Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities

By J. Andrew Royle, R. Dorazio. 2008

1,268 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2fdcd4456b740aaf63c7a88697ebd8ecfe86eb99

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Citations in the corpus

When can we ignore the problem of imperfect detection in comparative studies? (2012, methodology): “These methods are mostly based on generalised linear models and can incorporate a rich variety of factors possibly affectingN and p (see Royle & Dorazio 2008; King et al. 2009).”
Analysing and mapping species range dynamics using occupancy models (2013, methodology): “Models can also be easily implemented in tools for Bayesian inference such as WinBUGS (Lunn et al., 2000), OpenBUGS (Lunn et al., 2009) or JAGS (Plummer, 2003); code can be found in Royle & K ery (2007), Royle & Dorazio (2008) and K ery & Schaub (2012).”
Misidentification of freshwater mussel species (Bivalvia:Unionidae): contributing factors, management implications, and potential solutions (2011, methodology): “Bayesian modeling approaches could be used (e.g., Gelman and Hill 2007, Royle and Dorazio 2008, Link and Barker 2009) to include this a priori knowledge formally in modeling and estimation procedures to improve estimates from modeling approaches that accommodate species identification errors (Royle…”
What ' s on the horizon for macroecology? (2012, background): “2009), it can be argued that Bayesian approaches are more transparent since they lay open the mechanisms modeled as well as the sampling processes (Royle and Dorazio 2008).”
Direct human‐caused mortality of birds: improving quantification of magnitude and assessment of population impact (2012, methodology): “Use of different mortality values can reflect different precaution levels. back of HB methods is that the complex models used to generate credibility intervals may not be easily under-standable (Royle and Dorazio 2008).”
Accounting for non‐independent detection when estimating abundance of organisms with a Bayesian approach (2011, background): “However, the standard binomial mixture model assumes that organisms are detected independently of each other (Royle & Dorazio 2008).”
French citizens monitoring ordinary birds provide tools for conservation and ecological sciences (2012, methodology): “In the FBBS, we use capture-recapture models to estimate species richness and community dynamics, but similar framework have been developed to obtain estimates of abundance using counts or presenceeabsence data (Kéry et al., 2005; Royle et al., 2007; Royle and Dorazio, 2008; Dupuis et al., 2011).”
Occupancy dynamics in a tropical bird community: unexpectedly high forest use by birds classified as non‐forest species (2010, methodology): “We assumed that species identity and the habitat type (forest or non-for-est)were the onlyeﬀectsondetection: In the community hierarchal level of the model, the species-speciﬁc parameters were linked together through a common distribution (Royle & Dorazio 2008).”
Predicting species distributions from checklist data using site‐occupancy models (2010, background): “Finally, it has become clear that site-occupancy models are just a special case of a general hierarchical metapopulation modelling framework (Royle, 2004; Royle & Dorazio, 2006, 2008).”
Machine learning to classify animal species in camera trap images: applications in ecology (2018, background): “…developing statistically sound data for species observations is often difficult and expensive (Underwood, Chapman, & Connell, 2000) and significant effort has been devoted to correcting bias in more easily collected or opportunistic observation data (Royle & Dorazio, 2008; MacKenzie et al., 2017).”
Detecting small changes in populations at landscape scales: a bioacoustic site-occupancy framework (2019, methodology): “…L θ y Λ ( ) ( )A 0 The likelihood ratio statistic is then computed as −2 log(Λ), and if the null is true, it asymptotically follows a chi-squared distribution, χ2(v), where degrees of freedom v is the difference in the number of parameters to be estimated under HA and H0 (Royle and Dorazio, 2008).”
Estimating abundance of mountain lions from unstructured spatial sampling (2012, methodology): “We conducted a Bayesian analysis of the models using data augmentation (Royle and Dorazio 2008, Royle and Young 2008), a process in which the data set is augmented with a large number of undetected individuals, each having an all zero encounter history.”
A guide to Bayesian model selection for ecologists (2015, methodology): “We focus on the same species considered by Royle and Dorazio (2008), the Willow Tit (Parus montanus), a relatively common passerine in Europe that resembles the Chickadee of North America in appearance (see Plate 1).”