Habitation Sites Influence Tree Community Assemblages in the Great Bear Rainforest, British Columbia, Canada
Bibliographic record
Abstract
Identifying how past human actions have influenced their environment is essential for understanding the ecological factors that structure contemporary ecosystems. Intertidal resource use by Indigenous Peoples for thousands of years has led to habitation sites containing vast shell midden deposits and facilitating long-term impacts on soil chemistry and drainage. Here we examine how these shell middens have impacted various forest metrics, such as species diversity, community composition, canopy height, and regeneration recruitment to determine if forests on habitation sites differ from the surrounding matrix. We surveyed known habitation sites with archeological evidence indicating past year-round human occupation, within the Hakai Lúxvbálís Conservancy on Calvert and Hecate Islands within the Great Bear Rainforest along British Columbia’s Central Coast. Our results demonstrate that habitation sites exhibit lower tree species richness, less relative species abundances, as such, displayed lower Shannon diversity and inverse Simpson values. The composition of tree communities on habitation sites was statistically different, with western hemlock and western redcedar densities increasing on non-habitation sites. Conversely, regeneration diversity at habitation sites was more even and exhibited elevated Shannon diversity and inverse Simpson values. The community composition of regeneration was more consistent among habitation and non-habitation sites; however, western redcedar, western hemlock and Sitka spruce were more abundant at habitation sites. For all tree species, maximum height was higher within the habitation sites; however, this trend was the most notable in western redcedar and Sitka spruce, which increased by an average of 4.8 m relative to non-habitation sites. Collectively, our findings suggest that long-term habitation alters forest community compositions. The landscape alterations within habitation sites promote conditions needed to support diverse, even, and abundant regeneration communities and consequently increase the height of the dominant coastal tree species. Thus, our results offer evidence that long-term influence by Indigenous communities have a persistent influence on coastal forests.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".