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'RISDM': species distribution modelling from multiple data sources in R.

Authors :
Foster, Scott D.
Peel, David
Hosack, Geoffrey R.
Hoskins, Andrew
Mitchell, David J.
Proft, Kirstin
Yang, Wen‐Hsi
Uribe‐Rivera, David E
Froese, Jens G.
Source :
Ecography; Jun2024, Vol. 2024 Issue 6, p1-10, 10p
Publication Year :
2024

Abstract

Species distribution models (SDMs) are usually based on a single data type, such as presence‐only (PO), presence‐absence (PA) or abundance (AA). Results from SDMs using single sources of data will suffer from inherent biases and limitations to that data type. For example, PO data contain sampling‐bias and PA/AA data are often less expansive and more sparse. Integrated SDMs (ISDMs) combine multiple data types and have recently emerged as a way to leverage strengths and minimise weaknesses of the different data types. They pose a common (distribution) model and separate observation models for each of the data types. The 'RISDM' package for the R environment (www.r‐project.org) provides access to this modelling framework using functions for preparation, fitting, interpreting and diagnosing models. The functionality of the package is demonstrated here using synthetic data sets. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09067590
Volume :
2024
Issue :
6
Database :
Complementary Index
Journal :
Ecography
Publication Type :
Academic Journal
Accession number :
177627496
Full Text :
https://doi.org/10.1111/ecog.06964