A hierarchical model for spatial capture-recapture data.

By J. Andrew Royle, K. Young. Ecology, 2008

423 citations recorded by the source corpus.

Subfields: Ecology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/73b000793b79eaad7b06de872a191fc6edebb964

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Citations in the corpus

Comparison of density estimation methods for mammal populations with camera traps in the Kaa‐Iya del Gran Chaco landscape (2012, methodology): “On the other hand, the Bayesian approach may be more appropriate for small sample sizes, typical when sampling rare or elusive species (Kéry et al., 2011).”
Restoring apex predators can reduce mesopredator abundances (2019, methodology): “We used spatial capture–recapture (SCR) methods (Efford, 2004; Royle and Young, 2008) on recognizable species to model capture”
Spatially explicit models for inference about density in unmarked or partially marked populations (2011, background): “vity centers close to a trap are more likely to be captured than individuals whose activity centers are further away. Spatial capture– recapture (SCR) models [Eﬀord (2004), Borchers and Eﬀord (2008), Royle and Young (2008), Royle et al. (2009)] produce direct estimates of density or population size ”
Estimating true instead of apparent survival using spatial Cormack–Jolly–Seber models (2014, result): “The spatial CJS model introduced here assumes that encounters are possible everywhere within the study area, consistent with an ‘area search’ type of protocol (Royle & Young 2008) which is common in studies of birds and reptiles.”
Spatial capture–recapture models allowing Markovian transience or dispersal (2015, neutral): “Their model was an ‘‘area search’’ type of observation model (Royle and Young 2008) with a Cormack–Jolly–Seber state model for survival.”
Density Estimation in a Wolverine Population using Spatial Capture-Recapture Models (2011, methodology): “The parameter s can be related formally to movement in that it corresponds to the standard deviation of locations under a bivariate normal model, if space is uniformly sampled (Royle and Young 2008).”
Estimating jaguar densities with camera traps: Problems with current designs and recommendations for future studies (2013, methodology): “…to estimate activity centers, distance parameters (r), encounter rates at the activity center (k0), and abundance for all individuals in a pre-defined area, avoiding the choice of a buffer to estimate the ETA (Efford, 2004; Efford et al., 2009; Royle and Gardner, 2011; Royle and Young, 2008).”
camtrapR: an R package for efficient camera trap data management (2016, background): “…methods use repeated detections of marked individuals of a species at an array of sampling locations (camera trap stations) to estimate species density while accounting for imperfect detection and movement of individuals about their home ranges (Eﬀord 2004; Royle & Young 2008; Royle et al. 2014).”
Estimating Black Bear Density Using DNA Data From Hair Snares (2010, background): “…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).”
Separating mortality and emigration: modelling space use, dispersal and survival with robust‐design spatial capture–recapture data (2014, background): “Recently, spatial–capture–recapture (SCR)models have been developed to make use of information about the spatial locations of individual encounters (Efford 2004; Borchers & Efford 2008; Royle & Young 2008; Gardner, Royle & Wegan 2009).”
Bayesian inference in camera trapping studies for a class of spatial capture-recapture models. (2009, methodology): “Formalization of the use of auxiliary spatial information requires the precise definition of a model: the linkage of encounter location to some notion of territory or home range (and perhaps movements, see Royle and Young 2008).”