scholarly journals Species Distribution Models and Ecological Suitability Analysis for Potential Tick Vectors of Lyme Disease in Mexico

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Patricia Illoldi-Rangel ◽  
Chissa-Louise Rivaldi ◽  
Blake Sissel ◽  
Rebecca Trout Fryxell ◽  
Guadalupe Gordillo-Pérez ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Eastern Coast ◽  
Suitability Analysis ◽  
Amblyomma Cajennense ◽  
Distribution Models ◽  
Geographic Ranges ◽  
Lower Temperature ◽  
Tick Vectors

Species distribution models were constructed for tenIxodesspecies andAmblyomma cajennensefor a region including Mexico and Texas. The model was based on a maximum entropy algorithm that used environmental layers to predict the relative probability of presence for each taxon. For Mexico, species geographic ranges were predicted by restricting the models to cells which have a higher probability than the lowest probability of the cells in which a presence record was located. There was spatial nonconcordance between the distributions ofAmblyomma cajennenseand theIxodesgroup with the former restricted to lowlands and mainly the eastern coast of Mexico and the latter to montane regions with lower temperature. The risk of Lyme disease is, therefore, mainly present in the highlands where someIxodesspecies are known vectors; ifAmblyomma cajennenseturns out to be a competent vector, the area of risk also extends to the lowlands and the east coast.

scholarly journals Species distribution models for the eastern blacklegged tick, Ixodes scapularis, and the Lyme disease pathogen, Borrelia burgdorferi, in Ontario, Canada

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0238126
Author(s):  
Andreea M. Slatculescu ◽  
Katie M. Clow ◽  
Roman McKay ◽  
Benoit Talbot ◽  
James J. Logan ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Borrelia Burgdorferi ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Ixodes Scapularis ◽  
Distribution Models ◽  
Blacklegged Tick

Testing climate-based species distribution models with recent field surveys of pond-breeding amphibians in eastern Missouri

2011 ◽  
Vol 89 (11) ◽  
pp. 1074-1083 ◽  
Author(s):  
D.R. Trumbo ◽  
A.A. Burgett ◽  
J.H. Knouft
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Range ◽  
Environmental Data ◽  
Limited Attention ◽  
Distribution Models ◽  
Field Surveys ◽  
Geographic Ranges ◽  
Using Data ◽  
Study Species

Species distribution models (SDMs) have become an important tool for ecologists by providing the ability to predict the distributions of organisms based on species niche parameters and available habitat across broad geographic areas. However, investigation of the appropriate extent of environmental data needed to make accurate predictions has received limited attention. We investigate whether SDMs developed with regional climate and species locality data (i.e., within Missouri, USA) produce more accurate predictions of species occurrences than models developed with data from across an entire species range. To test the accuracy of the model predictions, field surveys were performed in 2007 and 2008 at 103 study ponds for eight amphibian study species. Models developed using data from across the entire species range did not accurately predict the occurrences of any study species. However, models developed using data only from Missouri produced accurate predictions for four study species, all of which are near the edge of their geographic ranges within the study area. These results suggest that species distribution modeling with regionally focused data may be preferable for local ecological and conservation purposes, and that climate factors may be more important for determining species distributions at the edge of their geographic ranges.

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 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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