Disturbance, habitat variability and biodiversity of a boreal carabid (Coleoptera: Carabidae) fauna 15 years after variable retention harvest
Bibliographic record
Abstract
Species diversity patterns and the processes that drive them are a central focus of modern ecology. Although the spatial effects of habitat variation and disturbance on aggregate metrics like diversity and abundance have received considerable attention at local scales, ecologists have virtually ignored the joint influences of habitat variability and disturbance on the temporal variability in communities. Many such studies have dealt with carabid beetles (ground beetles), which are widely used as bioindicators to evaluate faunal response to disturbance. In this dissertation, I have studied variation of boreal ground beetle assemblages in time, in relation to the joint influences of forest cover type and disturbance as a contribution to interpreting the findings of the EMEND (Ecosystem Management Emulating Natural Disturbance) experiment in NW Alberta, Canada. In chapter 1, I focused on α-diversity and studied how local assemblages respond to combinations of retention harvest prescription across four cover types of mixedwood forest, using a 15-year dataset. The work led to three main conclusions: (1) retention harvests better maintain ground beetle diversity than do traditional clear-cuts; (2) beetle assemblages start recovering toward pre-harvest condition immediately after harvest, instead of diverging from it; and (3) beetle assemblages in high retention treatments recover more quickly than do those in low retention treatments. Chapter 2 identifies the species that are mostly significantly affected by harvest responsible for large post-harvest species turnover (i.e., beta-diversity) among harvested stands and between them and un-harvested controls. My analysis establishes that (1) retention harvests are associated with lower species turnover than are traditional clear-cuts; (2) species turnover decreases with increased retention; and (3) species turnover in response to harvest is driven mostly by forest specialists, but that the extent of turnover will vary with cover-type. I found that seven reasonably abundant species contributed most to species turnover and classified them as ‘key species’ for conservation of these carabids in the context of mixedwood forestry. Chapter 3 links post-harvest variation to abundance of particular ground beetle species, and I model the abundance of key species, common species and rare species based on information about composition of vegetation. This chapter provides species-specific understanding of how harvest disturbance drives faunal turnover in these forests. Again, there are three main results: (1) habitat variation influences the patterns of abundance, and hence, distribution of ground beetle species and thus affects the structure of local assemblages in quite predictable ways; (2) such influences vary among ground beetle species because they have different habitat requirements; and (3) species distribution in stands harvested with retention are left more similar to un-harvested controls and recover toward forest characteristic of the early phases of boreal succession than in clear-cuts. Overall, my dissertation provides the most extensive analysis of forest ground beetle assemblages ever undertaken in relation to anthropogenic disturbance. My dissertation contributes to a habitat-based understanding of α- and beta-diversity for carabid beetles of forest systems, and clearly shows that the conservation-related effects of forestry flow from reductions in beta-diversity. Thus, in the more general view, this work increases ecological understanding about species diversity patterns that contributes to building better approaches for long-term biodiversity conservation in the context of sustainable forest management.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".