Department Systems Analysis, Integrated Assessment and Modelling

We review the main methods used to study spin glasses. In the first part, we focus on methods for fully connected models and systems defined on a tree. These include the replica method, the Thouless-Anderson-Palmer formalism, the cavity method, and the dynamical mean-field theory. In the second part, we deal with the description of low-dimensional systems, mostly in three spatial dimensions, which are mostly studied through numerical simulations. We conclude by mentioning some of the main open problems in the field.

Weunveil the multifractal behavior of Ising spin glasses in their low-temperature phase. Using the Janus II custom-built supercomputer, the spin-glass correlation function is studied locally. Dramatic fluctuations are found when pairs of sites at the same distance are compared. The scaling of these fluctuations, as the spin-glass coherence length grows with time, is characterized through the computation of the singularity spectrum and its corresponding Legendre transform. A comparatively small number of site pairs controls the average correlation that governs the response to a magnetic field. We explain how this scenario of dramatic fluctuations (at length scales smaller than the coherence length) can be reconciled with the smooth, self-averaging behavior that has long been considered to describe spin-glass dynamics.

Regulation of chemicals requires knowledge of their toxicological effects on a large number of species, which has traditionally been acquired through in vivo testing. The recent effort to find alternatives based on machine learning, however, has not focused on guaranteeing transparency, comparability and reproducibility, which makes it difficult to assess advantages and disadvantages of these methods. Also, comparable baseline performances are needed. In this study, we trained regression models on the ADORE “t-F2F” challenge proposed in [Schür et al., Nature Scientific data, 2023] to predict acute mortality, measured as LC50 (lethal concentration 50), of organic compounds on fishes. We trained LASSO, random forest (RF), XGBoost, Gaussian process (GP) regression models, and found a series of aspects that are stable across models: (i) using mass or molar concentrations does not affect performances; (ii) the performances are only weakly dependent on the molecular representations of the chemicals, but (iii) strongly on how the data is split. Overall, the tree-based models RF and XGBoost performed best and we were able to predict the log10-transformed LC50 with a root mean square error of 0.90, which corresponds to an order of magnitude on the original LC50 scale. On a local level, on the other hand, the models are not able to consistently predict the toxicity of individual chemicals accurately enough. Predictions for single chemicals are mostly influenced by a few chemical properties while taxonomic traits are not captured sufficiently by the models. We discuss technical and conceptual improvements for these challenges to enhance the suitability of in silico methods to environmental hazard assessment. Accordingly, this work showcases state-of-the-art models and contributes to the ongoing discussion on regulatory integration.

1. While many studies on insect diversity report declines, others show stable, fluctuating or increasing trends. For a thorough understanding of insect trends and their effects on ecosystem functioning, it is important to simultaneously assess insect richness, abundance and biomass, and to report trends for multiple taxa.
2. We analysed insect richness, abundance and biomass data for all insects and for eight insect taxa (Buprestidae, Cerambycidae, Carabidae, other Coleoptera, Aculeata, other Hymenoptera, Heteroptera and Lepidoptera) from 42 sites across Switzerland from 2000 to 2007, representing three major habitat types in Switzerland (agricultural, unmanaged [open and forested] and managed forest habitats). As potential drivers of temporal patterns, we evaluated weather- and land-use-related factors. As predictors, we included temperature and precipitation as well as the vegetation index and the habitat type, respectively.
3. We found a consistent pattern of stable or increasing trends for richness, abundance and biomass of insects in total and the eight taxa over 8 years. Both overall patterns and six out of eight taxa (except for Cerambycidae and Lepidotpera) showed the highest values in agricultural habitats. However, when accounting for elevation, there was no difference in open habitats regardless of whether they were used agriculturally.
4. Habitat types were the most important predictors, followed by weather- and vegetation-related factors. Modelled responses to mean temperature were unimodal, whereas the standard deviation of temperature showed positive and precipitation negative effects. Longer time series are needed to draw robust inferences and to investigate potential negative effects of future warming.

Increasing temperatures caused by anthropogenic climate change are leading to changes in the composition of local communities across biomes. This has implications for ecological assessment methods that rely on macroinvertebrates as bioindicators of water quality. To investigate the influence of changing water temperature on these assessment methods, we analysed macroinvertebrate data from Swiss national monitoring programs. We used a species distribution model to simulate temperature change effects on macroinvertebrate communities and estimated the resulting changes on three biological indices commonly used in Switzerland, namely the species richness of Ephemeroptera, Plecoptera and Trichoptera (EPT), the Swiss biological (IBCH) index along with its components, as well as the species at risk pesticides (SPEAR_pesticides) index. While results vary by temperature scenario and index, our model results for the most realistic water temperature increase scenario of + 2 °C across most sites in Switzerland suggest no, or only a minor, influence of temperature (not accounting for other hydrological changes). Our model projection predicted only a small increase in the probability of occurrence for 70 % of the studied families. The sensitivity to temperature as captured in our model is generally not very high and varies among the biological indices: on average across all sites, a + 2 °C increase in temperature resulted in a 7 % increase in EPT species richness, a 4 % increase in the IBCH index, and a less than 1 % increase in the SPEAR_pesticides index. Our study suggests the robustness of these biological indices to moderate warming and points towards the usefulness of these biological indices for the next few decades as tools for water quality assessment. Despite some limitations of statistical species distribution models (e.g., not accounting for dispersal limitation or biotic interactions, predictive performance varying by taxon), the study provides valuable insights into the complex relationships between environmental factors and macroinvertebrate communities, and the potential impacts of future temperature change. These findings can inform conservation and management efforts for these important ecological systems.