Resistance of plant–plant networks to biodiversity loss and secondary extinctions following simulated environmental changes
Bibliographic record
Abstract
Summary Plant interactions are fundamental processes for structuring plant communities and are an important mechanism governing the response of plant species and communities to environmental changes. Thus, understanding the role played by the interaction network in modulating the impact of environmental changes on plant community composition and diversity is crucial. Here, we aimed to develop a new analytical and conceptual framework to evaluate the responses of plant communities to environmental changes. This framework uses functional traits as sensitivity measures for simulated environmental changes and assesses the consequences of microhabitat loss. We show here its application to an alpine plant community where we recorded functional traits [specific leaf area ( SLA ) and leaf dry matter content ( LDMC )] of all plants associated with three foundation species or the surrounding open areas. We then simulated primary species loss based on different scenarios of environmental change and explored community persistence to the loss of foundation species. Generally, plant community responses differed among environmental change scenarios. In a scenario of increasing drought alone (i.e. species with lower LDMC were lost first) or increasing drought with increasing temperature (i.e. species with lower LDMC and higher SLA were lost first), the plant community resisted because drought‐tolerant foundation species tolerated those deteriorating conditions. However, in scenarios with increasing nitrogen input (i.e. species having lower SLA were lost earlier), foundation species accelerated species loss due to their early primary extinctions and the corresponding secondary extinctions of species associated to their microhabitat. The resistance of a plant community depends on the driver of environmental change, meaning that the prediction of the fate of this system is depending on the knowledge of the main driver of environmental change. Our framework provides a mechanistic understanding of an ecosystem response to such environmental changes thanks to the integration of biology‐informed criteria of species sensitivities to environmental factors into a network of interacting species. A lay summary is available for this article.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".