We conducted a 84-day nutritional feeding experiment with dried whole cells of DHA-rich marine microalga Schizochytrium sp. (Sc) to determine the optimum level of fish-oil substitution (partial or complete) for maximum growth of Nile tilapia. When we fully replaced fish oil with Schizochytrium (Sc100 diet), we found significantly higher weight gain and protein efficiency ratio (PER), and lower (improved) feed conversion ratio (FCR) and feed intake compared to a control diet containing fish oil (Sc0); and no significant change in SGR and survival rate among all diets. The Sc100 diet had the highest contents of 22:6n3 DHA, led to the highest DHA content in fillets, and consequently led to the highest DHA:EPA ratios in tilapia fillets. Schizochytrium sp. is a high quality candidate for complete substitution of fish oil in juvenile Nile tilapia feeds, providing an innovative means to formulate and optimize the composition of tilapia juvenile feed while simultaneously raising feed efficiency of tilapia aquaculture and to further develop environmentally and socially sustainable aquafeeds. Results show that replacing fish oil with DHA-rich marine Sc improves the deposition of n3 LC PUFA levels in tilapia fillet. These results support further studies to lower Schizochytrium production costs and to combine different marine microalgae to replace fish oil and fishmeal into aquafeeds.

This study supplements spatial panel econometrics techniques with qualitative GIS to analyse spatio-temporal changes in the distribution of integrated conservation–development projects relative to poaching activity and unauthorized resource use in Volcanoes National Park, Rwanda. Cluster and spatial regression analyses were performed on data from ranger monitoring containing > 35,000 combined observations of illegal activities in Volcanoes National Park, against tourism revenue sharing and conservation NGO funding data for 2006–2015. Results were enriched with qualitative GIS analysis from key informant interviews. We found a statistically significant negative linear effect of overall integrated conservation–development investments on unauthorized resource use in Volcanoes National Park. However, individually, funding from Rwanda's tourism revenue sharing policy did not have an effect in contrast to the significant negative effect of conservation NGO funding. In another contrast between NGO funding and tourism revenue sharing funding, spatial analysis revealed significant gaps in revenue sharing funding relative to the hotspots of illegal activities, but these gaps were not present for NGO funding. Insight from qualitative GIS analysis suggests that incongruity in prioritization by decision makers at least partly explains the differences between the effects of revenue sharing and conservation NGO investment. Although the overall results are encouraging for integrated conservation–development projects, we recommend increased spatial alignment of project funding with clusters of illegal activities, which can make investment decision-making more data-driven and projects more effective for conservation.

Forest degradation, generally defined as a reduction in the delivery of forest ecosystem services, can have long-term impacts on biodiversity, climate, and local livelihoods. The quantification of forest degradation, its dynamics and proximate causes can help prompt early action to mitigate carbon emissions and inform relevant land use policies. The Democratic Republic of the Congo is largely forested with a relatively low deforestation rate, but anthropogenic degradation has been increasing in recent years. We assess the impact of eight independent variables related to land cover, land use, infrastructure, armed conflicts, and accessibility on forest degradation, measured by the Forest Condition (FC) index, a measure of forest degradation based on biomass history and fragmentation that ranges from 0 (completely deforested) to 100 (intact). We employ spatial panel models with fixed effects using regular 25 × 25 km units over five 3-year intervals from 2002 to 2016. The regression results suggest that the presence of swamp ecosystems, low access (defined by high travel time), and forest concessions are associated with lower forest degradation, while built up area, fire frequency, armed conflicts result in greater forest degradation. The impact of neighboring units on FC shows that all variables within the 50 km spatial neighborhood have a greater effect on FC than the on-site spatial determinants, indicating the greater influence of drivers beyond the 25 km2 unit. In the case of protected areas, we unexpectedly find that protection in neighboring locations leads to higher forest degradation, suggesting a potential leakage effect, while protected areas in the local vicinity have a positive influence on FC. The Mann-Kendall trend statistic of occurrences of fires and conflicts over the time period and until 2020 show that significant increases in conflicts and fires are spatially divergent. Overall, our results highlight how assessing the proximate causes of forest degradation with spatiotemporal analysis can support targeted interventions and policies to reduce forest degradation but spillover effects of proximal drivers in neighboring areas need to be considered.

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The forests of the Greater Mekong Subregion, consisting of Myanmar, Thailand, Cambodia, Laos and Vietnam, are under high pressure from economic development and exploitation of natural resources, including but not limited to land concession, smallholder plantations and commercial agriculture, agroforestry development, mining, and road infrastructure development. While these threats are well-known, the magnitude and dynamics of their individual and interacting effects on forest cover are not fully understood. This pilot study aims to apply existing, publicly available macro, micro, and socioeconomic data in addition to remote sensing forest cover data to explore economic determinants of deforestation in the Greater Mekong, using the case of the Central Annamites Landscape (CAL) ecoregion of Vietnam. A longitudinal panel was constructed for 2000-2017, containing 1,658 observations for 144 variables across 95 Tier 2 (district) administrative units in CAL from 2000-2017 for modeling macroeconomic and socioeconomic conditions against annual tree cover loss aggregated to the Tier 2 administrative unit. The first phase of the study used tiered spatial regression analysis to correlatively identify which commodities, economic development activities, and social conditions have historically had the greatest effect on forest cover by magnitude in CAL. Based on first phase results, we selected a subset of these determinants for scenario modelling to predict possible deforestation outcomes given certain economic scenarios. The results, among others, indicate that, when spatially collocated, high poverty rates and smaller scale agricultural land conversion are key immediate determinants of deforestation. This therefore provides evidence to support programs targeting rubber, acacia harvesting, artisanal mining, and land conversion for cash crop plantations. Education is also key immediate socioeconomic factor, as poverty rate is consistently associated with tree cover loss and increased educational attainment is consistently associated with reduced tree cover loss, at approximately 12 ha per percentage increase in secondary school graduation. On the macro level, economic growth in China and Vietnam are correlatively associated with tree cover loss, as are rising trade in Myanmar and Laos. This study provides a methodological contribution to the current academic literature identifying socioeconomic dimensions of deforestation through spatial econometric analysis and scenario modelling at the landscape level. For practitioner work, this pilot provides a model or tool for strategic planning of conservation interventions in light of economic conditions and factors.