Data from: Modeling time to population extinction when individual reproduction is autocorrelated ...

In nature, individual reproductive success is seldom independent from year to year, due to factors such as reproductive costs and individual heterogeneity. However, population projection models that incorporate temporal autocorrelations in individual reproduction can be difficult to parameterize, pa...

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Main Authors: Lee, Aline Magdalena, Sæther, Bernt-Erik, Markussen, Stine Svalheim, Engen, Steinar, Saether, Bernt-Erik
Format: Dataset
Language:English
Published: Dryad 2018
Subjects:
Online Access:https://dx.doi.org/10.5061/dryad.5g0rg
https://datadryad.org/stash/dataset/doi:10.5061/dryad.5g0rg
id ftdatacite:10.5061/dryad.5g0rg
record_format openpolar
spelling ftdatacite:10.5061/dryad.5g0rg 2024-02-04T09:52:30+01:00 Data from: Modeling time to population extinction when individual reproduction is autocorrelated ... Lee, Aline Magdalena Sæther, Bernt-Erik Markussen, Stine Svalheim Engen, Steinar Saether, Bernt-Erik 2018 https://dx.doi.org/10.5061/dryad.5g0rg https://datadryad.org/stash/dataset/doi:10.5061/dryad.5g0rg en eng Dryad https://dx.doi.org/10.1111/ele.12834 Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 reproductive autocorrelation age structure Demographic Stochasticity Alces alces matrix model dynamic heterogeneity Moose Dataset dataset 2018 ftdatacite https://doi.org/10.5061/dryad.5g0rg10.1111/ele.12834 2024-01-05T04:39:59Z In nature, individual reproductive success is seldom independent from year to year, due to factors such as reproductive costs and individual heterogeneity. However, population projection models that incorporate temporal autocorrelations in individual reproduction can be difficult to parameterize, particularly when data are sparse. We therefore examine whether such models are necessary to avoid biased estimates of stochastic population growth and extinction risk, by comparing output from a matrix population model that incorporates reproductive autocorrelations to output from a standard age-structured matrix model that does not. We use a range of parameterizations, including a case study using moose data, treating probabilities of switching reproductive class as either fixed or fluctuating. Expected time to extinction from the two models is found to differ by only small amounts (under 10%) for most parameterizations, indicating that explicitly accounting for individual reproductive autocorrelations is in most ... : Individual histories for moose at Vega 1984-2012Individual reproductive and survival histories for female moose at Vega 1984-2012. Numbers according to states: 1: Produced 0 calves, 2: Produced 1 calf, 3: Produced 2 calves, 4: Hunted, and 5: Natural death.Vegafemc190515.txt ... Dataset Alces alces DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language English
topic reproductive autocorrelation
age structure
Demographic Stochasticity
Alces alces
matrix model
dynamic heterogeneity
Moose
spellingShingle reproductive autocorrelation
age structure
Demographic Stochasticity
Alces alces
matrix model
dynamic heterogeneity
Moose
Lee, Aline Magdalena
Sæther, Bernt-Erik
Markussen, Stine Svalheim
Engen, Steinar
Saether, Bernt-Erik
Data from: Modeling time to population extinction when individual reproduction is autocorrelated ...
topic_facet reproductive autocorrelation
age structure
Demographic Stochasticity
Alces alces
matrix model
dynamic heterogeneity
Moose
description In nature, individual reproductive success is seldom independent from year to year, due to factors such as reproductive costs and individual heterogeneity. However, population projection models that incorporate temporal autocorrelations in individual reproduction can be difficult to parameterize, particularly when data are sparse. We therefore examine whether such models are necessary to avoid biased estimates of stochastic population growth and extinction risk, by comparing output from a matrix population model that incorporates reproductive autocorrelations to output from a standard age-structured matrix model that does not. We use a range of parameterizations, including a case study using moose data, treating probabilities of switching reproductive class as either fixed or fluctuating. Expected time to extinction from the two models is found to differ by only small amounts (under 10%) for most parameterizations, indicating that explicitly accounting for individual reproductive autocorrelations is in most ... : Individual histories for moose at Vega 1984-2012Individual reproductive and survival histories for female moose at Vega 1984-2012. Numbers according to states: 1: Produced 0 calves, 2: Produced 1 calf, 3: Produced 2 calves, 4: Hunted, and 5: Natural death.Vegafemc190515.txt ...
format Dataset
author Lee, Aline Magdalena
Sæther, Bernt-Erik
Markussen, Stine Svalheim
Engen, Steinar
Saether, Bernt-Erik
author_facet Lee, Aline Magdalena
Sæther, Bernt-Erik
Markussen, Stine Svalheim
Engen, Steinar
Saether, Bernt-Erik
author_sort Lee, Aline Magdalena
title Data from: Modeling time to population extinction when individual reproduction is autocorrelated ...
title_short Data from: Modeling time to population extinction when individual reproduction is autocorrelated ...
title_full Data from: Modeling time to population extinction when individual reproduction is autocorrelated ...
title_fullStr Data from: Modeling time to population extinction when individual reproduction is autocorrelated ...
title_full_unstemmed Data from: Modeling time to population extinction when individual reproduction is autocorrelated ...
title_sort data from: modeling time to population extinction when individual reproduction is autocorrelated ...
publisher Dryad
publishDate 2018
url https://dx.doi.org/10.5061/dryad.5g0rg
https://datadryad.org/stash/dataset/doi:10.5061/dryad.5g0rg
genre Alces alces
genre_facet Alces alces
op_relation https://dx.doi.org/10.1111/ele.12834
op_rights Creative Commons Zero v1.0 Universal
https://creativecommons.org/publicdomain/zero/1.0/legalcode
cc0-1.0
op_doi https://doi.org/10.5061/dryad.5g0rg10.1111/ele.12834
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