An investigation of the effects of conservation incentive programs on management of invasive species by private landowners
Bibliographic record
Abstract
Abstract Invasive species are a large and growing threat to biodiversity and ecosystem service provisioning globally as well as in southern Ontario, Canada's most biodiverse region. As in many other world regions, most land in southern Ontario is privately owned and therefore conservation programs that aim at private lands are important. Conservation incentive programs that target private lands are increasingly popular, but little is known about their effectiveness in achieving conservation objectives. To address this knowledge gap, we used a large survey of 1,200 Ontario landowners to investigate how successful conservation incentive programs are at motivating landowners to engage in invasive species management. Utilizing a quasi‐experimental approach, we surveyed landowners participating in the Conservation Lands Tax Incentive Program (CLTIP), the Managed Forest Tax Incentive Program (MFTIP), or in both programs, as well as landowners who were program eligible but did not participate. Our results demonstrate the differential effects of participation in the two programs on specific landowner conservation behaviors: While participation in the MFTIP increased the likelihood of removing invasive species by a factor 2.5 and the planting of native species by a factor 4.3, participants in the CLTIP were no more likely to engage in these behaviors than landowners who did not participate in either program. We suggest that this behavioral disparity is due to the differences in the program designs: The CLTIP does not require a management plan, favors a short planning horizon, and mainly encourages passive management; in doing so it essentially breaks the causal chain that reinforces landowners' environmental awareness and a sense of responsibility for taking conservation actions. Our recommendations include requirements for impact evaluations of private land conservation incentive programs to ensure they achieve stated program outcomes, as well as conservation incentive program designs that oblige landowners to actively manage their land over longer time horizons.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".