Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges?

2016 ◽  
Vol 342 ◽  
pp. 135-146 ◽  
Author(s):  
Linda J. Beaumont ◽  
Erin Graham ◽  
Daisy Englert Duursma ◽  
Peter D. Wilson ◽  
Abigail Cabrelli ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Species Ranges ◽  
Broad Scale

Do species distribution models explain spatial structure within tree species ranges?

2009 ◽  
Vol 18 (6) ◽  
pp. 662-673 ◽  
Author(s):  
Daniel Montoya ◽  
Drew W. Purves ◽  
Itziar R. Urbieta ◽  
Miguel A. Zavala
Keyword(s):  
Spatial Structure ◽  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Species Ranges

scholarly journals Broad-scale species distribution models applied to data-poor areas

2019 ◽  
Vol 175 ◽  
pp. 198-207 ◽  
Author(s):  
Charlène Guillaumot ◽  
Jean Artois ◽  
Thomas Saucède ◽  
Laura Demoustier ◽  
Camille Moreau ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Broad Scale

scholarly journals Muddy Boots Beget Wisdom: Implications for Rare or Endangered Plant Species Distribution Models

Diversity ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 10 ◽  
Author(s):  
Nora Oleas ◽  
Kenneth Feeley ◽  
Javier Fajardo ◽  
Alan Meerow ◽  
Jennifer Gebelein ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Conservation Strategies ◽  
Distribution Models ◽  
Online Data ◽  
Species Ranges ◽  
Geographic Ranges ◽  
Standard Data ◽  
Herbarium Collections ◽  
Species Specific

Species distribution models (SDMs) are popular tools for predicting the geographic ranges of species. It is common practice to use georeferenced records obtained from online databases to generate these models. Using three species of Phaedranassa (Amaryllidaceae) from the Northern Andes, we compare the geographic ranges as predicted by SDMs based on online records (after standard data cleaning) with SDMs of these records confirmed through extensive field searches. We also review the identification of herbarium collections. The species’ ranges generated with corroborated field records did not agree with the species’ ranges based on the online data. Specifically, geographic ranges based on online data were significantly inflated and had significantly different and wider elevational extents compared to the ranges based on verified field records. Our results suggest that to generate accurate predictions of species’ ranges, occurrence records need to be carefully evaluated with (1) appropriate filters (e.g., altitude range, ecosystem); (2) taxonomic monographs and/or specialist corroboration; and (3) validation through field searches. This study points out the implications of generating SDMs produced with unverified online records to guide species-specific conservation strategies since inaccurate range predictions can have important consequences when estimating species’ extinction risks.

scholarly journals Scale effects in species distribution models: implications for conservation planning under climate change

2008 ◽  
Vol 5 (1) ◽  
pp. 39-43 ◽  
Author(s):  
Changwan Seo ◽  
James H Thorne ◽  
Lee Hannah ◽  
Wilfried Thuiller
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Conservation Planning ◽  
Species Distribution Models ◽  
Climate Model ◽  
Scale Effects ◽  
Global Climate ◽  
Global Climate Model ◽  
Distribution Models ◽  
Species Ranges

Predictions of future species' ranges under climate change are needed for conservation planning, for which species distribution models (SDMs) are widely used. However, global climate model-based (GCM) output grids can bias the area identified as suitable when these are used as SDM predictor variables, because GCM outputs, typically at least 50×50 km, are biologically coarse. We tested the assumption that species ranges can be equally well portrayed in SDMs operating on base data of different grid sizes by comparing SDM performance statistics and area selected by four SDMs run at seven grid sizes, for nine species of contrasting range size. Area selected was disproportionately larger for SDMs run on larger grid sizes, indicating a cut-off point above which model results were less reliable. Up to 2.89 times more species range area was selected by SDMs operating on grids above 50×50 km, compared to SDMs operating at 1 km 2 . Spatial congruence between areas selected as range also diverged as grid size increased, particularly for species with ranges between 20 000 and 90 000 km 2 . These results indicate the need for caution when using such data to plan future protected areas, because an overly large predicted range could lead to inappropriate reserve location selection.

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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