scholarly journals A field‐validated species distribution model to support management of the critically endangered Poweshiek skipperling ( Oarisma poweshiek ) butterfly in Canada

2019 ◽  
Vol 2 (3) ◽  
Author(s):  
Richard Westwood ◽  
Alana R. Westwood ◽  
Mahsa Hooshmandi ◽  
Kara Pearson ◽  
Kerienne LaFrance ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Critically Endangered ◽  
Distribution Model ◽  
Oarisma Poweshiek

scholarly journals Species Distribution Model Predictions of the Critically Endangered Grey Nurse Shark in Australia

2021 ◽  
Author(s):  
Guanfang Su
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Boosted Regression Trees ◽  
Critically Endangered ◽  
Distribution Model ◽  
Distribution Models ◽  
Nurse Shark ◽  
Distributional Range ◽  
Specificity And Sensitivity ◽  
Leave One Out

Abstract Species distribution models (SDMs) are commonly used to forecast how threatened species are influenced by climate change. The grey nurse shark (Carcharias tauras) is a critically endangered species inhabiting both the east and west coasts of Australia, with negligible genetic interchange between the two populations. I used Generalized Linear Models (GLM), Maximum Entropy (MaxEnt) models and Boosted Regression Trees (BRT) to predict the distribution of the grey nurse shark. The data were a sample of presence-only data, derived from the known grey nurse shark sighting locations, from the east coasts of Australia, with pseudo-absences generated and bootstrapped from a restricted background. I verified these models using leave-one-out cross validation and model metrics including AICc, BIC, percentage of deviance explained, leave-one-out cross-validated R2, AUC, maximum Cohen’s Kappa, specificity and sensitivity. Cross-validated R2 was used as an overall comparison method across model types. I performed out-of-source validation by comparing model projection with the distributional range of the ragged tooth shark (Carcharias taurus) in South Africa. The prediction of the selected model was consistent with the current distributional range of the ragged tooth shark.

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 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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