Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model

Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance...

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Main Authors: Whitlock, Rebecca, Mäntyniemi, Samu, Palm, Stefan, Koljenen, Marja - Liisa, Dannewitz, Johan, Östergren, Johan, Koljonen, Marja-Liisa
Format: Dataset
Language:unknown
Published: 2018
Subjects:
Online Access:https://zenodo.org/record/4984268
https://doi.org/10.5061/dryad.4pg37
id ftzenodo:oai:zenodo.org:4984268
record_format openpolar
spelling ftzenodo:oai:zenodo.org:4984268 2023-05-15T18:10:00+02:00 Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model Whitlock, Rebecca Mäntyniemi, Samu Palm, Stefan Koljenen, Marja - Liisa Dannewitz, Johan Östergren, Johan Koljonen, Marja-Liisa 2018-11-13 https://zenodo.org/record/4984268 https://doi.org/10.5061/dryad.4pg37 unknown doi:10.1111/2041-210x.12946 https://zenodo.org/communities/dryad https://zenodo.org/record/4984268 https://doi.org/10.5061/dryad.4pg37 oai:zenodo.org:4984268 info:eu-repo/semantics/openAccess https://creativecommons.org/publicdomain/zero/1.0/legalcode Baltic salmon 2014 Salmo salar Bayesian spatial model mixed fisheries info:eu-repo/semantics/other dataset 2018 ftzenodo https://doi.org/10.5061/dryad.4pg3710.1111/2041-210x.12946 2023-03-10T21:59:45Z Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement in an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses. Incorporation of prior information about stock abundance and movement resulted in more precise and plausible estimates of mixture compositions in time and space. Inclusion of a population dynamics model also allowed robust interpolation of expected catch composition at areas and times with no genetic observations. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species. Baltic_salmon_baseline_dataBaseline genotypes (17 microsatellite loci) for Baltic salmon from 27 stocks (3593 individuals of known stock of origin).Baltic_salmon_mixture_dataBaltic salmon mixture data: Genotypes (17 microsatellite loci) for 2058 Baltic salmon individuals sampled from Swedish and Finnish coastal trap net fisheries in 2014. Dataset Salmo salar Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language unknown
topic Baltic salmon
2014
Salmo salar
Bayesian
spatial model
mixed fisheries
spellingShingle Baltic salmon
2014
Salmo salar
Bayesian
spatial model
mixed fisheries
Whitlock, Rebecca
Mäntyniemi, Samu
Palm, Stefan
Koljenen, Marja - Liisa
Dannewitz, Johan
Östergren, Johan
Koljonen, Marja-Liisa
Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
topic_facet Baltic salmon
2014
Salmo salar
Bayesian
spatial model
mixed fisheries
description Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement in an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses. Incorporation of prior information about stock abundance and movement resulted in more precise and plausible estimates of mixture compositions in time and space. Inclusion of a population dynamics model also allowed robust interpolation of expected catch composition at areas and times with no genetic observations. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species. Baltic_salmon_baseline_dataBaseline genotypes (17 microsatellite loci) for Baltic salmon from 27 stocks (3593 individuals of known stock of origin).Baltic_salmon_mixture_dataBaltic salmon mixture data: Genotypes (17 microsatellite loci) for 2058 Baltic salmon individuals sampled from Swedish and Finnish coastal trap net fisheries in 2014.
format Dataset
author Whitlock, Rebecca
Mäntyniemi, Samu
Palm, Stefan
Koljenen, Marja - Liisa
Dannewitz, Johan
Östergren, Johan
Koljonen, Marja-Liisa
author_facet Whitlock, Rebecca
Mäntyniemi, Samu
Palm, Stefan
Koljenen, Marja - Liisa
Dannewitz, Johan
Östergren, Johan
Koljonen, Marja-Liisa
author_sort Whitlock, Rebecca
title Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
title_short Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
title_full Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
title_fullStr Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
title_full_unstemmed Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
title_sort data from: integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
publishDate 2018
url https://zenodo.org/record/4984268
https://doi.org/10.5061/dryad.4pg37
genre Salmo salar
genre_facet Salmo salar
op_relation doi:10.1111/2041-210x.12946
https://zenodo.org/communities/dryad
https://zenodo.org/record/4984268
https://doi.org/10.5061/dryad.4pg37
oai:zenodo.org:4984268
op_rights info:eu-repo/semantics/openAccess
https://creativecommons.org/publicdomain/zero/1.0/legalcode
op_doi https://doi.org/10.5061/dryad.4pg3710.1111/2041-210x.12946
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