scholarly journals Joint species distribution modelling for spatio-temporal occurrence and ordinal abundance data

2017 ◽  
Vol 27 (1) ◽  
pp. 142-155 ◽  
Author(s):  
Erin M. Schliep ◽  
Nina K. Lany ◽  
Phoebe L. Zarnetske ◽  
Robert N. Schaeffer ◽  
Colin M. Orians ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Modelling ◽  
Distribution Modelling ◽  
Abundance Data ◽  
Temporal Occurrence ◽  
Spatio Temporal

scholarly journals Species Distribution Modelling: Contrasting presence-only models with plot abundance data

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Vitor H. F. Gomes ◽  
Stéphanie D. IJff ◽  
Niels Raes ◽  
Iêda Leão Amaral ◽  
Rafael P. Salomão ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Modelling ◽  
Distribution Modelling ◽  
Abundance Data

scholarly journals Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change

2018 ◽  
Vol 373 (1761) ◽  
pp. 20170446 ◽  
Author(s):  
Scott Jarvie ◽  
Jens-Christian Svenning
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Climatic Conditions ◽  
First Century ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Major Barrier ◽  
Methodological Choices ◽  
Biodiversity And Ecosystem Function

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’.

Evolutionary algorithms for species distribution modelling: A review in the context of machine learning

2019 ◽  
Vol 392 ◽  
pp. 179-195 ◽  
Author(s):  
Sacha Gobeyn ◽  
Ans M. Mouton ◽  
Anna F. Cord ◽  
Andrea Kaim ◽  
Martin Volk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithms ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Distribution Modelling
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