Vuokko Heikinheimo

Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas

Social media data is increasingly used as a proxy for human activity in different environments, including protected areas, where collecting visitor information is often laborious and expensive, but important for management and marketing. Here, we compared data from Instagram, Twitter and Flickr, and assessed systematically how park popularity and temporal visitor counts derived from social media data perform against high-precision visitor statistics in 56 national parks in Finland and South Africa in 2014. We show that social media activity is highly associated with park popularity, and social media-based monthly visitation patterns match relatively well with the official visitor counts. However, there were considerable differences between platforms as Instagram clearly outperformed Twitter and Flickr. Furthermore, we show that social media data tend to perform better in more visited parks, and should always be used with caution. Based on stakeholder discussions we identified potential reasons why social media data and visitor statistics might not match: the geography and profile of the park, the visitor profile, and sudden events. Overall the results are encouraging in broader terms: Over 60% of the national parks globally have Twitter or Instagram activity, which could potentially inform global nature conservation.

Green environment and incident depression in South Africa: a geospatial analysis and mental health implications in a resource-limited setting

Summary Background Unprecedented levels of habitat transformation and rapid urbanisation are changing the way individuals interrelate with the natural environment in developing countries with high economic disparities. Although the potential benefit of green environments for mental health has been recognised, population-level evidence to this effect is scarce. We investigated the effect of green living environment in potentially countering incident depression in a nationally representative survey in South Africa. Methods We used panel data from the South African National Income Dynamics Study (SA-NIDS). Our study used SA-NIDS data from three waves: wave 1 (2008), wave 2 (2010), and wave 3 (2012). Households were sampled on the basis of a stratified two-stage cluster design. In the first stage, 400 primary sampling units were selected for inclusion. In the second stage, two clusters of 12 dwelling units each were drawn from within each primary sampling unit (or 24 dwelling units per unit). Household and individual adult questionnaires were administered to participants. The main outcome, incident depression (ie, incident cohort of 11 156 study participants without significant depression symptoms at their first entry into SA-NIDS), was assessed in the adult survey via a ten item version of the Center for Epidemiologic Studies Depression Scale; a total score of ten or higher was used as a cutoff to indicate significant depressive symptoms. Each participant was assigned a value for green living space via a satellite-derived normalised difference vegetation index (NDVI) based on the GPS coordinates of their household location. Findings Overall, we found uneven benefit of NDVI on incident depression among our study participants. Although the green living environment showed limited benefit across the study population as a whole, our final analysis based on logistic regression models showed that higher NDVI was a predictor of lower incident depression among middle-income compared with low-income participants (adjusted odds ratio [aOR] 0·98, 0·97–0·99, p<0·0001), although when this analysis was broken down by race, its positive effect was particularly evident amongst African individuals. Living in rural areas was linked to lower odds of incident depression (aOR 0·71, 0·55–0·92, p=0·011) compared with study participants residing in urban informal areas that often lack formal planning. Interpretation Our results imply the importance of green environments for mental wellbeing in sub-Saharan African settings experiencing rapid urbanisation, economic and epidemiological transition, reaffirming the need to incorporate environmental services and benefits for sustainable socioeconomic development. Funding South African Medical Research Council, National Institutes of Health, and Academy of Finland.

Social media reveal that charismatic species are not the main attractor of ecotourists to sub-Saharan protected areas

Charismatic megafauna are arguably considered the primary attractor of ecotourists to sub-Saharan African protected areas. However, the lack of visitation data across the whole continent has thus far prevented the investigation of whether charismatic species are indeed a key attractor of ecotourists to protected areas. Social media data can now be used for this purpose. We mined data from Instagram, and used generalized linear models with site- and country-level deviations to explore which socio-economic, geographical and biological factors explain social media use in sub-Saharan African protected areas. We found that charismatic species richness did not explain social media usage. On the other hand, protected areas that were more accessible, had sparser vegetation, where human population density was higher, and that were located in wealthier countries, had higher social media use. Interestingly, protected areas with lower richness in non-charismatic species had more users. Overall, our results suggest that more factors than simply charismatic species might explain attractiveness of protected areas, and call for more in-depth content analysis of the posts. With African countries projected to develop further in the near-future, more social media data will become available, and could be used to inform protected area management and marketing.

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600–2200 m. The field measurements were carried out in 215 plots in 2013–2015 and ALS flights conducted in 2014–2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R2 = 0.88, RMSE = 52.9 Mg ha−1). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region.