Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?
Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3520967?pdf=render |
| _version_ | 1811268733925064704 |
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| author | Tabitha A Graves J Andrew Royle Katherine C Kendall Paul Beier Jeffrey B Stetz Amy C Macleod |
| author_facet | Tabitha A Graves J Andrew Royle Katherine C Kendall Paul Beier Jeffrey B Stetz Amy C Macleod |
| author_sort | Tabitha A Graves |
| collection | DOAJ |
| description | Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method. |
| first_indexed | 2024-04-12T21:28:12Z |
| format | Article |
| id | doaj.art-9965903562ce43a48702402da40df660 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T21:28:12Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-9965903562ce43a48702402da40df6602022-12-22T03:16:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-01712e4941010.1371/journal.pone.0049410Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?Tabitha A GravesJ Andrew RoyleKatherine C KendallPaul BeierJeffrey B StetzAmy C MacleodUsing multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method.http://europepmc.org/articles/PMC3520967?pdf=render |
| spellingShingle | Tabitha A Graves J Andrew Royle Katherine C Kendall Paul Beier Jeffrey B Stetz Amy C Macleod Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models? PLoS ONE |
| title | Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models? |
| title_full | Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models? |
| title_fullStr | Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models? |
| title_full_unstemmed | Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models? |
| title_short | Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models? |
| title_sort | balancing precision and risk should multiple detection methods be analyzed separately in n mixture models |
| url | http://europepmc.org/articles/PMC3520967?pdf=render |
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