scholarly journals Data prevalence matters when assessing species' responses using data-driven species distribution models

2016 ◽  
Vol 32 ◽  
pp. 69-78 ◽  
Author(s):  
Shinji Fukuda ◽  
Bernard De Baets
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Data Driven ◽  
Distribution Models ◽  
Species Responses ◽  
Using Data

scholarly journals New findings on protected and strictly protected species confirm the value of the prime hoverfly area network

2018 ◽  
pp. 63-71
Author(s):  
Marina Jankovic ◽  
Marija Milicic ◽  
Dimitrije Radisic ◽  
Dubravka Milic ◽  
Ante Vujic
Keyword(s):  
Protected Areas ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Small Sample Size ◽  
Small Sample ◽  
Area Network ◽  
Distribution Models ◽  
Environmental Pressures ◽  
New Findings ◽  
Using Data

With environmental pressures on the rise, the establishment of pro?tected areas is a key strategy for preserving biodiversity. The fact that many species are losing their battle against extinction despite being within protected areas raises the question of their effectiveness. The aim of this study was to evaluate established Priority Hoverfly Areas (PHAs) and areas that are not yet but could potentially be included in the PHA network, using data from new field surveys. Additionally, species distribution models have been created for two new species recognized as important and added to the list of key hoverfly species. Maps of potential distribution of these species were superimposed on maps of protected areas and PHAs to quantify percentages of overlap. The results of this study are not statisti?cally significant, which could be influenced by a small sample size. However, the results of species distribution models and the extent of overlap with PHAs confirm the utility of these expert-generated designations.

Testing climate-based species distribution models with recent field surveys of pond-breeding amphibians in eastern Missouri

2011 ◽  
Vol 89 (11) ◽  
pp. 1074-1083 ◽  
Author(s):  
D.R. Trumbo ◽  
A.A. Burgett ◽  
J.H. Knouft
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Range ◽  
Environmental Data ◽  
Limited Attention ◽  
Distribution Models ◽  
Field Surveys ◽  
Geographic Ranges ◽  
Using Data ◽  
Study Species

Species distribution models (SDMs) have become an important tool for ecologists by providing the ability to predict the distributions of organisms based on species niche parameters and available habitat across broad geographic areas. However, investigation of the appropriate extent of environmental data needed to make accurate predictions has received limited attention. We investigate whether SDMs developed with regional climate and species locality data (i.e., within Missouri, USA) produce more accurate predictions of species occurrences than models developed with data from across an entire species range. To test the accuracy of the model predictions, field surveys were performed in 2007 and 2008 at 103 study ponds for eight amphibian study species. Models developed using data from across the entire species range did not accurately predict the occurrences of any study species. However, models developed using data only from Missouri produced accurate predictions for four study species, all of which are near the edge of their geographic ranges within the study area. These results suggest that species distribution modeling with regionally focused data may be preferable for local ecological and conservation purposes, and that climate factors may be more important for determining species distributions at the edge of their geographic ranges.

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.

Sign in / Sign up

Export Citation Format

Share Document