Optimizing cost-efficiency of long term monitoring programs by using spatially balanced sampling designs: The case of manila clams in Arcachon bay
|Author(s)||Kermorvant Claire1, Caill-Milly Nathalie2, Bru Nooelle1, D'Amico Franck1|
|Affiliation(s)||1 : Univ Pau & Pays Adour, CNRS, UPPA E2S, Lab Math & Leurs Applicat Pau MIRA,UMR5142, F-64600 Anglet, France.
2 : IFREMER, Lab Environm Ressources Areachon, PDG, ODE,LITTORAL,LERAR,FED MIRA 4155, F-64600 Anglet, France.
|Source||Ecological Informatics (1574-9541) (Elsevier Science Bv), 2019-01 , Vol. 49 , P. 32-39|
|Keyword(s)||Arcachon bay, Balanced acceptance sampling, Spatial sampling, Spatial variation, Temporal variation, Virtual ecology|
Funding lake is one major issue in ecology, in particular at local scale. It is known that sustainable management of a natural population requires a good understanding of its functioning, itself dependent on a good long term monitoring program. Such programs are usually very difficult to implement, especially for resources characterized by high spatio-temporal variation in their distribution, resulting in a trade off between efficiency and costs. Today, thanks to rapidly evolving statistical theory, new survey designs are developed, some with the characteristic of well balancing samples in the study area. This paper aims at demonstrating that theses advanced sampling designs perform better than the usual ones for long term monitoring program of local resources, with the added benefices of saving money and also increasing results accuracy. To prove it, and for it high spatio-temporal variation in it distribution, we choose the example of Manila clam's stock monitoring in Arcachon bay. This stock is under high scrutiny and last campaigns could not be done because of lack of funding (at least 50,000€/survey). We use a simulation study based on real data to assess and compare performances of news and older sampling designs on this survey. Three sampling designs are tested in both of the 6 past monitoring campaigns data and we estimate the cost of their application in the field. Selected sampling designs are: 1 - simple random sampling (SRS - the one used in the past years of this monitoring program), 2 - generalized tessellation sampling (GRTS - a recent spatially balanced sampling design known for its high performance) and, 3 - balanced acceptance sampling design (BAS - a newly developed spatially balanced sampling design, never tested yet in a real population). We first confirm that the two spatially balanced sampling designs perform better than simple random sampling. Both of the advanced sampling designs perform equally and allow achieving same accuracy in the results with almost half sampling intensity than SRS. This makes them so cost-effective that 30% of each campaign price could be saved if they were used. Moreover, the three sampling designs need a constant sample size thought years to achieve a fixed accuracy in results. This will permit us to fix one sample size that could be done for all future campaigns; and this, despite the existence of spatial and temporal variations in clam's distribution.