Managing multifunctional landscapes: Local insights from a Pacific Island Country context.

Across Pacific Island Countries, projects and policies are incorporating objectives related to managing landscape multifunctionality to sustain flows of multiple, valued ecosystem services. Strategies to manage natural resources are often not effective, or do not have intended outcomes, if they do not account for local contexts and the varied needs and constraints of stakeholders who rely upon natural resources for their livelihoods. Through fieldwork in Ba, Fiji, local insights were generated concerning the institutional, geographic, and socio-economic factors which determine and challenge i) different stakeholders' ability to access landscape resources, and ii) stakeholders' capacities to benefit from ecosystem services. The following insights were generated from this research which are important for guiding management of landscape multifunctionality. In Ba, hierarchical governance systems present barriers to effective management of landscape multifunctionality, and projects or policies with aims to manage landscapes should establish context appropriate multi-scale governance. Such governance systems should facilitate communication and interaction between different stakeholders, build upon community knowledge, and support communities as key actors in landscape management. Consideration of the spatial footprint of landscape resources, stakeholders' different physical and financial capacities, and the institutional structures that mediate access to resources should be central to landscape management and planning. Various climatic stressors affect flows of ecosystem services from the Ba landscape and people's capacity to access landscape resources; therefore, it is important that management of landscapes also builds resilience to climate stressors.

Turning down the heat: An enhanced understanding of the relationship between urban vegetation and surface temperature at the city scale.

Guiding urban planners on the cooling returns of different configurations of urban vegetation is important to protect urban dwellers from adverse heat impacts. To this end, we estimated statistical models that fused multi-temporal very fine spatial (20 cm) and vertical (1 mm) resolution imagery, that captures the complexity of urban vegetation, with remotely sensed temperature data to assess how urban vegetation configuration influences urban temperatures. Perth, Western Australia, was used as a case-study for this analysis. Panel regression models showed that within a location an increase in tree and shrub cover has a larger cooling effect than grass coverage. On average, holding all else equal, an approximate 1 km2 increase in shrub (tree) cover within a location reduces surface temperatures by 12 °C (5 °C). We included a range of robustness checks for the observed relationships between urban vegetation type and temperature. Geographically weighted regression models showed spatial variation in the cooling effect of different vegetation types; this indicates that i) unobserved factors moderate temperature-vegetation relationships across urban landscapes, and ii) that urban vegetation type and temperature relationships are complex. Machine learning models (Random Forests) were used to further explore complex and non-linear relationships between different urban vegetation configurations and temperature. The Random Forests showed that vegetation type explained 31.84% of the out-of-bag variance in summer surface temperatures, that increased cover of large vegetation within a location increases cooling, and that different configurations of urban vegetation structure can lead to cooling gains. The models in this study were trained with vegetation data capturing local detail, multiple time-periods, and entire city coverage. Thus, these models illustrate the potential to develop locally-detailed and spatially explicit tools to guide planning of vegetation configuration to optimise cooling at local- and city-scales.

Short-term memory capacity: magic number or magic spell?

Previous experiments have found that memory span is greater for items that can be pronounced more quickly. For a variety of materials the span equals the number of items that can be pronounced in about 1.5 s, presumably the duration of the verbal trace. This suggests a model for immediate recall: The probability of correctly recalling a list equals the probability that the time to recite the list is less than the variable duration of the trace. Recall probability for lists of various lengths was determined for six materials. Later, subjects read the lists aloud. The standard normal deviates corresponding to probability of correct recall were linear in pronunciation time. Evidently, over subjects, a normal distribution is a reasonable approximation of the distribution of the trace duration. The mean and variance of the trace duration were estimated. The mean (1.88 s) agrees well with previous estimates, and the model accounts for 95% of the variance in immediate recall.