Designing an optimized landscape restoration with spatially interdependent non-linear models.

Brazilian Atlantic Forest is a biodiversity hotspot drastically fragmented due to different land use practices. Our understanding on the impacts of fragmentation and restoration practices on ecosystem functionality significantly increased during the last decades. However, it is unknown to our knowledge how a precision restoration approach, integrated with landscape metrics, will affect the decision-making process of forest restoration. Here, we applied Landscape Shape Index and Contagion metrics in a genetic algorithm for planning forest restoration in watersheds at the pixel level. We evaluated how such integration may configure the precision of restoration with scenarios related to landscape ecology metrics. The genetic algorithm worked toward optimizing the site, shape, and size of forest patches across the landscape according to the results obtained in applying the metrics. Our results, obtained by simulations of scenarios, support aggregation of forest restoration zones as expected, with priority restoration areas indicated where most of the aggregation of forest patches occurs. Our optimized solutions for the study area (Santa Maria do Rio Doce Watershed) predicted an important improvement of landscape metrics (LSI = 44 %; Contagion/LSI = 73 %). Largest shifts are suggested based on LSI (i.e., three larger fragments) and Contagion/LSI (i.e., only one well-connected fragment) optimizations. Our findings indicate that restoration in an extremely fragmented landscape will promote a shift toward more connected patches and with reduction of the surface:volume ratio. Our work explores the use of genetic algorithms to propose forest restoration based on landscape ecology metrics in a spatially explicit innovative approach. Our results indicate that LSI and Contagion:LSI ratio may affect the choice concerning precise location of restoration sites based on forest fragments scattered in the landscape and reinforce the usefulness of genetic algorithms to yield an optimized-driven solution for restoration initiatives.

Microbial communities associated with honey bees in Brazil and in the United States.

Honey bee colony losses worldwide call for a more in-depth understanding of the pathogenic and mutualistic components of the honey bee microbiota and their relation with the environment. In this descriptive study, we characterized the yeast and bacterial communities that arise from six substrates associated with honey bees: corbicular pollen, beebread, hive debris, intestinal contents, body surface of nurses and forager bees, comparing two different landscapes, Minas Gerais, Brazil and Maryland, United States. The sampling of five hives in Brazil and four in the USA yielded 217 yeast and 284 bacterial isolates. Whereas the yeast community, accounted for 47 species from 29 genera, was dominated in Brazil by Aureobasidium sp. and Candida orthopsilosis, the major yeast recovered from the USA was Debaryomyces hansenii. The bacterial community was more diverse, encompassing 65 species distributed across 31 genera. Overall, most isolates belonged to Firmicutes, genus Bacillus. Among LAB, species from Lactobacillus were the most prevalent. Cluster analysis evidenced high structuration of the microbial communities, with two distinguished microbial groups between Brazil and the United States. In general, the higher difference among sites and substrates were dependents on the turnover effect (~ 93% of the beta diversity), with a more pronounced effect of nestedness (~ 28%) observed from Brazil microbiota change. The relative abundance of yeasts and bacteria also showed the dissimilarity of the microbial communities between both environments. These results provide a comprehensive view of microorganisms associated with A. mellifera, highlighting the importance of the environment in the establishment of the microbiota associated with honey bees.

Climate and soil attributes determine plant species turnover in global drylands.

AIM: Geographic, climatic, and soil factors are major drivers of plant beta diversity, but their importance for dryland plant communities is poorly known. This study aims to: i) characterize patterns of beta diversity in global drylands, ii) detect common environmental drivers of beta diversity, and iii) test for thresholds in environmental conditions driving potential shifts in plant species composition. LOCATION: 224 sites in diverse dryland plant communities from 22 geographical regions in six continents. METHODS: Beta diversity was quantified with four complementary measures: the percentage of singletons (species occurring at only one site), Whittake's beta diversity (ß(W)), a directional beta diversity metric based on the correlation in species occurrences among spatially contiguous sites (ß(R2)), and a multivariate abundance-based metric (ß(MV)). We used linear modelling to quantify the relationships between these metrics of beta diversity and geographic, climatic, and soil variables. RESULTS: Soil fertility and variability in temperature and rainfall, and to a lesser extent latitude, were the most important environmental predictors of beta diversity. Metrics related to species identity (percentage of singletons and ß(W)) were most sensitive to soil fertility, whereas those metrics related to environmental gradients and abundance ((ß(R2)) and ß(MV)) were more associated with climate variability. Interactions among soil variables, climatic factors, and plant cover were not important determinants of beta diversity. Sites receiving less than 178 mm of annual rainfall differed sharply in species composition from more mesic sites (> 200 mm). MAIN CONCLUSIONS: Soil fertility and variability in temperature and rainfall are the most important environmental predictors of variation in plant beta diversity in global drylands. Our results suggest that those sites annually receiving ~ 178 mm of rainfall will be especially sensitive to future climate changes. These findings may help to define appropriate conservation strategies for mitigating effects of climate change on dryland vegetation.