Access to direct-to-consumer genetic testing services has increased in recent years. However, disparities in knowledge and awareness of these services are not well documented. We examined awareness of genetic testing services by rural/urban and racial/ethnic status. Analyses were conducted using pooled cross-sectional data from 4 waves (2011–2014) of the Health Information National Trends Survey (HINTS). Descriptive statistics compared sample characteristics and information sources by rural/urban residence. Logistic regression was used to examine the relationship between geography, racial/ethnic status, and awareness of genetic testing, controlling for sociodemographic characteristics. Of 13,749 respondents, 16.7% resided in rural areas, 13.8% were Hispanic, and 10.1% were non-Hispanic black. Rural residents were less likely than urban residents to report awareness of genetic testing (OR = 0.74, 95% CI = 0.63–0.87). Compared with non-Hispanic whites, racial/ethnic minorities were less likely to be aware of genetic testing: Hispanic (OR = 0.68, 95% CI = 0.56–0.82); and non-Hispanic black (OR = 0.74, 95% CI = 0.61–0.90). Rural-urban and racial-ethnic differences exist in awareness of direct-to-consumer genetic testing. These differences may translate into disparities in the uptake of genetic testing, health behavior change, and disease prevention through precision and personalized medicine.

The carbon stored in vegetation varies across tropical landscapes due to a complex mix of climatic and edaphic variables, as well as direct human interventions such as deforestation and forest degradation. Mapping and monitoring this variation is essential if policy developments such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation) are to be known to have succeeded or failed. We produce a map of carbon storage across the watershed of the Tanzanian Eastern Arc Mountains (33.9 million ha) using 1,611 forest inventory plots, and correlations with associated climate, soil and disturbance data. As expected, tropical forest stores more carbon per hectare (182 Mg C ha-1) than woody savanna (51 Mg C ha-1). However, woody savanna is the largest aggregate carbon store, with 0.49 Pg C over 9.6 million ha. We estimate the whole landscape stores 1.3 Pg C, significantly higher than most previous estimates for the region. The 95% Confidence Interval for this method (0.9 to 3.2 Pg C) is larger than simpler look-up table methods (1.5 to 1.6 Pg C), suggesting simpler methods may underestimate uncertainty. Using a small number of inventory plots with two censuses (n = 43) to assess changes in carbon storage, and applying the same mapping procedures, we found that carbon storage in the tree-dominated ecosystems has decreased, though not significantly, at a mean rate of 1.47 Mg C ha-1 yr-1 (c. 2% of the stocks of carbon per year). The most influential variables on carbon storage in the region are anthropogenic, particularly historical logging, as noted by the largest coefficient of explanatory variable on the response variable. Of the non-anthropogenic factors, a negative correlation with air temperature and a positive correlation with water availability dominate, having smaller p-values than historical logging but also smaller influence. High carbon storage is typically found far from the commercial capital, in locations with a low monthly temperature range, without a strong dry season, and in areas that have not suffered from historical logging. The results imply that policy interventions could retain carbon stored in vegetation and likely successfully slow or reverse carbon emissions.

Comparative studies on plant species richness, endemism, floristic composition, and structure between protected and unprotected forests are few in the Eastern Arc Mountains, one of the most biodiverse ecosystems in Africa. This study from one mountain range, the East Usambaras, examines floristic and structural tree data from 41–0.5 ha plots in four types of Eastern Arc forest: active agroforests, recently abandoned agroforests, mature secondary forest, and natural forest. Active agroforests had significantly lower tree species richness, endemic species richness, and stand density compared to natural and mature secondary forest. Recently abandoned agroforests contained a higher tree species richness, density, and tree height than active agroforests. Active and abandoned agroforests were dominated by an invasive tree, Maesopsis eminii. This tree species makes up a large percentage of the stems in active agroforests (26%), recently abandoned agroforests (32%), and in the canopy of mature secondary forests ∼ 30 years post logging (30%). Through time the increasing dominance of this non-native tree in active agroforests is a concern when considering the role of agroforests in a landscape scale conservation strategy.

Monitoring landscape carbon storage is critical for supporting and validating climate change mitigation policies. These may be aimed at reducing deforestation and degradation, or increasing terrestrial carbon storage at local, regional and global levels. However, due to data-deficiencies, default global carbon storage values for given land cover types such as ‘lowland tropical forest’ are often used, termed ‘Tier 1 type’ analyses by the Intergovernmental Panel on Climate Change (IPCC). Such estimates may be erroneous when used at regional scales. Furthermore uncertainty assessments are rarely provided leading to estimates of land cover change carbon fluxes of unknown precision which may undermine efforts to properly evaluate land cover policies aimed at altering land cover dynamics. Here, we present a repeatable method to estimate carbon storage values and associated 95% confidence intervals (CI) for all five IPCC carbon pools (aboveground live carbon, litter, coarse woody debris, belowground live carbon and soil carbon) for data-deficient regions, using a combination of existing inventory data and systematic literature searches, weighted to ensure the final values are regionally specific. The method meets the IPCC ‘Tier 2’ reporting standard. We use this method to estimate carbon storage over an area of33.9 million hectares of eastern Tanzania, reporting values for 30 land cover types. We estimate that this area stored 6.33 (5.92–6.74) Pg C in the year 2000. Carbon storage estimates for the same study area extracted from five published Africa-wide or global studies show a mean carbon storage value of ∼50% of that reported using our regional values, with four of the five studies reporting lower carbon storage values. This suggests that carbon storage may have been underestimated for this region of Africa. Our study demonstrates the importance of obtaining regionally appropriate carbon storage estimates, and shows how such values can be produced for a relatively low investment.