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Title: Global reconstruction of life-history strategies: A case study using tunas
Authors: Horswill, Cat; Kindsvater, Holly K.; Jose Juan-Jorda, Maria and Dulvy, Nicholas K.; Mangel, Marc; Matthiopoulos, Jason
Citation: JOURNAL OF APPLIED ECOLOGY, 2019, 56, 855-865
Abstract: Measuring the demographic parameters of exploited populations is central to predicting their vulnerability and extinction risk. However, current rates of population decline and species loss greatly outpace our ability to empirically monitor all populations that are potentially threatened. The scale of this problem cannot be addressed through additional data collection alone, and therefore it is a common practice to conduct population assessments based on surrogate data collected from similar species. However, this approach introduces biases and imprecisions that are difficult to quantify. Recent developments in hierarchical modelling have enabled missing values to be reconstructed based on the correlations between available life-history data, linking similar species based on phylogeny and environmental conditions. However, these methods cannot resolve life-history variability among populations or species that are closely placed spatially or taxonomically. Here, theoretically motivated constraints that align with life-history theory offer a new avenue for addressing this problem. We describe a Bayesian hierarchical approach that combines fragmented, multispecies and multi-population data with established life-history theory, in order to objectively determine similarity between populations based on trait correlations (life-history trade-offs) obtained from model fitting. We reconstruct 59 unobserved life-history parameters for 23 populations of tuna that sustain some of the world's most valuable fisheries. Testing by cross-validation across different scenarios indicated that life-histories were accurately reconstructed when information was available for other populations of the same species. The reconstruction of several traits was also accurate for species represented by a single population, although credible intervals increased dramatically.Synthesis and applications. The described Bayesian hierarchical method provides access to life-history traits that are difficult to measure directly and reconstructs missing life-history information useful for assessing populations and species that are directly or indirectly affected by human exploitation of natural resources. The method is particularly useful for examining populations that are spatially or taxonomically similar, and the reconstructed life-history strategies described for the principal market tunas have immediate application to the world-wide management of these fisheries. The described Bayesian hierarchical method provides access to life-history traits that are difficult to measure directly and reconstructs missing life-history information useful for assessing populations and species that are directly or indirectly affected by human exploitation of natural resources. The method is particularly useful for examining populations that are spatially or taxonomically similar, and the reconstructed life-history strategies described for the principal market tunas have immediate application to the world-wide management of these fisheries.
Keywords: Bayesian imputation; data limited; demography; fecundity; life-history theory; missing data; principal market tuna; Scombridae; POPULATION-DYNAMICS; EXTINCTION RISK; BLUEFIN TUNA; PARAMETERS; ATLANTIC; INFORMATION; ESTIMATORS; STEEPNESS; RATIOS
Issue Date: 2019
Publisher: WILEY
Type: Article
Language: 
DOI: 10.1111/1365-2664.13327
URI: http://dspace.azti.es/handle/24689/990
ISSN: 0021-8901
E-ISSN: 1365-2664
Funder: Natural Environment Research CouncilNERC Natural Environment Research Council [NE/P004180/1]
National Science FoundationNational Science Foundation (NSF) [DEB-1555729, DEB-1556779]
Marine Alliance for Science and Technology Scotland [SG411]
Marie Curie International Outgoing FellowshipEuropean Union (EU)
Appears in Publication types:Artículos científicos



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