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Adaptive monitoring using causative conceptual models: assessment of ecological integrity of aquatic ecosystems

Authors :
Jonathan C. Marshall
Peter M. Negus
Joanna Blessing
Sara E. Clifford
Source :
Australasian Journal of Environmental Management. 27:224-240
Publication Year :
2020
Publisher :
Informa UK Limited, 2020.

Abstract

Ecosystem monitoring often fails to provide the right information to evaluate and guide environmental stewardship due to a lack of diagnostic capacity, long-term operational resources, explicit monitoring objectives and rigorous sampling designs. Our objective is to describe a monitoring framework that addresses these failures by including causative conceptual models and the concepts of adaptive monitoring and management. Resources are rarely available to monitor all ecosystem components, so identifying priorities is vital for the success of a monitoring program. An ecological risk assessment combining available information and expert opinion on threats and their consequences to the ecosystem can be used to prioritise monitoring and identify explicit objectives. A Pressure-Stressor-Response conceptual model forms the causative understanding of the ecosystem and the model components underpin the factors in the risk assessment. In this way, field sampling can validate the priority of ecosystem threats; provide information for refinement of conceptual understandings and guide efficient management activity. Repeated risk assessments using updated data and information can identify successful management and the increase and establishment of threats. Updated risk assessments can change threat priorities and therefore monitoring and assessment hypotheses and objectives can change. This ability to change underlies the concepts of adaptive monitoring and management.

Details

ISSN :
21595356 and 14486563
Volume :
27
Database :
OpenAIRE
Journal :
Australasian Journal of Environmental Management
Accession number :
edsair.doi...........394735c9263c93e2c2601813a8c9014a
Full Text :
https://doi.org/10.1080/14486563.2020.1750494