Discriminating and mapping the C3 and C4 composition of grasslands in the northern Great Plains,USA

There is uncertainty about the extent and distribution of grasslands following the C3 and C4 photosynthetic pathways. Since these grasses have an asynchronous seasonal profile it should be possible to estimate and map the C3–C4 composition of grasslands from multi-temporal remote sensing imagery. This potential was evaluated using 30 weekly composite MERIS MTCI images for South Dakota, USA. Derived relationships between the remotely sensed response and composition of grasslands were significant, with R² ∼ 0.6. It also appears possible to map broad classes of grassland composition, with a three class (high, medium and low C3 cover) classification having an accuracy of 77.8%.     

Altitude modifies species richness–nutrient indicator value relationships in a country-wide survey of grassland vegetation

Nutrient enrichment is a threat to botanical diversity in Europe, and its assessment is part of biodiversity monitoring schemes. In Switzerland, this is done by calculating the average nutrient (N) indicator value of the vegetation based on a country-wide systematic vegetation survey. However, it is questionable whether N values indicate eutrophication and resulting species loss equally well across an entire country, which includes wide topographic gradients and distinct biogeographic regions. Here we analyze vascular plant species lists from 415 grassland plots (10 m²) between 365 and 2770 m a.s.l. throughout Switzerland to investigate how the relationship between N value and species richness differs with altitude and among regions. The N value strongly decreased with altitude (piecewise regression: r² = 0.77), particularly between 800 and 2000 m a.s.l., where this decrease was related to a decreasing proportion of fertilized grasslands. In the alpine belt, lower N values were associated with a greater frequency of acidic soils and a restricted species pool. Vascular plant species richness was maximal at intermediate altitude (piecewise regression: r² = 0.33) and intermediate N value (polynomial regression: r² = 0.46). When analyzed separately by altitudinal belt, the relationship between species richness and N value was negative in the lowlands and montane belt but unimodal in the subalpine belt. In the alpine belt, soil pH (R indicator values) explained most of the variation in species richness. Two indices of between-plot diversity (floristic dissimilarity and the contribution of individual plots to total species richness) were negatively related to N values from the lowlands to the subalpine belt but not in the alpine belt. All relationships differed little among the biogeographic regions of Switzerland, but they might be modified by changes in management and by the expansion of common lowland species into mountain grasslands.     

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.     

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.     

Airborne hyperspectral data predict Ellenberg indicator values for nutrient and moisture availability in dry grazed grasslands within a local agricultural landscape

Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m × 4 m grassland) plots on the Baltic island of Öland was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m × 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R² values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape.     

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.     

Utilization of ground-based digital photography for the evaluation of seasonal changes in the aboveground green biomass and foliage phenology in a grassland ecosystem

We investigated the usefulness of a ground-based digital photography to evaluate seasonal changes in the aboveground green biomass and foliage phenology in a short-grass grassland in Japan. For ground-truthing purposes, the ecological variables of aboveground green biomass and spectral reflectance of aboveground plant parts were also measured monthly. Seasonal change in a camera-based index (rG: ratio of green channel) reflected the characteristic events of the foliage phenology such as the leaf-flush and leaf senescence. In addition, the seasonal pattern of the rG was similar to that of the aboveground green biomass throughout the year. Moreover, there was a positive linear relationship between rG and aboveground green biomass (R² = 0.81, p < 0.05), as was the case with spectra-based vegetation indices. On the basis of these results, we conclude that continuous observation using digital cameras is a useful tool that is less labor intensive than conventional methods for estimating aboveground green biomass and monitoring foliage phenology in short-grass grasslands in Japan.     

Long-term effects of grazing on subalpine and alpine grasslands in the Central Alps,Austria

The effects of grazing exclusion on species diversity and functional diversity were analyzed along an elevation gradient from the subalpine (1960 m a.s.l.) to the lower and upper alpine zone (2275 m–2650 m a.s.l.) in the Austrian Central Alps for 15 years. Nine sites were chosen, including grasslands at different elevations, a bog and a glacier foreland site at the lower alpine zone and a snowbed at the upper alpine zone. Data were acquired by frequency counts in 1 m² permanent plots inside each fenced area (three plots per site) and outside in grazed areas (three plots). Diversity indices and functional diversity were analyzed by means of generalized linear mixed models (GLMMs). Exclosure, duration of exclusion and exclosure*years (interaction effect) were defined as predictor variables. Multivariate ordination techniques were used to (i) determine species responses to grazing exclusion (pRDA, partial redundancy analysis) and (ii) to create a distance matrix representing the changes between exclosures and control plots per year (NMDS, non-metric multidimensional scaling). At the subalpine grassland, first differences between exclosure and control plots occurred already only after three years, at the upper alpine zone after four and five years. Contrary to our expectation, dwarf shrubs did not increase within the exclosures of the subalpine grassland. Instead, mainly the tall forb Geranium sylvaticum increased. Species richness significantly decreased at the exclosures of the subalpine zone, the snowbed and at one upper alpine grassland sites. The communities of the glacier foreland and the bog were hardly affected by grazing exclusion.We conclude that plant species and communities react individually depending on elevation and grazing animals. Grazing exclusion studies at high elevations should definitely be carried out in the long-term.     

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.     

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.     

Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysis

Mapping, monitoring and managing the environmental condition of riparian zones are major focus areas for local and state governments in Australia. New remotely sensed data techniques that can provide the required mapping accuracies, complete spatial coverage and processing and mapping transferability are currently being developed for use over large spatial extents. The research objective was to develop and apply an approach for mapping riparian condition indicators using object-based image analysis of airborne Light Detection and Ranging (LiDAR) data. The indicators assessed were: streambed width; riparian zone width; plant projective cover (PPC); longitudinal continuity; coverage of large trees; vegetation overhang; and stream bank stability. LiDAR data were captured on 15 July 2007 for two 5 km stretches along Mimosa Creek in Central Queensland, Australia. Field measurements of riparian vegetation structural and landform parameters were obtained between 28 May and 5 June 2007. Object-based approaches were developed for mapping each riparian condition indicator from the LiDAR data. The validation and empirical modelling results showed that the object-based approach could be used to accurately map the riparian condition indicators (R² = 0.99 for streambed width, R² = 0.82 for riparian zone width, R² = 0.89 for PPC, R² = 0.40 for bank stability). These research findings will be used in a 26,000 km mapping project assessing riparian vegetation and physical form indicators from LiDAR data in Victoria, Australia.     

Influence of management and precipitation on carbon fluxes in great plains grasslands

Suitable management and sufficient precipitation on grasslands can provide carbon sinks. The net carbon accumulation of a site from the atmosphere, modeled as the Net Ecosystem Productivity (NEP), is a useful means to gauge carbon balance. Previous research has developed methods to integrate flux tower data with satellite biophysical datasets to estimate NEP across large regions. A related method uses the Ecosystem Performance Anomaly (EPA) as a satellite-derived indicator of disturbance intensity (e.g., livestock stocking rate, fire, and insect damage). To better understand the interactions among management, climate, and carbon dynamics, we evaluated the relationship between EPA and NEP data at the 250 m scale for grasslands in the Central Great Plains, USA (ranging from semi-arid to mesic). We also used weekly estimates of NEP to evaluate the phenology of carbon dynamics, classified by EPA (i.e., by level of disturbance impact). Results show that the cumulative carbon balance over these grasslands from 2000 to 2008 was a weak net sink of 13.7 g C m⁻² yr⁻¹. Overall, NEP increased with precipitation (R² = 0.39, P < 0.05) from west to east. Disturbance influenced NEP phenology; however, climate and biophysical conditions were usually more important. The NEP response to disturbance varies by ecoregion, and more generally by grassland type, where the shortgrass prairie NEP is most sensitive to disturbance, the mixed-grass prairie displays a moderate response, and tallgrass prairie is the least impacted by disturbance (as measured by EPA). Sustainable management practices in the tallgrass and mixed-grass prairie may potentially induce a period of average net carbon sink until a new equilibrium soil organic carbon is achieved. In the shortgrass prairie, management should be considered sustainable if carbon stocks are simply maintained. The consideration of site carbon balance adds to the already difficult task of managing grasslands appropriately to site conditions. Results clarify the seasonal and interannual dynamics of NEP, specifically the influence of disturbance and moisture availability.     

Multivariate approach to the classification and ordination of the forest ecosystem of Nandiar valley western Himalayas

QuestionsDoes the vegetation composition of the forests of Nandiar Valley correlate with climatic, topographic and edaphic variables? Is it possible to identify plant communities through indicator species in relation to environmental gradients? Can this approach of classification and ordination will be helpful for conservation planning?LocationForests of Nandiar Valley, Moist temperate Western Himalayas Pakistan.MethodsEighty stands were selected for quantitative and qualitative characteristic of vegetation between an elevations of 525–3817 m. Species composition was recorded by using 400 m long transects. GPS, climatic, edaphic and topographic data were recorded for each sampling site. The relationship between habitat types, species composition and distribution along with climatic, edaphic and topographic variables were analyzed using TWINSPAN, Cluster analysis and DCA ordination.ResultsSum 325 vascular plants species belonging to 97 families were recorded. Diversity index and species richness was maximum in the moist temperate zone. Classification and ordination showed that the variance in species data was 7.07. Two-ways indicator species analysis classified the vegetation into eight plant communities. Indicator species analysis revealed that slope aspect, wind speed, temperature, dew point, wet bulb, pH, organic matter and phosphorous were the strongest parameters (p ≤ 0.05) determining plant community composition and indicator species in each habitat. The results also show the strength of the environment – species relationship using Monte Carlo procedures. DCA ordination grouped different species having similar habitat and habitats having common species.ConclusionsThe multivariate analysis of the vegetation along with environmental variables of Nandiar valley confirmed the indicators of each sort of vegetation communities/microclimatic zones which could further be used in conservation planning and management not only in studied area but also in the adjacent regions as well as in the areas exhibit similar sort of climatic, edaphic and topographic conditions.     

Plant species and communities assessment in interaction with edaphic and topographic factors; an ecological study of the mount Eelum District Swat,Pakistan

The current analyses of vegetation were aimed to study the different effects of environmental variables and plant species and communities interaction to these variables, identified threats to local vegetation and suggestion for remedial measures in the Mount Eelum, Swat, Pakistan. For assessment of environmental variability quantitative ecological techniques were used through quadrats having sizes of 2 × 2, 5 × 5 and 10 × 10 m² for herbs, shrubs and trees respectively. Result of the present study revealed 124 plant species in the study area. Canonical Correspondence Analysis (CCA) was used to analyze the ecological gradient of vegetation. The environmental data and species abundance were used in CANOCO software version 4.5. The presence absence data of plant species were elaborated with Cluster and Two Way Cluster Analysis techniques using PC-ORD version 5 to show different species composition that resulted in five plant communities. Findings indicate that elevation, aspect and soil texture are the strongest variables that have significant effect on species composition and distribution of various communities shown with P value 0.0500. It is recommended to protect and use sensibly whole of the Flora normally and rare species particularly in the region.     

Comparison of four techniques to estimate milk production in singleton-rearing non-dairy ewes

Accurate estimates of milk production or milk intake are difficult, as all methods interfere to some degree with the natural behaviour of the dam and her young, and potentially alter milk yield itself. The present study compared milk yield obtained by the “oxytocin” method, udder dimensions (UD), the isotope dilution method, and live weight change of the lamb, in an attempt to select the most accurate and convenient way of measuring milk production in non-dairy sheep. In addition, the study investigated which of the three milk-estimation techniques was an accurate predictor of growth rates of lambs. Thirty-seven singleton-bearing and rearing ewes were milked once a week, for seven consecutive weeks, using the “oxytocin” method. Prior to each afternoon milking, the external dimensions of the ewe's udder were measured. Lambs were weighed weekly for the first seven weeks of life and live weight change was calculated. The deuterium oxide (D₂O) dilution technique was used to estimate milk intake of the lambs and performed at approximately 7 days post-partum and finishing on approximately day 14. Pearson's correlation coefficients and multiple regression coefficients among techniques were calculated. The UD-models at d7 (R² = 0.35), d35 (R² = 0.36) and d42 (R² = 0.34), were the best models explaining variation in milk yield (concordance correlation coefficient (CCC) = 0.49; 0.53; 0.51; for d7, d35 and d42, respectively). The lamb live weight-change model explained the variation in milk yield best at d28 (R² = 0.32; CCC = 0.49), at d35 (R² = 0.22; CCC = 0.36) and at d42 (R² = 0.28; CCC = 0.44). At d14, the intake of milk by lambs as measured by the D₂O technique, did not explain the variation in milk yield. In conclusion, udder dimensions, lamb live weight change and lamb milk intake are relatively poor estimators of the milk yield of singleton-rearing ewes obtained by the “oxytocin” method. Additionally, udder dimensions, milk yield and lamb milk intake do not give an accurate prediction of growth rates of singleton lambs. These results emphasize that there is a difference between ewe milk production potential and lamb milk intake, which need to be considered when estimating milk production in non-dairy animals.     

Response of ecosystem CO2 exchange to biomass productivity in a high yield grassland

We measured the biomass production and ecosystem carbon CO₂ exchange in a high yield grassland dominated by Miscanthus sinensis. The experimental grassland is managed by mowing once a year in winter every year and the harvested biomass on the ground is left to become the humus. The maximum aboveground and belowground biomasses were 1117 and 2803 g d.w. m^?2 in our grassland. Although the high potential of our grassland for biomass production led to higher carbon uptake than with other types of grassland, the large biomass contributed to a higher respired carbon loss. Biomass increase led to a linear increase in ecosystem respiration. Over the 3 years, RE₁₀ increased with increasing aboveground biomass. The potential gross primary production at a photosynthetic photon flux density of 2000 μmol m² s^?1 logarithmic increased with LAI. These responses of CO₂ exchange to biomass production suggest this grassland behaved as weak CO₂ sink or near carbon neutral (?78 and 17 g C m^?2 year^?1) in current management.     

Year-round cattle and horse grazing supports the restoration of abandoned,dry sandy grassland and heathland communities by supressing Calamagrostis epigejos and enhancing species richness

The spread of competitive grasses, changes in species composition and vegetation structure are direct consequences of grassland and heathland abandonment. As an alternative to more costly management measures such as traditional pastoralism, year-round low-intensity grazing with large herbivores is increasingly used to restore and maintain semi-open habitats. However, the suitability of this grazing regime has not yet been investigated for long-abandoned, highly degraded but nutrient-poor sandy grassland and heathland communities. In particular, it is unclear if year-round grazing is suitable for preventing the further spread of highly competitive grasses such as Calamagrostis epigejos while simultaneously maintaining or improving characteristic species richness and vegetation structure. Hence, we conducted a comprehensive field study on two spatial scales (plot-level: 25 m², macroplot-level: 1 ha) to analyse the impacts of year-round low-intensity cattle and horse grazing on the development of the highly competitive grass Calamagrostis epigejos, as well as the vegetation structure and plant species richness of long-abandoned but nutrient-poor dry sandy grassland and heathland communities, their mosaics and Calamagrostis stands within an 800 ha heathland between 2008 and 2015. Finally, we assessed the local conservation status of the habitat types after seven years of grazing in comparison to long-abandoned sites.Grazing successfully reduced the coverage of Calamagrostis epigejos, whereby Calamagrostis stands developed towards species-rich sandy grasslands after seven years of grazing. In addition, the quality of the vegetation structure was improved by enhancing the proportion of bare soil, while litter and grass cover, litter thickness and height of the field layer as well as the coverage of ruderal indicators were significantly reduced on grazed sites in comparison to ungrazed sites in 2015. Moreover, we found an overall positive grazing effect on species richness: Total species number, number of target species as well as subordinated target species significantly increased within the vegetation types over time.Thus, year-round low-intensity cattle and horse grazing is a suitable management tool for restoring, maintaining and even improving long-abandoned, nutrient-poor sandy grassland and heathland communities, and thus to enhance the local conservation status of the habitat types. However, if there is a high initial cover of woody species (e.g. shrubs, tree rejuvenation), then an extensive shrub and tree clearance will be necessary, with manual shrub cutting being crucial to reduce the heavy regrowth of the woody species. In addition, a one-time mowing should be implemented in highly degraded heaths to facilitate the vegetative rejuvenation of degenerate stands of Calluna vulgaris, thus improving its attractiveness for the grazing animals.     

Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data

Monitoring soil respiration (R_s) at regional scales using images from operational satellites remains a challenge because of the problem in scaling local R_s to the regional scales. In this study, we estimated the spatial distribution of R_s in the Tibetan alpine grasslands as a product of vegetation index (VI). Three kinds of vegetation indices (VIs), that is, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and modified soil adjusted vegetation index (MSAVI), derived from Landsat Thematic Mapper (TM) and Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product were selected to test our method. Different statistical models were used to analyze the relationships among the three VIs and R_s. The results showed that, based on the remote sensing data from either MODIS or Landsat TM, exponential function was the optimal fit function for describing the relationships among VIs and R_s during the peak growing season of alpine grasslands. Additionally, NDVI consistently showed higher explanation capacity for the spatial variation in R_s than EVI and MSAVI. Thus, we used the exponential function of TM-based NDVI as the R_s predictor model. Since it is difficult to achieve full spatial coverage of the entire study area with Landsat TM images only, we used the MODIS 8-day composite images to obtain the spatial extrapolation of plot-level R_s after converting the NDVI_MODIS into its corresponding NDVI_TM. The performance of the R_s predictor model was validated by comparing it with the field measured R_s using an independent dataset. The TM-calibrated MODIS-estimated R_s was within an accuracy of field measured R_s with R² of 0.78 and root mean square error of 1.45 gC m⁻² d⁻¹. At the peak growing season of alpine grasslands, R_s was generally much higher in the southeastern part of the Tibetan Plateau and gradually decreased toward the northwestern part. Satellite remote sensing demonstrated the potential for the large scale mapping of R_s in this study.     

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.     

Effects of plant diversity on microbial biomass and community metabolic profiles in a full-scale constructed wetland

There has been less understanding of relations of microbial community patterns with plant diversity in constructed wetlands. We conducted a single full-scale subsurface vertical flow constructed wetland (SVFCW, 1000 m²) study focusing on domestic wastewater processing. This study measured the size and structure of microbial community using fumigation extraction and BIOLOG Ecoplate? techniques, to examine the effects of macrophyte diversity on microbial communities that are critical in treatment efficiency of constructed wetlands. We also determined the relationship of plant diversity (species richness) with its biomass production under disturbance of the same wastewater supply. Linear regression analysis showed that plant biomass production strongly correlated with plant species richness (R = 0.407, P < 0.001). Increase in plant species richness increased microbial biomass carbon and nitrogen (R = 0.494, P < 0.001; R = 0.465, P < 0.001) and utilization of amino acids on Ecoplates (R = 0.235, P = 0.03), but limited the utilization of amine/amides (R = ?0.338, P = 0.013). Principal components analysis (PCA) showed that the diversity and community-level physiological profiles (CLPP) of microbial community at 168 h of incubation strongly depended on the presence or absence of plant species in the SVFCW system, but not on the species richness. This is the first step toward understanding relations of plant diversity with soil microbial community patterns in constructed wetlands, but the effect of species diversity on microbial community should be further studied.