Adaptive behavior of single individuals plays a crucial role in ecosystems and societies. It not only influences the dynamics of the whole socio-economic-ecological network but also has important implications for its sustainable management. The topic is studied as part of two different projects: animal movements and conservation of biodiversity, and the sustainable management of fisheries.
I investigated animal movement behavior in order to help improve wildlife and biodiversity conservation. The ultimate aim is to improve protected areas and the design of ecological corridors for the conservation of jaguars (Panthera onca) in Atlantic Forest, Brazil. Atlantic forest habitat is very fragmented, and each isolated patch contains no more than 50 individuals of this keystone species. Understanding jaguar movement behavior plays a key role in the conservation of the species in the area. Different statistical methods are available for conservation biologists to analyze movement behavior, and information about home range, migration routes, resource, and habitat selection can be extrapolated from movement data. However, there is no common agreement or systematic analysis of the effectiveness of these methods. The project therefore aims to provide a systematic analysis of their performance. In particular, an Individual-Based Movement Model has been implemented to simulate movement trajectories caused by different behavioral patterns. The model takes into account step length, directional persistence, and habitat preference. The trajectories obtained with the model are then statistically analyzed and the performance of each method assessed for each of the behavioral types. A catalog of how these methods perform can be used by conservation ecologists to make an informed choice of method in terms of the species they are studying.
In the second project, I investigated adaptive behavior on the part of fishers in order to improve sustainable management of fisheries. The study area is north-eastern Hokkaido, Japan, where fishing is the major source of income for the community. Significant changes in the composition of species caught has been observed in the last four decades. In mixed fisheries, fishers can actively choose their target species to maximize their profit in relation to social, economic, and ecological factors. Understanding the dynamics of their choice is crucial for sustainable fishing policies. An integrated bio-economic model was developed to study the dynamics of fishing effort allocation among the three main target species in the area. Catch and income data (collected by Fisheries Cooperative Associations), together with socioeconomic information (collected through questionnaires in the study area) were used to parametrize the model. Effort allocation and stock dynamics predicted by the model are in quite good agreement with the observed trends: a significant switch from traditional salmon fishing to seaweed collection and oyster aquaculture is due both to stock decline and economic factors such as market prices and fishing costs. Moreover, individual vessel fishing behavior and strategy are incorporated in a theoretical bio-economic-evolutionary model to assess the effect of fleet dynamics on the evolutionarily sustainable management of fisheries.
Pietro Landi is an Italian citizen and is a Postdoctoral Scholar in the Evolution and Ecology Program.
Last edited: 02 March 2016
Davis KF, Yu K, Herrero M, Havlik P, Carr JA, & D’Odorico P (2015). Historical trade-offs of livestock’s environmental impacts. Environmental Research Letters 10 (12): p. 125013. DOI:10.1088/1748-9326/10/12/125013.
Wilson C & Grubler A (2015). Historical Characteristics and Scenario Analysis of Technological Change in the Energy System. In: Technology and Innovation for Sustainable Development. Eds. Vos, R. & Alarcon, D., pp. 45-80 Norwich, UK: Bloomsbury Academic. ISBN 978-1-4725-8079-510.5040/9781472580795.ch-003.
Duarte R, Feng K, Hubacek K, Sanchez-Choliz J, Sarasa C, & Sun L (2015). Modeling the carbon consequences of pro-environmental consumer behavior. Applied Energy 184: 1207-1216. DOI:10.1016/j.apenergy.2015.09.101.
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