Effects of experimental warming on Betula nana epidermal cell growth tested over its maximum climatological growth range

Numerous long-term, free-air plant growth facilities currently explore vegetation responses to the ongoing climate change in northern latitudes. Open top chamber (OTC) experiments as well as the experimental set-ups with active warming focus on many facets of plant growth and performance, but information on morphological alterations of plant cells is still scarce. Here we compare the effects of in-situ warming on leaf epidermal cell expansion in dwarf birch, Betula nana in Finland, Greenland, and Poland. The localities of the three in-situ warming experiments represent contrasting regions of B. nana distribution, with the sites in Finland and Greenland representing the current main distribution in low and high Arctic, respectively, and the continental site in Poland as a B. nana relict Holocene microrefugium. We quantified the epidermal cell lateral expansion by microscopic analysis of B. nana leaf cuticles. The leaves were produced in paired experimental treatment plots with either artificial warming or ambient temperature. At all localities, the leaves were collected in two years at the end of the growing season to facilitate between-site and within-site comparison. The measured parameters included the epidermal cell area and circumference, and using these, the degree of cell wall undulation was calculated as an Undulation Index (UI). We found enhanced leaf epidermal cell expansion under experimental warming, except for the extremely low temperature Greenland site where no significant difference occurred between the treatments. These results demonstrate a strong response of leaf growth at individual cell level to growing season temperature, but also suggest that in harsh conditions other environmental factors may limit this response. Our results provide evidence of the relevance of climate warming for plant leaf maturation and underpin the importance of studies covering large geographical scales.Peer reviewe

Drivers of phytoplankton community structure change with ecosystem ontogeny during the Quaternary

Freshwater species are particularly sensitive to climate fluctuations, but little is known of their response to the large-scale environmental change that took place during the Quaternary. This is partly due to the scarcity of continuously preserved freshwater sedimentary records with orbital chronology. We use a 1.363 Ma high-resolution fossil record of planktonic diatoms from ancient Lake Ohrid to evaluate the role of global and regional versus local-scale environmental change in driving temporal community dynamics. By using a Bayesian joint species distribution model, we found that communities were mostly driven by the local-scale environment. Its effects decreased over time, becoming less important than global and regional environment at the onset of the penultimate glacial, 0.183 Ma. Global and regional control over the environment became important with successive deepening of the lake at around 1.0 Ma, and its influence remained persistent until the present. Our high-resolution data demonstrate the critical role of lake depth and its thermal dynamics in determining phytoplankton response to environmental change by influencing lake mixing, nutrient and light availability. With this study we demonstrate the relative impact of various environmental factors and their scale dependant effect on the phytoplankton communities during the Quaternary, emphasizing the importance of not only considering climate fluctuations in driving their structure and temporal dynamics but also the local environment. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Use of Bayesian networks in forensic soil casework

Forensic soil comparisons can be of high evidential value in a forensic case, but become complex when multiple methods and factors are considered. Bayesian networks are well suited to support forensic practitioners in complex casework. This study discusses the structure of a Bayesian network, elaborates on the in- and output data and evaluates two examples, one using source level propositions and one using activity level propositions. These examples can be applied as a template to construct a case specific network and can be used to assess sensitivity of the target output to different factors and identify avenues for research

An object-based image analysis approach to assess irrigation-water consumption from MODIS products in Ethiopia

Efficient water-resource management is essential with regard to food security, growing populations and climate change. This is especially important for low- and middle-income (LMC) countries where food is often locally produced by traditional smallholder farming. Detailed knowledge of the spatio-temporal distribution of irrigation-water consumption provides valuable information to anticipate local food shortages and water scarcity as a result of climate variability. Yet, adequate techniques to quantify irrigation-water consumption at field level over large areas are lacking. Irrigation estimates generally have a coarse resolution making them inadequate for field-level assessments. This study developed a remote-sensing-based approach to quantify spatio-temporal patterns of irrigation-water consumption at field level using the MODIS evapotranspiration product (MOD16A2) and existing land-use maps on the spatio-temporal distribution of irrigated agriculture. Object-based image analysis was used to establish local evapotranspiration differences between irrigated and rainfed fields on a monthly basis, which are the irrigation-water consumption rates of the irrigated fields. This novel method was applied to a study area in the Central Rift Valley in Ethiopia where smallholder farming is dominant and only a few large-scale farms are present. Comparison with irrigation-water-consumption values of a local irrigation scheme showed that the monthly temporal dynamics were captured quite well, but lower values were calculated compared to the scheme's field data. Comparison with two validated remote-sensing based studies in Africa showed good agreement as irrigation-water-consumption estimates were in the same order of magnitude. Irrigation-water consumption follows the temporal rainfall pattern, i.e. irrigation practices intensify with increased water availability. Surface water is commonly used for irrigation in the study area. Our study shows that smallholder practices have a lower irrigation-water consumption compared to modern large-scale farms by approximately a factor 3. Irrigation-water consumption in the area is considerable, especially during the dry season. On average 32 % of excess water (precipitation – evapotranspiration) is consumed for irrigation. For smallholder irrigation and large-scale irrigation specifically this is 28 % and 63 % respectively. The object-based approach presented here is an operational mapping method for field-level irrigation-water-consumption over large areas. MOD16A2 is a global open-source readily-available evapotranspiration product used here although an evapotranspiration product with a higher spatial resolution might be preferred. Our approach can provide irrigation-water-consumption estimates over large areas in data-poor regions, which will increase the understanding of spatio-temporal patterns of smallholder irrigation and provide information to optimize water use

North Atlantic Oscillation seesaw effect in leaf morphological records from dwarf birch shrubs in Greenland and Finland

The North Atlantic Oscillation (NAO) determines wind speed and direction, seasonal heat, moisture transport, storm tracks, cloudiness and sea-ice cover through atmospheric mass balance shifts between the Arctic and the subtropical Atlantic. The NAO is characterized by the typical, yet insufficiently understood, seesaw pattern of warmer winter and spring temperatures over Scandinavia and cooler temperatures over Greenland during the positive phase of the NAO, and vice versa during the negative phase. We tested the potential to reconstruct NAO variation beyond the meteorological record through the application of a microphenological proxy. We measured the Undulation Index (UI) in Betula nana epidermal cells from herbarium leaf samples and fossil peat fragments dating back to 1865—exceeding most meteorological records in the Arctic—to estimate imprints of spring thermal properties and NAO in Greenland and Finland. We found negative relations between Greenland UI and late winter, spring and early summer NAO, and mostly positive, but not significant, relations between Finland UI and NAO in years with pronounced NAO expression. The direction of the UI response in this common circumpolar species is, therefore, likely in line with the NAO seesaw effect, with leaf development response to NAO fluctuations in northern Europe opposing the response in Greenland and vice versa. Increased knowledge of the UI response to climate may contribute to understanding ecological properties of key Arctic species, whilst additionally providing a proxy for NAO dynamics

Unmixing water and mud: Characterizing diffuse boundaries of subtidal mud banks from individual satellite observations

Mapping of subtidal banks in mud-dominated coastal systems is crucial as they influence not only shoreline and ecosystem dynamics but also economic activities and livelihoods of local communities. Due to associated spatiotemporal variations in suspended particulate matter concentrations, subtidal mudbanks are often confined by diffuse and rapidly changing boundaries. To avoid inaccurate representations of these mudbanks in remote sensing images, it is necessary to unmix distinctive reflectance signals into representative landcover fractions. Yet, extracting mud fractions, in order to characterize such diffuse boundaries, is challenging because of the spectral similarity between subtidal- and intertidal features. Here we show that an unsupervised decision tree, used to derive spatially explicit and spectrally coherent image endmembers, facilitates robust linear spectral unmixing on an image-to-image basis, enabling the separation of these coastal features. We found that resulting abundance maps represent cross-shore gradients of vegetation, water and mud fractions present at the coast of Suriname. Furthermore, we confirmed that it is possible to separate land, water and an initial estimate of intertidal zones on individual images. Thus, spectral signatures of end-member candidates, determined from relevant index histograms within these initial estimates, are consistent. These results demonstrate that spectral information from well-defined spatial neighbourhoods facilitates the detection of diffuse boundaries of mudbanks with a spectral unmixing approach

Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method

Canopy nitrogen (N) influences carbon (C) uptake by vegetation through its important role in photosynthetic enzymes. Global Vegetation Models (GVMs) predict C assimilation, but are limited by a lack spatial canopy N input. Mapping canopy N has been done in various ecosystems using remote sensing (RS) products, but has rarely considered environmental variables as additional predictors. Our research objective was to estimate spatial patterns of canopy N in European forests and to investigate the degree to which including environmental variables among the predictors would improve the models compared to using remotely sensed products alone. The environmental variables included were climate, soil properties, altitude, N deposition and land cover, while the remote sensing products were vegetation indices and NIR reflectance from MODIS and MERIS sensors, the MOD13Q1 and MTCI products, respectively. The results showed that canopy N could be estimated both within and among forest types using the random forests technique and calibration data from ICP Forests with good accuracy (r2 = 0.62, RRMSE = 0.18). The predicted spatial pattern shows higher canopy N in mid-western Europe and relatively lower values in both southern and northern Europe. For all subgroups tested (All plots, Evergreen Needleleaf Forest (ENF) plots and Deciduous Broadleaf Forest (DBF) plots), including environmental variables improved the predictions. Including environmental variables was especially important for the DBF plots, as the prediction model based on remotely sensed data products predicted canopy N with the lowest accuracy

Predictive performance of deep-learning-enhanced remote-sensing data for ecological variables of tidal flats over time

Tidal flat systems with a diverse benthic community (e.g., bivalves, polychaetes and crustaceans) is important in the food chain for migratory birds and fish. The geographical distribution of macrozoobenthos depends on physical factors, among which sediment characteristics are key aspects. Although high-resolution and high-frequency mapping of benthic indices (i.e., sediment composition and benthic fauna) of these coastal systems are essential to coastal management plans, it is challenging to gather such information on tidal flats through in-situ measurements. The Synoptic Intertidal Benthic Survey (SIBES) database provides this field information for a 500m grid annual for the Dutch Wadden Sea, but continuous coverage and seasonal dynamics are still lacking. Remote sensing may be the only feasible monitoring method to fill in this gap, but it is hampered by the lack of spectral contrast and variation in this environment. In this study, we used a deep-learning model to enhance the information extraction from remote-sensing images for the prediction of environmental and ecological variables of the tidal flats of the Dutch Wadden Sea. A Variational Auto Encoder (VAE) deep-learning model was trained with Sentinel-2 satellite images with four bands (blue, green, red and near-infrared) over three years (2018, 2019 and 2020) of the tidal flats of the Dutch Wadden Sea. The model was trained to derive important characteristics of the tidal flats as image features by reproducing the input image. These features contain representative information from the four input bands, like spatial texture and band ratios, to complement the low-contrast spectral signatures. The VAE features, the spectral bands and the field-collected samples together were used to train a random forest model to predict the sediment characteristics: median grain size and silt content, and macrozoobenthic biomass and species richness. The prediction was done on the tidal flats of Pinkegat and Zoutkamperlaag of the Dutch Wadden sea. The encoded features consistently increased the accuracy of the predictive model. Compared to a model trained with just the spectral bands, the use of encoded features improved the prediction (coefficient of determination, R2) by 10-15% points for 2018, 2019 and 2020. Our approach improves the available techniques for mapping and monitoring of sediment and macrozoobenthic properties of tidal flat systems and thereby contribute towards their sustainable management

Projections of salt intrusion in a mega-delta under climatic and anthropogenic stressors

Rising temperatures, rapid urbanization and soaring demand for natural resources threaten deltas worldwide and make them vulnerable to rising seas, subsidence, droughts, floods, and salt intrusion. However, climate change projections in deltas often address climate-driven stressors in isolation and disregard parallel anthropogenic processes, leading to insufficient socio-political drive. Here, using a combination of process-based numerical models that integrate both climatic and anthropogenic environmental stressors, we project salt intrusion within the Mekong mega-Delta, in the next three decades. We assess the relative effects of various drivers and show that anthropogenic forces such as groundwater extraction-induced subsidence and riverbed level incisions due to sediment starvation can increase the salinity-affected areas by 10–27% compared to the present-day situation, while future sea level rise adds another 6–19% increase. These projections provide crucial input for adaptation policy development in the Mekong Delta and the methodology inspires future systemic studies of environmental changes in other deltas

Vegetation and peat accumulation steer Holocene tidal–fluvial basin filling and overbank sedimentation along the Old Rhine River, The Netherlands

In the transformation from tidal systems to freshwater coastal landscapes, plants act as eco-engineering species that reduce hydrodynamics and trap sediment, but nature and timing of the mechanisms of land creation along estuaries remains unclear. This article focuses on the Old Rhine estuary (The Netherlands) to show the importance of vegetation in coastal landscape evolution, predominantly regarding tidal basin filling and overbank morphology. This estuary hosted the main outflow channel of the river Rhine between ca 6500 to 2000 cal bp, and was constrained by peat during most of its existence. This study reconstructs its geological evolution, by correlating newly integrated geological data and new field records to varying conditions. Numerical modelling was performed to test the inferred mechanisms. It was found that floodbasin vegetation and resulting organic accumulation strongly accelerated back-barrier infill, by minimizing tidal influence. After tidal and wave transport had already sufficiently filled the back-barrier basin, reed rapidly expanded from its edges under brackish conditions, as shown by diatom analysis and datings. Reed growth provided a positive infilling feedback by reducing tidal flow and tidal prism, accelerating basin infilling. New radiocarbon dates show that large-scale crevassing along the Old Rhine River – driven by tidal backwater effect – only started as nutrient-rich river water transformed the floodbasin into an Alder carr in a next phase of estuary evolution. Such less dense vegetation promotes crevassing as sediments are more easily transported into the floodbasin. As river discharge increased and estuary mouth infilling progressed, crevasse activity diminished around 3800 to 3000 cal bp, likely due to a reduced tidal backwater effect. The insights from this data-rich Holocene study showcase the dominant role that vegetation may have in the long-term evolution of coastal wetlands. It provides clues for effective use of vegetation in vulnerable wetland landscapes to steer sedimentation patterns to strategically adapt to rising water levels