Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence

By D. MacKenzie. 2005

2,674 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/39f71db3644086329e0a1ce79717e5db89582ec1

How researchers see this paper

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

Citations in the corpus

Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches (2010, methodology): “These data can be modeled to estimate NC and population growth rates (MacKenzie et al. 2006).”
Comparative interpretation of count, presence–absence and point methods for species distribution models (2012, background): “Such occupancy data (MacKenzie et al. 2005) are often modelled as a Bernoulli random variable.”
Inference About Species Richness and Community Structure Using Species-Specific Occupancy Models in the National Swiss Breeding Bird Survey MHB (2009, background): “An estimate of Z enables rich inference about a metacommunity; for instance, summing across species yields quadrat-specific species richness and summing across quadrats essentially yields occupancy for each species, another central quantity in ecology and its applications (MacKenzie et al. 2006).”
Contemporary climate change alters the pace and drivers of extinction (2011, result): “Our finding of high detectability in O. princeps contrasts with very low detectability that is typical for most mammals (MacKenzie et al., 2006), but mirrors findings from other studies of the species.”
Seven considerations about dingoes as biodiversity engineers: the socioecological niches of dogs in Australia (2012, background): “However, assessments of broad-scale distribution patterns often cannot describe the fine-scale relationships between predator and prey (MacKenzie et al. 2006; Allen 2011; McLachlan and Ladle 2011).”
Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range (2015, methodology): “Methods for estimating species distribution include ‘hierarchical’ models that explicitly separate measurement and biological process models (MacKenzie et al. 2005; Royle & Wikle 2005), regression-based methods that correlate detection and count data to measured variables*Correspondence author.”
Predicting carnivore occurrence with noninvasive surveys and occupancy modeling (2011, methodology): “We used likelihood-based occupancy modeling (MacKenzie et al. 2002; MacKenzie and Bailey 2004; MacKenzie et al. 2006) to estimate both site occurrence (w; probability that the species occurred at a site) and detectability (p; probability that the species was detected if present) from our detection– ”
Spatially explicit integrated population models (2014, background): “Although such survey data can be useful for studying trends in abundance or patch dynamics (Chandler & King 2011; Mackenzie et al., 2006), they contain little information about the demographic processes governing population dynamics (Brooks, King & Morgan 2004).”