The freshwater landscape: lake, wetland, and stream abundance and connectivity at macroscales
Bibliographic record
Abstract
Abstract Inland water bodies and their surface hydrologic connections are active components in the landscape, influencing multiple ecological processes that can propagate to broad‐scale phenomena such as regional nutrient and carbon cycles and metapopulation dynamics. However, while lake, wetland, and stream abundance has been estimated at regional and global extents, less attention has been paid to freshwater connectivity attributes among aquatic systems at macroscales. Thus, regional to continental patterns of freshwater abundance and connectivity are poorly understood. We measured lake, wetland, and stream abundance and surface connectivity attributes (i.e., landscape position within stream networks) at a subcontinental extent in the Midwest and Northeast United States to characterize macroscale spatial patterns of the freshwater landscape (i.e., abundance and connectivity attributes of lakes, wetlands, and streams). We found that lake and wetland abundance exhibited opposite spatial patterns from stream density that generally followed glaciation extent boundaries—lake and wetland abundance was high north of the glaciation boundary, whereas stream density was high south of the glaciation boundary. Freshwater connectivity attributes exhibited distinct spatial patterns as defined by our integrated freshwater clusters and revealed a layer of complexity not captured by abundance measures. Patterns of freshwater abundance and connectivity in the study extent were associated primarily with glaciation and secondarily with hydrogeomorphic (e.g., surficial geology and topography), climate (e.g., runoff), and land‐use (e.g., agriculture) variables, providing insight into potential drivers of freshwater composition and distribution. The connectivity spatial patterns observed suggest that relying solely on freshwater abundance measures in macroscale analyses omits unique information on the structural attributes of freshwater systems that can be critical to key ecological processes. Adopting an integrated freshwater landscape framework to study and manage freshwaters is essential as freshwater systems face broad‐scale disturbances that may alter hydrologic connections and subsequently may impact ecosystem processes and services.
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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 | 0.000 | 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 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".