scholarly journals Invasion risk assessment using trait-environment and species distribution modelling techniques in an arid protected area: Towards conservation prioritization

2021 ◽  
Vol 129 ◽  
pp. 107951
Author(s):  
Reham F. El-Barougy ◽  
Mohammed A. Dakhil ◽  
Marwa W. Halmy ◽  
Sarah M. Gray ◽  
Mohamed Abdelaal ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hsin-Ting Yeh ◽  
Harn-Yeu Cheah ◽  
Ming-Chih Chiu ◽  
Jhih-Rong Liao ◽  
Chiun-Cheng Ko

AbstractPest risk assessment is typically performed by expert taxonomists using a pest’s biological data. However, the biological data or expert taxonomists may be difficult to find. Here, we used species distribution modelling to predict potential invasion in which phytophagous quarantine pests survive in Taiwan; the pests (unrecorded yet in Taiwan) included were three notorious quarantine whiteflies (Crenidorsum aroidephagus, Aleurothrixus trachoides, and Paraleyrodes minei) and three aphids (Nasonovia ribisnigri, Macrosiphum euphorbiae, and Viteus vitifoliae). In brief, maximum entropy modelling (MaxEnt) was used to predict the suitability of the pests’ habitats under certain climatic conditions, and then receiver operating characteristic curve analysis was performed (to verify the prediction result). We then analysed environmental variables affecting the habitat suitability and matched them with Taiwan’s crop cultivation areas for the assessment of potential invasion. We observed that the habitat suitability of the cultivation areas of host plants was low for C. aroidephagus, A. trachoides, and N. ribisnigri but was high for the remaining three species. Moreover, precipitation of coldest quarter negatively affected habitat suitability for C. aroidephagus, P. minei, N. ribisnigri, and M. euphorbiae. Seasonal temperature changes also negatively affected the habitat suitability for A. trachoides. This is the first study to demonstrate the use of species distribution modelling as the preliminary step for the pest risk assessment of these emerging pests with limited biological data before their invasion.


2018 ◽  
Vol 95 ◽  
pp. 311-319 ◽  
Author(s):  
Manuele Bazzichetto ◽  
Marco Malavasi ◽  
Vojtěch Bartak ◽  
Alicia Teresa Rosario Acosta ◽  
Duccio Rocchini ◽  
...  

Author(s):  
D. Rocchini ◽  
A. Comber ◽  
C. X. Garzon-Lopez ◽  
M. Neteler ◽  
A. M. Barbosa ◽  
...  

Species distribution models represent an important approach to map the spread of plant and animal species over space (and time). As all the statistical modelling techniques related to data from the field, they are prone to uncertainty. In this study we explicitly dealt with uncertainty deriving from field data sampling; in particular we propose i) methods to map sampling effort bias and ii) methods to map semantic bias.


Oryx ◽  
2018 ◽  
Vol 54 (5) ◽  
pp. 699-705 ◽  
Author(s):  
Rafael M. Rabelo ◽  
Jonas R. Gonçalves ◽  
Felipe E. Silva ◽  
Daniel G. Rocha ◽  
Gustavo R. Canale ◽  
...  

AbstractThe rate of deforestation in the Amazon is increasing. Predictive models estimate that as a result of agricultural expansion 40% of these forests will be lost by 2050. As a consequence the habitat of forest-dwelling species such as the Endangered black-faced black spider monkey Ateles chamek is being lost, particularly along the arc of deforestation in the Brazilian Amazon. We used species distribution modelling to (1) define the distribution of this spider monkey, using environmental predictors, (2) calculate the area of this distribution covered by the protected area network, and (3) calculate the expected loss of the species’ habitat under future scenarios of deforestation. We found that the species occupies only c. 28% of its extent of occurrence. Only 32% of the species’ area of occupancy is legally protected, and the modelling suggests that 31–40% of the species’ habitat will be lost by 2050. We highlight three unprotected regions with extensive forest cover that are predicted to become severely deforested by 2050 as priority regions for expanding the protected area network. We also propose landscape management and restoration in three human-modified regions. Our study provides an example of how species distribution modelling can be applied to assess threats to species and support decision makers in implementing conservation actions.


2018 ◽  
Vol 373 (1761) ◽  
pp. 20170446 ◽  
Author(s):  
Scott Jarvie ◽  
Jens-Christian Svenning

Trophic rewilding, the (re)introduction of species to promote self-regulating biodiverse ecosystems, is a future-oriented approach to ecological restoration. In the twenty-first century and beyond, human-mediated climate change looms as a major threat to global biodiversity and ecosystem function. A critical aspect in planning trophic rewilding projects is the selection of suitable sites that match the needs of the focal species under both current and future climates. Species distribution models (SDMs) are currently the main tools to derive spatially explicit predictions of environmental suitability for species, but the extent of their adoption for trophic rewilding projects has been limited. Here, we provide an overview of applications of SDMs to trophic rewilding projects, outline methodological choices and issues, and provide a synthesis and outlook. We then predict the potential distribution of 17 large-bodied taxa proposed as trophic rewilding candidates and which represent different continents and habitats. We identified widespread climatic suitability for these species in the discussed (re)introduction regions under current climates. Climatic conditions generally remain suitable in the future, although some species will experience reduced suitability in parts of these regions. We conclude that climate change is not a major barrier to trophic rewilding as currently discussed in the literature.This article is part of the theme issue ‘Trophic rewilding: consequences for ecosystems under global change’.


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