Species distribution modeling with Gaussian processes: A case study with the youngest stages of sea spawning whitefish (Coregonus lavaretus L. s.l.) larvae

2012 ◽  
Vol 228 ◽  
pp. 49-58 ◽  
Author(s):  
Jarno Vanhatalo ◽  
Lari Veneranta ◽  
Richard Hudd
Keyword(s):  
Species Distribution ◽  
Gaussian Processes ◽  
Species Distribution Modeling ◽  
Coregonus Lavaretus ◽  
Distribution Modeling

scholarly journals Combining connectivity and species distribution modeling to define conservation and restoration priorities for multiple species: A case study in the eastern Amazon

2021 ◽  
Vol 257 ◽  
pp. 109148
Author(s):  
Leonardo de Sousa Miranda ◽  
Marcelo Awade ◽  
Rodolfo Jaffé ◽  
Wilian França Costa ◽  
Leonardo Carreira Trevelin ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Modeling ◽  
Distribution Modeling ◽  
Multiple Species

Predicting the Goshawk’s habitat area using Species Distribution Modeling: Case Study area

2015 ◽  
Vol 29 (3) ◽  
pp. 333-343 ◽  
Author(s):  
Hae-Jin Cho ◽  
◽  
Dal-Ho Kim ◽  
Man-Seok Shin ◽  
Tehan Kang ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Modeling ◽  
Habitat Area ◽  
Distribution Modeling ◽  
Case Study Area

Computational Techniques for Biologic Species Distribution Modeling

2011 ◽  
pp. 308-325
Author(s):  
Pedro Corrêa ◽  
Mariana Carvalhaes ◽  
Antonio Saraiva ◽  
Fabrício Rodrigues ◽  
Elisângela Rodrigues ◽  
...  
Keyword(s):  
Climate Change ◽  
Maximum Entropy ◽  
Species Distribution ◽  
Potential Distribution ◽  
Species Distribution Modeling ◽  
Computational Techniques ◽  
Distribution Modeling ◽  
Occurrence Pattern ◽  
Conservation Of Biodiversity

Computational modeling techniques for species geographic distribution are critical to support the task of identifying areas with high risk of loss of Biodiversity. These tools can assist in the conservation of Biodiversity, in planning the use of non-inhabited regions, in estimating the risk of invasive species, in the proposed reintroduction programs for species and even in planning the protecting endangered species. Furthermore, such techniques can help to understand the effects of climate change and other changes in the geographical distribution of species. This chapter presents concepts related to the species distribution modeling and algorithms based on Neural Networks and Maximum Entropy as alternatives for modeling of species distribution. The algorithms were integrated into the open source tool called openModeller used by biologists and other researchers in this area. A case study of modeling the distribution of babaçu (Orbignya phalerata) in the Piauí State – Brazil is presented, evaluating the potential distribution of this species used to produce bioenergy. Fifty models were generated and merged the ten models with best accuracy for each algorithm. The results show that the models obtained by both are consistent. The models obtained with Maximum Entropy seem to reflect best the reality, considering the occurrence pattern of babaçu as a secondary species.

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