Published January 23, 2025 | Version 1

Potential spatial mismatches between marine predators and their prey in the Southern Hemisphere in response to climate change

  • 1. ROR icon Institut de Ciències del Mar
  • 2. EDMO icon Department of Evolutionary Biology, Ecology and Environmental Science, Barcelona University
  • 3. ROR icon Fundació Clínic per a la Recerca Biomèdica
  • 4. ROR icon Universitat de València
  • 5. EDMO icon Centre for Ecology and Conservation, University of Exeter
  • 6. ROR icon Ecopath International Initiative

Description

This repository contains various information related to the analysis of species distribution models conducted in this work. It includes the following scripts and datasets:

1. Scripts classified in the following folders:

  • 1. Presences and Absences: Scripts for creating pseudo-absence points for each study area (other data is also included, e.g., bathymetry).
  • 2. Environmental Data: Scripts for extracting, cleaning, and preparing environmental data for analysis.
  • 3. Fitting: Scripts used to fit species distribution models, calculate the cutoff, and analyze spatial autocorrelation.
  • 4. Predict: Scripts for making predictions based on the fitted models.

2. Datasets:

  • Occurrence Data: The datasets containing species occurrence records after cleaning.
    • Bas_etal_2025/1. Presences and absences/1. NZAU/CSV_ocurrences_NZAU/

    • Bas_etal_2025/1. Presences and absences/2. South_America/CSV_ocurrences_South_America/

    • Bas_etal_2025/1. Presences and absences/3. Southern_Africa/CSV_ocurrences_Southern_Africa/

  • Pseudo-absence Data: The datasets created after the generation of pseudo-absences for modeling species distributions.

    • Bas_etal_2025/1. Presences and absences/1. NZAU/CSV_pseudoabsences_NZAU/

    • Bas_etal_2025/1. Presences and absences/2. South_America/CSV_pseudoabsences_South_America/

    • Bas_etal_2025/1. Presences and absences/3. Southern_Africa/CSV_pseudoabsences_Southern_Africa/

  • Environmental Data: The datasets consisting of the extraction of the environmental variables for each occurrence and pseudo-absence.

    • Bas_etal_2025/2. Environmental data/Dataset after 6. extract_environmentalData_SH_SDM/NZAU/

    • Bas_etal_2025/2. Environmental data/Dataset after 6. extract_environmentalData_SH_SDM/South_America/

    • Bas_etal_2025/2. Environmental data/Dataset after 6. extract_environmentalData_SH_SDM/Southern_Africa/

Note: NZAU or australiaNZ refer to Australia and New Zealand study area; Southern_Africa, southAfrica or SAf refer to Southern Africa study area; and South_America, southAmerica or SAm refer to South America study area. Species can be found in various ways, for example, Arctocephalus australis can be found as A. australis or AAUS (see Table 1 from the main work for all species included).

These materials are intended to facilitate the reproducibility of the analysis presented in the associated publication.

Files

Bas_etal_2025.zip

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Additional details

Software

Programming language
R