Data for improving SDM transferability of global freshwater invaders with non-conservative niches
Data files
Mar 31, 2026 version files 15.13 GB
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Bio1-Bio19_for_1800-1890.zip
7.33 GB
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Bio1-Bio19_for_1900-1990.zip
7.25 GB
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Bio1-Bio19_for_1990-2020.zip
179.83 MB
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Biodiversity.asc
38.73 MB
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Code_for_starting_Wallace.docx
13.75 KB
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Code_for_starting_Wallace.R
236 B
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Distance_to_water.asc
49.66 MB
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Elevation.asc
39.25 MB
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Freashwater_area.asc
143.84 MB
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Human_footprint.asc
24.08 MB
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README.md
2.68 KB
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Soil_property.asc
37.78 MB
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Water_direction.asc
39.25 MB
Abstract
Species distribution models (SDMs) are powerful tools for addressing global ecological challenges. Many species, particularly invaders, exhibit dynamic niches in novel environments, which reduces SDM transferability, raising concerns about prediction accuracy. Improving model transferability and validating predictions with robust methods and independent occurrence data are essential to address these issues. However, the extent of transferability improvement required to achieve reliable predictions remains unclear. This work aims to quantitatively assess how niche dynamics influence model transferability and prediction accuracy, using freshwater invasive golden, zebra, and quagga mussels as a case study. We categorized mussel occurrence data based on invasion histories and quantified niche dynamics using bioclimatic variables. We analyzed the improvements in SDM transferability by optimizing occurrence data and various environmental variables. Finally, we established the relationship between niche dynamics and transferability-related indices, identifying their threshold ranges necessary for accurate distribution predictions. Zebra mussels maintained high niche stability during invasions, while golden and quagga mussels exhibited niche expansion and unfilling. Incorporating invasive-range data and diverse environmental variables improved model transferability and prediction accuracy for zebra mussels but was less effective for the other two species. We found positive correlations between SDM transferability and niche stability and negative correlations with niche expansion and unfilling. We also identified potential minimum thresholds for accurate mussel distribution predictions: a Continuous Boyce Index of 0.900, a Linear Correlation of Suitable Habitat Area of 0.800, and a Schoener’s D of 0.200. For freshwater invaders with non-conservative niches, accurate predictions can be achieved by improving transferability when niche dynamics meet specific thresholds. Our findings therefore offer strategies for refining SDMs to improve prediction accuracy for species with dynamic niches and provide valuable insights for managing freshwater ecosystems in the context of global change.
Dataset DOI: 10.5061/dryad.rbnzs7hr7
Description of the data and file structure
We selected environmental variables—including bioclimatic, human-related, geographical, hydrological, and biological variables—based on their perceived relevance to freshwater mussel distributions. A total of 19 bioclimatic variables (Bio1–Bio19, 2.5 arc-minutes), covering temperature and precipitation, were sourced from WorldClim v2.1, Climate Data Store, and CHELSA-TraCE21k, reflecting conditions from 1800 to 2020. Human-related variables included the Human Footprint Index (1 km resolution) and Distance to Freshwater Body (5 arc-minutes), sourced from the Socioeconomic Data and Applications Center and relevant literature. Geographical variables comprised Harmonised Soil Property (30 arc-seconds resolution) from a literature and Water Body Elevation (2.5 arc-minutes) from WorldClim v2.1. Hydrological variables were derived from Flow Direction (5 arc-minutes resolution from HydroSHEDS v2) and Land and Water Area (2.5 arc-minutes from SEDAC v4.0, https://sedac.ciesin.columbia.edu). Finally, a biological variable, Global Biodiversity (2.5 arc-minutes), was obtained from the literature. All variables were organized as raster files, projected onto the geographic coordinate reference system GCS_WGS_1984 (EPSG: 4326), and standardized to 2.5 arc-minutes (5 km × 5 km).
Files and variables
File: Code_for_starting_Wallace.docx
Description: Code for starting Wallace Platform
File: Code_for_starting_Wallace.R
Description: Code for starting Wallace Platform in plain text
File: Biodiversity.asc
Description: Environmental variable
File: Elevation.asc
Description: Environmental variable
File: Distance_to_water.asc
Description: Environmental variable
File: Freashwater_area.asc
Description: Environmental variable
File: Human_footprint.asc
Description: Environmental variable
File: Soil_property.asc
Description: Environmental variable
File: Water_direction.asc
Description: Environmental variable
File: Bio1-Bio19_for_1990-2020.zip
Description: 19 Bioclimatic variables from 1990 to 2020
File: Bio1-Bio19_for_1800-1890.zip
Description: 19 Bioclimatic variables from 1800 to 1890
File: Bio1-Bio19_for_1900-1990.zip
Description: 19 Bioclimatic variables from 1900 to 1990
Code/software
The R code for starting Wallace platform is available within this submission.
