Modelling connectivity among resource wave hotspots: bears and spawning salmon of coastal British Columbia
Bibliographic record
Abstract
Understanding how important landscape features are connected can provide key information for the management of ecological systems, particularly insight into how species move through and interact with their environment. Landscape features and their spatial arrangement can either promote or deter movement among resource patches by individuals. Over microevolutionary time periods, such movement allows for the persistence of population structure via the exchange of genetic material, particularly in metapopulations spatially separated over fragmented landscapes. Over shorter ecological periods, and less understood, the presence and distribution of important food patches can influence habitat connectivity for mobile species, such as large carnivores. In this work, we examined how five species of Pacific salmon (Oncorhynchus spp.), which spawn across varying times (~late April to ~late December) and spatial locations throughout coastal British Columbia (BC), might be associated with movement by grizzly bears (Ursus arctos horribilis). Following “resource waves”, spatial data show how these mobile consumers track salmon spawns as runs become available over space and time throughout the spawning season. In coastal BC, where bears have never been radio-collared, we know little about how landscape features might affect their ability to travel among salmon spawns. Such information is essential to proactive landscape planning for forest management. Following circuit theory, we used Circuitscape to predict movement among these important resource patches within a 17,000km2 study area. Variables affecting grizzly movement were parameterized during collaborative meetings with the Heiltsuk Integrated Resource Management Department (HIRMD) and incorporated Indigenous and local knowledge. The modelled current flows suggested important areas of high predicted connectivity between salmon spawns within and among watersheds. Furthermore, we illustrated potential corridors within the unprotected forest matrix for consideration by HIRMD. Broadly, this work unites connectivity modelling and resource waves research to consider movement among food patches, and directly informs conservation planning by an Indigenous government.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".