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Related Experiment Video

Updated: Oct 24, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

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Published on: October 11, 2016

Predicting species distributions and community composition using satellite remote sensing predictors.

Jesús N Pinto-Ledezma¹, Jeannine Cavender-Bares²

¹Department of Ecology, Evolution and Behavior, University of Minnesota, 1479 Gortner Ave, Saint Paul, MN, 55108, USA. jpintole@umn.edu.

Scientific Reports

|August 13, 2021

Summary

Next-generation biodiversity models using stacked species distribution models (S-SDMs) show promise for predicting species composition at macroecological scales. However, these models do not accurately predict species at finer ecological scales.

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Area of Science:

Ecological modeling
Biodiversity informatics
Remote sensing applications

Background:

Biodiversity is rapidly changing due to climate change and human activities.
Accurate predictions of species composition and diversity are crucial for conservation and management.

Purpose of the Study:

To construct and evaluate next-generation biodiversity models using stacked species distribution models (S-SDMs) with satellite remote sensing data.
To assess the performance of S-SDMs in predicting species composition and diversity at various spatial scales.

Main Methods:

Utilized satellite remote sensing products as covariates.
Constructed stacked species distribution models (S-SDMs) within a Bayesian framework.
Assessed model performance using oak assemblages from the National Ecological Observatory Network (NEON).

Main Results:

Stacked species distribution models (S-SDMs) did not improve predictions when constraints were applied.
These models failed to accurately predict species composition at plot and community scales (400 m²).
Reasonable predictions of species identity were achieved at macroecological scales (NEON sites, ~27 km²).

Conclusions:

Next-generation biodiversity models using S-SDMs show potential for macroecological predictions but lack accuracy at finer scales.
Future research should explore integrating S-SDMs with image spectroscopy for improved biodiversity monitoring.
Findings offer insights for enhancing biodiversity prediction accuracy across diverse spatial scales globally.