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The existing literature estimates a significantly reduced odds of hospitalisation and death among individuals. However, though less severe than other variants, the Omicron variant may still lead to excess mortality compared to pre-pandemic years.A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, excess of mortality is estimated.In Italy, 14 and 11 regions suffered from relevant excess mortality in January and February, respectively. However, the situation is far from being as critical as during previous waves.We can conclude that no matter which variant (or multiple inter-variant recombination) we are facing, excess mortality will appear in correspondence of any incidence peak.

eLife 12, e81916 (2023). doi:10.7554/eLife.81916, Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.Methods:We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance.Results:Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models.Conclusions:Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks., Published by eLife Sciences Publications, Cambridge

During the COVID-19 pandemic, postexposure-vaccine-prophylaxis is not a practice. Following exposure, only patient isolation is imposed. Moreover, no therapeutic prevention approach is applied. We asked whether evidence exists for reduced mortality rate following postexposure-vaccine-prophylaxis. To estimate the effectiveness of postexposure-vaccine-prophylaxis, we obtained data from the Israeli Ministry of Health registry. The study population consisted of Israeli residents aged 12 years and older, identified for the first time as PCR-positive for SARS-CoV-2, between December 20th, 2020 (the beginning of the vaccination campaign) and October 7th, 2021. We compared "recently injected" patients-that proved PCR-positive on the same day or on 1 of the 5 consecutive days after first vaccination (representing an unintended postexposure-vaccine-prophylaxis)s-to unvaccinated control group. Among Israeli residents identified PCR-positive for SARS-CoV-2, 11 687 were found positive on the day they received their first vaccine injection (BNT162b2) or on 1 of the 5 days thereafter. In patients over 65 years, 143 deaths occurred among 1412 recently injected (10.13%) compared to 255 deaths among the 1412 unvaccinated (18.06%), odd ratio (OR) 0.51 (95% confidence interval [CI]: 0.41-0.64; p 0.001). A significant reduction in the death toll was observed among the 55-64 age group, with 8 deaths occurring among the 1320 recently injected (0.61%) compared to 24 deaths among the 1320 unvaccinated control (1.82%), OR 0.33 (95% CI: 0.13-0.76; p = 0.007). Postexposure-vaccine-prophylaxis is effective against death in COVID-19 infection.

When Covid-19 arrived in Italy in early 2020, a group of statisticians came together to provide tools to make sense of the unfolding epidemic and to counter misleading media narratives. Here, members of StatGroup-19 reflect on their work to date

In the last few years, diabetes mellitus and obesity revealed to be one of the fastest-growing chronic diseases in youth in the United States. The number of new diabetes cases is dramatically increasing, and, for the moment, effective therapy does not exist. Experts believe that one of the causes of this increase is the decline in exercise behavior. The California Education Code requires local educational agencies (LEAs) to administer the FITNESSGRAM, the Physical Fitness Test (PFT), to Californian students of public schools. This test evaluates six fitness areas, and experts defined that a passing result on all six areas of the test represents a fitness level that offers some protection against the diseases associated with physical inactivity. We consider 2015-2016 data provided by the California Department of Education (CDE): for each Californian county ( m=57 ), we aim at estimating the county-level proportion of students with a score equal to six. To account for the heterogeneity of the phenomenon and the presence of outlying counties, we extend the standard area-level model by specifying the random effects as a symmetric α -stable (S α S) distribution that can accommodate different types of outlying observations. The model can accurately estimate the county-level proportion of students with a score equal to six. Results highlight some interesting relationships with social and economic situations in each county. The performance of the proposed model is also investigated through an extensive simulation study.

Within the context of Sustainable Development Goals, progress towards Target 12.3 can be measured and monitored with the Food Loss Index. A major challenge is the lack of data, which dictated many methodology decisions. Therefore, the objective of this work is to present a possible improvement to the modeling approach used by the Food and Agricultural Organization in estimating the annual percentage of food losses by country and commodity. Our proposal combines robust statistical techniques with the strict adherence to the rules of the official statistics. In particular, the case study focuses on cereal crops, which currently have the highest (yet incomplete) data coverage and allow for more ambitious modeling choices. Cereal data is available in 66 countries and 14 different cereal commodities from 1991 to 2014. We use the annual food loss as response variable, expressed as percentage over production, by country and cereal commodity. The estimation work is twofold: it aims at selecting the most important factors explaining losses worldwide, comparing two Bayesian model selection approaches, and then at predicting losses with a Beta regression model in a fully Bayesian framework.

Background: Excess mortality (EM) can reliably capture the impact of a pandemic, this study aims at assessing the numerous factors associated with EM during the COVID-19 pandemic in Italy. Methods: Mortality records (ISTAT 2015–2021) aggregated in the 610 Italian Labour Market Areas (LMAs) were used to obtain the EM P-scores to associate EM with socioeconomic variables. A two-step analysis was implemented: (1) Functional representation of EM and clustering. (2) Distinct functional regression by cluster. Results: The LMAs are divided into four clusters: 1 low EM; 2 moderate EM; 3 high EM; and 4 high EM-first wave. Low-Income showed a negative association with EM clusters 1 and 4. Population density and percentage of over 70 did not seem to affect EM significantly. Bed availability positively associates with EM during the first wave. The employment rate positively associates with EM during the first two waves, becoming negatively associated when the vaccination campaign began. Conclusions: The clustering shows diverse behaviours by geography and time, the impact of socioeconomic characteristics, and local governments and health services’ responses. The LMAs allow to draw a clear picture of local characteristics associated with the spread of the virus. The employment rate trend confirmed that essential workers were at risk, especially during the first wave.

BackgroundDuring the COVID-19 pandemic, post-exposure-prophylaxis is not a practice. Following exposure, only patient isolation is imposed. Moreover, no therapeutic prevention approach is applied. We asked whether evidence exists for reduced mortality rate following post-exposure-prophylaxis.MethodsTo estimate the effectiveness of post-exposure-prophylaxis, we obtained data from the Israeli Ministry of Health (MoH) registry. The study population consisted of Israeli residents aged 12 years and older, identified for the first time as PCR-positive for SARS-CoV-2, between December 20th, 2020 (the beginning of the vaccination campaign) and October 7th, 2021. We compared “recently injected” patients - that proved PCR-positive on the same day or on one of the five consecutive days after first vaccination (representing an unintended post-exposure-prophylaxis), to unvaccinated control group.ResultsAmong Israeli residents identified PCR-positive for SARS-CoV-2, 11,690 were found positive on the day they received their first vaccine injection (BNT162b2) or on one of the 5 days thereafter. In patients over 65 years, 143 deaths occurred among 1413 recently injected (10.12%) compared to 280 deaths among the 1413 unvaccinated (19.82%), odd ratio (OR) 0.46 (95% confidence interval (CI), 0.36 to 0.57; PConclusionPost-exposure-prophylaxis is effective against death in COVID-19 infection.Israeli MoH Registry NumberHMO-0372-20

We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of both the first and the second wave of COVID-19 in Italy, i.e. from February 2020 to July 2020 and from July 2020 to December 2020, respectively. We analyse data at the regional level and, interestingly enough, prove that substantial spatial and temporal dependence occurred in both waves, although strong restrictive measures were implemented during the first wave. Accurate predictions are obtained, improving those of the model where independence across regions is assumed.

We compare the expected all-cause mortality with the observed one for different age classes during the pandemic in Lombardy, which was the epicenter of the epidemic in Italy and still is the region most affected by the pandemic. A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, a significant excess of mortality is estimated in 2020, leading to approximately 35000 more deaths than expected, mainly arising during the first wave. In 2021, instead, the excess mortality is not significantly different from zero, for the 85+ and 15-64 age classes, and significant reductions with respect to the 2020 estimated excess mortality are estimated for other age classes.

Introduction: Excess mortality (EM) is a valid indicator of COVID-19’s impact on public health. Several studies regarding the estimation of EM have been conducted in Italy, and some of them have shown conflicting values. We focused on three estimation models and compared their results with respect to the same target population, which allowed us to highlight their strengths and limitations. Methods: We selected three estimation models: model 1 (Maruotti et al.) is a Negative-Binomial GLMM with seasonal patterns; model 2 (Dorrucci et al.) is a Negative Binomial GLM epidemiological approach; and model 3 (Scortichini et al.) is a quasi-Poisson GLM time-series approach with temperature distributions. We extended the time windows of the original models until December 2021, computing various EM estimates to allow for comparisons. Results: We compared the results with our benchmark, the ISS-ISTAT official estimates. Model 1 was the most consistent, model 2 was almost identical, and model 3 differed from the two. Model 1 was the most stable towards changes in the baseline years, while model 2 had a lower cross-validation RMSE. Discussion: Presently, an unambiguous explanation of EM in Italy is not possible. We provide a range that we consider sound, given the high variability associated with the use of different models. However, all three models accurately represented the spatiotemporal trends of the pandemic waves in Italy.

The Costa Concordia shipwreck permitted to assess how multiple disturbances affected marine biota at different spatial and temporal scales, evaluating the effects of mechanical and physical disturbances on Posidonia oceanica (L.) Delile, an endemic seagrass species of the Mediterranean Sea. To assess the impacts of the shipwreck and its salvaging from 2012 to 2017 at a population and a landscape level, a diversified approach was applied based on the application of a geographical information system coupled with seascape metrics and structural descriptors. Benthic habitat maps and seascape metrics highlighted cenotic transitions, as well as fragmentation and erosion phenomena, resulting in 9952 m2 of seagrass area impacted. Regression of the meadow was unveiled by both multivariate and interpolation analysis, revealing a clear spatio-temporal gradient of impacts based on distances from the wreck. Results highlighted the effectiveness of the descriptors involved that permitted to reveal temporal changes at both fine and large scales.

Any forest management potentially affects the availability and quality of resources for forest-dwelling wildlife populations, including endangered species. One such species is the Apennine brown bear, a small and unique population living in the central Apennines of Italy. The conservation of this relict bear population is hampered by the lack of knowledge of the fine-scale relationships between productivity of key foods and forest structure, as this prevents the design and implementation of effective forest management plans. To address this issue, we sampled the main structural stand attributes within the bear’s range and used multivariate generalized linear mixed models in a Bayesian framework to relate forest structural attributes to proxies of productivity of key bear foods. We found that hard mast was positively associated with both forest typology and high forest system, but negatively related to both the time elapsed since the last forest utilization and the amount of deadwood. The availability of soft-mast producing species was positively related to past forestry practices but negatively associated with steep slopes historically managed with high tree densities and a low silvicultural disturbance. Our findings also suggest that herb cover was negatively affected by terrain steepness and basal area, while herb productivity was positively affected by northern and southern exposure. Additionally, richness of forest ants was associated with forests characterized by low volume and high density. Our findings confirm that the productivity of natural bear foods is strongly affected by forest structural and topographical characteristics and are relevant as preliminary information for forest management practices to support the long-term conservation of Apennine bears.

In the last few years, diabetes mellitus and obesity revealed to be one of the fastest‐growing chronic diseases in youth in the United States. The number of new diabetes cases is dramatically increasing, and, for the moment, effective therapy does not exist. Experts believe that one of the causes of this increase is the decline in exercise behavior. The California Education Code requires local educational agencies (LEAs) to administer the FITNESSGRAM, the Physical Fitness Test (PFT), to Californian students of public schools. This test evaluates six fitness areas, and experts defined that a passing result on all six areas of the test represents a fitness level that offers some protection against the diseases associated with physical inactivity. We consider 2015–2016 data provided by the California Department of Education (CDE): for each Californian county (m=57), we aim at estimating the county‐level proportion of students with a score equal to six. To account for the heterogeneity of the phenomenon and the presence of outlying counties, we extend the standard area‐level model by specifying the random effects as a symmetric α‐stable (SαS) distribution that can accommodate different types of outlying observations. The model can accurately estimate the county‐level proportion of students with a score equal to six. Results highlight some interesting relationships with social and economic situations in each county. The performance of the proposed model is also investigated through an extensive simulation study.

Presence-only data are a typical occurrence in species distribution modeling. They include the presence locations and no information on the absence. Their modeling usually does not account for detection biases. In this work, we aim to merge three different sources of information to model the presence of marine mammals. The approach is fully general and it is applied to two species of dolphins in the Central Tyrrhenian Sea (Italy) as a case study. Data come from the Italian Environmental Protection Agency (ISPRA) and Sapienza University of Rome research campaigns, and from a careful selection of social media (SM) images and videos. We build a Log Gaussian Cox process where different detection functions describe each data source. For the SM data, we analyze several choices that allow accounting for detection biases. Our findings allow for a correct understanding of Stenella coeruleoalba and Tursiops truncatus distribution in the study area. The results prove that the proposed approach is broadly applicable, it can be widely used, and it is easily implemented in the R software using INLA and inlabru. We provide examples' code with simulated data in the supplementary materials.

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided. publishedVersion

In the last years we are witnessing a progressive shift toward the adoption of the cloud-computing model for a large variety of application domains. High-performance computing (HPC) is not an exception to this trend, with a steadily increasing amount of performance-heavy scientific experiments that are conducted on the cloud. However, this approach may pose some serious security, operational and economical issues.In this paper we report about our experience with this scenario, that culminated with the development of a small-scale on-premise HPC facility called TeraStat, available through our Department. This facility has been purposely deployed and customized so to simplify and accelerate the execution of performance-heavy computational experiments.The success of TeraStat is confirmed by the large number of experiments conducted in these years. Here, we focus on two of the most significant case studies, outlining the positive role played by TeraStat in their solution. The first is a large scale analysis of climate data with annual cycles. The second is a study about the advantages of using compression techniques while processing large genomic data by means of a distributed approach.

The availability of intensive care beds during the COVID‐19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short‐term prediction of COVID‐19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area‐specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave‐last‐out rationale. The approach has been set up and validated during the first epidemic wave in Italy. A report of its performance for predicting ICU occupancy at regional level is included.

Seagrasses rank among the most productive yet highly threatened ecosystems on Earth. Loss of seagrass habitat because of anthropogenic disturbances and evidence of their limited resilience have provided the impetus for investigating and monitoring habitat restoration through transplantation programmes. Although Structure from Motion (SfM) photogrammetry is becoming a more and more relevant technique for mapping underwater environments, no standardised methods currently exist to provide 3-dimensional high spatial resolution and accuracy cartographic products for monitoring seagrass transplantation areas. By synthesizing various remote sensing applications, we provide an underwater SfM-based protocol for monitoring large seagrass restoration areas. The data obtained from consumer-grade red-green-blue (RGB) imagery allowed the fine characterization of the seabed by using 3D dense point clouds and raster layers, including orthophoto mosaics and Digital Surface Models (DSM). The integration of high spatial resolution underwater imagery with object-based image classification (OBIA) technique provided a new tool to count transplanted Posidonia oceanica fragments and estimate the bottom coverage expressed as a percentage of seabed covered by such fragments. Finally, the resulting digital maps were integrated into Geographic Information Systems (GIS) to run topographic change detection analysis and evaluate the mean height of transplanted fragments and detect fine-scale changes in seabed vector ruggedness measure (VRM). Our study provides a guide for creating large-scale, replicable and ready-to-use products for a broad range of applications aimed at standardizing monitoring protocols in future seagrass restoration actions.

The ectoparasite communities of two juvenile Diplodus species, D. sargus and D. puntazzo, were studied in a rocky coast of the Central Tyrrhenian sea (Mediterranean Sea) where three neighbouring nursery areas showed a differential availability of microhabitats due to a gradual protection gradient capable of influencing local hydrodynamic conditions. Five parasite forms were detected on juvenile hosts: Peniculus fistula, the two larval forms of gnathiids (praniza and zuphea stages), Caligus sp. and Anilocra physodes. Among these species an increasing rates of infestation (up to 57%), from the less protected to the most sheltered site, was detected. The largest infestation rate occurred in the most enclosed site, where P. fistula was the most infective species, also capable of affecting the body condition of juvenile fishes. In addition, to investigate behavioural processes among infected fish, both gut content analyses and in situ HD video sequences were used. Our results demonstrate that ectoparasites cannot be considered as accidental food items, implying an active removal of parasites among conspecifics. The highest frequencies of cleaning interactions were recorded during high ambient light conditions, suggesting the role of visual displays as an important factor in stimulating cleaning interactions.

CircSpaceTime is the only R package, currently available, that implements Bayesian models for spatial and spatio-temporal interpolation of circular data. Such data are often found in applications w...

CircSpaceTime is the only R package currently available that implements Bayesian models for spatial and spatio-temporal interpolation of circular data. Such data are often found in applications where, among the many, wind directions, animal movement directions, and wave directions are involved. To analyze such data we need models for observations at locations s and times t, as the so-called geostatistical models, providing structured dependence assumed to decay in distance and time. The approach we take begins with Gaussian processes defined for linear variables over space and time. Then, we use either wrapping or projection to obtain processes for circular data. The models are cast as hierarchical, with fitting and inference within a Bayesian framework. Altogether, this package implements work developed by a series of papers; the most relevant being Jona Lasinio, Gelfand, and Jona Lasinio (2012); Wang and Gelfand (2014); Mastrantonio, Jona Lasinio, and Gelfand (2016). All procedures are written using Rcpp. Estimates are obtained by MCMC allowing parallelized multiple chains run. The implementation of the proposed models is considerably improved on the simple routines adopted in the research papers. As original running examples, for the spatial and spatio-temporal settings, we use wind directions datasets over central Italy.

We introduce a Bayesian multivariate hierarchical framework to estimate a space-time model for a joint series of monthly extreme temperatures and amounts of precipitation. Data are available for 360 monitoring stations over 60 years, with missing data affecting almost all series. Model components account for spatio-temporal correlation and annual cycles, dependence on covariates and between responses. Spatio-temporal dependence is modeled by the nearest neighbor Gaussian process (GP), response multivariate dependencies are represented by the linear model of coregionalization and effects of annual cycles are included by a circular representation of time. The proposed approach allows imputation of missing values and interpolation of climate surfaces at the national level. It also provides a characterization of the so called Italian ecoregions, namely broad and discrete ecologically homogeneous areas of similar potential as regards the climate, physiography, hydrography, vegetation and wildlife. To now, Italian ecoregions are hierarchically classified into 4 tiers that go from 2 Divisions to 35 Subsections and are defined by informed expert judgments. The current climatic characterization of Italian ecoregions is based on bioclimatic indices for the period 1955–2000.

In this work we propose the use of functional data analysis (FDA) to deal with a very large dataset of atmospheric aerosol size distribution resolved in both space and time. Data come from a mobile measurement platform in the town of Perugia (Central Italy). An OPC (Optical Particle Counter) is integrated on a cabin of the Minimetro, an urban transportation system, that moves along a monorail on a line transect of the town. The OPC takes a sample of air every six seconds and counts the number of particles of urban aerosols with a diameter between 0.28 μm and 10 μm and classifies such particles into 21 size bins according to their diameter. Here, we adopt a 2D functional data representation for each of the 21 spatiotemporal series. In fact, space is unidimensional since it is measured as the distance on the monorail from the base station of the Minimetro. FDA allows for a reduction of the dimensionality of each dataset and accounts for the high space-time resolution of the data. Functional cluster analysis is then performed to search for similarities among the 21 size channels in terms of their spatiotemporal pattern. Results provide a good classification of the 21 size bins into a relatively small number of groups (between three and four) according to the season of the year. Groups including coarser particles have more similar patterns, while those including finer particles show a more different behavior according to the period of the year. Such features are consistent with the physics of atmospheric aerosol and the highlighted patterns provide a very useful ground for prospective model-based studies.

Acquiring seabed, landform or other topographic data in the field of marine ecology has a pivotal role in defining and mapping key marine habitats. However, accessibility for this kind of data with a high level of detail for very shallow and inaccessible marine habitats has been often challenging, time consuming. Spatial and temporal coverage often has to be compromised to make more cost effective the monitoring routine. Nowadays, emerging technologies, can overcome many of these constraints. Here we describe a recent development in remote sensing based on a small unmanned drone (UAVs) that produce very fine scale maps of fish nursery areas. This technology is simple to use, inexpensive, and timely in producing aerial photographs of marine areas. Both technical details regarding aerial photos acquisition (drone and camera settings) and post processing workflow (3D model generation with Structure From Motion algorithm and photo-stitching) are given. Finally by applying modern algorithm of semi-automatic image analysis and classification (Maximum Likelihood, ECHO and Object-based Image Analysis) we compared the results of three thematic maps of nursery area for juvenile sparid fishes, highlighting the potential of this method in mapping and monitoring coastal marine habitats.

Nitrogen inputs in aquatic ecosystems are increasing and climate change is likely to exacerbate cultural eutrophication. The recovery of aquatic ecosystem functionality requires strenuous efforts and entails considerable costs. Therefore, the development of early warning ecological indicators that can help arrest the phenomenon in its early stages is highly desirable. Stable isotope analysis of Nitrogen in algal primary producers has proved useful in determining the origins of Nitrogen inputs in several marine and freshwater ecosystems. Nitrogen signatures are often assigned to impact or non-impact classes by comparing the Nitrogen signature of samples with the Nitrogen signature ranges of potential sources, which can hinder objective ecological evaluation when sample signatures are close to the upper/lower boundaries of source ranges. To overcome this problem, we obtained the Nitrogen signatures of the epilithic associations collected in the littoral zone of Lake Bracciano (Central Italy), covering a pre-drought (2015–2016) and ongoing drought (2017–2019) period. The Bayesian Gaussian Mixture Model determined four probability distributions, each associated with a Nitrogen impact class, and assigned the observed epilithic signatures to the most appropriate classes. Application of the approach at various spatial and temporal scales allowed us to compare the pre-drought and ongoing drought Nitrogen input dynamics. At each spatial and temporal scale, we observed differences in the input dynamics arising from the side effects of the drought on human activities, which were reflected in changes in the probability of Nitrogen signatures belonging to one or the other impact class. Based on this probability, the proposed analytical protocol provided a useful tool for prioritizing specific management measures in areas affected by specific Nitrogen inputs. Moreover, with a few recalibrations, the model proposed for Lake Bracciano can be extended to other contexts.

The River Continuum Concept (RCC) and the Riverine Ecosystem Synthesis (RES) are two different theories proposed by river ecologists to describe the response of biotic communities to environmental variability. River network directional patterns are conveniently described by asymmetric eigenvector maps, an eigenfunction-based spatial filtering method specifically proposed for situations where a hypothesized directional spatial process influences the species distribution. In this work asymmetric eigenvector maps are used in conjunction with canonical redundancy analysis and variation partitioning analysis to describe the distribution of macroinvertebrate communities of a river system in Northern Italy and to test the link between the river theories and the available data. Benthic macrofauna data were collected during the summer of 2009–2013 in 16 rivers, for a total of 283 replicates. We investigate the effects of some measured environmental factors on the benthic macrofauna community, accounting for directional effects intrinsic to the river network structure. The proposed protocol allows to highlight and discuss some of the features relevant to the two river theories. According to the RCC theory, altitude and temperature were relevant factors affecting the macrozoobenthic community, together with the distance from the spring and water depth. Environmental factors representing local and lateral dimensions were less relevant for explaining the variability of the community composition. Nonetheless a role of the surrounding land use was also found, suggesting the presence of lateral effects due to human activities. Overall, the results demonstrated that RCC is a reliable model to describe the distribution of macrobenthic communities in river networks. In socio-ecological systems, the local and lateral dimensions postulated by the RES theory could be mainly related to surrounding land use and naturalness degree.

We analyze variations in α -diversity of benthic macroinvertebrate communities in an Italian lagoon system using Bayesian hierarchical models with nested random effects. Our aim is to understand how spatial scales influence microhabitat definition. Tsallis entropy measures diversity and spike-and-slab regression selects predictors.