Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models

By W. Gu, Robert Swihart. 2004

796 citations recorded by the source corpus.

Subfields: Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/c06e1152d9be7c4cf16de4575e5c63a9f68699f6

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In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Generalized site occupancy models allowing for false positive and false negative errors. (2006, positive): “As incremental methodological extensions have been developed (e.g., Royle and Nichols 2003, MacKenzie et al. 2003, 2005, Tyre et al. 2003, Gu and Swihart 2004, MacKenzie and Bailey 2004, Royle 2004a, b), the site occupancy framework is becoming widely adopted in survey and monitoring activities in…”
Imperfect detection is the rule rather than the exception in plant distribution studies (2013, background): “…observed occurrence and species distributions as the true occurrence and distribution, that is failing to make amendments for imperfect detection, may lead to problems in species distribution studies (Kéry 2011), habitat models (Gu & Swihart 2004) and biodiversity management (Chades et al. 2008).”
Spatial and Temporal Structure of a Mesocarnivore Guild in Midwestern North America (2015, background): “Collectively, detection models reinforced the importance of accounting for imperfect detection of carnivores when modeling species distributions based on non-invasive survey methods (Gu and Swihart 2004, Field et al. 2005, MacKenzie et al. 2006, Rota et al. 2011).”
Selecting pseudo‐absences for species distribution models: how, where and how many? (2012, background): “As false absence data can have negative effects on SDM (Gu & Swihart 2004), different strategies have been proposed to improve the selection of an appropriate pseudo-absence data set.”
Accounting for uncertainty when mapping species distributions: The need for maps of ignorance (2011, background): “…shifts from year to year (thus being temporarily absent when the surveys are carried out), may be missed simply because the habitat or site in question has not been accurately sampled (Gu and Swihart, 2004; Lobo et al., 2007; Stohlgren, 2007), or may have been misidentified (Berendsohn, 1995).”
Use of Maximum Entropy Modeling in Wildlife Research (2009, background): “Additionally, absence data is difficult to verify given the potential for a species to be present at a site but not observed resulting in substantially biased wildlife–habitat relationships [1,2].”
Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation (2013, background): “the species is present but has not been observed (Gu and Swihart, 2004).”
A behavioural ecology approach to understand volunteer surveying for citizen science datasets (2012, background): “In addition, absences of species records might reflect differences in species detectability, survey effort or observer ability rather than true absences, leading to erroneous estimates of species distribution and richness if species are actually present but remain undetected (Gu and Swihart 2004).”
ESTIMATING AND DEALING WITH DETECTABILITY IN OCCUPANCY SURVEYS FOR FOREST OWLS AND ARBOREAL MARSUPIALS (2005, background): “…much lower than 1 (Kery 2002, Tyre et al. 2003, Bailey et al. 2004) and that false absences may have a negative impact on species-habitat analyses (Tyre et al. 2003, Gu and Swihart 2004), metapopulation models (Moilanen 2002), and monitoring studies (MacKenzie et al. 2003, Rhodes et al. 2005).”
Modelling occurrence and abundance of species when detection is imperfect (2005, methodology): “This theory incorporates the basic structnral elements that were the focus of He and Gaston (2003), and includes inference procedures based on observable qnantities as elaborated by Ke ´ry (2002), MacKenzie et al. (2002, 2003), Royle and Nichols (2003), Tyre et al. (2003), Gu and Swihart (2004) , a”
Community structure and differential responses of aquatic turtles to agriculturally induced habitat fragmentation (2006, background): “Failure to detect a species during sampling can result in serious errors in subsequent logistic regression models (Gu and Swihart 2004).”
Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities (2017, background): “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.”