Phylogenetic diversity and co‐evolutionary signals among trophic levels change across a habitat edge
Bibliographic record
Abstract
Incorporating the evolutionary history of species into community ecology enhances understanding of community composition, ecosystem functioning and responses to environmental changes. Phylogenetic history might partly explain the impact of fragmentation and land-use change on assemblages of interacting organisms and even determine potential cascading effects across trophic levels. However, it remains unclear whether phylogenetic diversity of basal resources is reflected at higher trophic levels in the food web. In particular, phylogenetic determinants of community structure have never been incorporated into habitat edge studies, even though edges are recognized as key factors affecting communities in fragmented landscapes. Here, we test whether phylogenetic diversity at different trophic levels (plants, herbivores and parasitoids) and signals of co-evolution (i.e. phylogenetic congruence) among interacting trophic levels change across an edge gradient between native and plantation forests. To ascertain whether there is a signal of co-evolution across trophic levels, we test whether related consumer species generally feed on related resource species. We found differences across trophic levels in how their phylogenetic diversity responded to the habitat edge gradient. Plant and native parasitoid phylogenetic diversity changed markedly across habitats, while phylogenetic variability of herbivores (which were predominantly native) did not change across habitats, though phylogenetic evenness declined in plantation interiors. Related herbivore species did not appear to feed disproportionately on related plant species (i.e. there was no signal of co-evolution) even when considering only native species, potentially due to the high trophic generality of herbivores. However, related native parasitoid species tended to feed on related herbivore species, suggesting the presence of a co-evolutionary signal at higher trophic levels. Moreover, this signal was stronger in plantation forests, indicating that this habitat may impose stresses on parasitoids that constrain them to attack only host species for which they are best adapted. Overall, changes in land use across native to plantation forest edges differentially affected phylogenetic diversity across trophic levels, and may also exert a strong selective pressure for particular co-evolved herbivore-parasitoid interactions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".