Nutrient availability and herbivory can regulate primary production in ecosystems, but little is known about how, or whether, they may interact with one another. Here I investigate how nitrogen availability and insect herbivory interact to alter above- and belowground plant community biomass in an old-field ecosystem. In 2004, 36 experimental plots were established in which soil nitrogen (N) availability (at three levels) was manipulated and insect abundance (at two levels) in a completely randomized plot design. In 2009, after six years of treatment, I measured aboveground biomass and assessed root production at peak growth. Overall, I found a significant effect of soil N availability on both above- and belowground plant biomass while insects affected only aboveground biomass of subdominant plant species and coarse root production; there were no statistical interactions between N availability and insect herbivory for any response variable. Specifically, responses of aboveground and belowground community biomass to nutrients were driven by reductions in soil N, but not additions, indicating that soil N may not be primarily limiting production in this ecosystem. Insect herbivory altered the aboveground biomass of the subdominant plant species and altered allocation patterns to coarse root production belowground. Overall, the results of six years of nutrient amendments and insect removals suggest strong bottom-up influences on total plant community productivity.

Nutrient availability and herbivory can regulate primary production in ecosystems, but little is known about how, or whether, they may interact with one another. Here I investigate how nitrogen availability and insect herbivory interact to alter above- and belowground plant community biomass in an old-field ecosystem. In 2004, 36 experimental plots were established in which soil nitrogen (N) availability (at three levels) was manipulated and insect abundance (at two levels) in a completely randomized plot design. In 2009, after six years of treatment, I measured aboveground biomass and assessed root production at peak growth. Overall, I found a significant effect of soil N availability on both above- and belowground plant biomass while insects affected only aboveground biomass of subdominant plant species and coarse root production; there were no statistical interactions between N availability and insect herbivory for any response variable. Specifically, responses of aboveground and belowground community biomass to nutrients were driven by reductions in soil N, but not additions, indicating that soil N may not be primarily limiting production in this ecosystem. Insect herbivory altered the aboveground biomass of the subdominant plant species and altered allocation patterns to coarse root production belowground. Overall, the results of six years of nutrient amendments and insect removals suggest strong bottom-up influences on total plant community productivity.

This work is part of a study of wet meadows within the project GA CR 526/09/1545. The objective of the project is to determine the importance of newly assimilated carbon for the plat-soil interactions of plants with in wet grassland ecosystems in changing environmental conditions. As part of this project, a wet grassland ecosystem near Hamr situated in the Nežárka river floodplain was assessed in terms of aboveground production. This work includes data from 2010 and 2011. Each year the biomass was sampled two times. At each sampling, 24 samples were collected from plots differing in the intensity of fertilization. The treatments included high intensity of fertilization, low intensity of fertilization and no fertilization. In 2010, the annual production of aboveground biomass was 863.88 gm-2 on plots with a high intensity of fertilization, 788.46 gm-2 on plots with low intensity of fertilization and areas 839.69 gm-2 on unfertilized plots. In 2011 the annual production of aboveground biomass was 1149.71 gm-2 on plots with high fertilization, 953.73 gm-2 in plots with low fertilization, and 930.25 gm-2 on plots without fertilization.

Master of Science / Department of Biology / Jesse B. Nippert / Jesse B. Nippert / Anthropogenic climate change is likely to alter the function and composition of ecosystems worldwide through increased precipitation variability and temperatures. To predict ecosystem responses, a greater understanding of the physiological and growth responses of plants is required. Dominant species drive ecosystem responses, and it is essential to understand how they respond to understand potential ecosystem changes. Dominant species, such as switchgrass (Panicum virgatum L.), posses large genotypic and phenotypic variability, which will impact the degree of responses to projected climate changes. I studied the physiological and growth responses of switchgrass, a common perennial warm-season C4 grass that is native to the tallgrass prairie, to alterations in precipitation amount and temperature. The first experiment I conducted focused on the responses of three ecotypes of P. virgatum to three precipitation regimes (average, 25% below, 25% above). I concluded that the physiological responses of photosynthesis, stomatal conductance, transpiration, dark-adapted fluorescence, and mid-day water potential in P. virgatum were explained by ecotypic differences. Robust responses to altered precipitation were seen in the water use efficiency, mid-day water potential, and aboveground biomass. Ecotypic differences were also seen in several aboveground biomass variables, and most strikingly in flowering times and rates. There were few interactions between ecotype and precipitation, suggesting precipitation is a strong driver of biomass production, whereas adaption of ecotypes to their local environment affects physiological processes. A second experiment studied the response of local populations of P. virgatum to nocturnal warming. Results showed significant differences in daytime E, daytime gs, and flowering phenology between treatments. Differences in aboveground biomass were between topographic positions. I concluded that water availability, based on topographic position, is a strong driver of P. virgatum aboveground biomass production, but nocturnal warming has the potential to impact flowering phenology, physiological responses, and exacerbate plant water stress. I also reviewed the literature on the ecological effects of implementing switchgrass cultivation for biofuel. From the literature review, I concluded that large-scale switchgrass cultivation will have widespread ecological impacts. If landscape heterogeneity is maintained through harvest rotations, no till farming, and mixed species composition, ecosystem services can be maintained while providing economic value.

Above-Ground Biomass (AGB) assessment using remote sensing has been an active area of research since the 1970s. However, improvements in the reported accuracy of wide scale studies remain relatively small. Therefore, there is a need to improve error analysis to answer the question: Why is AGB assessment accuracy still under doubt? This project aimed to develop and implement a systematic quantitative methodology to analyse the uncertainty of remotely sensed AGB, including all perceptible error types and reducing the associated costs and computational effort required in comparison to conventional methods. An accuracy prediction tool was designed based on previous study inputs and their outcome accuracy. The methodology used included training a neural network tool to emulate human decision making for the optimal trade-off between cost and accuracy for forest biomass surveys. The training samples were based on outputs from a number of previous biomass surveys, including 64 optical data based studies, 62 Lidar data based studies, 100 Radar data based studies, and 50 combined data studies. The tool showed promising convergent results of medium production ability. However, it might take many years until enough studies will be published to provide sufficient samples for accurate predictions. To provide field data for the next steps, 38 plots within six sites were scanned with a Leica ScanStation P20 terrestrial laser scanner. The Terrestrial Laser Scanning (TLS) data analysis used existing techniques such as 3D voxels and applied allometric equations, alongside exploring new features such as non-plane voxel layers, parent-child relationships between layers and skeletonising tree branches to speed up the overall processing time. The results were two maps for each plot, a tree trunk map and branch map. An error analysis tool was designed to work on three stages. Stage 1 uses a Taylor method to propagate errors from remote sensing data for the products that were used as direct inputs to the biomass assessment process. Stage 2 applies a Monte Carlo method to propagate errors from the direct remote sensing and field inputs to the mathematical model. Stage 3 includes generating an error estimation model that is trained based on the error behaviour of the training samples. The tool was applied to four biomass assessment scenarios, and the results show that the relative error of AGB represented by the RMSE of the model fitting was high (20-35% of the AGB) in spite of the relatively high correlation coefficients. About 65% of the RMSE is due to the remote sensing and field data errors, with the remaining 35% due to the ill-defined relationship between the remote sensing data and AGB. The error component that has the largest influence was the remote sensing error (50-60% of the propagated error), with both the spatial and spectral error components having a clear influence on the total error. The influence of field data errors was close to the remote sensing data errors (40-50% of the propagated error) and its spatial and non-spatial Overall, the study successfully traced the errors and applied certainty-scenarios using the software tool designed for this purpose. The applied novel approach allowed for a relatively fast solution when mapping errors outside the fieldwork areas.

The MSc thesis is part of the project of Ministry of Environment of the Czech Republic entitled Czech Terra, which aims at assessing the carbon budget and cycle in the main types of ecosystems in the Czech Republic. This thesis is focussed on the production of aboveground biomass of Carex acuta, which dominates the unmanaged and permanently flooded part of the Wet Meadows. The seasonal dynamics of aboveground plant production was followed using a series of 9 destructive harvests during the vegetation season. On each date, four 0.5x0.5m2 samples were taken from the wetter, and four samples from the drier part of the stand. The maximum value of live biomass of C. acuta (550.8 g.m-2) was recorded on 13.6 2008. The highest value of live biomass of all species reached 602.4 g.m-2. The maximum total biomass (without litter) reached 994.6 g. m-2. The highest value of productivity of C. acuta (12.46 g.m-2.day-1) was recorded on 24. 5.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第21807号 / 農博第2320号 / 新制||農||1065(附属図書館) / 学位論文||H31||N5179(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)教授 大澤 晃, 教授 北島 薫, 教授 神﨑 護 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Thesis (M.Sc. (Geography)) -- University of Limpopo, 2020 / Wetland vegetation provides a variety of goods and services such as carbon sequestration, flood control, climate regulation, filtering contamination, improve and maintain water quality, ecological functioning. However, changes in land cover and uses, overgrazing and environmental changes have resulted in the transformation of the wetland ecosystem. So far, a lot of focus has been biased towards large wetlands neglecting wetlands at a local scale. Smaller wetlands continue to receive massive degradation by the surrounding communities.Therefore, this study seeks to assess and map wetland vegetation as an indicator of ecological productivity on a small scale. The Sentinel-2 MSI image was used to map wetland plant species diversity and above-ground biomass (AGB). Four key diversity indices; the Shannon Wiener (H), Simpson (D), Pielou (J), and Species richness (S) were used to measure species diversity. A multilinear regression technique was applied to establish the relationship between remotely sensed data and diversity indices and AGB. The results indicated that Simpson (D) has a high relationship with combined vegetation indices and spectral band, yielding the highest accuracy when compared to other diversity indices. For example, an R² of 0.75, and the RMSE of 0.08 and AIC of -191.6 were observed. Further, vegetation AGB was estimated with high accuracy of an R² of 0.65, the RMSE 29.02, and AIC of 280.21. These results indicate that Maungani wetland has high species abundance largely dominated by one species (Cyperus latifidius) and highly productive. The findings of this work underscore the relevance of remotely sensed to estimate and monitor wetland plant species diversity with high accuracy.

No description available.

Leaves in forests assimilate carbon from the atmosphere and woody components store the net production of that assimilation. Separate structure measurements of leaves and woody components advance the monitoring and modeling of forest ecosystem functions. This dissertation provides a method to determine, for the first time, the 3-D spatial arrangement and the amount of leafy and woody materials separately in a forest by classification of lidar returns from a new, innovative, lidar scanner, the Dual-Wavelength Echidna® Lidar (DWEL). The DWEL uses two lasers pulsing simultaneously and coaxially at near-infrared (1064 nm) and shortwave-infrared (1548 nm) wavelengths to locate scattering targets in 3-D space, associated with their reflectance at the two wavelengths. The instrument produces 3-D bispectral "clouds" of scattering points that reveal new details of forest structure and open doors to three-dimensional mapping of biophysical and biochemical properties of forests. The three parts of this dissertation concern calibration of bispectral lidar returns; retrieval of height profiles of leafy and woody materials within a forest canopy; and virtual reconstruction of forest trees from multiple scans to estimate their aboveground woody biomass. The test area was a midlatitude forest stand within the Harvard Forest, Petersham, Massachusetts, scanned at five locations in a 1-ha site in leaf-off and leaf-on conditions in 2014. The model for radiometric calibration assigned accurate values of spectral apparent reflectance, a range-independent and instrument-independent property, to scattering points derived from the scans. The classification of leafy and woody points, using both spectral and spatial context information, achieved an overall accuracy of 79±1% and 75±2% for leaf-off and leaf-on scans, respectively. Between-scan variation in leaf profiles was larger than wood profiles in leaf-off seasons but relatively similar to wood profiles in leaf-on seasons, reflecting the changing spatial heterogeneity within the stand over seasons. A 3-D structure-fitting algorithm estimated wood volume by modeling stems and branches from point clouds of five individual trees with cylinders. The algorithm showed the least variance for leaf-off, woody-points-only data, validating the value of separating leafy and woody points to the direct biomass estimates through the structure modeling of individual trees.