Dark diversity in temperate forests of northeastern China: drivers and implications
Bibliographic record
Abstract
Understanding the species composition of a community, including those species present and those absent but potentially able to occur, is vital for assessing biodiversity changes and informing conservation planning. Typically, studies focus on observed taxonomic diversity but ignore undetected species expected to be present based on co‐occurrence patterns – referred to as dark diversity. Dark diversity serves as a sensitive indicator of biodiversity change, often responding earlier than observed diversity. However, its underlying drivers, especially environmental and anthropogenic factors, remain poorly understood in forest ecosystems. In this study, we quantified both dark and observed diversity and applied logistic regression to identify traits influencing species' likelihood of belonging to dark diversity at the species level. Variance partitioning and spatial autoregressive models were used to disentangle the effects of environmental and human drivers at the plot level. Our results revealed that key traits such as mycorrhizal type, specific leaf area and tree height determine a species' likelihood of belonging to dark diversity, and that dark and observed diversity respond differently to environmental and anthropogenic factors. Dark diversity was mainly influenced by the interaction of environmental factors and human impacts, with annual mean temperature being the strongest environmental driver. Observed diversity, in contrast, was most influenced by annual mean precipitation. Dark diversity increased with annual mean temperature, declined under the combined influence of human footprint and precipitation, and increased with the interaction between human footprint and precipitation seasonality. Observed diversity was positively affected by precipitation and altitude, and negatively influenced by human footprint–temperature and altitude–roadless area interactions. Our findings highlight the importance of addressing climate and human pressures in conservation planning, as dark diversity signals hidden constraints on species establishment and persistence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".