Comparing population growth rates between census and recruitment‐mortality models
ABSTRACT In forested ecosystems, estimating the abundance or trend of most wildlife populations is difficult. Therefore, vital rates are often used to model population change, but validating such models is important. Using data from woodland caribou ( Rangifer tarandus ), we compared estimates of po...
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crwiley:10.1002/jwmg.21185 2024-06-02T08:13:39+00:00 Comparing population growth rates between census and recruitment‐mortality models Serrouya, Robert Gilbert, Sophie McNay, R. Scott Mclellan, Bruce N. Heard, Douglas C. Seip, Dale R. Boutin, Stan 2016 http://dx.doi.org/10.1002/jwmg.21185 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjwmg.21185 http://onlinelibrary.wiley.com/wol1/doi/10.1002/jwmg.21185/fullpdf en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor The Journal of Wildlife Management volume 81, issue 2, page 297-305 ISSN 0022-541X 1937-2817 journal-article 2016 crwiley https://doi.org/10.1002/jwmg.21185 2024-05-03T11:38:47Z ABSTRACT In forested ecosystems, estimating the abundance or trend of most wildlife populations is difficult. Therefore, vital rates are often used to model population change, but validating such models is important. Using data from woodland caribou ( Rangifer tarandus ), we compared estimates of population change (λ) based on vital rate models to λ based on aerial censuses. We modeled λ using Hatter and Bergerud's (1991) recruitment‐mortality (R‐M) equation (λ = survival/[1 − recruitment]). We estimated survival and recruitment from a sample of 317 radio‐collared caribou from 9 subpopulations in British Columbia, Canada. In this ecosystem, woodland caribou have high sightability (>85%) in winter and thus are easy to census compared to most forest wildlife. We found that the R‐M equation overestimated λ compared to census‐based λ across most of the observed range of data (e.g., if R‐M estimated λ of 1.1, census‐based λ was 0.99, and if R‐M was 0.90, census‐based λ was 0.89). We then assessed whether recruitment, survival, a linear model of both parameters, or the R‐M equation best predicted census‐based λ. The R‐M equation explained 60% of the variation in census‐based λ, more than double the next‐best approach (i.e., the simple linear model), even though identical parameters were included. Further, we simulated variability due to the unknown sex (M:F) ratio in the sample, and found that the R‐M equation remained the best predictor of census‐based λ. Although the R‐M equation was the most precise and accurate approach, our results reaffirm that it is important to periodically validate trend estimates based on vital rate models with estimates of absolute abundance, particularly for species of management concern. © 2016 The Wildlife Society. Article in Journal/Newspaper Rangifer tarandus Wiley Online Library British Columbia ENVELOPE(-125.003,-125.003,54.000,54.000) Canada The Journal of Wildlife Management 81 2 297 305 |
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Open Polar |
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Wiley Online Library |
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crwiley |
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English |
description |
ABSTRACT In forested ecosystems, estimating the abundance or trend of most wildlife populations is difficult. Therefore, vital rates are often used to model population change, but validating such models is important. Using data from woodland caribou ( Rangifer tarandus ), we compared estimates of population change (λ) based on vital rate models to λ based on aerial censuses. We modeled λ using Hatter and Bergerud's (1991) recruitment‐mortality (R‐M) equation (λ = survival/[1 − recruitment]). We estimated survival and recruitment from a sample of 317 radio‐collared caribou from 9 subpopulations in British Columbia, Canada. In this ecosystem, woodland caribou have high sightability (>85%) in winter and thus are easy to census compared to most forest wildlife. We found that the R‐M equation overestimated λ compared to census‐based λ across most of the observed range of data (e.g., if R‐M estimated λ of 1.1, census‐based λ was 0.99, and if R‐M was 0.90, census‐based λ was 0.89). We then assessed whether recruitment, survival, a linear model of both parameters, or the R‐M equation best predicted census‐based λ. The R‐M equation explained 60% of the variation in census‐based λ, more than double the next‐best approach (i.e., the simple linear model), even though identical parameters were included. Further, we simulated variability due to the unknown sex (M:F) ratio in the sample, and found that the R‐M equation remained the best predictor of census‐based λ. Although the R‐M equation was the most precise and accurate approach, our results reaffirm that it is important to periodically validate trend estimates based on vital rate models with estimates of absolute abundance, particularly for species of management concern. © 2016 The Wildlife Society. |
format |
Article in Journal/Newspaper |
author |
Serrouya, Robert Gilbert, Sophie McNay, R. Scott Mclellan, Bruce N. Heard, Douglas C. Seip, Dale R. Boutin, Stan |
spellingShingle |
Serrouya, Robert Gilbert, Sophie McNay, R. Scott Mclellan, Bruce N. Heard, Douglas C. Seip, Dale R. Boutin, Stan Comparing population growth rates between census and recruitment‐mortality models |
author_facet |
Serrouya, Robert Gilbert, Sophie McNay, R. Scott Mclellan, Bruce N. Heard, Douglas C. Seip, Dale R. Boutin, Stan |
author_sort |
Serrouya, Robert |
title |
Comparing population growth rates between census and recruitment‐mortality models |
title_short |
Comparing population growth rates between census and recruitment‐mortality models |
title_full |
Comparing population growth rates between census and recruitment‐mortality models |
title_fullStr |
Comparing population growth rates between census and recruitment‐mortality models |
title_full_unstemmed |
Comparing population growth rates between census and recruitment‐mortality models |
title_sort |
comparing population growth rates between census and recruitment‐mortality models |
publisher |
Wiley |
publishDate |
2016 |
url |
http://dx.doi.org/10.1002/jwmg.21185 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjwmg.21185 http://onlinelibrary.wiley.com/wol1/doi/10.1002/jwmg.21185/fullpdf |
long_lat |
ENVELOPE(-125.003,-125.003,54.000,54.000) |
geographic |
British Columbia Canada |
geographic_facet |
British Columbia Canada |
genre |
Rangifer tarandus |
genre_facet |
Rangifer tarandus |
op_source |
The Journal of Wildlife Management volume 81, issue 2, page 297-305 ISSN 0022-541X 1937-2817 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1002/jwmg.21185 |
container_title |
The Journal of Wildlife Management |
container_volume |
81 |
container_issue |
2 |
container_start_page |
297 |
op_container_end_page |
305 |
_version_ |
1800737220955471872 |