Modelling occurrence and abundance of species when detection is imperfect

By J. Andrew Royle, James D. Nichols, Marc Kéry. 2005

383 citations recorded by the source corpus.

Relationships between species abundance and occupancy are of considerable interest in metapopulation biology and in macroecology. Such relationships may be described concisely using probability models that characterize variation in abundance of a species. However, estimation of the parameters of these models in most ecological problems is impaired by imperfect detection. When organisms are detected imperfectly, observed counts are biased estimates of true abundance, and this induces bias in stated occupancy or occurrence probability. In this paper we consider a class of models that enable estimation of abundance/occupancy relationships from counts of organisms that result from surveys in which detection is imperfect. Under such models, parameter estimation and inference are based on conventional likelihood methods. We provide an application of these models to geographically extensive breeding bird survey data in which alternative models of abundance are considered that include factors that influence variation in abundance and detectability. Using these models, we produce estimates of abundance and occupancy maps that honor important sources of spatial variation in avian abundance and provide clearly interpretable characterizations of abundance and occupancy adjusted for imperfect detection.

Subfields: Ecology

View Paper: https://www.semanticscholar.org/paper/8bfd51be608856c292f7038226bf48dd5065e4d6

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In Ecology

Citations in the corpus

Making Great Leaps Forward: Accounting for Detectability in Herpetological Field Studies (2007, methodology): “—Point count models have been used primarily in ornithological contexts (Kéry et al., 2005; Royle et al., 2005).”
Global mapping of Jatropha curcas yield based on response of fitness to present and future climate (2010, methodology): “, 2009) and has been used to assess species absolute density distribution (Royle et al., 2005; Gaston et al., 2006).”
Predicting species distributions: a critical comparison of the most common statistical models using artificial species (2007, background): “…et al., 2005); differences in species prevalence (Segurado & Araújo, 2004); variability among observers and habitat types (Buckland et al., 2005; Royle et al., 2005); biased sampling intensity due to limited access of certain areas (Austin & Heyligers, 1989; Wessels et al., 1998); effects of…”
Use of niche models in invasive species risk assessments (2011, background): “…of species are much more readily available than data documenting absences (Soberón and Peterson 2005), thanks mainly to the difficulty of discriminating genuine absence from inadequate survey effort (MacKenzie et al. 2002; Royle et al. 2005; Anderson 2003; Lobo 2008a; Gu and Swihart 2004).”
Realising the full potential of citizen science monitoring programs (2013, methodology): “The addition of temporal replication by BBS datasets allows for the development of new trend analysis techniques that incorporate variability and imperfect detection (Lele, 2006; Royle et al., 2005).”
Difficult decisions: Strategies for conservation prioritization when taxonomic, phylogenetic and functional diversity are not spatially congruent (2018, background): “Despite some difficulties in estimating species abundances or richness from imperfect data (e.g., Chao and Jost, 2012; Chen et al., 2009; Kellner and Swihart, 2014; MacKenzie et al., 2009; Royle et al., 2005), taxonomic-based metrics are far easier to estimate than functional or phylogenetic ones.”
Synthesizing multiple data types for biological conservation using integrated population models (2018, background): “The importance of accounting for potential biases due to imperfect detection during sampling has been demonstrated repeatedly in studies estimating the population dynamics and trends of species (e.g., Royle et al., 2005; MacKenzie et al., 2009; Nichols et al., 2009).”
Measuring community responses to large‐scale disturbance in conservation biogeography (2009, background): “The most important factor affecting the local detectability of a given species is its abundance, N, which induces heterogeneity in site-specific detection probabilities (Royle et al., 2005).”