Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective

By Marc Kéry, Michael Schaub. 2011

1,012 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2314b3d2e34f36151ce7d38f936e0528ae86abd9

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Citations in the corpus

Integrated population models facilitate ecological understanding and improved management decisions (2018, background): “In 1995, the United States Fish and Wildlife Service (USFWS) implemented adaptive harvest management models for midcontinent mallards to better understand the role of harvest in affecting population dynamics and to help guide annual decision making (Johnson et al. 1997, Nichols et al. 2007).”
Diversification practices reduce organic to conventional yield gap (2015, methodology): “[43] rice France 9 no no no [44] hazelnut Turkey 1 no yes no [45] tomato, maize United States 45 no yes yes”
Wildlife in a Politically Divided World: Insularism Inflates Estimates of Brown Bear Abundance (2016, methodology): “The complement of the survival parameter 1- ϕ represents the joint probability of mortality and permanent emigration, whereas γ is a nuisance parameter without direct biological interpretation because it represents transition from the pool of (augmented) available individuals (Kéry & Schaub 2012).”
Estimating true instead of apparent survival using spatial Cormack–Jolly–Seber models (2014, methodology): “To implement the model, we use a state-space formulation (Gimenez et al. 2007; Royle 2008; K ery & Schaub 2012).”
Evaluating population viability and efficacy of conservation management using integrated population models (2018, methodology): “We fitted the IPM and BPVA using a Bayesian approach (Kéry & Schaub, 2012; Schaub, Gimenez, Sierro, & Arlettaz, 2007; Schaub & Abadi, 2011).”
Integrated population models: Model assumptions and inference (2019, methodology): “For each simulated dataset, we fit Bayesian CJS models (Cormack, 1964; Jolly, 1965; Seber, 1965) for the capture‐recapture data (M), state‐space models (Kéry & Schaub, 2012) for the count data (y) and Poisson regression models for the fecundity (M) data, as well as two IPMs with the combined data.”
Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE (2015, background): “Many books use BUGS to teach Bayesian statistical modeling (e.g., Lancaster 2004; Kery and Schaub 2011; Vidakovic 2011), and domain scientists find that it helps them to reason clearly about models (Kery and Schaub 2011).”
Abundance, biomass production, nutrient content, and the possible role of terrestrial salamanders in Missouri Ozark forest ecosystems (2014, methodology): “01; Gelman and Hill 2007) and performed posterior predictive checks (Bayesian P value) to confirm adequate model fit (Kéry and Schaub 2012).”
Monitoring in the presence of species misidentification: the case of the Eurasian lynx in the Alps (2012, methodology): “We fitted the model in the Bayesian framework of inference using Markov Chain Monte Carlo simulation techniques implemented in the WinBUGS software (Gilks, Thomas & Spiegelhalter, 1994; Lunn et al., 2000), as did Royle & Kéry (2007) and Kéry & Schaub (2011).”
Unstructured citizen science data fail to detect long‐term population declines of common birds in Denmark (2016, unknown): “…is the standard analytical procedure for estimating trends from bird count data when no ancillary data (e.g. distance to detected birds, continuous covariates affecting detectability) or repeat visits are available to account for imperfect detection (K ery & Schaub, 2012; Inger et al., 2014).”
Analysing and mapping species range dynamics using occupancy models (2013, methodology): “…is estimated, thus leading to incorrect inferences regarding habitat relationships (Tyre et al., 2003; Gu & Swihart, 2004; MacKenzie, 2006), a problem which is greatest when detectability is itself a function of the habitat covariates (K ery, 2010; K ery et al., 2010a; K ery & Schaub, 2012).”
Imperfect detection is the rule rather than the exception in plant distribution studies (2013, background): “…been developed to correct for imperfect detection in population analyses for inference about distribution, abundance and vital rates (Seber 1982; Buckland et al. 2001; Borchers, Buckland & Zucchini 2002; Williams, Nichols & Conroy 2002; Royle & Dorazio 2008; King et al. 2010; Kéry & Schaub 2012).”