Understanding the adaptive capacity of ecosystems to cope with change is crucial to management. However, unclear and often confusing definitions of adaptive capacity make application of this concept difficult. In this paper, we revisit definitions of adaptive capacity and operationalize the concept. We define adaptive capacity as the latent potential of an ecosystem to alter resilience in response to change. We present testable hypotheses to evaluate complementary attributes of adaptive capacity that may help further clarify the components and relevance of the concept. Adaptive sampling, inference and modeling can reduce key uncertainties incrementally over time and increase learning about adaptive capacity. Such improvements are needed because uncertainty about global change and its effect on the capacity of ecosystems to adapt to social and ecological change is high.
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Adaptive Metrics for Adaptive Samples
We generalize the local-feature size definition of adaptive sampling used in surface reconstruction to relate it to an alternative metric on Euclidean space. In the new metric, adaptive samples become uniform samples, making it simpler both to give adaptive sampling versions of homological inference results and to prove topological guarantees using the critical points theory of distance functions. This ultimately leads to an algorithm for homology inference from samples whose spacing depends on their distance to a discrete representation of the complement space.
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- Award ID(s):
- 2017980
- NSF-PAR ID:
- 10211933
- Date Published:
- Journal Name:
- Algorithms
- Volume:
- 13
- Issue:
- 8
- ISSN:
- 1999-4893
- Page Range / eLocation ID:
- 200
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/a13080200
