A novel survey design for modeling species distribution of beavers in Algonquin Park, Canada
Bibliographic record
Abstract
Abstract Understanding spatial and temporal variation in beaver abundance is a central goal for a wide range of management issues, ranging from species reintroductions to mitigation of environmental and economic impacts. Yet due to high costs associated with surveys, many studies are limited to a single regional estimate, or a complete census of a smaller study area extrapolated to the surrounding landscape. We present a survey design that allows for predicting beaver abundance across the broader landscape through interpolation. In October 2019, we conducted an aerial survey in a 15,000 km 2 study area around Algonquin Provincial Park in Ontario, Canada. We counted 145 colonies on 73 plots that averaged 4.5 km 2 (+/−3.27 SD). Our regional estimate for beaver abundance of 0.55 (95% CI = +/−0.18) colonies/km 2 is comparable to historical surveys conducted in the region in the 1970s. We then predicted beaver abundance in unsampled plots using a Poisson generalized additive model (adjusted R 2 = 0.81, deviance explained = 55.9%) that included non‐linear responses to elevation ( P < 0.001), shoreline complexity ( P = 0.003), and availability of shade‐tolerant hardwoods ( P = 0.001). Our species distribution model predicted strong east‐west patterns in beaver abundance across the study region associated with spatial patterns in elevation and forest composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".