Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela

By R. P. Anderson. 2003

162 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/8fbb3fcd448721a18596a1fac0760bc1b4d23e07

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data (2011, background): “(Dennis & Hardy 1999; Dennis et al. 1999; Dennis & Thomas 2000; Dennis et al. 2006; Zaniewski et al. 2002; Anderson 2003; Reutter et al. 2003; Graham et al. 2004; Soberón & Peterson 2004; Martínez-Meyer 2005).”
Profile or group discriminative techniques? Generating reliable species distribution models using pseudo‐absences and target‐group absences from natural history collections (2010, methodology): “3; Anderson, 2003; Loiselle et al., 2003), except in the case of Maxent, because the technique is designed to work with presence versus background data, and therefore assumes that the background data will include both absences and presences (Phillips et al., 2009).”
Maximum entropy modeling of species geographic distributions (2006, background): “Similarly, sampling intensity and sampling methods often vary widely across the study area(Anderson, 2003) .”
Comparison of six correlative models in predictive vegetation mapping on a local scale (2012, methodology): “We also considered model performance based on objective intrinsic criteria (Anderson 2003).”
Predicting the spatial distribution of an invasive plant species (Eupatorium adenophorum) in China (2007, background): “The high predictive power of distribution ranges from niche modeling is widely reported (Anderson 2003; Raxworthy et al. 2003).”
A framework for species distribution modelling with improved pseudo-absence generation (2015, background): “…important in order to reduce330 uncertainties when binary presence/absence maps are required for decision331 making and/or management plans.332 Furthermore, the lack of records from suitable regions may simply derive333 from an inadequate sampling (Anderson, 2003; Hanspach et al., 2011).”
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation (2008, methodology): “We investigated whether the set of occurrence data for an entire ‘‘target group’’ of species that may be captured or observed using the same methods (Ponder et al. 2001, Anderson 2003) can be regarded as a random sample from the sampling distribution and thus used as background data.”
Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? (2007, background): “However, when models are projected into geographical space, species are often depicted to occur in areas where no previous record of occurrence is known (Anderson, 2003; Martı́nez-Meyer, 2005).”
Novel methods improve prediction of species' distributions from occurrence data (2006, unknown): “This is particularly apparent in the CAN bird data set, in which the data display many of the biases possible in presence-only data (Anderson 2003).”
spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models (2015, background): “of sampling (i.e. via records of a target group; Anderson 2003), and integrated into model calibration to correct for associated biases in environmental space (Phillips et al. 2009).”
KnowBR: An application to map the geographical variation of survey effort and identify well-surveyed areas from biodiversity databases (2018, background): “…geographic ranges, the extant databases are typically characterized by incompleteness and biases (e.g., Dennis and Hardy, 1999; Soberón et al., 2000; Zaniewski et al., 2002; Anderson, 2003; Martínez-Meyer, 2005; Dennis et al., 2006; Lobo et al., 2007; Hortal et al., 2008; Stropp et al., 2016).”
Factors affecting detection probability in plant distribution studies (2009, background): “Geographical range size and extent of occurrence will be underestimatedwith imperfect detection (Kéry 2002; Anderson 2003).”
ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models (2014, methodology): “To reduce the influence of spatial sampling bias, we applied a weighted-target group approach (Anderson 2003) by using 22,858 tree species occur- rence localities throughout Puerto Rico as background locali- ties (Phillips et al. 2009).”
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).”
Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques (2019, methodology): “Based on optimum combinations of error components, the 10 best-subset models were chosen out of the 100 repeats (Anderson, 2003; Sobek-Swant et al., 2012).”
Analysing botanical collecting effort in Amazonia and correcting for it in species range estimation (2007, background): “We accept these principles but, in addition, we believe that the buffer around a certain point should extend further in a direction where general collecting activity is less than in another direction of greater overall collecting activity (cf. Anderson, 2003; Loiselle et al., 2003).”