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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
Published on: October 11, 2016
Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges
Michael Kearney1, Warren Porter
1Department of Zoology, The University of Melbourne, Melbourne, Vic. 3010, Australia. mrke@unimelb.edu.au
Ecology Letters
|March 19, 2009
Summary
Mechanistic species distribution models (SDMs) integrate biophysical ecology to predict species ranges. This approach offers more robust predictions for climate change and invasions than correlative SDMs.
Area of Science:
- Ecology
- Biophysics
- Biogeography
Background:
- Species distribution models (SDMs) are crucial ecological tools for mapping species' ranges and habitat suitability.
- Current correlative SDMs statistically link species occurrences with environmental data.
- There is a need for models that incorporate mechanistic understanding of species-environment interactions.
Purpose of the Study:
- To review the principles of biophysical ecology for developing mechanistic SDMs.
- To demonstrate the application of physiologically based SDMs across diverse organisms and environments.
- To highlight the potential of integrating mechanistic and correlative SDM approaches.
Main Methods:
- Linking spatial data with physiological responses and constraints of organisms using biophysical ecology principles.
- Developing physiologically based species distribution models (SDMs).
- Comparing mechanistic SDMs with traditional correlative approaches.
Main Results:
- Mechanistic SDMs provide a physiologically grounded view of the fundamental niche, mappable to infer range constraints.
- Physiologically based SDMs can be developed for various organisms and environmental settings.
- Mechanistic SDMs offer distinct advantages and disadvantages compared to correlative models.
Conclusions:
- Integrating biophysical principles into SDMs enhances predictions of species' fundamental niches and range limits.
- Mechanistic SDMs are vital for robustly predicting range shifts under novel conditions like climate change and invasions.
- Future research should explore the integration of mechanistic and correlative SDM approaches for improved ecological forecasting.
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