Spatially Explicit Maximum Likelihood Methods for Capture–Recapture Studies
By David L. Borchers, M. Efford. Biometrics, 2008
864 citations recorded by the source corpus.
Subfields: Ecology, Epidemiology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/07fa9993dd1a70c3ddded9648bf693c3826572cb
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Citations in the corpus
- Spatial capture–recapture models allowing Markovian transience or dispersal (2015, neutral): “For each of scenarios A, B and C, we simulated 200 data sets and fit an ordinary SCR model by maximizing the ‘‘full likelihood’’ which contains the parameter N (Borchers and Efford 2008; ch. 6 in Royle et al. 2014, 2015).”
- Estimating Black Bear Density Using DNA Data From Hair Snares (2010, methodology): “…Columbia, Canada (Mowat and Strobeck 2000, Poole et al. 2001, Romain-Bondi et al. 2004) and Glacier National Park, USA (Boulanger et al. 2008, Kendall et al. 2008, Kendall et al. 2009); on brown bears in France (Taberlet et al. 1997) and Italy (Lorenzini et al. 2004); and on black bears in…”
- Bird population density estimated from acoustic signals (2009, methodology): “Equivalent inhomogeneous Poisson models allow for spatial trend or habitat effects (Borchers & Efford 2008; Efford et al. 2009b).”
- Estimating population size by spatially explicit capture–recapture (2013, methodology): “Models may be fitted by simulation and inverse prediction (Efford 2004), likelihood maximization (Borchers and Efford 2008) or Markov chain Monte Carlo (MCMC) methods (Royle and Young 2008, Gardner et al. 2009).”
- Estimating bird density using passive acoustic monitoring: a review of methods and suggestions for further research (2021, methodology): “This approach allows researchers to apply the spatially explicit capture – recapture (SECR) method (Borchers & Efford 2008).”
- A critique of density estimation from camera-trap data† (2012, methodology): “, Efford 2004, Borchers and Efford 2008, Royle et al. 2009b), show promise as they do not require the intermediate step of estimating the ETA, and formally link the encounter location to the home range (Royle and Gardner 2011). Ivan (2011) compared the relative performance of MMDM and SECR methods u”
- Spotting the difference: towards fully-automated population monitoring of African penguins Spheniscus demersus (2010, background): “This would limit the individual resighting frequency that the system could achieve and would need to be considered in subsequent data analysis (Borchers & Efford 2008, Royle et al. 2009).”
- Comparison of density estimation methods for mammal populations with camera traps in the Kaa‐Iya del Gran Chaco landscape (2012, methodology): “…directly estimates animal density by using information on capture histories in combination with the location of the individual capture under either a Bayesian (Royle & Young, 2008; Gardner et al., 2009; Royle et al., 2009a) or a likelihood analysis framework (Efford, 2004; Borchers & Efford, 2008).”
- Spatially explicit models for inference about density in unmarked or partially marked populations (2011, background): “Of course, this is also known in the application of standard spatial capturerecapture models (Borchers and Efford, 2008) where individual identity is preserved across trap encounters, but it is seldom, if ever, considered in the design of more traditional count surveys.”
- Assessing the performance of a semi‐automated acoustic monitoring system for primates (2015, methodology): “The most widely applied monitoring methods for terrestrial animals are transect and plot sampling, as well as various types of capture–mark–recapture methods (Krebs 1998; Borchers, Buckland & Zucchini 2002; Buckland et al. 2004; Borchers & Efford 2008).”
- Estimating jaguar densities with camera traps: Problems with current designs and recommendations for future studies (2013, methodology): “While it would be possible to build models that include heterogeneity for r either as a fixed or continuous mixture (Borchers and Efford, 2008), this would further increase the necessary sample size.”