Density estimation in live‐trapping studies

By M. Efford. 2004

748 citations recorded by the source corpus.

Subfields: Ecology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3cf4f5e8aab26d91bf995707a347b223bda7ec9f

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In Ecology

In Statistics and Probability

Citations in the corpus

camtrapR: an R package for efficient camera trap data management (2016, methodology): “…utilize camera trap data, including estimation of occupancy probabilities (MacKenzie et al. 2002) or abundance, density and demographic rates with capture – recapture (Karanth 1995; Silver et al. 2004) and spatial capture – recapture models (Eﬀord 2004; Royle et al. 2009; Gardner et al. 2010).”
"Which camera trap type and how many do I need?" A review of camera features and study designs for a range of wildlife research applications (2013, methodology): “Recently, a method that uses the locationspecific individual capture histories, to construct a spatial capturerecapture (SCR) model was developed by Efford (2004) and Royle et al. (2009a,b).”
Estimating population size by spatially explicit capture–recapture (2013, methodology): “The last few years have seen progress on the related problem of estimating population density (Efford 2004, Borchers and Efford 2008, Royle and Young 2008), defining a set of methods known as spatially explicit capture– recapture (SECR).”
Estimating Black Bear Density Using DNA Data From Hair Snares (2010, methodology): “…models provide a rigorous and integrated way of using individual capture histories and trap coordinates to directly estimate density without ad hoc determination of an effective trapping area (Efford 2004; Borchers and Efford 2008; Royle and Young 2008; Gardner et al. 2009; Royle et al. 2009b).”
Animal movement affects interpretation of occupancy models from camera‐trap surveys of unmarked animals (2018, background): “The common practice of analyzing the CT time series as independent detections at a much coarser resolution (e.g., detections grouped within daily or weekly sampling occasions) erodes the information embedded in these spatiotemporal patterns (Efford 2004).”
Bayesian inference in camera trapping studies for a class of spatial capture-recapture models. (2009, methodology): “A natural framework for developing spatial models of abundance is based on point process models, and point processes have been considered as the basis for spatial capture–recapture models in a number of recent efforts (Efford 2004, Borchers and Efford 2008, Royle and Young 2008).”
Spatially explicit integrated population models (2014, methodology): “…data and the survey data (either count or detection/non-detection data) can be modelled conditional on the point process using ideas from recently developed spatial capture–recapture (SCR) models (Efford 2004; Borchers & Efford 2008; Gardner et al. 2010; Chandler &Royle 2013; Royle et al. 2013).”
Estimating animal population density using passive acoustics (2012, background): “(d ) Spatially explicit capture recapture The recent development of spatially explicit capture recapture (SECR; Efford, 2004; Borchers & Efford, 2008; Royle & Young, 2008) was motivated by two key issues in MR: (i) unmodelled heterogeneity in detected animals (i.e. not all animals have the same…”
Abundance, Density and Relative Abundance: A Conceptual Framework (2011, neutral): “Relative precision was density dependent (Efford 2004) between 0.”
Comparison of density estimation methods for mammal populations with camera traps in the Kaa‐Iya del Gran Chaco landscape (2012, methodology): “Unfortunately, this approach has no theoretical mechanism to link abundance with the survey area in order to estimate density (Williams, Nichols & Conroy, 2002) and has been questioned due to the ad hoc nature of estimating the survey area (Efford, 2004; Gardner, Royle & Wegan, 2009; Royle et al., 2”
Bird population density estimated from acoustic signals (2009, methodology): “Other studies suggest density estimates by SECR are robust to mis-specification of the distribution (Efford 2004; Efford et al. 2009a), and we anticipate this will apply also to acoustic estimates.”
Individual heterogeneity and capture–recapture models: what, why and how? (2018, background): “In CR models, the probability of detection can be assumed to vary between individuals in relation to, e.g. sex or age-related behavioral differences and more recently to space (Efford 2004, Borchers 2012).”
Density Estimation in a Wolverine Population using Spatial Capture-Recapture Models (2011, methodology): “The standard assumption is that si are uniformly distributed in space (Efford 2004, Borchers and Efford 2008, Royle and Young 2008).”
Density Estimation by Spatially Explicit Capture–Recapture: Likelihood-Based Methods (2009, methodology): “Robustness of density estimates to the shape of the fitted detection function (step function vs halfnormal) was also found in simulations using inverse prediction (Efford 2004).”
Improving density estimates for elusive carnivores: Accounting for sex-specific detection and movements using spatial capture–recapture models for jaguars in central Brazil (2011, background): “Spatially explicit capture–recapture (SECR) models are a recent development in this field and overcome the conceptual problem of interpreting abundance estimates from traditional nonspatial capture–recapture models (Efford, 2004; Royle and Young, 2008).”