Methods and tools for addressing natural disturbance dynamics in conservation planning for wilderness areas
Bibliographic record
Abstract
Abstract Aim New conservation approaches that account for broad‐scale ecological processes must underpin decisions about conservation planning in the world's remaining wilderness areas. Our goal is to make the relevant tools and methods that have been developed by conservation scientists accessible to conservation practitioners working towards wilderness preservation. Location Wilderness areas, in particular theNorthAmerican boreal region. Methods We describe prominent spatial tools from natural resource management, landscape ecology and conservation biology for incorporating natural disturbance dynamics into systematic conservation planning. Then, we identify emerging methods that combine and customize these types of tools to account for interacting ecological processes in wilderness conservation plans with a specific focus on conserving natural disturbances in theNorthAmerican boreal region. Results Two classes of tools are well suited to the task of conservation planning in dynamic landscapes: site‐selection tools (e.g.Marxan andZonation) and process‐based modelling tools (e.g.CONSERVandLANDIS‐II). Four methods for explicitly including natural disturbance dynamics into conservation plans emerge from the combination of these tools: spatial catalysts combined with site‐selection tools, probability theory combined with site‐selection tools, spatial simulation models and spatial simulation models combined with site‐selection tools. Main conclusions Globally, there are few wilderness areas remaining; therefore, there is increasing impetus to effectively protect the world's remaining intact areas. Careful combinations of probabilistic models, such as Markov chain models, or spatial simulation tools, such asCONSERVandSpatiallyExplicitLandscapeEventSimulator, with site‐selection tools, such as Benchmark Builder and Marxan, are promising approaches for accounting for natural disturbance dynamics when land use planning in wilderness areas such as theNorthAmerican boreal region. The protection of natural disturbance dynamics will play an increasingly important role in the long‐term persistence of biodiversity in earth's remaining wilderness areas as ongoing anthropogenic disturbances and climate change imperil broad‐scale ecological processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".