Ruffs, Philomachus pugnax, and Distribution Models: Can Leks Be Regarded as Patches?

Oikos ◽  
1998 ◽  
Vol 82 (2) ◽  
pp. 370 ◽  
Author(s):  
Jacob Höglund ◽  
Fredrik Widemo ◽  
William J. Sutherland ◽  
Helena Nordenfors ◽  
Jacob Hoglund
Keyword(s):  
Distribution Models ◽  
Philomachus Pugnax

Distribution Models and Spatial Analyses Provide Robust Assessments of Conservation Status of Orchid Species in Colombia: The Case of Lephantes mucronata

2020 ◽  
Vol 25 (1) ◽  
pp. 111
Author(s):  
Juan Sebastián Moreno ◽  
Stephania Sandoval-Arango ◽  
Rubén Darío Palacio ◽  
Nestor Fabio Alzate ◽  
Milton Rincón ◽  
...  
Keyword(s):  
Conservation Status ◽  
Spatial Analyses ◽  
Distribution Models ◽  
Orchid Species

Predictive distribution models of European hake in the south-central Mediterranean Sea

Hydrobiologia ◽  
2017 ◽  
Vol 821 (1) ◽  
pp. 153-172 ◽  
Author(s):  
G. Garofalo ◽  
S. Fezzani ◽  
F. Gargano ◽  
G. Milisenda ◽  
O. Ben Abdallah ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Predictive Distribution ◽  
The South ◽  
Distribution Models ◽  
Central Mediterranean ◽  
South Central ◽  
Central Mediterranean Sea ◽  
European Hake

scholarly journals Endemic Juniperus Montane Species Facing Extinction Risk under Climate Change in Southwest China: Integrative Approach for Conservation Assessment and Prioritization

Biology ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 63
Author(s):  
Mohammed A. Dakhil ◽  
Marwa Waseem A. Halmy ◽  
Walaa A. Hassan ◽  
Ali El-Keblawy ◽  
Kaiwen Pan ◽  
...  
Keyword(s):  
Climate Change ◽  
Potential Distribution ◽  
Extinction Risk ◽  
In Situ Conservation ◽  
Integrative Approach ◽  
Conservation Assessment ◽  
Distribution Models ◽  
Area Of Occupancy ◽  
Annual Range ◽  
The Impact

Climate change is an important driver of biodiversity loss and extinction of endemic montane species. In China, three endemic Juniperus spp. (Juniperuspingii var. pingii, J.tibetica, and J.komarovii) are threatened and subjected to the risk of extinction. This study aimed to predict the potential distribution of these three Juniperus species under climate change and dispersal scenarios, to identify critical drivers explaining their potential distributions, to assess the extinction risk by estimating the loss percentage in their area of occupancy (AOO), and to identify priority areas for their conservation in China. We used ensemble modeling to evaluate the impact of climate change and project AOO. Our results revealed that the projected AOOs followed a similar trend in the three Juniperus species, which predicted an entire loss of their suitable habitats under both climate and dispersal scenarios. Temperature annual range and isothermality were the most critical key variables explaining the potential distribution of these three Juniperus species; they contribute by 16–56.1% and 20.4–38.3%, respectively. Accounting for the use of different thresholds provides a balanced approach for species distribution models’ applications in conservation assessment when the goal is to assess potential climatic suitability in new geographical areas. Therefore, south Sichuan and north Yunnan could be considered important priority conservation areas for in situ conservation and search for unknown populations of these three Juniperus species.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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