Trend estimation in populations with imperfect detection

By Marc Kéry, R. Dorazio, L. Soldaat, A. Strien, A. Zuiderwijk, J. Andrew Royle. 2009

248 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/874c67aaa6d01ab46aab5425758848ee2f44d6b3

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Citations in the corpus

The North American Breeding Bird Survey 1966–2011: Summary Analysis and Species Accounts (2013, methodology): “…methods is now available than could have been employed when the BBS was started, computer advances have revolutionized data management, and modern analytical methods can incorporate these approaches by adding additional components to the hierarchical model described above (e.g., Kéry et al. 2009).”
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).”
Bias and information in biological records (2015, background): “‘recordable’) on a site, because detection is less than perfect (Kéry et al., 2009; Chen et al., 2013).”
Intrinsic heterogeneity in detection probability and its effect on N‐mixture models (2016, background): “In order formixturemodels to performwell (high accuracy), it may be most important to have high detection probabilities (≥0 5) (Royle 2004; K ery et al. 2009; Couturier et al. 2013) more so than to completely avoid intrinsic detection heterogeneity.”
Long-term monitoring of tropical bats for anthropogenic impact assessment: Gauging the statistical power to detect population change (2010, methodology): “It would further ensure that newly developed hierarchical mixture models that explicitly correct for imperfect detection (Kéry et al., 2009; Kéry and Royle, 2010) can be applied at the analysis stage.”
The first 50 years of the North American Breeding Bird Survey (2017, background): “In each, the underlying population size (at stops or routes, depending on the analysis) is viewed as a latent parameter, and modeled connections of the counts to the underlying population sizes form the basis of inference (Kéry et al. 2009).”
Decomposing trends in Swedish bird populations using generalized additive mixed models (2016, background): “In other cases, the sampling design may allow correction for the variation in the proportionality constant (K ery et al. 2009), for example by including estimates of detection probabilities (Harrison et al. 2014) or the use of covariates believed to capture temporal or spatial variation in the…”
Needles in haystacks: Estimating detection probability and occupancy of rare and cryptic snakes (2011, background): “45; Roughton and Seddon, 2006) and for Lacerta agilis in the Netherlands (0.05–0.40; Kéry et al., 2009).”
Population decline assessment, historical baselines, and conservation (2010, background): “Bayesian inference can accommodate some data limitations such as imperfect detection and census gaps (e.g., Royle & Kéry 2007; Brooks et al. 2008; Kéry et al. 2009).”
Unstructured citizen science data fail to detect long‐term population declines of common birds in Denmark (2016, background): “In particular, imperfect detection is a well-known problem for the monitoring of wild animals (Royle & Nichols, 2003; K ery et al., 2009).”