scholarly journals Hindcasting the impacts of land-use changes on bird communities with species distribution models of Bird Atlas data

2018 ◽  
Vol 28 (7) ◽  
pp. 1867-1883 ◽  
Author(s):  
Adrián Regos ◽  
Louis Imbeau ◽  
Mélanie Desrochers ◽  
Alain Leduc ◽  
Michel Robert ◽  
...  
Keyword(s):  
Land Use ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Land Use Changes ◽  
Bird Communities ◽  
Distribution Models ◽  
Atlas Data

Finessing atlas data for species distribution models

2011 ◽  
Vol 17 (6) ◽  
pp. 1173-1185 ◽  
Author(s):  
Aidin Niamir ◽  
Andrew K. Skidmore ◽  
Albertus G. Toxopeus ◽  
Antonio R. Muñoz ◽  
Raimundo Real
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Atlas Data

Multispecies and Watershed Approaches to Freshwater Fish Conservation

2019 ◽  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Fisheries Management ◽  
Species Distribution ◽  
Large Scale ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Climate Conditions ◽  
Resource Sustainability ◽  
Riparian Protection

<em>Abstract</em>.—Increasingly, fisheries managers must make important decisions in complex environments where rapidly changing landscape and climate conditions interact with historical impacts to influence resource sustainability. Successful fisheries management in this setting will require that we adapt traditional management approaches to incorporate information on these complex interacting factors—a process referred to as resilient fisheries management. Large-scale species distribution data and predictive models have the potential to enhance the management of freshwater fishes through improved understanding of how past, present, and future natural and anthropogenic factors combine to determine species vulnerability and resiliency. Here we describe a resilient fisheries management framework that provides guidance on how and when these models can be incorporated into traditional approaches to meet specific goals and objectives for resource sustainability. In addition to elucidating complex drivers of distributional patterns and change, species distribution models can inform the prioritization, application, and implementation of management activities such as restoration (e.g., instream habitat and riparian), protection (e.g., areas where additional land use would result in a change in species distribution), and regulations (e.g., harvest restriction) in a way that informs resiliency to land use and climate change. Although considerable progress has been made with respect to applying species distribution models to the management of Brook Trout <em>Salvelinus fontinalis </em>and other aquatic species, there are several areas where a more unified research and management effort could increase the ability of distribution models to inform resilient management. Future efforts should aim to improve (1) data availability, consistency (sampling methodology), and quality (accounting for detection); (2) partnerships among researchers, agencies, and managers; and (3) model accessibility and understanding of limitations and potential benefits to managers (e.g., incorporation into publicly available decision support systems). The information and recommendations provided herein can be used to promote and advance the use of models in resilient fisheries management in the face of continued large-scale land use and climate change.

The economics of land use reveals a selection bias in tree species distribution models

2016 ◽  
Vol 26 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Jean-Sauveur Ay ◽  
Joannès Guillemot ◽  
Nicolas Martin-StPaul ◽  
Luc Doyen ◽  
Paul Leadley
Keyword(s):  
Land Use ◽  
Selection Bias ◽  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Distribution Models

Use of taxonomy to delineate spatial extent of atlas data for species distribution models

2015 ◽  
Vol 25 (2) ◽  
pp. 227-237 ◽  
Author(s):  
Aidin Niamir ◽  
Andrew K. Skidmore ◽  
Albertus G. Toxopeus ◽  
Raimundo Real
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Spatial Extent ◽  
Distribution Models ◽  
Atlas Data

How many predictors in species distribution models at the landscape scale? Land use versus LiDAR-derived canopy height

2014 ◽  
Vol 28 (8) ◽  
pp. 1723-1739 ◽  
Author(s):  
Gentile Francesco Ficetola ◽  
Anna Bonardi ◽  
Caspar A. Mücher ◽  
Niels L.M. Gilissen ◽  
Emilio Padoa-Schioppa
Keyword(s):  
Land Use ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Landscape Scale ◽  
Canopy Height ◽  
Distribution Models

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.

Sign in / Sign up

Export Citation Format

Share Document