Evaluating forest resilience to global threats using functional response traits and network properties
Bibliographic record
Abstract
Ecosystem functions provided by forests are threatened by direct and indirect effects of global change drivers such as climate warming land-use change, biological invasions, and shifting natural disturbance regimes. To develop resilience-based forest management, new tools and methods are needed to quantitatively estimate forest resilience to management and future natural disturbances. We propose a multidimensional evaluation of ecological resilience based on species functional response traits (e.g., functional response diversity and functional redundancy) and network properties of forested patches (e.g., connectivity, modularity, and centrality). Using a fragmented rural landscape in temperate south-eastern Canada as a reference landscape, we apply our multidimensional approach to evaluate two alternative management strategies at three levels of intensity: (1) functional enrichment of current forest patches and (2) multi-species plantations in previously non-forested patches. Within each management strategy, planted species are selected to maximize functional diversity, drought tolerance, or pest resistance. We further compare how ecological resilience under these alternative management strategies responds to three simulated disturbances: drought, pest outbreak, and timber harvesting. We found that both management strategies enhance resilience at the landscape scale by increasing functional response diversity and connectivity. Specifically, when the less functionally diverse patches are prioritized for management, functional enrichment is more effective than the establishment of new multi-species plantations in increasing resilience. In addition, randomly allocated multi-species plantations increased connectivity more than those allocated in riparian areas. Our results show that across various management strategies, planting species to enhance biodiversity led to the highest increase in functional response diversity while planting pest-resistant species led to the highest increase in landscape connectivity. Planting biodiversity-enhancing species (i.e., species that maximize functional diversity) mitigated drought effects equally well as planting with drought-tolerant species. Our multidimensional approach facilitates the characterization at the landscape scale of forest resilience to disturbances using both functional diversity and network properties while accounting for the importance of response traits to future disturbances. The simulation approach we used can be applied to forest landscapes across different biomes for the evaluation and comparison of forest management initiatives to enhance resilience.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".