Optimization of an ecological integrity monitoring program for protected areas: Case study for a network of national parks
Bibliographic record
Abstract
Over the last few years, several of the world's national park networks have implemented ecological integrity monitoring programs. These programs are based on a series of indicators to detect changes in ecosystem integrity. There are many scientific and logistical challenges in developing these programs due to limits in both our knowledge of ecosystems functioning and the resources for implementing such programs. Thus, the relatively quick and simple implementation of many monitoring programs has been to the detriment of their scientific validity. Few studies have focused on evaluating an entire monitoring program. This project presents an approach to evaluate the ecological and statistical relevance of ecosystem integrity indicators measured within a program with the goal of iterative optimization. The approach is based on three complementary elements: (1) spatial characterization of park ecosystems based on the classification of satellite imagery, (2) ecological validation of indicators based on ecosystem conceptual models and (3) statistical validation of indicators based on analyses of statistical power. This innovative approach allows a systematic, quantified, reproducible and generalizable review of the indicators of an ecological integrity monitoring program. It provides managers with an overview of the spatial representativeness of indicators, their ecological and statistical relevance according to different parameters such as the period monitored, the amount of change to be detected, and the degree of significance. Thus, the approach identifies monitoring gaps and offers various alternatives for improving sampling. The approach was developed and tested in the network of Quebec national parks, more specifically in the Frontenac, Jacques-Cartier and Bic national parks. The results clearly identify the strengths and weaknesses of the current program in place and possible improvements are proposed for these parks. This approach is a relevant tool for park networks, particularly for those that have limited resources for monitoring ecological integrity.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".