Publications

3843

High levels of ‘Candidatus Phytoplasma pini’ have produced extensive forest mortality on Pinus halepensis Mill forests in eastern Spain. This has led to the widespread levels of forest mortality. We used archival Landsat imagery and shapes algorithm implemented in the Google Earth Engine to explore the potential of the LandTrendr algorithm and its outputs, together with field observations, to analyze and predict the health status in P. halepensis stands affected by ‘Candidatus Phytoplasma pini’ in Andalusia (south-eastern Spain). We found that the Landsat time series algorithm (LandTrendr) has captured both long- and short-duration trends and changes in spectral reflectance related to phytoplasma disturbance in the Aleppo pine forest stands investigated. The normalized burn ratio (NBR) trends were positively associated with environmental variables: Annual precipitation, mean temperature, soil depth, percent base saturation and aspect. Environmental variables were tested for their contributions to the mapping of changes in Aleppo pine cover in the study area, as an empirical modeling approach to disturbance mapping in forests of south-eastern Spain. The methodology outlined in this paper has produced valuable results that indicate new possibilities for the use in forest management of remote-sensing technologies based on spectral trajectories associated with pest-diseases defoliation. Given the likely increase in pest risks in the forests of southern Europe, accurate assessment and map of pest outbreaks on forests will become increasingly important, both for research and for practical applications in forest management.

ABSTRACTChain elongation is emerging as a bioprocess to produce valuable medium-chain fatty acids (MCFA; 6 to 8 carbons in length) from organic waste streams by harnessing the metabolism of anaerobic microbiomes. Although our understanding of chain elongation physiology is still evolving, the reverse β-oxidation pathway has been identified as a key metabolic function to elongate the intermediate products of fermentation to MCFA. Here, we describe two chain-elongating microorganisms that were enriched in an anaerobic microbiome transforming the residues from a lignocellulosic biorefining process to short- and medium-chain fatty acids. Based on a multi-omic analysis of this microbiome, we predict thatCandidatusWeimerbacter bifidus, gen. nov., sp. nov. used xylose to produce MCFA, whereasCandidatusPseudoramibacter fermentans, sp. nov., used glycerol and lactate as substrates for chain elongation. Both organisms are predicted to use an energy conserving hydrogenase to improve the overall bioenergetics of MCFA production.IMPORTANCEMicrobiomes are vital to human health, agriculture, environmental processes, and are receiving attention as biological catalysts for production of renewable industrial chemicals. Chain elongation by MCFA-producing microbiomes offer an opportunity to produce valuable chemicals from organic streams that otherwise would be considered waste. However, the physiology and energetics of chain elongation is only beginning to be studied, and we are analyzing MCFA production by self-assembled communities to complement the knowledge that has been acquired from pure culture studies. Through a multi-omic analysis of an MCFA-producing microbiome, we characterized metabolic functions of two chain elongating bacteria and predict previously unreported features of this process.

Monitoring the health of Huanglongbing-affected citrus trees by following changes in leaf Candidatus Liberibacter asiaticus (CLas) titer has an inherent element of imprecision because CLas titer varies considerably within the tree canopy and with calendar seasons. In addition, the destructive sampling method used to determine CLas titer entails a different set of leaves per sampling period adding to the inconsistency and inexactitude of the results. To overcome these ambiguities and to reduce the numerical variability between samples, we developed an experimental method that analyzes portions of the same treated leaves for up to four sampling periods. By assaying subsamples of adjacent locations of the same leaf, random variability was significantly reduced, and comparative analysis can be carried out with greater precision.