Chemical constituents from the stem bark of Acer tegmentosum

A new phenolic glycoside, 4-hydroxyphenylethyl-1-O-β-D-[6′-O-(4-hydroxybenzoyl)]-glucopyranoside (1) was isolated from the stem bark of Acer tegmentosum, along with seven known phenolic compounds (2–8). The structure of compound 1 was determined by spectral analyses, including HR-ESI-MS, 1D and 2D NMR (COSY, HMQC and HMBC) experiments. Compounds 3 and 4 were found in the family Aceraceae for the first time.     

Antiproliferative constituents from Aphananthe aspera leaves

Extensive screening for the antiproliferative activity of different compounds found in trees was performed by extracting the leaves of Aphananthe aspera (Thunb.) Planch and then using chromatographic separation to afford 2 new compounds, (2S,4R)-2-carboxy-4-(E)-p-caffeoyl-1-methyl-hydroxyproline (1) and 5-O-caffeoyl quinic acid-(7′R,8′S,7′′E)-3′,4′,3′′-dihydroxy-4′′,7′-epoxy-8′,5′′-neolign-7′-ene-9- carboxyl (2). In addition, 6 known compounds were discovered from the leaves of this plant. The structural determination of all compounds, including their absolute configurations, was established by UV, IR, HRESIMS, 1D and 2D NMR, and CD spectroscopy. The novel compound 1 showed strong antiproliferative activity against human breast adenocarcinoma cells MCF-7 and MDA-MB-231.     

The potential of generalized additive modelling for the prediction of radial growth of Norway spruce from Central Germany

During the past decades managed forest ecosystems in Central Europe underwent vast changes, induced by extreme climate conditions and occasionally adverse forest management. Tree ring width patterns mirror these changes and thus have been widely examined as environmental archives and reliable empirical data sources in ‘tree growth modelling’. Dendrochronologists often suppose linear co-variation among the covariates, variable independence and homoscedasticity. Conventionally, these assumptions were achieved by eliminating biological age trends (detrending) and removing the autocorrelation from the time series (pre-whitening). Particularly detrending might be biased according to the scientific problem and sometimes inflexible age models. In this study, we tackle these issues and examine the suitability of a flexible Generalized Additive Model (GAM) on recently developed tree ring width time series of 30 Norway spruce stands (Picea abies [L.] H. Karst) from Central Germany.The model was established to simultaneously cope with the mentioned detrending issue, to unravel nonlinear climate-growth relationships and to predict mean ring width time series for spruce stands in the region. Particularly the latter was of primary interest, since recent forest planning relies on static yield tables that often underestimate the actual growth.The model reliably captured the empirical data, indicated by a small Generalized Cross Validation criterion (GCV = 0.045) and a deviance explained of 88.6 %. The flexible additive smoothers accounted for the social status of individual trees, captured low frequency variations of changing growth conditions adequately and displayed a rather flat biological age trend. The radial increment responded positively to summer season precipitation of the current and previous year. Positive temperature responses were found during the early vegetation period, whereas high summer season temperatures negatively affected the radial growth. The seasonal transition from spring to summer in June induced a shift in the climate response of the linear predictor, leading to a distinct negative effect of temperature and a no-role of precipitation on the linear predictor.Most important, utilizing the calibrated GAM for the purely climate-driven prediction of mean ring width time series from five independent spruce sites revealed proper coherencies. Herein, the mean ring width for sites located within the climatic-optimum for spruce growth were more exactly predicted than for sites with adverse spruce growth conditions. In addition, large mean ring widths were systematically underestimated, whereas small mean ring widths were precisely predicted. Overall, we strongly recommend GAMs as a powerful tool for the investigation of nonlinear climate-growth relationships and for the prediction of radial growth in managed forest ecosystems.     

Evaluation of chromium phytoremediation potential of some plant species of Dir Lower,Khyber Pakhtunkhwa,Pakistan

Chromium is the second most common metal pollutant in the soil, sediments and groundwater, due to its extensive industrial application, hence posing a serious environmental concern. Various remediation approaches including phytoremediation have been proposed to remediate chromium polluted waters and soils. In the present research, a total of sixty-one plant species belongs to thirty families were analyzed for the concentration of Chromium. Chromium was analyzed in the soil of the root zone, root and shoot of each plant. The concentration of chromium in the soil of different sites and plant parts (roots and shoots) was found in mg/kg in the range of 0.33–48.73, 8–1233.3 and 10.23–568.33 respectively. The highest concentration of chromium was found in the soil of site Site 41 (48.73 mg/kg) followed by Site 18 (47.83 mg/kg) and Site 6 (45.33 mg/kg). Among the analyzed plants, the highest concentration of chromium in mg/kg was found in the root of Cannabis sativa (1233.3) while its highest concentration was found in the shoot of Allium griffithianum (568.33). Phytoremediation potential of the analyzed plants was evaluated by the calculation of Bioconcentration Factor (BCF), Translocation Factor (TF) and Biological Accumulation Coefficient (BAC). Thirty-eight plant species showed feasibility for the phytostabilization of chromium (Cr_Excluders) based on BCF value and the concentration of chromium in the root. Plants i.e. Argyrolobium stenophyllum, Silybum marianum, Bryophyllum daigremontianum, Limonium macrorhabdon, Calendula arvensis and Delphinium suave were found the most efficient plant for the phytostabilization of chromium. Fifteen plant species showed feasibility for the phytoextraction of chromium (Cr_Indicators) based on TF value. The most efficient plant's species among them for the phytoextraction of chromium are Rosularia adenotricha, Catharanthus roseus, Allium griffithianum, Himalaiella heteromalla, Stellaria media, Salvia moorcroftiana and Marrubium vulgare. Based on BCFs, TFs and BACs value and the concentration of chromium in plant shoot six plant species Allium griffithianum, Catharanthus roseus, Himalaiella heteromalla, Geranium rotundifolium, Marrubium vulgare and Solanum nigrum were found chromium hyperaccumulators.     

In-vitro efficacy of bio-control agent and essential oils against leaf blight of chickpea caused by Alternaria alternata

Alternaria leaf blight is one of the most destructive foliar diseases of chickpea in different countries including Pakistan and has caused huge losses ranging from 5 to 100% around the world. This study was carried out to check the in-vitro effectiveness and sustainability of a bio-control agent Trichoderma viride and some essential oils like Castor, Jasmine, Clove, Sesame, Neem, Coconut, Henna, Black seed, and Mint oil at different doses of 1%, 2%, 4%, and 6% by food poisoned method against the mycelial colony growth inhibition of A. alternata which causes leaf blight in chickpea. The results indicate that maximum germination (80%) was recorded in control, (63%) under soil infestation and a minimum germination of (60%) was recorded in seed infestation. Maximum shoot weight (0.5630 g) in control, (0.2751 g) under soil infestation and (0.2064 g) in seed infestation. Maximum root weight (0.5937 g) in control, (0.4359 g) under soil infestation method, and (0.4102 g) in seed infestation method. Maximum shoot length (20.00 cm) in control, (10.23 cm) under soil infestation method and (7.053 cm) in seed infestation method. Maximum root length (7.19 cm) in control, (4.80 cm) under soil infestation method and (4.80 cm) in seed infestation. Bio-control agent Trichoderma viride showed (74.44%) growth inhibition compared to control (1.00%) growth inhibition of A. alternata. Maximum colony growth inhibition of A. alternata was (80.00%) Sesame, Coconut (77.04%), Henna (72.59%), Mint (66.07%), Black seed (71.85%), Jasmine (64.07%), Clove (70.74%), Neem (73.33%), Castor (58.89%) and minimum of (1.00%) was recorded in control. The results of this study will be very helpful for researchers and farming community for better management of this destructive disease of chickpea.     

Consistency of growth response to precipitation by Pseudopiptadenia contorta in two Atlantic Forest remnants

Tree growth sensitivity to climate can vary over space and time. This variability generates inconsistency in growth response to climate, which makes it difficult to assess the effects of past climate and global climate change on tree growth. A previous short-term study of Pseudopiptadenia contorta found a consistent growth response to climate in distinct locations, which raises the question, is the growth response of P. contorta to climate consistent over the long-term? We aimed to assess whether there is a common pattern of variation in tree-ring width, build tree-ring width chronologies, and verify the consistency of the climate-growth response of P. contorta in two Atlantic Forest remnants. Wood samples were collected in Reserva Biológica de Poço das Antas (RBPA) and Reserva Biológica de Tinguá (RBT) in the state of Rio de Janeiro, Brazil. Conventional dendrochronology methods were used for cross-dating, to build chronologies and to assess the climate-growth relationship. A common growth pattern was detected for P. contorta, and two tree-ring width chronologies were constructed. A congruent growth response was found for trees of RBPA and RBT to annual and spring precipitation as well as precipitation in the rainy months. Other climate-growth relationships were detected with other precipitation and temperature variables. Considering that P. contorta is a widespread species, occurring in other Brazilian biomes and forest formations, it is a promising model for developing further dendrochronological research including regional networks of replicated site chronologies, which could facilitate the reconstruction of historical climatic series and predictions of future impacts of climate change in tropical areas.     

A comparison of presence-only analytical techniques and their application in forest pest modeling

Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel.     

Estimating linear temporal trends from aggregated environmental monitoring data

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.