Mcleod D.S.A., English D.R., Armstrong B.K., Clarke M.W., Ebeling P.R., Hartel G., Kimlin M.G., O’connell R.L., Pham H., Harris R.M.R., Van Der Pols J.C., Venn A.J., Webb P.M., Whiteman D.C. & Neale R.E. (2020). Predicting deseasonalised serum 25 hydroxy vitamin D concentrations in the D-Health Trial: an analysis using boosted regression trees. MedRxiv, 2020.08.23.20180422. https://doi.org/10.1101/2020.08.23.20180422
Here's a quick illustrated tutorial on modEvA. Read the functions' help files for full documentation. There's also a (somewhat outdated) course manual on model building with fuzzySim and model evaluation with modEvA (in Spanish)
Click here for further info on the package and its origins.
The R-Forge project summary page you can find here.