Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal Machine Learning

Paper describes a data-driven framework based on spatio-temporal ensemble machine learning to produce distribution maps for 16 forest tree species (Abies alba Mill., Castanea sativa Mill. , Corylus avellana L., Fagus sylvatica L., Olea europaea L., Picea abies L. H. Karst., Pinus halepensis Mill., Pinus nigra J. F. Arnold, Pinus pinea L., Pinus sylvestris L., Prunus avium L., Quercus cerris L., Quercus ilex L., Quercus robur L., Quercus suber L. and Salix caprea L.) at high spatial resolution (30 m). Tree occurrence data for a total of 3 million of points was used to train different Machine Learning (ML) algorithms: random forest, gradient-boosted trees, generalized linear models, k-nearest neighbors, CART and an artificial neural network. A stack of 585 coarse and high resolution covariates representing spectral reflectance (Landsat bands, spectral indices; time-series of seasonal composites), different biophysical conditions (i.e. temperature, precipitation, elevation, lithology) and biotic competition (other species distribution maps) was used as predictors for realized distributions, while potential distribution was modelled with environmental predictors only. Logloss and computing time were used to select the three best algorithms to train an ensemble model based on stacking with a logistic regressor as a meta-learner for each species. High resolution (30 m) probability and model uncertainty maps of realized distribution were produced for each species using a time window of 4 years for a total of 6 distribution maps per species for the studied period, while for potential distributions only one map per species was produced. Results of spatial cross validation show that Olea europaea and Quercus suber achieved the best performances in both potential and realized distribution, while Pinus sylvestris and Salix caprea achieved the worst. Further analysis shows that fine-resolution models consistently outperformed coarse resolution models (250 m) for realized distribution (average decrease in logloss: +53%). Realized distribution models achieved higher predictive performances than potential distribution ones. Importance of predictor variables differed across species and models, with the green band for summer and the NDWI and NDVI for fall for realized distribution and the diffuse irradiation and precipitation of the driest quarter being the most important and frequent for potential distribution. The ensemble model outperformed or performed as good as the best individual model in all potential species distributions, while for ten species it performed worse than the best individual model in modeling realized distributions. The framework shows how combining continuous and consistent EO time series data with state of the art ML can be used to derive dynamic distribution maps. The produced time-series occurrence predictions can be used to quantify temporal trends and detect potential forest degradation.

Gut Microbial Signatures for Glycemic Responses of GLP-1 Receptor Agonists in Type 2 Diabetic Patients: A Pilot Study

BackgroundsGlucagon-like peptide-1 receptor agonist (GLP-1 RA) is probably one of more effective antidiabetic agents in treatment of type 2 diabetes mellitus (T2D). However, the heterogenicity in responses to GLP-1 RA may be potentially related to gut microbiota, although no human evidence has been published. This pilot study aims to identify microbial signatures associated with glycemic responses to GLP-1 RA.Materials and MethodsMicrobial compositions of 52 patients with T2D receiving GLP-1 RA were determined by 16S rRNA amplicon sequencing. Bacterial biodiversity was compared between responders versus non-responders. Pearson’s correlation and random forest tree algorithm were used to identify microbial features of glycemic responses in T2D patients and multivariable linear regression models were used to validate clinical relevance.ResultsBeta diversity significantly differed between GLP-1 RA responders (n = 34) and non-responders (n = 18) (ADONIS, P = 0.004). The top 17 features associated with glycohemoglobin reduction had a 0.96 diagnostic ability, based on area under the ROC curve: Bacteroides dorei and Roseburia inulinivorans, the two microbes having immunomodulation effects, along with Lachnoclostridium sp. and Butyricicoccus sp., were positively correlated with glycemic reduction; Prevotella copri, the microbe related to insulin resistance, together with Ruminococcaceae sp., Bacteroidales sp., Eubacterium coprostanoligenes sp., Dialister succinatiphilus, Alistipes obesi, Mitsuokella spp., Butyricimonas virosa, Moryella sp., and Lactobacillus mucosae had negative correlation. Furthermore, Bacteroides dorei, Lachnoclostridium sp. and Mitsuokella multacida were significant after adjusting for baseline glycohemoglobin and C-peptide concentrations, two clinical confounders.ConclusionsUnique gut microbial signatures are associated with glycemic responses to GLP-RA treatment and reflect degrees of dysbiosis in T2D patients.

Forest tree growth is linked to mycorrhizal fungal composition and function across Europe

Most trees form symbioses with ectomycorrhizal fungi (EMF) which influence access to growth-limiting soil resources. Mesocosm experiments repeatedly show that EMF species differentially affect plant development, yet whether these effects ripple up to influence the growth of entire forests remains unknown. Here we tested the effects of EMF composition and functional genes relative to variation in well-known drivers of tree growth by combining paired molecular EMF surveys with high-resolution forest inventory data across 15 European countries. We show that EMF composition was linked to a three-fold difference in tree growth rate even when controlling for the primary abiotic drivers of tree growth. Fast tree growth was associated with EMF communities harboring high inorganic but low organic nitrogen acquisition gene proportions and EMF which form contact versus medium-distance fringe exploration types. These findings suggest that EMF composition is a strong bio-indicator of underlying drivers of tree growth and/or that variation of forest EMF communities causes differences in tree growth. While it may be too early to assign causality or directionality, our study is one of the first to link fine-scale variation within a key component of the forest microbiome to ecosystem functioning at a continental scale.

The Effect of Quercus infectoria Acorn on Clinical, Hematobiochemical Parameters of Pregnant Albino Rats

Background: Quercus infectoria is a kind of forest tree widely used for livestock feeding within the northern parts of Iraq with little was known about the Quercus infectoria toxicity mainly in pregnant animals. Therefore, this study aimed to investigate the potential effect of Quercus infectoria acorn on the pregnant albino rats. Methods: This study was conducted on the Wistar pregnant rats, using aqueous extract of the acorn at doses of 2 grams/ rat for a period of 10 days. The effect of the quercus acorn on the animal behavior, pregnancy hematobiochemical parameters was investigated compared to the control groups. Result: Neither mortality, nor significant changes in animals’ behavior were detected in treated rats; whereas, abortion and early parturition with lower weight of the pups were observed in treated pregnant rats. Significant increases in red blood cells, packed cell volume, hemoglobin concentration, means corpuscular volume and means corpuscular hemoglobin and levels of neutrophils and eosinophil were reported in treated rats compared to the control groups. Besides, blood urea nitrogen and aspartate aminotransferase were significantly increased in the treated group. Study results suggest that the aqueous extract of Quercus infectoria acorn has potential toxic effects on pregnancy.

Challenges and Perspectives in the Epigenetics of Climate Change-Induced Forests Decline

Forest tree species are highly vulnerable to the effects of climate change. As sessile organisms with long generation times, their adaptation to a local changing environment may rely on epigenetic modifications when allele frequencies are not able to shift fast enough. However, the current lack of knowledge on this field is remarkable, due to many challenges that researchers face when studying this issue. Huge genome sizes, absence of reference genomes and annotation, and having to analyze huge amounts of data are among these difficulties, which limit the current ability to understand how climate change drives tree species epigenetic modifications. In spite of this challenging framework, some insights on the relationships among climate change-induced stress and epigenomics are coming. Advances in DNA sequencing technologies and an increasing number of studies dealing with this topic must boost our knowledge on tree adaptive capacity to changing environmental conditions. Here, we discuss challenges and perspectives in the epigenetics of climate change-induced forests decline, aiming to provide a general overview of the state of the art.

Effectiveness of biotechnology on the basis of associative microorganisms at the early stages of forestry process of the Pinus sylvestris L.

The transition to biologically based technologies is one of the priority scientific areas of agriculture and forestry in Russia. The aim of the work is to develop and modify the technology of using biological products on forest tree species and to test it on Scots pine. The effect of pre-sowing inoculation of pine seeds with biological products on soil germination, safety of one- and two-year-old seedlings and their biometric characteristics was studied. The results of the analysis of soil germination of seeds and the safety of 1-year-old seedlings showed that these biological products can be introduced into the technology of growing planting material of Scots pine. It was revealed that the seeds of sensitive trees of the Stupinskaya population turned out to be the most responsive to inoculation with biological products. A stimulating effect has been established on the survival rate of seedlings, preservation, height, growth and diameter of 2-year-old plants in a forestry area (Vernadsky forestry, Tambov region). The height of 2-year-old seedlings of pine 'Ostrogozhskaya' exceeds the control by an average of 18.1%. The use of this biotechnology in forestry production will create favorable conditions for the germination of pine seeds, the growth of seedlings and their higher survival rate.

Aboveground Biomass Stockpile of Trees in Vallanadu Blackbuck Sanctuary, Peninsular India

A forest tree inventory study was conducted in Vallanadu Black buck sanctuary, Tuticorin. The current study was conducted to assess tree density, species richness, basal area (BA) and aboveground biomass (AGB) stockpile. The study area has been classified as Southern Thorn Forest (SFT). One hundred square plots (total area 1 ha), each 10m × 10m (100 m2 each) laid randomly across study area. All live trees with ≥5 cm diameter at breast height (DBH) measured at 137 cm above the ground. As the whole, 1335 individual trees ≥5cm DBH recorded. A total number of 18 species recorded from 14 genera and 11 families in study area. The family Mimosaceae has maximum number of species (7 species) followed by Rhamnaceae (2 species), while 9 families had just single species’ each. The total basal area recorded was 22.046 m2 ha-1, while, the mean wood density (WD) of trees estimated as 0.70±0.093 g cm-3. Total amount of 50.065 Mg ha-1 present in STF. The contribution of different species in terms of total AGB varied significantly. Commiphora berryi stocked 45.13% (22.588 Mg ha-1) of AGB followed by A. planifrons (23.31%, 11.669 Mg ha-1), A. mellifera (7.233%, 3.621 Mg ha-1), whereas remaining 15 species collectively stocked 24.327% (12.187 Mg ha-1) AGB. The STF had a large number of trees compared to some dry forests within Tamil Nadu. Southern Thorn Forest endowed with a moderate number of trees species. Aboveground biomass stockpile of trees is comparable with the range recorded from Indian dry forests. The study area experiences lesser mean annual rainfall and >6 months dry season. Further, endowed with short-bole and smaller leaved trees, hence stocked a relatively lesser AGB in trees.