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

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Benchmarking novel approaches for modelling species range dynamics.

Damaris Zurell¹, Wilfried Thuiller^2,3, Jörn Pagel⁴

¹Dynamic Macroecology, Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903, Birmensdorf, Switzerland.

Global Change Biology

|February 13, 2016

Summary

Biodiversity loss from climate change requires better predictive models. Dynamic range models improve climate change projections, but no single model excels across all scenarios, highlighting the need for further development.

Keywords:

climate change demographic models dispersal population viability prediction simulated data species distribution models virtual ecologist approach

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Area of Science:

Ecology
Climate Change Biology
Computational Biology

Background:

Biodiversity loss is a major global challenge, exacerbated by climate change.
Accurate modeling of species' range dynamics and extinction risks is crucial for conservation.
Existing correlative Species Distribution Models (SDMs) have limitations in predicting responses to climate change.

Purpose of the Study:

To benchmark the performance of various range (dynamic) models using simulated data.
To evaluate the impact of demographic and community processes on model accuracy.
To compare classical SDMs with dynamic range models (DRMs) and SDM hybrids.

Main Methods:

Utilized process-based, simulated data for benchmarking five range models: classical SDMs, SDM hybrids (with dispersal or population dynamics), and a hierarchical Bayesian DRM.
Assessed model performance under current climate and projected climate change scenarios.
Investigated the influence of demographic and community processes on predictive accuracy.

Main Results:

Dynamic range models (DRMs) showed marginally better performance under current climate.
Under climate change, predictive performance varied, with no single model consistently outperforming others.
All range dynamic models significantly improved predictions compared to correlative SDMs under climate change.
Population dynamic models provided reasonable extinction risk predictions.
SDM hybrids incorporating dispersal were reliable when complex demographic/community processes were simulated.
Model structural decisions significantly impact accuracy; prior system knowledge can reduce uncertainty.

Conclusions:

Dynamic approaches are valuable for modeling species' responses to climate change.
Further improvements in model development and data are needed for robust projections.
Combining multiple models and incorporating system knowledge can enhance accuracy and operationalize projections for more species.