Generalized site occupancy models allowing for false positive and false negative errors.

By J. Andrew Royle, W. Link. Ecology, 2006

377 citations recorded by the source corpus.

Subfields: Ecology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8258db87147a22a7a3b7a3a93ebc6db5c8494797

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In Ecology

In Statistics and Probability

Citations in the corpus

Statistical solutions for error and bias in global citizen science datasets (2014, background): “…have been published for handling systematic biases 512 common to CS data, including false absences (Farmer 2012) and false 513 presences (i.e., mistakenly recording a species as present when it was in fact 514 absent, Royle and Link, 2006), and species misidentifications (Conn et al, 515 2013).”
Genetic and Manual Survey Methods Yield Different and Complementary Views of an Ecosystem (2017, methodology): “Occupancy Modeling Species (or “site”) occupancy modeling provides an accessible framework for interpreting taxon detections in eDNA studies (Lahoz-Monfort et al., 2015; see also Ficetola et al., 2015; more broadly, seeMiller et al., 2011; Royle and Link, 2006), andwe used this method in a simple si”
Dealing with false‐positive and false‐negative errors about species occurrence at multiple levels (2017, methodology): “These approaches are based on the mixture model in Royle & Link (2006), which describes detections as being generated by either true detections at occupied sites, or false detections at unoccupied sites.”
A plan for the North American Bat Monitoring Program (NABat) (2015, background): “As in occupancy analyses, abundance estimates are conditioned on confirmed identifications (Kéry and Schmidt 2008; MacKenzie and others 2003, 2009; Royle and Link 2006).”
Occupancy dynamics in a tropical bird community: unexpectedly high forest use by birds classified as non‐forest species (2010, methodology): “Current methodology in occupancy modelling accounts for the likelihood of false absences by incorporating detection probabilities, generating more accurate predictions with regards to the true state of occupancy and related dynamics (Royle & Link 2006; Nichols et al. 2008).”
Predicting species distributions from checklist data using site‐occupancy models (2010, background): “The assumption that the data contain no false positives is important, because the latter can greatly inflate occupancy estimates (Royle & Link, 2006).”
Imperfect detection and its consequences for monitoring for conservation (2008, background): “…(the closure assumption, see case study 2 for how closure problems may be relaxed), (ii) species are not falsely recorded when absent (but see Royle and Link 2006), (iii) all sites have the same probability of occupancy, extinction, colonisation and detection, except insofar as differences…”
Making Great Leaps Forward: Accounting for Detectability in Herpetological Field Studies (2007, positive): “For instance, Royle and Link (2006) developed models where the species can be wrongly identified as present when it is absent from the site (e.g., for cases where volunteers in a monitoring program have variable experience).”
Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data (2015, background): “However, misidentification of species is possible even with traditional ecological data, and SOMs have thus been expanded to account for potential false presences (Royle & Link 2006; Miller et al. 2011).”
Advances and applications of occupancy models (2014, background): “…errors (< 5% of all detections) caused severe overestimation of site occupancy, colonization and local extinction probabilities, as well as spurious relationships between these parameters and explanatory variables (Royle & Link 2006; McClintock et al. 2010a; Miller et al. 2011, 2013).”
Predicting species distributions for conservation decisions (2013, background): “…reducing uncertainty, it is useful to consider whether the uncertainty is reducible (Barry & Elith 2006) and whether a reduction in uncertainty might lead to decisions that yield better management outcomes (Regan et al. 2005), a concept generally known as value of information (Runge et al. 2011).”
Misidentification of freshwater mussel species (Bivalvia:Unionidae): contributing factors, management implications, and potential solutions (2011, methodology): “In contrast, false positive errors tend to cause overestimation of population size and species presence, but relatively few modeling approaches have been developed to accommodate this source of bias in survey data (but see Royle and Link 2006, Runge et al. 2007).”
Hierarchical Species Distribution Models (2016, methodology): “Also, data models have been recently developed to deal with false-positives [2, 13, 87, 88] and species misidentification [76].”