Land use and river-lake connectivity: Biodiversity determinants of lake ecosystems
Bibliographic record
Abstract
Lake ecosystems confront escalating challenges to their stability and resilience, most intuitively leading to biodiversity loss, necessitating effective preservation strategies to safeguard aquatic environments. However, the complexity of ecological processes governing lake biodiversity under multi-stressor interactions remains an ongoing concern, primarily due to insufficient long-term bioindicator data, particularly concerning macroinvertebrate biodiversity. Here we utilize a unique, continuous, and in situ biomonitoring dataset spanning from 2011 to 2019 to investigate the spatio-temporal variation of macroinvertebrate communities. We assess the impact of four crucial environmental parameters on Lake Dongting and Lake Taihu, i.e., water quality, hydrology, climate change, and land use. These two systems are representative of lakes with Yangtze-connected and disconnected subtropical floodplains in China. We find an alarming trend of declining taxonomic and functional diversities among macroinvertebrate communities despite improvements in water quality. Primary contributing factors to this decline include persistent anthropogenic pressures, particularly alterations in human land use around the lakes, including intensified nutrient loads and reduced habitat heterogeneity. Notably, river-lake connectivity is pivotal in shaping differential responses to multiple stressors. Our results highlight a strong correlation between biodiversity alterations and land use within a 2–5 km radius and 0.05–2.5 km from the shorelines of Lakes Dongting and Taihu, respectively. These findings highlight the importance of implementing land buffer zones with specific spatial scales to enhance taxonomic and functional diversity, securing essential ecosystem services and enhancing the resilience of crucial lake ecosystems.
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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 | 0.000 |
| 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".