Modelling distribution and abundance with presence‐only data

By J. Pearce, M. Boyce. 2006

659 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/286eaaad4a533c3150853a376cb92d9cbe5c4a55

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Citations in the corpus

Where will species go? Incorporating new advances in climate modelling into projections of species distributions (2007, methodology): “…Zimmermann, 2000), and generally models that allow the use of both presence and absence data result in more accurate projections of the current potential distributions of species than those that utilize presence-only data (e.g. see Ferrier & Watson, 1997; Elith et al., 2006; Pearce & Boyce, 2006).”
Habitat suitability modelling to correlate gene flow with landscape connectivity (2008, methodology): “Models of habitat suitability or species distribution (Guisan and Zimmermann 2000; Guisan and Thuiller 2005; Pearce and Boyce 2006 ) can help select reserve networks (Tole 2006; Zielinski et al. 2006) and evaluate connectivity (Binzenho ¨fer et al. 2005), because these models predict the distributi”
Error and uncertainty in habitat models (2006, methodology): “The most statistically accurate way to model with pseudo-absence data is through the use of specialized models such as case control (Keating & Cherry 2004; Pearce and Boyce 2006) and resource selection functions (Manly et al.”
Using presence‐only modelling to predict Asian elephant habitat use in a tropical forest landscape: implications for conservation (2010, methodology): “The HS model was validated using a continuous Boyce validation technique available within Biomapper software (Hirzel et al., 2006; Pearce & Boyce, 2006).”
What shapes Eurasian lynx distribution in human dominated landscapes: selecting prey or avoiding people? (2009, methodology): “We had data on observations of lynx throughout the study area, based on a presence-only design (Pearce and Boyce 2006).”
A statistical explanation of MaxEnt for ecologists (2011, methodology): “So far we have treated presence or absence as a binary event, but in reality defining the response variable is not straightforward, and in this regard, presence-only data are quite different from presence-absence data (Pearce & Boyce, 2006).”
Species distribution modelling of marine benthos: a North Sea case study (2011, methodology): “Thus, Bayesian and other modelling ap - proaches will be necessary tools in future prediction of quantitative characteristics of species distribution (Pearce & Boyce 2006, Potts & Elith 2006, Anadón et al. 2010, Chakraborty et al. 2010).”
Making better biogeographical predictions of species’ distributions (2006, methodology): “presence-only) data, for example bioclimatic envelopes, ecological niche factor analysis and standard methods employing pseudo-absences ( Pearce & Boyce 2006 ), problems remain in the evaluation of predictions from these models.”
Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection (2017, background): “…is plentifully available from the socalled ‘opportunistic surveys’ and can be found in museum and herbarium collections, historical database records (Pearce & Boyce 2006), and is now becoming increasingly available via online repositories such as theGlobal Biodiversity Information Facility…”
Evaluating the ability of habitat suitability models to predict species presences (2006, background): “This map may result from any statistical analysis (Guisan and Zimmermann, 2000; Pearce and Boyce, 2006).”
Variation in niche and distribution model performance: The need for a priori assessment of key causal factors (2012, methodology): “, 2011); Desktop GARP uses pseudoabsences to fit some of its component methods (Stockwell and Peters, 1999); and GAM and multivariate regression methods use either pseudoabsence or true absence data, but the interpretations of models based on one or the other are not the same (Pearce and Boyce, 2006”
Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula (2009, methodology): “Although these three above-mentioned methods have been usually used with presence-absence data, they can be applied to presence-only situations by using pseudo-absence selected randomly from areas from which the species is not known (Brotons et al. 2004; Pearce and Boyce 2006 ).”
Piloting a Non-Invasive Genetic Sampling Method for Evaluating Population-Level Benefits of Wildlife Crossing Structures (2010, background): “…et al. 2003, Piggot and Taylor 2003, Pearse and Crandall 2004, Romain-Bondi et al. 2004), develop species occupancy models (MacKenzie et al. 2006, Pearce and Boyce 2006, Long et al. 2008), calculate migration rates (Manel et al. 2005, Dixon et al. 2006), and quantify the genetic population…”
Recent decline in suitable environmental conditions for African great apes (2012, methodology): “Species presence localities can be used to model suitable environmental conditions (SEC) (Pearce & Boyce, 2006).”