Draft:Systematic Conservation Planning

Systematic conservation planning (SCP) is an approach to identifying spatial priorities for biodiversity conservation that sets explicit objectives and uses data and transparent rules to compare alternative areas for action.^[1] It is commonly used to inform decisions about protected areas and other place-based conservation measures, including the configuration of networks intended to represent biodiversity features while accounting for constraints such as cost, land use, and feasibility.^[1][2]

SCP is sometimes described as a response to earlier “opportunistic” reserve establishment, where sites were added largely because they were inexpensive or faced low development pressure rather than because they completed a representative network.^[3]

Concepts that later became central to SCP - especially complementarity (selecting sites that add new biodiversity features not yet captured by existing areas) - were developed in the 1980s and early 1990s alongside algorithmic approaches to reserve selection.^[4] Early work in Tasmania is often cited in histories of reserve-selection algorithms.^[5]

A frequently cited synthesis of SCP as an “operational” planning model was published by Margules and Pressey in 2000, which set out a structured approach linking objectives, data, and decision rules to area selection and implementation.^[6] Subsequent work extended the framework to emphasise implementation conditions and iterative feedback between planning stages (e.g., stakeholder engagement, feasibility, and monitoring).^[7]

SCP literature commonly discusses several recurring concepts:

• Representativeness and adequacy (capturing biodiversity features in sufficient amount and condition to support persistence).^[8]
• Complementarity (priorities depend on what is already protected or managed).^[9]
• Irreplaceability and related measures of how difficult it would be to meet objectives if a site were lost from consideration.^[10]
• Connectivity (the arrangement of sites can affect movement, dispersal, and ecological processes).^[10]
• Threat and vulnerability (risk of loss or degradation can influence what actions are prioritised and where).^[11]

One widely cited description of SCP presents an 11-stage planning process, often treated as iterative rather than strictly linear.^[12] The stages are commonly summarised as:

1. Scope and cost the planning exercise (define the region, decision context, and resources).
2. Identify and involve stakeholders and decision-makers.
3. Describe the context (governance, constraints, and relevant policy).
4. Identify conservation goals for the region.
5. Compile data on biodiversity features (e.g., species, habitats, ecological processes).
6. Compile data on socio-economic variables and threats relevant to decisions.
7. Set explicit objectives (e.g., representation or condition targets).
8. Assess current achievement of objectives (gap analysis).
9. Select additional areas (or actions) to meet objectives, often under constraints.
10. Implement conservation actions (e.g., designation, management, incentives).
11. Maintain and monitor outcomes, updating plans as conditions and data change.^[12][13]

SCP analyses often use decision-support tools to compare spatial options against objectives and constraints. Tools vary in the optimisation methods they use and in whether outputs are intended as a set of areas (a “solution”) or a ranked surface of relative priority.

Examples include:

• Marxan and Marxan with Zones, which use heuristic optimisation (including simulated annealing) to search for sets of planning units that meet targets while minimising a cost function; Marxan with Zones extends this to multiple zone types for land or sea-use planning.^[14]
• Zonation, which produces a hierarchical ranking by iteratively removing locations that contribute least to overall conservation value under the model’s assumptions.^[15]
• prioritizr, an R package that formulates conservation planning problems as integer programming models and solves them using exact or near-exact optimisation solvers.^[16]
• CLUZ, a QGIS plugin intended to support interactive, “on-screen” planning and to interface with Marxan and Marxan with Zones workflows.^[17]

SCP has been applied in terrestrial, freshwater, and marine contexts, including:

• designing or expanding protected area networks and related ecological networks;^[18][19]
• marine spatial planning and zoning processes;^[20]
• national and regional biodiversity assessments and plans, including programmes in South Africa described as “systematic biodiversity planning.”^[21]

Recent reviews describe methodological developments such as incorporating ecosystem services, evaluating multiple actions and zones, and adapting objectives under climate change and uncertainty.^[22]

International biodiversity agreements include targets that refer to spatial planning, restoration, and area-based conservation. The Kunming–Montreal Global Biodiversity Framework (adopted in 2022 under the Convention on Biological Diversity) includes targets on integrated spatial planning (Target 1), restoration (Target 2), and conserving at least 30% of land and sea through protected areas and other effective area-based conservation measures (Target 3).^[23][24] SCP is one approach that has been used in practice to support planning toward such spatially explicit targets, although implementation depends on governance, finance, and monitoring capacity beyond the prioritisation analysis itself.^[25][26]