Advanced Distance Sampling: Estimating Abundance of Biological Populations

By T. Schweder. 2007

409 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e58bb1cbd8a2b1476d178cabb3c60eb1a56dddfe

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Citations in the corpus

Evaluating the Feasibility of Eradicating an Invasion (2006, background): “Both distance sampling theory (Burnham et al. 1980; Buckland et al. 2004) and search theory (Koopman 1980) offer techniques that may help determine the probability of detection as a function of effort expended in different search environments.”
Making Great Leaps Forward: Accounting for Detectability in Herpetological Field Studies (2007, background): “A new class of distance models has emerged to deal with cases where the assumption of perfect detection at distance zero cannot be met (Buckland et al., 2004).”
Estimating abundance of killer whales in the nearshore waters of the Gulf of Alaska and Aleutian Islands using line-transect sampling (2007, methodology): “Both Conventional Distance Sampling (CDS) and Multiple Covariate Distance Sampling (MCDS) approaches were used (Buckland et al. 2001, 2004; Marques and Buckland 2003).”
Best practice guidelines for surveys and monitoring of great ape populations (2008, methodology): “Currently the most widely used approach is distance sampling, which has been described extensively (Buckland et al. 1993, 2001, 2004).”
Automated distance estimation for wildlife camera trapping (2022, background): “Distance sampling relies much more on low bias than the magnitude of random errors (Buckland et al., 2004).”
Confusion reigns? A review of marine megafauna interactions with tidal-stream environments (2015, background): “…in narrow tidal straits between islands where currents may preclude an unbiased distribution of survey effort, edge effects may be significant, and water movement may cause animals to be non-randomly distributed relative to survey transects (Buckland et al. 2001, 2004, Wilson et al. 2013).”
Rethinking Avian Response to Tamarix on the Lower Colorado River: A Threshold Hypothesis (2008, methodology): “To assure that the use of raw count data did not bias our findings, we used the Multiple Covariate Distance Sampling engine in the program DISTANCE (Buckland et al. 2004) to determine if detection probability varied with the relative index of Tamarix.”
Distance sampling with camera traps (2017, methodology): “Fortunately, provided that detection is certain at the zerodistance, the pooling robustness property ensures that estimation is unbiased in the presence of heterogeneity in detectability among individuals (Buckland et al. 2004), and this also applies to heterogeneity caused by differences in angle a”
Effective monitoring of freshwater fish (2019, background): “Distance sampling (Buckland et al., 2001, 2004, 2011) typically involves the investigator noting the distance of each individual from a transect or point.”
Factors affecting the detection distances of reef fish: implications for visual counts (2011, methodology): “Probability of detection can be estimated using the Distance software (Thomas et al. 2010) by fitting a detection function to the distribution of sighting distances, allowing the proportion of fish missed by the observer to be estimated (see Buckland et al. 2001, 2004 ).”