Landscape spatial patterns and forest fragmentation in managed forests in southeast British Columbia : perceptions, measurements, and scale
Bibliographic record
Abstract
Forest spatial patterns are a central topic in contemporary landscape ecology, largely because of concerns about forest fragmentation. Forest fragmentation is thought to be a major threat to biodiversity because remnant forest patches, left from human disturbances such as logging, would support fewer species and be more prone to local extinctions because they are small and isolated from each other, as predicted from an extension of island biogeography theory. These and other theoretical predictions stemming from the forest fragmentation paradigm remain virtually unchallenged by empirical data. I investigated landscape spatial patterns in managed forests of the Slocan Valley in southeast British Columbia, and focused my investigations on theoretical predictions concerning forest fragmentation and the distinction between habitat amount and spatial configuration effects. I first investigated human perception of fragmentation to assess the usefulness of current methods in quantifying landscape spatial pattern and to investigate definitions and confusion about fragmentation. I then used traditional landscape indices to test predictions about fragmentation trends in the Slocan Valley by focusing on the effect of forest harvesting on old growth forest fragmentation. I then created a unique method of assessing landscape connectivity, the inverse of fragmentation, using a scale-dependent, organism-centered technique based on an organism's ability to move between habitat patches. Finally, I tested mule deer scale-dependant selection of forest edges, patch size, and logging roads relative to amount of forest, since these landscape elements are implicated in the fragmentation issue and are either untested or unresolved for mule deer. I found people associate fragmentation with high patch density, which was highly correlated with amount of harvesting, illustrating the confusion between habitat amount and spatial configuration. Landscape indices were of very limited use in deriving absolute values of fragmentation, and are likely best used to compare landscapes and pattern trends. I found little evidence of an old growth forest fragmentation trend in the Slocan Valley. Most predictions concerning a fragmentation trend were falsified. Using an organism-centered method to assess connectivity among old growth patches, I found the landscape to be accessible to all old growth associates at maximum dispersal distances, with the exception of the northern flying squirrel (Glaucomys sabrinus). At median dispersal distances however, only larger more vagile carnivorous birds could access all old growth patches in the landscape. Of particular concern are flying squirrels which had access to only 10% of the landscape at median dispersal distances. Mule deer displayed selection of landscape elements at the landscape scale only. The best predictors of mule deer winter use were mature forest patch size and amount of mature forest. Because of high correlation between these two variables, distinction between them was difficult and illustrates this persistent problem in empirical work. Empirical field studies are direly needed to test the existing fragmentation theoretical framework. Future work must distinguish between habitat loss effects and independent fragmentation effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".