Modeling the spatial distribution of larval fish abundance provides essential information for management

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http://hdl.handle.net/10138/193020

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Kallasvuo , M , Vanhatalo , J & Veneranta , L 2017 , ' Modeling the spatial distribution of larval fish abundance provides essential information for management ' , Canadian Journal of Fisheries and Aquatic Sciences , vol. 74 , no. 5 , pp. 636-649 . https://doi.org/10.1139/cjfas-2016-0008

Title: Modeling the spatial distribution of larval fish abundance provides essential information for management
Author: Kallasvuo, Meri; Vanhatalo, Jarno; Veneranta, Lari
Contributor: University of Helsinki, Department of Mathematics and Statistics
Date: 2017-05
Language: eng
Number of pages: 14
Belongs to series: Canadian Journal of Fisheries and Aquatic Sciences
ISSN: 0706-652X
URI: http://hdl.handle.net/10138/193020
Abstract: Productive fisheries are strongly linked to the ecological state of the essential habitats. In this study, we developed a methodology to assess the most important reproduction habitats of fish by using larval survey data and Bayesian species distribution models that predict the spatial distribution and abundance of fish larvae. Our case study with four commercially and ecologically important fish species in the coastal zone of the northern Baltic Sea demonstrated that the production of fish stocks can be concentrated to an extremely limited area compared to the entire suitable production area. The area suitable for larval production varied from 3.7% to 99.8% between species, but the smallest area responsible for 80% of the cumulative larval production was two to five times more limited, varying from 1.4% to 52.9% between species. Hence, instead of the traditional approach of modeling only habitat suitability for fish production, marine spatial planning and management should take into account the areal production potential. Moreover, the developed methodology enables linking of the total production potential across the whole distribution area to fisheries stock assessment and management.
Subject: 1172 Environmental sciences
112 Statistics and probability
1181 Ecology, evolutionary biology
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