Environmental justice in a very green city: Spatial inequality in exposure to urban nature, air pollution and heat in Oslo, Norway.

Poorer citizens are often more exposed to environmental hazards due to spatial inequalities in the distribution of urban blue-green space. Few cities have managed to prevent spatial and social inequality despite sustainable development strategies like compact city planning. We explore whether environmental injustice exists in a city where one would least expect to find it: a city with abundant nature, an affluent population governed by a left leaning social democratic city council, and an aggressive densification strategy; Oslo, Norway. Green space was measured with a satellite-derived vegetation index which captures the combined availability of gardens, street trees, parks and forest. Blue space was defined by the proximity of residential areas to the closest lake, river or fjord. We found that poorer city districts, often with greater immigrant populations, have less available blue-green spaces and are disproportionately exposed to hazardous air pollution levels, but not extreme heat compared to wealthier city districts. Citizens living within 100 m of a water body are likely to earn US$ 20,000 more per year than citizens living 500 m away from water, and a US$ 3000 increase in annual income corresponds to a 10 % increase in green space availability. Hazardous air pollution concentrations in the poorest city districts were above levels recommended by the WHO and Oslo municipality. Historical trends showed that districts undergoing population densification coincide with the lowest availability of blue-green space, suggesting that environmental justice has been overlooked in compact city planning policy. Despite Oslo's affluence and egalitarian ideals, the patterns of inequality we observed mirror the city's historical east-west class divide and point to spatial concentration of wealth as a core factor to consider in studies of green segregation. Urban greening initiatives in Oslo and other cities should not take spatial equality for granted, and instead consider socio-economic geographies in their planning process.

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway.

Many publications make use of opportunistic data, such as citizen science observation data, to infer large-scale properties of species' distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species' home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS-telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species' ecology. Models naïvely using opportunistic observations in habitat-use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species' RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat-use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat-use studies.

The unique spatial ecology of human hunters.

Human hunters are described as 'superpredators' with a unique ecology. Chronic wasting disease among cervids and African swine fever among wild boar are emerging wildlife diseases in Europe, with huge economic and cultural repercussions. Understanding hunter movements at broad scales has implications for how to control the spread of these diseases. Here we show, based on analysis of the settlement patterns and movements of hunters of reindeer (n = 9,685), red deer (n = 47,845), moose (n = 60,365) and roe deer (n = 42,530) from across Norway (2001-2017), that hunter density was more closely linked to human density than prey density and that hunters were largely migratory, aggregated with increasing regional prey densities and often used dogs. Hunter movements extended across Europe and to other continents. Our results provide extensive evidence that the broad-scale movements and residency patterns of postindustrial hunters relative to their prey differ from those of large carnivores.