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.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".