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.

Coexistence of large mammals and humans is possible in Europe's anthropogenic landscapes.

A critical question in the conservation of large mammals in the Anthropocene is to know the extent to which they can tolerate human disturbance. Surprisingly, little quantitative data is available about large-scale effects of human activity and land use on their broad scale distribution in Europe. In this study, we quantify the relative importance of human land use and protected areas as opposed to biophysical constraints on large mammal distribution. We analyze data on large mammal distribution to quantify the relative effect of anthropogenic variables on species' distribution as opposed to biophysical constraints. We finally assess the effect of anthropogenic variables on the size of the species' niche by simulating a scenario where we assumed no anthropogenic pressure on the landscape. Results show that large mammal distribution is primarily constrained by biophysical constraints rather than anthropogenic variables. This finding offers grounds for cautious optimism concerning wildlife conservation in the Anthropocene.

Using locational data in a novel mixed-methods sequence design: Identifying critical health care barriers for people with disabilities in Malawi.

The primary aim of this study was to determine which health care barriers were most important for people with disabilities in Malawi. To accomplish this, we devised a sequential mixed-methods research design that integrated locational survey data and qualitative data from field studies. Our secondary aim was to evaluate this research design not only as a design-solution to our particular research objective, but as a tool with more general applicability within social sciences. Malawi has one of the most underserved health service populations in the world with chronic resource shortages and long travel distances where people with disabilities are at a particular disadvantage. Nevertheless, our results show that even in a resource scarce society such as Malawi it is the interpersonal relationships between patients and health service providers that has the largest impact on the perception of access among patients. Our results also suggest that the sequential mixed-methods design is effective in guiding researchers towards models with strong specifications.

Combining survey data, GIS and qualitative interviews in the analysis of health service access for persons with disabilities.

BACKGROUND: Equitable access to health services is a key ingredient in reaching health for persons with disabilities and other vulnerable groups. So far, research on access to health services in low- and middle-income countries has largely relied on self-reported survey data. Realizing that there may be substantial discrepancies between perceived and actual access, other methods are needed for more precise knowledge to guide health policy and planning. The objective of this article is to describe and discuss an innovative methodological triangulation where statistical and spatial analysis of perceived distance and objective measures of access is combined with qualitative evidence. METHODS: The data for the study was drawn from a large household and individual questionnaire based survey carried out in Namibia and Malawi. The survey data was combined with spatial data of respondents and health facilities, key informant interviews and focus group discussions. To analyse access and barriers to access, a model is developed that takes into account both measured and perceived access. The geo-referenced survey data is used to establish four outcome categories of perceived and measured access as either good or poor. Combined with analyses of the terrain and the actual distance from where the respondents live to the health facility they go to, the data allows for categorising areas and respondents according to the four outcome categories. The four groups are subsequently analysed with respect to variation in individual characteristics and vulnerability factors. The qualitative component includes participatory map drawing and is used to gain further insight into the mechanisms behind the different combinations of perceived and actual access. RESULTS: Preliminary results show that there are substantial discrepancies between perceived and actual access to health services and the qualitative study provides insight into mechanisms behind such divergences. CONCLUSION: The novel combination of survey data, geographical data and qualitative data will generate a model on access to health services in poor contexts that will feed into efforts to improve access for the most vulnerable people in underserved areas.

VisAdapt: A Visualization Tool to Support Climate Change Adaptation.

The web-based visualization VisAdapt tool was developed to help laypeople in the Nordic countries assess how anticipated climate change will impact their homes. The tool guides users through a three-step visual process that helps them explore risks and identify adaptive actions specifically modified to their location and house type. This article walks through the tool's multistep, user-centered design process. Although VisAdapt's target end users are Nordic homeowners, the insights gained from the development process and the lessons learned from the project are applicable to a wide range of domains.