The response of litter decomposition to extreme drought modified by plant species, plant part, and soil depth in a temperate grassland.

Plant litter decomposition is a key ecosystem process in carbon and nutrient cycling, and is heavily affected by changing climate. While the direct effects of drought on decomposition are widely studied, in order to better predict the overall drought effect, indirect effects associated with various drought-induced changes in ecosystems should also be quantified. We studied the effect of an extreme (5-month) experimental drought on decomposition, and if this effect varies with two dominant perennial grasses, plant parts (leaves vs. roots), and soil depths (0-5 cm vs. 10-15 cm) in a semi-arid temperate grassland. After 12 months, the average litter mass loss was 43.5% in the control plots, while only 25.7% in the drought plots. Overall, mass loss was greater for leaves (44.3%) compared to roots (24.9%), and for Festuca vaginata (38.6%) compared to Stipa borysthenica (30.5%). This variation was consistent with the observed differences in nitrogen and lignin content between plant parts and species. Mass loss was greater for deep soil (42.8%) than for shallow soil (26.4%). Collectively, these differences in decomposition between the two species, plant parts, and soil depths were similar in magnitude to direct drought effect. Drought induces multiple changes in ecosystems, and our results highlight that these changes may in turn modify decomposition. We conclude that for a reliable estimate of decomposition rates in an altered climate, not only direct but also indirect climatic effects should be considered, such as those arising from changing species dominance, root-to-shoot ratio, and rooting depth.

Field experiments underestimate aboveground biomass response to drought.

Researchers use both experiments and observations to study the impacts of climate change on ecosystems, but results from these contrasting approaches have not been systematically compared for droughts. Using a meta-analysis and accounting for potential confounding factors, we demonstrate that aboveground biomass responded only about half as much to experimentally imposed drought events as to natural droughts. Our findings indicate that experimental results may underestimate climate change impacts and highlight the need to integrate results across approaches.

Beyond the Forest-Grassland Dichotomy: The Gradient-Like Organization of Habitats in Forest-Steppes.

Featuring a transitional zone between closed forests and treeless steppes, forest-steppes cover vast areas, and have outstanding conservation importance. The components of this mosaic ecosystem can conveniently be classified into two basic types, forests and grasslands. However, this dichotomic classification may not fit reality as habitat organization can be much more complex. In this study, our aim was to find out if the main habitat types can be grouped into two distinct habitat categories (which would support the dichotomic description), or a different paradigm better fits this complex ecosystem. We selected six main habitats of sandy forest-steppes, and, using 176 relevés, we compared their vegetation based on species composition (NMDS ordination, number of common species of the studied habitats), relative ecological indicator values (mean indicators for temperature, soil moisture, and light availability), and functional species groups (life-form categories, geoelement types, and phytosociological preference groups). According to the species composition, we found a well-defined gradient, with the following habitat order: large forest patches, medium forest patches, small forest patches, north-facing edges, south-facing edges, and grasslands. A considerable number of species were shared among all habitats, while the number of species restricted to certain habitat types was also numerous, especially for north-facing edges. The total (i.e., pooled) number of species peaked near the middle of the gradient, in north-facing edges. The relative ecological indicator values and functional species groups showed mostly gradual changes from the large forest patches to the grasslands. Our results indicate that the widely used dichotomic categorization of forest-steppe habitats into forest and grassland patches is too simplistic, potentially resulting in a considerable loss of information. We suggest that forest-steppe vegetation better fits the gradient-based paradigm of landscape structure, which is able to reflect continuous variations.

Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods.

For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area.

Application of Electrical Capacitance Method for Prediction of Plant Root Mass and Activity in Field-Grown Crops.

The root electrical capacitance (C R ) method is suitable for assessing root growth and activity, but soil water content (SWC) strongly influences the measurement results. This study aimed to adapt the method for field monitoring by evaluating the effect of SWC on root capacitance to ensure the comparability of C R detected at different SWC. First a pot experiment was conducted with maize and soybean to establish C R -SWC functions for the field soil. Ontogenetic changes in root activity were monitored under field conditions by simultaneously measuring C R and SWC around the roots. The C R values were normalized using SWC data and experimental C R -SWC functions to obtain C R*, the comparable indicator of root activity. The effect of arbuscular mycorrhizal fungi (AMF) inoculation on the C R* and biomass of field-grown soybean was investigated. The pot trial showed an exponential increase in C R with SWC. C R -SWC functions proved to be species-specific. C R showed strong correlation with root dry mass (R2 = 0.83-0.87). The root activity (C R*) of field-grown crops increased until flowering, then decreased during maturity. This was consistent with data obtained with other methods. AMF inoculation of soybean resulted in significantly higher C R* during the late vegetative and early flowering stages, when destructive sampling concurrently showed higher shoot biomass. The results demonstrated that the root capacitance method could be useful for time course studies on root activity under field conditions, and for comparing single-time capacitance data collected in areas with heterogeneous soil water status.