Biodiversity change under adaptive community dynamics
Bibliographic record
Abstract
Compositional change is a ubiquitous response of ecological communities to environmental drivers of global change, but is often regarded as evidence of declining "biotic integrity" relative to historical baselines. Adaptive compositional change, however, is a foundational idea in evolutionary biology, whereby changes in gene frequencies within species boost population-level fitness, allowing populations to persist as the environment changes. Here, we present an analogous idea for ecological communities based on core concepts of fitness and selection. Changes in community composition (i.e., frequencies of genetic differences among species) in response to environmental change should normally increase the average fitnessof community members. We refer to compositional changes that improve the functional match, or "fit," between organisms' traits and their environment as adaptive community dynamics. Environmental change (e.g., land-use change) commonly reduces the fit between antecedent communities and new environments. Subsequent change in community composition in response to environmental changes, however, should normally increase community-level fit, as the success of at least some constituent species increases. We argue that adaptive community dynamics are likely to improve or maintain ecosystem function (e.g., by maintaining productivity). Adaptive community responses may simultaneously produce some changes that are considered societally desirable (e.g., increased carbon storage) and others that are undesirable (e.g., declines of certain species), just as evolutionary responses within species may be deemed desirable (e.g., evolutionary rescue of an endangered species) or undesirable (e.g., enhanced virulence of an agricultural pest). When assessing possible management interventions, it is important to distinguish between drivers of environmental change (e.g., undesired climate warming) and adaptive community responses, which may generate some desirable outcomes. Efforts to facilitate, accept, or resist ecological change require separate consideration of drivers and responses, and may highlight the need to reconsider preferences for historical baseline communities over communities that are better adapted to the new conditions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".