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dc.contributor.authorAddison, Prudence Frances Elizabeth
dc.date.accessioned2014-12-16T06:01:35Z
dc.date.available2014-12-16T06:01:35Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/11343/43241
dc.description© 2014 Dr. Prudence Frances Elizabeth Addison
dc.description.abstractLong-term biological monitoring data are becoming increasingly available to inform conservation efforts internationally. However long-term monitoring data, like many other forms of scientific evidence, are often not used effectively in conservation management. In this thesis I explore: i) what are the current barriers to the use of scientific evidence in conservation management?; and ii) what practical solutions can improve the use of scientific evidence, particularly long-term monitoring data, in conservation management? My research targets the science–management interface, acknowledging that both scientists and managers play vital roles in the creation and use of scientific evidence in conservation management. I first make a broad assessment of the common objections to the use of models to interpret scientific evidence in environmental decision-making. In response to these objections, I propose a series of practical solutions to assist scientists improve the effectiveness of models in conservation decision-making. These practical solutions highlight the role of structured decision-making as a framework to guide good modelling practice. I also recommend scientists develop skills to facilitate the social aspects of decision-making, something that is rarely included in conventional scientific training. In my following two research chapters I assess the barriers to the use of long-term monitoring data in marine protected area (MPA) management. This qualitative research revealed that long term biological monitoring data are not being used comprehensively in the evaluation of management effectiveness of Australian MPAs. In response, I develop a series of recommendations to assist both scientists and managers improve the use of long-term monitoring data in MPA management. In my final two research chapters, I introduce two methodological approaches to improve the use of long-term monitoring data in evidence-based conservation management. These approaches focus on the development of management thresholds to assist with interpreting monitoring data and alert when management intervention is required to address undesirable ecosystem changes. I illustrate these approaches using a case study of the management of a key ecological species, Hormosira banksii, on rocky intertidal reefs in MPAs. The first approach uses the theory of control charts to set statistically informed conservation management thresholds for environmental indicators. The second is a participatory modelling approach, designed to be useful when conservation management thresholds must be set for multi-objective conservation problems by participants with limited modelling experience. Both approaches follow the steps of structured decision-making, emphasising the need for logical and transparent model development in conservation. My PhD research advances the understanding of the barriers to using scientific evidence in conservation management. Many factors are at play that transcend the science–management interface. The practical solutions developed in my PhD are globally applicable and substantively address many important barriers and promote the comprehensive use of scientific evidence in conservation management.en_US
dc.subjectmarine protected areasen_US
dc.subjectmonitoringen_US
dc.subjectconservation managementen_US
dc.subjectstructured decision-makingen_US
dc.subjectmanagement effectiveness evaluationen_US
dc.subjectcontrol chartsen_US
dc.subjectmanagement thresholdsen_US
dc.titleTargeting the science–management interface: improving the use of long-term monitoring in conservation managementen_US
dc.typePhD thesisen_US
melbourne.affiliation.departmentSchool of Botany
melbourne.affiliation.facultyScience
melbourne.contributor.authorAddison, Prudence Frances Elizabeth
melbourne.accessrightsOpen Access


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