Learn Search Topics Filter CategoriesClear AllFundamentalsMarxan 101Advanced ConceptsMarxan with ConnectivityMarxan with Zones What is conservation planning? And why is it important? The CARE Principles. Connectivity, Adequacy, Representativeness, Efficiency Problem formulation: What are you trying to achieve? The most important question in conservation planning A framework for systematic conservation planning. In this section we summarise the framework presented in Pressey and Bottrill (2009). What is Marxan? Learn Marxan comprises a suite of different software applications to support conservation planning decisions by providing cost-efficient solutions to complex conservation problems. Planning units. Planning units are the building blocks of any conservation or zoning plan. Targets and target setting. Targets represent how much you want to conserve of a particular feature. Costs. Learn In conservation planning, cost data may reflect any variety of socioeconomic factors, which if minimized, might help the conservation plan be implemented more effectively and reduce conflicts with other uses. Gap Analysis. Learn A gap analysis is a traditional planning evaluation based on the assessment of existing or proposed protected-area networks. Stratification. Learn Sometimes we want to stratify (break-up, arrange, or classify) features by spatial units or even other features, for example, ecosystems or local administration units. Thresholding. Learn Refers to what percentage of a planning unit must be covered by a protected area (in this case), to be considered “protected”. Locking planning units in or out. Learn Manually including or excluding individual planning units is useful where a real-world issue affects where new protected areas or conservation actions can be designated. Scheduling. Learn It is rarely possible that priority areas for conservation action identified through systematic conservation planning can realistically be implemented in a single time step. Scenario Development. Scenario planning consists of using a few contrasting scenarios to explore the uncertainty surrounding the future consequences of a decision. Understanding trade-offs and trade-off curves. Learn A trade-off is a decision that involves diminishing or losing one quality, quantity, or property of a set or design in return for gains in other aspects. Using condition data. Learn In spatial conservation planning ecosystem condition plays an increasingly important role for identifying priorities for conservation action. Understanding connectivity data. Learn One of the challenges associated with integrating data of ecological connectivity in spatial planning is the wide variety of entities that move (e.g. What is Marxan with Connectivity? Learn The following sections are a summary of Daigle et al. Using connectivity data in Marxan. Learn There are several different quantitative methods to directly incorporate connectivity data into the standard Marxan workflow . What is Marxan with Zones? Learn Marxan with Zones is based on the same principles standard Marxan, but allows for multiple zones, zoning contributions, costs, and the spatial relationships between zones to all be considered in spatial optimization.
What is conservation planning? And why is it important? The CARE Principles. Connectivity, Adequacy, Representativeness, Efficiency Problem formulation: What are you trying to achieve? The most important question in conservation planning A framework for systematic conservation planning. In this section we summarise the framework presented in Pressey and Bottrill (2009). What is Marxan? Learn Marxan comprises a suite of different software applications to support conservation planning decisions by providing cost-efficient solutions to complex conservation problems. Planning units. Planning units are the building blocks of any conservation or zoning plan. Targets and target setting. Targets represent how much you want to conserve of a particular feature. Costs. Learn In conservation planning, cost data may reflect any variety of socioeconomic factors, which if minimized, might help the conservation plan be implemented more effectively and reduce conflicts with other uses. Gap Analysis. Learn A gap analysis is a traditional planning evaluation based on the assessment of existing or proposed protected-area networks. Stratification. Learn Sometimes we want to stratify (break-up, arrange, or classify) features by spatial units or even other features, for example, ecosystems or local administration units. Thresholding. Learn Refers to what percentage of a planning unit must be covered by a protected area (in this case), to be considered “protected”. Locking planning units in or out. Learn Manually including or excluding individual planning units is useful where a real-world issue affects where new protected areas or conservation actions can be designated. Scheduling. Learn It is rarely possible that priority areas for conservation action identified through systematic conservation planning can realistically be implemented in a single time step. Scenario Development. Scenario planning consists of using a few contrasting scenarios to explore the uncertainty surrounding the future consequences of a decision. Understanding trade-offs and trade-off curves. Learn A trade-off is a decision that involves diminishing or losing one quality, quantity, or property of a set or design in return for gains in other aspects. Using condition data. Learn In spatial conservation planning ecosystem condition plays an increasingly important role for identifying priorities for conservation action. Understanding connectivity data. Learn One of the challenges associated with integrating data of ecological connectivity in spatial planning is the wide variety of entities that move (e.g. What is Marxan with Connectivity? Learn The following sections are a summary of Daigle et al. Using connectivity data in Marxan. Learn There are several different quantitative methods to directly incorporate connectivity data into the standard Marxan workflow . What is Marxan with Zones? Learn Marxan with Zones is based on the same principles standard Marxan, but allows for multiple zones, zoning contributions, costs, and the spatial relationships between zones to all be considered in spatial optimization.
