Modeling Seasonal Distribution of Irrawaddy Dolphins (Orcaella brevirostris) in a Transnational Important Marine Mammal Area
Bibliographic record
Abstract
Fishing activities continue to decimate populations of marine mammals, fish, and their habitats in the coastal waters of the Kep Archipelago, a cluster of tropical islands on the Cambodia-Vietnam border. In 2019, the area was recognized as an Important Marine Mammal Area, largely owing to the significant presence of Irrawaddy dolphins (Orcaella brevirostris). Understanding habitat preferences and distribution aids in the identification of areas to target for monitoring and conservation, which is particularly challenging in data-limited nations of Southeast Asia. Here, we test the hypothesis that accurate seasonal habitat models, relying on environmental data and species occurrences alone, can be used to describe the ecological processes governing abundance for the resident dolphin population of the Kep Archipelago, Cambodia. Leveraging two years of species and oceanographic data—depth, slope, distance to shore and rivers, sea surface temperature, and chlorophyll-a concentration—we built temporally stratified models to estimate distribution and infer seasonal habitat importance. Overall, Irrawaddy dolphins of Kep displayed habitat preferences similar to other populations, and were predominately encountered in three situations: (1) water depths ranging from 3.0 to 5.3 m, (2) surface water temperatures of 27–32°C, and (3) in close proximity to offshore islands (< 7.5 km). With respect to seasonality, statistical tests detected significant differences for all environment variables considered except seafloor slope. Four predictor sets, each with a unique combination of variables, were used to map seasonal variation in dolphin habitat suitability. Models with highest variable importance scores were water depth, pre- and during monsoon season (61–62%), and sea surface temperature, post-monsoon (71%), which suggests that greater freshwater flow during the wet season may alter primary productivity and dolphin prey abundance. Importantly, findings show the majority of areas with highest habitat suitability are not currently surveyed for dolphins and located outside Kep’s Marine Fisheries Management Area. This research confirms the need to expand monitoring to new areas where high-impact fisheries and other human activities operate. Baseline knowledge on dolphin distribution can guide regional conservation efforts by taking into account the seasonality of the species and support the design of tailored management strategies that address transboundary threats to an Important Marine Mammal Area.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".