Planning and management of marine protected areas : methodological approaches to cope with data scarcity

Tese de doutoramento, Biologia (Biologia Marinha e Aquacultura), Universidade de Lisboa, Faculdade de Ciências, 2015

Mapping multi-species habitat use for marine conservation planning

Tese de Doutoramento, Ciências do Mar (Biologia Marinha)

Marine Spatial Planning is coming to an ocean near you

There are too many conflicting uses of the ocean in a time where resources are rapidly dwindling. Marine Spatial Planning is catching on globally, and may soon come to Long Island Sound, but it may be difficult to decide who gets to do what, where.

Adaptive management, international co-operation and planning for marine conservation hotspots in a changing climate

Acknowledgements This work received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) and their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (Grant reference HR09011) and contributing institutions.

Adaptive management, international co-operation and planning for marine conservation hotspots in a changing climate

Acknowledgements This work received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) and their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (Grant reference HR09011) and contributing institutions.

Projections of change in key ecosystem indicators for planning and management of marine protected areas: An example study for European seas

Marine Protected Areas (MPAs) are widely used as tools to maintain biodiversity, protect habitats and ensure that development is sustainable. If MPAs are to maintain their role into the future it is important for managers to understand how conditions at these sites may change as a result of climate change and other drivers, and this understanding needs to extend beyond temperature to a range of key ecosystem indicators. This case study demonstrates how spatially-aggregated model results for multiple variables can provide useful projections for MPA planners and managers. Conditions in European MPAs have been projected for the 2040s using unmitigated and globally managed scenarios of climate change and river management, and hence high and low emissions of greenhouse gases and riverborne nutrients. The results highlight the vulnerability of potential refuge sites in the north-west Mediterranean and the need for careful monitoring at MPAs to the north and west of the British Isles, which may be affected by changes in Atlantic circulation patterns. The projections also support the need for more MPAs in the eastern Mediterranean and Adriatic Sea, and can inform the selection of sites.

Projections of change in key ecosystem indicators for planning and management of marine protected areas: An example study for European seas

Marine Protected Areas (MPAs) are widely used as tools to maintain biodiversity, protect habitats and ensure that development is sustainable. If MPAs are to maintain their role into the future it is important for managers to understand how conditions at these sites may change as a result of climate change and other drivers, and this understanding needs to extend beyond temperature to a range of key ecosystem indicators. This case study demonstrates how spatially-aggregated model results for multiple variables can provide useful projections for MPA planners and managers. Conditions in European MPAs have been projected for the 2040s using unmitigated and globally managed scenarios of climate change and river management, and hence high and low emissions of greenhouse gases and riverborne nutrients. The results highlight the vulnerability of potential refuge sites in the north-west Mediterranean and the need for careful monitoring at MPAs to the north and west of the British Isles, which may be affected by changes in Atlantic circulation patterns. The projections also support the need for more MPAs in the eastern Mediterranean and Adriatic Sea, and can inform the selection of sites.

Informing Marine Spatial Planning (MSP) with numerical modelling: A case-study on shellfish aquaculture in Malpeque Bay (Eastern Canada)

A moratorium on further bivalve leasing was established in 1999–2000 in Prince Edward Island (Canada). Recently, a marine spatial planning process was initiated explore potential mussel culture expansion in Malpeque Bay. This study focuses on the effects of a projected expansion scenario on productivity of existing leases and available suspended food resources. The aim is to provide a robust scientific assessment using available datasets and three modelling approaches ranging in complexity: (1) a connectivity analysis among culture areas; (2) a scenario analysis of organic seston dynamics based on a simplified biogeochemical model; and (3) a scenario analysis of phytoplankton dynamics based on an ecosystem model. These complementary approaches suggest (1) new leases can affect existing culture both through direct connectivity and through bay-scale effects driven by the overall increase in mussel biomass, and (2) a net reduction of phytoplankton within the bounds of its natural variation in the area.

Transboundary dimensions of marine spatial planning: Fostering inter-jurisdictional relations and governance

There is broad agreement that marine spatial planning (MSP) should incorporate transboundary considerations, reflecting the cross-border nature of marine and coastal ecosystem dynamics and maritime resources and activities. This is recognised in the European Union's recent legislation on MSP, and experience in transboundary approaches is developing through official processes and pilot studies.

Robot path planning in dynamic environments using a simulated annealing based approach

Mobile robots are widely used in many industrial fields. Research on path planning for mobile robots is one of the most important aspects in mobile robots research. Path planning for a mobile robot is to find a collision-free route, through the robot’s environment with obstacles, from a specified start location to a desired goal destination while satisfying certain optimization criteria. Most of the existing path planning methods, such as the visibility graph, the cell decomposition, and the potential field are designed with the focus on static environments, in which there are only stationary obstacles. However, in practical systems such as Marine Science Research, Robots in Mining Industry, and RoboCup games, robots usually face dynamic environments, in which both moving and stationary obstacles exist. Because of the complexity of the dynamic environments, research on path planning in the environments with dynamic obstacles is limited. Limited numbers of papers have been published in this area in comparison with hundreds of reports on path planning in stationary environments in the open literature. Recently, a genetic algorithm based approach has been introduced to plan the optimal path for a mobile robot in a dynamic environment with moving obstacles. However, with the increase of the number of the obstacles in the environment, and the changes of the moving speed and direction of the robot and obstacles, the size of the problem to be solved increases sharply. Consequently, the performance of the genetic algorithm based approach deteriorates significantly. This motivates the research of this work. This research develops and implements a simulated annealing algorithm based approach to find the optimal path for a mobile robot in a dynamic environment with moving obstacles. The simulated annealing algorithm is an optimization algorithm similar to the genetic algorithm in principle. However, our investigation and simulations have indicated that the simulated annealing algorithm based approach is simpler and easier to implement. Its performance is also shown to be superior to that of the genetic algorithm based approach in both online and offline processing times as well as in obtaining the optimal solution for path planning of the robot in the dynamic environment. The first step of many path planning methods is to search an initial feasible path for the robot. A commonly used method for searching the initial path is to randomly pick up some vertices of the obstacles in the search space. This is time consuming in both static and dynamic path planning, and has an important impact on the efficiency of the dynamic path planning. This research proposes a heuristic method to search the feasible initial path efficiently. Then, the heuristic method is incorporated into the proposed simulated annealing algorithm based approach for dynamic robot path planning. Simulation experiments have shown that with the incorporation of the heuristic method, the developed simulated annealing algorithm based approach requires much shorter processing time to get the optimal solutions in the dynamic path planning problem. Furthermore, the quality of the solution, as characterized by the length of the planned path, is also improved with the incorporated heuristic method in the simulated annealing based approach for both online and offline path planning.

An integrated approach to tourism planning in a developing nation : a case study from Beloi (Timor-Leste)

In this chapter we present a case study set in Beloi, a fishing village located on Ataúro Island, 30 km across the sea from Díli, capital of Timor-Leste (East-Timor). We explore the tension between tourism development, food security and marine conservation in a developing country context. In order to better understand the relationships between the social, ecological and economic issues that arise in tourism planning we use an approach and associated methodology based on storytelling, complexity theory and concept mapping. Through testing scenarios with this methodology we hope to evaluate which trade-offs are acceptable to local people in return for the hoped-for economic boost from increased tourist visitation and associated developments.

Real-time image classification for adaptive mission planning using an Autonomous Underwater Vehicle

Real-time image analysis and classification onboard robotic marine vehicles, such as AUVs, is a key step in the realisation of adaptive mission planning for large-scale habitat mapping in previously unexplored environments. This paper describes a novel technique to train, process, and classify images collected onboard an AUV used in relatively shallow waters with poor visibility and non-uniform lighting. The approach utilises Förstner feature detectors and Laws texture energy masks for image characterisation, and a bag of words approach for feature recognition. To improve classification performance we propose a usefulness gain to learn the importance of each histogram component for each class. Experimental results illustrate the performance of the system in characterisation of a variety of marine habitats and its ability to operate onboard an AUV's main processor suitable for real-time mission planning.