megaSDM: Integrating dispersal and time-step analyses into species distribution models
Open accessAbstract
Understanding how species ranges shift as climates rapidly change informs us how to effectively conserve vulnerable species. Species distribution models (SDMs) are an important method for examining these range shifts. The tools for performing SDMs are ever improving. Here, we present the megaSDM R package. This package facilitates realistic spatiotemporal SDM analyses by incorporating dispersal probabilities, creating time‐step maps of range change dynamics and efficiently handling large datasets and computationally intensive environmental subsampling techniques. Provided a list of species and environmental data, megaSDM synthesizes GIS processing, subsampling methods, MaxEnt modelling, dispersal rate restrictions and additional statistical tools to create a variety of outputs for each species, time period and climate scenario requested. For each of these, megaSDM generates a series of distribution maps and outputs visual representations of statistical data. megaSDM offers several advantages over other commonly used SDM tools. First, many of the functions in megaSDM natively implement parallelization, enabling the package to handle large amounts of data efficiently without the need for additional coding. megaSDM also implements environmental subsampling of occurrences, making the technique broadly available in a way that was not possible before due to computational considerations. Uniquely, megaSDM generates maps showing the expansion and contraction of a species range across all considered time periods (time‐maps), and constrains both presence/absence and continuous suitability maps of species ranges according to species‐specific dispersal constraints. The user can then directly compare non‐dispersal and dispersal‐limited distribution predictions. This paper discusses the unique features and highlights of megaSDM, describes the structure of the package and demonstrates the package's features and the model flow through examples.
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The ensemble of replicate maps used the median value of each pixel, reducing the effect of outliers.
“The same function then projected all models onto the speci fi ed future environments, across all RCP scenarios, and created the ensemble of all replicate maps using the median value of each pixel, thus reducing the effect of outliers (Arau ́ jo and New, 2007; Shipley et al., 2022).”
From Projecting future climate change impacts on the distribution of the ‘Octopus vulgaris species complex’ (2022) · cites this paper as Shipley et al., 2022
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“In short, the training area, where the occurrence and background points are located, and the study area, where the model will be projected, were defined using the functions ‘TrainStudyEnv’ and ‘PredictEnv’, which take specific raster stacks and manipulates them to standardize the present and future period’s environmen…”
BackgroundMethodInfluential
- Authors
- Shipley, B. R., Bach, R., Do, Y., Strathearn, H., McGuire, J. L., & Dilkina, B.
- Affiliations
- Georgia Institute of Technology (US)
- Purdue University West Lafayette (US)
- University of Southern California (US)
- Corresponding author
- Shipley, Benjamin R.
- Venue
- Ecography
- Publisher
- Wiley
- Published
- 2021-10-20
- Volume
- 2022
- Issue
- 1
- ISSN
- 0906-7590 (ISSN-L)
- 1600-0587
- OpenAlex
- W3206367002
- Cited by
- 80
- References
- 63
- Citations by year
- 2026: 19 · 2025: 26 · 2024: 15 · 2023: 10 · 2022: 10
- FWCI
- 37.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Ecography (publishedVersion, cc-by)
- Funders
- National Science Foundation (1655898, 1945013, 1763108)
- U.S. Department of Agriculture
- National Institute of Food and Agriculture (2016-67032-24986)
- Topics
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Keywords
- species distribution modeling, dispersal probability, range shifts, climate change, MaxEnt, dispersal limitation, parallel computing, R package
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