Timing recovery of ecosystems in sequential remotely sensed and simulated data

van Belzen, J.; van de Koppel, J.; van der Wal, D.; Herman, P.M.J.; Dakos, V.; Kéfi, S.; Scheffer, M.; Bouma, T.J.

doi:/10.1038/protex.2017.038

Timing recovery of ecosystems in sequential remotely sensed and simulated data

van Belzen, J.; van de Koppel, J.; van der Wal, D.; Herman, P.M.J.; Dakos, V.; Kéfi, S.; Scheffer, M.; Bouma, T.J. (2017). Timing recovery of ecosystems in sequential remotely sensed and simulated data . Protocol Exchange June 2017. https://dx.doi.org/10.1038/protex.2017.038

In: Protocol Exchange. Nature Publishing Group: London. ISSN 2043-0116; e-ISSN 2043-0116, meer

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Author keywords

disturbance; ecosystem dynamics; recovery; resilience; stability; tipping points

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van Belzen, J., meer van de Koppel, J., meer van der Wal, D., meer Herman, P.M.J., meer	Dakos, V. Kéfi, S. Scheffer, M. Bouma, T.J., meer

Abstract

The time needed for ecosystems to recover from a disturbance has been proposed as a generic indicator of ecosystem resilience. The lengthening of the recovery time with increasing stress is referred to as “Critical Slowing Down” and has been proposed as an early warning of a nearing tipping point. Hence, methodologies for measuring recovery rates and critical slowing down in remotely sensed data might provide a powerful way to synoptically asses ecosystem resilience. Here, we present a protocol using an algorithm to measure the recovery time after a disturbance from sequential spatial data. The algorithm can be applied to both empirical, e.g. remotely sensed, and simulated spatial data.

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