Draft:Systematic Conservation Planning

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Submission declined on 25 August 2025 by Pythoncoder (talk). Your draft shows signs of having been generated by a large language model, such as ChatGPT. Wikipedia guidelines prohibit the use of LLMs to write articles from scratch. In addition, LLM-generated articles usually have multiple quality issues, to include: Promotional tone, editorializing and other words to watch Vague, generic, and speculative statements extrapolated from similar subjects Essay-like writing Hallucinations (plausible-sounding, but false information) and non-existent references Close paraphrasing Please address these issues. The best way is usually to read reliable sources and summarize them, instead of using a large language model. See our help page on large language models. If you would like to continue working on the submission, click on the "Edit" tab at the top of the window. If you have not resolved the issues listed above, your draft will be declined again and potentially deleted. If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors. Please do not remove reviewer comments or this notice until the submission is accepted. Where to get help If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews. If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed. How to improve a draft Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia. Help:Wikitext – how to use the markup Help:Referencing for beginners – how to include references Wikipedia:Article development – how to develop your article Wikipedia:Writing better articles – how to improve your article Wikipedia:Verifiability – make sure your article includes reliable third-party sources You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Add tags to your draft Editor resources Easy tools: Citation bot (help) | Advanced: Fix bare URLs Declined by Pythoncoder 6 months ago. Last edited by J barton 234 7 days ago. Reviewer: Inform author. This draft has been resubmitted and is currently awaiting re-review.

Submission declined on 5 August 2025 by JesusisGreat7 (talk). Your draft shows signs of having been generated by a large language model, such as ChatGPT. Wikipedia guidelines prohibit the use of LLMs to write articles from scratch. In addition, LLM-generated articles usually have multiple quality issues, to include: Promotional tone, editorializing and other words to watch Vague, generic, and speculative statements extrapolated from similar subjects Essay-like writing Hallucinations (plausible-sounding, but false information) and non-existent references Close paraphrasing Please address these issues. The best way is usually to read reliable sources and summarize them, instead of using a large language model. See our help page on large language models. Declined by JesusisGreat7 6 months ago.

Submission declined on 7 June 2025 by CanonNi (talk). This submission reads more like an essay than an encyclopedia article. Submissions should summarise information in secondary, reliable sources and not contain opinions or original research. Please write about the topic from a neutral point of view in an encyclopedic manner. Declined by CanonNi 8 months ago.

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]