TY - JOUR

T1 - Evaluating methods for setting thresholds for good status in marine ecosystems.

AU - McKellar, Lorna

AU - Hiddink, Jan Geert

AU - McQuatters-Gollop, Abigail

AU - Vina-Herbon, Cristina

AU - Valanko, Sebastian

AU - Chaigneau, Tomas

PY - 2025/3/3

Y1 - 2025/3/3

N2 - Estimating thresholds to distinguish between good and degraded ecosystem states is key for assessing and managing marine environments. Numerous methods are used to estimate thresholds, however there is no standardised framework to evaluate their accuracy and reliability which reduces the consistency and transparency of thresholds estimated for ‘good’ status. Statistical robustness of four methods was evaluated by varying stochastic noise, sample size, and shape of the pressure-state relationship, of simulated indicator data. Range of natural variation and statistically detectable change methods, which quantify natural variation in undisturbed reference conditions, reliably estimated status thresholds for noisy, small datasets, but thresholds were lower than what would have been estimated without noise present or with a greater sample size. Tipping points and distance to degradation methods, which estimate the point at which a system is about to reach, or has reached, a degraded state, failed to estimate thresholds or fit models that were consistent with the underlying relationship as noise increased and sample size decreased. Therefore, for small or noisy datasets, range of natural variation is most suitable to estimate ecologically meaningful, reliable, and transparent thresholds for good status, while for larger datasets with low noise levels all four methods are likely to be useful.

AB - Estimating thresholds to distinguish between good and degraded ecosystem states is key for assessing and managing marine environments. Numerous methods are used to estimate thresholds, however there is no standardised framework to evaluate their accuracy and reliability which reduces the consistency and transparency of thresholds estimated for ‘good’ status. Statistical robustness of four methods was evaluated by varying stochastic noise, sample size, and shape of the pressure-state relationship, of simulated indicator data. Range of natural variation and statistically detectable change methods, which quantify natural variation in undisturbed reference conditions, reliably estimated status thresholds for noisy, small datasets, but thresholds were lower than what would have been estimated without noise present or with a greater sample size. Tipping points and distance to degradation methods, which estimate the point at which a system is about to reach, or has reached, a degraded state, failed to estimate thresholds or fit models that were consistent with the underlying relationship as noise increased and sample size decreased. Therefore, for small or noisy datasets, range of natural variation is most suitable to estimate ecologically meaningful, reliable, and transparent thresholds for good status, while for larger datasets with low noise levels all four methods are likely to be useful.

KW - Marine Strategy Framework Directive

KW - BBNJ Agreement

KW - UK Marine Strategy

KW - Good Environmental Status

KW - Kunming-Montreal Global Biodiversity Framework

U2 - 10.1093/icesjms/fsaf019

DO - 10.1093/icesjms/fsaf019

M3 - Article

VL - 82

JO - ICES Journal of Marine Science

JF - ICES Journal of Marine Science

SN - 1054-3139

IS - 3

M1 - fsaf019

ER -