Rates at which a community recovers after disturbance, or its resilience, can be accelerated by increased net primary productivity and recolonization dynamics such as recruitment. These mechanisms can vary across biogeographic gradients, such as latitude, suggesting that biogeography is likely important to predicting resilience. To test whether community resilience, informed by functional and compositional recovery, hinges on geographic location, we employed a standardized replicated experiment on marine invertebrate communities across four regions from the tropics to the subarctic zone. Communities assembled naturally on standardized substrate while experiencing distinct levels of biomass removal (no removal, low disturbance, and high disturbance), which opened space for new colonizers, thereby providing a pulse of limited resource to these communities. We then quantified functional (space occupancy and biomass) and compositional recovery from these repeated pulse disturbances across two community assembly timescales (early and late at 3 and 12 months, respectively). We documented latitudinal variation in resilience across 47° latitude, where speed of functional recovery was higher toward lower latitudes yet incomplete at late assembly in the tropics and subtropics. The degree of functional recovery did not coincide with compositional recovery, and regional differences in recruitment and growth likely contributed to functional recovery in these communities. While biogeographic variation in community resilience has been predicted, our results are among the first to examine functional and compositional recovery from disturbance in a single large‐scale standardized experiment.
Understanding post-disaster population recovery patterns
Despite the rising importance of enhancing community resilience to disasters, our understandings on when, how and why communities are able to recover from such extreme events are limited. Here, we study the macroscopic population recovery patterns in disaster affected regions, by observing human mobility trajectories of over 1.9 million mobile phone users across three countries before, during and after five major disasters. We find that, despite the diversity in socio-economic characteristics among the affected regions and the types of hazards, population recovery trends after significant displacement resemble similar patterns after all five disasters. Moreover, the heterogeneity in initial and long-term displacement rates across communities in the three countries were explained by a set of key common factors, including the community’s median income level, population, housing damage rates and the connectedness to other cities. Such insights discovered from large-scale empirical data could assist policymaking in various disciplines for developing community resilience to disasters.
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- Award ID(s):
- 1638311
- NSF-PAR ID:
- 10204513
- Date Published:
- Journal Name:
- Journal of The Royal Society Interface
- Volume:
- 17
- Issue:
- 163
- ISSN:
- 1742-5689
- Page Range / eLocation ID:
- 20190532
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Objective Policy-makers and practitioners have a need to assess community resilience in disasters. Prior efforts conflated resilience with community functioning, combined resistance and recovery (the components of resilience), and relied on a static model for what is inherently a dynamic process. We sought to develop linked conceptual and computational models of community functioning and resilience after a disaster. Methods We developed a system dynamics computational model that predicts community functioning after a disaster. The computational model outputted the time course of community functioning before, during, and after a disaster, which was used to calculate resistance, recovery, and resilience for all US counties. Results The conceptual model explicitly separated resilience from community functioning and identified all key components for each, which were translated into a system dynamics computational model with connections and feedbacks. The components were represented by publicly available measures at the county level. Baseline community functioning, resistance, recovery, and resilience evidenced a range of values and geographic clustering, consistent with hypotheses based on the disaster literature. Conclusions The work is transparent, motivates ongoing refinements, and identifies areas for improved measurements. After validation, such a model can be used to identify effective investments to enhance community resilience. ( Disaster Med Public Health Preparedness . 2018;12:127–137)more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1098/rsif.2019.0532
