Land use and river-lake connectivity: Biodiversity determinants of lake ecosystems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it