Predicting species distributions from checklist data using site‐occupancy models

By Marc Kéry, Beth Gardner, C. Monnerat. 2010

232 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/6597bdbec1f23e644e7eb966e67bfb00c07550d6

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. G. Guillera‐Arroita
2. J. Guélat
3. J. Lahoz‐Monfort
4. Beth Gardner
5. K. Ma
6. Guoke Chen
7. F. Archaux
8. P. Henry

In Ecology

1. G. Guillera‐Arroita
2. J. Guélat
3. Beth Gardner
4. J. Lahoz‐Monfort
5. P. Henry
6. K. Ma
7. Guoke Chen
8. Olivier Gimenez

In Nature and Landscape Conservation

1. G. Guillera‐Arroita
2. J. Guélat
3. Beth Gardner
4. F. Archaux
5. Olivier Gimenez
6. P. Henry
7. J. Lahoz‐Monfort
8. Marc Kéry

Citations in the corpus

• Statistical solutions for error and bias in global citizen science datasets (2014, methodology): “642 643 Highly clustered presence-only data, which seem to represent the worst of all 644 possible scenarios, have received recent attention in SDMs.”
• Analysing and mapping species range dynamics using occupancy models (2013, background): “…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 is itself a function of the habitat covariates (K ery, 2010; K ery et al., 2010a; K ery & Schaub, 2012).”
• When can we ignore the problem of imperfect detection in comparative studies? (2012, methodology): “This approach relies on estimating the mean detectability and its standard deviation by replicating counts over a representative part of the study plots only (Bart & Earnst 2002; Pollock et al. 2002) and has been successfully used with patch occupancy models (Kéry et al. 2010b).”
• Effects of spatial autocorrelation and imperfect detection on species distribution models (2018, methodology): “Site- occupancy models for presence/absence data have been increasingly used as a powerful framework for SDMs (Altwegg, Wheeler, & Erni, 2008; Guillera- Arroita et al., 2015; Kéry, 2011; Kéry et al., 2010; Lahoz- Monfort et al., 2014).”
• Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities (2017, methodology): “Disregarding false absences can lead to biased inference about species distributions and overstated precision about estimated parameters (Tyre et al. 2003, Gu and Swihart 2004, Kéry et al. 2010a, Lahoz-Monfort et al. 2014), even when detectability is constant.”
• Imperfect detection is the rule rather than the exception in plant distribution studies (2013, background): “…approach to estimate detection probability, and therefore to obtain unbiased estimates of occurrence, may not always be an option (though see, for instance, Kéry et al. 2010b; who extracted replicated detection–nondetection data from what is essentially a multispecies presence-only data base).”
• Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates (2020, background): “Models for detection probability can be constructed from repeated sampling of the same locations (cf. Kery et al. 2010).”