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Monitoring temporal trends in spatially structured populations: how should sampling effort be allocated between space and time?

Rhodes, Jonathan R. and Jonzén, Niclas LU (2011) In Ecography 34(6). p.1040-1048
Abstract
Estimating temporal trends in spatially structured populations has a critical role to play in understanding regional changes in biological populations and developing management strategies. Designing effective monitoring programmes to estimate these trends requires important decisions to be made about how to allocate sampling effort among spatial replicates (i.e. number of sites) and temporal replicates (i.e. how often to survey) to minimise uncertainty in trend estimates. In particular, the optimal mix of spatial and temporal replicates is likely to depend upon the spatial and temporal correlations in population dynamics. Although there has been considerable interest in the ecological literature on understanding spatial and temporal... (More)
Estimating temporal trends in spatially structured populations has a critical role to play in understanding regional changes in biological populations and developing management strategies. Designing effective monitoring programmes to estimate these trends requires important decisions to be made about how to allocate sampling effort among spatial replicates (i.e. number of sites) and temporal replicates (i.e. how often to survey) to minimise uncertainty in trend estimates. In particular, the optimal mix of spatial and temporal replicates is likely to depend upon the spatial and temporal correlations in population dynamics. Although there has been considerable interest in the ecological literature on understanding spatial and temporal correlations in species population dynamics, little attention has been paid to its consequences for monitoring design. We address this issue using model-based survey design to identify the optimal allocation of sampling effort among spatial and temporal replicates for estimating population trends under different levels of spatial and temporal correlation. Based on linear trends, we show that how we should allocate sampling effort among spatial and temporal replicates depends crucially on the spatial and temporal correlations in population dynamics, environmental variation, observation error and the spatial variation in temporal trends. When spatial correlation is low and temporal correlation is high, the best option is likely to be to sample many sites infrequently, particularly when observation error and/or spatial variation in temporal trends are high. When spatial correlation is high and temporal correlation is low, the best option is likely to be to sample few sites frequently, particularly when observation error and/or spatial variation in temporal trends are low. When abundances are spatially independent, it is always preferable to maximise spatial replication. This provides important insights into how spatio-temporal monitoring programmes should be designed to estimate temporal trends in spatially structured populations. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Ecography
volume
34
issue
6
pages
1040 - 1048
publisher
Wiley-Blackwell
external identifiers
  • wos:000297738200014
  • scopus:80052722779
ISSN
1600-0587
DOI
10.1111/j.1600-0587.2011.06370.x
language
English
LU publication?
yes
id
fc6d5f07-9f99-4a1a-8807-66a6578aa9db (old id 2333149)
date added to LUP
2012-02-06 15:13:35
date last changed
2017-07-09 04:12:21
@article{fc6d5f07-9f99-4a1a-8807-66a6578aa9db,
  abstract     = {Estimating temporal trends in spatially structured populations has a critical role to play in understanding regional changes in biological populations and developing management strategies. Designing effective monitoring programmes to estimate these trends requires important decisions to be made about how to allocate sampling effort among spatial replicates (i.e. number of sites) and temporal replicates (i.e. how often to survey) to minimise uncertainty in trend estimates. In particular, the optimal mix of spatial and temporal replicates is likely to depend upon the spatial and temporal correlations in population dynamics. Although there has been considerable interest in the ecological literature on understanding spatial and temporal correlations in species population dynamics, little attention has been paid to its consequences for monitoring design. We address this issue using model-based survey design to identify the optimal allocation of sampling effort among spatial and temporal replicates for estimating population trends under different levels of spatial and temporal correlation. Based on linear trends, we show that how we should allocate sampling effort among spatial and temporal replicates depends crucially on the spatial and temporal correlations in population dynamics, environmental variation, observation error and the spatial variation in temporal trends. When spatial correlation is low and temporal correlation is high, the best option is likely to be to sample many sites infrequently, particularly when observation error and/or spatial variation in temporal trends are high. When spatial correlation is high and temporal correlation is low, the best option is likely to be to sample few sites frequently, particularly when observation error and/or spatial variation in temporal trends are low. When abundances are spatially independent, it is always preferable to maximise spatial replication. This provides important insights into how spatio-temporal monitoring programmes should be designed to estimate temporal trends in spatially structured populations.},
  author       = {Rhodes, Jonathan R. and Jonzén, Niclas},
  issn         = {1600-0587},
  language     = {eng},
  number       = {6},
  pages        = {1040--1048},
  publisher    = {Wiley-Blackwell},
  series       = {Ecography},
  title        = {Monitoring temporal trends in spatially structured populations: how should sampling effort be allocated between space and time?},
  url          = {http://dx.doi.org/10.1111/j.1600-0587.2011.06370.x},
  volume       = {34},
  year         = {2011},
}