Complex Metrics Research Articles

Storylines of projected summer warming in Iberia using atmospheric circulation, soil moisture and sea surface temperature as drivers of uncertainty

This study explores the uncertainty of future summer warming over Iberia using storylines constructed from climate model simulations of the Climate Model Intercomparison Project Phase 6. Unlike prior storyline approaches focusing on remote drivers and global teleconnections of atmospheric circulation, we use regional factors that directly influence summer temperatures: ridging activity, soil moisture and Mediterranean sea surface temperature. These drivers explain a substantial portion of the observed variability across climate models, with ridging activity and soil moisture showing the strongest influence on Iberian warming. Under a high radiative forcing scenario (SSP5–8.5), the storylines of Iberian warming based on these two drivers range between 7 and 9 °C for the end of the 21st century. The storyline leading to the largest warming is characterised by a drying out of the soil conditions and an increase in the anticyclonic activity over Iberia. We find similar conclusions for simple extreme heat indicators, though the approach struggles with more complex heatwave metrics. We also propose a novel modification of the storyline approach to increase the data sample of climate responses by using different time intervals throughout the 21st century. This modification would allow the application of more complex statistical models, the exploration of non-linear relationships and the identification of other drivers shaping the regional climate projections.

Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification

Workflows orchestrate a collection of computing tasks to form a complex workflow logic. Different from the traditional monolithic workflow management systems, modern workflow systems often manifest high throughput, concurrency and scalability. As service-based systems, execution time monitoring is an important part of maintaining the performance for those systems. We developed a trace profiling approach that leverages quantitative verification (also known as probabilistic model checking) to analyse complex time metrics for workflow traces. The strength of probabilistic model checking lies in the ability of expressing various temporal properties for a stochastic system model and performing automated quantitative verification. We employ semi-Makrov chains (SMCs) as the formal model and consider the first passage times (FPT) measures in the SMCs. Our approach maintains simple mergeable data summaries of the workflow executions and computes the moment parameters for FPT efficiently. We describe an application of our approach to AWS Step Functions, a notable workflow web service. An empirical evaluation shows that our approach is efficient for computer high-order FPT moments for sizeable workflows in practice. It can compute up to the fourth moment for a large workflow model with 10,000 states within 70 s.

The Role of AI in Start-Up Innovation: Driving Efficiency & Competitive Edge

Abstract: Thanks to artificial intelligence, the corporate has been transforming rapidly and positively in the start-up culture. The study delves into how AI is affecting start-ups and looks at the potential it has to change innovation in business. Our study is undertaken to uncover places where marketing-only AI has been implemented, since we have not access to data and cases necessary for the revealing of complex metrics in this paper. The results showed better decision-making, increased Bicer processing times and significant cost savings with AI application. It is costly to implement, demands particularized knowledge and involvement of sporadically moral estimation. The paper offers implications for AI industrialization in start-ups and future work related to the advantages, as well as hurdles of incorporating this technology. The above has a significant impact to startups — while AI provides vast opportunities for introducing innovation and smart features from data analysis or deep learning paradigms in their offerings, achieving successful adoption as well as ongoing stability is reliant on navigating these constraints proficiently. Thanks to artificial intelligence, the corporate has been transforming rapidly and positively in the start-up culture. The study delves into how AI is affecting start-ups, and looks at the potential it has to change innovation in business. Our study is undertaken to uncover places where marketing-only AI has been implemented, since we have not access to data and cases necessary for the revealing of complex metrics in this paper. The results showed better decision-making, increased Bicer processing times and significant cost savings with AI application. It is costly to implement, demands particularized knowledge and involvement of sporadically moral estimation. The paper offers implications for AI industrialization in start-ups and future work related to the advantages, as well as hurdles of incorporating this technology. The above has a significant impact to startups — while AI provides vast opportunities for introducing innovation and smart features from data analysis or deep learning paradigms in their offerings, achieving successful adoption as well as ongoing stability is reliant on navigating these constraints proficiently

Study on controllability assessment of a new type of power system based on lagrangian function integrated assessment method

The complex structure and multiple controllability metrics of the new power system require a comprehensive assessment approach. We propose a controllability assessment method based on the Lagrangian function, analyzing key factors that influence controllability. Our index system includes distributed power supply utilization rate, energy storage station charge state, power factor, and voltage offset. Using Lagrange shadow price theory, we construct a guideline layer index system treating bottom indexes as constraints. We formulate an optimization function utilizing the Lagrangian shadow price theory and considering weights calculated from the bottom index’s shadow price. These weights inform primary evaluation. By combining primary evaluation results, weights, and variable coefficients using the percentage system method, we determine comprehensive evaluation results for the new power system’s controllability. Experimental results demonstrate the high reliability of this method’s evaluation indexes, indicating their effectiveness. This research provides valuable insight for the advancement of the new power system. Region 1 had the lowest overall evaluation score of 51.57 and was rated general. The overall score of Region 2 was 77.24, which was considered good. Region 3 received the highest overall score of 86.29 and was rated as excellent.Graphical

Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring

The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems.

General approach on shadow radius and photon spheres in asymptotically flat spacetimes and the impact of mass-dependent variations

Recent observations of black hole shadows have revolutionized our ability to probe gravity in extreme environments. This manuscript presents a novel analytic method to calculate, in leading-order terms, the key parameters of photon sphere and shadow radius. This method offers advantages for complex metrics where traditional approaches are cumbersome. We further explore the impact of black hole mass on the photon sphere radius, providing insights into black hole interactions with their surroundings. Our findings contribute significantly to black hole physics and gravity under extreme conditions. By leveraging future advancements in observations, such as the next-generation Event Horizon Telescope (ngEHT), this work paves the way for even more precise tests of gravity near black holes.

Diagnostic and prognostic value of an ejection fraction adjusted for myocardial remodeling.

Ejection fraction (EF) is widely used to evaluate heart function during heart failure (HF) due to its simplicity compared but it may misrepresent cardiac function during ventricular hypertrophy, especially in heart failure with preserved EF (HFpEF). To resolve this shortcoming, we evaluate a correction factor to EF, which is equivalent to computing EF at the mid-wall layer (without the need for mid-layer identification) rather than at the endocardial surface, and thus better complements other complex metrics. The retrospective cohort data was studied, consisting of 2,752 individuals (56.5% male, age 69.3 ± 16.4 years) admitted with a request of a troponin test and undergoing echocardiography as part of their clinical assessment across three centres. Cox-proportional regression models were constructed to compare the adjusted EF (EFa) to EF in evaluating risk of heart failure admissions. Comparing HFpEF patients to non-HF cases, there was no significant difference in EF (62.3 ± 7.6% vs. 64.2 ± 6.2%, p = 0.79), but there was a significant difference in EFa (56.6 ± 6.4% vs. 61.8 ± 9.9%, p = 0.0007). Both low EF and low EFa were associated with a high HF readmission risk. However, in the cohort with a normal EF (EF ≥ 50%), models using EFa were significantly more associative with HF readmissions within 3 years, where the leave one out cross validation ROC analysis showed a 18.6% reduction in errors, and Net Classification Index (NRI) analysis showed that risk increment classification of events increased by 12.2%, while risk decrement classification of non-events decreased by 16.6%. EFa is associated with HF readmission in patients with a normal EF.

A Network Interference Approach to Analyzing Change Propagation in Requirements

Abstract Requirements are frequently revised due to the iterative nature of the design process. If not properly managed, these changes may result in financial and time losses due to undesired propagating effect. Currently, predictive models to assist designers in making well-informed decisions prior to change implementation do not exist. Current modeling methods for managing requirements do not offer formal reasoning necessary to manage requirement change and its propagation. The ability to predict change during the design process may lead to valuable insights in designing artifacts more efficiently by minimizing unanticipated changes due to mismanaged requirement changes. Two research questions are addressed in this paper: (1) How do complex network metrics of requirements, considering both node and edge interferences, influence the predictability of requirement change propagation across different case studies? (2) How does the performance of the complex network metrics approach compare to the refined automated requirement change propagation prediction (R-ARCPP) tool, developed from our prior study, in accurately predicting requirement change propagation? Requirement changes are simulated by applying the node interference and the edge interference methods. It is found that complex network metrics can be used to predict requirement change propagation. Based on the studied data, the performance ranking of metrics is characterized by edge interference across the changes. The results reveal that the R-ARCPP tool ranks higher than comparatively performing complex network metrics.

PSI-21 Optimizing sample size for accurate measurements and prediction equations for intestinal morphology in broiler chickens

Abstract Histological measurements of intestinal morphology, including villi height (VH) and crypt depth (CD), are routinely used to evaluate intestinal health and the nutrient absorptive capacity in monogastric animals. These metrics suffer from significant natural variation, and there is no established standard for the number of unique measurements required to robustly estimate these variables for a given animal. Furthermore, the potential utility of additional variables, including villus width (VW), perimeter (VP), or villus area (VA), has not been explored. Therefore, the objective of the current experiment was to estimate the minimum number of measurements required to control the intrasample variance in each variable and determine if more complex or time-consuming metrics, such as VP and/or VA, can be accurately estimated from linear measurements. A set of 1283 intestinal morphology data, including VH, CD, VW, VP, and VA, measured from 68 intestinal tissues (46 ileum and 22 jejunum) of 4 experiments were used in the current study (Figure 1). Retrospective calculation of the CV for all combinations of N measurements per sample was used to determine the impact of measurement number on the quality of data. Data for the jejunum and ileum were pooled together, and the NLIN procedure of SAS was used to estimate the optimal measurement for intestinal morphology measurements using a quadratic broken line. The VA was estimated using geometric calculation with the assumption that VA equals 0.67 × base width × VH. A paired t-test was conducted to compare measured and calculated VA. The optimal number of measurements to minimize within-sample CV for VH, CD, VW, VP, and VA ranged from 10.6 to 10.8. The VH was highly correlated (P < 0.05) with VP (r = 0.95 and 0.87) and VA (r = 0.77 and 0.63) in the jejunum and ileum, respectively (Table 1 and 2). Additionally, there was a significant difference (P < 0.05) between measured (0.110 µm2) and calculated VA (0.086 µm2). In conclusion, this study shows that measuring 11 villi per sample is required for minimizing variations within the jejunal or ileal tissue in broiler chickens. Also, estimating VA based on idealized geometry was found to be inaccurate, necessitating either direct measurement or more complex prediction equations.

Century-long recovery of mycorrhizal interactions in European beech forests after mining

Abstract Background and aims Ecological restoration strategies are emerging globally to counteract biodiversity loss and ecosystem degradation. However, restored ecosystems may not reach undisturbed biodiversity and functionality. One reason of this limited success may be a focus on short-term recovery of diversity, composition, or isolated functions. These simplified metrics may underestimate the real time ecosystems need to recover. Thus, studies of more complex metrics, like biotic interactions, at larger timescales, are essential to understand ecosystem recovery. Methods Using molecular identification, we assessed the recovery of the interactions between ectomycorrhizal (EcM) fungi and European beech (Fagus sylvatica L.) in two opencast iron mines in use since the fourteenth century and abandoned over 107 and 148 years. Results Species richness, species diversity, Basidiomycota/Ascomycota abundance ratio and taxonomic distinctness of EcM fungi recovered to undisturbed values, whereas species composition was still different. Certain fungal functional traits (i.e. exploration and sporocarp types) also reached undisturbed values. Differences in soil pH and NH4+ affected the composition of the EcM communities associated with beech, suggesting that mining caused a long-term impact in soil biogeochemistry, that directly impacted beech-EcM interactions. Conclusion Mycorrhizal interactions require more than 150 years to recover following mining. Contrary to the rapid recovery response provided by simple metrics like species richness, recovery metrics with more ecological information, like the identity of plant-EcM interactions, may be still capturing signals of incomplete recovery.

Characterization of invariant complex Finsler metrics on the complex Grassmann manifold

Let P:=U(p+q)/U(p)×U(q) be the complex Grassmann manifold and F:T1,0P→[0,+∞) be an arbitrary U(p+q)-invariant strongly pseudoconvex complex Finsler metric. We prove that F is necessary a Kähler-Berwald metric which is not necessary Hermitian quadratic. We also prove that F is Hermitian quadratic if and only if F is a constant multiple of the canonical U(p+q)-invariant Kähler metric on P. In particular on the complex projective space CPn=U(n+1)/U(n)×U(1), there exists no U(n+1)-invariant strongly pseudoconvex complex Finsler metric other than a constant multiple of the Fubini-Study metric. These invariant metrics are of particular interesting since they are the most important examples of strongly pseudoconvex complex Finsler metrics on P which are elliptic metrics in the sense that they enjoy very similar holomorphic sectional curvature and bisectional curvature properties as that of the U(p+q)-invariant Kähler metrics on P, nevertheless, these invariant metrics are not necessary Hermitian quadratic, hence provide nontrivial explicit examples for complex Finsler geometry in the compact cases.

Impact of the Russia–Ukraine Conflict on Global Marine Network Based on Massive Vessel Trajectories

Maritime transportation plays a vital role in global trade, and studying the resilience of the global maritime network is crucial for ensuring its sustainable development. Currently, the ongoing conflict between Russia and Ukraine has garnered significant global attention. However, there is a lack of specific research on the impact of the conflict on maritime shipping, particularly the resilience of the global maritime network. This paper proposes a resilience assessment framework under the influence of significant events by combining complex network metrics and network performance indicators from the resilience triangle model. It quantitatively evaluates the resilience changes in the global maritime network before and after the outbreak of the Russia–Ukraine conflict. The experiment utilizes real automatic identification system (AIS) maritime trajectory data to quantify and visualize the changes in global maritime traffic during a 20-day period before and after the conflict, constructing the global maritime network for resilience calculations. The research findings indicate the following changes occurred after the Russia–Ukraine conflict. Firstly, the global maritime industry experienced overall growth, with increased ship transportation between ports. Transportation in certain regions was negatively affected, with a significant decrease in ship activities in the Black Sea and Adriatic Sea areas. The positions of Russia and Ukraine in the world maritime industry noticeably declined. Secondly, the network connectivity, network size, and network density of the global maritime network significantly increased, indicating an enhanced network resilience. According to our quantitative results, from a topological perspective, we observed the following changes: network connectivity increased by 27.2%, network scale increased by 36.6%, network density increased by 32.4%, and network resilience increased by 18.6%. Thirdly, the global maritime network is characterized by a high degree of heterogeneity, and the impact of conflicts on the heterogeneity of the shipping network is not significant. Finally, the network exhibited a slower performance decline under random attacks, while deliberate attacks led to a sharp decline. Due to the adaptive nature of the maritime network, the resilience of the network improves in terms of its topology following the outbreak of conflicts. After conflict incidents, the rate of performance decline during simulated attacks is lower compared to the pre-conflict period.

Multi-perspective analysis of sustainability metrics characterising the debris environment

To assess the effectiveness of mitigation measures, long-term simulations of the debris environment are often analysed and evaluated using simple metrics such as the total number of objects or the frequency of fragmentation events. In addition, more complex criticality metrics have been developed in the past to rank objects according to their environmental impact and evaluate the overall state of the debris environment. In this context, three different perspectives are particularly worth highlighting: object-specific metrics that characterise the criticality of individual objects and orbits, spatial metrics that can be used to identify particularly vulnerable orbital regions, and global metrics which describe the sustainability of the environment as a whole. Typically, the criticality metrics are specific to one of the respective perspectives, but unification across and convertibility between perspectives would allow for a more comprehensive assessment of sustainability. In this context, a methodology is developed to transform object-specific and spatial metrics into one another. The conversion concept enables a comparison of two structurally different metrics from their respective initial object-specific and spatial domain. Furthermore, the third perspective is taken into account by using different aggregation methods to retrieve a global metric from the initial object-specific and spatial description. The three perspectives then are compared by conducting environment evolution simulations for various baseline scenarios using a common space debris population in low Earth orbit. The comparative analysis identifies commonalities and differences of the chosen metrics in the three different perspectives and how the conversion between them affects their quantification of sustainability. The multi-perspective convertibility and aggregation of single-perspective metrics represents a contribution to the generalisation of sustainability metrics for different scopes of application.

Using naturalistic and driving simulator data to model driver responses to unintentional lane departures

Unintentional lane departures on straight roads cause many road fatalities each year. The objective of this study was to explore and model drivers’ recovery steering maneuvers in unintentional drift situations, to enable the prospective safety benefit assessment of lane departure warning and avoidance systems through counterfactual simulations. The timing and amplitude of the steering adjustments drivers make to avoid lane departure were studied over three data sets with different origins, consisting of both naturalistic data and experimental data from a driving simulator study. With respect to timing, the main finding was that visually distracted drivers often initiate the corrective steering response prior to looking back towards the road, demonstrating that lane-keeping information in the visual periphery is sufficient to trigger the response. As for steering amplitude, the observed amplitudes were correlated against different lane departure risk metrics from the literature, resulting in a model capable of accounting for human behavior across all three data sets with good performance. Surprisingly, a very simple model (which describes the steering amplitude as increasing quadratically with the vehicle’s orientation to the road) predicted the amplitude of the primary corrective steering adjustment better than models based on more complex lane departure risk metrics. This result indicates that drivers scale the amplitude of their steering adjustment to the steering input needed to get the vehicle back in the lane already at first response. However, it was possible to obtain a similar model fit using a more complex threshold model, with different dynamics depending on the vehicle’s current orientation to the road. We discuss how these findings can be applied to models of human steering for safety benefit assessment.

Percentage of area protected can substitute for more complicated structural metrics when monitoring protected area connectivity

Protected area (PA) targets have become a hallmark of global conservation policies such as the recent Kunming-Montreal Global Biodiversity Framework, which requires signatory countries to establish new PAs while also monitoring gains to both PA network coverage and connectivity at large spatial scales. Policy makers tasked with implementing and monitoring progress toward these targets face the difficult decision about which connectivity metric to use, which is not always straightforward given existing data and software limitations. We empirically compare 17 of the most widely used structural connectivity metrics to determine how they capture change in a PA network as additional protected areas are added and assess whether mathematically simple metrics are a reasonable substitute for more complex metrics. We find that simply reporting the percentage of the total area that is protected is a viable way to capture connectivity gains in most landscapes. If a more involved metric is desired, we recommend the Integral Index of Connectivity, which was highly correlated with the percentage of area protected, produced similar results when measuring change in PA networks, and incorporates stepping stone movements through transboundary regions.

Dynamic structural analysis-based epitope prediction of Exendin-4 in aqueous solution.

The study of epitopes has a broad range of applications in drug discovery, vaccine design, and immunotherapy. In this study, an epitope prediction method was developed based on the dynamic structure of protein antigens. Solvent accessible surface area, charge, and root mean square fluctuation were introduced as the key residue property parameters. The epitope prediction algorithm was established by constructing a three-parameter complex metrics of seven-peptide groups. The method was applied to predict the epitopes of Exendin-4, an effective antidiabetic drug. The epitopes of both the natural and C-terminal amidated forms of Exendin-4 were predicted and compared in their folded and intermediate states. In the folded state, the epitopes of natural Exendin-4 (His1-Phe6 and Asp9-Val19) were found to be nearly identical to the epitopes of C-terminal aminated Exendin-4 (His1-Thr7 and Asp9-Val19). In the intermediate state, however, the epitopes of natural Exendin-4 (His1-Gly4, Phe6 and Lys12-Arg20) covered fewer amino acids than the epitopes of C-terminal aminated Exendin-4 (His1-Gly4, Phe6, Asp9-Val19 and Trp25-Lys27). The comparison with the results from other prediction tools demonstrates the reliability of our predicted epitopes of Exendin-4.

The lipidomic correlates of epigenetic aging across the adult lifespan: A population-based study.

Lipid signaling is involved in longevity regulation, but which specific lipid molecular species affect human biological aging remains largely unknown. We investigated the relation between complex lipids and DNA methylation-based metrics of biological aging among 4181 participants (mean age 55.1 years (range 30.0-95.0)) from the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany. The absolute concentration of 14 lipid classes, covering 964 molecular species and 267 fatty acid composites, was measured by Metabolon Complex Lipid Panel. DNA methylation-based metrics of biological aging (AgeAccelPheno and AgeAccelGrim) were calculated based on published algorithms. Epigenome-wide association analyses (EWAS) of biological aging-associated lipids and pathway analysis were performed to gain biological insights into the mechanisms underlying the effects of lipidomics on biological aging. We found that higher levels of molecular species belonging to neutral lipids, phosphatidylethanolamines, phosphatidylinositols, and dihydroceramides were associated with faster biological aging, whereas higher levels of lysophosphatidylcholine, hexosylceramide, and lactosylceramide species were associated with slower biological aging. Ceramide, phosphatidylcholine, and lysophosphatidylethanolamine species with odd-numbered fatty acid tail lengths were associated with slower biological aging, whereas those with even-numbered chain lengths were associated with faster biological aging. EWAS combined with functional pathway analysis revealed several complex lipids associated with biological aging as important regulators of known longevity and aging-related pathways.

Supplier selection strategy – NPV comparison

The supply chain has gained prominence in recent years, both in specialized literature and in economic practice, being defined and analysed as an operational component of the behaviour of economic actors, both those with global activities and those with localized activities. The main aspects related to this thematic area are associated with complex concepts that have a quantifiable economic impact in terms of money, time, quality, and competitiveness. Supplier selection has become a matter of strategic partnership within global supply chains enabling customer performance and competitiveness, thus increasing supply chain leaders’ awareness to develop and implement complex tools and metrics for supplier comparison before business award, one of the representative being net present value comparison. Net Present Value is defined as the sum of the investment's expected cash inflows and outflows discounted back to their present value at a company-agreed risk-adjusted rate. This study is focused on showing how an assessment of comparative net present values can clarify a business case and improve the decision making process towards a particular supplier selection, together with meeting project budget, internal targets, financial risk assessment, and strategic planning.

Maternal mortality in Colombia during the COVID-19 pandemic: time series and social inequities

ObjectiveThe impact of the COVID-19 pandemic goes beyond morbidity and mortality from that disease. Increases in maternal mortality have also been described but have not been extensively studied to date....

Environment and shipping drive environmental DNA beta-diversity among commercial ports.

The spread of nonindigenous species by shipping is a large and growing global problem that harms coastal ecosystems and economies and may blur coastal biogeographical patterns. This study coupled eukaryotic environmental DNA (eDNA) metabarcoding with dissimilarity regression to test the hypothesis that ship-borne species spread homogenizes port communities. We first collected and metabarcoded water samples from ports in Europe, Asia, Australia and the Americas. We then calculated community dissimilarities between port pairs and tested for effects of environmental dissimilarity, biogeographical region and four alternative measures of ship-borne species transport risk. We predicted that higher shipping between ports would decrease community dissimilarity, that the effect of shipping would be small compared to that of environment dissimilarity and shared biogeography, and that more complex shipping risk metrics (which account for ballast water and stepping-stone spread) would perform better. Consistent with our hypotheses, community dissimilarities increased significantly with environmental dissimilarity and, to a lesser extent, decreased with ship-borne species transport risks, particularly if the ports had similar environments and stepping-stone risks were considered. Unexpectedly, we found no clear effect of shared biogeography, and that risk metrics incorporating estimates of ballast discharge did not offer more explanatory power than simpler traffic-based risks. Overall, we found that shipping homogenizes eukaryotic communities between ports in predictable ways, which could inform improvements in invasive species policy and management. We demonstrated the usefulness of eDNA metabarcoding and dissimilarity regression for disentangling the drivers of large-scale biodiversity patterns. We conclude by outlining logistical considerations and recommendations for future studies using this approach.