A review of the intact forest landscape concept in the Canadian boreal forest: its history, value, and measurement
Bibliographic record
Abstract
Loss of global forest, and in particular forest that has little human disturbance, is a standard against which we measure progress to conserve Earth’s forests. The value of intact forest landscapes has taken hold in the global psyche. We provide a brief history of the intact forest landscape concept and discuss how this has moved to an operational definition used as a global and regional metric of forest conservation. We distinguish between a conceptual intact forest landscape and an operational definition. For the purposes of this paper we will use the term IFL to mean the operational definition and intact forest landscapes to mean the conceptual idea. We provide an overview of the science that supports the value of intact forest landscapes in a Canadian boreal context and analyse issues with using a standard operationalized IFL definition to both measure and promote conservation of forests at global and regional scales. We found many arguments for protecting large, intact forest landscapes that are relevant to the Canadian boreal forest, including conservation of biodiversity, ecological processes and ecosystem services, existence values, application of the precautionary principle, and the need for scientific benchmarks. But it is clear that the standard operational IFL size threshold of 50 000 ha in the boreal forest is inadequate to meet these broad conservation objectives. However, the concept of intact forest being large enough to allow for all natural processes and biodiversity is likely not logistically feasible in Canada’s managed boreal forest. The scale at which the most extensive processes (e.g., fire and insects) occur and species (e.g., woodland caribou) function is likely too large. Management options incorporating local knowledge of conservation needs and the specifics of ecosystem function and composition are more likely to be effective in conservation than rigid IFL requirements. A standardized approach is useful for global tracking of IFLs but it is not the best approach to meet more regional forest conservation goals. Intact forest landscapes have exceptional value but should be managed in the context of integrated land use planning that includes protected areas, sustainable forest management, species at risk management, and ecosystem restoration.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.026 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".