Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities
Abstract Close‐kin mark–recapture (CKMR) is a method for estimating abundance and vital rates from kinship relationships observed in genetic samples. CKMR inference only requires animals to be sampled once (e.g., lethally), potentially widening the scope of population‐level inference relative to tra...
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Online Access: | https://doi.org/10.1002/ece3.6296 |
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doaj-6d3fc8c9ad6f436bad4f8db63e1c59262021-04-02T10:55:08ZengWileyEcology and Evolution2045-77582020-06-0110125558556910.1002/ece3.6296Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilitiesPaul B. Conn0Mark V. Bravington1Shane Baylis2Jay M. Ver Hoef3Marine Mammal Laboratory Alaska Fisheries Science Center NOAA National Marine Fisheries Service Seattle WA USACSIRO Marine Lab Hobart TAS AustraliaCSIRO Marine Lab Hobart TAS AustraliaMarine Mammal Laboratory Alaska Fisheries Science Center NOAA National Marine Fisheries Service Seattle WA USAAbstract Close‐kin mark–recapture (CKMR) is a method for estimating abundance and vital rates from kinship relationships observed in genetic samples. CKMR inference only requires animals to be sampled once (e.g., lethally), potentially widening the scope of population‐level inference relative to traditional monitoring programs. One assumption of CKMR is that, conditional on individual covariates like age, all animals have an equal probability of being sampled. However, if genetic data are collected opportunistically (e.g., via hunters or fishers), there is potential for spatial variation in sampling probability that can bias CKMR estimators, particularly when genetically related individuals stay in close proximity. We used individual‐based simulation to investigate consequences of dispersal limitation and spatially biased sampling on performance of naive (nonspatial) CKMR estimators of abundance, fecundity, and adult survival. Population dynamics approximated that of a long‐lived mammal species subject to lethal sampling. Naive CKMR abundance estimators were relatively unbiased when dispersal was unconstrained (i.e., complete mixing) or when sampling was random or subject to moderate levels of spatial variation. When dispersal was limited, extreme variation in spatial sampling probabilities negatively biased abundance estimates. Reproductive schedules and survival were well estimated, except for survival when adults could emigrate out of the sampled area. Incomplete mixing was readily detected using Kolmogorov–Smirnov tests. Although CKMR appears promising for estimating abundance and vital rates with opportunistically collected genetic data, care is needed when dispersal limitation is coupled with spatially biased sampling. Fortunately, incomplete mixing is easily detected with adequate sample sizes. In principle, it is possible to devise and fit spatially explicit CKMR models to avoid bias under dispersal limitation, but development of such models necessitates additional complexity (and possibly additional data). We suggest using simulation studies to examine potential bias and precision of proposed modeling approaches prior to implementing a CKMR program.https://doi.org/10.1002/ece3.6296abundance estimationincomplete mixingsampling biasspatial heterogeneity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Paul B. Conn Mark V. Bravington Shane Baylis Jay M. Ver Hoef |
spellingShingle |
Paul B. Conn Mark V. Bravington Shane Baylis Jay M. Ver Hoef Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities Ecology and Evolution abundance estimation incomplete mixing sampling bias spatial heterogeneity |
author_facet |
Paul B. Conn Mark V. Bravington Shane Baylis Jay M. Ver Hoef |
author_sort |
Paul B. Conn |
title |
Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities |
title_short |
Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities |
title_full |
Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities |
title_fullStr |
Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities |
title_full_unstemmed |
Robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities |
title_sort |
robustness of close‐kin mark–recapture estimators to dispersal limitation and spatially varying sampling probabilities |
publisher |
Wiley |
series |
Ecology and Evolution |
issn |
2045-7758 |
publishDate |
2020-06-01 |
description |
Abstract Close‐kin mark–recapture (CKMR) is a method for estimating abundance and vital rates from kinship relationships observed in genetic samples. CKMR inference only requires animals to be sampled once (e.g., lethally), potentially widening the scope of population‐level inference relative to traditional monitoring programs. One assumption of CKMR is that, conditional on individual covariates like age, all animals have an equal probability of being sampled. However, if genetic data are collected opportunistically (e.g., via hunters or fishers), there is potential for spatial variation in sampling probability that can bias CKMR estimators, particularly when genetically related individuals stay in close proximity. We used individual‐based simulation to investigate consequences of dispersal limitation and spatially biased sampling on performance of naive (nonspatial) CKMR estimators of abundance, fecundity, and adult survival. Population dynamics approximated that of a long‐lived mammal species subject to lethal sampling. Naive CKMR abundance estimators were relatively unbiased when dispersal was unconstrained (i.e., complete mixing) or when sampling was random or subject to moderate levels of spatial variation. When dispersal was limited, extreme variation in spatial sampling probabilities negatively biased abundance estimates. Reproductive schedules and survival were well estimated, except for survival when adults could emigrate out of the sampled area. Incomplete mixing was readily detected using Kolmogorov–Smirnov tests. Although CKMR appears promising for estimating abundance and vital rates with opportunistically collected genetic data, care is needed when dispersal limitation is coupled with spatially biased sampling. Fortunately, incomplete mixing is easily detected with adequate sample sizes. In principle, it is possible to devise and fit spatially explicit CKMR models to avoid bias under dispersal limitation, but development of such models necessitates additional complexity (and possibly additional data). We suggest using simulation studies to examine potential bias and precision of proposed modeling approaches prior to implementing a CKMR program. |
topic |
abundance estimation incomplete mixing sampling bias spatial heterogeneity |
url |
https://doi.org/10.1002/ece3.6296 |
work_keys_str_mv |
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