Using datasets of different taxonomic detail to assess the influence of floodplain characteristics on terrestrial arthropod assemblages

Four arthropod datasets of different taxonomic detail were compared on their discriminatory power for various environmental characteristics in a lowland floodplain area along the river Rhine. The arthropod datasets comprised ground-dwelling arthropods at class-order level (n = 10), beetle families (n = 32), ground beetle genera (n = 30) and ground beetle species (n = 68). Environmental characteristics included vegetation characteristics, hydro-topographic setting, physical–chemical soil properties and soil contamination levels. Relations between arthropod assemblages and environmental factors were assessed with variance partitioning: a multivariate statistical approach that attributes variation in community composition to specific explaining variables. The variance partitioning showed comparable results for the four datasets. A substantial part of the variation (31–38%) could be ascribed to vegetation characteristics. Variance could further be attributed to physical–chemical soil properties (7–10%), hydro-topographic setting (3–7%) and soil metal contamination (2–4%). Thus, in strongly heterogeneous landscapes like lowland river floodplains, relatively coarse taxonomic data can already provide a valuable indication of the relative importance of different environmental factors for structuring arthropod communities. However, the ground beetles showed a higher specificity for different vegetation types and a more distinct relation to soil contamination levels than the other arthropod datasets. Hence, a higher degree of taxonomic detail will be beneficial for investigating the consequences of for example environmental pollution or vegetation characteristics in terms of taxonomic diversity or community composition.     

Integrated knowledge mining,genome-scale modeling,and machine learning for predicting Yarrowia lipolytica bioproduction

Predicting bioproduction titers from microbial hosts has been challenging due to complex interactions between microbial regulatory networks, stress responses, and suboptimal cultivation conditions. This study integrated knowledge mining, feature extraction, genome-scale modeling (GSM), and machine learning (ML) to develop a model for predicting Yarrowia lipolytica chemical titers (i.e., organic acids, terpenoids, etc.). First, Y. lipolytica production data, including cultivation conditions, genetic engineering strategies, and product information, was manually collected from literature (~100 papers) and stored as either numerical (e.g., substrate concentrations) or categorical (e.g., bioreactor modes) variables. For each case recorded, central pathway fluxes were estimated using GSMs and flux balance analysis (FBA) to provide metabolic features. Second, a ML ensemble learner was trained to predict strain production titers. Accurate predictions on the test data were obtained for instances with production titers >1 g/L (R² = 0.87). However, the model had reduced predictability for low performance strains (0.01–1 g/L, R² = 0.29) potentially due to biosynthesis bottlenecks not captured in the features. Feature ranking indicated that the FBA fluxes, the number of enzyme steps, the substrate inputs, and thermodynamic barriers (i.e., Gibbs free energy of reaction) were the most influential factors. Third, the model was evaluated on other oleaginous yeasts and indicated there were conserved features for some hosts that can be potentially exploited by transfer learning. The platform was also designed to assist computational strain design tools (such as OptKnock) to screen genetic targets for improved microbial production in light of experimental conditions.     

A Robust Technique for Mapping Vegetation Condition Across a Major River System

Ecologists need to develop tools that allow characterization of vegetation condition over scales that are pertinent to species’ persistence and appropriate for management actions. Our study shows that stand condition can be mapped accurately over the floodplain of a major river system (ca 100,000 ha of forest over 1600 km of river)—the Murray River in southeastern Australia. It demonstrates the value of using quantitative ground surveys in conjunction with remotely sensed data to model vegetation condition over very large spatial domains. A comparison of four modelling methods found that stand condition was best modelled using the multivariate adaptive regression spline (MARS) method (R ² = 0.85), although there was little difference among the methods (R ² = 0.77–0.85). However, a subsequent validation survey of condition at new locations showed that use of artificial neural networks had substantially higher predictive power (R ² = 0.78) than the MARS model (R ² = 0.28). This discrepancy demonstrates the value of using several modelling approaches to determine relationships among vegetation responses and environmental variables, and stresses the importance of validating ecological models with predictive surveys conducted after model building. The artificial neural network was used to produce a stand condition map for the whole floodplain, which predicted that only 30% of the area containing Eucalyptus camaldulensis stands is currently in good condition. There is a downstream decline in stand condition, which is related to more extreme declines in flooding, due to water harvesting, and drier climate found in the Lower Murray region. Rigorous surveying and modelling approaches, such as those used here, are necessary if vegetation health is to be effectively monitored and managed. Author Contribution: SCC wrote the paper and was involved in all parts of the research; RM conceived and designed the study, and contributed to analysis and writing; JR designed the study, and contributed to writing; PJB designed the study and contributed to writing; MW contributed to design and modelling; JRT contributed to modelling; PG contributed new methods and modelling.     

Estimation of wetland vegetation height and leaf area index using airborne laser scanning data

Wetland vegetation is a core component of wetland ecosystems. Wetland vegetation structural parameters, such as height and leaf area index (LAI) are important variables required by earth-system and ecosystem models. Therefore, rapid, accurate, objective and quantitative estimations of wetland vegetation structural parameters are essential. The airborne laser scanning (also called LiDAR) is an active remote sensing technology and can provide accurate vertical vegetation structural parameters, but its accuracy is limited by short, dense vegetation canopies that are typical of wetland environments. The objective of this research is to explore the potential of estimating height and LAI for short wetland vegetation using airborne discrete-return LiDAR data.The accuracies of raw laser points and LiDAR-derived digital elevation models (DEM) data were assessed using field GPS measured ground elevations. The results demonstrated very high accuracy of 0.09 m in raw laser points and the root mean squared error (RMSE) of the LiDAR-derived DEM was 0.15 m.Vegetation canopy height was estimated from LiDAR data using a canopy height model (CHM) and regression analysis between field-measured vegetation heights and the standard deviation (σ) of detrended LiDAR heights. The results showed that the actual height of short wetland vegetation could not be accurately estimated using the raster CHM vegetation height. However, a strong relationship was observed between the σ and the field-measured height of short wetland vegetation and the highest correlation occurred (R² = 0.85, RMSE = 0.14 m) when sample radius was 1.50 m. The accuracy assessment of the best-constructed vegetation height prediction model was conducted using 25 samples that were not used in the regression analysis and the results indicated that the model was reliable and accurate (R² = 0.84, RMSE = 0.14 m).Wetland vegetation LAI was estimated using laser penetration index (LPI) and LiDAR-predicted vegetation height. The results showed that the vegetation height-based predictive model (R² = 0.79) was more accurate than the LPI-based model (the highest R² was 0.70). Moreover, the LAI predictive model based on vegetation height was validated using the leave-one-out cross-validation method and the results showed that the LAI predictive model had a good generalization capability. Overall, the results from this study indicate that LiDAR has a great potential to estimate plant height and LAI for short wetland vegetation.     

River phytoplankton prediction model by Artificial Neural Network: Model performance and selection of input variables to predict time-series phytoplankton proliferations in a regulated river system

In this study, a comparison between statistical regression model and Artificial Neural Network (ANN) is given on the effectiveness of ecological model of phytoplankton dynamics in a regulated river. From the results of the study, the effectiveness of ANN over statistical method was proposed. Also feasible direction of increasing ANN models' performance was provided. A hypertrophic river data was used to develop prediction models (chlorophyll a (chl. a) 41.7 ± 56.8 μg L^− 1; n = 406). Higher time-series predictability was found from the ANN model. Failure of statistical methods would be due to the complex nature of ecological data in the regulated river ecosystems. Reduction of ANN model size by decreasing the number of input variables according to the sensitivity analysis did not have effectiveness with respect to the predictability on testing data set (RMSE of the ANN with all 27 variables, 25.7; 47.9 from using 2 highly sensitive variables; 42.9 from using 5 sensitive variables; 33.1 from using 15 variables). Even though the ANN model presented high performance in prediction accuracy, more efficient methods of selecting feasible input information are strongly requested for the prediction of freshwater ecological dynamics.     

Wildfire spatial pattern analysis in the Zagros Mountains,Iran: A comparative study of decision tree based classifiers

Knowledge of wildfire behavior is of key importance for planning and allocating resources to fire suppression efforts. In this study, we analyzed the spatial pattern of wildfires with five decision tree based classifiers, including alternating decision tree (ADT), classification and regression tree (CART), functional tree (FT), logistic model tree (LMT), and Naïve Bayes tree (NBT). The classifiers were trained using historical fire locations in the Zagros Mountains (Iran) from the years 2007–2014 and a set of fifteen explanatory variables (i.e., slope degree, aspect, altitude, plan curvature, topographic position index (TPI), topographic roughness index (TRI), topographic wetness index (TWI), mean annual temperature and rainfall, wind effect, soil type, land use, and proximity to settlements, roads, and rivers) that were first optimized with a twostep process using multicollinearity analysis and the Gain Ratio variable selection method. The classifiers were then validated using the Kappa index and several statistical index-based evaluators (i.e., accuracy, sensitivity, specificity, precision, and F-measure). The global performance of the classifiers was measured using the ROC-AUC method. In this comparative study, the ADT classifier demonstrated the highest performance both in terms of goodness-of-fit with the training dataset (accuracy = 99.8%, AUC = 0.991) and the capability to predict future wildfires (accuracy = 75.7%, AUC = 0.903). This study contributes to the suite of research that evaluates data mining methods for the prediction of natural hazards.     

Performance limitation and the role of core temperature when wearing light-weight workwear under moderate thermal conditions

The objective of this investigation was to achieve an understanding about the relationship between heat stress and performance limitation when wearing a two-layerfire-resistant light-weight workwear (full-clothed ensemble) compared to an one-layer short sports gear (semi-clothed ensemble) in an exhaustive, stressful situation under moderate thermal condition (25 °C). Ten well trained male subjects performed a strenuous walking protocol with both clothing ensembles until exhaustion occurred in a climatic chamber. Wearing workwear reduced the endurance performance by 10% (p=0.007) and the evaporation by 21% (p=0.003), caused a more pronounced rise in core temperature during submaximal walking (0.7±0.3 vs. 1.2±0.4 °C; p≤0.001) and from start till exhaustion (1.4±0.3 vs. 1.8±0.5 °C; p=0.008), accelerated sweat loss (13±2 vs. 15±3 g min⁻¹; p=0.007), and led to a significant higher heart rate at the end of cool down (103±6 vs. 111±7 bpm; p=0.004). Correlation analysis revealed that core temperature development during submaximal walking and evaporation may play important roles for endurance performance. However, a critical core temperature of 40 °C, which is stated to be a crucial factor for central fatigue and performance limitation, was not reached either with the semi-clothed or the full-clothed ensemble (38.3±0.4 vs. 38.4±0.5 °C). Additionally, perceived exertion did not increase to a higher extent parallel with the rising core temperature with workwear which would substantiate the critical core temperature theory. In conclusion, increased heat stress led to cardiovascular exercise limitation rather than central fatigue.     

Spatio–temporal variation of vegetation heterogeneity in groundwater dependent ecosystems within arid environments

Climate change, land cover change and the over–abstraction of groundwater threaten the existence of Groundwater-Dependent Ecosystems (GDE), despite these environments being regarded as biodiversity hotspots. The vegetation heterogeneity in GDEs requires routine monitoring in order to conserve and preserve the ecosystem services in these environments. However, in–situ monitoring of vegetation heterogeneity in extensive, or transboundary, groundwater resources remain a challenge. Inherently, the Spectral Variation Hypothesis (SVH) and remotely-sensed data provide a unique way to monitor the response of GDEs to seasonal or intra–annual environmental stressors, which is the key for achieving the national and regional biodiversity targets. This study presents the first attempt at monitoring the intra–annual, spatio–temporal variations in vegetation heterogeneity in the Khakea–Bray Transboundary Aquifer, which is located between Botswana and South Africa, by using the coefficient of variation derived from the Landsat 8 OLI Operational Land Imager (OLI). The coefficient of variation was used to measure spectral heterogeneity, which is a function of environmental heterogeneity. Heterogenous environments are more diverse, compared to homogenous environments, and the vegetation heterogeneity can be inferred from the heterogeneity of a landscape. The coefficient of variation was used to calculate the α- and β measures of vegetation heterogeneity (the Shannon–Weiner Index and the Rao's Q, respectively), whilst the monotonic trends in the spatio–temporal variation (January–December) of vegetation heterogeneity were derived by using the Mann–Kendall non–parametric test. Lastly, to explain the spatio–temporal variations of vegetation heterogeneity, a set of environmental variables were used, along with a machine-learning algorithm (random forest). The vegetation heterogeneity was observed to be relatively high during the wet season and low during the dry season, and these changes were mainly driven by landcover- and climate–related variables. More specifically, significant changes in vegetation heterogeneity were observed around natural water pans, along roads and rivers, as well as in cropping areas. Furthermore, these changes were better predicted by the Rao's Q (MAE = 5.81, RMSE = 6.63 and %RMSE = 42.41), than by the Shannon–Weiner Index (MAE = 30.37, RMSE = 33.25 and %RMSE = 63.94). These observations on the drivers and changes in vegetation heterogeneity provide new insights into the possible effects of future landcover changes and climate variability on GDEs. This information is imperative, considering that these environments are biodiversity hotspots that are capable of supporting many livelihoods. More importantly, this work provides a spatially explicit framework on how GDEs can be monitored to achieve Sustainable Development Goal (SDG) Number 15.     

Estimation of big sagebrush leaf area index with terrestrial laser scanning

Accurate monitoring and quantification of the structure and function of semiarid ecosystems is necessary to improve carbon and water flux models that help describe how these systems will respond in the future. The leaf area index (LAI, m² m⁻²) is an important indicator of energy, water, and carbon exchange between vegetation and the atmosphere. Remote sensing techniques are frequently used to estimate LAI, and can provide users with scalable measurements of vegetation structure and function. We tested terrestrial laser scanning (TLS) techniques to estimate LAI using structural variables such as height, canopy cover, and volume for 42 Wyoming big sagebrush (Artemisia tridentata subsp. wyomingensis Beetle & Young) shrubs across three study sites in the Snake River Plain, Idaho, USA. The TLS-derived variables were regressed against sagebrush LAI estimates calculated using specific leaf area measurements, and compared with point-intercept sampling, a field method of estimating LAI. Canopy cover estimated with the TLS data proved to be a good predictor of LAI (r² = 0.73). Similarly, a convex hull approach to estimate volume of the shrubs from the TLS data also strongly predicted LAI (r² = 0.76), and compared favorably to point-intercept sampling (r² = 0.78), a field-based method used in rangelands. These results, coupled with the relative ease-of-use of TLS, suggest that TLS is a promising tool for measuring LAI at the shrub-level. Further work should examine the structural measures in other similar shrublands that are relevant for upscaling LAI to the plot-level (i.e., hectare) using data from TLS and/or airborne laser scanning and to regional levels using satellite-based remote sensing.     

Accuracy assessment of MODIS active fire products in southern African savannah woodlands

Satellite remote sensing offers a cost‐effective method for monitoring fire occurrence in savannah systems, for proper fire management. However, the ability of satellite fire products to detect active fire is known to vary depending on the terrestrial ecosystems and sensor characteristics. In this study, the overall accuracy, kappa coefficient of agreement and true skill statistic (TSS) were used to assess the accuracy of two MODIS fire products (MOD14A1 and MCD14ML) to detect active fire at two savannah woodland sites dominated by Baikiaea plurijuga and Brachystegia spiciformis in Zimbabwe. In both sites, MOD14A1 with a coarse spatial resolution of 1 km had a poor index of agreement with ground fire data (kappa = 0, TSS = 0 and overall accuracy ≤ 0.4). By contrast, a moderate to strong agreement between MCD14ML and active fires measured on the ground was observed at both study sites (overall accuracy ≥ 0.7, kappa ≥ 0.6 and TSS ≥ 0.6; Table  1 ). It was therefore concluded that MCD14ML, with a spatial resolution of 375 m, is a more suitable product for detecting active fires in both Baikiaea plurijuga and Brachystegia‐dominated savannah woodlands of southern Africa compared to MOD14A1.     

Ecological niche modelling of Cantharellus species in Benin,and revision of their conservation status

Of the eight Cantharellus species known from Benin, seven have been encountered under similar macroecological conditions. The present work attempts to generate a more complete distribution of these seven species. Forty-eight occurrences of the target species and four explanatory variables including three bioclimatic variables and a land cover variable were used to build an ensemble model from five modelling approaches under the Biomod2 package of R software. Results showed a distribution restricted to the Bassila and Atacora mountain range phytogeographic districts with excellent statistical performance (TSS = 0.98, AUC = 0.99). This distribution is governed mainly by high soil moisture and high potential evapotranspiration, thus defining only gallery forests as the most suitable habitat for chanterelles in Sudano-guinean and Soudanese ecozones of Benin. Based on IUCN criterion B1 and sub-criteria B1a and B1c(i), these seven species were categorized under the Endangered (EN) threat category according to our results.     

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC).We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets.We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine).According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML.     

Antioxidant Enzyme Isoforms on Gels in Two Poplar Clones Differing in Sensitivity After Exposure to Ozone

The effect of acute ozone (O₃) fumigation on isozyme patterns of superoxide dismutase (SOD), peroxidase (POD) and ascorbate peroxidase (APX) in mature (ML) and young leaves (YL) of two poplar clones, contrasting in O₃-sensitivity was analysed. Untreated leaves of both the O₃-sensitive (O₃-S) clone Eridano of Populus deltoides×P. maximowiczii and the O₃-resistant (O₃-R) clone I-214 of P.×euramericana showed four distinct SOD isoforms with a relative mobility (R_f) of 0.54 (MnSOD), 0.60 (Cu/ZnSOD), 0.65 (unidentified), and 0.71 (Cu/ZnSOD). After O₃-fumigation the activity of the SOD isoforms showed only quantitative variations with respect to control plants. In ML of untreated O₃-R plants seven POD isoforms (R_f= 0.13, 0.19, 0.34, 0.59, 0.64, 0.70 and 0.75) were found, while in YL one isoform (R_f= 0.34) was undetected. Only three POD isoforms in both ML and YL of untreated O₃-S plants were resolved. The electrophoretic pattern of POD in O₃-S leaves was greatly modified by acute O₃-fumigation with the appearance of new isoforms in both YL and ML and the disappearance of an isoform (R_f= 0.13) in YL. Additionally, O₃-exposure induced the appearance of two APX isoforms in YL (R_f= 0.66 and 0.70), and one isoform in ML (R_f= 0.70) of the O₃-S clone. By contrast, the activity of the three APX isoformes (R_f= 0.64, 0.70 and 0.76) detected in O₃-R leaves showed only quantitative variation with respect to untreated plants. From these data it is concluded that: 1) in these poplar hybrids antioxidant enzyme activity is developmentally regulated and greatly affected by acute O₃ stress treatments and 2) the different enzymes activity displayed by the two poplar clones, especially for POD and APX isoformes, could partly explain their distinct O₃-sensitivity.     

Bioethanol production in batch mode by a native strain of <Emphasis Type="Italic">Zymomonas mobilis</Emphasis>

Two wild strains of Zymomonas mobilis were isolated (named as ML1 and ML2) from sugar cane molasses obtained from different farms of Santander, Colombia. Initially, selection of the best ethanol-producer strains was carried out using ethanol production parameters obtained with a commercial strain Z. mobilis DSM 3580. Three isolated strains were cultivated in a culture medium containing yeast extract, peptone, glucose and salts, at pH 6 and 32°C with stirring rate of 65 rpm during 62 h. The best results of ethanol production were obtained with the native strain ML1, reaching a maximum ethanol concentration of 79.78 g l⁻¹. ML1 and ML2 strains were identified as Z. mobilis, according to the morphology, biochemical tests and molecular characterization by PCR of specific DNA sequences from Z. mobilis. Subsequently, the effect of different nitrogen sources on production of ethanol was evaluated. The best results were obtained using urea at a 0.73 g/l. In this case, maximum concentration of ethanol was 83.81 g l⁻¹, with kinetic parameters of yield of ethanol on biomass (Y_P/X) = 69.01(g g⁻¹), maximum volumetric productivity of ethanol (Qp_max) = 2.28 (g l⁻¹ h⁻¹), specific productivity of ethanol (q_P) = 3.54 (h⁻¹) and specific growth rate (μ) = 0.12 h⁻¹. Finally, we studied the effect of different culture conditions (pH, temperature, stirring, C/N ratio) with a Placket-Burman′s experimental design. This optimization indicated that the most significant variables were temperature and stirring. In the best culture conditions a significant increase in all variables of response was achieved, reaching a maximum ethanol concentration of 93.55 g l⁻¹.     

Applying a framework for landscape planning under climate change for the conservation of biodiversity in the Finnish boreal forest

Conservation strategies are often established without consideration of the impact of climate change. However, this impact is expected to threaten species and ecosystem persistence and to have dramatic effects towards the end of the 21st century. Landscape suitability for species under climate change is determined by several interacting factors including dispersal and human land use. Designing effective conservation strategies at regional scales to improve landscape suitability requires measuring the vulnerabilities of specific regions to climate change and determining their conservation capacities. Although methods for defining vulnerability categories are available, methods for doing this in a systematic, cost‐effective way have not been identified. Here, we use an ecosystem model to define the potential resilience of the Finnish forest landscape by relating its current conservation capacity to its vulnerability to climate change. In applying this framework, we take into account the responses to climate change of a broad range of red‐listed species with different niche requirements. This framework allowed us to identify four categories in which representation in the landscape varies among three IPCC emission scenarios (B1, low; A1B, intermediate; A2, high emissions): (i) susceptible (B1 = 24.7%, A1B = 26.4%, A2 = 26.2%), the most intact forest landscapes vulnerable to climate change, requiring management for heterogeneity and resilience; (ii) resilient (B1 = 2.2%, A1B = 0.5%, A2 = 0.6%), intact areas with low vulnerability that represent potential climate refugia and require conservation capacity maintenance; (iii) resistant (B1 = 6.7%, A1B = 0.8%, A2 = 1.1%), landscapes with low current conservation capacity and low vulnerability that are suitable for restoration projects; (iv) sensitive (B1 = 66.4%, A1B = 72.3%, A2 = 72.0%), low conservation capacity landscapes that are vulnerable and for which alternative conservation measures are required depending on the intensity of climate change. Our results indicate that the Finnish landscape is likely to be dominated by a very high proportion of sensitive and susceptible forest patches, thereby increasing uncertainty for landscape managers in the choice of conservation strategies.     

Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region,Türkiye

Above-ground biomass (AGB) is an important component for identifying carbon stocks, monitoring the impacts of climate change, and evaluating merchantable timber. Accurate prediction of forest AGB is central to the correct interpretation of these components and to produce usable data for planners and researchers. In this study, remotely sensed time-series data derived from Landsat 8 (reflectance (R) and vegetation indices (VI)), topographic (T) and climate (C) data were used as independent variables to predict AGB of pure Calabrian pine (Pinus brutia Ten.) stands using multiple regression analysis (MLR) and support vector machines (SVM) methods. The AGB modeling was done by using independent variables individually and by combining variables, and the AGB maps of the most successful models obtained from MLR and SVM methods were produced. It was determined that the most successful variable group was the VI when the independent variables were used one by one (MLR Training R² = 0.50, SVM Training R² = 0.67). The most successful predictions in AGB modeling were obtained with combining all independent variables and using the SVM method (Training R² = 0.85, Validation R² = 0.69). In the combination of independent variables, VI and C data made the greatest contribution to the success of the AGB prediction. The ‘green leaf index’ vegetation indices had the most significant effect on the modeling AGB. In this study, T and C in addition to spectral data has increased the AGB estimation performance. It has been found that the SVM method yielded higher model accuracy than MLR method in predicting AGB. Overall, the spectral data and the SVM method can contribute to improving the accuracy of AGB estimates and provide an effective approach towards the capability for forest ecosystem monitoring.     

Comparative impacts of dam water level regimes on herbaceous plant growth strategies in cascade reservoirs and downstream reaches of a major river

1. Flow regulation is a prolific and growing influence on rivers world-wide. Nine cascade hydropower dams were constructed along the 1,150-km Wujiang River in China over the past 30 years, disrupting longitudinal continuity. Water level fluctuations in the associated reservoirs range between daily, weekly, seasonal, and annual, depending on the type of regulation, but the comparative impacts of these regimes on plant growth strategies, or the extent of their downstream influence, is unknown.
2. Competitor, stress-tolerator, and ruderal (CSR) plant strategies were used to assess the impact of reservoir regulation type on the riparian herbaceous plant community based on sampling the inundation zone of nine reservoirs and their downstream river reaches during 2017 and 2018.
3. Our results revealed profound differences in CSR plant strategies of the dominant vegetation with respect to water level regime. While ruderal plants dominated (45%–60% of species), irrespective of regulation type, vegetation in reservoirs exhibited a strong shift from stress-tolerators (e.g., Cynodon dactylon, C-11.9:S-41.5:R-46.5%) to competitors (e.g., Reynoutria japonica, C-77.9:S-0:R-22.0%) with increasing intensity of water level fluctuation, reflecting the shift from annual to daily regulation. The width of the inundation zone was the best overall variable in explaining the CSR strategies of riparian vegetation, both in the reservoir inundation zone (r²-adj = 15.4%) and the downstream river (r²-adj = 7.3%). Retention time significantly explained variation in CSR plant strategies in the reservoir inundation zone (r²-adj = 3.7%, p = 0.002) but not downstream (p > 0.01). There was also a clear scale dependency of CSR plant strategies, with an increase in stress tolerators (average slope = 0.7%/km) and decline of competitor (average slope = −0.3%/km) and ruderal plants (average slope = −0.9%/km) with increasing distance downstream from dams.
4. The growth strategies of the dominant riparian vegetation changed with the magnitude and frequency of water level fluctuations caused by differences in regulation type, and local environmental conditions. Clear scale dependency in the CSR plant strategies was observed with distance from the dam, with ruderals dominating closest to the reservoirs and declining gradually downstream as stress tolerators increased.
5. Our study helps to evaluate the impact of river damming on the functional traits of riparian vegetation and to predict the resilience and restoration potential of riparian vegetation under different forms of human disturbance.

     

1,8-Naphthalimide–Cu(ІІ) ensemble based turn-on fluorescent probe for the detection of thiols in organic aqueous media

A 1,8-naphthalimide–Cu(II) ensemble was rationally designed and synthesized as a new turn-on fluorescent probe utilizing the ‘chemosensing ensemble’ method for detections of thiols (Cys, Hcy and GSH) with high selectivity over other α-amino acids at pH 7.4 in organic aqueous media (EtOH/HEPES, v/v = 9:1). The recognition mechanism was attributed to the remove Cu(II) from the 1,8-naphthalimide–Cu(II) ensemble by thiols and the release of flurescence of ligand 1. Remarkable fluorescence enhancements were therefore observed in the sensing process of thiols by the 1,8-naphthalimide–Cu(II) ensemble. Furthermore, the 1,8-naphthalimide–Cu(II) ensemble was successfully applied to the fluorescence imaging of thiols in CHO cells with high sensitivity and selectivity.     

Population parameters and reproduction of the piranha Serrasalmus marginatus in the Negro river,Pantanal, Brazil

This paper aimed to define the reproductive period and population parameters of Serrasalmus marginatus relative to local environmental features, such as day length, rainfall and mean river level. The study site was a floodplain in the Negro river, Pantanal, Brazil, and samples were collected bimonthly using gill nets and cast nets with meshes from 1.5 to 8 cm between adjacent knots. The reproduction period, as determined by gonadosomatic index (GSI) and percentage of gonad stages, varied significantly along the year (F_4,116 = 77.5; p < 0.01), but it was well defined from October to December. Reproduction period was positively correlated with rainfall (rs = 0.97; p < 0.01) and photoperiod (rs = 0.92; p = 0.02), but not with the rise of river level (rs = 0.10; p = 0.86). Rainfall and photoperiod may act as predictive factors, providing cues to fish to begin reproduction in order to find the best conditions for offspring in the following months, during the flooding season, when the river level reaches its peak and extends to the plain. We have herein reported the first population parameters described for this species in the Brazilian Pantanal, including growth rate (k = 0.53/year), asymptotic length (L∞ = 32.74), lifespan (A_0.95 = 5.65) and mortality (M = 1.11), showing that this population has better growth performance when compared to data reported for this same species in the Paraná river since the population in the Negro river is characterized by lower growth and mortality rates, and S. marginatus achieved longer lengths and lifespan. This latter parameter was compatible with six cohorts estimated by Electronic Length Frequency Analysis (ELEFAN). The size at first maturity (L50 = 18.26 cm A50 = 1.56 years) was also larger and later than the population in the Paraná river, likely a consequence of the the lower mortality and the maintenance of larger specimens in the Negro river.