Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys

By Marc Kéry, J. Andrew Royle. 2008

216 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/37eff13d76a14549b463a20f0beea09eaf69fe39

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. Christoph F. J. Meyer
2. Marc Kéry
3. E. Kalko
4. Kirsten Jung
5. J. Andrew Royle
6. Beth Gardner
7. Elise F. Zipkin
8. A. Dhondt

In Ecology

1. Marc Kéry
2. Beth Gardner
3. E. Kalko
4. M. Kelly
5. J. Andrew Royle
6. Kirsten Jung
7. Christoph F. J. Meyer
8. Elise F. Zipkin

In Nature and Landscape Conservation

1. Marc Kéry
2. Beth Gardner
3. Elise F. Zipkin
4. A. Dhondt
5. V. Ruiz‐Gutierrez
6. J. Andrew Royle
7. C. Monnerat
8. M. Kelly

Citations in the corpus

• Inference About Species Richness and Community Structure Using Species-Specific Occupancy Models in the National Swiss Breeding Bird Survey MHB (2009, methodology): “Not surprisingly, compared to a quadrat-by-quadrat analysis of species richness, integrated modelling of the metacommunity yields large benefits in terms of precision of the estimates, and in some quadrats with very small numbers of observed species enables an estimate in the first place (Kéry and R”
• Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community (2016, methodology): “Hyper-parameters specify the mean response and variation among species within the community to a covariate (K ery & Royle 2008).”
• Predicting species distributions from checklist data using site‐occupancy models (2010, background): “Site-occupancy models also form the basis for the recently developed models for inference about communities, both static (Dorazio & Royle, 2005; Gelfand et al., 2005; Dorazio et al., 2006; Latimer et al., 2006; Kéry & Royle, 2008a,b) and dynamic (Kéry et al., 2009; Dorazio et al., in press).”
• Effects of drought on avian community structure (2009, methodology): “This diverse region, with its wide range of temperature, precipitation, and elevation, includes both eastern and western United States bird species.”
• Accounting for detectability improves estimates of species richness in tropical bat surveys (2011, methodology): “Thus, comparisons of species richness over temporal or spatial scales will be distorted if based on simple counts of species (Boulinier et al. 1998; Pollock et al. 2002; Kéry & Royle 2008).”
• Occupancy dynamics in a tropical bird community: unexpectedly high forest use by birds classified as non‐forest species (2010, methodology): “As in previous applications of this method (e.g. Ke´ry & Royle 2008) we assumed that occupancy for species i at site j in year t , denoted z ( i , j , t ), was a latent variable (i.e. imperfectly observed) such that z ( i , j , t ) = 1 when species i was present and zero otherwise.”
• Realising the full potential of citizen science monitoring programs (2013, methodology): “…within these datasets is also 233 useful for improving the performance of spatial analysis methods such as species distribution modelling (e.g. 234 Araujo et al. 2005; Bahn and McGill 2007; Brotons et al. 2007; Luoto et al. 2007), and estimation of species 235 richness (Kery and Royle 2008).”