Understanding consequences of adaptive monitoring protocols on data consistency: application to the monitoring of giant clam densities impacted by massive mortalities in Tuamotu atolls, French Polynesia. (23rd January 2019)

• Record Type: Journal Article
• Authors: Georget, Stéphane; Van Wynsberge, Simon; Andréfouët, Serge
• Editors: Birchenough, Silvana
• Is Part Of: ICES journal of marine science. Volume 76:Number 4(2019)
• Page Start: 1062
• Page End: 1071
• Publication Date: 2019-01-23
• Subjects: belt-transect -- ecological monitoring -- fishery -- line-intersect transects -- mass mortality -- quadrats -- sampling methods -- time series -- Tridacna maxima.; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsy189
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: During long-term monitoring, protocols suitable in the initial context may have to change afterward because of unforeseen events. The outcome for management can be important if the consequences of changing protocols are not understood. In Tuamotu Archipelago atolls, French Polynesia, the density of giant clams ( Tridacna maxima ) has been monitored for 12 years, but massive mortalities and collapsing densities forced to shift from a line-intercept transects and quadrats (LIT-Q) method to a belt-transect (BT) method. We investigated with a simulation approach the conditions (density, size structure, aggregation of giant clam populations) under which the two methods provided different results. A statistical model relating the BT density to the LIT-Q density was validated using new field data acquired on the same sites with both protocols, on two atolls. The BT method usually provided higher estimates of density than the LIT-Q method, but the opposite was found for very high densities. The shape of the relationship between measurements depended on population size structure and on aggregation. Revisiting with the model the historical LIT-Q densities suggested that densities have been underestimated in the past but previously detected trends in population trajectories remained valid. The implication of these findings for management are discussed.