WAS IT THERE? DEALING WITH IMPERFECT DETECTION FOR SPECIES PRESENCE/ABSENCE DATA †

By D. MacKenzie. 2005

163 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e57e5b7cc8e9cd9275331ca69829839191b3e124

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. Jane Elith
2. R. P. Anderson
3. Rune Halvorsen
4. Steven J. Phillips
5. Miroslav Dud'ik
6. C. Yates
7. J. Andrew Royle
8. A. Townsend Peterson

In Ecology

1. Jane Elith
2. D. MacKenzie
3. Steven J. Phillips
4. Miroslav Dud'ik
5. J. Andrew Royle
6. C. Yates
7. Rune Halvorsen
8. Trevor J. Hastie

In Global and Planetary Change

1. J. Andrew Royle
2. D. MacKenzie
3. C. Yates
4. Jane Elith
5. Steven J. Phillips
6. Gregory B. Pauly
7. B. R. Schmidt
8. L. Bailey

Citations in the corpus

• Citizen Science as a Tool for Augmenting Museum Collection Data from Urban Areas (2017, background): “…sampling is haphazard and so may not be even across the study region, and because participants may differ in their ability to detect different species, it is difficult to use citizen science to reliably document species absence or abundance (Kéry, 2002; Mackenzie, 2005; Dickinson et al., 2010).”
• A statistical explanation of MaxEnt for ecologists (2011, background): “As an aside we note, however, that absence data are plagued by issues of detection probability (Wintle et al., 2004; MacKenzie, 2005) so that even presence-absence data may not yield a good estimate of prevalence.”
• Probabilistic historical biogeography: new models for founder-event speciation, imperfect detection, and fossils allow improved accuracy and model-testing (2013, unknown): “ 2003;  Mackenzie  2005;  Royle,  Nichols  &   Kéry  2005;  Bailey  et  al.”
• WHAT ARE THE ISSUES WITH PRESENCE–ABSENCE DATA FOR WILDLIFE MANAGERS? (2005, methodology): “MacKenzie (2005a) also illustrated this, where a variable that affected detection probability was mistakenly identified as being important in a resource selection application when using a simple logistic regression approach.”
• Designing occupancy studies: general advice and allocating survey effort (2005, methodology): “Unaccounted for ‘false absences’ will lead to underestimates of the true level of occupancy, and estimates of the relative change in occupancy will only be valid if the surveyors’ ability to detect the species is identical in the two time periods (MacKenzie 2005a).”