Changing Ecological Conditions in the Marine Environment Generate Different Microbial Food Web Structures in a Repeatable Manner

The mechanisms responsible for the development of various structural and functional features of the microbial food web (MFW) and their dynamics at spatial and temporal scales, which are important for predicting their responses to future environmental changes, are largely unknown. More than 3000 datasets of environmental and microbial variables collected over a decade on a seasonal and large spatial scale in the Adriatic Sea were analyzed. The sets of environmental variables were classified into four clusters (representing different environmental states) using Neural Gas analysis and the differences in MFW structure between the clusters were analyzed. Different variants of MFW evolve in the different clusters in terms of the abundance of MFW components, their ratios, growth and grazing rates, predator preference in prey selection, the strength of predator-prey interaction, and the relative importance of top-down and bottom-up control. However, these clusters are neither spatially nor temporally fixed; rather, the area studied represents a mosaic of different environmental conditions that alternate from one state to another on a time scale. In each of the environmental states, a distinct structure of MFW develops that shows consistent and repeatable changes that strictly follow the switching in environmental conditions from one state to another.

Short-lived species move uphill faster under climate change

Climate change is pushing species ranges and abundances towards the poles and mountain tops. Although many studies have documented local altitudinal shifts, knowledge of general patterns at a large spatial scale, such as a whole mountain range, is scarce. From a conservation perspective, studying altitudinal shifts in wildlife is relevant because mountain regions often represent biodiversity hotspots and are among the most vulnerable ecosystems. Here, we examine whether altitudinal shifts in birds’ abundances have occurred in the Scandinavian mountains over 13 years, and assess whether such shifts are related to species’ traits. Using abundance data, we show a clear pattern of uphill shift in the mean altitude of bird abundance across the Scandinavian mountains, with an average speed of 0.9 m per year. Out of 76 species, 7 shifted significantly their abundance uphill. Altitudinal shift was strongly related to species’ longevity: short-lived species showed more pronounced uphill shifts in abundance than long-lived species. The observed abundance shifts suggest that uphill shifts are not only driven by a small number of individuals at the range boundaries, but the overall bird abundances are on the move. Overall, the results underscore the wide-ranging impact of climate change and the potential vulnerability of species with slow life histories, as they appear less able to timely respond to rapidly changing climatic conditions.

Perception and Attitudes of Residents Towards Green Spaces in Croatia – an Exploratory Study

Green spaces are important parts of urban infrastructure. COVID-19 pandemic and lockdown periods around the world have confirmed benefits that people derive from using green spaces for their physical and mental health. Green spaces need to meet the needs of users so that people can use them and benefit over time. It is important to consider users' perceptions and attitudes. User input proves beneficial in improving management practices. We investigated the differences in attitudes and perceptions of respondents from different large settlements in Croatia towards green spaces. Data on the use and perception of green spaces were collected in the first lockdown period in Europe and processed the part of the questionnaire on attitudes and perceptions towards green spaces. People have similar, mostly positive perceptions of green spaces regardless of the size of the settlement. Differences were found in the perception of disadvantages and needs related to the management of green spaces. This is the first study of the attitudes and perceptions on a large spatial scale in Croatia, so the results are exploratory and important. This study contributes to research on the social aspects of green spaces by investigating the influence of environmental context on perceptions and attitudes.

Mapping Risks Associated with Soil Copper Contamination Using Availability and Bio-Availability Proxies at the European Scale

Soil contamination by trace elements like copper (Cu) can affect soil functioning. Environmental policies with guidelines and soil survey measurements still refer to the total contents of Cu in soils. However, Cu contents in soil solution or free Cu contents have been shown to be better proxies of risks of Cu mobility or (bio)availability for soil organisms. Several empirical equations have been defined at the local scale to predict the amounts of Cu in soil solution based on both total soil Cu contents and main soil parameters involved in the soil/solution partitioning. Nevertheless, despite the relevance for risk assessment, these equations are not applied at a large spatial scale due to difficulties to perform changes from local to regional. To progress in this challenge, we collected several empirical equations from literature and selected those allowing estimation of the amount of Cu in solution, used as a proxy of available Cu, from the knowledge of both total soil Cu contents and soil parameters. We did the same for the estimation of free Cu in solution, used as a proxy of bioavailable Cu These equations were used to provide European maps of (bio-)available Cu based on the one of total soil Cu over Europe. Results allowed comparing the maps of available and bio-available Cu at the European scale. This was done with respective median values of each form of Cu to identify specific areas of risks linked to these two proxies. Higher discrepancies were highlighted between the map of bioavailable Cu and the map of soil total Cu compared to the Cu available map. Such results can be used to assess environmental-related issues for land use planning.

The Sub-Daily Variability of Aerosol Loading and Associated Radiative Forcing Over the Indian Region

The sub-daily variability of aerosols affects the estimates of daily mean aerosol loading. However, large spatial scale estimates of their climate effects are mostly based on snapshots from low orbit satellites that may bias the mean estimate for daily, monthly, or annual timescales. In this study, an attempt is made to estimate the magnitude of such bias based on ground and satellite-based datasets. Using ground-based measurements, we show an apparent asymmetry (of the order of 10–50%) in the sub-daily variability of aerosol loading over the Indian region. For the first time, it is reported that this sub-daily variability has a spatial pattern with an increasing amplitude toward the east of the subcontinent. We also find this variability in aerosol loading is well-captured by the satellites but with a lower amplitude. Our study shows that such differences could alter the annual surface radiative forcing estimates by more than ∼15 W m−2 over this region. We find that NASA’s Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), a state-of-the-art model-based chemical reanalysis, is unable to capture these sub-daily variabilities. This implies that both model and satellite-based radiative forcing estimates for large spatial scales should improve aerosol sub-daily information/variabilities for obtaining reliable radiative forcing estimates.

Dominant species determine ecosystem stability across scales in Inner Mongolian grassland

There is an urgent need to extend knowledge on ecosystem temporal stability to larger spatial scales because presently available local-scale studies generally do not provide effective guide for management and conservation decisions at the level of an entire region with diverse plant communities. We investigated temporal stability of plant biomass production across spatial scales and hierarchical levels of community organization and analyzed impacts of dominant species, species diversity and climatic factors using a multi-site survey of Inner Mongolian grassland. We found that temporal stability at a large spatial scale, i.e. a large area aggregating multiple local communities, was related to temporal stability of and asynchrony among spatially separated local communities and large-scale population dynamics of dominant species, yet not to species richness. Additionally, a lower mean and higher variation of yearly precipitation destabilized communities at local and large scales by destabilizing dominant species population dynamics. We argue that, for semi-arid temperate grassland, dynamics and precipitation responses of dominant species and asynchrony among local communities stabilize ecosystems at large spatial scales. Our results indicate that reduced amounts and increased variation of precipitation may present key threats to the sustainable provision of biological products and services to human well-being in this region.

Geographic delay characterization of railway systems

Railway systems provide pivotal support to modern societies, making their efficiency and robustness important to ensure. However, these systems are susceptible to disruptions and delays, leading to accumulating economic damage. The large spatial scale of delay spreading typically make it difficult to distinguish which regions will ultimately affected from an initial disruption, creating uncertainty for risk assessment. In this paper, we identify geographical structures that reflect how delay spreads through railway networks. We do so by proposing a graph-based, hybrid schedule and empirical-based model for delay propagation and apply spectral clustering. We apply the model to four European railway systems: the Netherlands, Germany, Switzerland and Italy. We characterize these geographical delay structures in the railway systems of these countries and interpret these regions in terms of delay severity and how dynamically disconnected they are from the rest. The method also allows us to point out important differences between these countries’ railway systems. For practitioners, such geographical characterization of railways provides natural boundaries for local decision-making structures and risk assessment.

Marine Robotics for Recurrent Morphological Investigations of Micro-Tidal Marine-Coastal Environments. A Point of View

Coastal zones are subjected to a wide range of phenomena acting on very different temporal and spatial scales: from decades to days and from hundreds of kilometers to tens of meters. Planning the management of such areas, thus, requires an accurate and updated knowledge of the ongoing processes. While standard monitoring activities are functional for the medium-long time scale and medium-large spatial scale, they struggle to provide adequate information concerning the short period (i.e., days) and small range (i.e., few meters). In addition, such operations are affected by high costs and logistic complexity since they generally involve the deployment of specific aircraft or maritime vehicles. On the contrary, the employment of robotic devices can represent a solution to these issues. Their proper use can allow for frequent surveys and enhance the coverage of the acquired data due to optimized mission strategies. Marine robotics has the potential to arise as an efficient complementary tool to standard monitoring techniques. Nevertheless, the use of marine robots is still limited and should be improved. The purpose of this paper is to discuss the current state of robotic technology, identifying both the benefits and shortcomings of its use for micro-tidal marine-coastal monitoring. The discussion will be supported by actual results, taken as an example, achieved using FeelHippo AUV, the compact Autonomous Underwater Vehicle (AUV) developed by the Department of Industrial Engineering at the University of Florence, Italy.

Early Monitoring of Health Status of Plantation-Grown Eucalyptus pellita at Large Spatial Scale via Visible Spectrum Imaging of Canopy Foliage Using Unmanned Aerial Vehicles

Eucalyptus is a diverse genus from which several species are often deployed for commercial industrial tree plantation due to their desirable wood properties for utilization in both solid wood and fiber products, as well as their growth and productivity in many environments. In this study, a method for monitoring the health status of a 22.78 ha Eucalyptus pellita plantation stand was developed using the red-green-blue channels captured using an unmanned aerial vehicle. The ortho-image was generated, and visual atmospheric resistance index (VARI) indices were developed. Herein, four classification levels of pest and disease were generated using the VARI-green algorithm. The range of normalized VARI-green indices was between −2.0 and 2.0. The results identified seven dead trees (VARI-green index −2 to 0), five trees that were severely infected (VARI-green index 0 to 0.05), 967 trees that were mildly infected (VARI-green index 0.06 to 0.16), and 10,090 trees that were considered healthy (VARI-green index 0.17 to 2.00). The VARI-green indices were verified by manual ground-truthing and by comparison with normalized difference vegetation index which showed a mean correlation of 0.73. This study has shown practical application of aerial survey of a large-scale operational area of industrial tree plantation via low-cost UAV and RGB camera, to analyze VARI-green images in the detection of pest and disease.

A Clustering Approach for Predicting Dune Morphodynamic Response to Storms Using Typological Coastal Profiles: A Case Study at the Dutch Coast

Dune erosion driven by extreme marine storms can damage local infrastructure or ecosystems and affect the long-term flood safety of the hinterland. These storms typically affect long stretches (∼100 km) of sandy coastlines with variable topo-bathymetries. The large spatial scale makes it computationally challenging for process-based morphological models to be used for predicting dune erosion in early warning systems or probabilistic assessments. To alleviate this, we take a first step to enable efficient estimation of dune erosion using the Dutch coast as a case study, due to the availability of a large topo-bathymetric dataset. Using clustering techniques, we reduce 1,430 elevation profiles in this dataset to a set of typological coastal profiles (TCPs), that can be employed to represent dune erosion dynamics along the whole coast. To do so, we use the topo-bathymetric profiles and historic offshore wave and water level conditions, along with simulations of dune erosion for a number of representative storms to characterize each profile. First, we identify the most important drivers of dune erosion variability at the Dutch coast, which are identified as the pre-storm beach geometry, nearshore slope, tidal level and profile orientation. Then using clustering methods, we produce various sets of TCPs, and we test how well they represent dune morphodynamics by cross-validation on the basis of a benchmark set of dune erosion simulations. We find good prediction skill (0.83) with 100 TCPs, representing a 93% input and associated computational costs reduction. These TCPs can be used in a probabilistic model forced with a range of offshore storm conditions, enabling national scale coastal risk assessments. Additionally, the presented techniques could be used in a global context, utilizing elevation data from diverse sandy coastlines to obtain a first order prediction of dune erosion around the world.