N‐Mixture Models for Estimating Population Size from Spatially Replicated Counts

By J. Andrew Royle. Biometrics, 2004

1,544 citations recorded by the source corpus.

Subfields: Ecology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8b8a7c61f18fb74a10190797eed84663a2b2c038

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Citations in the corpus

MODELING ABUNDANCE EFFECTS IN DISTANCE SAMPLING (2004, background): “See Royle (2004) for details.”
Evaluating relative abundance indices for terrestrial herbivores from large-scale camera trap surveys (2018, background): “Implementing the recently developed approaches for unmarked animals (e.g., Royle, 2004; Rowcliffe, Field, Turvey, & Carbone, 2008; Chandler & Royle, 2013) is not always feasible: these methods require complex and computationally intensive statistics, their results can be difficult to interpret,…”
In Defense of Indices: The Case of Bird Surveys (2008, methodology): “This model has performed well in some simulations (Royle 2004) and field applications (Kéry et al. 2005), even when the same explanatory variables influenced population size and detectability (Kéry 2008).”
Modelling occurrence and abundance of species when detection is imperfect (2005, methodology): “Royle (2004a) ela- borated on an extension of this approach to modeling abundance from counts that are assumed to have a binomial sampling distribution.”
A picture is worth a thousand words: the application of camera trapping to the study of birds (2008, methodology): “The methods of Royle and Nichols (2003) and Royle (2004) have been applied to avian point count surveys (Breeding Bird Surveys [Robbins et al. 1986]) where it is often difficult to identify the number of unique individuals observed.”
Inferences About Landbird Abundance from Count Data: Recent Advances and Future Directions (2009, methodology): “Specifically, he focused on replicate counts as a basis for inference about abundance (Royle 2004) and occupancy (Royle and Nichols 2003).”
Making Great Leaps Forward: Accounting for Detectability in Herpetological Field Studies (2007, methodology): “The aim of call index models is to estimate the latent abundance index (Royle, 2004a; Royle and Link, 2005).”
Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities (2017, methodology): “One example is the so-called N-mixture models (Royle 2004, Kéry et al. 2009a, Kéry and Royle 2016, Ch. 6-7), which require data on replicated counts of individuals with no double counting (hence individuals are in some way “marked”, at least during the period of a count).”
Trend estimation in populations with imperfect detection (2009, methodology): “Alternatives would include the adoption of a negative-binomial distribution for abundance ( Royle 2004a; but see Joseph et al. 2009) or a ‘non-parametric’, data-based representation of the variation in density among sites (see Dorazio et al. 2008).”
Estimating animal population density using passive acoustics (2012, methodology): “Methods based on presence-absence (Royle & Nichols, 2003) or counts (Royle, 2004) of a given species over repeated visits to a given number of sites have also been used to estimate abundance.”
Predicting species distributions: a critical comparison of the most common statistical models using artificial species (2007, background): “Interpreting data collected in the real world is often confounded by problems of differing species detectability (Boulinier et al., 1998; Royle, 2004; Buckland et al., 2005); differences in species prevalence (Segurado & Araújo, 2004); variability among observers and habitat types (Buckland et…”