Estimating spatio-temporal dynamics of size-structured populations

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Spatial distributions of structured populations are usually estimated by fitting
abundance surfaces for each stage and at each point of time separately, ignoring correlations that emerge from growth of individuals. Here, we present a statistical model that combines spatio-temporal correlations with simple stock dynamics, to estimate simultaneously how size distributions and spatial distributions develop in time. We demonstrate the method for a cod population sampled by trawl surveys. Particular attention is paid to correlation between
size classes within each trawl haul due to clustering of individuals with similar size. The model estimates growth, mortality and reproduction, after which any aspect of size-structure, spatio-temporal population dynamics, as well as the sampling process can be probed. This is illustrated by two applications: 1) tracking the spatial movements of a single cohort through time, 2) predicting the risk of by-catch of undersize individuals. The method demonstrates that it is possible to combine stock assessment and spatio-temporal dynamics, however at
a high computational cost. The model can be extended by increasing its ecological fidelity, although computational feasibility eventually becomes limiting
Original languageEnglish
JournalCanadian Journal of Fisheries and Aquatic Sciences
Volume71
Issue number2
Pages (from-to)326-336
ISSN0706-652X
DOIs
Publication statusPublished - 2014

Cite this

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title = "Estimating spatio-temporal dynamics of size-structured populations",
abstract = "Spatial distributions of structured populations are usually estimated by fitting abundance surfaces for each stage and at each point of time separately, ignoring correlations that emerge from growth of individuals. Here, we present a statistical model that combines spatio-temporal correlations with simple stock dynamics, to estimate simultaneously how size distributions and spatial distributions develop in time. We demonstrate the method for a cod population sampled by trawl surveys. Particular attention is paid to correlation between size classes within each trawl haul due to clustering of individuals with similar size. The model estimates growth, mortality and reproduction, after which any aspect of size-structure, spatio-temporal population dynamics, as well as the sampling process can be probed. This is illustrated by two applications: 1) tracking the spatial movements of a single cohort through time, 2) predicting the risk of by-catch of undersize individuals. The method demonstrates that it is possible to combine stock assessment and spatio-temporal dynamics, however at a high computational cost. The model can be extended by increasing its ecological fidelity, although computational feasibility eventually becomes limiting",
author = "Kasper Kristensen and Thygesen, {Uffe H{\o}gsbro} and Andersen, {Ken Haste} and Beyer, {Jan E.}",
year = "2014",
doi = "10.1139/cjfas-2013-0151",
language = "English",
volume = "71",
pages = "326--336",
journal = "Canadian Journal of Fisheries and Aquatic Sciences",
issn = "0706-652X",
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}

Estimating spatio-temporal dynamics of size-structured populations. / Kristensen, Kasper; Thygesen, Uffe Høgsbro; Andersen, Ken Haste; Beyer, Jan E.

In: Canadian Journal of Fisheries and Aquatic Sciences, Vol. 71, No. 2, 2014, p. 326-336.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Estimating spatio-temporal dynamics of size-structured populations

AU - Kristensen, Kasper

AU - Thygesen, Uffe Høgsbro

AU - Andersen, Ken Haste

AU - Beyer, Jan E.

PY - 2014

Y1 - 2014

N2 - Spatial distributions of structured populations are usually estimated by fitting abundance surfaces for each stage and at each point of time separately, ignoring correlations that emerge from growth of individuals. Here, we present a statistical model that combines spatio-temporal correlations with simple stock dynamics, to estimate simultaneously how size distributions and spatial distributions develop in time. We demonstrate the method for a cod population sampled by trawl surveys. Particular attention is paid to correlation between size classes within each trawl haul due to clustering of individuals with similar size. The model estimates growth, mortality and reproduction, after which any aspect of size-structure, spatio-temporal population dynamics, as well as the sampling process can be probed. This is illustrated by two applications: 1) tracking the spatial movements of a single cohort through time, 2) predicting the risk of by-catch of undersize individuals. The method demonstrates that it is possible to combine stock assessment and spatio-temporal dynamics, however at a high computational cost. The model can be extended by increasing its ecological fidelity, although computational feasibility eventually becomes limiting

AB - Spatial distributions of structured populations are usually estimated by fitting abundance surfaces for each stage and at each point of time separately, ignoring correlations that emerge from growth of individuals. Here, we present a statistical model that combines spatio-temporal correlations with simple stock dynamics, to estimate simultaneously how size distributions and spatial distributions develop in time. We demonstrate the method for a cod population sampled by trawl surveys. Particular attention is paid to correlation between size classes within each trawl haul due to clustering of individuals with similar size. The model estimates growth, mortality and reproduction, after which any aspect of size-structure, spatio-temporal population dynamics, as well as the sampling process can be probed. This is illustrated by two applications: 1) tracking the spatial movements of a single cohort through time, 2) predicting the risk of by-catch of undersize individuals. The method demonstrates that it is possible to combine stock assessment and spatio-temporal dynamics, however at a high computational cost. The model can be extended by increasing its ecological fidelity, although computational feasibility eventually becomes limiting

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