From headwaters to outlets: Bird species accrual curves are faster downstream with different implications for varying landcovers and ecoregions
Bibliographic record
Abstract
Terrestrial and aquatic systems are geographically connected, yet these systems are typically studied independently of each other. This approach omits a large amount of ecological information as landscapes are best described as mosaics in watersheds. Species Accumulation Curves (SACs) that incorporate sampling effort are familiar models of how biodiversity will change when landcovers are lost. In land-based systems, the consistent pattern of increased species richness with increasing number of sites sampled is an ecological norm. In freshwater systems, fish species discharge relationships are analogous to species-area relationships in terrestrial systems, but the relationship between terrestrial species and discharge remains largely unexplored. Although some studies investigate the effect of terrestrial systems on neighboring aquatic species, less work has been done on exploring the effect of aquatic systems on terrestrial species. Additionally, creating statistical models to observe these interactions need to be explored further. Using data from the Ontario Breeding Bird Atlas (2001–2005), we created bird SACs to explore how increases in diversity with sites sampled varies with watershed position on the Canadian side of the Great Lakes Basin (GLB). The mosaic landscape of the GLB was characterized using six majority land cover classes at a 15 m resolution. This work shows that rates of species accrual and potential maximum species richness vary as a function of watershed position, underlying land cover, and the Ecoregion in which sampling was performed. We also found that Urban landcover has the potential to retain relatively high levels of species richness, which is further modified by Ecoregion and watershed position. Through our ‘world building,’ we believe that we can increase knowledge around the importance of land-water interactions and further the goals of viewing landscapes as mosaic watersheds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".