Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
For all species, abiotic factors directly affect performance, survival and reproduction, and consequently, their geographic distribution. Species distribution models (SDMs) are important tools to predict the influence of abiotic factors in species distributions and has been more applied over the years. However, these models can be built under different algorithms and using different methods to select environmental predictors, which can lead to different results. Five different algorithms and two sets of environmental predictors were compared to predict the geographic distribution of the blowfly Sarconesia chlorogaster (Wiedemann) (Diptera: Calliphoridae). This species has several occurrence points and a considerable amount of biological data available, which makes S. chlorogaster a good model system to compare environmental predictors. Two sets of environmental predictors (mainly derived from temperature and humidity) were built, and the set based on the influence of abiotic variables on the ecophysiology of S. chlorogaster showed better results than the principal component analysis (PCA) approach using 19 climatic variables. We also employed five modeling algorithms-Envelope Score, Mahalanobis Distance, GARP, Support Vector Machines, and Maxent-and the latter two showed the best performances. The results indicate that temperature is the main factor shaping geographic distribution of S. chlorogaster through its effect on fitness. Furthermore, we showed that this species is mainly distributed in south, southeastern, and some northwestern and southwestern sites of South America. In addition, our results also predicted suitable areas in Ecuador and Colombia, countries without previous records.
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Source |
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http://dx.doi.org/10.1093/ee/nvx124 | DOI Listing |
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