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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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Published on: October 11, 2016

Reversing desertification as a spatial resonance problem.

Yair Mau¹, Lev Haim², Ehud Meron³

¹Department of Physics, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel and Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina 27708, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|February 14, 2015

Summary

This summary is machine-generated.

Restoring desertified ecosystems requires careful landscape modulation. Partial vegetation implantation offers greater resilience to rainfall variability than complete implantation, ensuring productive vegetation restoration.

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Area of Science:

Environmental science
Ecology
Pattern formation

Background:

Desertification is a major environmental challenge in water-limited ecosystems.
Vegetation restoration often employs landscape modulations to capture water.
Existing methods may lack resilience to environmental variability.

Purpose of the Study:

To investigate the resilience of vegetation restoration patterns to rainfall variability.
To compare different strategies for spatial pattern control in ecological restoration.
To identify optimal methods for reversing desertification.

Main Methods:

Utilizing a realistic vegetation model and a simplified pattern formation model.
Analyzing the system as a spatial resonance problem.
Investigating the role of inherent spatial modes and partial vegetation implantation.

Main Results:

Simple landscape modulations with complete vegetation response show poor resilience to rainfall variability.
Less intuitive approaches using inherent spatial modes and partial implantation are highly resilient.
Both methods can achieve comparable productivity.

Conclusions:

Partial vegetation implantation is a more resilient strategy for desertification reversal.
Spatial resonance principles can guide ecological restoration in variable conditions.
Findings are applicable to various spatially forced systems near instability points.