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
Spatial analyses of ecosystem system services that are directly relevant to both forest management decision making and conservation in the subtropics are rare. Also, frameworks that identify and map carbon stocks and corresponding forest management drivers using available regional, national, and international-level forest inventory datasets could provide insights into key forest structural characteristics and management practices that are optimal for carbon storage. To address this need we used publicly available USDA Forest Service Forest Inventory and Analysis data and spatial analyses to develop a framework for mapping "carbon hotspots" (i.e. areas of significantly high tree and understory aboveground carbon stocks) across a range of forest types using the state of Florida, USA as an example. We also analyzed influential forest management variables (e.g. forest types, fire, hurricanes, tenure, management activities) using generalized linear mixed modeling to identify drivers associated with these hotspots. Most of the hotspots were located in the northern third of the state some in peri-urban areas, and there were no identifiable hotspots in South Florida. Forest silvicultural treatments (e.g. site preparation, thinning, logging, etc) were not significant predictors of hotspots. Forest types, site quality, and stand age were however significant predictors. Higher site quality and stand age increased the probability of forests being classified as a hotspot. Disturbance type and time since disturbance were not significant predictors in our analyses. This framework can use globally available forest inventory datasets to analyze and map ecosystems service provision areas and bioenergy supplies and identify forest management practices that optimize these services in forests.
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Source |
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http://dx.doi.org/10.1016/j.jenvman.2012.10.020 | DOI Listing |
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