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 ◽  
...  
2021 ◽  
Author(s):  
Guanfang Su

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.


2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang

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.


2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  

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