Using predictive modelling to guide the conservation of a critically endangered coastal wetland amphibian

Tarrant, Jeanne; Armstrong, Adrian J.

doi:10.1016/j.jnc.2013.03.006

Cited by 14 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Range-size is one of the more important metrics determining the result of the evaluation of vulnerability according to IUCN methodology [ 126 ] and are used world-wide as criteria to prioritization of conservation efforts. SDM was considered as a possible surrogate for extent of occurrence (EOO), which could inform species categorization under IUCN categories [ 85 , 127 ], but there are also claims to use SDM to estimate area of occurrence (AOO) [ 128 , 129 ]. Our results support that all methods may provide accurate estimations of the absolute range-size and help inform species evaluation under the IUCN framework.…”

Section: Discussionmentioning

confidence: 99%

Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation

2018

View full text Add to dashboard Cite

The increasing use of species distribution modeling (SDM) has raised new concerns regarding the inaccuracies, misunderstanding, and misuses of this important tool. One of those possible pitfalls − collinearity among environmental predictors − is assumed as an important source of model uncertainty, although it has not been subjected to a detailed evaluation in recent SDM studies. It is expected that collinearity will increase uncertainty in model parameters and decrease statistical power. Here we use a virtual species approach to compare models built using subsets of PCA-derived variables with models based on the original highly correlated climate variables. Moreover, we evaluated whether modelling algorithms and species data characteristics generate models with varying sensitivity to collinearity. As expected, collinearity among predictors decreases the efficiency and increases the uncertainty of species distribution models. Nevertheless, the intensity of the effect varied according to the algorithm properties: more complex procedures behaved better than simple envelope models. This may support the claim that complex models such as Maxent take advantage of existing collinearity in finding the best set of parameters. The interaction of the different factors with species characteristics (centroid and tolerance in environmental space) highlighted the importance of the so-called “idiosyncrasy in species responses” to model efficiency, but differences in prevalence may represent a better explanation. However, even models with low accuracy to predict suitability of individual cells may provide meaningful information on the estimation of range-size, a key species-trait for macroecological studies. We concluded that the use of PCA-derived variables is advised both to control the negative effects of collinearity and as a more objective solution for the problem of variable selection in studies dealing with large number of species with heterogeneous responses to environmental variables.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation

2018

View full text Add to dashboard Cite

show abstract

“…The International Union for Conservation of Nature (IUCN) protocol for vulnerability analysis (IUCN 2001) uses the term extension of occurrence (EOO) for the range distribution of a particular species. Other model developers have interpreted the SDM predicted distribution as a surrogate for the EOO (Sergio et al 2007, Tarrant and Armstrong 2013, Pena et al 2014, thereby giving a broad meaning to the vulnerability analysis. Therefore, we tentatively use those categories to evaluate D. nigra vulnerability because the IUCN (2001) supports the use of predicted distributions.…”

Section: Vulnerability Analysismentioning

confidence: 99%

Modeling the distribution of a rare Amazonian odonate in relation to future deforestation

et al. 2015

View full text Add to dashboard Cite

“…ENMs have been used to predict suitable habitats of rare species for their conservation management (e.g. Armstrong 2009, Tarrant andArmstrong 2013) as well as to predict niche shifts of species due to future climate change (Erasmus et al 2002, Coetzee et al 2009, García-Domínguez et al 2014. Climate change is expected to cause significant biodiversity losses in the future, including in South Africa (Erasmus et al 2002, Thomas et al 2004, Hannah et al 2005, Pereira et al 2010, McCain and Colwell 2011.…”

Section: Introductionmentioning

confidence: 99%

Past, present, and future distribution of Afromontane rodents (Muridae: Otomys) reflect climate-change predicted biome changes

et al. 2016

View full text Add to dashboard Cite

Climate change constitutes a potential threat to montane biodiversity, particularly in low-altitude, tropical mountains; however, few data exist for the Afromontane taxa. In South Africa, the temperate grassland and fynbos biomes are mostly associated with the Great Escarpment and the high-lying central plateau. Varying contractions of the grassland and fynbos biomes are predicted under different climate scenarios by 2050. Animal taxa adapted to these biomes should suffer similar range declines and can be used to independently test the vegetation models. We constructed MaxEnt models from 271 unique locality records for three species of montane and submontane vlei rats that are closely associated with grassland angoniensis) due to historically documented changes from a grassland-dominated to thicket-dominated landscape.

show abstract

Using predictive modelling to guide the conservation of a critically endangered coastal wetland amphibian

Cited by 14 publications

References 37 publications

Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation

Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation

Modeling the distribution of a rare Amazonian odonate in relation to future deforestation

Past, present, and future distribution of Afromontane rodents (Muridae: Otomys) reflect climate-change predicted biome changes

Contact Info

Product

Resources

About