Protected area effectiveness: evaluation of biological outcomes in protected areas
Bibliographic record
Abstract
Protected areas are the primary form of intervention applied to achieve biodiversity conservation in response to anthropogenic threat. More than 14% of terrestrial land surface area is within protected areas worldwide, and the CBD Strategic Plan developed in Nagoya established new targets of 17% global coverage of protected areas. However, biodiversity continues to decline, including within some protected areas. The ability of protected areas to retain their conservation values has been questioned. Understanding of whether protected areas are retaining their biodiversity values, and under what circumstances is limited. In particular, the crucial role of protected area performance in maintaining populations of species remains poorly understood. Long-term systematic population monitoring data are exceptionally rare, but critical for determining species and community level changes in natural values. Given the level of investment in protected areas, especially in terms of opportunity cost, it is critical that we understand the mechanisms underlying protected area effectiveness in retaining biological values. I use a variety of tools to evaluate the trends in fauna within protected areas, and to identify critical correlates of effectiveness in maintaining species populations. I undertook a comprehensive literature analysis to evaluate the impact of protected areas and factors likely to impact their success. I identified a broad suite of potential variables likely to influence outcomes in protected areas. These included design, ecology, management and socio-economic factors. In general protected areas appear effective for protecting habitats, although leakage (the impact still occurring, but in another place) is a problem and enforcement is important. However, a key finding was poor counterfactual monitoring in species population studies, making relative impact difficult to determine. To evaluate the influence of critical correlates I lead a global evaluation to identify key correlates of biological effectiveness of protected areas using trends in terrestrial vertebrates. Using mixed effects analysis several emergent trends were identified. Species body mass and indicators of human wellbeing were strongly positively correlated with population trends of monitored vertebrate fauna (birds and mammals), a finding that is consistent across taxonomic classes and geographic realms. I also explored the influence of factors for which it is difficult to obtain good broadscale data (such as resources) using a case study in Canada. Investment and staff time appear to be the best predictors of species outcomes in Canada, although the model explained little of the overall variance, indicating that there are also likely to be factors that have not been considered at play. In iiaddition to the rarity of population monitoring data, a key limitation of protected areas impact evaluation is the lack of monitoring outside of protected areas. Further, I investigated and developed methods to estimate relative impact of protection using species list data (lists of species found on a single visit in a defined geographic location) collected by volunteers and citizen scientists. In the absence of other available data, or historical data, this approach can give us a first approximation of potential trends for all species in a community. I have applied these methods (List Length Analysis) to a case study in the Australian Wet Tropics (AWT). Using this approach I was able to finding that although absolute trends are variable, most endemics are stable in the AWT bioregion. However there was no difference in endemic avifaunal trends within and outside of protected areas: rather any remaining habitat is equally as good at retaining these species. The outcomes of this work are likely to yield tangible conservation benefits through application to policy and practice in both the short and long term. Better understanding of the impact of protection and the mechanisms that may be underlying protected area effectiveness in retaining biological values will facilitate improved outcomes by informing the management process and policy and investment decision-making. It will therefore be possible to maximise the marginal benefit of existing and new protected areas.
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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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".