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...
Main Authors: | , , , , , , |
---|---|
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 |
_version_ |
1766182725856788480 |