megaSDM: integrating dispersal and time‐step analyses into species distribution models

Abstract

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.

Copepod functional traits and groups show divergent biogeographies in the global ocean

“…4). This uncertainty calls for more studies combining distribution models with dispersal rates and limitations (D’Amen et al, 2018; Shipley et al, 2021) to study the link between zooplankton functional traits and the “seascape” (Sommeria-Klein et al, 2021; Richter et al, 2020).…”

Projecting Future Climate Change-Mediated Impacts in Three Paralytic Shellfish Toxins-Producing Dinoflagellate Species

“…The following procedures were conducted using the package megaSDM [ 79 ], which allows the efficient synthesis of SDMs for several species, time periods, and climate scenarios, presenting a simple and intuitive workflow. Since megaSDM relies on MaxEnt modeling, all the predictor raster layers were reprojected to an equal-area projection (i.e., specifically, the cylindrical equal-area projection: “+proj = cea + lat_ts = 0 + lon_0 = 0 + x_0 = 0 + y_0 = 0 + datum = WGS84 + no_defs”, using the nearest neighbour method).…”

“…MaxEnt randomly samples cells from the available geographic space, implicitly assuming cells of equal area in the entire extent of each predictor layer, which bears the need for reprojecting the layers a priori [ 80 ]. At this point, the functions ‘TrainStudyEnv’ and ‘PredictEnv’ were employed to manipulate and standardize the present and future time periods’ input environmental data, by re-projecting, clipping, and resampling the raster predictors [ 79 ]. The former function also defines the training area, where the occurrence and background points are located, the study area where the model will be projected, and the habitat suitability predicted.…”

Korean endemic species no more: on the occurrence of Pelophylax chosenicus in China

“…To analyse the landscape features used by P. chosenicus , we combined the data obtained from our field surveys with records from the Global Biodiversity Information Facility (GBIF; DOI: 10.15468/dl.6kvymf. ), collected through the megaSDM package in R (Shipley et al 2022), by running the ‘OccurrenceCollection’ function. The combined datasets resulted in a total of 403 occurrence points (Figure 1).…”