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.     

Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes

Assessing the spatial variability of ecosystem structure and functioning is an important step towards developing monitoring systems to detect changes in ecosystem attributes that could be linked to desertification processes in drylands. Methods based on ground-collected soil and plant indicators are being increasingly used for this aim, but they have limitations regarding the extent of the area that can be measured using them. Approaches based on remote sensing data can successfully assess large areas, but it is largely unknown how the different indices that can be derived from such data relate to ground-based indicators of ecosystem health. We tested whether we can predict ecosystem structure and functioning, as measured with a field methodology based on indicators of ecosystem functioning (the landscape function analysis, LFA), over a large area using spectral vegetation indices (VIs), and evaluated which VIs are the best predictors of these ecosystem attributes. For doing this, we assessed the relationship between vegetation attributes (cover and species richness), LFA indices (stability, infiltration and nutrient cycling) and nine VIs obtained from satellite images of the MODIS sensor in 194 sites located across the Patagonian steppe. We found that NDVI was the VI best predictor of ecosystem attributes. This VI showed a significant positive linear relationship with both vegetation basal cover (R² = 0.39) and plant species richness (R² = 0.31). NDVI was also significantly and linearly related to the infiltration and nutrient cycling indices (R² = 0.36 and 0.49, respectively), but the relationship with the stability index was weak (R² = 0.13). Our results indicate that VIs obtained from MODIS, and NDVI in particular, are a suitable tool for estimate the spatial variability of functional and structural ecosystem attributes in the Patagonian steppe at the regional scale.     

Development of a macrophyte-based index of biotic integrity for Minnesota lakes

Traditional approaches for managing aquatic resources have often failed to account for effects of anthropogenic disturbances on biota that are not directly reflected by chemical and physical proxies of environmental condition. The index of biotic integrity (IBI) is a potentially effective assessment method to integrate ecological, functional, and structural aspects of aquatic systems. A macrophyte-based IBI was developed for Minnesota lakes to assess the ability of aquatic plant communities to indicate environmental condition. The index was developed using quantitative point intercept vegetation surveys for 97 lakes that represent a range of limnological and watershed characteristics. We followed an approach similar to that used in Wisconsin to develop the aquatic macrophyte community index (AMCI). Regional adaptation of the AMCI required the identification of species representative of macrophyte communities in Minnesota. Metrics and scaling methods were also substantially modified to produce a more empirically robust index. Regression analyses indicated that IBI scores reflected statewide differences in lake trophic state (R² = 0.57, F = 130.3, df = 1, 95, p < 0.005), agricultural (R² = 0.51, F = 83.0, df = 1, 79, p < 0.005), urban (R² = 0.22, F = 23.0, df = 1, 79, p < 0.005), and forested land uses (R² = 0.51, F = 84.7, df = 1, 79, p < 0.005), and county population density (R² = 0.14, F = 16.6, df = 1, 95, p < 0.005). Variance partitioning analyses using multiple regression models indicated a unique response of the IBI to human-induced stress separate from a response to natural lake characteristics. The IBI was minimally affected by differences in sample point density as indicated by Monte Carlo analyses of reduced sampling effort. Our analysis indicates that a macrophyte IBI calibrated for Minnesota lakes could be useful for identifying differences in environmental condition attributed to human-induced stress gradients.     

LiDAR derived ecological integrity indicators for riparian zones: Application to the Houille river in Southern Belgium/Northern France

Riparian zones are central landscape features providing several ecosystem services and are exceptionally rich in biodiversity. Despite their relatively low area coverage, riparian zones consequently represent a major concern for land and water resource managers confirmed within several European directives. These directives involve effective multi-scale monitoring to assess their conditions and their ability to carry out their functions. The objective of this research was to develop automated tools to provide from a single aerial LiDAR dataset new mapping tools and keystone riparian zone attributes assessing the ecological integrity of the riparian zone at a network scale (24 km).Different metrics were extracted from the original LiDAR point cloud, notably the Digital Terrain Model and Canopy Height Model rasters, allowing the extraction of riparian zones attributes such as the wetted channel (0.89 m; mean residual) and floodplain extents (6.02 m; mean residual). Different riparian forest characteristics were directly extracted from these layers (patch extent, overhanging character, longitudinal continuity, relative water level, mean and relative standard deviation of tree height). Within the riparian forest, the coniferous stands were distinguished from deciduous and isolated trees, with high accuracy (87.3%, Kappa index).Going further the mapping of the indicators, our study proposed an original approach to study the riparian zone attributes within different buffer width, from local scale (50 m long channel axis reach) to a network scale (ca. 2 km long reaches), using a disaggregation/re-agraggation process. This novel approach, combined to graphical presentations of the results allow natural resource managers to visualise the variation of upstream–downstream attributes and to identify priority action areas.In the case study, results showed a general decrease of the riparian forests when the river crosses built-up areas. They also highlighted the lower flooding frequency of riparian forest patches in habitats areas.Those results showed that LiDAR data can be used to extract indicators of ecological integrity of riparian zones in temperate climate zone. They will enable the assessment of the ecological integrity of riparian zones to be undertaken at the regional scale (13,000 km, completely covered by an aerial LIDAR survey in 2014).     

Forest biomass and volume estimation using airborne LiDAR in a cool-temperate forest of northern Hokkaido,Japan

Trees are recognized as a carbon reservoir, and precise and convenient methods for forest biomass estimation are required for adequate carbon management. Airborne light detection and ranging (LiDAR) is considered to be one of the solutions for large-scale forest biomass evaluation. To clarify the relationship between mean canopy height determined by airborne LiDAR and forest timber volume and biomass of cool-temperate forests in northern Hokkaido, Japan, we conducted LiDAR observations covering the total area of the Teshio Experimental Forest (225 km²) of Hokkaido University and compared the results with ground surveys and previous studies. Timber volume and aboveground tree carbon content of the studied forest stands ranged from 101.43 to 480.40 m³ ha^–1 and from 30.78 to 180.54 MgC ha^–1, respectively. The LiDAR mean canopy height explained the variation among stands well (volume: r² = 0.80, RMSE = 55.04 m³ ha^–1; aboveground tree carbon content: r² = 0.78, RMSE = 19.10 MgC ha^–1) when one simple linear regression equation was used for all types (hardwood, coniferous, and mixed) of forest stands. The determination of a regression equation for each forest type did not improve the prediction power for hardwood (volume: r² = 0.84, RMSE = 62.66 m³ ha^–1; aboveground tree carbon content: r² = 0.76, RMSE = 27.05 MgC ha^–1) or coniferous forests (volume: r² = 0.75, RMSE = 51.07 m³ ha^–1; aboveground tree carbon content: r² = 0.58, RMSE = 19.00 MgC ha^–1). Thus, the combined regression equation that includes three forest types appears to be adequate for practical application to large-scale forest biomass estimation.     

Applying remote sensing techniques to monitoring seasonal and interannual changes of aquatic vegetation in Taihu Lake,China

Knowledge of the composition and areal distribution of aquatic vegetation types, as well as their seasonal and interannual variations, is crucial for managing and maintaining the balance of lake ecosystems. In this study, a series of remotely sensed images with a resolution of 30 m (HJ-CCD and Landsat TM) were collected and used to map the distribution of aquatic vegetation types in Taihu Lake, China. Seasonal and interannual dynamics of aquatic vegetation types were explored and analyzed. The distribution areas of Type I (emergent, floating-leaved and floating vegetation) and Type II (submerged vegetation) were used to model their growing season phenology by double logistic functions. The resulting double logistic models showed, the area of Type I reached its peak in mid-August, and the maximum area for Type II occurred in mid-September. From 1984 to 2013, Type I area increased continuously from 59.75 km² to 148.00 km² (R² = 0.84), whereas the area covered by Type II first increased and then decreased, with a trend conforming to a significant quadratic curve (R² = 0.83). The eutrophication and stable state of Taihu Lake was assessed using a simple indicator which was expressed as a ratio of Type II area to Type I area. The results showed that the eutrophication in the lake might have been increasing in the area studied since 2000. Additionally, the results showed that air temperature had likely a direct effect on the growth of Type I (R² = 0.66) and a significant, but delayed, effect on the growth of Type II.     

The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures

Retrieving leaf chlorophyll content at a range of spatio-temporal scales is central to monitoring vegetation productivity, identifying physiological stress and managing biological resources. However, estimating leaf chlorophyll over broad spatial extents using ground-based traditional methods is time and resource heavy. Satellite-derived spectral vegetation indices (VIs) are commonly used to estimate leaf chlorophyll content, however they are often developed and tested on broadleaf species. Relatively little research has assessed VIs for different leaf structures, particularly needle leaves which represent a large component of boreal forest and significant global ecosystems. This study tested the performance of 47 published VIs for estimating foliar chlorophyll content from different leaf and canopy structures (broadleaf and needle). Coniferous and deciduous sites were selected in Ontario, Canada, representing different dominant vegetation species (Picea mariana and Acer saccharum) and a variety of canopy structures. Leaf reflectance data was collected using an ASD Fieldspec Pro spectroradiometer (400–2500 nm) for over 300 leaf samples. Canopy reflectance data was acquired from the medium resolution imaging spectrometer (MERIS). At the canopy level, with both leaf types combined, the DD-index showed the strongest relationship with leaf chlorophyll (R² = 0.78; RMSE = 3.56 μg/cm²), despite differences in leaf structure. For needleleaf trees alone the relationship with the top VI was weaker (D_[red], R² = 0.71; RMSE = 2.32 μg/cm²). A sensitivity study using simulated VIs from physically-modelled leaf (PROSPECT) and canopy (4-Scale) reflectance was performed in order to further investigate these results and assess the impacts of different background types and leaf area index on the VIs’ performance. At the leaf level, the MNDVI8 index showed a strong linearity to changing chlorophyll and negligible difference to leaf structure/type. At canopy level, the best performing VIs were relatively consistent where LAI ≥ 4, but responded strongly to differences in background at low canopy coverage (LAI = 2). This research provides comprehensive assessments for the use of spectral indices in retrieval of spatially-continuous leaf chlorophyll content at the leaf (MTCI: R² = 0.72; p < 0.001) and canopy (DD: R² = 0.78; p < 0.001) level for resource management over different spatial and temporal scales.     

Whole crop rice silage: Predictions of yield and content of metabolizable energy,metabolizable protein and other nutrients for dairy cows from crop maturity and botanical fractions at harvest

The study investigated the suitability of stage of maturity and botanical fractions of whole crop rice (WCR) to predict yield and nutritive value of ensiled WCR for dairy cows. Eight varieties of WCR (i.e., Akichikara, Fukuhibiki, Habataki, Hamasari, Hokuriku 168, Kusanami, Tamakei 96, Yumetoiro) were harvested at four stages of maturity (i.e., 10, 22, 34, 45 days after flowering [DAF]) and ensiled. Dry matter (DM) yield at each harvest was determined. Silage samples were fractionated into four botanical fractions being: leaf blade, leaf sheath, stem and head. Silage samples were also analyzed for chemical composition, fermentation characteristics, in situ DM and N disappearance. Metabolizable energy (ME) and metabolizable protein (MP) content of samples were estimated according to Terada et al. (1988) and AFRC (1993), respectively. Relationships between maturity or proportions of botanical fractions and contents of WCR silage in terms of DM, ME and MP, and their yields, were estimated by correlation and regression analysis. Stage of maturity was positively related (P<0.001) to ME content (R² = 0.46; y = 4.53 + 0.08X) and MP content (R² = 0.56; y = 22.26 + 0.76X), and DM yield (R² = 0.63; y = 9.21 + 0.12X), ME yield (R² = 0.68, y = 36931 + 1708X) and MP yield (R² = 0.72, y = 161.0 + 14.15X) of WCR. Proportion of leaf was negatively related to yields and nutritive value of ensiled WCR, whilst proportion of head was positively related (P<0.05 to <0.001). Proportion of head was best related to the ME content (R² = 0.72; y = 3.26 + 0.009X), MP content (R² = 0.72; y = 12.31 + 0.079X), and DM yield (R² = 0.41; y = 9.02 + 0.009X), ME yield (R² = 0.76, y = 19494 + 165.5X), and MP yield (R² = 0.75, y = 34.37 + 1.32X) of WCR. Results suggest that to optimize yield and nutritive value, WCR should be ensiled within 40 DAF and the proportion of head should be equal to or more than 500 g per kg DM of WCR silage. Stage of maturity and proportion of head of WCR predict yields of DM, ME and MP of WCR, and their contents, in WCR silage with acceptable accuracy. However, these relationships need to be validated using large data sets and in vivo studies.     

Electromyographic and mechanomyographic responses across repeated maximal isometric and concentric muscle actions of the leg extensors

The purpose of the present study was to examine the patterns of responses for torque, electromyographic (EMG) amplitude, EMG mean power frequency (MPF), mechanomyographic (MMG) amplitude, and MMG MPF across 30 repeated maximal isometric (ISO) and concentric (CON) muscle actions of the leg extensors. Twelve female subjects (21.1 ± 1.4 yrs; 63.3 ± 7.4 kg) performed ISO and CON fatigue protocols with EMG and MMG signals recorded from the vastus lateralis. The relationships for torque, EMG amplitude, EMG MPF, MMG amplitude, and MMG MPF versus repetition number were examined using polynomial regression. The results indicated there were decreases (p < 0.05) across the ISO muscle actions for torque (r² = 0.95), EMG amplitude (R² = 0.44), EMG MPF (r² = 0.62), and MMG MPF (r² = 0.48), but no change in MMG amplitude (r² = 0.07). In addition, there were decreases across the CON muscle actions for torque (R² = 0.97), EMG amplitude (R² = 0.46), EMG MPF (R² = 0.86), MMG amplitude (R² = 0.44), and MMG MPF (R² = 0.80). Thus, the current findings suggested that the mechanisms of fatigue and motor control strategies used to modulate torque production were similar between maximal ISO and CON muscle actions.     

Evaluation of (Z)-2-((1-benzyl-1H-indol-3-yl)methylene)-quinuclidin-3-one analogues as novel,high affinity ligands for CB1 and CB2 cannabinoid receptors

A small library of N-benzyl indolequinuclidinone (IQD) analogs has been identified as a novel class of cannabinoid ligands. The affinity and selectivity of these IQDs for the two established cannabinoid receptor subtypes, CB1 and CB2, was evaluated. Compounds 8 (R = R² = H, R¹ = F) and 13 (R = COOCH₃, R¹ = R² = H) exhibited high affinity for CB2 receptors with K_i values of 1.33 and 2.50 nM, respectively, and had lower affinities for the CB1 receptor (K_i values of 9.23 and 85.7 nM, respectively). Compound 13 had the highest selectivity of all the compounds examined, and represents a potent cannabinoid ligand with 34-times greater selectivity for CB2R over CB1R. These findings are significant for future drug development, given recent reports demonstrating beneficial use of cannabinoid ligands in a wide variety of human disease states including drug abuse, depression, schizophrenia, inflammation, chronic pain, obesity, osteoporosis and cancer.     

Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Vegetation biomass is a key biophysical parameter for many ecological and environmental models. The accurate estimation of biomass is essential for improving the accuracy and applicability of these models. Light Detection and Ranging (LiDAR) data have been extensively used to estimate forest biomass. Recently, there has been an increasing interest in fusing LiDAR with other data sources for directly measuring or estimating vegetation characteristics. In this study, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China. A series of LiDAR and hyperspectral metrics were calculated to obtain the optimal biomass estimation model. To assess the prediction ability of the fused data, single and fused LiDAR and hyperspectral metrics were regressed against field-observed belowground biomass (BGB), aboveground biomass (AGB) and total forest biomass (TB). The partial least squares (PLS) regression method was used to reduce the multicollinearity problem associated with the input metrics. It was found that the estimation accuracy of forest biomass was affected by LiDAR plot size, and the optimal plot size in this study had a radius of 22 m. The results showed that LiDAR data alone could estimate biomass with a relative high accuracy, and hyperspectral data had lower prediction ability for forest biomass estimation than LiDAR data. The best estimation model was using a fusion of LiDAR and hyperspectral metrics (R² = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively, with p < 0.0001). Compared with LiDAR metrics alone, the fused LiDAR and hyperspectral data improved R² by 5.8%, 2.2% and 2.6%, decreased AIC value by 1.9%, 1.1% and 1.2%, and reduced RMSE by 8.6%, 7.9% and 8.3% for BGB, AGB and TB, respectively. These results demonstrated that biomass accuracies could be improved by the use of fused LiDAR and hyperspectral data, although the improvement was slight when compared with LiDAR data alone. This slight improvement could be attributed to the complementary information contained in LiDAR and hyperspectral data. In conclusion, fusion of LiDAR and other remotely sensed data has great potential for improving biomass estimation accuracy.     

Estimation of gross primary production of irrigated maize using Landsat-8 imagery and Eddy Covariance data

A study was conducted to understand the potential of Landsat-8 in the estimation of gross primary production (GPP) and to quantify the productivity of maize crop cultivated under hyper-arid conditions of Saudi Arabia. The GPP of maize crop was estimated by using the Vegetation Photosynthesis Model (VPM) utilizing remote sensing data from Landsat-8 reflectance (GPP_VPM) as well as the meteorological data provided by Eddy Covariance (EC) system (GPP_EC), for the period from August to November 2015. Results revealed that the cumulative GPP_EC for the entire growth period of maize crop was 1871 g C m⁻². However, the cumulative GPP determined as a function of the enhanced vegetation index – EVI (GPP_EVI) was 1979 g C m⁻², and that determined as a function of the normalized difference vegetation index – NDVI (GPP_NDVI) was 1754 g C m⁻². These results indicated that the GPP_EVI was significantly higher than the GPP_EC (R² = 0.96, P = 0.0241 and RMSE = 12.6%). While, the GPP_NDVI was significantly lower than the GPP_EC (R² = 0.93, P = 0.0384 and RMSE = 19.7%). However, the recorded relative error between the GPP_EC and both the GPP_EVI and the GPP_NDVI was −6.22% and 5.76%, respectively. These results demonstrated the potential of the landsat-8 driven VPM model for the estimation of GPP, which is relevant to the productivity and carbon fluxes.     

Trispyrazolylmethane piano stool complexes of iron(II) and cobalt(II)

A series of cationic trispyrazolylmethane complexes of the general form [Tm^RM(CH₃CN)₃]²⁺ (Tm = tris(pyrazolyl)methane, 1, R = 3,5-Me₂, M = Fe(II); 2, R = 3-Ph, M = Fe(II); 3, R = 3,5-Me₂, M = Co(II); 4, R = 3-Ph, M = Co(II)) with ‘piano-stool’ structures was prepared by the reaction of the N₃tripodal ligands (Tm^R)with [(CH₃CN)₆M](BF₄)₂ in a 1:1 stoichiometric ratio. Magnetic susceptibility measurements indicate that all four complexes with BF₄⁻ counter anions are paramagnetic, high-spin systems in the solid state with μ_eff at high temperatures of 5.2 (1, S = 2), 5.4 (2, S = 2), 4.9 (3, S = 3/2) and 4.6 (4, S = 3/2) BM, respectively. Comparisons of bond lengths from the metal centre to the Tm^R nitrogen donors, and from the metal centre to the acetonitrile nitrogen donors indicate that the neutral tripodal ligands appear to be more weakly coordinated to the metal centre than are the acetonitrile ligands. Reactions of these tripodal complexes with bidentate phosphine ligands, such as 1,2-diphosphinoethane or 1,2-bis(diallylphosphino)ethane leads to displacement of the tripodal ligand, or to the formation of more thermally stable bis-ligand complexes M(Tm^R)₂ (R = 3,5-dimethyl).     

Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system

Canopy height (H_canopy) and aboveground biomass (AGB) of crops are two basic agro-ecological indicators that can provide important indications on the growth, light use efficiency, and carbon stocks in agro-ecosystems. In this study, hundreds of stereo images with very high resolution were collected to estimate H_canopy and AGB of maize using a low-cost unmanned aerial vehicle (UAV) system. Millions of point clouds that are related to the structure from motion (SfM) were produced from the UAV stereo images through a photogrammetric workflow. Metrics that are commonly used in airborne laser scanning (ALS) were calculated from the SfM point clouds and were tested in the estimation of maize parameters for the first time. In addition, the commonly used spectral vegetation indices calculated from the UAV orthorectified image were also tested. Estimation models were established based on the UAV variables and field measurements with cross validation, during which the performance of the UAV variables was quantified. Finally, the following results were achieved: (1) the spatial patterns of maize H_canopy and AGB were predicted by a multiple stepwise linear (SWL) regression model (R² = 0.88, rRMSE = 6.40%) and a random forest regression (RF) model (R² = 0.78, rRMSE = 16.66%), respectively. (2) The UAV-estimated maize parameters were proved to be comparable to the field measurements with a mean error (ME) of 0.11 m for H_canopy, and 0.05 kg/m² for AGB. (3) The SfM point metrics, especially the mean point height (H_mean) greatly contributed to the estimation model of maize H_canopy and AGB, which can be promising indicators in the detection of maize biophysical parameters. To conclude, the variations in spectral and structural attributes for maize canopy should be simultaneously considered when only simple RGB images are available for estimating maize AGB. This study provides some suggestions on how to make full use of the low-cost and high-resolution UAV stereo images in precision agro-ecological applications and management.     

Process length variation in cysts of a dinoflagellate,Lingulodinium machaerophorum,in surface sediments: Investigating its potential as salinity proxy

A biometrical analysis of the dinoflagellate cyst Lingulodinium machaerophorum [Deflandre, G., Cookson, I.C., 1955. Fossil microplankton from Australia late Mesozoic and Tertiary sediments. Australian journal of Marine and Freshwater Research 6: 242–313.] Wall, 1967 in 144 globally distributed surface sediment samples revealed that the average process length is related to summer salinity and temperature at a water depth of 30 m by the equation (salinity/temperature) = (0.078?average process length + 0.534) with R² = 0.69. This relationship can be used to reconstruct palaeosalinities, albeit with caution. The particular ecological window can be associated with known distributions of the corresponding motile stage Lingulodinium polyedrum (Stein) Dodge, 1989. Confocal laser microscopy showed that the average process length is positively related to the average distance between process bases (R² = 0.78), and negatively related to the number of processes (R² = 0.65). These results document the existence of two end members in cyst formation: one with many short, densely distributed processes and one with a few, long, widely spaced processes, which can be respectively related to low and high salinity/temperature ratios. Obstruction during formation of the cysts causes anomalous distributions of the processes. From a biological perspective, processes function to facilitate sinking of the cysts through clustering.     

Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Chesapeake Bay

The aim of this study was to determine the effects of catchment and riparian stream buffer-wide urban and non-urban land cover/land use (LC/LU) on total nitrogen (TN) and total phosphorus (TP) runoff to the Chesapeake Bay. The effects of the composition and configuration of LC/LU patches were explored in particular. A hybrid-statistical-process model, the SPAtially Referenced Regression On Watershed attributes (SPARROW), was calibrated with year 1997 watershed-wide, average annual TN and TP discharges to Chesapeake Bay. Two variables were predicted: (1) yield per unit watershed area and (2) mass delivered to the upper estuary. The 166,534 km² watershed was divided into 2339 catchments averaging 71 km². LC/LU was described using 16 classes applied to both the catchments and also to riparian stream buffers alone. Seven distinct landscape metrics were evaluated. In all, 167 (TN) and 168 (TP) LC/LU class metric combinations were tested in each model calibration run. Runs were made with LC/LU in six fixed riparian buffer widths (31, 62, 125, 250, 500, and 1000 meters (m)) and entire catchments. The significance of the non-point source type (land cover, manure and fertilizer application, and atmospheric deposition) and factors affecting land-to-water delivery (physiographic province and natural or artificial land surfaces) was assessed. The model with a 31 m riparian stream buffer width accounted for the highest variance of mean annual TN (r² = 0.9366) and TP (r² = 0.7503) yield (mass for a specified time normalized by drainage area). TN and TP loadings (mass for a specified time) entering the Chesapeake Bay were estimated to be 1.449 × 10⁸ and 5.367 × 10⁶ kg/yr, respectively. Five of the 167 TN and three of the 168 TP landscape metrics were shown to be significant (p-value ≤ 0.05) either for non-point sources or land-to-water delivery variables. This is the first demonstration of the significance of riparian LC/LU and landscape metrics on water quality simulation in a watershed as large as the Chesapeake Bay. Land cover metrics can therefore be expected to improve the precision of estimated TN and TP annual loadings to the Chesapeake Bay and may also suggest changes in land management that may be beneficial in control of nutrient runoff to the Chesapeake Bay and similar watersheds elsewhere.     

Satellite- versus temperature-derived green wave indices for predicting the timing of spring migration of avian herbivores

According to the green wave hypothesis, herbivores follow the flush of spring growth of forage plants during their spring migration to northern breeding grounds. In this study we compared two green wave indices for predicting the timing of the spring migration of avian herbivores: the satellite-derived green wave index (GWI), and an index of the rate of acceleration in temperature (GDDjerk). The GWI was calculated from MODIS normalized difference vegetation index (NDVI) satellite imagery and GDDjerk from gridded temperature data using products from the global land data assimilation system (GLDAS). To predict the timing of arrival at stopover and breeding sites, we used four years (2008–2011) of tracking data from 12 GPS-tagged barnacle geese, a long-distance herbivorous migrant, wintering in the Netherlands, breeding in the Russian Arctic. The stopover and breeding sites for these birds were identified and the relations between date of arrival with the date of 50% GWI and date of peak GDDjerk at each site were analyzed using mixed effect linear regression. A cross-validation method was used to compare the predictive accuracy of the GWI and GDDjerk indices. Significant relationships were found between the arrival dates at the stopover and breeding sites for the dates of 50% GWI as well as the peak GDDjerk (p < 0.01). The goose arrival dates at both stopover and breeding sites were predicted more accurately using GWI (R²_cv = 0.68, RMSD_cv = 5.9 and  R²_cv= 0.71, RMSD_cv = 3.9 for stopover and breeding sites, respectively) than GDDjerk. The GDDjerk returned a lower accuracy for prediction of goose arrival dates at stopover ( R²_cv = 0.45, RMSD_cv = 7.79) and breeding sites (R²_cv = 0.55, RMSD_cv = 4.93). The positive correlation between the absolute residual values of the GDDjerk model and distance to the breeding sites showed that this index is highly sensitive to latitude. This study demonstrates that the satellite-derived green wave index (GWI) can accurately predict the timing of goose migration, irrespective of latitude and therefore is suggested as a reliable green wave index for predicting the timing of avian herbivores spring migration.     

Testing the effectiveness of surrogates for assessing biological diversity of arthropods in cereal agricultural landscapes

Agricultural intensification is altering biodiversity patterns worldwide. Rapid and effective methods are needed to monitor these changes in farmland biodiversity, but it becomes both a cost- and time-prohibitive task, particularly for hyper-diverse groups such as arthropods. We evaluated the effectiveness of surrogates in irrigated and rainfed wheat fields in a Mediterranean farmland in NW Spain in order to get a rapid tool to assess arthropod biodiversity. We studied six groups with different ecological needs (i.e. Aphididae, Aphidiinae, Coccinellidae, Formicidae, Heteroptera and Syrphidae) at species level (147 species), genus (105), family (10, only Heteroptera) and order (19) level. Higher taxa, cross-taxa and subset-taxa or total richness approaches were tested as well as the correlation in composition between levels for the selected groups, and the influence of farming regime. Genus richness was a good surrogate of species richness in all six groups studied (R² = 0.38–0.60), like family and order were for Heteroptera (R² = 0.37 and 0.29, respectively). Cross-taxa analyses showed that Aphididae and Aphidiinae genera (R² = 0.19 and 0.30, respectively) and species (R² = 0.20 and 0.28, respectively) were good surrogates for Aphidiinae and Aphididae species respectively. Coccinellidae genera (R² = 0.26) and species (R² = 0.25) were good surrogates for Heteroptera species. Finally, Aphididae and Coccinellidae both at genera (R² = 0.14 and 0.20, respectively) and at species levels (R² = 0.12–0.22, respectively) were good surrogates for total species richness of all groups. Genera composition was the best surrogate for the species composition within each group. Farming regime had no influence on the relationships between surrogates and species patterns in most cases. Our results suggest that genera level is a useful surrogate for all the studied groups and family is appropriate for Heteroptera. Genus level provided a saving of 15% of identification time in Aphididae and 80% for Coccinellidae. This proves its usefulness to asses and monitor biodiversity in wheat croplands and the possibility to reduce costs.     

Synthesis and anti-acetylcholinesterase properties of novel β- and γ-substituted alkoxy organophosphonates

Activated organophosphate (OP) insecticides and chemical agents inhibit acetylcholinesterase (AChE) to form OP-AChE adducts. Whereas the structure of the OP correlates with the rate of inhibition, the structure of the OP-AChE adduct influences the rate at which post-inhibitory reactivation or aging phenomena occurs. In this report, we prepared a panel of β-substituted ethoxy and γ-substituted propoxy phosphonoesters of the type p-NO₂PhO-P(X)(R)[(O(CH₂)_nZ] (R = Me, Et; X = O, S; n = 2, 3; Z = halogen, OTs) and examined the inhibition of three AChEs by select structures in the panel. The β-fluoroethoxy methylphosphonate analog (R = Me, Z = F, n = 2) was the most potent anti-AChE compound comparable (k_i ～6 × 10⁶ M^?1 min^?1) to paraoxon against EEAChE. Analogs with Z = Br, I, or OTs were weak inhibitors of the AChEs, and methyl phosphonates (R = Me) were more potent than the corresponding ethyl phosphonates (R = Et). As expected, analogs with a thionate linkage (PS) were poor inhibitors of the AChEs.     

Climatic dependency of soil organic carbon isotopic composition along the S–N Transect from 34°N to 52°N in central-east Asia

We explored the relationships between surface-soil (1–20 cm) organic carbon isotopic signatures and associated climatic factors in central-east Asia in an attempt to develop transfer functions that can be used to retrieve the paleoclimatic information stored in the thick eolian–paleosol sequences within the area. Our analysis shows that the negative correlation between the surface-soil organic δ¹³C values and the mean annual precipitation is robust (R² = 0.453; n = 196; p < 0.05) and the negative correlation with the growing-season (April–September) precipitation is more significant (R² = 0.4966; n = 196; p < 0.05). Our study further shows that the positive correlation between the surface-soil organic δ¹³C values and mean growing-season aridity is most significant (R² = 0.5805; n = 196; p < 0.05). We have smoothed both the organic δ¹³C values and the mean growing-season aridity values using a 3-point moving-window average-filter method in an attempt to remove some of random errors and found that the positive correlation between the two is further increased (R² =  0.7784; n =  192; p < 0.05). These robust linear relationships demonstrate their value in reconstructing paleoclimate changes in the study area. The documented climatic dependency of the surface-soil carbon isotopic composition in the study area might have resulted both from the humidity-related isotopic enrichment processes of the dominant C₃ plants (stomatal conductance and photosynthetic discrimination) and from the aridity-related abundance of C₄ plants (mainly Chenopodiaceae species) along the S–N bioclimatic gradient.