Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence

2020 ◽  
Vol 415 ◽  
pp. 108837 ◽  
Author(s):  
Sara M. Melo-Merino ◽  
Héctor Reyes-Bonilla ◽  
Andrés Lira-Noriega
2019 ◽  
Vol 2 (2) ◽  
pp. 05
Author(s):  
Deborah Veranea Espinosa Martínez

La elaboración de modelos de nicho ecológico (MNE/Ecological Niche Models) y/o modelos de distribución de especies (MDE/Species Distribution Models) requieren de diferentes fuentes de información para poder hacer la inferencia de la distribución de las especies en un espacio geográfico y ecológico interactuante (Ríos-Muñoz & Espinosa-Martínez, 2019; Peterson et al., 2011).  La información biológica está representada por los sitios geográficos donde las especies han sido registradas (datos de ocurrencia) y por lo tanto, bajo la perspectiva de los MNE/MDE, representan los sitios donde existen las condiciones ambientalmente viables (tanto físicas como ecológicas) para que las especies puedan mantenerse a largo plazo (Pulliam, 2000). Por esta razón, es necesario considerar diferentes aspectos referentes a la calidad de la información que representan los datos de ocurrencia de las especies y hacer énfasis en algunas consideraciones que deben tenerse con este tipo de datos. Esta es la segunda entrega de la serie de editoriales que están enfocadas a este proceso.


2005 ◽  
Vol 32 (2) ◽  
pp. 117-128 ◽  
Author(s):  
CHRIS J. JOHNSON ◽  
MICHAEL P. GILLINGHAM

The widespread use of spatial planning tools in conjunction with increases in the availability of geographic information systems and associated data has led to the rapid growth in the exploration and application of species distribution models. Conservation professionals can choose from a considerable number of modelling techniques, but there has been relatively little evaluation of predictive performance, data requirements, or type of inference of these models. Empirical data for woodland caribou Rangifer tarandus caribou was used to examine four species distribution models, namely a qualitative habitat suitability index and quantitative resource selection function, Mahalanobis distance and ecological niche models. Models for three sets of independent variables were developed and then a temporally independent set of caribou locations evaluated predictive performance. The similarity of species distribution maps among the four modelling approaches was also quantified. All of the quantitative species distribution models were good predictors of the validation data set, but the spatial distribution of mapped habitats differed considerably among models. These results suggest that choice of model and variable set could influence the identification of areas for conservation emphasis. Model choice may be limited by the type of species locations or desired inference. Conservation professionals should choose a model and variable set based on the question, the ecology of the species and the availability of requisite data.


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.


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