Sustainability Institute

How communities are structured into functional groups and trophic layers is key to understanding ecosystem functioning. Nonetheless, we lack insights about spatiotemporal variation in guild composition of communities and its causes. To investigate spatial and temporal patterns and drivers of variation in insect feeding guilds, we combined data from a nationwide survey of Swedish insects using Malaise traps and DNA metabarcoding with a comprehensive trait database. We assigned species into one of three feeding guilds (phytophages, saprophages, predators) or into one of three associated parasitoid guilds. We then analysed patterns in species richness for each guild. Species richness declined with latitude in all guilds. Beyond this gradient, local variation in species richness matched between hosts and their parasitoids. Yet, hosts and their parasitoids responded differently to habitat. The phenological peak of parasitoid species richness appeared later than the peak of their hosts, but the length of time lags varied among guilds. Spatiotemporal patterns were driven by guild-specific responses to temperature, though much variation remained between seasons and locations even when controlling for temperature. Overall, these patterns suggest that shifts in both climate and land use may alter the synchrony of insect trophic layers, with unknown consequences.

The zero-excess lithium metal batteries (ZELMBs) offer a higher energy density and better manufacturing safety compared with the conventional LMBs. However, the practical application of such cells is hindered by the severe dendrite growth originating from the uneven Li+ distribution at the copper substrate. Here, we combine a top layer of polyacrylonitrile (PAN) and a bottom layer of poly(vinylidene difluoride) (PVDF), fabricated by electrospinning, to serve as an artificial solid electrolyte interphase (ASEI) promoting membrane, denoted as Cu@PAN + PVDF, for effectively dealing with this challenge. This configuration facilitates the desolvation and uniform flux of Li+, leading to form an inorganic-rich SEI layer to favor the uniform lithium deposition. In contrast, reversing the layer order (i.e., PVDF as the top layer and PAN as the bottom layer, denoted as Cu@PVDF + PAN) results in a high nucleation barrier and an uneven lithium deposit. The morphological evolution is further examined using a newly designed half-stripping experiment where lithium is plated and partially stripped at 1 mA cm-2 for 1 and 0.5 h, respectively. The Cu@PAN + PVDF electrode maintains a dense, uniform surface, whereas Cu@PVDF + PAN exhibits midlayer voids and disconnected "dead Li", indicating the uneven delithiation. The Cu@PAN + PVDF||Li half-cell achieves over 160 cycles with a Coulombic efficiency (CE) exceeding 95%, outperforming the Cu@PVDF + PAN||Li (100 cycles) and bare Cu||Li (110 cycles) cells. This work identifies layer orientation as a governing parameter for the ASEI design and introduces a practical half-stripping methodology for evaluating the interfacial reversibility of the negative electrode in ZELMBs.

Microplastics (MPs), defined as plastic particles ranging from 1 µm to 5 mm, have emerged as ubiquitous environmental contaminants due to the widespread use and persistence of plastics. They originate both from the degradation of larger plastic debris and from intentionally produced small particles. MPs have been detected across environmental compartments, including freshwater systems, and even within the human body, raising concerns about potential exposure and risks. This thesis aimed to investigate the occurrence of MPs in freshwater environments and related consumption products, specifically drinking water and fish, while contributing to the advancement of reliable analytical methodologies. Pyrolysis–gas chromatography–mass spectrometry (Py-GC-MS) was employed as the primary analytical technique due to its ability to provide size-independent, quantitative, and polymer-specific data. Complementary use of micro-Fourier transform infrared spectroscopy (µ-FTIR) enabled particle-based characterization. Urban surface water in Amsterdam was analyzed across seasons and locations, revealing higher MP concentrations in summer and in densely urbanized canals. Polypropylene (PP) and polyethylene (PE) were the dominant polymers, and MP concentrations were positively correlated with suspended particulate matter. In drinking water systems, raw water contained relatively high MP concentrations, while treatment processes achieved removal efficiencies of 97–98%. Consequently, tap water contained only trace levels of MPs. Methodological challenges, particularly background contamination, highlighted the importance of strict quality control. The occurrence and potential accumulation of MPs in Nile tilapia were also investigated. Low concentrations were detected in fillet tissues, with heterogeneous distribution across samples. A controlled exposure experiment demonstrated minimal translocation of MPs into edible tissues, with most particles being excreted, suggesting limited human exposure through fish consumption. Overall, this thesis provides mass-based evidence of MP occurrence across environmental and biological matrices and demonstrates the applicability of Py-GC-MS for robust MP quantification. The findings contribute to improved methodological standardization and enhance the understanding of MP distribution and potential human exposure pathways.

The capacity of nutrient-limited forests to enhance carbon (C) sequestration under elevated CO2 (eCO2) remains a critical uncertainty in C cycle modeling. While existing evidence suggests that low phosphorus (P) bioavailability may constrain CO2 fertilization effects on plant growth, the extent to which this limitation modulates ecosystem responses to eCO2 in forests adapted to P-deficient soils remains poorly understood. Here, using eight P-enabled models, we simulated the magnitudes and mechanisms through which P bioavailability interacts with eCO2, emulating an ecosystem-scale P enrichment experiment at a P-limited Eucalyptus forest undergoing long-term Free-Air CO2 Enrichment. While models predicted pronounced P effects on tree growth, P enrichment unexpectedly did not increase the CO2 effects on tree growth and ecosystem C sequestration. Models prioritized either CO2-driven or P-driven growth, but rarely both. This tradeoff emerged due to model-specific assumptions on 1) partitioning of the extra P in soil labile versus nonlabile pools; 2) plant photosynthetic acclimation to P deficiency; 3) C and nutrient use strategies regulating plant size and allocation; and 4) microbial-driven soil decomposition processes. By generating divergent yet biologically plausible outcomes, these predictions establish critical testable hypotheses for empirical research and highlight multiple P-related pathways that may influence the future land C sink.

Ishakidris Bolton, 1984 is a monotypic ant genus, previously known only from a solitary specimen collected in Sarawak, Borneo. Unusual morphology of the genus has kept scientists intrigued since its description. Here we report on the discovery of Ishakidris hastifera sp. nov. from Peninsular Malaysia that provides important new insights into the taxonomy of this enigmatic genus and sheds light on its previously unknown diversity and distribution. We present a detailed account of the worker’s diagnostic features, aided by a micro-CT–based 3D model of the holotype, together with a cybertype dataset for the new species and an identification key for the genus. In addition, a 3D model of non-type material is made available for the already established species, Ishakidris ascitaspis Bolton 1984, using additional workers newly collected in Sabah, Borneo. Our analysis of both species’ internal structures, enabled by the 3D models, revealed a shelf-like invagination of the propodeum into the mesosomal cavity – an unprecedented character in ants. Using the morphological data, we discuss the support for the current systematic placement of the genus in Myrmicinae subfamily, or alternative hypothesis for Agroecomyrmecinae, that calls for molecular data to be yet tested.

Exploring nexus between particulate pollution and urban land using land use regression (LUR) and machine learning models: a case of study of Delhi, India.Kamna Sachdeva1 and Divansh Sharma21Professor Department of sustainability sciences, Delhi Skill and Entrepreneurship University (email: kamna.sachdeva@dseu.ac.in)2 Research Fellow, Division of air Quality The energy and Resource Institute (TERI) (email: divyansh.sharma@teri.res.in)Investigating the environmental repercussions of urban growth dynamics is essential for sustainable urban development. Urbanization affects air pollutants through urban expansion and emission growth, inevitably shifting the health risks associated with air pollution. The interaction between temporal variations of pollutants and spatial heterogeneity further complicates the dynamics of urban air pollution. To cater such heterogeneity regression models are integral they provide detailed insights into the relationships between air pollutants and various influencing factors. These models correlate air pollutants with independent variables, including anthropogenic emissions, meteorological parameters, and the concentrations of other air pollutants. The air quality of Delhi where transboundary emissions, local emissions, land use changes/patters and different seasonal patterns interplays, can only be explained by land use regression models. Land use regression (LUR) modeling, which offers refined insights into the spatial distribution of pollutants by incorporating land use characteristics. The integration of machine learning into land use regression (LUR) modeling further enhance its capability to predict air pollution levels with greater accuracy and spatial resolution. The study was planned to investigate the application of Land Use Regression (LUR) models to explore the relationship between particulate pollution and urban land use in Delhi, incorporating geographic, meteorological, and machine-learning approaches. The study highlights the effectiveness of traditional LUR models, Random Forest (RF), and Deep Neural Networks (DNN) in capturing spatial and temporal variability of PM2.5 and PM10 concentrations. Traditional LUR models were developed for both annual and seasonal predictions, with key variables selected based on their statistical significance and impact direction on pollutant levels. For instance, the annual model for PM2.5 included variables like green cover, building area, and wind speed, while the seasonal models adjusted variables to reflect specific environmental conditions of each period. This methodical selection and modeling process formed the basis for further analysis using advanced techniques. Advanced machine learning models, including RF and DNN, were applied to enhance the traditional LUR models. These models demonstrated improved predictive accuracy and robustness, effectively handling nonlinear interactions and complex data patterns. The study revealed some unexpected trends, particularly in terms of the temporal persistence of pollutants and understanding intensity of pollution hotspots across Delhi.

Multiple disasters that occur simultaneously or in short succession, with impacts that overlap in space and time, are referred to as multi-hazard events. Such events can create societal impacts that can be significantly worse than the sum of the individual events, due to the dynamics and interconnected nature of the disasters. Additionally, response and recovery become more complex, for instance due to depletion of financial and human resources and damaged infrastructure. Quantitative, large-scale studies that assess systematic differences between single- and multi-hazard events remain limited due to the lack of suitable, consistent, and scalable data. There are a few studies that do provide more quantitative generalized comparisons between single and multi-hazard impacts on a large scale, using disaster impact databases like EMDAT and DESINVENTAR, but these are not able to assess the dynamic changes in impact and recovery that occur after the event. In this study, we use consistent Visible Infrared Imaging Radiometer Suite Nighttime Light (VIIRS NTL) daily Black Marble data as a satellite-based data proxy for disaster impact and recovery. We provide a global-scale analysis of different geological, meteorological, and hydrological hazards between 2012-2019, comparing areas affected by a single hazard to areas affected by multiple disaster events with a time lag of 14 and 28 days. The results reveal systematic differences in impact and recovery profiles between single- and multi-hazard events. These findings demonstrate the potential of satellite-based proxies for generalisable, large-scale assessments of disaster impacts and recovery dynamics, supporting policymakers, humanitarian organisations, and risk assessment studies in anticipating emerging challenges in a future where increasingly frequent and intense hazards increase the likelihood of consecutive disasters.