Title

Complex Interactions Among Successional Trajectories and Climate Govern Spatial Resilience After Severe Windstorms in Central Wisconsin, USA

Published In

Landscape Ecology

Document Type

Citation

Publication Date

12-1-2019

Abstract

Context

Resilience is a concept central to the field of ecology, but our understanding of resilience is not sufficient to predict when and where large changes in species composition might occur following disturbances, particularly under climate change.

Objectives

Our objective was to estimate how wind disturbance shapes landscape-level patterns of engineering resilience, defined as the recovery of total biomass and species composition after a windstorm, under climate change in central Wisconsin.

Methods

We used a spatially-explicit, forest simulation model (LANDIS-II) to simulate how windstorms and climate change affect forest succession and used boosted regression tree analysis to isolate the important drivers of resilience.

Results

At mid-century, biomass fully recovered to current conditions, but neither biomass nor species composition completely recovered at the end of the century. As expected, resilience was lower in the south, but by the end of the century, resilience was low throughout the landscape. Disturbance and species’ characteristics (e.g., the amount of area disturbed and the number of species) explained half of the variation in resilience, while temperature and soil moisture comprised only 17% collectively.

Conclusions

Our results illustrate substantial spatial patterns of resilience at landscape scales, while documenting the potential for overall declines in resilience through time. Species diversity and windstorm size were far more important than temperature and soil moisture in driving long term trends in resilience. Finally, our research highlights the utility of using machine learning (e.g., boosted regression trees) to discern the underlying mechanisms of landscape-scale processes when using complex spatially-interactive and non-deterministic simulation models.

Description

© 2019 Springer Nature Switzerland AG.

DOI

10.1007/s10980-019-00929-1

Persistent Identifier

https://archives.pdx.edu/ds/psu/30618

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