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A Unifying Model for Capture-Recapture and Distance Sampling Surveys of Wildlife Populations.

Borchers DL, Stevenson BC, Kidney D, Thomas L, Marques TA - J Am Stat Assoc (2015)

Bottom Line: In addition to unifying CR and DS models, the class provides a means of improving inference from SECR models by adding supplementary location data, and a means of incorporating measurement error into DS and MRDS models.We illustrate their utility by comparing inference on acoustic surveys of gibbons and frogs using only capture locations, using estimated angles (gibbons) and combinations of received signal strength and time-of-arrival data (frogs), and on a visual MRDS survey of whales, comparing estimates with exact and estimated distances.Supplementary materials for this article are available online.

View Article: PubMed Central - PubMed

ABSTRACT

A fundamental problem in wildlife ecology and management is estimation of population size or density. The two dominant methods in this area are capture-recapture (CR) and distance sampling (DS), each with its own largely separate literature. We develop a class of models that synthesizes them. It accommodates a spectrum of models ranging from nonspatial CR models (with no information on animal locations) through to DS and mark-recapture distance sampling (MRDS) models, in which animal locations are observed without error. Between these lie spatially explicit capture-recapture (SECR) models that include only capture locations, and a variety of models with less location data than are typical of DS surveys but more than are normally used on SECR surveys. In addition to unifying CR and DS models, the class provides a means of improving inference from SECR models by adding supplementary location data, and a means of incorporating measurement error into DS and MRDS models. We illustrate their utility by comparing inference on acoustic surveys of gibbons and frogs using only capture locations, using estimated angles (gibbons) and combinations of received signal strength and time-of-arrival data (frogs), and on a visual MRDS survey of whales, comparing estimates with exact and estimated distances. Supplementary materials for this article are available online.

No MeSH data available.


Smoothed simulated sampling distributions of estimated whale cue density when capture history and exact distances are observed (“mrds”) and when capture history and estimated distances are used (“dist”). The down arrow marks true density, the horizontal axis is percentage deviation from true density, and the up arrows are the means of the sampling distributions.
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f0007: Smoothed simulated sampling distributions of estimated whale cue density when capture history and exact distances are observed (“mrds”) and when capture history and estimated distances are used (“dist”). The down arrow marks true density, the horizontal axis is percentage deviation from true density, and the up arrows are the means of the sampling distributions.

Mentions: We conducted a simulation study (500 simulations) to investigate the effect of neglecting measurement error on density estimates, using the parameters estimated from the model that incorporates measurement error, with mean sample size of 70, and with error CVs of 12%, 32%, and 50%. Results for the 32% case are shown in Figure 7. On the 1987 NASS survey measurement error CV was estimated to be 32% compared to 12% on the 2001 survey—see Borchers et al. (2009). All estimators were found to be positively biased but those from the MRDS model were (in order of increasing measurement error CVs) larger by 14%, 34%, and 68%, respectively. Biases using the SECR model with measurement error were 7.7%, 7.0%, and 8.2%. As the model estimates six parameters from only 70 observations, we believe this to be small-sample bias.


A Unifying Model for Capture-Recapture and Distance Sampling Surveys of Wildlife Populations.

Borchers DL, Stevenson BC, Kidney D, Thomas L, Marques TA - J Am Stat Assoc (2015)

Smoothed simulated sampling distributions of estimated whale cue density when capture history and exact distances are observed (“mrds”) and when capture history and estimated distances are used (“dist”). The down arrow marks true density, the horizontal axis is percentage deviation from true density, and the up arrows are the means of the sampling distributions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4440664&req=5

f0007: Smoothed simulated sampling distributions of estimated whale cue density when capture history and exact distances are observed (“mrds”) and when capture history and estimated distances are used (“dist”). The down arrow marks true density, the horizontal axis is percentage deviation from true density, and the up arrows are the means of the sampling distributions.
Mentions: We conducted a simulation study (500 simulations) to investigate the effect of neglecting measurement error on density estimates, using the parameters estimated from the model that incorporates measurement error, with mean sample size of 70, and with error CVs of 12%, 32%, and 50%. Results for the 32% case are shown in Figure 7. On the 1987 NASS survey measurement error CV was estimated to be 32% compared to 12% on the 2001 survey—see Borchers et al. (2009). All estimators were found to be positively biased but those from the MRDS model were (in order of increasing measurement error CVs) larger by 14%, 34%, and 68%, respectively. Biases using the SECR model with measurement error were 7.7%, 7.0%, and 8.2%. As the model estimates six parameters from only 70 observations, we believe this to be small-sample bias.

Bottom Line: In addition to unifying CR and DS models, the class provides a means of improving inference from SECR models by adding supplementary location data, and a means of incorporating measurement error into DS and MRDS models.We illustrate their utility by comparing inference on acoustic surveys of gibbons and frogs using only capture locations, using estimated angles (gibbons) and combinations of received signal strength and time-of-arrival data (frogs), and on a visual MRDS survey of whales, comparing estimates with exact and estimated distances.Supplementary materials for this article are available online.

View Article: PubMed Central - PubMed

ABSTRACT

A fundamental problem in wildlife ecology and management is estimation of population size or density. The two dominant methods in this area are capture-recapture (CR) and distance sampling (DS), each with its own largely separate literature. We develop a class of models that synthesizes them. It accommodates a spectrum of models ranging from nonspatial CR models (with no information on animal locations) through to DS and mark-recapture distance sampling (MRDS) models, in which animal locations are observed without error. Between these lie spatially explicit capture-recapture (SECR) models that include only capture locations, and a variety of models with less location data than are typical of DS surveys but more than are normally used on SECR surveys. In addition to unifying CR and DS models, the class provides a means of improving inference from SECR models by adding supplementary location data, and a means of incorporating measurement error into DS and MRDS models. We illustrate their utility by comparing inference on acoustic surveys of gibbons and frogs using only capture locations, using estimated angles (gibbons) and combinations of received signal strength and time-of-arrival data (frogs), and on a visual MRDS survey of whales, comparing estimates with exact and estimated distances. Supplementary materials for this article are available online.

No MeSH data available.