The importance of facilitative interactions in mediating climate change impact on biodiversity
Bibliographic record
Abstract
Global change is a multifaceted, unprecedented crisis hitting the life support system of our planet. Among global changes, climate change is regarded as one of the most grave threats to biodiversity because of its direct impacts on species and ecosystems integrity and because of its indirect consequences through synergistic effects with other global change factors such as biological invasions. The challenges presented to either reduce or mitigate this biodiversity crisis derived from climate change require novel synthesis and innovation in ecological and evolutionary theory. Positive species interactions within and between trophic levels can play a key role in the resilience of ecological communities. Depending on the tolerance of nurse species to different aspects of climate change, communities can be more or less resilient to those changes. This knowledge has important implications for both natural communities and agroecosystems. Further, our fundamental understanding of the role of positive interactions can also enable both effective conservation and restoration levers in space and time. This special issue includes studies addressing the role of facilitative interactions on the response of ecological systems to climate change. Key concepts examined included stress, gradients, nurse species, spatial scale, translocation, phylogenetics alongside physiochemicals, and variation in the capacity of species to buffer changes. Alpine, tundra, drylands and temperate forests were directly tested, but salient principles were relevant to all ecosystems including a contribution on soil biota and also a call to more open data and collaborative science. Together, this corpus of work highlighted the significance of facilitative interactions in mitigating many of the effects of climate change on biodiversity.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".