Tracking the Conservation Promise of Movement Ecology
Bibliographic record
Abstract
From butterflies to elephants, the rapidly developing science of movement ecology is providing increasingly detailed spatio-temporal data on a wide array of mobile animals. Thus, this discipline also holds great promise for improving the conservation of wildlife. To measure progress towards this promise, we investigated the degree to which movement ecology research is connected to conservation goals as well as the proportion of studies that were incorporated into federal and international status assessments for mobile species at risk. We examined 13,349 “movement ecology” papers published between 1990 and 2014 and found that explicit connections to conservation and management were made in 35% (n = 4, 672) of these papers, with the number of connections increasing over time. We then measured the uptake of movement ecology research into species status assessment and recovery plans (n = 72 documents) produced by three different governance agencies for 12 endangered mobile species. We found that on average 60 % of available movement ecology research was used in the status assessment process, demonstrating that when movement ecology research is available, it is generally being utilized in conservation planning. However, for 25% of these species, there was little movement research available to be used, highlighting that knowledge gaps remain for some at-risk species despite the general growth of movement ecology research. We outline opportunities for movement ecology to promote more effective conservation of taxa that move.
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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 | 0.045 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".