Assessing mangrove restoration practices using species‐interaction networks
Bibliographic record
Abstract
Mangroves are uniquely important ecosystems, for preserving biodiversity, sustaining livelihoods, and mitigating against climate change. However, they are degraded globally and are therefore a priority for ecosystem restoration. To date, the assessment of mangrove restoration outcomes is generally poor, and the limited studies that do exist are focused largely on forest area. Thus, more holistic ways of assessing the outcomes of mangrove restoration projects on biodiversity and associated ecological processes are urgently needed. Ecological networks are a useful tool for simultaneously examining both. Here, we assessed the utility of using species‐interaction networks for evaluating mangrove restoration outcomes for the first time. We compared the structure and complexity of mangrove ecological networks in replicated “monoculture reforestation,” “mixed species regeneration” and “reference forest” plots in two study areas in Sulawesi, Indonesia, an estuarine, and a coastal fringe mangrove system. We also combined and evaluated sampling methods, utilizing traditional plant–animal sampling while also integrating video recording data in a novel way. We found significant differences in the structure and complexity of mangrove networks between restored and natural plots, with contrasting effects between the two sites. Our results show differences in the complex ways in which taxa interact in mangrove restoration projects, which would be overlooked if common biodiversity metrics, such as species richness, were used alone, with consequences for the restoration of ecosystem functioning. We also highlight the utility of video recording data collection for constructing species‐interaction networks, overcoming the detrimental impacts of observer presence for some key species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".