[MARMAM] New paper on creating ensembles of SDM predictions
swoodman213 at gmail.com
Thu Oct 17 15:45:52 PDT 2019
Dear MARMAM community,
On behalf of my co-authors, I am pleased to announce the following
publication in Methods in Ecology and Evolution:
Woodman, S.M., Forney, K.A., Becker, E.A., DeAngelis, M.L., Hazen, E.L.,
Palacios, D.M., Redfern, J.V. (2019). eSDM: A tool for creating and
exploring ensembles of predictions from species distribution and abundance
models. Methods in Ecology and Evolution. 2019;00:1-11.
The paper is open access and is available at
The abstract is below, while more information about the eSDM R package and
accompanying GUI can be found at https://github.com/smwoodman/eSDM (the
package is also on CRAN).
1. Species distribution models (SDMs) are a valuable statistical approach
for both understanding species distributions and identifying potential
impacts of environmental changes or management decisions to species, but
multiple SDMs for the same species in a region can create confusion in
2. One solution is to create ensembles (i.e. combinations) of predictions
from existing SDMs. However, creating ensembles can be challenging if the
predictions were made at different spatial resolutions, using different
data sources, or with different prediction value types (e.g. abundance and
probability of occurrence).
3. We present eSDM, an R package that allows users to create an ensemble of
SDM predictions overlaid onto a single base geometry. These predictions can
be evaluated (e.g. through among‐model uncertainty or AUC, TSS and RMSE
metrics), mapped, and exported. eSDM includes a built‐in GUI created using
the R package shiny, which makes the package accessible to non‐R users.
4. We provide an overview of eSDM functionality and use eSDM to create an
ensemble of predictions from three blue whale (*Balaenoptera musculus*) SDMs
for the California Current Ecosystem.
Harvey Mudd College 2016
Mathematical and Computational Biology
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