Predicting regional cumulative effects of future development on coastal ecosystems to support Indigenous governance
Bibliographic record
Abstract
Abstract To achieve better biodiversity outcomes and match local governance capacity, cumulative effects assessment frameworks that combine Indigenous and western knowledge to predict future development impacts on biodiversity are needed. We developed a spatial future‐focused model informed by inclusive elicitation and strategic foresight to assess the regional cumulative effects of development on ecosystem health across the land and sea. We collaborated with three First Nations on the Central Coast of British Columbia, Canada, enabling Indigenous priorities, knowledge and values to drive the process, from the choice of priority ecosystem components (including salmon, herring, seabirds and bears), to identifying future development scenarios (based on forestry, energy/mining, tourism and salmon aquaculture sectors). Bayesian networks were populated with empirical data and expert judgement elicited from knowledge holders to predict the cumulative effects of current and future pressures on species and ecosystems. Under current conditions, the lowest probability of persistence was predicted for Pacific salmon (37%), followed by Pacific herring (43%). Under future conditions, the greatest declines in species health were associated with the intense development of mining, tourism and forestry, with up to a 54% decline from the current baseline health estimates predicted for Marbled Murrelets and old‐growth forest. Future outcomes for overall ecosystem health were predicted to be worst in scenarios with high future forestry activities (>60% decline in some areas). The continuation or development of all four industries resulted in an 8% decline overall in ecosystem health across the Central Coast. In contrast, predicted ecosystem health in the tourism economy scenario increased up to 15% in some marine areas, primarily driven by the removal of salmon aquaculture and forestry activities. Synthesis and applications . Our study demonstrates an inclusive, regional approach to assessing the cumulative effects of future development on coastal species. The novel participatory tools and predictive framework draw upon and interweave multiple forms of knowledge, enabling Indigenous values to drive the process, and appropriately integrate Indigenous knowledge into regional cumulative effects assessment. Our interactive web application provides First Nations partners access to all outputs, supporting Indigenous‐led governance and in situ ecosystem‐based management of their lands and water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".