Biodiversity and ecosystem services in forest ecosystems: a research agenda for applied forest ecology
Bibliographic record
Abstract
Summary Given the substantial contributions of forest biodiversity and ecosystem services to society, forest sciences have a large potential to contribute to the integrity and sustainability of our future. This is especially true when the roles of biodiversity for sustaining ecosystem services are considered. The rapid expansion of sustainable forest management ( SFM ) has resulted in the adoption of various forest management frameworks intended to safeguard biodiversity. Concurrently, the importance of forest ecosystem services has been increasingly recognized. Although some initiatives aimed at conserving both biodiversity and ecosystem services are emerging, knowledge gaps still exist about their relationships and potential trade‐offs in forests. Given recent advancements, increasing opportunities and some lags in forest ecology, further research on biodiversity, ecosystem functions and services will play substantial roles in the development of SFM practices. Here, we identified key issues including (i) relationships between biodiversity and ecosystem function as a foundation of ecological integrity, (ii) resilience thinking to better prepare for and adapt to environmental changes, (iii) social–ecological perspectives that facilitate real‐world conservation and management and (iv) theory‐driven restoration that bridges science and practice. Thus, we illustrate priorities and future possibilities in applied ecology studies in forests, which will help society and ecosystems to build capacity and resilience to face uncertainty in the changing environment. Synthesis and applications . Under substantial human influences, forests are highly likely to be largely altered, potentially leading to the emergence of novel ecosystems or alternative stable states. Management thus needs more flexible, novel measures to address the significant uncertainty this generates. Resilience‐based approaches are important to respond adaptively to future changes and cope with surprises, potentially providing multiple options. Although challenges exist, theory should play an important role in managing, conserving and restoring forest ecosystems. The issues discussed here should receive further attention in the context of the multiple goals 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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".