Temporal Community Research Articles

Selective Enrichment of Methylococcaceae versus Methylocystaceae Methanotrophs via Control of Methane Feeding Schemes.

Methanotrophs are crucial in keeping environmental CH4 emissions in check. However, the contributions of different groups of methanotrophs at terrestrial CH4-oxidation hotspots, such as the oxic-anoxic interface of rice paddies, have shown considerable inconsistency across observations. To address the knowledge gap regarding this inconsistency, methanotrophic microbiomes were enriched from paddy soils in well-mixed CH4-fed batch reactors under six different incubation conditions, prepared as combinations of two CH4 mixing ratios (0.5 and 10%) and three supplemented Cu2+ concentrations (0, 2, and 10 μM). Monitoring of temporal community shifts in these cultures revealed a dominance of Methylocystis spp. in all 0.5%-CH4 cultures, while methanotrophs affiliated to Gammaproteobacteria dominated the 10%-CH4 cultures that were less consistent both temporally and across conditions. The shotgun metagenome analyses of the 0.5%-CH4 cultures corroborated the Methylocystis dominance and, interestingly, showed that copper deficiency did not select for mmoXYZ-possessing methanotrophs. Instead, a mbn cluster, accounting for approximately 5% of the Methylocystis population, was identified, suggesting the ecological significance of methanobactin in Cu-deficient methanotrophy. These findings underscore the important role of Methylocystis spp. in mitigating emissions from terrestrial CH4 hotspots and suggest the feasibility of directed enrichment and/or isolation of Methylocystis spp. for utilization in, for example, methanobactin and polyhydroxybutyrate production.

Seasonal dynamics of Mediterranean fish communities revealed by eDNA: Contrasting compositions across depths and Marine Fully Protected Area boundaries

Marine fish communities suffer from anthropogenic pressures and climate change, which influence their spatio-temporal dynamics. Marine Protected Areas (MPAs) have been established worldwide to preserve these communities, while mesophotic ecosystems could provide natural refugia. Assessing the extent to which MPAs and deeper ecosystems can mitigate human and climate change impacts requires regular monitoring of temporal community dynamics. Environmental DNA (eDNA) surveys – being time- and cost-effective – can provide valuable insights on biodiversity change. Here, we initiated a long-term study based on eDNA monitoring in an MPA in the north-western Mediterranean Sea that includes areas with various protection levels. Specifically, from June 2021 to January 2023, we collected eDNA samples during the summer, fall, and winter seasons from shallow water (20 m depth), at 40 m depth, and from the mesophotic zone (80 m depth) in a Fully Protected Area (FPA) and in a nearby Lightly Protected Area (LPA) in the Riou archipelago (France). In this short period and relatively small area, we detected a total of 113 actinopterygian and chondrichthyan taxa. Species with high fishing vulnerability had higher detection rates in the FPA than in the LPA, suggesting a positive impact of FPAs on the conservation of these threatened species. A marked seasonal signal in species detections, including significantly lower detections of several species in winter, indicated a combined effect of species biological changes and migration behavior. The seasonality trend was stronger in the FPA than in the LPA, indicating that such areas may modify sub-yearly patterns in communities and ecosystem processes. Fish composition was associated with water depth, with marked species dissimilarities between shallow waters and the mesophotic zone, implying that multiple depths should be considered in MPA monitoring to fully capture the response of biodiversity to management. Our results point to the importance of temporal information combined with extensive sampling across depths and protection levels to fully understand the ecological dynamics and structure of coastal fish communities.

Aridity-dependent shifts in biodiversity-stability relationships but not in underlying mechanisms.

Climate change will affect the way biodiversity influences the stability of plant communities. Although biodiversity, associated species asynchrony, and species stability could enhance community stability, the understanding of potential nonlinear shifts in the biodiversity-stability relationship across a wide range of aridity (measured as the aridity index, the precipitation/potential evapotranspiration ratio) gradients and the underlying mechanisms remain limited. Using an 8-year dataset from 687 sites in Mongolia, which included 5496 records of vegetation and productivity, we found that the temporal stability of plant communities decreased more rapidly in more arid areas than in less arid areas. The result suggests that future aridification across terrestrial ecosystems may adversely affect community stability. Additionally, we identified nonlinear shifts in the effects of species richness and species synchrony on temporal community stability along the aridity gradient. Species synchrony was a primary driver of community stability, which was consistently negatively affected by species richness while being positively affected by the synchrony between C3 and C4 species across the aridity gradient. These results highlight the crucial role of C4 species in stabilizing communities through differential responses to interannual climate variations between C3 and C4 species. Notably, species richness and the synchrony between C3 and C4 species independently regulated species synchrony, ultimately affecting community stability. We propose that maintaining plant communities with a high diversity of C3 and C4 species will be key to enhancing community stability across Mongolian grasslands. Moreover, species synchrony, species stability, species richness and the synchrony between C3 and C4 species across the aridity gradient consistently mediated the impacts of aridity on community stability. Hence, strategies aimed at promoting the maintenance of biological diversity and composition will help ecosystems adapt to climate change or mitigate its adverse effects on ecosystem stability.

Validation and development of eDNA metabarcoding primers for comprehensive assessment of Chinese amphibians.

Environmental DNA (eDNA) metabarcoding has emerged as a powerful, non-invasive tool for biodiversity assessments. However, the accuracy and limitations of these assessment techniques are highly dependent on the choice of primer pairs being used. Although several primer sets have been used in eDNA metabarcoding studies of amphibians, there are few comparisons of their reliability and efficiency. Here, we employed lab- and field-tested sets of publicly available and de novo-designed primers in amplifying 83 species of amphibian from all three orders (Anura, Caudata, and Gymnophiona) and 13 families present in China to evaluate the versatility and specificity of these primers sets in amphibian eDNA metabarcoding studies. Three pairs of primers were highly effective, as they could successfully amplify all the major clades of Chinese amphibians in our study. A few non-amphibian taxa were also amplified by these primers, which implies that further optimization of amphibian-specific primers is still needed. The simultaneous use of three primer sets can completely cover all the species obtained by conventional survey methods and has even effectively distinguished quite a number of species (n = 20) in the Wenshan National Nature Reserve. No single primer set could individually detect all of the species from the studied region, indicating that multiple primers might be necessary for a comprehensive survey of Chinese amphibians. Besides, seasonal variations in amphibian species composition were also revealed by eDNA metabarcoding, which was consistent with traditional survey methods. These results indicate that eDNA metabarcoding has the potential to be a powerful tool for studying spatial and temporal community changes in amphibian species richness.

Environmental DNA time series analysis of a temperate stream reveals distinct seasonal community and functional shifts

AbstractEnvironmental DNA (eDNA) extracted from water is routinely used in river biodiversity research, and via metabarcoding eDNA can provide comprehensive taxa lists with little effort and cost. However, eDNA‐based species detection in streams and rivers may be influenced by sampling season and other key factors such as water temperature and discharge. Research linking these factors and also informing on the potential of eDNA metabarcoding to detect shifts in ecological signatures, such as species phenology and functional feeding groups across seasons, is missing. To address this gap, we collected water samples every 2 weeks for 15 months at a long‐term ecological research (LTER) site and at three different positions in the river's cross section, specifically the water surface, riverbed, and riverbank. For these 102 samples, we analyzed macroinvertebrate species and molecular operational taxonomic unit (OTU) richness and temporal community turnover across seasons based on cytochrome c oxidase subunit I (COI) metabarcoding data. Using Generalized Additive Models, we found a significant influence of sampling season on species richness. Community turnover followed a cyclic pattern, reflecting the continuous change of the macroinvertebrate community throughout the year (“seasonal clock”). Although water temperature had no influence on the inferred species richness, higher discharge reduced the number of Annelida and Ephemeroptera species detectable with eDNA. Most macroinvertebrate taxa showed the highest species richness in spring, in particular merolimnic species with univoltine life cycles. Further, we detected an increase in the proportion of shredders in winter and parasites in summer. Our results show the usefulness of highly resolved eDNA metabarcoding time series data for ecological research and biodiversity monitoring in streams and rivers.

Ground air pollutants explanation based on multiple visibility graph of complex network by temporal community division.

In air pollution studies, the correlation analysis of environmental variables has usually been challenged by parametric diversity. Such variable variations are not only from the extrinsic meteorological conditions and industrial activities but also from the interactive influences between the multiple parameters. A promising solution has been motivated by the recent development of visibility graph (VG) on multi-variable data analysis, especially for the characterization of pollutants' correlation in the temporal domain, the multiple visibility graph (MVG) for nonlinear multivariate time series analysis has been verified effectively in different realistic scenarios. To comprehensively study the correlation between pollutant data and season, in this work, we propose a multi-layer complex network with a community division strategy based on the joint analysis of the atmospheric pollutants. Compared to the single-layer-based complex networks, our proposed method can integrate multiple different atmospheric pollutants for analysis, and combine them with multivariate time series data to obtain higher temporary community division for ground air pollutants interpretation. Substantial experiments have shown that this method effectively utilizes air pollution data from multiple representative indicators. By mining community information in the data, it successfully achieves reasonable and strong interpretive analysis of air pollution data.

Measuring Spatiotemporal Civil War Dimensions Using Community-Based Dynamic Network Representation (CoDNet)

Civil war exhibits complex geospatial trends over time, which may be missed by models that rely on count-based operationalizations. Here, the spatial and temporal correlation values of monthly civil war events are transformed into their influence degree symbol, which measures geospatial concentration, spread, and intensity of civil war. We then measure variation in these degrees over time to identify relevant spatiotemporal civil war aspects. The network model is constructed using 0.5 degree grid locations as nodes, counting nearby and over time connections. We then extract the temporal community structure behind the data. We use ground-truth data to visualize how our measures correlate with observed patterns, thereby illustrating our method provides accurate depictions of geospatial civil war dynamics. We also evaluate the impact of several indicators highlighted by past research and our community-based spatiotemporal measures and comparing it to the preprocessed count indicator. Our findings indicate that the relationship between state capacity and climate stress show opposite correlations with civil war as those identified by studies that use count-based indicators. Counterintuitively, our results show that conflict intensifies and spreads in locations where the state is stronger and where climate conditions are improved.

QTCS: Efficient Query-Centered Temporal Community Search

Temporal community search is an important task in graph analysis, which has been widely used in many practical applications. However, existing methods suffer from two major defects: (i) they only require that the target result contains the query vertex q , leading to the temporal proximity between q and other vertices being ignored. Thus, they may find many temporal irrelevant vertices (these vertices are called query-drifted vertices) concerning q for satisfying their objective functions; (ii) their methods are NP-hard, incurring high costs for exact solutions or compromised qualities for approximate/heuristic algorithms. In this paper, we propose a new problem named query-centered temporal community search to overcome these limitations. Specifically, we first present a novel concept of Time-Constrained Personalized PageRank to characterize the temporal proximity between q and other vertices. Then, we introduce a model called β -temporal proximity core, which can seamlessly combine temporal proximity and structural cohesiveness. Subsequently, our problem is formulated as an optimization task that finds a β -temporal proximity core with the largest β. We theoretically prove that our problem can circumvent these query-drifted vertices. To solve our problem, we first devise an exact and near-linear time greedy removing algorithm that iteratively removes unpromising vertices. To improve efficiency, we then design an approximate two-stage local search algorithm with bound-based pruning techniques. Finally, extensive experiments on eight real-life datasets and nine competitors show the superiority of the proposed solutions.

No positive effects of biodiversity on ecological resilience of lake ecosystems

<p>Ecological resilience is critical for ecosystems to persist in the face of perturbations without shifting to a different state. Global biodiversity loss in multiple ecosystems is considered to be associated with decreasing ecological resilience and increasing the risk of ecosystem collapse. However, how temporal changes in biodiversity affect ecological resilience in natural ecosystems remains poorly elucidated. By analyzing subfossil records of diatoms, chironomids, and cladocerans from 53 lake sediment cores across the globe, we found that species richness showed an increasing trend with time, while beta diversity and ecological resilience presented a decreasing temporal trend when ecosystems are approaching the abrupt shift. Asynchronous fluctuations among species and temporal stability at species level are suggested to be the mechanisms that contribute to the maintenance of temporal community stability. We found species richness and beta diversity have positive effects on species asynchrony but negative effects on species stability. However, we found that species asynchrony and species stability had negative relationships with ecological resilience, with species richness and beta diversity overall having no positive effects on ecological resilience. We highlighted that biodiversity effects on ecological resilience are not only the role of species richness, but also the species assemblage and network complexity of species-species interactions. Our study indicates that increased species diversity and community heterogeneity may be not beneficial for the ecosystem to recover from disturbances at a lake ecosystem scale, which has great implications for the assessment of ecological resilience and predicting ecosystem collapse in future global environmental change scenarios.</p>

A biogeographic–macroecological perspective on the rising novelty of the biosphere in the Anthropocene

AbstractThe concept of novel ecosystems has been discussed for over more than a decade to describe ecosystems that have an altered species composition and function, such that the community has crossed a threshold forbidding a return to its historical state. While spatial and temporal community compositional change has been well studied in biogeography, studies on novel ecosystems in a modern context are few and tend towards classifying novelty based on a static baseline. Given that the abiotic and biotic drivers of novelty are in a state of rapid change, and reaching levels unprecedented within the last hundred thousand to million years, defining such a threshold requires additional thought. Here, we discuss a biogeographical–macroecological perspective on novel ecosystems, exploring how such a threshold for novelty can be defined in an environment undergoing progressive global change and suggesting pathways through which the emergence and spread of novelty can be further explored, understood and managed.

Exploring Temporal Community Structure via Network Embedding.

Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made significant progress, and one can exploit deep learning superiority to network tasks. Although some methods for static networks have shown promising results in boosting community detection by integrating community embedding, they are not suitable for temporal networks and unable to capture their dynamics. Furthermore, the dynamic embedding methods only model network varying without considering community structures. Hence, in this article, we propose a novel unsupervised dynamic community detection model, which is based on network embedding and can effectively discover temporal communities and model dynamic networks. More specifically, we propose the community prior by introducing the Gaussian mixture model (GMM) in the variational autoencoder, which can obtain community information and better model the evolutionary characteristics of community structure and node embedding by utilizing the variant of gated recurrent unit (GRU). Extensive experiments conducted in real-world and artificial networks demonstrate that our proposed model has a better effect on improving the accuracy of dynamic community detection.

HB-DSBM: Modeling the Dynamic Complex Networks From Community Level to Node Level.

A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies with time. As the most popular and successful network model, the stochastic block model (SBM) has been extended and applied to community detection, link prediction, anomaly detection, and evolution analysis of dynamic networks. However, all current models based on the SBM for modeling dynamic networks are designed at the community level, assuming that nodes in each community have the same dynamic behavior, which usually results in poor performance on temporal community detection and loses the modeling of node abnormal behavior. To solve the above-mentioned problem, this article proposes a hierarchical Bayesian dynamic SBM (HB-DSBM) for modeling the node-level and community-level dynamic behavior in a dynamic network synchronously. Based on the SBM, we introduce a hierarchical Dirichlet generative mechanism to associate the global community evolution with the microscopic transition behavior of nodes near-perfectly and generate the observed links across the dynamic networks. Meanwhile, an effective variational inference algorithm is developed and we can easy to infer the communities and dynamic behaviors of the nodes. Furthermore, with the two-level evolution behaviors, it can identify nodes or communities with abnormal behavior. Experiments on simulated and real-world networks demonstrate that HB-DSBM has achieved state-of-the-art performance on community detection and evolution. In addition, abnormal evolutionary behavior and events on dynamic networks can be effectively identified by our model.

High frequency environmental DNA metabarcoding provides rapid and effective monitoring of fish community dynamics

AbstractLong‐term monitoring is critical to measure the response of biodiversity patterns and processes to human‐mediated environmental pressures. This is particularly pertinent in freshwaters, where recent estimates indicated a third of all fish species are threatened with extinction, making ongoing biomonitoring essential for conservation management. High frequency annual monitoring is critical for identifying temporal changes in fish community composition; however, traditional survey methods are typically less practical over such timeframes. While environmental (e)DNA measurement represents a potentially powerful tool for monitoring temporal community dynamics, studies are lacking. To address this deficit, we generated a high frequency time‐series dataset of entire fish communities using eDNA metabarcoding, to directly assess the repeatability and sensitivity of this method for detecting annual population trends. We targeted two differing environments (freshwater vs. intertidal) within the Thames catchment, UK, where detailed historical records from traditional monitoring were available for comparison. To test how robust eDNA data is for inferring the known community, we applied a hierarchical, nested design encompassing short and longer‐term variation in eDNA data. Our analyses showed that irrespective of environment, eDNA metabarcoding represented known seasonal shifts in fish communities, where increased relative read abundance of eDNA coincided with known migratory and spawning events, including those of the critically endangered native species Anguilla anguilla (European eel). eDNA species detections across a single year included over 75% of species recorded in a ca. 30‐year historical dataset, highlighting the power of eDNA for species detection. Our findings provide greater insight into the utility of eDNA metabarcoding for recovering temporal trends in fish communities from dynamic freshwater systems and insight into the potential best sampling strategy for future eDNA surveys.

Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar, which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.

Relic DNA obscures DNA-based profiling of multiple microbial taxonomic groups in a river-reservoir ecosystem.

Numerous studies have investigated the spatiotemporal variability in water microbial communities, yet the effects of relic DNA on microbial community profiles, especially microeukaryotes, remain far from fully understood. Here, total and active bacterial and microeukaryotic community compositions were characterized using propidium monoazide (PMA) treatment coupled with high-throughput sequencing in a river-reservoir ecosystem. Beta diversity analysis showed a significant difference in community composition between both the PMA untreated and treated bacteria and microeukaryotes; however, the differentiating effect was much stronger for microeukaryotes. Relic DNA only resulted in underestimation of the relative abundances of Bacteroidota and Nitrospirota, while other bacterial taxa exhibited no significant changes. As for microeukaryotes, the relative abundances of some phytoplankton (e.g. Chlorophyta, Dinoflagellata and Ochrophyta) and fungi were greater after relic DNA removal, whereas Cercozoa and Ciliophora showed the opposite trend. Moreover, relic DNA removal weakened the size and complexity of cross-trophic microbial networks and significantly changed the relationships between environmental factors and microeukaryotic community composition. However, there was no significant difference in the rates of temporal community turnover between the PMA untreated and treated samples for either bacteria or microeukaryotes. Overall, our results imply that the presence of relic DNA in waters can give misleading information of the active microbial community composition, co-occurrence networks and their relationships with environmental conditions. More studies of the abundance, decay rate and functioning of nonviable DNA in freshwater ecosystems are highly recommended in the future.

Between news and history: identifying networked topics of collective attention on Wikipedia

The digital information landscape has introduced a new dimension to understanding how we collectively react to new information and preserve it at the societal level. This, together with the emergence of platforms such as Wikipedia, has challenged traditional views on the relationship between current events and historical accounts of events, with an ever-shrinking divide between “news” and “history”. Wikipedia’s place as the Internet’s primary reference work thus poses the question of how it represents both traditional encyclopaedic knowledge and evolving important news stories. In other words, how is information on and attention towards current events integrated into the existing topical structures of Wikipedia? To address this, we develop a temporal community detection approach towards topic detection that takes into account both short term dynamics of attention as well as long term article network structures. We apply this method to a dataset of one year of current events on Wikipedia to identify clusters of Wikipedia articles related to news events, distinct from those that would be found solely from page view time series correlations or static network structure. We are able to resolve the topics that more strongly reflect unfolding current events vs more established knowledge by the relative importance of collective attention dynamics vs link structures. We also offer important developments by identifying and describing the emergent topics on Wikipedia. This work provides a means of distinguishing how these information and attention clusters are related to Wikipedia’s twin faces of encyclopaedic knowledge and current events—crucial to understanding the production and consumption of knowledge in the digital age.

Temporal community structure and seasonal climatic migration of coastal sharks and large teleost fishes in the northeast Gulf of Mexico

We conducted monthly fishery-independent sampling using scientific gillnets and longlines at two seagrass shoals and surrounding soft-bottom habitat in the northeast Gulf of Mexico. Using non-metric multidimensional scaling, we explored the effects of abiotic variables on temporal patterns of fish community structure, analyzed temporal patterns using permutational multivariate analysis of variance, and tested for cyclicity in community structure. Additionally, we used generalized additive models to explore the effects of environmental variables on immigration and emigration phases of dominant taxa. We found strong seasonality and cyclicity in assemblages captured by both gear types, with depauperate winter communities and diverse assemblages in warmer month. Our results suggest that temperature may determine the timing of immigration and duration of the residency period of dominant taxa, but photoperiod may cue immigration and emigration in adults when the purpose of those migrations includes predicable reproductive functions (e.g., parturition). We found evidence of partial migration in juvenile life stages of some coastal sharks and hypothesize that rising temperatures due to climate change may have variable effects on residency patterns over ontogeny.

Temporal assembly patterns of microbial communities in three parallel bioreactors treating low-concentration coking wastewater with differing carbon source concentrations

Carbon source is an important factor of biological treatment systems, the effects of which on their temporal community assembly patterns are not sufficiently understood currently. In this study, the temporal dynamics and driving mechanisms of the communities in three parallel bioreactors for low-concentration coking wastewater (CWW) treatment with differing carbon source concentrations (S0 with no glucose addition, S1 with 200 mg/L glucose addition and S2 with 400 mg/L glucose addition) were comprehensively studied. High-throughput sequencing and bioinformatics analyses including network analysis and Infer Community Assembly Mechanisms by Phylogenetic bin-based null model (iCAMP) were used. The communities of three systems showed turnover rates of 0.0029∼0.0034 every 15 days. Network analysis results showed that the S0 network showed higher positive correlation proportion (71.43%) and clustering coefficient (0.33), suggesting that carbon source shortage in S0 promoted interactions and cooperation of microbes. The neutral community model analysis showed that the immigration rate increased from 0.5247 in S0 to 0.6478 in S2. The iCAMP analysis results showed that drift (45.89%) and homogeneous selection (31.68%) dominated in driving the assembly of all the investigated microbial communities. The contribution of homogeneous selection increased with the increase of carbon source concentrations, from 27.92% in S0 to 36.08% in S2. The OTUs participating in aerobic respiration and tricarboxylic acid (TCA) cycle were abundant among the bins mainly affected by deterministic processes, while those related to the metabolism of refractory organic pollutants in CWW such as alkanes, benzenes and phenols were abundant in the bins dominated by stochastic processes.

Stable plant community biomass production despite species richness collapse under simulated extreme climate in the European Alps

Direct observation of biodiversity loss in response to abrupt climate change can resolve fundamental questions about temporal community dynamics and clarify the controversial debate of biodiversity loss impacts on ecosystem functioning. We tracked local plant species loss and the corresponding change of aboveground biomass of native and non-native species by actively pushing mountain grassland ecosystems beyond their ecological thresholds in a five-year, multisite translocation experiment across the European Alps. Our results show that species loss (ranging from a 73 % to 94 % reduction in species richness) caused by simulated climate extremes (strong warming interacting with drought) did not decrease community biomass. Even without non-native species colonization, the community biomass of native species remained stable during native species richness collapse. Switching our research focus from local extinction in the face of climate change towards the beneficial impacts of persisting native species (in addition to novel plant-plant interactions) might yield insights on transformative opportunities for boosting climate resilience.

Environmental DNA metabarcoding reveals spatial and seasonal patterns in the fish community in the Venice Lagoon

Environmental DNA (eDNA) is an emerging tool for assessing biodiversity and understanding spatial and temporal community patterns and processes, directly from DNA sequencing of environmental samples such as air, water, and sediments. We applied eDNA methods to monitor bony fish communities, detecting as well locally allochthonous species, and to reveal seasonal patterns at two sites in the Venice Lagoon. We analyzed 17 water samples collected over 12 months at two ecologically distinct sites by using available primers for teleosts and High Throughput Illumina sequencing. We identified 1,289 amplicon sequence variants (ASVs) assigned to 62 fish taxa. Most of the species known to inhabit or to enter the Venice Lagoon were detected, with eDNA data reflecting differences in fish communities between the internal (freshwater associated) and the external (sea associated) part of the lagoon. Moreover, seasonal trends of migration have been portrayed, highlighting the most involved species and disclosing possible clashes between migration events and the temporary interruption of sea-lagoon connectivity due to MOSE (MOdulo Sperimentale Elettromeccanico). Of interest, the first-time detection of Oceanic puffer (Lagocephalus lagocephalus) DNA in the Venice Lagoon provides evidence of the further northward expansion of this species in the high Adriatic Sea. eDNA successfully profiled fish communities by season and habitat in the Venice Lagoon. Our results support routine application of eDNA to monitor potential ecological consequences of MOSE closures in this World Heritage site.