scholarly journals Migratory connectivity of Swan Geese based on species' distribution models, feather stable isotope assignment and satellite tracking

2020 ◽  
Vol 26 (8) ◽  
pp. 944-957
Author(s):  
Qin Zhu ◽  
Keith A. Hobson ◽  
Qingshan Zhao ◽  
Yiqi Zhou ◽  
Iderbat Damba ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Satellite Tracking ◽  
Migratory Connectivity ◽  
Distribution Models

scholarly journals Combining citizen science species distribution models and stable isotopes reveals migratory connectivity in the secretive Virginia rail

2016 ◽  
Vol 54 (2) ◽  
pp. 618-627 ◽  
Author(s):  
Auriel M. V. Fournier ◽  
Alexis R. Sullivan ◽  
Joseph K. Bump ◽  
Marie Perkins ◽  
Mark C. Shieldcastle ◽  
...  
Keyword(s):  
Stable Isotopes ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Migratory Connectivity ◽  
Distribution Models

scholarly journals Using citizen science monitoring data in species distribution models to inform isotopic assignment of migratory connectivity in wetland birds

2017 ◽  
Author(s):  
Auriel M. V. Fournier ◽  
Kiel L. Drake ◽  
Douglas C. Tozer
Keyword(s):  
South Carolina ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Monitoring Program ◽  
The United States ◽  
Monitoring Data ◽  
Migratory Connectivity ◽  
Distribution Models ◽  
The Pacific

AbstractStable isotopes have been used to estimate migratory connectivity in many species. Estimates are often greatly improved when coupled with species distribution models (SDMs), which temper estimates in relation to occurrence. SDMs can be constructed using from point locality data from a variety of sources including extensive monitoring data typically collected by citizen scientists. However, one potential issue with SDM is that these data oven have sampling bias. To avoid this potential bias, an approach using SDMs based on marsh bird monitoring program data collected by citizen scientists and other participants following protocols specifically designed to maximize detections of species of interest at locations representative of the species range. We then used the SDMs to refine isotopic assignments of breeding areas of autumn-migrating and wintering Sora (Porzana Carolina), Virginia Rails (Rallus limicola), and Yellow Rails (Coturnicops noveboracensis) based on feathers collected from individuals caught at various locations in the United States from Minnesota south to Louisiana and South Carolina. Sora were assigned to an area that included much of the western U.S. and prairie Canada, covering parts of the Pacific, Central, and Mississippi Flyways. Yellow Rails were assigned to a broad area along Hudson and James Bay in northern Manitoba and Ontario, as well as smaller parts of Quebec, Minnesota, Wisconsin, and Michigan, including parts of the Mississippi and Atlantic Flyways. Virginia Rails were from several discrete areas, including parts of Colorado, New Mexico, the central valley of California, and southern Saskatchewan and Manitoba in the Pacific and Central Flyways. Our study demonstrates extensive data from organized citizen science monitoring programs are especially useful for improving isotopic assignments of migratory connectivity in birds, which can ultimately lead to better informed management decisions and conservation actions.

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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