Integrated framework for stakeholder participation: Methods and tools for identifying and addressing human–wildlife conflicts
Bibliographic record
Abstract
Abstract As wild areas disappear and agricultural lands expand, understanding how people and wildlife can coexist becomes increasingly important. Human–wildlife conflicts (HWCs) are obstacles to coexistence and negatively affect both wildlife populations and the livelihood of people. To facilitate coexistence, a number of frameworks have been developed to both understand the drivers of conflict and then to find solutions that mitigate conflict. However, each framework has different foci and strengths in particular stages of analysis. Here, we propose an integrated framework that leverages the individual strengths of previously fairly isolated methodologies, allowing for holistic HWC analysis. The framework for participatory impact assessment ( FoPIA ) provides a toolset for developing wildlife scenarios, selecting assessment indicators and assessing the impact of different scenarios. The social‐ecological framework of ecosystem services and disservices ( SEEDS ) analyzes the ecosystem services trade‐offs related to scenarios, and the 3i stakeholder analysis approach, supports the identification of stakeholders and provides a mechanism to explore, in detail stakeholders' interests, relative influence, and how outcomes of research are likely to impact different stakeholders. We apply these approaches to eastern Germany, where the increase in several wildlife populations (i.e., wild boar, common crane, gray wolf, and European bison) has contributed to conflict with people. We demonstrate the complementarity of FoPIA, SEEDS, and 3i in identifying stakeholder needs and showing how wildlife dynamics may affect coexistence and create imbalanced ecosystem service and disservice distributions. The integrated framework introduced here provides guidelines for analyzing the multistage process of stakeholder participation and enables a comprehensive approach to the complex challenge of HWCs.
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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 | 0.069 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".