Species distribution model for the ‘Northern’ Oak hairstreak (Satyrium favonius ontario) with comments on its conservation status in the northeastern United States

2017 ◽  
Vol 21 (5-6) ◽  
pp. 781-790 ◽  
Author(s):  
Benedict L. Gagliardi ◽  
David L. Wagner ◽  
Jenica M. Allen
Keyword(s):  
United States ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Conservation Status ◽  
Distribution Model ◽  
Northeastern United States

scholarly journals Redescription, geographic distribution and ecological niche modeling of Elapomorphus wuchereri (Serpentes: Dipsadidae)

2017 ◽  
Vol 16 (2) ◽  
pp. 225 ◽  
Author(s):  
Omar Machado Entiauspe-Neto ◽  
Márcia Ferret Renner ◽  
Conrado Mario-da-Rosa ◽  
Arthur Diesel Abegg ◽  
Daniel Loebmann ◽  
...  
Keyword(s):  
Species Distribution ◽  
Ecological Niche ◽  
Species Distribution Model ◽  
Ecological Niche Modeling ◽  
Conservation Status ◽  
Original Description ◽  
Niche Modeling ◽  
Distribution Model ◽  
Taxonomic History ◽  
History Of

The original description of Elapomorphus wuchereri Günther, 1861 included a drawing and brief comments about the morphology of three specimens; two of the latter belong to another species and the holotype is lost. Based on the discovery of new specimens, we redescribe Elapomorphus wuchereri and designate a neotype. We discuss the variation and the taxonomic history of the species, and based on the results of a species distribution model analysis (SDM), we describe the distribution, extent of occurrence, and conservation status.

Ensemble Species Distribution Model Identifies Survey Opportunities for At-Risk Bearded Beaksedge (Rhynchospora crinipes) in the Southeastern United States

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Carlos Ramirez-Reyes ◽  
Garrett Street ◽  
Francisco J. Vilella ◽  
D. Todd Jones-Farrand ◽  
M. Scott Wiggers ◽  
...  
Keyword(s):  
United States ◽  
At Risk ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Southeastern United States ◽  
Distribution Model

Identifying suitable habitats and current conservation status of a rare and elusive reptile in Iran

2018 ◽  
Vol 39 (3) ◽  
pp. 355-362 ◽  
Author(s):  
Rosa M. Chefaoui ◽  
Mahboubeh Sadat Hosseinzadeh ◽  
Meysam Mashayekhi ◽  
Barbod Safaei-Mahroo ◽  
Seyed Mahdi Kazemi
Keyword(s):  
Protected Areas ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Conservation Status ◽  
Distribution Model ◽  
Reliable Estimate ◽  
Current Conservation ◽  
Mountain Ranges ◽  
Maximum Entropy Modeling ◽  
Suitable Habitats

Abstract Knowledge gaps regarding species distribution and abundance are great in remote regions with political instability, and they might be even larger concerning elusive and rare species. We predict the potential distribution for Hierophis andreanus, a poorly known endemic snake in the Iranian Plateau, and assess its conservation status in relation to existing protected areas. We used a maximum entropy modeling tool and Mahalanobis distance to produce an ensemble species distribution model. The most suitable habitats where located mainly in mountain ranges and adjacent areas of Iran and Afghanistan. Mean temperature and slope were the most important predictors for our models. Furthermore, just five localities for H. andreanus were inside the Iranian protected areas. A 10 km expansion from existing boundaries of protected areas in all directions would double protected localities to 10, and a 20 km buffer would result in 13 protected localities. Our findings are particularly valuable to select locations to conduct new surveys and produce a more reliable estimate of current population size to improve conservation and management for this reptile in the Irano-Anatolian region.

scholarly journals Assessing the effect of fish size on species distribution model performance in southern Chilean rivers

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7771
Author(s):  
Daniel Zamorano ◽  
Fabio A. Labra ◽  
Marcelo Villarroel ◽  
Shaw Lacy ◽  
Luca Mao ◽  
...  
Keyword(s):  
Body Size ◽  
Species Distribution ◽  
Riparian Vegetation ◽  
Species Distribution Model ◽  
Performance Metrics ◽  
Conservation Status ◽  
Management Tool ◽  
Distribution Model ◽  
Neural Network Modeling ◽  
Fish Size

Despite its theoretical relationship, the effect of body size on the performance of species distribution models (SDM) has only been assessed in a few studies, and to date, the evidence shows unclear results. In this context, Chilean fishes provide an ideal case to evaluate this relationship due to their short size (fishes between 5 cm and 40 cm) and conservation status, providing evidence for species at the lower end of the worldwide fish size distribution and representing a relevant management tool for species conservation. We assessed the effect of body size on the performance of SDM in nine Chilean river fishes, considering the number of records, performance metrics, and predictor importance. The study was developed in the Bueno and Valdivia basins of southern Chile. We used a neural network modeling algorithm, training models with a cross-validation scheme. The effect of fish size on selected metrics was assessed using linear models and beta regressions. While no relationship between fish size and the number of presences was found, our results indicate that the model specificity increases with fish size. Additionally, the predictive importance of Riparian Vegetation and Within-Channel Structures variables decreases for larger species. Our results suggest that the relationship between the grain of the dataset and the home range of the species could bias SDM, leading in our case, to overprediction of absences. We also suggest that evolutionary adaptation to low slopes among Chilean fishes increases the relevance of riparian vegetation in the SDMs of smaller species. This study provides evidence on how species size may bias SDM, which could potentially be corrected by adjusting the model grain.

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 Species distribution model reveals only highly fragmented suitable patches remaining for giant armadillo in the Brazilian Cerrado

2021 ◽  
Author(s):  
Katia Maria Paschoaletto Micchi de Barros Ferraz ◽  
Bruna Gomes de Oliveira ◽  
Nina Attias ◽  
Arnaud Leonard Jean Desbiez
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Brazilian Cerrado

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact
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