Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas

By H. Owens, L. Campbell, L. Dornak, E. Saupe, N. Barve, Jorge Soberón, K. Ingenloff, A. Lira‐Noriega, C. Hensz, C. Myers and 1 more. 2012

624 citations recorded by the source corpus.

Software for 1-dimensional plots of species' responses to environment, and for MOP calculations.

Subfields: Ecological Modeling, Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/6717285487bdd026367978fd6e07a58e7d9d8d4e

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Citations in the corpus

kuenm: an R package for detailed development of ecological niche models using Maxent (2019, methodology): “…output of this evaluation (a table with the results) is written directly to the directory. kuenm_mmop Calculates the mobility-oriented parity (MOP; Owens et al., 2013 ) metric for comparing sets of environmental conditions between the calibration area ( M ) and multiple areas or scenarios to…”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, result): “In particular, our results pinpoint the difficulty of extrapolative predictions, which has direct implications for model transferability across systems, space and time (Wenger and Olden 2012, Owens et al. 2013).”
Mechanistic and Correlative Models of Ecological Niches (2015, background): “2012), particularly when models are to be transferred to other landscapes or times (Owens et al. 2013).”
Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment (2014, methodology): “The ‘fade-by-clamping’ option was used to prevent extrapolations outside the environmental range of the training data (Owens et al. 2013).”
ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches (2020, neutral): “…2014), biomod2 (Thuiller, Lafourcade, Engler, & Araújo, 2009), dismo (Hijmans, Phillips, Leathwick, & Elith, 2019), sdm (Naimi & Araújo, 2016), SSDM (Schmitt, Pouteau, Justeau, de Boissieu, & Birnbaum, 2017), SDMToolbox (Brown, Bennett, & French, 2017), sklearn (Pedregosa et al., 2011) and others.”
Creating individual accessible area hypotheses improves stacked species distribution model performance (2018, background): “…predictions of the species’ true occupied geographical distribution (i.e., the occupied area), increasing the predictive power of SDMs in environmental space and eliminating overlap of allopatric species complexes (Figure 4; Barve et al., 2011; Owens et al., 2013; Sober on & Peterson, 2005).”