Warming enhances dominance of vascular plants over cryptogams across northern wetlands.

Climate warming causes profound effects on structure and function of wetland ecosystem, thus affecting regional and global hydrological cycles and carbon budgets. However, how wetland plants respond to warming is still poorly understood. Here, we synthesized observations from 273 independent sites to explore responses of northern wetland plants to warming. Our results show that warming enhances biomass accumulation for vascular plants including shrubs and graminoids, whereas it reduces biomass accumulation for cryptogams including moss and lichen. This divergent response of vascular plants and cryptogams is particularly pronounced in the high latitudes where permafrost prevails. As warming continues, this divergent response is amplified, however, the reduction in cryptogams is more drastic. Warming leads to declined surface soil moisture and lowered water table, thereby shifting wetlands from a wet system dominated by cryptogams to a drier system with increased cover of vascular plants. Under a high-emission scenario of Shared Socioeconomic Pathways (SSP5), a 4.7-5.1°C mean global temperature rise will cause more than fivefold loss of cryptogams compared with current climate. As cryptogams are largely concentrated at northern high latitudes, where warming will likely be greater than the projected global mean, modification in wetland plant composition and major reduction in cryptogams are expected to occur even much earlier than 2100.

Simultaneous removal of heavy metals and bioelectricity generation in microbial fuel cell coupled with constructed wetland: an optimization study on substrate and plant types.

A microbial fuel cell coupled with constructed wetland (CW-MFC) was built to remove heavy metals (Zn and Ni) from sludge. The performance for the effects of substrates (granular activated carbon (GAC), ceramsite) and plants (Iris pseudacorus, water hyacinth) towards the heavy metal treatment as well as electricity generation was systematically investigated to determine the optimal constructions of CW-MFCs. The CW-MFC systems possessed higher Zn and Ni removal efficiencies as compared to CW. The maximal removal rates of Zn (76.88%) and Ni (66.02%) were obtained in system CW-MFC based on GAC and water hyacinth (GAC- and WH-CW-MFC). Correspondingly, the system produced the maximum voltage of 534.30 mV and power density of 70.86 mW·m-3, respectively. Plant roots and electrodes contributed supremely to the removal of heavy metals, especially for GAC- and WH-CW-MFC systems. The coincident enrichment rates of Zn and Ni reached 21.10% and 26.04% for plant roots and 14.48% and 16.50% for electrodes, respectively. A majority of the heavy metals on the sludge surface were confirmed as Zn and Ni. Furthermore, the high-valence Zn and Ni were effectively reduced to low-valence or elemental metals. This study provides a theoretical guidance for the optimal construction of CW-MFC and the resource utilization of sludge containing heavy metals.

Introducing ARTMO's Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape.

Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible. To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO's software framework developed in Matlab which implements most of the state-of-the-art methods in the machine learning community. To demonstrate its utility, we chose a heterogeneous case study scene, a landscape in Southwest Iran to map PTs. In this area, four main PTs were identified, consisting of shrub land, grass land, semi-shrub land, and shrub land-grass land vegetation. Having developed 21 MLCAs using the same training and validation, datasets led to varying accuracy results. Gaussian process classifier (GPC) was validated as the top-performing classifier, with an overall accuracy (OA) of 90%. GPC follows a Laplace approximation to the Gaussian likelihood under the supervised classification framework, emerging as a very competitive alternative to common MLCAs. Random forests resulted in the second-best performance with an OA of 86%. Two other types of ensemble-learning algorithms, i.e., tree-ensemble learning (bagging) and decision tree (with error-correcting output codes), yielded an OA of 83% and 82%, respectively. Following, thirteen classifiers reported OA between 70% and 80%, and the remaining four classifiers reported an OA below 70%. We conclude that GPC substantially outperformed all classifiers, and thus, provides enormous potential for the classification of a diversity of land-cover types. In addition, its probabilistic formulation provides valuable band ranking information, as well as associated predictive variance at a pixel level. Nevertheless, as these are supervised (data-driven) classifiers, performances depend on the entered training data, meaning that an assessment of all MLCAs is crucial for any application. Our analysis demonstrated the efficacy of ARTMO's MLCA toolbox for an automated evaluation of the classifiers and subsequent thematic mapping.

Variations in hydrogen isotopic fractionation in higher plants and sediments across different latitudes: Implications for paleohydrological reconstruction.

Sedimentary δDn-alkane value is widely utilized as a reliable proxy for paleo-hydrological reconstruction. Applications of this proxy must be based upon a globally clear understanding of the relationship between leaf wax δDn-alkane values and precipitation δD (δDp), defined as apparent fractionation (εapp). However, there is a critical concern about whether relatively constant εapp values exist across different latitudes. In this study, we systematically analyzed the variations of available εapp with latitudes based upon two compiled-new databases of higher plants and sediments over the world. We found that the total average εapp was relatively constant, i.e., -116 ±â€¯5‰ (n = 941), in higher plants across different latitudes without consideration of plant types (e.g., dicots, monocots, gymnosperms), and was still constant but slightly lower average εapp, i.e., -125 ±â€¯6‰ (n = 460), in sediments across the latitudes. The slightly lower average εapp in sediments relative to higher plants probably derived from the contribution of aquatic plants with isotopically D-depleted εapp in lake sediments. Interestingly, with consideration of plant types, average εapp increased in dicots but decreased in monocots slightly from low to high latitudes. The counteraction of these competing trends generates relatively constant average εapp values in higher plants, and resultantly constant average εapp values occur in sediments at the global scale. It is important to elaborate relatively constant εapp values from higher plants and sediments across different latitudes when sedimentary δDn-alkane is utilized as a proxy for paleohydrological reconstruction.

Winners always win: growth of a wide range of plant species from low to future high CO2.

Evolutionary adaptation to variation in resource supply has resulted in plant strategies that are based on trade-offs in functional traits. Here, we investigate, for the first time across multiple species, whether such trade-offs are also apparent in growth and morphology responses to past low, current ambient, and future high CO 2 concentrations. We grew freshly germinated seedlings of up to 28 C3 species (16 forbs, 6 woody, and 6 grasses) in climate chambers at 160 ppm, 450 ppm, and 750 ppm CO 2. We determined biomass, allocation, SLA (specific leaf area), LAR (leaf area ratio), and RGR (relative growth rate), thereby doubling the available data on these plant responses to low CO 2. High CO 2 increased RGR by 8%; low CO 2 decreased RGR by 23%. Fast growers at ambient CO 2 had the greatest reduction in RGR at low CO 2 as they lost the benefits of a fast-growth morphology (decoupling of RGR and LAR [leaf area ratio]). Despite these shifts species ranking on biomass and RGR was unaffected by CO 2, winners continued to win, regardless of CO 2. Unlike for other plant resources we found no trade-offs in morphological and growth responses to CO 2 variation, changes in morphological traits were unrelated to changes in growth at low or high CO 2. Thus, changes in physiology may be more important than morphological changes in response to CO 2 variation.