Untitled

doi:10.1111/ddi.2008.14.issue-5

Cited by 11 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The predicted LULC map (for 2026), climate data and occurrence records of glossy buckthorn were then inputted into a species distribution model called MaxEnt for studying the impact of future LUCC on the spatial distribution of glossy buckthorn. MaxEnt is a technique for modelling of geographic distributions of species with presence-only data, and it has the advantage of achieving high predictive accuracy (Wisz et al 2008;Elith and Leathwick 2009). The relative suitability was calculated for glossy buckthorn with the predicted 2026 LULC map.…”

Section: Methodsmentioning

confidence: 99%

“…We chose the maximum entropy modelling via the software program MaxEnt (Phillips, Anderson, and Schapire 2006) because we had presenceonly observations. MaxEnt is the preferred method for modelling with presence-only data due to its performance relative to alternative methods (Wisz et al 2008;Elith and Leathwick 2009;Merow, Smith, and Silander 2013) and because it does not require selection of pseudo-absences (where the assumption is that the environments are unsuitable), but rather background points (which describes the available landscape, but does not assume unsuitability, Merow, Smith, and Silander 2013). Presence records were aggregated to the 60-m scale to match the climatic and LULC predictors for model fitting (Merow et al 2016).…”

Section: Species Distribution Modelmentioning

confidence: 99%

See 1 more Smart Citation

Predicting land use/cover change in Long Island Sound Watersheds and its effect on invasive species: a case study for glossy buckthorn

et al. 2018

View full text Add to dashboard Cite

Land use/cover change (LUCC) is a major threat to ecosystems. It may affect the abundance and distribution of species. Despite the importance of LUCC to ecological patterns and processes, few quantitative projections are available for use in ecological modelling. To fill in this literature gap, we constructed a LUCC model for Long Island Sound Watersheds (LISW) and explored the potential effect of the future LUCC on the range size of an invasive species (glossy buckthorn, Frangula alnus). We first applied the multi-layer perceptron-Markov chain model to predict the future LUCC in the LISW area within New England, USA, and then used the predicted land use/ cover data as input into a species distribution model to simulate the future range size of glossy buckthorn. Our results indicate that under the current LUCC trend, there is a continued loss of forest and an increase of developed land in the near future, and this LUCC affects the relative suitability for glossy buckthorn.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Species Distribution Modelmentioning

confidence: 99%

Predicting land use/cover change in Long Island Sound Watersheds and its effect on invasive species: a case study for glossy buckthorn

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Yet despite the widespread use and numerous applications of SDMs, how to effectively model species distribution remains a key issue. In the past decade, much research on effective modelling has been done, with the majority of studies concentrating on one or more of the following issues: (i) determining the significance of different environmental variables for species occurrence from statistical and ecological points of view (Stockwell and Peterson 2002); (ii) sampling issues, including sample size and generation of pseudoabsence points (Wisz et al 2008;VanDerWal et al 2009); and (iii) exploration, development and evaluation of new SDMs (De'Ath 2002;Phillips and Dudík 2008). Here, we concentrate on the first issuehow to select environmental variables for use in modelsas it is a precondition to successful modelling.…”

Section: Introductionmentioning

confidence: 99%

Study on selecting sensitive environmental variables in modelling species spatial distribution

et al. 2016

View full text Add to dashboard Cite

This study explores the effects of different environmental variables on the accuracy of species distribution models. Forest inventory and analysis data sets were used to generate absence and pseudo-absence points of chestnut oak (Quercus prinus) in the central and southern Appalachian mountain region of the US. We simulate chestnut oak distribution using different criteria for selecting environmental variables: (1) the selection of sensitive variables using factor analysis and the calculation of a sensitivity index, (2) principal components analysis. Factor analysis to environmental variables at both occurrence and pseudo-absence points was conducted to calculate the sensitivity index for each environmental variable. The identification of sensitive variables may use the factor loadings of first one or two factors of environmental variables. Modelling with sensitive variables (mean Kappa > 0.60; mean true skill statistic (TSS) > 0.60) can enhance model accuracy more than using PCA variables or all available environmental variables (mean Kappa ranges from 0.45 to 0.65; mean TSS ranges from 0.40 to 0.70). Modelling with leading principal components (larger than 90% variations) can achieve similar or higher accuracy than modelling with all variables. The influence of redundant information on species modelling varies with the model used. Our results suggest that selecting environmental variables using a sensitivity index defined by factor analysis may improve model accuracy and reduce redundant information in species modelling. The proposed method for selecting sensitive variables is easy to implement and has strong ecological interpretability. ARTICLE HISTORY

show abstract

Detection and mapping of bracken fern weeds using multispectral remotely sensed data: a review of progress and challenges

Matongera

Mutanga

Dube

et al. 2016

Geocarto International

View full text Add to dashboard Cite

Untitled

Cited by 11 publications

References 0 publications

Predicting land use/cover change in Long Island Sound Watersheds and its effect on invasive species: a case study for glossy buckthorn

Predicting land use/cover change in Long Island Sound Watersheds and its effect on invasive species: a case study for glossy buckthorn

Study on selecting sensitive environmental variables in modelling species spatial distribution

Detection and mapping of bracken fern weeds using multispectral remotely sensed data: a review of progress and challenges

Contact Info

Product

Resources

About