Can Exclusion of Feral Ecosystem Engineers Improve Coastal Floodplain Resilience to Climate Change? Insight from a Case Study in North East Arnhem Land, Australia
Bibliographic record
Abstract
Global climate change can interact with local drivers, such as ecosystem engineers, to exacerbate changes in ecosystem structure and function, with socio-ecological consequences. For regions of Indigenous interest, there may also be cultural consequences if species and areas affected are culturally significant. Here we describe a participatory approach between the Indigenous (Yolngu) Yirralka Rangers and non-Indigenous researchers that explored the interaction between sea level rise and feral ungulate ecosystem engineers on culturally significant floodplains in the Laynhapuy Indigenous Protected Area (IPA), northern Australia. A feral ungulate exclusion fence array (12 fenced and 12 unfenced plots) was stratified by elevation/salinity to disentangle the effects of salinity and ungulates on floodplain soil and vegetation. We found that exclusion of feral ungulates improved ground cover vegetation, which, according to our literature-derived ecosystem process model, may enhance soil trapping and reduce evapotranspiration to provide the antecedent conditions needed to improve floodplain resilience to sea level rise. The mid-zone of the supratidal floodplain study site was suggested as the region where the benefits of fencing were most pronounced after two years and ground cover species diversity was highest. Ongoing monitoring is required to investigate whether removal of feral ungulates can increase resilience against sea level rise and recruitment of eco-culturally significant Melaleuca species. An interview with a key Yolngu Traditional Owner of the study site demonstrated the importance and effectiveness of the partnership. Yolngu land owners and rangers were active co-researchers and will decide if, when and how to integrate results into feral ungulate management and climate adaptation responses, highlighting the importance of industry-university partnerships in maximising biocultural conservation outcomes.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".