Incoherent dimensionality in fisheries management: consequences of misaligned stock assessment and population boundaries

Abstract Fisheries policy inherently relies on an explicit definition of management boundaries that delineate the spatial extent over which stocks are assessed and regulations are implemented. However, management boundaries tend to be static and determined by politically negotiated or historically identified population (or multi-species) units, which create a potential disconnect with underlying, dynamic population structure. The consequences of incoherent management and population or stock boundaries were explored through the application of a two-area spatial simulation–estimation framework. Results highlight the importance of aligning management assessment areas with underlying population structure and processes, especially when fishing mortality is disproportionate to vulnerable biomass among management areas, demographic parameters (growth and maturity) are not homogenous within management areas, and connectivity (via recruitment or movement) unknowingly exists among management areas. Bias and risk were greater for assessments that incorrectly span multiple population segments (PSs) compared to assessments that cover a subset of a PS, and these results were exacerbated when there was connectivity between PSs. Directed studies and due consideration of critical PSs, spatially explicit models, and dynamic management options that help align management and population boundaries would likely reduce estimation biases and management risk, as would closely coordinated management that functions across population boundaries.

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Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada

The Condor ◽

10.1093/condor/duaa007 ◽

2020 ◽

Vol 122 (2) ◽

Author(s):

Péter Sólymos ◽

Judith D Toms ◽

Steven M Matsuoka ◽

Steven G Cumming ◽

Nicole K S Barker ◽

...

Keyword(s):

North America ◽

Time Of Day ◽

Lessons Learned ◽

Population Estimates ◽

Spatially Explicit ◽

Species Variation ◽

Detection Distance ◽

Spatially Explicit Models ◽

Population Sizes ◽

Partners In Flight

Abstract Estimating the population abundance of landbirds is a challenging task complicated by the amount, type, and quality of available data. Avian conservationists have relied on population estimates from Partners in Flight (PIF), which primarily uses roadside data from the North American Breeding Bird Survey (BBS). However, the BBS was not designed to estimate population sizes. Therefore, we set out to compare the PIF approach with spatially explicit models incorporating roadside and off-road point-count surveys. We calculated population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, using land cover and climate as predictors. We also developed a framework to evaluate how the differences between the detection distance, time-of-day, roadside count, and habitat representation adjustments explain discrepancies between the 2 estimators. We showed that the key assumptions of the PIF population estimator were commonly violated in this region, and that the 2 approaches provided different population estimates for most species. The average differences between estimators were explained by differences in the detection-distance and time-of-day components, but these adjustments left much unexplained variation among species. Differences in the roadside count and habitat representation components explained most of the among-species variation. The variation caused by these factors was large enough to change the population ranking of the species. The roadside count bias needs serious attention when roadside surveys are used to extrapolate over off-road areas. Habitat representation bias is likely prevalent in regions sparsely and non-representatively sampled by roadside surveys, such as the boreal region of North America, and thus population estimates for these regions need to be treated with caution for certain species. Additional sampling and integrated modeling of available data sources can contribute towards more accurate population estimates for conservation in remote areas of North America.

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Improving management advice through spatially explicit models and sharing information

Fisheries Research ◽

10.1016/j.fishres.2009.07.006 ◽

2009 ◽

Vol 100 (3) ◽

pp. 191-199 ◽

Cited By ~ 6

Author(s):

Carlos Montenegro ◽

Mark N. Maunder ◽

Maximiliano Zilleruelo

Keyword(s):

Spatially Explicit ◽

Spatially Explicit Models ◽

Management Advice

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A spectral theory for Wright’s inbreeding coefficients and related quantities

PLoS Genetics ◽

10.1371/journal.pgen.1009665 ◽

2021 ◽

Vol 17 (7) ◽

pp. e1009665

Author(s):

Olivier François ◽

Clément Gain

Keyword(s):

Population Structure ◽

Genetic Variation ◽

Population Genetic ◽

Principal Component ◽

Large Data ◽

Population Subdivision ◽

Adaptive Genetic Variation ◽

Genomic Locus ◽

Inbreeding Coefficients ◽

Definition Of

Wright’s inbreeding coefficient, FST, is a fundamental measure in population genetics. Assuming a predefined population subdivision, this statistic is classically used to evaluate population structure at a given genomic locus. With large numbers of loci, unsupervised approaches such as principal component analysis (PCA) have, however, become prominent in recent analyses of population structure. In this study, we describe the relationships between Wright’s inbreeding coefficients and PCA for a model of K discrete populations. Our theory provides an equivalent definition of FST based on the decomposition of the genotype matrix into between and within-population matrices. The average value of Wright’s FST over all loci included in the genotype matrix can be obtained from the PCA of the between-population matrix. Assuming that a separation condition is fulfilled and for reasonably large data sets, this value of FST approximates the proportion of genetic variation explained by the first (K − 1) principal components accurately. The new definition of FST is useful for computing inbreeding coefficients from surrogate genotypes, for example, obtained after correction of experimental artifacts or after removing adaptive genetic variation associated with environmental variables. The relationships between inbreeding coefficients and the spectrum of the genotype matrix not only allow interpretations of PCA results in terms of population genetic concepts but extend those concepts to population genetic analyses accounting for temporal, geographical and environmental contexts.

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Model of the Factors Influencing Competitiveness of the Latvian Fisheries Sector Cluster

Economics and Business ◽

10.1515/eb-2016-0011 ◽

2016 ◽

Vol 28 (1) ◽

pp. 76-82

Author(s):

Inese Biukšāne

Keyword(s):

Environmental Factors ◽

Policy Making ◽

Scientific Literature ◽

Social Economic ◽

Factors Influencing ◽

Fisheries Policy ◽

Definition Of

Abstract The aim of the research is to elaborate the model of factors influencing competitiveness of the Latvian fisheries sector cluster. Based on the studied scientific literature, the research provides an improved definition of the sector competitiveness and defines the factors influencing the competitiveness of the sector. As a result of the analysis, the author has discovered that there are several internal and external social, economic, political and environmental factors that influence the competitiveness of the Latvian fisheries sector cluster. It is advisable to the institutions involved in fisheries policy-making to take into account the identified factors, influencing the competitiveness, and their changes when making and improving the general policy of the sector.

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Range limits in spatially explicit models of quantitative traits

Journal of Mathematical Biology ◽

10.1007/s00285-012-0628-4 ◽

2012 ◽

Vol 68 (1-2) ◽

pp. 207-234 ◽

Cited By ~ 2

Author(s):

Judith R. Miller ◽

Huihui Zeng

Keyword(s):

Quantitative Traits ◽

Range Limits ◽

Spatially Explicit ◽

Spatially Explicit Models

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An Object-Oriented Approach to Managing Fuzziness in Spatially Explicit Models Coupled to a Geographic Database

Advances in Fuzzy Object-Oriented Databases ◽

10.4018/978-1-59140-384-5.ch009 ◽

2011 ◽

pp. 269-300 ◽

Cited By ~ 2

Author(s):

Vincent B. Robinson ◽

Phil A. Graniero

Keyword(s):

Spatial Data ◽

Spatial Databases ◽

Ecological Modeling ◽

Simulation Models ◽

Spatially Explicit ◽

Loosely Coupled ◽

Spatially Explicit Models ◽

Modeling Problem ◽

Object Oriented Approach ◽

Oriented Approach

This chapter uses a spatially explicit, individual-based ecological modeling problem to illustrate an approach to managing fuzziness in spatial databases that accommodates the use of nonfuzzy as well as fuzzy representations of geographic databases. The approach taken here uses the Extensible Component Objects for Constructing Observable Simulation Models (ECO-COSM) system loosely coupled with geographic information systems. ECO-COSM Probe objects flexibly express the contents of a spatial database within the context of an individualized fuzzy schema. It affords the ability to transform traditional nonfuzzy spatial data into fuzzy sets that capture the uncertainty inherent in the data and model’s semantic structure. The ecological modeling problem was used to illustrate how combining Probes and ProbeWrappers with Agent objects affords a flexible means of handling semantic variation and is an effective approach to utilizing heterogeneous sources of spatial data.

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