Navigating Change: Mapping Seasonal Drivers of Endangered Finless Porpoise Habitat in Hong Kong for Dynamic Marine Protection

Hong Kong waters host the Indo-Pacific finless porpoise (Neophocaena phocaenoides), a Vulnerable (IUCN Red List; CITES Appendix I) small cetacean. Monitoring surveys since 1996 by various parties provide baseline abundance and distribution data, with peak-season sightings of only 176–220 individuals. Marine Protected Areas (MPAs), often compensatory for infrastructure, lack holistic evaluation of coverage and effectiveness amid seasonal ecosystem fluctuations. Habitat suitability is governed by seasonally varying environmental factors, compounded by human activities and climate change. Statistical models offer a powerful framework to elucidate and forecast population trends by identifying key environmental and anthropogenic drivers of distribution. By revealing spatio-temporal patterns and mechanisms, they inform targeted mitigation, such as dynamic zoning or seasonal fishing restrictions. This project, for the first time, leverages a >30-year dataset (1996–2029) from long-term governmental and agency monitoring programs, complemented by environmental and anthropogenic parameters from diverse sources. Through rigorous integration and advanced modelling, we will address three pivotal conservation objectives. 1. Distribution patterns: Analyze seasonal variations in habitat distribution using longterm data from line-transect surveys. Historical records show seasonal core habitat shifts—offshore in dry season (November–April), inshore in wet season (May–October)—linked to oceanographic changes and prey dynamics. We will unify disparate sighting data, correct for survey effort biases, and use kernel density estimation and hotspot analysis in ArcGIS to quantify spatio-temporal hotspots, revealing seasonal trends and climate-driven displacements. 2. Habitat drivers: Identify key environmental and anthropogenic variables shaping core habitat suitability via Species Distribution Models (SDMs). We will integrate oceanographic variables (bathymetry, chlorophyll-α, dissolved oxygen, salinity, sea surface temperature) with anthropogenic layers (traffic density, fishing intensity, pollution levels) to assess relative importance and cumulative effects of variables through permutation tests and response curves, calculating ensemble habitat suitability indices to uncover degradation thresholds and inform risk maps for threats like vessel strikes and fishery bycatch. 3. Gap analysis: Quantify marine protection deficits relative to dynamic core habitat shifts under the influence of seasonal and climatic changes. We will overlay SDM outputs on existing MPAs to evaluate proportional coverage and efficacy using a panel of statistical tests, integrating threat layers and exposing temporal mismatches to prioritize risk-based adaptations like boundary revisions or dynamic zoning. Evidencebased analyses will empower Hong Kong and Greater Bay Area agencies to implement adaptive measures, delivering recommendations to stakeholder, safeguarding this vulnerable species and fostering sustainable human-wildlife coexistence.