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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 gibbon call density when only spatial capture history is used in estimation (“simple”) and when capture history and observed angles are used (“angle”). The down arrow marks true (simulated) 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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f0003: Smoothed simulated sampling distributions of estimated gibbon call density when only spatial capture history is used in estimation (“simple”) and when capture history and observed angles are used (“angle”). The down arrow marks true (simulated) density, the horizontal axis is percentage deviation from true density, and the up arrows are the means of the sampling distributions.

Mentions: To investigate the cause of the differences we plotted estimated locations of calling groups using Equation (5), and we conducted a simulation study (with 500 simulations) in which true parameter values were equal to those estimated using and . Illustrative examples of location contours are shown in Figure 2 and the simulated sampling distributions of the two estimators is shown in Figure 3.


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 gibbon call density when only spatial capture history is used in estimation (“simple”) and when capture history and observed angles are used (“angle”). The down arrow marks true (simulated) 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

f0003: Smoothed simulated sampling distributions of estimated gibbon call density when only spatial capture history is used in estimation (“simple”) and when capture history and observed angles are used (“angle”). The down arrow marks true (simulated) density, the horizontal axis is percentage deviation from true density, and the up arrows are the means of the sampling distributions.
Mentions: To investigate the cause of the differences we plotted estimated locations of calling groups using Equation (5), and we conducted a simulation study (with 500 simulations) in which true parameter values were equal to those estimated using and . Illustrative examples of location contours are shown in Figure 2 and the simulated sampling distributions of the two estimators is shown in Figure 3.

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.