Estimating abundance indices of juvenile fish in estuaries using Geostatistics: An example of European sea bass (Dicentrarchus labrax)

Type Article
Date 2022-05
Language English
Author(s) Roy AmedeeORCID, Lebigre ChristopheORCID, Drogou Mickael, Woillez MathieuORCID
Affiliation(s) DECOD (Ecosystem Dynamics and Sustainability), IFREMER, INRAE, Institut Agro, Plouzané, France
Source Estuarine Coastal And Shelf Science (0272-7714) (Elsevier BV), 2022-05 , Vol. 269 , P. 107799 (13p.)
DOI 10.1016/j.ecss.2022.107799
WOS© Times Cited 1
Keyword(s) Pre-recruitment indices, Transitive kriging, Intrinsic kriging, Kriging with external drift, Tidal dynamics, Seabass

Estuaries play a fundamental role in the renewal of fisheries resources, as they hold nurseries for many juvenile fish species. Estimating juveniles’ abundance in estuaries is therefore key to improve stock assessment models, anticipate future recruitment and prevent crises related to biomass collapse. While geostatistical methods have been widely used in fisheries science to estimate species’ abundance during offshore scientific surveys, difficulties arise when using these methods in estuaries. Indeed, these ecosystems are characterized by their irregular and often non-convex morphology, their environmental gradients (salinity, depth), and their tidal dynamics which question the validity of the hypothesis of second-order stationarity, fundamental to the theory of intrinsic geostatistics. Therefore, we tested the performance of different geostatistical methods to account for the complexity of these ecosystems and quantify robust indices of abundance adapted to estuaries. We used density data of juvenile sea bass (Dicentrarchus labrax) sampled with demersal trawls in the Loire River collected over three consecutive years and tested a metric space for which the distance along the estuary is considered. We took into account the non-stationarity of densities with either a transitive approach or an intrinsic approach with spatio-temporal external drifts, which takes into account the effects of tides and environmental gradients. These geostatistical methods allowed us to produce densities distribution maps and had substantially greater predictive capabilities than the stratified random estimator (classical reference estimator). However, geostatistical methods consistently had larger CVs than the stratified random estimator because the latter ignores the spatio-temporal distribution of sampling points leading to uncertainties underestimates and hence overly optimistic confidence intervals. The use of geostatistically computed abundance indices in an assessment model appears to be a conservative approach, whose uncertainties would allow a more robust adjustment trade-off between different indices when estimating recruitment in estuaries.

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