Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?

By Tomás Václavík, Ross K. Meentemeyer. 2009

290 citations recorded by the source corpus.

Subfields: Ecological Modeling, Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/13c94137e07a0ce552dbe117ff89c179d203895b

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Citations in the corpus

Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions (2021, background): “The occupancy is the expected probability that cells are occupied by wood thrush Václavík & Meentemeyer, 2009).”
Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, background): “…with the environment in non-native regions, and (b) possibly already occupying a large proportion of their fundamental niches across native and invasive distributions (Araújo & Pearson, 2005; Václavík & Meentemeyer, 2009), thereby making them prime candidates for transferability assessments.”
Accounting for multi‐scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM) (2012, background): “Accounting for SAC in iSDMs may be particularly important for capturing fine-scale contagious processes of invasion that lead to geographically structured distributions and violate the assumption that species are in equilibrium with environmental controls (Václavı́k & Meentemeyer, 2009).”
Predicting distributions of invasive species (2013, methodology): “ (2009); De’ath & Fabricius (2000) (b) Václavík & Meentemeyer 2009 (only in invasive range) Machine Learning Ensembles of trees: boosted regression trees (BRT), or random forests (RF) Various Some Most invasive species examples are within ensembles;…”
Species distribution modelling of marine benthos: a North Sea case study (2011, methodology): “In order to account for mobility and dispersal abilities of individual species, some approaches incorporate dispersal into distribution modelling approaches (e.g. by coupling SDM to dispersal simulations; Bahn et al. 2008, Václavík & Meentemeyer 2009).”
The elephant in the room: the role of failed invasions in understanding invasion biology (2013, background): “For potential distribution models of invasive species, records of failed invasions represent true absences that might significantly improve model calibration and validation and decrease the uncertainties surrounding the predictions (Duncan et al. 2009, Václavík and Meentemeyer 2009).”
Use of niche models in invasive species risk assessments (2011, background): “Predicting actual distributions require true-absence information (Jiménez-Valverde et al. 2008; Václavı́k and Meentemeyer 2009; Lobo et al. 2010), but we suggest avoiding the use of absence data in estimating potential distributions as those required in invader risk maps; because they can misreprese”
CliMond: global high‐resolution historical and future scenario climate surfaces for bioclimatic modelling (2012, background): “Methods in Ecology and Evolution 2011 British Ecological Society, Methods in Ecology and Evolution, 3, 53–64 are specified (Chefaoui & Lobo 2008; VanDerWal et al. 2009).”
Global climate suitability of citrus huanglongbing and its vector, the Asian citrus psyllid, using two correlative species distribution modeling approaches, with emphasis on the USA (2016, background): “…environment (they have fully filled their environmental niche) and they may fail to correctly project the distribution of a species that is on the move, invading a new area or more particularly a species that experiences a range shift in response to climate change (Vaclavik and Meentemeyer 2009).”
Epidemiological modeling of invasion in heterogeneous landscapes: spread of sudden oak death in California (1990–2030) (2011, background): “To date, spatially-explicit models of disease spread have tended to use either a biogeographical, pattern-based approach (e.g., Vaclavik and Meentemeyer 2009) or an epidemiological, process-based approach (e.g., Gilligan 2008).”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, background): “…or among the best (Hernandez et al. 2006, Guisan et al. 2007, Sérgio et al. 2007, Wang et al. 2007, Wisz et al. 2008, Roura-Pascual et al. 2009, Tognelli et al. 2009, Václávik & Meentemeyer 2009, Veloz 2009, Williams et al. 2009, Mateo et al. 2010, Rebelo & Jones 2010, Rupprecht et al. 2011).”
Species distribution models of the Spotted Wing Drosophila ( Drosophila suzukii , Diptera: Drosophilidae) in its native and invasive range reveal an ecological niche shift (2018, background): “…models are sometimes referred to as invasive species distribution models (iSDM; Uden, Allen, Angeler, Corral, & Fricke, 2015), and can be employed to identify likely starting points of biological invasions and to prioritise proactive ecological management efforts (Václavík & Meentemeyer, 2009).”