Estimating demographic models for the range dynamics of plant species

By J. Cabral, Frank M. Schurr. 2010

90 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/494f1eafc30905981a73fe50a2e83a4aa00cf7e0

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Predicting the distribution of vulnerable marine ecosystems in the deep sea using presence-background models (2014, background): “…primarily from shortfalls in the available data, i.e. data sets containing sample selection bias (Araújo & Guisan, 2006; Phillips et al., 2009; Cabral and Schurr, 2010), autocorrelation (Dormann, 2007a; Legendre, 1993; Segurado et al., 2006) and imperfect measurement (Barry and Elith, 2006;…”
Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? (2012, background): “Mechanistic approaches that account for demographic processes that drive species’ distribution and abundance better approximate observed spatial patterns of abundance and geographic range size (Cabral & Schurr, 2010).”
Mechanistic and Correlative Models of Ecological Niches (2015, background): “Finally, several recent efforts have aimed to develop process-based models and simulations of the joint action of the suite of factors that together determine species’ geographic distributions (Cabral & Schurr 2010; Dormann et al. 2012).”
Tools for integrating range change, extinction risk and climate change information into conservation management (2013, background): “The disadvantages of NPMs are that spatial abundance data from across the species’ range is needed to validate model structure and in some cases fit the model (Cabral and Schurr 2010), which are rarely available.”
Fine‐scale environmental variation in species distribution modelling: regression dilution, latent variables and neighbourly advice (2011, background): “These observation errors may be derived from sampling biases in data collection (e.g. Cabral & Schurr 2010) and biases introduced by imperfect measurement (Huston 2002; Clark 2005; Araújo & Rahbek 2006; Austin 2007).”
The virtual ecologist approach: simulating data and observers (2010, methodology): “An example was performed by Cabral and Schurr (2009) using hybrid models of species distribution (Fig.”
Variation in niche and distribution model performance: The need for a priori assessment of key causal factors (2012, unknown): “Very recent processoriented methods are beginning to take into account dispersal and movements (Cabral and Schurr, 2010; Smolik et al., 2010; Barve et al., 2011), but this gap is a limitation for the set of methods most commonly used: it is simply impossible to inform them directly about M, which is”
Ditch the niche – is the niche a useful concept in ecology or species distribution modelling? (2012, methodology): “In contrast, we may attempt a dynamic model that follows representations of real-world steps (e.g. Cabral & Schurr, 2010), and where the properties emerge from systems’ history and feedbacks (Fig.”
Hypervolume concepts in niche‐ and trait‐based ecology (2018, background): “…incorporate spatial effects (Phillips et al. 2006, Renner and Warton 2013), and those that incorporate demographic processes (Hooten and Wikle 2008, Cabral and Schurr 2010, Soberón 2010, Nenzén et al. 2012), dispersal limitation (Hooten and Wikle 2008, Engler and Guisan 2009), and species…”
Mechanistic simulation models in macroecology and biogeography: state‐of‐art and prospects (2017, background): “Finally, certain models incorporate physiology indirectly through defining the suitable habitat (e.g. demographic hybrid niche models, Keith et al. 2008, Anderson et al. 2009, Cabral and Schurr 2010), or ad-hoc environmental optima and tolerances (Hortal et al. 2009).”