Uneven Rates of Landscape Change as a Source of Bias in Roadside Wildlife Surveys
Bibliographic record
Abstract
ABSTRACT Roadside survey data have been used frequently to assess species occurrence and population trends and to establish conservation priorities. However, most studies using such data assume that samples are representative of either the amount of habitat or its rate of change at larger spatial scales. We tested both of these assumptions for the Breeding Bird Survey (BBS) from 1974 to 2001 in New Brunswick, Canada. Our study focused on mature forest—a cover type that we predicted would be characterized by rapid change due to human activities and that is of high ecological importance. We also sought to determine whether land cover changes adjacent to BBS routes were related to bird population trends detected in BBS data. Within all 3 time periods examined (1970s, 1980s, and 1990s), the amount of mature forest adjacent to BBS routes was significantly lower than in surrounding 1° blocks of latitude and longitude. This could be problematic for studies that use roadside data to compare the relative abundance of species. On average, mature forest declined at a rate of‐1.5% per year over the 28‐year study period. We detected no significant difference in the rate of change between degree blocks and BBS routes over this time span. However, in the 1970s and 1980s, mature forest declined more rapidly in degree blocks (‐2.7%/yr) than adjacent to BBS routes (‐0.5/yr). We also found that the BBS trend for a mature forest‐associated species, blackburnian warbler ( Dendroica fusca ), was correlated with the trend in mature forest along BBS routes. This, combined with slower rates of mature forest change along routes in the 1970s and 1980s, suggests that BBS data may have underestimated population declines during this period. It is important that research be conducted to test for potential biases in roadside surveys caused by uneven rates of landscape change, particularly in regions characterized by rapid habitat alteration.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".