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Linking human harvesting behaviour to fisheries food webs: Human interactions in managed ecosystems

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Title: Linking human harvesting behaviour to fisheries food webs: Human interactions in managed ecosystems
Author: Bieg, Carling
Department: Department of Integrative Biology
Program: Integrative Biology
Advisor: McCann, Kevin
Abstract: Effective fisheries management requires an understanding of harvesting impacts and how they are influenced by human behaviour. Clearly not all harvesting impacts are the same, so it is important to classify fishing behaviour based on fisheries catches and human effort and decision-making. This thesis provides an important basis for classifying global fisheries based on human impacts and a framework for understanding the dynamics of holistic social-ecological harvesting systems. Chapter 1 presents a novel approach to characterizing harvesting behaviour based on the topology and distribution of human interactions throughout fisheries food webs, and shows that global fisheries exist along a continuum of human fishing generality. Further, I show that global patterns in fishing behaviour can be explained by geographic location and socio-economic conditions. With an understanding of societal motivations driving harvesting behaviour, holistic social-ecological resource management models that consider human decision-making and harvesting effort can be developed. Chapter 2 investigates the dynamical implications of harvesting in a simplified social-ecological model as a first step towards characterizing differences in human impacts based on harvesting behaviour. The results show that harvesting has the potential to drive stability in a resource at weak to intermediate levels of human impacts. However even a simple model has the potential to produce extremely complex dynamics, including human-driven cycles, chaos, long transients and alternate states. This thesis illustrates the importance of including human behaviour in resource management models by developing thorough social-ecological harvesting models.
URI: http://hdl.handle.net/10214/10039
Date: 2016-09
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