How COVID-19 affects voting for incumbents: Evidence from local elections in France.

How do voters react to an ongoing natural threat? Do voters sanction or reward incumbents even when incumbents cannot be held accountable because an unforeseeable natural disaster is unfolding? We address this question by investigating voters' reactions to the early spread of COVID-19 in the 2020 French municipal elections. Using a novel, fine-grained measure of the circulation of the virus based on excess-mortality data, we find that support for incumbents increased in areas that were particularly hard hit by the virus. Incumbents from both left and right gained votes in areas more strongly affected by COVID-19. We provide suggestive evidence for two mechanisms that can explain our findings: an emotional channel related to feelings of fear and anxiety, and a prospective-voting channel, related to the ability of incumbents to act more swiftly against the diffusion of the virus than challengers.

Assessing the Relative Toxicity of Different Road Salts and Effect of Temperature on Salinity Toxicity: LCx Values versus No-Effect Concentration (NEC) Values.

Freshwater biota are at risk globally from increasing salinity, including increases from deicing salts in cold regions. A variety of metrics of toxicity are used when estimating the toxicity of substances and comparing the toxicity between substances. However, the implications of using different metrics are not widely appreciated. Using the mayfly Colobruscoides giganteus (Ephemeroptera: Colobruscoidea), we compare the toxicity of seven different salts where toxicity was estimated using two metrics: (1) the no-effect concentrations (NEC) and (2) the lethal concentrations for 10, 25 and 50% of the test populations (LCx). The LCx values were estimated using two different models, the classic log-logistic model and the newer toxicokinetic-toxicodynamic (TKTD) model. The NEC and both types of LCx values were estimated using Bayesian statistics. We also compared the toxicity of two salts (NaCl and CaCl2) for C. giganteus at water temperatures of 4 °C, 7 °C and 15 °C using the same metrics of toxicity. Our motivation for using a mayfly to assess salinity toxicity was because mayflies are generally salt sensitive, are ecologically important and are common in Australian (sub-)alpine streams. The temperature ranges were chosen to mimic winter, spring and summer water temperatures for Australian (sub-)alpine streams. Considering 144-h classical LCx values, we found toxicity differed between various salts, i.e., the lowest 144-h LC50 (8 mS/cm) for a salt used by a ski resort was half that of the highest 144-h LC50 from artificial marine salts and CaCl2 applied to roads (16 mS/cm). The analytical grade NaCl (as shown by 144-h LC50 value at 7 °C) was substantially more toxic (7.3 mS/cm) compared to analytical grade CaCl2 (12.5 mS/cm). Yet for NEC values, there were comparably fewer differences in toxicity between salts and none between the same salts at different temperatures. We conclude that LCx values are better suited to compare the difference in toxicity between substances or between the same substance at different test temperatures, while NEC values are better suited to estimating concentrations of substances that have no effect to the test species and endpoint measured under laboratory conditions.

Can SPEcies At Risk of pesticides (SPEAR) indices detect effects of target stressors among multiple interacting stressors?

Pesticides are increasingly recognised as a threat to freshwater biodiversity, but their specific ecological effects remain difficult to distinguish from those of co-occurring stressors and environmental gradients. Using mesocosms we examined the effects of an organophosphate insecticide (malathion) on stream macroinvertebrate communities concurrently exposed to a suite of stressors typical of streams in agricultural catchments. We assessed the specificity of the SPEcies At Risk index designed to determine pesticide effects in mesocosm trials (SPEARmesocosm). This index determines the log abundance proportion of taxa that are considered physiologically sensitive to pesticides. Geographic variation in pesticide sensitivity within taxa, coupled with variation between pesticides and the effects of co-occurring stressors may decrease the accuracy of SPEARmesocosm. To examine this, we used local pesticide sensitivity assessments based on rapid toxicity tests to develop two new SPEAR versions to compare to the original SPEARmesocosms index using mesocosm results. We further compared these results to multivariate analyses and community indices (e.g. richness, abundance, Simpson's diversity) commonly used to assess stressor effects on biota. To assess the implications of misclassifying species sensitivity on SPEAR indices we used a series of simulations using artificial data. The impacts of malathion were detectable using SPEARmesocosm, and one of two new SPEAR indices. All three of the SPEAR indices also increased when exposed to other agricultural non-pesticide stressors, and this change increased with greater pesticide concentrations. Our results support that interactions between other non-pesticide stressors with pesticides can affect SPEAR performance. Multivariate analysis and the other indices used here identified a significant effect of malathion especially at high concentrations, with little or no evidence of effects from the other agricultural stressors.

Constructing Time-Resolved Species Sensitivity Distributions Using a Hierarchical Toxico-Dynamic Model.

Classical species sensitivity distribution (SSD) is used to assess the threat to ecological communities posed by a contaminant and derive a safe concentration. It suffers from several well-documented weaknesses regarding its ecological realism and statistical soundness. Criticism includes that SSD does not take time-dependence of the data into account, that safe concentrations obtained from SSD might not be entirely protective of the target communities, and that there are issues of statistical representativity and of uncertainty propagation from the experimental data. We present a hierarchical toxico-dynamic (TD) model to simultaneously address these weaknesses: TD models incorporate time-dependence and allow improvement of the ecological relevance of safe concentrations, while the hierarchical approach affords appropriate propagation of uncertainty from the original data. We develop this model on a published data set containing the salinity tolerance over 72 h of 217 macroinvertebrate taxa, obtained through rapid toxicity testing (RTT). The shrinkage properties of the hierarchical model prove particularly adequate for modeling inhomogeneous RTT data. Taking into account the large variability in the species response, the model fits the whole data set well. Moreover, the model predicts a time-independent safe concentration below that obtained with classical SSD at 72 h, demonstrating under-protectiveness of the classical approach.

Hierarchical modelling of species sensitivity distribution: development and application to the case of diatoms exposed to several herbicides.

The species sensitivity distribution (SSD) is a key tool to assess the ecotoxicological threat of contaminants to biodiversity. For a contaminant, it predicts which concentration is safe for a community of species. Widely used, this approach suffers from several drawbacks: (i) summarizing the sensitivity of each species by a single value entails a loss of valuable information about the other parameters characterizing the concentration-effect curves; (ii) it does not propagate the uncertainty on estimated sensitivities into the SSD; (iii) the hazardous concentration estimated with SSD only indicates the threat to biodiversity, without any insight about a global response of the community related to the measured endpoint. To remedy these drawbacks, we built a global hierarchical model including the concentration-effect model together with the distribution law of the SSD. We revisited the current SSD approach to account for more sources of variability and uncertainty into the prediction than the traditional analysis and to assess a global response for the community. Working within a Bayesian framework, we were able to compute an SSD taking into account the uncertainty from the original raw data. We also developed a quantitative indicator of a global response of the community to the contaminant. We applied this methodology to study the toxicity and the risk of six herbicides to benthic diatoms from Lake Geneva, based on the biomass endpoint. Our approach highlighted a wide variability within the set of diatom species for all the parameters of the concentration-effect model and a potential correlation between them. Remarkably, variability of the shape parameter of the model and correlation had not been considered before. Comparison between the SSD and the global response of the community revealed that protecting 95% of the species might preserve only 80-86% of the global response. Finally, propagating the uncertainty on the estimated sensitivity showed that building an SSD on a low level of effect, such as EC10, might be unreasonable as it induces a large uncertainty on the result.

On the use of human mobility proxies for modeling epidemics.

Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study.

MOSAIC_SSD: a new web tool for species sensitivity distribution to include censored data by maximum likelihood.

Censored data are seldom taken into account in species sensitivity distribution (SSD) analysis. However, they are found in virtually every dataset and sometimes represent the better part of the data. Stringent recommendations on data quality often entail discarding a lot of these meaningful data, resulting in datasets of reduced size which lack representativeness of any realistic community. However, it is reasonably simple to include censored data in SSD by using an extension of the standard maximum likelihood method. The authors detail this approach based on the use of the R-package fitdistrplus, dedicated to the fit of parametric probability distributions. The authors present the new Web tool MOSAIC_SSD, that can fit an SSD on datasets containing any type of data, censored or not. The MOSAIC_SSD Web tool predicts any hazardous concentration and provides bootstrap confidence intervals on the predictions. Finally, the authors illustrate the added value of including censored data in SSD, taking examples from published data.