A Model‐Based Approach for Making Ecological Inference from Distance Sampling Data

Johnson, Devin S.; Laake, Jeffrey L.; Hoef, Jay M. Ver

doi:10.1111/j.1541-0420.2009.01265.x

Cited by 82 publications

(118 citation statements)

References 33 publications

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“…Estimation of density and habitat relationships may also be combined in a single analysis (Hedley et al 2004, Royle et al 2004, Johnson et al 2010, Niemi & Fernández 2010. Inference about population processes is improved when combined with an observation model into a single likelihood framework (Goodman 2004, Buckland et al 2007, Royle & Dorazio 2008.…”

Section: Discussionmentioning

confidence: 99%

Precautionary design of a marine protected area based on a habitat model

Gerrodette¹,

Eguchi²

2011

Endang. Species. Res.

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Section: Discussionmentioning

confidence: 99%

Precautionary design of a marine protected area based on a habitat model

Gerrodette¹,

Eguchi²

2011

Endang. Species. Res.

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“…Johnson et al (2010) is a recent example of this approach. Like Johnson et al (2010), we assume that [S; φ] is a nonhomogeneous Poisson process (NHPP), as in Eq. (1).…”

Section: Distance Sampling As a Thinned Point Processmentioning

confidence: 99%

From distance sampling to spatial capture–recapture

Borchers

Marques

2017

AStA Adv Stat Anal

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Distance sampling and capture-recapture are the two most widely used wildlife abundance estimation methods. capture-recapture methods have only recently incorporated models for spatial distribution and there is an increasing tendency for distance sampling methods to incorporated spatial models rather than to rely on partly design-based spatial inference. In this overview we show how spatial models are central to modern distance sampling and that spatial capture-recapture models arise as an extension of distance sampling methods. Depending on the type of data recorded, they can be viewed as particular kinds of hierarchical binary regression, Poisson regression, survival or time-to-event models, with individuals' locations as latent variables and a spatial model as the latent variable distribution. Incorporation of spatial models in these two methods provides new opportunities for drawing explicitly spatial inferences. Areas of likely future development include more sophisticated spatial and spatio-temporal modelling of individuals' locations and movements, new methods for integrating spatial capture-recapture and other kinds of ecological survey data, and methods for dealing with the recapture uncertainty that often arise when "capture" consists of detection by a remote device like a camera trap or microphone.

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“…This definition strictly holds only when region B is at least as large as the region of integration used to fit the model; only with this condition can we be sure all n detected animals have centres within B. The sampling variance of R(N ), technically a mean square prediction error (Johnson et al 2010), is approximated by summing the expected Poisson variance of the true number of undetected animals and a delta-method estimate of its sampling variance, obtained as for E(N ).…”

Section: Notementioning

confidence: 99%

On the Fast Track to Recovery: Island Foxes on the Northern Channel Islands

Coonan¹,

Bakker

Hudgens³

et al. 2014

Monographs of the Western North American Naturalist

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Description Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection. License GPL (>= 2)LazyData yes Spatially explicit capture-recapture is a set of methods for studying marked animals distributed in space. Data comprise the locations of detectors (traps, searched areas, etc. described in an object of class 'traps'), and the detection histories of individually marked animals. Individual histories are stored in an object of class 'capthist' that includes the relevant 'traps' object.Models for population density (animals per hectare) and detection are defined in secr using symbolic formula notation. Density models may include spatial or temporal trend. Possible predictors for detection probability include both pre-defined variables (t, b, etc.) corresponding to 'time', 'behaviour' and other effects), and user-defined covariates of several kinds. Habitat is distinguished from nonhabitat with an object of class 'mask'.Models are fitted in secr by maximizing either the full likelihood or the likelihood conditional on the number of individuals observed (n). Conditional likelihood models are limited to homogeneous Poisson density, but allow continuous individual covariates for detection. A model fitted with secr.fit is an object of class secr. Generic methods (plot, print, summary, etc.) are provided for each object class.A link at the bottom of each help page takes you to the help index. Several vignettes complement the help pages:General interest secr-overview.pdf general introduction secr-datainput.pdf data formats and input functions secr-manual.pdf consolidated help pages secr-tutorial.pdf introductory tutorial secr-habitatmasks.pdf buffers and habitat masks secr-models.pdf linear models in secr secr-troubleshooting.pdf problems with secr.fit, including speed issuesMore specialised topics secr-densitysurfaces.pdf modelling density surfaces secr-finitemixtures.pdf mixture models for individual heterogeneity secr-markresight.pdf mark-resight data and models secr-multisession.pdf multi-session capthist objects and models secr-noneuclidean.pdf non-Euclidean distances secr-parameterisations.pdf alternative parameterisations sigmak, a0 secr-polygondetectors.pdf using polygon and transect detector types secr-sound.pdf analysing data from microphone arrays secr-varyingeffort.pdf variable effort in SECR modelsThe datasets possum, skink, ovenbird, housemouse, deermouse, ovensong, hornedlizard and stoatDNA include examples of fitted models.Two add-on packages extend the capability of secr and are documented separately. secrlinear enables the estimation of linear density (e.g., animals per km) for populations in linear habitats such as stream networks (secrlinear-vignette.pdf). secrdesign enables the assessment of alternative study designs by Monte ...

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A Model‐Based Approach for Making Ecological Inference from Distance Sampling Data

Cited by 82 publications

References 33 publications

Precautionary design of a marine protected area based on a habitat model

Precautionary design of a marine protected area based on a habitat model

From distance sampling to spatial capture–recapture

On the Fast Track to Recovery: Island Foxes on the Northern Channel Islands

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