Effect of Non-local Grazing on Dry-land Vegetation Dynamics
Mrinal Kanti Pal, Swarup Poria

TL;DR
This paper investigates how non-local, spatially weighted grazing impacts dry-land vegetation patterns and ecosystem resilience, revealing that heterogeneity in grazing significantly influences system dynamics and stability.
Contribution
It introduces a non-local grazing model based on spatially weighted vegetation density, extending the Klausmeier plant-water model to better reflect real herbivory effects.
Findings
Non-local grazing enhances ecosystem resilience to aridity.
Spatial heterogeneity influences vegetation pattern stability.
Multiple stable states depend on grazing history.
Abstract
Dry-land ecosystem has turned into a matter of grave concern, due to growing threat of land degradation and bioproductivity-loss. Self-organized vegetation patterns are a remarkable characteristic of these ecosystems; apart from being visually captivating, patterns modulate the system-response to increasing environmental stress. Empirical studies hinted that herbivory is one the key regulatory mechanism behind pattern formation and overall ecosystem functioning. However most of the mathematical models have taken a mean-field strategy to grazing; foraging has been considered to be independent of spatial distribution of vegetation. To this end, an extended version of the celebrated plant-water model due to Klausmeier, has been taken as the base here. To encompass the effect of heterogeneous vegetation distribution on foraging intensity and subsequent impact on entire ecosystem, grazing is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEcosystem dynamics and resilience · Evolutionary Game Theory and Cooperation · Ecology and Vegetation Dynamics Studies
MethodsBalanced Selection
