scholarly journals VBORNET Gap Analysis: Sand Fly Vector Distribution Models Utilised to Identify Areas of Potential Species Distribution in Areas Lacking Records

2016 ◽  
Vol 4 (1) ◽  
pp. 5
Author(s):  
Bulent Alten ◽  
Veerle Versteirt ◽  
Wim Van Bortel ◽  
Herve Zeller ◽  
William Wint ◽  
...  
Keyword(s):  
Species Distribution ◽  
Gap Analysis ◽  
Sand Fly ◽  
Distribution Models ◽  
Vector Distribution

scholarly journals VBORNET gap analysis: Mosquito vector distribution models utilised to identify areas of potential species distribution in areas lacking records.

2016 ◽  
Vol 4 (1) ◽  
pp. 6 ◽  
Author(s):  
Francis Schaffner ◽  
Veerle Versteirt ◽  
Wim Van Bortel ◽  
Hervé Zeller ◽  
William Wint ◽  
...  
Keyword(s):  
Species Distribution ◽  
Gap Analysis ◽  
Mosquito Vector ◽  
Distribution Models ◽  
Vector Distribution

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 The distribution of woody species in relation to climate and fire in Yosemite National Park, California, USA

Fire Ecology ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Jan W. van Wagtendonk ◽  
Peggy E. Moore ◽  
Julie L. Yee ◽  
James A. Lutz
Keyword(s):  
Plant Species ◽  
Sierra Nevada ◽  
Species Distribution ◽  
National Park ◽  
Woody Plant ◽  
Fire Regime ◽  
Yosemite National Park ◽  
Distribution Models ◽  
Ecological Zones ◽  
Woody Plant Species

Abstract Background The effects of climate on plant species ranges are well appreciated, but the effects of other processes, such as fire, on plant species distribution are less well understood. We used a dataset of 561 plots 0.1 ha in size located throughout Yosemite National Park, in the Sierra Nevada of California, USA, to determine the joint effects of fire and climate on woody plant species. We analyzed the effect of climate (annual actual evapotranspiration [AET], climatic water deficit [Deficit]) and fire characteristics (occurrence [BURN] for all plots, fire return interval departure [FRID] for unburned plots, and severity of the most severe fire [dNBR]) on the distribution of woody plant species. Results Of 43 species that were present on at least two plots, 38 species occurred on five or more plots. Of those 38 species, models for the distribution of 13 species (34%) were significantly improved by including the variable for fire occurrence (BURN). Models for the distribution of 10 species (26%) were significantly improved by including FRID, and two species (5%) were improved by including dNBR. Species for which distribution models were improved by inclusion of fire variables included some of the most areally extensive woody plants. Species and ecological zones were aligned along an AET-Deficit gradient from cool and moist to hot and dry conditions. Conclusions In fire-frequent ecosystems, such as those in most of western North America, species distribution models were improved by including variables related to fire. Models for changing species distributions would also be improved by considering potential changes to the fire regime.

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