A method for detecting positive growth autocorrelation without marking individuals.
In most ecological studies, within-group variation is a nuisance that obscures patterns of interest and reduces statistical power. However, patterns of within-group variability often contain information about ecological processes. In particular, such patterns can be used to detect positive growth au...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3810375?pdf=render |
| _version_ | 1818432391832666112 |
|---|---|
| author | Mollie E Brooks Michael W McCoy Benjamin M Bolker |
| author_facet | Mollie E Brooks Michael W McCoy Benjamin M Bolker |
| author_sort | Mollie E Brooks |
| collection | DOAJ |
| description | In most ecological studies, within-group variation is a nuisance that obscures patterns of interest and reduces statistical power. However, patterns of within-group variability often contain information about ecological processes. In particular, such patterns can be used to detect positive growth autocorrelation (consistent variation in growth rates among individuals in a cohort across time), even in samples of unmarked individuals. Previous methods for detecting autocorrelated growth required data from marked individuals. We propose a method that requires only estimates of within-cohort variance through time, using maximum likelihood methods to obtain point estimates and confidence intervals of the correlation parameter. We test our method on simulated data sets and determine the loss in statistical power due to the inability to identify individuals. We show how to accommodate nonlinear growth trajectories and test the effects of size-dependent mortality on our method's accuracy. The method can detect significant growth autocorrelation at moderate levels of autocorrelation with moderate-sized cohorts (for example, statistical power of 80% to detect growth autocorrelation ρ (2) = 0.5 in a cohort of 100 individuals measured on 16 occasions). We present a case study of growth in the red-eyed tree frog. Better quantification of the processes driving size variation will help ecologists improve predictions of population dynamics. This work will help researchers to detect growth autocorrelation in cases where marking is logistically infeasible or causes unacceptable decreases in the fitness of marked individuals. |
| first_indexed | 2024-12-14T16:04:28Z |
| format | Article |
| id | doaj.art-435c0099f59142fdb8e4d4257d93ee98 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-14T16:04:28Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-435c0099f59142fdb8e4d4257d93ee982022-12-21T22:55:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01810e7638910.1371/journal.pone.0076389A method for detecting positive growth autocorrelation without marking individuals.Mollie E BrooksMichael W McCoyBenjamin M BolkerIn most ecological studies, within-group variation is a nuisance that obscures patterns of interest and reduces statistical power. However, patterns of within-group variability often contain information about ecological processes. In particular, such patterns can be used to detect positive growth autocorrelation (consistent variation in growth rates among individuals in a cohort across time), even in samples of unmarked individuals. Previous methods for detecting autocorrelated growth required data from marked individuals. We propose a method that requires only estimates of within-cohort variance through time, using maximum likelihood methods to obtain point estimates and confidence intervals of the correlation parameter. We test our method on simulated data sets and determine the loss in statistical power due to the inability to identify individuals. We show how to accommodate nonlinear growth trajectories and test the effects of size-dependent mortality on our method's accuracy. The method can detect significant growth autocorrelation at moderate levels of autocorrelation with moderate-sized cohorts (for example, statistical power of 80% to detect growth autocorrelation ρ (2) = 0.5 in a cohort of 100 individuals measured on 16 occasions). We present a case study of growth in the red-eyed tree frog. Better quantification of the processes driving size variation will help ecologists improve predictions of population dynamics. This work will help researchers to detect growth autocorrelation in cases where marking is logistically infeasible or causes unacceptable decreases in the fitness of marked individuals.http://europepmc.org/articles/PMC3810375?pdf=render |
| spellingShingle | Mollie E Brooks Michael W McCoy Benjamin M Bolker A method for detecting positive growth autocorrelation without marking individuals. PLoS ONE |
| title | A method for detecting positive growth autocorrelation without marking individuals. |
| title_full | A method for detecting positive growth autocorrelation without marking individuals. |
| title_fullStr | A method for detecting positive growth autocorrelation without marking individuals. |
| title_full_unstemmed | A method for detecting positive growth autocorrelation without marking individuals. |
| title_short | A method for detecting positive growth autocorrelation without marking individuals. |
| title_sort | method for detecting positive growth autocorrelation without marking individuals |
| url | http://europepmc.org/articles/PMC3810375?pdf=render |
| work_keys_str_mv | AT mollieebrooks amethodfordetectingpositivegrowthautocorrelationwithoutmarkingindividuals AT michaelwmccoy amethodfordetectingpositivegrowthautocorrelationwithoutmarkingindividuals AT benjaminmbolker amethodfordetectingpositivegrowthautocorrelationwithoutmarkingindividuals AT mollieebrooks methodfordetectingpositivegrowthautocorrelationwithoutmarkingindividuals AT michaelwmccoy methodfordetectingpositivegrowthautocorrelationwithoutmarkingindividuals AT benjaminmbolker methodfordetectingpositivegrowthautocorrelationwithoutmarkingindividuals |