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Moisture availability is a strong determinant of decomposition rates in forests worldwide. Climate models suggest that many terrestrial ecosystems are at risk from future droughts, suggesting moisture limiting conditions will develop across a range of forests worldwide. The impacts of increasing drought conditions on forest carbon (C) fluxes due to shifts in organic matter decay rates may be poorly characterised due to limited experimental research. To appraise this question, we conducted a meta-analysis of forest drought experiment studies worldwide, examining spatial limits, knowledge gaps and potential biases. To identify limits to experimental knowledge, we projected the global distribution of forest drought experiments against spatially modelled estimates of (i) future precipitation change, (ii) ecosystem total above-ground C and (iii) soil C storage. Our assessment, involving 115 individual experimental study locations, found a mismatch between the distribution of forest drought experiments and regions with higher levels of future drought risk and C storage, such as Central America, Amazonia, the Atlantic Forest of Brazil, equatorial Africa and Indonesia. Decomposition rate responses in litter and soil were also relatively under-studied, with only 30 experiments specifically examining the potential experimental impacts of drought on C fluxes from soil or litter. We propose new approaches for engaging experimentally with forest drought research, utilising standardised protocols to appraise the impacts of drought on the C cycle, while targeting the most vulnerable and relevant forests.

Some Parties (Countries) to the UNFCCC decided to include the carbon uptake by harvested wood products (HWP) in a new general accounting framework after 2012 (post Kyoto). The analysis aims to make a comparison between the cascaded use of HWP and the use of wood for energy. We combine the new HWP framework with an assumed increased 50 million m3harvest level in Canada and evaluate the impact of the GHG emissions over a 100-year period. Our reference case assumes all harvested wood is an immediate CO2emission (IPCC default) and no substitution effects, i.e. annual GHG emissions of 41 million tonnes CO2eq. In our wood utilization scenario's, harvested trees are allocated (in varying shares) to three end-products: construction wood, paper products and pellets for power production. In comparison with our base case, a combination of fossil fuel substitution, material substitution and temporary carbon uptake by HWP leads to significant decreases in GHG emissions. All scenario's show annual GHG emission between 18 and 21 million tonnes CO2eqexcept for triple use without recycling (at least 24 million tonnes CO2eq). We conclude that GHG emissions of our scenarios are substantially lower than IPCC default. However, it is difficult to incorporate one single method to account for GHG uptake and emissions by HWP, due to end use efficiency and recycling options. Further GHG allocation over individual countries is not straightforward and needs further research. © 2013 Elsevier Ltd.

AbstractAlthough our observing capabilities of solar‐induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in‐situ SIF observing capability especially in “data desert” regions, improving cross‐instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

The deep-sea, defined as the area 200 m below the surface, is facing emerging chemical, physical and biological stressors. Currently, very little is known regarding deep-sea ecosystems both globally and in the Arctic. In this thesis I undertook a literature review on the current understanding of global deep-sea ecosystems through the use of stable isotopes. Specifically, I synthesized the available literature on spatial variation, energy pathways, depth, temporal variation, feeding behaviour, niche, trophic position and body size isotopic trends. This thesis then presents a case study examining the isotopic niche of five teleost and two decapod species within Arctic deep-sea food webs across the localized latitudinal gradient of Baffin Island. Spatial variation in isotopic niche was quantified using 13C and 15N for seven deep-sea species at three locations on Baffin Island, Nunavut to determine whether the “Latitudinal Niche Breadth Hypothesis” which states that niche breadth should increase with latitude holds true in the Arctic. Overall, isotopic patterns in global deep-sea ecosystem are variable; consistent trends are not observed across all taxa and habitats. It was concluded that niche breadth did not consistently increase with latitude in the eastern Canadian Arctic; localized conditions (e.g. sea ice, temperature) and individual condition (e.g. hepatosomatic index) may contribute more to a species’ niche than latitude. Overall, this thesis improves our understanding of deep-sea ecosystems globally, contributes baseline data for future monitoring, and by investigating multiple species and locations it will provide input on how climate change may impact Arctic food web diversity, energy dynamics and ecosystem structure to aid in sustainable fishery development.

Abstract Smart agriculture aims at generating high harvest yields with an efficient resource management, such as the estimation of crop irrigation. One of the factors on which a productive crop irrigation depends on is evapotranspiration, defined as the water loss process from the soil. This is mainly measured by empirical equations, even though they are conditioned by the specific climatological variables they require. In recent years, data mining techniques are proposed as a powerful alternative to predict evapotranspiration. Among them, ensembles are notable in that they provide accurate estimators in different scenarios. Stacking is an ensemble-building technique aimed at strengthening the prediction capabilities of the system by the combined learning from the original features in the data and synthetic features created from the predictions of multiple models. This research proposes the usage of stacking for evapotranspiration prediction, which has been overlooked in the specialized literature, with the aim of a more sustainable management of water resources. The proposal is compared to other state-of-the-art empirical equations and data mining methods over several real-world climatological datasets of different agricultural areas in Spain. This comparison is performed considering separate datasets with features based on temperature, mass transfer, radiation and, finally, using the main meteorological variables together. The results obtained show that stacking is the best approach in all datasets and each group of features evaluated, running as good alternative to predict evapotranspiration when using data of a different nature and under different conditions.

Climate change is a growing concern across the globe, and the Provincial Government of Ontario recognizes that climate change impacts need to be considered in all decision-making. In Southern Ontario, a critical and ongoing challenge is balancing the competing water demands under changing climate for various uses to ensure prosperity and sustainability in the future. A better understanding and quantification of impacts of possible climate change on regional hydrology are necessary for sustainable water resources management and maintaining healthy ecosystems in this region. In order to study the impacts of future climate on the regional water resources, a large-scale hydrologic model was developed for Southern Ontario within the Great Lakes basin using the Soil and Water Assessment Tool (SWAT). The study area includes four basins: Eastern Georgian Bay, Eastern Lake Huron, Northern Lake Erie, and Lake Ontario and Niagara Peninsula basins, covering a total area of about 84,650 km2. The hydrologic model was calibrated and validated using monthly observed streamflow data at 40 gauging stations, and spatially validated at another 40 gauging stations across the study area. The developed model was employed to estimate water budget components for a reference period (1971-2000), and to assess climate change impacts on the hydrologic regime during the mid-century (2041-2070) and the end-century (2071-2100). Projected climate data from five GCM-RCMs simulations for RCP4.5 and RCP8.5 scenarios were obtained from the NA-CORDEX archive. After bias correction, climate data sets were used in the SWAT model for the impact assessment. Based on the model calibration and validation results, the overall performance of the model was found to be satisfactory. Its performance was better in the predominantly agricultural Northern Lake Erie and Eastern Lake Huron basins than the other two basins. The average annual precipitation, evapotranspiration (ET), surface runoff and water yields for the study area over the period 1971-2000 were estimated at 979 mm, 540 mm, 183 mm and 410 mm, respectively. The average annual precipitation in the four basins varied from 923 mm to 1049 mm, and water yields were found to vary between 377 mm and 465 mm. The projected increases in mean annual temperature are 3.0oC and 2.4oC by the mid-century, while the increases are 5.2oC and 3.2oC by the end-century for RCP8.5 and RCP4.5 scenarios, respectively. The average annual precipitation of the study area is projected to increase by 8% to 16%, depending on the scenario and time period. The possible increases in precipitation are relatively high for the RCP8.5 scenario and likely to vary between 13% and 18% in the four basins by the end of the 21st century. By the mid-century, the average annual water yields in the four basins are predicted to increase by 7% to 20%, and 5% to 13% under RCP8.5 and RCP4.5 scenarios, respectively. By the end-century, the projected increases in the annual water yields of the basins are 5% to 26% for RCP8.5 scenario and 3% to 11% for RCP4.5 scenario. In general, the average monthly water yield in the study area is likely to increase during December to February, but decrease in the months of March and April. The results are also presented spatially for the subwatersheds across the study area. The study results would help in planning and management of water resources, and in developing climate change adaptation plans and strategies.

Miscanthus (ANDERSSON) is considered a promising perennial industrial crop for providing biomass in a growing bioeconomy. One approach to increasing the biodiversity-enhancing ecosystem services of Miscanthus is the co-cultivation of flower-rich native wild plant species (WPS), for example, the perennial WPS common tansy (Tanacetum vulgare L.) and mugwort (Artemisia vulgaris L.), as well as the biennial WPS wild teasel (Dipsacus fullonum L.) and yellow melilot (Melilotus officinalis L.). This study tested whether these selected WPS would be as suitable for combustion as Miscanthus, in this case the sterile hybrid Miscanthus x giganteus Greef et Deuter, allowing for a mixing of the biomasses. By doing so, no additional value chain (e.g. biogas production) would be necessary to economically exploit the diversification of the agricultural system for bioenergy production. Feedstock samples of Miscanthus and the four above-mentioned WPS from a field trial in southwest Germany were used to investigate the combustion characteristics as well as the higher heating value (HHV). It was found that all WPS exhibited better combustion properties than Miscanthus with respect to ash melting behavior at similar HHVs of 16.3–17.5 MJ kg−1. From an admixture of >30% WPS to the Miscanthus biomass, a significant increase in the ash melting temperature by 20% from 1000 to 1200 °C was shown. Thus, the mixture of WPS and Miscanthus could potentially improve the combustion quality, leading to reduced costs in the incineration plant operation process. However, the reduced costs of incineration should be greater than the loss in productivity due to the lower biomass yields from the WPS. This is highly dependent on the particular site conditions and the establishment success of the WPS and needs to be investigated in long-term studies.

Exotic annual grasses invasion across northern Great Basin rangelands has promoted a grass-fire cycle that threatens the sagebrush (Artemisia spp.) steppe ecosystem. In this sense, high accumulation rates and persistence of litter from annual species largely increase the amount and continuity of fine fuels. Here, we highlight the potential use and transferability of remote sensing-derived products to estimate litter biomass on sagebrush rangelands in southeastern Oregon, and link fire regime attributes (fire-free period) with litter biomass spatial patterns at the landscape scale. Every June, from 2018 to 2021, we measured litter biomass in 24 field plots (60 m × 60 m). Two remote sensing-derived datasets were used to predict litter biomass measured in the field plots. The first dataset used was the 30-m annual net primary production (NPP) product partitioned into plant functional traits (annual grass, perennial grass, shrub, and tree) from the Rangeland Analysis Platform (RAP). The second dataset included topographic variables (heat load index -HLI- and site exposure index -SEI-) computed from the USGS 30-m National Elevation Dataset. Through a frequentist model averaging approach (FMA), we determined that the NPP of annual and perennial grasses, as well as HLI and SEI, were important predictors of field-measured litter biomass in 2018, with the model featuring a high overall fit (R2 = 0.61). Model transferability based on extrapolating the FMA predictive relationships from 2018 to the following years provided similar overall fits (R2 ≈ 0.5). The fire-free period had a significant effect on the litter biomass accumulation on rangelands within the study site, with greater litter biomass in areas where the fire-free period was <10 years. Our findings suggest that the proposed remote sensing-derived products could be a key instrument to equip rangeland managers with additional information towards fuel management, fire management, and restoration efforts.

While clonal integration can improve the performance of rhizomatous plants, it remains unclear whether their clonal integration strategy changes under contrasting clipping and saline-alkali homogeneous and heterogeneous environments. Leymus chinensis is a clonal grass native to the Songnen grassland where heavy grazing and patchy saline-alkali stress are serious environmental and ecological problems. We hypothesized that L. chinensis overcomes these stresses through clonal integration, in particular the transfer of nitrogen and carbohydrates.A pot experiment was carried out with 15N isotope soil labeling method to study clonal integration strategy in the connected mother and daughter ramets of L. chinensis. The connected ramet pairs were grown in homogeneous (both connected ramets were treated) and heterogeneous (only daughter ramets were treated) environments with four treatments: control, clipping (60% aboveground biomass removal), saline-alkali (3.45 g of NaCl, NaHCO3, and Na2CO3 per pot), and clipping × saline-alkali.A significant amount (22.5%) of 15N was transferred from mother to daughter ramets under non-stressed conditions. When homogeneously stressing both mother and daughter ramets, N transfer was significantly reduced to 8.5--14.6%, independent of the nature of the stress. When only daughters were stressed (heterogeneous stress), saline-alkali stress led to a division of labor where daughters had enhanced photosynthesis, and mother ramets had increased 15N uptake and growth. Clipping only daughters reduced biomass and 15N uptake of both daughter and mother ramets.Our results demonstrated that clonal integration also occurs in homogeneous favorable environments but is reduced under homogeneous stress. In heterogeneous environments, clonal integration is used to translocate resource after clipping and a division of labor is established to overcome saline-alkali stress. Clonal integration continued even when daughters were severely stressed by the combined treatments. Our findings suggest that these mechanisms are key to the success of L. chinensis in the Songnen grassland.