Modeling the Probability of Resource Use: The Effect of, and Dealing with, Detecting a Species Imperfectly

By D. MacKenzie. 2006

369 citations recorded by the source corpus.

Subfields: Ecology, Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/77745554ca6112ee7e985927ee5a156d4bb49839

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In Ecology

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Citations in the corpus

Designing occupancy studies: general advice and allocating survey effort (2005, background): “Importantly, resulting inferences about the ‘value’ of different habitats could be severely misleading if detection probabilities are correlated with occupancy probabilities (Gu & Swihart 2004; MacKenzie 2006).”
MEASURING HABITAT QUALITY: A REVIEW (2007, background): “Note that this discussion relates to temporal patterns in occupancy; examining spatial patterns of occupancy (e.g., ‘‘occupancy modeling’’ with presence/absence analyses) is a form of useavailability habitat selection analysis (MacKenzie 2006), which is discussed above.”
Population-level resource selection by sympatric brown and American black bears in Alaska (2009, background): “A disadvantage of this approach is that areas considered unused may actually be used but not detected because of low sampling frequency or animal detection rates of \1 (Johnson et al. 2006; MacKenzie 2006).”
Sampling in ecology and evolution – bridging the gap between theory and practice (2010, methodology): “To avoid the problems of sampling-induced bias due to the detectability of sampling units an important topic not evaluated in our simulation we assumed that the variable of interest had a detectability of 1.0, occurred everywhere, and is stable over time (Wintle et al. 2005, Mackenzie 2006).”
Domestic dogs shape the landscape-scale distribution of a threatened forest ungulate (2012, methodology): “First we used our global model for use of space to select the model that better explained probability of detection (Lebreton et al., 1992; MacKenzie, 2006).”
The role of climate, habitat, and species co‐occurrence as drivers of change in small mammal distributions over the past century (2011, methodology): “Using detailed field notes on trap captures in the historical and modern surveys, we were able to calculate the probability of detection of each species at each site following the methods of MacKenzie et al. (2002), MacKenzie (2006).”
Analysing and mapping species range dynamics using occupancy models (2013, background): “Otherwise, rather than capturing the actual distribution of the species, the area where the species is detected 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 ”
Improving distribution data of threatened species by combining acoustic monitoring and occupancy modelling (2016, methodology): “We chose a 2-step approach to select the most parsimonious model as proposed by MacKenzie (2006): first, we kept the occurrence part of the model constant (intercept-only) and tested the effect of each variable (e.g. elevation, forest type, hour) separately in the detection parameter.”
Occupancy dynamics in a tropical bird community: unexpectedly high forest use by birds classified as non‐forest species (2010, background): “However, most of the predictions based on occupancy dynamics in fragmented landscapes under the BIDE and CE models assume that an individual or species, respectively, was absent when not observed in a habitat patch (Moilanen 2002; MacKenzie 2006).”
Predicting species distributions from checklist data using site‐occupancy models (2010, background): “…in conventional species distribution modelling: the quantity being modelled, which is the apparent, not the true, distribution of a species, and the risks of erroneous inferences inherent in this approach (MacKenzie et al., 2002, 2003, 2006; Tyre et al., 2003; MacKenzie, 2006; Eraud et al., 2007).”