Holocene insights for tackling Anthropocene environmental challenges in Malesia
Bibliographic record
Abstract
Understanding long-term environmental change is essential for informed decision making in response to the threats posed by anthropogenically altered critical Earth Systems in the Anthropocene. This thesis focused on generating and synthesising novel datasets to contribute towards tackling two of these environmental challenges: introduction and establishment of alien plant species, and the degradation and loss of resilient ecosystems. Less research has been conducted on these phenomena in tropical regions, especially in Southeast Asia and Africa. Therefore, the geographic scope of this research is focussed on one of these areas and is best delimited by the phytogeographic region of Malesia which stretches from Peninsular Malaysia in the west, to New Guinea in the east. Analysis of distribution information at the level of island/island group for 31,580 native and naturalized (alien and established) plant species identified that 1,177 naturalized species have been introduced since prehistory, with many introduced due to European colonialism, and this has resulted in taxonomic homogenization (increase in similarity) of the flora. Most naturalized plant species in the region occur in anthropogenic, followed by open forest, habitat-types and the floras of Java and the Lesser Sunda Islands increased in similarity the most. For the second environmental challenge of focus for the thesis, multi-proxy palaeoecological data from three sediment cores in North Sulawesi, Indonesia, are presented as a case study for using palaeoenvironmental data to support mangrove management and conservation through the study of recovery and resilience. This revealed the rich biodiversity of mangrove ecosystems in North Sulawesi and their resilience over millennia to many forms of natural disturbance as well as phenomena to which they are more vulnerable. Assessing mangrove pollen abundance and composition indicated that the contemporary mangroves (at Mantehage and Likupang) are currently recovering and are on a trajectory towards their respective Holocene baselines. The results from both case studies enhance our understanding of key ecological processes (invasion and resilience), can be galvanised to support ecosystem management across scales (e.g., watchlist of naturalized taxa at risk of spread into a new island, guiding species selection for mangrove restoration), and directly support international commitments - especially the Kunming-Montreal Global Biodiversity Framework Targets 2, 3, and 6.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".