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Related Experiment Video

Updated: Apr 1, 2026

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

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

Published on: October 11, 2016

Spatial predictions at the community level: from current approaches to future frameworks.

Manuela D'Amen¹, Carsten Rahbek², Niklaus E Zimmermann³

¹Department of Ecology and Evolution (DEE), University of Lausanne, Biophore, CH-1015, Lausanne, Switzerland.

Biological Reviews of the Cambridge Philosophical Society

|October 2, 2015

Summary

This summary is machine-generated.

Ecological models predict species richness and community patterns by integrating historical, environmental, biotic, and stochastic drivers. New integrative frameworks offer improved predictions for conservation under climate change.

Keywords:

biotic interactions dispersal environmental filter evolutionary forces modelling framework species pool stochasticity

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Last Updated: Apr 1, 2026

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Area of Science:

Ecology
Ecological Modeling
Biodiversity Science

Background:

Understanding ecological patterns and predicting changes under varying conditions is a fundamental research goal.
Modeling species assemblages is crucial for predicting spatial patterns of species richness and community attributes across scales.
Existing modeling approaches vary in their emphasis on community structuring processes and predictive goals.

Purpose of the Study:

To review and synthesize current approaches for modeling spatially explicit species assemblages.
To examine different modeling methods based on theoretical foundations, included drivers, data sources, and outputs.
To highlight novelties, limitations, and future directions in community-level modeling, especially for conservation under climate change.

Main Methods:

Review of existing literature on ecological modeling and community assembly theory.
Categorization of community assembly drivers into historical/evolutionary, environmental, biotic, and stochastic factors.
Analysis of various modeling approaches, focusing on their strengths, weaknesses, and applicability.

Main Results:

Four main categories of drivers (historical, environmental, biotic, stochastic) influence community assembly.
Different modeling approaches exist, each with specific theoretical bases, data requirements, and outputs.
Integrative frameworks are emerging to incorporate multiple drivers for more comprehensive predictions.

Conclusions:

Accurate prediction of species richness and community composition requires integrating diverse drivers.
Novel integrative frameworks show promise for advancing community-level modeling and conservation efforts.
Addressing shortcomings and extending existing methods is crucial for future progress in ecological prediction.