Using the MAXENT program for species distribution modelling to assess invasion risk.

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

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’.


2019 ◽  
Vol 392 ◽  
pp. 179-195 ◽  
Author(s):  
Sacha Gobeyn ◽  
Ans M. Mouton ◽  
Anna F. Cord ◽  
Andrea Kaim ◽  
Martin Volk ◽  
...  

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