Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities

By G. Guillera‐Arroita. 2017

396 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/a4444df87554e25c20c41a60608252fc2a0c4d0d

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

How to make more out of community data? A conceptual framework and its implementation as models and software. (2017, methodology): “The first perspective relates to the need to explicitly account for the observation process when modelling the data, thus allowing one to separate the processes of interest from biases introduced by the observer (Guillera-Arroita 2017; Beissinger et al. 2016; Warton et al. 2016).”
A quantitative review of abundance-based species distribution models (2021, background): “A lack of observed presence is not necessarily a ‘true absence’, but instead suggests species were undetectable with a reasonable sampling effort (Guillera-Arroita 2017).”
Data Integration for Large-Scale Models of Species Distributions. (2020, background): “dance in space and time, can be built [8,9].”
Using environmental DNA methods to improve winter surveys for rare carnivores: DNA from snow and improved noninvasive techniques (2019, methodology): “…and Dreitz, 2016, see Table 1) and modern statistical modeling tools such as Bayesian hierarchical models (e.g., Royle and Nichols, 2003; Royle and Dorazio, 2008; Kéry and Schaub, 2012; Iknayan et al., 2014; Guillera-Arroita, 2017, see Table 4) that can explicitly account for missed detections.”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “portion of the zeros are likely to result from lack of observation or observation error rather than true absences (Guillera-Arroita 2017).”
A standard protocol for reporting species distribution models (2020, methodology): “Low detection probability will inevitably yield false absences (Guillera-Arroita 2017).”