Non‐random loss of phylogenetically distinct rare species degrades phylogenetic diversity in semi‐natural grasslands
Bibliographic record
Abstract
Abstract Although biodiversity loss is a critically important topic, our understanding of how both land abandonment and land‐use intensification in semi‐natural grasslands alters the community diversity and assembly mechanisms is very limited. Large‐scale economic drivers of land‐use change might inadvertently result in the loss of vulnerable species and reduce ecosystem service provisioning. In this study, we assessed non‐random community change by examining patterns of low‐abundance species loss, and community assembly in semi‐natural grasslands due to land abandonment and intensification in southwest Japan. We analysed relationships between evolutionary distinctiveness and abundance for each species. In addition, we used metrics of species and phylogenetic diversity and phylogenetic structure to assess patterns of non‐random biodiversity loss due to both abandoned and intensified land‐use. We demonstrated that low‐abundance species were more evolutionarily distinct compared to high‐abundance (i.e. dominant) species. Furthermore, land‐use intensification resulted in further declines in low‐abundance species, whereas land abandonment resulted in declines in all species, regardless of their initial abundance. We found that both forms of land‐use change (abandonment and intensification) resulted in non‐random patterns of community change, with traditional land‐use maintaining the highest biodiversity and land‐use change coinciding with decreased species and phylogenetic diversity measures. Intensified land‐use caused phylogenetic community structure to be more closely related than expected by chance, whereas the metrics of how distant species are from one another on average based on phylogenetic tree (i.e. mean pairwise phylogenetic distance) did not change from land abandonment. Synthesis and applications . We show that the loss of phylogenetic diversity, and especially of low‐abundance species with high value of phylogenetic distinctiveness, resulted in non‐random community disassembly. Our results argue that in order to maintain biodiversity in these semi‐natural grasslands, traditional management practices should be encouraged over intensification and simple abandonment. Government agencies should adopt policies or provide incentives that encourage the maintenance of traditional practices in rural Japan, and elsewhere where the combination of land consolidation and abandonment are important conservation issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".