scholarly journals Transferability and scalability of species distribution models: a test with sedentary marine invertebrates

2017 ◽  
Vol 74 (5) ◽  
pp. 766-778 ◽  
Author(s):  
Aaron M. Eger ◽  
Janelle M.R. Curtis ◽  
Marie-Josée Fortin ◽  
Isabelle M. Côté ◽  
Frédéric Guichard
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Marine Invertebrates ◽  
Predictive Accuracy ◽  
Distribution Models ◽  
Initial Information ◽  
Reference Models ◽  
Different Levels ◽  
Better Than

We found the predictive accuracy of species distribution models (SDMs) for sedentary marine invertebrates to be dependent on the methodology of their application. We explored three applications of SDMs: first a model tested at a scale smaller than at which it was trained (downscaled), second a model tested at scale larger than its training scale (upscaled), and third a model tested at the same scale but outside the extent for which it was trained (transferred). The accuracies of these models were compared with the “reference” models that were trained and tested at the same scale and extent. We found that downscaled SDMs had higher predictive accuracy than reference SDMs. Transferred and upscaled models had lower predictive accuracy than their reference counterparts but still performed better than random, making them potentially acceptable alternatives where information is lacking for imminent decisions or in cost-restricted scenarios. Our results provide insights into the techniques available for researchers and managers developing SDMs at varying scales, with different species, and with different levels of initial information.

Distribution of benthic marine invertebrates at northern latitudes ― An evaluation applying multi-algorithm species distribution models

2014 ◽  
Vol 85 ◽  
pp. 241-254 ◽  
Author(s):  
Karin Meißner ◽  
Dario Fiorentino ◽  
Sarah Schnurr ◽  
Pedro Martinez Arbizu ◽  
Falk Huettmann ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Marine Invertebrates ◽  
Distribution Models ◽  
Northern Latitudes

scholarly journals Embracing ensemble species distribution models to inform at-risk species status assessments

2021 ◽  
Author(s):  
Carlos Ramirez-Reyes ◽  
Mona Nazeri ◽  
Garrett Street ◽  
D. Todd Jones-Farrand ◽  
Francisco Vilella ◽  
...  
Keyword(s):  
At Risk ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Predictive Accuracy ◽  
Calibration Data ◽  
Species Status ◽  
Distribution Models ◽  
Modeling Approach ◽  
Modeling Techniques ◽  
Consequent Improvement

Conservation planning depends on reliable information regarding the geographic distribution of species. However, our knowledge of species' distributions is often incomplete, especially when species are cryptic, difficult to survey, or rare. The use of species distribution models has increased in recent years and proven a valuable tool to evaluate habitat suitability for species. However, practitioners have yet to fully adopt the potential of species distribution models to inform conservation efforts for information-limited species. Here, we describe a species distribution modeling approach for at-risk species that could better inform U.S. Fish and Wildlife Service’s species status assessments and help facilitate conservation decisions. We applied four modeling techniques (generalized additive, maximum entropy, generalized boosted, and weighted ensemble) to occurrence data for four at-risk species proposed for listing under the U.S. Endangered Species Act (Papaipema eryngii, Macbridea caroliniana, Scutellaria ocmulgee and Balduina atropurpurea) in the Southeastern U.S. The use of ensemble models reduced uncertainty caused by differences among modeling techniques, with a consequent improvement of predictive accuracy of fitted models. Incorporating an ensemble modeling approach into species status assessments and similar frameworks is likely to benefit survey efforts, inform recovery activities, and provide more robust status assessments for at-risk species. We emphasize that co-producing species distribution models in close collaboration with species experts has the potential to provide better calibration data and model refinements, which could ultimately improve reliance and use of model outputs.

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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