Imperfect detection and its consequences for monitoring for conservation

By Marc Kéry, B. R. Schmidt. 2008

346 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/2ce8e89bac3818851239f71d6438acc670e91603

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

Predicting species distributions from checklist data using site‐occupancy models (2010, background): “Finally, any pattern in detection, for instance a relationship with habitat or a time trend, will be reflected in the species distribution model unless p is properly accounted for (MacKenzie et al., 2006; Kéry & Schmidt, 2008).”
French citizens monitoring ordinary birds provide tools for conservation and ecological sciences (2012, methodology): “Only Switzerland has a systematic sampling design monitoring (Kéry et al., 2005).”
Next‐generation monitoring of aquatic biodiversity using environmental DNA metabarcoding (2016, background): “Their distributions are often poorly known because the detection probabilities using traditional survey methods can be very low and may vary because of local environmental conditions (K ery & Schmidt 2008; Tanadini & Schmidt 2011).”
Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data (2015, background): “…traditional ecological field studies, individuals and species that are present at one site are not always all detected, and failure in accounting for imperfect detection may result in biased inference (Yoccoz et al. 2001; MacKenzie et al. 2006; Kery & Schmidt 2008; Lahoz-Monfort et al. 2014).”
Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring (2018, background): “The first is imperfect detectability: the probability of successfully detecting a vocalising animal depends on its distance from the sensor, vocalising behaviour, call parameters, and site- specific environmental factors (Darras et al., 2016; Kéry & Schmidt, 2008).”
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).”
How will the ‘molecular revolution’ contribute to biological recording? (2015, background): “…prevalence, estimate detection probability, determine the number of samples and site visits needed to obtain a high probability of detection (Kéry & Schmidt, 2008; Pilliod et al., 2013; Schmidt et al., 2013; Ficetola et al., 2014) and test for the effects of site characteristics, such as…”
Analysing and mapping species range dynamics using occupancy models (2013, background): “…as ‘occupancy modelling’ (MacKenzie et al., 2002; Tyre et al., 2003), that formally accounts for one of the hallmarks of ecological data: imperfect detection (Yoccoz et al., 2001; K ery, 2002; K ery & Schmidt, 2008), an issue also relevant for sessile organisms such as plants (Chen et al., 2013).”
Effects of spatial autocorrelation and imperfect detection on species distribution models (2018, background): “Imperfect detection is especially ubiquitous in ecological datasets of distribution and abundance (Kellner & Swihart, 2014; Kéry & Schmidt, 2008; Royle, Kéry, Gautier, & Schmid, 2007).”
Probabilistic historical biogeography: new models for founder-event speciation, imperfect detection, and fossils allow improved accuracy and model-testing (2013, neutral): “ 2009) and conservation biology (Kéry & Schmidt 2008; Link & Barker 2009).”