Accommodating the role of site memory in dynamic species distribution models

Ecology ◽  
2021 ◽  
Author(s):  
Graziella V. DiRenzo ◽  
David A.W. Miller ◽  
Blake R. Hossack ◽  
Brent H. Sigafus ◽  
Paige E. Howell ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models

The role of demography, intra-species variation, and species distribution models in species' projections under climate change

Ecography ◽  
2014 ◽  
Vol 38 (3) ◽  
pp. 221-230 ◽  
Author(s):  
Rebecca M. Swab ◽  
Helen M. Regan ◽  
Diethart Matthies ◽  
Ute Becker ◽  
Hans Henrik Bruun
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Species Variation ◽  
Distribution Models

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.

scholarly journals Vulnerability to climate change of a microendemic lizard species from the central Andes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Laspiur ◽  
J. C. Santos ◽  
S. M. Medina ◽  
J. E. Pizarro ◽  
E. A. Sanabria ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Distribution Models ◽  
Activity Patterns ◽  
Geographic Range ◽  
Behavioral Thermoregulation ◽  
Rapid Loss ◽  
Distribution Models ◽  
Lizard Species ◽  
Vulnerability To Climate Change

AbstractGiven the rapid loss of biodiversity as consequence of climate change, greater knowledge of ecophysiological and natural history traits are crucial to determine which environmental factors induce stress and drive the decline of threatened species. Liolaemus montanezi (Liolaemidae), a xeric-adapted lizard occurring only in a small geographic range in west-central Argentina, constitutes an excellent model for studies on the threats of climate change on such microendemic species. We describe field data on activity patterns, use of microhabitat, behavioral thermoregulation, and physiology to produce species distribution models (SDMs) based on climate and ecophysiological data. Liolaemus montanezi inhabits a thermally harsh environment which remarkably impacts their activity and thermoregulation. The species shows a daily bimodal pattern of activity and mostly occupies shaded microenvironments. Although the individuals thermoregulate at body temperatures below their thermal preference they avoid high-temperature microenvironments probably to avoid overheating. The population currently persists because of the important role of the habitat physiognomy and not because of niche tracking, seemingly prevented by major rivers that form boundaries of their geographic range. We found evidence of habitat opportunities in the current range and adjacent areas that will likely remain suitable to the year 2070, reinforcing the relevance of the river floodplain for the species’ avoidance of extinction.

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