Point process models for presence‐only analysis

By Ian W. Renner, Jane Elith, Adrian Baddeley, William Fithian, Trevor J. Hastie, Steven J. Phillips, Gordana C. Popovic, David I. Warton. 2015

409 citations recorded by the source corpus.

Subfields: Applied Mathematics, Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/39165175180c39e3aacb4b5cce519070fd7076d3

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Citations in the corpus

Hierarchical Species Distribution Models (2016, methodology): “As a result, variants of the Poisson point process model are popular for modeling species’ distributions using presenceonly data [83, 99].”
Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species (2018, methodology): “All of these methods are closely related to the Poisson point process model recently viewed as an appropriate way to analyse presence-only data in a regression framework (Aarts, Fieberg, & Matthiopoulos, 2012; Renner & Warton, 2013; Renner et al., 2015; Warton & Shepherd, 2010).”
Predicting distributions of invasive species (2013, methodology): “ A statistical explanation of MaxEnt for ecologists.”
Data Integration for Large-Scale Models of Species Distributions. (2020, methodology): “Several methods exist to fit point processes to data [66].”
A standard protocol for reporting species distribution models (2020, background): “Most algorithms then require background data (also called ‘pseudo-absences’ or ‘quadrature points’) against which the observed presences are compared (Renner et al. 2015).”
The area under the precision‐recall curve as a performance metric for rare binary events (2019, methodology): “…that multiple analytical frameworks for modelling species presence data can be specified such that they are equivalent, including Maxent, Poisson point process models, generalized linear models and resource selection functions (Fithian & Hastie, 2013; Warton & Aarts, 2013; Renner et al., 2015).”
Will remote sensing shape the next generation of species distribution models? (2015, background): “Given the opportunities provided by remote sensing presented above, along with data collected from well-designed experiments, field plots and in situ sensors, the NG-SDMs could develop rapidly, building upon recent development in SDMs (Lurgi et al. 2015; Renner et al. 2015).”
blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models (2018, background): “These can be coupled with a large sample of points across the landscape, known as background data (Renner et al., 2015).”
Spatial modelling with R-INLA: A review (2018, background): “However, in many cases the usual assumption of either “window sampling” or the “small world model” (Baddeley et al., 2015) does not necessarily hold.”
Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection (2017, background): “Therefore, models of spatial point processes (which include Poisson processes) arguably provide a conceptual unification for the analysis of PB data (Dorazio 2014; Renner et al. 2015).”
Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities (2017, methodology): “Examples include the very popular MaxEnt (Phillips et al. 2006, Elith et al. 2011) and related point process methods (Warton and Shepherd 2010, Renner et al. 2015).”
How to make more out of community data? A conceptual framework and its implementation as models and software. (2017, methodology): “Within the HMSC framework, such data could be analysed e.g. by linking the linear predictor to a spatially varying intensity of an underlying point process reflecting the sampling effort (Renner et al. 2015).”