A Bayesian Approach to Carrying Capacity Estimate: The Case of Greek Coastal Cage Aquaculture

A Bayesian Approach to Carrying Capacity Estimate: The Case of Greek Coastal Cage Aquaculture

The estimation of the carrying capacity (CC) is a fundamental process in integrated environmental management, policy making, and decision making. Aquaculture carrying capacity has been studied since the 1960s to allow estimation of the production limits of aquaculture projects and, hence, their maxi...

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Bibliographic Details
Published in:Journal of marine science and engineering Vol. 10; no. 7; p. 940
Main Authors: Conides, Alexis, Zoulias, Theodoros, Pavlidou, Alexandra, Zachioti, Panagiota, Androni, Afroditi, Kabouri, Georgia, Rouselaki, Eleni, Konstantinopoulou, Aggeliki, Pagou, Kaliopi, Klaoudatos, Dimitris
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-07-2022
Subjects:
Algorithms
Aquaculture
Bayesian analysis
Bayesian theory
Cages
Carrying capacity
Chlorophyll
Chlorophyll a
coastal cage aquaculture
Coastal zone
coastal zone management
Coasts
Decision making
Economics
Environmental economics
Environmental management
Environmental quality
Eutrophication
Farms
Fish
Fish culture
Fish farms
Fisheries
Geomorphology
Growth models
Legislation
Marine environment
Nitrogen
Nutrients
Particulate matter
Phosphorus
Polyculture (aquaculture)
Population
Probability theory
Salinity
Salinity effects
Seawater
Silicates
Silicon
Suspended particulate matter
Trophic state
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Summary:The estimation of the carrying capacity (CC) is a fundamental process in integrated environmental management, policy making, and decision making. Aquaculture carrying capacity has been studied since the 1960s to allow estimation of the production limits of aquaculture projects and, hence, their maximum economic performance within sustainable limits for the local environment. One major drawback of these approaches is that they can provide CC estimates after a fish farm is installed and operates in a certain location (ex post approaches). This paper approaches the estimation of CC using a Bayesian/CHAID model of profiling information on the environmental quality, geomorphology, and human activities on the adjacent coastal area (land side) using as an indicator the trophic state of the marine area in terms of chlorophyll-a concentration (upper mesotrophic). This way, having the above information for a certain site, it is possible to calculate the maximum annual production of a cage fish farm so that the trophic state of the area will not exceed the environmental goal of the upper mesotrophic level. We examined the effects of 27 different physical, chemical, social and geomorphological parameters on CC (in fish biomass terms). CC was found to be correlated by particulate nitrogen (PN), silicates (Si-SiO4), salinity, and suspended particulate matter (SPM). The overall relationship found is: Biomassat CC level = +473.762[Chl-a] − 6856.64[PN] + 9.302[Salinity] − 473.5[Si-SiO4] + 341.864[SPM] − 207.046. The analysis performed allowed us to estimate the maximum levels for each factor to maintain a eutrophication status up to the upper mesotrophic level: particulate nitrogen < 0.018 mg/L, silicates < 0.137 mg/L, salinity > 38 PSU and SPM > 0.815 mg/L. Finally, the current fish farm licensing legislation in Greece concerning the CC estimation algorithm is discussed.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse10070940