Traditional knowledge (TK) on weather and climate is an important aspect of community life in the Pacific. Used for generations, this knowledge is derived from observing biological and meteorological variables and contributes to building community resilience to weather extremes. Most of this knowledge is passed on orally and is in danger of being lost due to generational changes, leading communities to seek to preserve the knowledge in other ways. This paper provides guidance on the successful collection and documentation of weather and climate TK in the Pacific by considering four key components: the legal and national context, in-country partnerships, the role of community, and national and community protocols. At the regional level legislation focuses on the protection of culture/TK and intellectual property, which are linked to national policies and laws. Within the national context consideration of the governance structure is critical, including obtaining approvals to conduct the studies. The next consideration is developing partnerships to establish and implement the projects, including working with appropriate ministries, media, donor organizations, and community groups. Community involvement in all aspects of the projects is critical, built on trust between partners and ensuring outputs are aligned with community needs. Following community protocols and procedures allows for effective sharing of TK. We document common protocols that were piloted and tested across four Pacific Island nations, illustrating similarities and differences between cultural groups, including recognizing cultural sensitivities and ensuring custodian rights are protected.

In most countries, national meteorological services either generate or have access to seasonal climate forecasts. However, in a number of regions, the uptake of these forecasts by local communities can be limited, with the locals instead relying on traditional knowledge to make their climate forecasts. Both approaches to seasonal climate forecasting have benefits, and the incorporation of traditional forecast methods into contemporary forecast systems can lead to forecasts that are locally relevant and better trusted by the users. This in turn could significantly improve the communication and application of climate information, especially to remote communities. A number of different methodologies have been proposed for combining these forecasts. Through considering the benefits and limitations of each approach, practical recommendations are provided on selecting a method, in the form of a decision framework, that takes into consideration both user and provider needs. The framework comprises four main decision points: 1) consideration of the level of involvement of traditional-knowledge experts or the community that is required, 2) existing levels of traditional knowledge of climate forecasting and its level of cultural sensitivity, 3) the availability of long-term data—both traditional-knowledge and contemporary-forecast components, and 4) the level of resourcing available. No one method is suitable for everyone and every situation; however, the decision framework helps to select the most appropriate method for a given situation.

Comparisons of recent estimations of home range sizes for the critically endangered black rhinoceros in Hluhluwe-iMfolozi Park (HiP), South Africa, with historical estimates led reports of a substantial (54%) increase, attributed to over-stocking and habitat deterioration that has far-reaching implications for rhino conservation. Other reports, however, suggest the increase is more likely an artefact caused by applying various home range estimators to non-standardised datasets. We collected 1939 locations of 25 black rhino over six years (2004–2009) to estimate annual home ranges and evaluate the hypothesis that they have increased in size. A minimum of 30 and 25 locations were required for accurate 95% MCP estimation of home range of adult rhinos, during the dry and wet seasons respectively. Forty and 55 locations were required for adult female and male annual MCP home ranges, respectively, and 30 locations were necessary for estimating 90% bivariate kernel home ranges accurately. Average annual 95% bivariate kernel home ranges were 20.4 ± 1.2 km2, 53 ±1.9% larger than 95% MCP ranges (9.8 km2 ± 0.9). When home range techniques used during the late-1960s in HiP were applied to our dataset, estimates were similar, indicating that ranges have not changed substantially in 50 years. Inaccurate, non-standardised, home range estimates and their comparison have the potential to mislead black rhino population management. We recommend that more care be taken to collect adequate numbers of rhino locations within standardized time periods (i.e., season or year) and that the comparison of home ranges estimated using dissimilar procedures be avoided. Home range studies of black rhino have been data deficient and procedurally inconsistent. Standardisation of methods is required.