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Related Experiment Video

Updated: Mar 14, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Towards Process-based Range Modeling of Many Species.

Margaret E K Evans1, Cory Merow2, Sydne Record3

  • 1Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ 85721, USA; Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA.

Trends in Ecology & Evolution
|September 25, 2016
PubMed
Summary

Process-based range models can be created for many species using hierarchical and inverse modeling. This approach integrates diverse data, fills gaps, and models across scales, advancing biodiversity science.

Keywords:
data fusionecological forecastinghierarchical modelinverse modelingspecies distribution models

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Area of Science:

  • Biodiversity Science
  • Ecological Modeling
  • Global Change Biology

Background:

  • Forecasting species' geographic distributions under global change is crucial.
  • Current approaches often force a choice between correlative models (many species) and process-based models (few species).

Purpose of the Study:

  • To propose a scalable framework for generating process-based species range models for numerous species.
  • To demonstrate how hierarchical and inverse modeling can overcome limitations of current methods.

Main Methods:

  • Reviewing statistical ecology and population/range modeling literature.
  • Utilizing hierarchical and inverse modeling techniques to "borrow strength" across species.
  • Integrating diverse ecological datasets and addressing data gaps.

Main Results:

  • Identified opportunities to develop process-based range models for a wider array of species.
  • Demonstrated the feasibility of fusing diverse data sets and modeling across biological and spatial scales.
  • Highlighted existing large ecological datasets suitable for these advanced modeling approaches.

Conclusions:

  • Hierarchical and inverse modeling offer a powerful, scalable solution for process-based species distribution modeling.
  • This integrated approach can significantly advance our ability to forecast biodiversity responses to global change.
  • The development of such models is timely, with relevant data and organisms available.