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: 3122
Function: getPubMedXML
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
The Prince Edward Island (PEI) mussel industry has faced challenges associated with invasive tunicate species over the past two decades. Field experiments to find suitable mitigation strategies require considerable time and are resource intensive. This study demonstrates the application of a mathematical model to assess several control strategies against populations under different temperature conditions in a mussel production area in PEI. A temperature dependent compartmental model was used to model the total abundance of . Each mitigation strategy was defined in terms of a combination of timing and frequency of treatments. Various strategies were explored to obtain the combination that maximized the difference in predicted abundances between the control (untreated) and the different mitigation strategies. Treatment frequency was allowed to vary between one and four times over a given production year. The model was assessed under baseline conditions, which mimicked water temperatures from Georgetown Harbor, PEI, in 2008; as well as under scenarios that reflected prolonged summer or warm spring temperatures. Furthermore, the sensitivity of the model to variations in presumed treatment efficacy was evaluated. The use of all four available treatments, starting around the first week of July and correctly timed thereafter, provided the most effective strategy, assuming the baseline temperature scenario. However, the effectiveness of this mitigation strategy depended on temperature conditions. The mathematical model developed in this study allows decision makers to explore different strategies to control the abundance of in mussel production areas under different environmental conditions. In addition, the modeling framework developed could be adapted to simulate comparable ectoparasitic infestation in aquatic environments.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6710327 | PMC |
http://dx.doi.org/10.3389/fvets.2019.00271 | DOI Listing |
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