R P Prabath

K Jayasinghe

José M Rico

Inês Machado
email: mimachado@fc.ul.pt

José Lino Costa

Henrique Cabral

Response of Food-Webs Indicators to Human Pressures, in the Scope of the Marine Strategy Framework Directive

Keywords: ecosystem-based assessment, trophic webs, anthropogenic pressures, good environmental status, marine monitoring

Understanding food webs environmental condition is a challenging task since evaluations are limited by data on key ecosystem elements, by the availability of indicators that incorporate relevant guilds and by the difficulty in establishing causeeffect relations between pressures and health status, as multiple overlapping pressures can affect taxonomic elements differently. The present work aims to investigate food webs assessment under the Marine Strategy Framework Directive (MSFD), revealing gaps and future research needs in the North Eastern Atlantic. To understand reporting patterns, information on the criteria employed and the resulting assessment trends of Descriptor 4-Food webs were surveyed from the MSFD reports. A multivariate analysis was applied to food webs assessment status and spatially overlapping anthropogenic pressures to understand if the assessment was detecting pressures, considering fish elements. Results revealed that reporting strategies varied between Member States. High reporting effort was exhibited by the United Kingdom in opposition to Ireland or France. Reporting of other groups other than fish and plankton was limited to the United Kingdom due to the availability of monitoring programs and data. The analysis applied to criteria considering fish elements reinforced that reporting strategies and trends differed between countries, although some similarities were found for the Bay of Biscay and Iberian coast and the Celtic Seas. Food webs assessment trends for fish were variable in Spain and were stable or increased in Portugal and the United Kingdom. Anthropogenic drivers significantly influencing food web trends for fish elements were fishing, and climate anomalies in the southern Bay of Biscay and Iberian coast, while eutrophication and chemical contamination had effects on trends in the Celtic Sea and the North Sea. Results allowed to establish a relation between anthropogenic effects and food web patterns, however, these were limited since food webs assessment is incongruent in terms of criteria used and data is still limited at relevant scales. This study reinforced the necessity to increase Member States harmonization and calibration to improve our understanding of food webs environmental status.

INTRODUCTION

Healthy marine systems depend on monitoring plans and management measures that consider competing societal interests such as the sustainability and productivity of the systems, human well-being, and the development of human activities (European Commission, 2008, 2020;[START_REF] Korpinen | Combined effects of human pressures on Europe's marine ecosystems[END_REF]. Several policies and legal instruments have been developed for that purpose and, in that scope, the Marine Strategy Framework Directive (MSFD), published in 2008 and revised in 2017, aims to implement an Ecosystem Based Approach (EBA) to the assessment and development of management measures in the European marine systems (European Commission, 2017a,b). The directive is structured in 11 ambitious descriptors that are in different stages of development. Among the descriptors that target biodiversity, Descriptor 4-Food webs (D4) aims to assess the status of the food chains, i.e., the network of predatorprey interactions between coexisting species and populations. This descriptor represents one of the most complex and unknown aspects of marine ecosystems, since the identification of simple indicators able to assess the status of the system with dynamic species interactions and the identification of underlying responses to pressures is challenging [START_REF] Shephard | Making progress towards integration of existing sampling activities to establish joint monitoring programmes in support of the MSFD[END_REF][START_REF] Otto | A quantitative framework for selecting and validating food web indicators[END_REF]. The assessment of the food-web descriptor includes criteria classified as primary-D4C1 Trophic guild species diversity and D4C2 Abundance across trophic, and secondary-D4C3 Trophic guild size distribution and D4C4 Trophic guild productivity (European Commission, 2017a). Nonetheless, the indicators and the methodology adopted may be different between Member States (MSs), which, are conditioned by existing data from national monitoring programs. Legislative updates have improved reporting consistency, considering the criteria selected and the spatial scale of the assessment (European Commission, 2017a,b). However, in the 2018 assessment, more than 60% of the coastal food webs were considered "not assessed, " while the shelf ecosystems were either "not assessed" or "unknown" for 90% of the cases (European Commission, 2020). The reasons behind low reporting and/or misreporting were the lack of appropriate metrics, the inexistence of appropriate datasets (that need to address an extensive number of ecosystem components), and the lack of knowledge on direct cause-effect relationships in Europe's seas (European Commission, 2020).

There has been an attempt to develop fully operational indicators that can integrate trophic structure and functions, together with their interactions. But the lack of comparable data between taxonomic groups has made such integration difficult [START_REF] Rombouts | Food web indicators under the marine strategy framework directive: from complexity to simplicity?[END_REF][START_REF] Tam | Towards ecosystem-based management: identifying operational food-web indicators for marine ecosystems[END_REF][START_REF] Machado | Assessment level and time scales of biodiversity indicators in the scope of the marine strategy framework directive -a case study for the NE Atlantic[END_REF][START_REF] Do | Reavaliação do Estado Ambiental e Definição de Metas: Parte D, Subdivisão do Continente. Estratégia Marinha, Relatório do 2 • ciclo[END_REF]. According to [START_REF] Tam | Towards ecosystem-based management: identifying operational food-web indicators for marine ecosystems[END_REF], food web indexes should be sensitive to the magnitude and direction of response to underlying attribute/pressure, have a basis in theory, be specific, be responsive at an appropriate time scale, and be cost-effective to monitor or to update [START_REF] Shin | Using indicators for evaluating, comparing, and communicating the ecological status of exploited marine ecosystems. 2. setting the scene[END_REF][START_REF] Rombouts | Food web indicators under the marine strategy framework directive: from complexity to simplicity?[END_REF][START_REF] Otto | A quantitative framework for selecting and validating food web indicators[END_REF]. The choice of a specific set of food web indicators can imply that some aspects of marine food webs are valued more than others. Therefore, a well-balanced selection process for indicators is required to encompass all currently known properties of marine food webs [START_REF] Tam | Towards ecosystem-based management: identifying operational food-web indicators for marine ecosystems[END_REF]. As a result, indicators considering ecosystem components such as fish and Phyto/zooplankton elements have been further implemented due to long-term stock assessment programs, implemented by the European Commission (EC) through the Common Fisheries Policy (CFP) and the Continuous Plankton Recorder monitoring program in the Celtic Seas (CS) and Greater North Sea (GNS) (European Commission, 2020;[START_REF] Machado | Identifying assessment scales for food web criteria in the NE Atlantic: implications for the marine strategy framework directive[END_REF]. Even though these indexes enable the evaluation of trophic guilds within ecosystem elements, they do not address the connectivity amidst ecosystem elements, hindering the assessment. As a result, a set of descriptors is commonly a recommended practice [START_REF] Tam | Towards ecosystem-based management: identifying operational food-web indicators for marine ecosystems[END_REF] and further emphasis on spatial and temporal resolutions should be further added [START_REF] Machado | Identifying assessment scales for food web criteria in the NE Atlantic: implications for the marine strategy framework directive[END_REF]. When trying to disentangle if ecosystem status is directly linked to pressures, further difficulties arise, since the environment is exposed to existing multiple pressures, and food web indicators lack the establishment of a clear and direct relation with anthropogenic pressures (pressurestatus relationship) [START_REF] Henriques | Efficacy of adapted estuarine fish-based multimetric indices as tools for evaluating ecological status of the marine environment[END_REF][START_REF] Shin | Using indicators for evaluating, comparing, and communicating the ecological status of exploited marine ecosystems. 2. setting the scene[END_REF][START_REF] Crise | A MSFD complementary approach for the assessment of pressures, knowledge and data gaps in Southern European Seas: the PERSEUS experience[END_REF][START_REF] Probst | A benchmarking and assessment framework to operationalise ecological indicators based on time series analysis[END_REF][START_REF] Preciado | Small-scale spatial variations of trawling impact on food web structure[END_REF]. As a consequence, some authors have considered them as surveillance indicators, due to their limited interpretation of direct effects [START_REF] Ices | Report of the Workshop on Guidance for the Review of MSFD Decision Descriptor 4 -Foodwebs II (WKGMSFDD4-I)[END_REF]. On the other hand, results of food web surveillance can provide signals and indications on what multiple or combinations of pressure may be behind alterations. As a result, relevant indicators are those identifying emergent properties of food webs, which can address cumulative impacts, integrated dynamics, and responses to pressures, detect indirect and unintended consequences [START_REF] Lynam | Interaction between top-down and bottom-up control in marine food webs[END_REF][START_REF] Tam | Towards ecosystem-based management: identifying operational food-web indicators for marine ecosystems[END_REF]. These are also often used in the context of evaluating trade-offs in management and mitigate impacts on food webs. In the last evaluation, indicators employed were considered short to show emergent proprieties that reflect the myriad of overlapping human pressures on food webs. Indices used incorporate a section of the system (fish elements) and mostly detect pressures driven by fisheries. In many instances, food webs assessments were incomplete, associated with high uncertainty, or are simply impossible due to a lack of suitable data. To overcome these obstacles, modeling approaches have been considered promising [START_REF] Coll | Ecological indicators to capture the effects of fishing on biodiversity and conservation status of marine ecosystems[END_REF], but they also lack the appropriate data sets, what hinders their implementation. Solutions such as the use of long-term data series and cross-regional cooperation have been pointed out to further facilitate improved and consistent assessments (European Commission, 2020).

This study aim is to systematize D4-Food web assessment methods and environmental status, reported by MS under the MSFD framework, to identify existing inconsistencies and knowledge gaps. The environmental status and trends estimated by MSs were further used to disentangle if cause-effect relations, exerted by anthropogenic pressures, are being detected at the sea basin level. To achieve this purpose, the methodological criteria adopted to assess food webs in the second MSFD reporting cycle and their resulting assessment trends were surveyed, analyzed and statistically compared across MSs to characterize reporting strategy and congruency across the North Eastern Atlantic MSs. In a second phase, a multivariate analysis was applied to food web criteria status, considering fish ecosystem elements trends, to understand if these are significantly influenced by spatially overlapping anthropogenic pressures occurring in the sea basin. Information of human activities was used as a proxy of anthropogenic pressures to obtain information about potential exposure of food webs to anthropogenic pressure. It was hypothesized that fish food web indicators are detecting effects of anthropogenic pressure and therefore, they are contributing toward the assessment of anthropogenic effects on food webs functioning and structure.

MATERIALS AND METHODS

Data Collection

The MSFD national reports included in the present work were from Ireland (IR), UK, France (FR), Spain (SP), and Portugal (PT), including the reports from the Autonomous Regions of the latter two countries. The existing information on food-web criteria assessed by each MS was retrieved from all reports. This survey collected the following data from each report: MSFD subregion, MS, food web criteria, ecosystem elements (target), human pressures, indicator, spatial scale, temporal scale, resulting trends: significance (i.e., significant, non-significant), direction (i.e., increase, decrease) and status of the assessment (i.e., GES, below GES). All these aspects were listed and counted, to understand MSs reporting patterns for this descriptor. If results were only available graphically, an image processing method was used to extract results from mean and standard error (e.g., Image J, software GraphClick, etc.). For comprehensive and in-depth analysis, spatial stratification groups were also devised for each MS (e.g., divisions and sub-divisions), whenever this information was available. The assessments were grouped based on the spatial unit of analysis (MSFD subregions, MSs divisions, and sub-divisions) available in the reports and regional estimations were obtained for each geographical scale of assessment. This Frontiers in Marine Science | www.frontiersin.org allowed exploring the local pooled effects by classifying divisions and sub-divisions within MSFD sub-regions, whenever possible. Statistical maps were plotted to show the spatial distribution for the pooled results.

Since the only ecosystem element common to all MSs was Fish, a subset of the first database was built only including fish elements. This new dataset included food web criteria targeting Fish elements that were reported more than once, by more than one MS, to enable statistical significance and comparison across assessments. The food webs criteria fulfilling these thresholds (considering the Fish group) are identified in Table 1. Geographically referenced data on relevant anthropogenic pressures occurring in the marine environment was retrieved for each spatial unit of assessment. The pressures considered were fisheries, climate anomalies, noise, and input of nutrients, as these are the source of the most prevailing effects exerted by human activities in the European seas [START_REF] Halpern | A global map of human impact on marine ecosystems[END_REF][START_REF] Crise | A MSFD complementary approach for the assessment of pressures, knowledge and data gaps in Southern European Seas: the PERSEUS experience[END_REF][START_REF] Korpinen | Combined effects of human pressures on Europe's marine ecosystems[END_REF]. The data collected, its source and the temporal range are showed in Table 2.

Data Analysis

To compare the food webs assessment made to answer MSFD requirements for each subregion, MS, ecosystem element (target) and criterion, the number of reported indicators and the corresponding assessment trend were estimated for each of these parameters. Assessment trends for each indicator were transformed into dummy variables: -1-Decreasing, 0-Stable, and 1-Increasing. A generalized linear model (GLM), using negative binomial distribution, was applied to the assessment trend, to understand if trend patterns were related to the categorical factors under study: subregion, MS, ecosystem element and criteria/indicator. Prior to the analysis, data were tested for normality (Shapiro-Wilk test) and homogeneity of variance (Cochran test) [START_REF] Zar | Biostatistical Analysis[END_REF].

Since criteria addressing fish elements were common to all MSs, these criteria were selected for subsequent analysis. Multivariate analysis was used to find similarities between food web assessment trends across MSs units and to understand if anthropogenic pressures significantly explained variations observed. To delineate groups with distinct reporting strategy a hierarchical agglomerative clustering using arithmetic averages (CLUSTER) was used based on the Bray-Curtis similarity measure [START_REF] Clarke | Non-parametric multivariate analyses of changes in community structure[END_REF] after the fourth-root transformation of reporting trends, according to [START_REF] Field | A practical strategy for analysing multispecies distribution patterns[END_REF]. Afterward, a principal coordinates analysis (PCO) was performed on anthropogenic pressures normalized data using the Euclidean 2001). A permutational multivariate analysis of variance (PERMANOVA) was used to test the hypothesis for significant differences among the cluster groups (defined by the Bray-Curtis measure on fourth root transformed data) [START_REF] Anderson | Permanova+ for Primer: Guide to Software and Statisticl Methods[END_REF]. The similarity percentages (SIMPER) routine was applied to identify which indicators contributed the most to the withincluster similarity and the between-cluster dissimilarity [START_REF] Clarke | Non-parametric multivariate analyses of changes in community structure[END_REF]. Finally, the BEST (BIOENV) procedure was used to find the subset of pressure variables that significantly explained the clusters of food webs assessment trends determined in the cluster analysis, using the Spearman correlation [START_REF] Clarke | Non-parametric multivariate analyses of changes in community structure[END_REF]. All statistical analysis were developed in R software environment (R Core Team., 2019) and multivariate analyses were performed in PRIMER 6.0 software [START_REF] Clarke | PRIMER V6: User Manual-Tutorial[END_REF].

RESULTS

A total number of 258 food web criteria were reported in the studied area. The number of criteria implemented per subregion, MS, ecosystem element and criteria/indicator type is shown in Supplementary Table 1. Food web criteria were more reported in the CS subregion (115), followed by GNS (75) (only including the UK section) and BBIC (65) subregions. The MS with the highest number of reported criteria was the United Kingdom (159), followed by PT (38) and SP (35). Ireland and France showed a very low effort for D4 implementation with 22 and 12 criteria, respectively. The most reported ecosystem elements were Fish (121), followed by Plankton (100) and Marine Mammals (25) (Figure 2A). Ecosystem elements targeted varied between MSs: the UK assessment included a wider number of ecosystem elements such as Fish, Plankton, Marine Mammals, and Marine birds), while other MSs only reported Fish and Plankton (IR) or just Fish (PT, FR, SP). Criteria reported varied between subregions and MS (Figures 2B,C). The most reported Criteria was the relative abundance of pairs (RAP), followed by maximum mean length (MML) and typical length (TyL)-all reported by the United Kingdom. Several criteria were only reported once across all studied MSs, i.e., Shannon diversity (kg), Shannon diversity (n), Species richness, Trophic diversity, Trophic diversity (vol), Trophic richness. General linear models showed that factors influencing food webs average assessment trend were MSs, ecosystem elements and criteria, explaining 34.1% of existing deviance (p < 0.05; Table 3). MSs and ecosystem elements explained 3.7 and 3.8% of existing deviance, respectively. MSs average trends significantly decreased in FR (-0.46; SE = 0.16), while other MSs presented stable trends (Figure 3A). Significantly increasing trends were found for Marine mammal (0, 16; SE = 0.17), while a decrease was found for Marine birds (-0.42; SE = 0.19); however, these two ecosystem groups were only reported by the UK (Figure 3B). As for criteria, results showed that this factor the highest percent of existing deviance with 21.22%; Fishing in Balance (FiB), Primary Production Required (PPR) and Fullness index (FI) presented significantly higher average trends (with 1; SE = 0.00), while Trophic Diversity (n), Species richness (n), Trophic richness and Relative abundance presented the lowest significant trend (with -1; SE = 0.00) (Figure 3C). When looking at food webs assessment targeting exclusively fish ecosystem elements, of the 115 criteria reported, 95 respected the established thresholds (were implemented more than once and were common to, at least, two MSs). These were reported mostly by BBIC (42), followed by the CS (33). As for MSs, UK was the country with the highest reporting number (42), followed by PT (30), while IR exhibited the lowest (2). The most reported criteria were TyL, MML, and Large Fish Indicator (LFI)/Mean Trophic Level (MTL) (with 19, 18, and 11 criteria, respectively) (Figure 4A). There was a division between subregions: MSs reporting for the GNS and CS (UK and IR) subregion used TyL, MML, and LFI fish criteria, while BBIC and MAC (SP and PT) used MTL, MTL with thresholds (using survey and landing data) and LFI (only in PT assessment) (Figure 4A). The average trend for fish criteria varied between 0 (SE = 0.00) in MAC and 0.2 (SE = 0.77) in GNS subregions. The MS with the highest average trend was IR, and PT, with 0.5 (SE = 0.71) and 0.2 (SE = 0.53), while the lowest trends were reported by SP and UK with a 0.1 average trend (SE = 0.62 and SE = 0.72, respectively). When analyzing criteria, results showed that the highest trends were obtained for LFI and MTL_3.25 (0.64; SE = 0.50 and 0.43; SE = 0.53, respectively), while the lowest trends were exhibited by MTL_3.25_landings and MML (-0.29; SE = 0.76 and -0.11; SE = 0.68, respectively) that exhibited decreasing patterns (Figure 4B).

In regard to the multivariate assessment, the cluster analysis revealed three groups (d, e, and g), three separate units (b, c and f) and one outlier (a) at the distance level of 54.9% (Figure 5A). When these clusters were superimposed with the spatial areas of assessment of each MS, a pattern could be observed (Figure 5B). The first group included eight UK assessment units (cluster d) and presented an average similarity of ∼86.99% within units. Two units also reported by the UK were separated from this cluster (cluster b and c, with 50-55% of similarity to cluster d).

The second cluster included all PT units (including the Azores) and exhibited 93.24% of within-group similarity (cluster g). This cluster presented an average similarity of 61.69% to a single SP assessment unit (Cluster f). The third cluster included two SP assessment units (Cluster e) and presented 100% of within similarity (average trends were identical). The cluster plot showed that PT units had similarities to SP and UK reporting units (with 61.69 and 15.11% of similarity). The similarity between SP and PT was due to MTL reporting, and the similarity between PT and UK was due to LFI reporting, while the SP units presented no similarity (0%) with the UK ones (Figure 5A). In what concerns the outliers, a single SP assessment unit presented a dissimilarity of 97.5% from the overall units. Results showed that assessment for SP units was more heterogeneous, since it was separated in two cluster groups and one outlier, while PT and UK were more homogeneous (Figures 5A,B).

The PCO analysis for pressure variables showed a relevant pattern for sst, ssl, cadmium, mean fishing, Marine Traffic, and number of ports across Axis 1, which explained 28.1% of the variation. High values for these variables explained most of the PT and SP average trend results (clusters e, f, and g). Axis 2 explained 23% of the variation and was associated with a high amount of chemical nutrients (Lead, Copper, Zinc) and a high number of offshore wind turbines. This axis explained the UK units from the GNS and CS (cluster d and b) and SP (North Bay of Biscay units). Axis 3 explained 13% of variability, including variables such as no3, po4, port distance, and explained UK trend estimates (including clusters c and d). Axis 4 and 5, explained 10.1 and 9.1 21% of the variation, respectively. These had a high influence of CPUE and zinc, respectively. These two axes explained average trend patterns for partial units from the PT cluster (Zone B) and SP (North Bay of Biscay units) (Figure 6).

PERMANOVA analysis showed significant differences between clusters (F = 86.26; P(perm) < 0.001) and the pair-wise analysis revealed significant differences among UK and SP units (d, e) and UK and PT units (d-g). Cluster e showed significantly higher trends (e = 1.00; SE = 0.22 average trend), while cluster a had significantly lower average trends (a = -1.00; SE = 0.30 average trend), evidencing heterogeneity in the SP reporting.

The SIMPER analysis showed that the within-group similarity ranged from 85.61 to 100% (groups d and e, respectively) and that the criteria that mostly contributed to this were MML, and TyL in group d, and MTL in group e. The between-groups dissimilarity varied between 39.31 and 100%, and the main discriminating species are listed in Supplementary Table 2.

The results of the BEST analysis, using anthropogenic data, revealed that the combination of sst, and mean bottom trawl provided the best match to explain the average reported trends. The correlation values were high (r = 0.592) and their influence was statistically significant (p < 0.01). When added to the combination, po4, Marine Traffic, and Zinc also presented a significant correlation with the average reported trends (r = 0.589).

Anthropogenic pressures identified above have been analyzed using descriptive (graphical) and statistical (correlation; spearman test) analysis to determine if they correlate with food web trends summaries for fish. The analyses showed that, in BBIC, an increase in sst and CPUE were associated with decreasing trends for food webs. While in the CS and GNS, high marine traffic and the number of offshore installations corresponded to food webs decreasing trends. However, none of these variables was significant.

DISCUSSION

The new MSFD report was published in 2020, pointing out broad progresses in relation to the 1 st MSFD report, published in 2012. Improvements were largely driven by the recently published legal documents that defined aspects such as criteria, ecosystem components, anthropogenic pressures and spatial scales for reporting (European Commission, 2017a,b), but were also motivated by MSs effort, that revealed lessons learned from the 1 st cycle and increased reporting coherence (European Commission, 2020). However, food webs descriptors are still not properly assessed, are poorly coordinated within each subregion and are underreported due to a lack of data and consensus on the indicators or indexes that should be used. Food webs (D4) assessment is complex by nature as it needs to measure energy flow across guilds through simple parameters, that require detecting changes on energy transfer [START_REF] Ices | Report of the Workshop on Guidance for the Review of MSFD Decision Descriptor 4 -Foodwebs II (WKGMSFDD4-I)[END_REF].

This work assessed D4 implementation and assessment results across the NE Atlantic basin, including four subregions and six MSs, to disentangle if the current assessment encompassed the ecological aspects of food webs at subregion scale (European Commission, 2017a) and if trends detected emergent properties from single or cumulative anthropogenic pressures.

Results revealed that reporting strategies varied between MSs since each country supplied a distinct level of information. Relevant knowledge gaps were identified for IR and FR, which reported two or one indicators, while MSs such as UK, SP, and PT developed fit-fo-purpose methods [START_REF] Ices | Report of the Workshop on Guidance for the Review of MSFD Decision Descriptor 4 -Foodwebs II (WKGMSFDD4-I)[END_REF][START_REF] Tam | Towards ecosystem-based management: identifying operational food-web indicators for marine ecosystems[END_REF]. As a result, reporting differences were found between MSs, ecosystem elements and criteria selected, showing a lack of congruency in D4 implementation. The UK was the only MS addressing other ecological elements than Fish and Plankton (i.e., marine mammals, marine birds) using specific data sets and indicators. This was due to the adoption of long-term monitoring programs and data availability. Also, the UK has closely followed OSPAR guidance in the implementation of indicators, what largely contributed to the choice of criteria (OSPAR., 2017; UK Marine Monitoring, Assessment Strategy, Uk Monitoring, and Assessment Reporting Group., 2019). That approach was not followed by other MSs in the CS, such as IR and FR.

In the BBIC subregion, SP and PT implemented food webs metrics indicated by [START_REF] Ices | Report of the Workshop on Guidance for the Review of MSFD Decision Descriptor 4 -Foodwebs II (WKGMSFDD4-I)[END_REF] or [START_REF] Tam | Towards ecosystem-based management: identifying operational food-web indicators for marine ecosystems[END_REF] and were coherent to some extent. On the other hand, the FR assessment reported raw information from stock assessment, not implementing food webs indicators and revealing lower reporting level and average trends. In the BBIC subregion, FIGURE 6 | Principal coordinates analysis (PCO) for the anthropogenic pressures' variables identified in Table 2. Clusters as obtained in the cluster analysis represented in Figure 4A.

no higher-trophic (seabird and megafauna productivity) and lower-trophic (primary production) ecosystem elements were addressed. Indicators assessing such groups are needed to reflect processes viewed from the opposite ends of the food web (e.g., PPR, zooplankton index, seabird productivity index, etc.). For example, PPR is an integrative indicator that represents the amount of primary productivity to sustain a fishery and enables the comparison of energy requirements across different fisheries [START_REF] Pauly | Primary production required to sustain global fisheries[END_REF][START_REF] Chassot | Global marine primary production constrains fisheries catches[END_REF][START_REF] Tam | Towards ecosystem-based management: identifying operational food-web indicators for marine ecosystems[END_REF], while seabird productivity is an indicator of ecosystems health, through food availability (forage fish), accumulation of contaminants and environmental pollutants, and physiologic stress caused by environmental change [START_REF] Mallory | Seabirds as indicators of aquatic ecosystem conditions: a case for gathering multiple proxies of seabird health[END_REF].

According to the Directive, EU Member States can monitor as many guilds as deemed appropriate (with a minimum of three), but at least two non-fish guilds should be addressed to ensure that not only fish are monitored (European Commission, 2017a). Even though indicators based on fish abundance and biomass can inform on the structural properties of food webs, they provide only partial information about its functioning, failing to consider complex trophic interactions and wholesystem energy flow [START_REF] Rombouts | Food web indicators under the marine strategy framework directive: from complexity to simplicity?[END_REF]. However, only the UK evaluated two non-fish guilds. The causes pointed out to explain this inequality are knowledge gaps in long term monitoring or the inexistence of minimum quality data that can support these assessments. Currently, the best data available comes from commercially exploited fish and shellfish stocks for which extensive monitoring programs exist and from phytoand zooplankton communities obtained through the Continuous Plankton Recorder; but even these are limited to certain areas of the North Atlantic Ocean (European Commission, 2020). To overcome this issue, the EU has identified strategies such as the use of theoretical and empirical models to identify potential impacts and key properties that should be monitored and the necessity of harmonized monitoring programs to generate proper assessments for trophic levels (and marine regions) (European Commission, 2020).

The average trends established in the assessments were stable or improved in the UK, SP, PT and IR, and decreased in the FR assessment. The UK presented a significantly decreasing trend for Marine birds, that were considered below GES or at risk in the CS and GNS. Decreasing patterns are of concern and direct management actions could be either top-down control rules aimed at relieving fishing pressure on lower-trophic species or bottom-up policies directed to improve water quality or habitat, which may also include improved management at landsea interfaces [START_REF] Mallory | Seabirds as indicators of aquatic ecosystem conditions: a case for gathering multiple proxies of seabird health[END_REF][START_REF] Tam | Towards ecosystem-based management: identifying operational food-web indicators for marine ecosystems[END_REF]. On the other hand, marine mammals exhibited the highest average trend, evidencing increasing populations in the UK and indicating a recovery of mammal's populations. For the fish group, the average trend reported varied significantly between the type of metric employed, which resulted in high reporting heterogeneity.

When analyzing food webs assessment approaches employed, the multivariate analysis applied to fish criteria revealed three similar food webs assessment groups including UK units, PT units, and SP units. Some units from IR and SP were considered dissimilar from the main groups. Within group similarities were based on MS and showed that UK, IR, PT, and SP adopted distinctive reporting metrics and therefore resulting trends. Although the UK and IR used similar indicators (MML and TyL), IR data sets did not enable a comprehensive assessment such as the UK, because the survey time-series from Irish waters is comparatively shorter [START_REF] Machado | Assessment level and time scales of biodiversity indicators in the scope of the marine strategy framework directive -a case study for the NE Atlantic[END_REF]; what has resulted in a dissimilar assessment for IR (European Commission, 2020). To some extent, PT and SP used identical indicators (MTL and MTL with thresholds) increasing similarities. However, the PT assessment adopted indicators equally across all its spatial units, while the SP assessment employed indexes heterogeneously across its territory. The SP assessment reported research findings obtained by EU funded projects (EcArpha project), and peerreviewed publications [START_REF] Arroyo | Towards coherent GES assessments at sub-regional level: signs of fisheries expansion processes in the Bay of Biscay using an OSPAR food web indicator, the mean trophic level[END_REF][START_REF] Preciado | Small-scale spatial variations of trawling impact on food web structure[END_REF]. These studies included fit for purpose outputs concerning food webs assessment, but the metrics, and the temporal and spatial scales were dissimilar between them, what could explain part of the heterogeneity obtained in the SP assessments and the significant differences found between the SP clusters'.

When looking at anthropogenic pressures and how they overlap with food webs assessment trends, results for UK units in the CS basin were influenced by high input of organic nutrients (no3, po4) and port distance, while GNS units were influenced by primary production (chl), chemical nutrients (Lead, Zinc, Cadmium) and the number of offshore installations. The high input of nutrients in the UK-CS units evidenced eutrophication and higher primary productivity, which can increase bottom-up effects [START_REF] Cury | Optimal environmental window and pelagic fish recruitment success in upwelling areas[END_REF]. In the NE Atlantic, eutrophication has been recorded in the southern parts of the North Sea and along the North western coast of France. Nevertheless, nutrient inputs from point sources have significantly decreased; although inputs from diffuse sources, i.e., losses from agricultural activities, are still too high (European Commission, 2020). In the GNS, chemical contamination has been decreasing due to regulations adopted, however, Cd levels are increasing in the Southern North Sea and need to be investigated. The assessment of chemical contaminants under the WFD showed that the worst scenarios can be been found in the Baltic and the GNS, with 55 and 51% of the area assessed below GES (European Commission, 2020). As for offshore installations (e.g., offshore renewable energies), long term impacts are still relatively unknown: offshore wind farms are a recently developed sector for which there are no long-term monitoring data. As a consequence, there is still a high level of uncertainty on the impacts of offshore wind parks on ecosystem structures and processes [START_REF] Alexander | Challenges of achieving good environmental status in the Northeast Atlantic[END_REF]. Noise affects especially marine mammals, but existing studies also show that habitat change, by adding artificial hard substrate in areas where mainly soft substrate occurs, can cause food webs shifts. Artificial reefs, such as offshore wind farms, are used by benthopelagic and benthic species as feeding grounds for prolonged periods [START_REF] Mavraki | Offshore wind farms and the attraction-production hypothesis: insights from a combination of stomach content and stable isotope analyses[END_REF].

In PT and SP units, anthropogenic pressures such as fishing (bottom trawl and mean fishing), seabed litter, sst, ssl, ph, and marine traffic explained food webs trends. Similar results were found by [START_REF] Korpinen | Combined effects of human pressures on Europe's marine ecosystems[END_REF] for this region, identifying global warming (increasing sst), fisheries and shipping (underwater noise) as the major challenges that need to be addressed when considering cumulative anthropogenic effects. This region is characterized by narrow shelf areas [START_REF] Korpinen | Combined effects of human pressures on Europe's marine ecosystems[END_REF], were trawling activities occur more intensely [START_REF] Eigaard | The footprint of bottom trawling in European waters: distribution, intensity, and seabed integrity[END_REF]. Studies on the ratio of the trawling footprint over the landings showed that the highest ratios occurred in the Iberian Portuguese area, reflecting the higher level of exploitation when compared with some of the Atlantic management areas where fishing effort has been reduced [START_REF] Eigaard | The footprint of bottom trawling in European waters: distribution, intensity, and seabed integrity[END_REF]. Regarding marine traffic, this activity is widely distributed in all EU marine regions and its intensity is highest along shipping corridors and near ports. Underwater noise from commercial shipping is considered one of the most pervasive noise sources. Underwater distribution and noise effects occurring in Europe are still unknown, however, impacts have been observed on all trophic levels, from invertebrates to fish, marine mammals, and diving seabirds [START_REF] Dekeling | Monitoring Guidance for Underwater Noise in European Seas, Part I: Executive Summary. A guidance document within the Common Implementation Strategy for the Marine Strategy Framework Directive by MSFD Technical Subgroup on Underwater Noise[END_REF][START_REF] Barnett | Underwater Noise-The Neglected Threat to Marine Life[END_REF][START_REF] Farcas | Validated shipping noise maps of the Northeast Atlantic[END_REF]. Climate anomalies (i.e., sst, ssl, and ph) also explained food web average trends in the southern countries. Climate change effects on latitudes of species transition, such as the Iberian Peninsula, are expected to have wider degradation effects on food webs as the cold water species habitat may be contracted, and warmer species habitat may expand [START_REF] Serrat | Condition and reproductive strategy of the Argentina sphyraena, a cold-water species in the Mediterranean sea[END_REF], changing food webs structure and resilience [START_REF] Lynam | Interaction between top-down and bottom-up control in marine food webs[END_REF]. Even minor temperature changes can have significant effects on the onset of the spring phytoplankton bloom, the relative abundance of zooplankton, and the abundance and distribution of commercial fish species [START_REF] Alexander | Challenges of achieving good environmental status in the Northeast Atlantic[END_REF].

The resulting matrix showed significantly high correlation values and as a result, it was possible to establish links between trends of abundance and distribution of fish elements and human pressures, such as fishing and climate anomalies and marine traffic in the southern region of the BBIC or the input of nutrients and/or chemicals in the UK waters. These findings indicate a relationship between food webs pressure and state and highlight the most relevant anthropogenic disturbances across the marine areas under study. Nevertheless, the methodological approach applied in this work, presented limitations since the assessment data set was limited by MSs reporting (only enabling the comparison of fish elements), it did not account for spatial and temporal variability and anthropogenic pressures were difficult to unequivocally distinguish from environmental variability. Notwithstanding, this work provides relevant insights on aspects that are hindering the detection of impacts and need to be considered in future assessments such as (1) the metrics/indicators and ecosystem elements assessed should be harmonized across the same subregion, (2) the improvement of data mining and modeling for well monitored indicators (status and pressures) [START_REF] Walmsley | Guidance for assessments under article 8 of the marine strategy framework directive[END_REF][START_REF] Borja | Yes, we can! large-scale integrative assessment of European regional seas, using open access databases[END_REF], (3) further development of monitoring networks, (4) improve the spatial coverage and resolution of the assessment, since not all MSs are adopting similar scales [START_REF] Machado | Assessment level and time scales of biodiversity indicators in the scope of the marine strategy framework directive -a case study for the NE Atlantic[END_REF][START_REF] Machado | Effects of scale on the assessment of fish biodiversity in the marine strategy framework directive context[END_REF], ( 5) the methodologies and standards should account for the specificities of the region and detect region-specific sensitivity values (European Commission, 2020).

Future work on food webs should use data that are often automatically recorded (e.g., automatic identification system (AIS) for shipping, vessel monitoring system (VMS) for fisheries), stored in permit databases (e.g., marine construction, dredging, dumping, fish catches), or observed from satellites (e.g., sst, chl, ph, oil spills, etc.). The use of these data sets could improve the assessment of single or cumulative effects on food webs, which in the past have been limited by data availability [START_REF] Borja | Yes, we can! large-scale integrative assessment of European regional seas, using open access databases[END_REF][START_REF] Korpinen | Combined effects of human pressures on Europe's marine ecosystems[END_REF]. However, more work is still needed in food web index implementation and development, to include non-linear responses and synergistic and antagonistic effects of pressures on ecosystem elements [START_REF] Korpinen | A global review of cumulative pressure and impact assessments in marine environments[END_REF][START_REF] Taherzadeh | A trait-based framework for explaining non-additive effects of multiple stressors on plankton communities[END_REF].

In general, although the EU has surpassed previous assessments, food web evaluation is still lacking an appropriate ecological dimension. The present study emphasized the need for EU MSs further improve their coordination and calibration at sea basin level, concerning ecosystems elements, criteria, indicators, and the spatial and temporal scales used in food webs assessment and monitoring programmes. Only afterword's it will be possible to determine coordinated objectives and targets and having effective measures tackling the right pressures. The analysis proposed here allows, as a first step, to define the highest pressures by which managers can steer toward food web targets in the studied basins. Insights to this discussion are both timely and relevant, especially as EU MSs are preparing their program of measures to fulfill the aims of the MSFD.

CONCLUSION

The present work highlights relevant aspects that need to be tackled in the assessment of food web in the context of the MSFD. The criteria and indicators selected need to be further calibrated, concerning the target element addressed and the metric employed, at subregion level. Although some advances have been made in this direction with the legal diploma 848/2017, the present work showed that food webs assessment is largely dependent on MS reporting. Only by using a harmonized set of indicators it will be possible to assess food webs status at an ecosystem level (at the subregion and sea basin level), to understand the effects of different human pressures, and to define effective management decisions. Importantly, the direct or indirect anthropogenic pressures were detected by trends assessed in the MSFD. The pressures of concern were fisheries, and climate change in the Iberian Peninsula, while eutrophication and chemical contamination affected CS and GNS surveyed regions. Overall, the MSFD assessment showed that human activities are not at an environmentally sustainable level and that pressures exert combined effects on food webs ecosystem components, i.e., fish. At the moment, the most urgent step would be to ensure a coherent assessment that foresees ecological relevant aspects of food webs and considers effects of relevant and on-going human pressures [START_REF] Elliott | Activity-footprints, pressuresfootprints and effects-footprints -walking the pathway to determining and managing human impacts in the sea[END_REF], therefore promoting sea basin level calibration. A basis for this would be to continue to promote monitoring programs and data rich platforms that can support assessments [START_REF] Korpinen | Combined effects of human pressures on Europe's marine ecosystems[END_REF].

A

  literature review was applied to MSFD reports submitted in the Central Data Repository (CDR) of the European Environment Information and Observation Network (EIONET) Portal and the Marine Information System For Europe (WISE Marine) database (EEA, 2020, 2021) to survey results on the assessment of food webs criteria obtained within the scope of Descriptor 4-Food webs. The search included all reports submitted until 2020, concerning the 2nd assessment cycle (2012-2018) in the Northeast Atlantic basin. The survey included the subregions Macaronesia (MAC), Bay of Biscay and Iberian coast (BBIC), Celtic Seas (CS) and the United Kingdom (UK) part of the GNS (Greater North Sea), as defined in Article 4(2) of Directive 2008/56/EC (European Commission, 2008; Figure 1). The list of ecosystem elements (targets) and human-driven pressures considered are defined in the MSFD, for Descriptor 4-Food webs, in the Commission Decision 2017/845 and 2017/848 (European Commission, 2017a,b).

FIGURE 1 |

 1 FIGURE 1 | Subregions included in the analyze (UK-United Kingdom).

FIGURE 3 |

 3 FIGURE 3 | Average assessment trend for food web criteria per MS (A), ecosystem element (B) and criteria (C) resulting from the GLM analysis applied to Descriptor 4-Food webs data in the NE Atlantic.

FIGURE 4 |

 4 FIGURE 4 | Number of fish criteria reported per Member State (A) and average assessment trend for fish food web indicators (B).

FIGURE 5 |

 5 FIGURE 5 | Dendrogram resulting from cluster analysis of Food webs transformed average trend, reported by Member States (A) and spatial distribution of the cluster assemblages (B). Five assemblages and three outliers were identified at the 57% level of similarity.

TABLE 1 |

 1 Food web criteria selected by each MS concerning fish taxonomic elements. if there is a spatial pattern of pressure variables that were associated with MSs food webs assessment [pressures data used: fisheries mean, ph, silica, seabed litter, beach litter, sea surface temperature anomaly, sea surface level, chlorophyll, phosphates (po4), nitrates (no3), port number, distance to port, bottom trawl (mean), marine traffic, number of wind turbines, the average number of wind turbines, CPUE, zinc, lead, cadmium, copper, and fluor across reported units (see data description in Table 2)] (Clarke and Warwick,

	Food web criteria using fish ecosystem elements	MS

Mean Trophic Level (MTL) surveys (and thresholds) SP, PT Mean Trophic Level (MTL) landings (and thresholds) SP, PT Large Fish Indicators (LFI) UK, PT Typical Length (TyL) UK, IR Mean maximum length (MML) UK, IR distance to determine

TABLE 2 |

 2 Anthropogenic data retrieved for the study: type of pressure, data, source, and temporal scale of the dataset.

	Pressure	Type of data	Source of data	Temporal range
	Fisheries	VMS data fisheries	EMODNET (EMODnet Human Activities)	2010-2018
		VMS bottom trawl fisheries	AIS derived high-resolution fishing effort layer for European	2014-2015
			trawlers of more than 15 meters long (Vespe et al., 2016)
		Number of hour fishing: (VMS data	Daily Fishing Effort at 10th Degree Resolution by MMSI, version	(2012-2018)
		2012-2016)	2.0 (Kroodsma et al., 2018)
		ICES landings (ICES database)	Official Nominal Catches. Catches in FAO area 27 by country,	2006-2018
			species, area, and year as provided by the national authorities
			(ICES, 2021b)
	Nutrients	Chemical nutrients	Chemical on biota (Mytilus spp.): Zinc, Cadmium, Copper, Fluor,	2000-2016
			Lead (ICES, 2021a)
		No3, Po4, Chlorophyll	Copernicus-Marine Data: (mean data) (EU Copernicus Marine	2010-2018
			Service Information, 2021a)
		Discharge points	EMODnet (2021a)	2010-2018
		Beach litter	EMODnet (2021a)	2010-2018
		Seabed litter	EMODnet (2021a)	2010-2018
	Climate change	SST and sea surface level	Calculated as a climate anomaly (mean data from 2010-2018,	2000-2018
			subtracted to data from the mean 2000-2010) (EU Copernicus
			Marine Service Information, 2021b)
		Climate anomaly	Air temperature changes (NCAR community, 2012)	2000-2018

Ph, Si Calculated as ph/si anomaly (mean data from 2012-2018, subtracted to data from the mean 2000-2012) (EU Copernicus Marine Service Information, 2021a) 2000-2018 Noise Nr of ports EMODnet (2021b) 2018 MRE and Offshore wind installations (nr of installations) EMODnet (2021b) 2018 Marine Traffic (VMS): All traffic EMODnet (2021b) 2010-2018 FIGURE 2 | Number of reported criteria per ecosystem element (A), Member state (B) and subregion (C).

TABLE 3 |

 3 Generalized linear models (GLMs) with gamma distribution applied to the average trend data for Descriptor 4-Food webs, per subregion, Member State, criteria, ecosystem element, and their corresponding interaction.

		Residual Deviance % explained p-value
	Subregion	0.14	22.24	0.64	0.534
	Member state	0.83	21.56	3.69	0.014
	Ecosystem element (Target)	0.85	21.53	3.81	0.005
	Criteria	4.75	17.64	21.22	< 0.001
	Subregion: Criteria	0.49	21.90	2.20	0.942
	Subregion: Target	0.00	22.39	0.00	-
	Criteria: MS	0.58	21.81	2.57	0.187
	Target: MS	0.00	22.39	0.00	-
	Total explained			34.14

Frontiers in Marine Science | www.frontiersin.org

October 2021 | Volume 8 | Article 699566

FUNDING

This publication was financed by the Portuguese national funds through FCT-Fundação IP under project reference UIDB/04292/2020-granted to MARE, project reference UIDB/50019/2020-granted to IDL. This publication was also funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement N810139: Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation -PORTWIMS. IM has received funding support from the FCT-Fundação IP through a Ph.D. fellowship (PD/BD/135065/2017).

DATA AVAILABILITY STATEMENT

Publicly available datasets were analyzed in this study. This data can be found here: https://cdr.eionet.europa.eu/ and https:// water.europa.eu/marine/about/wise-marine.

AUTHOR CONTRIBUTIONS

IM carried out data analysis and wrote the manuscript. JC and HC provided guidance on the analysis and provided edits for the manuscript. All authors contributed to the article and approved the submitted version.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars. 2021.699566/full#supplementary-material

Conflict of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Shakeel A Sheikh

Md Sahidullah

Fabrice Hirsch

Slim Ouni

Robust Stuttering Detection via Multi-task and Adversarial Learning

Keywords: stuttering, disfluency, multi-tasking, adversarial, speech disorder

By automatic detection and identification of stuttering, speech pathologists can track the progression of disfluencies of persons who stutter (PWS). In this paper, we investigate the impact of multi-task (MTL) and adversarial learning (ADV) to learn robust stutter features. This is the first-ever preliminary study where MTL and ADV have been employed in stuttering identification (SI). We evaluate our system on the SEP-28k stuttering dataset consisting of ≈ 20 hours of data from 385 podcasts. Our methods show promising results and outperform the baseline in various disfluency classes. We achieve up to 10%, 6.78%, and 2% improvement in repetitions, blocks, and interjections respectively over the baseline.

I. INTRODUCTION

Speech impairments are speech disorders in which a speaker toils to create the speech sounds required for communication [START_REF] Guitar | Stuttering: An Integrated Approach to its Nature and Treatment[END_REF]. These speech impairments can take different forms which include apraxia, cluttering, dysarthria, stuttering, etc [START_REF] Sheikh | Machine learning for stuttering identification: Review, challenges & future directions[END_REF]. Among the speech disorders, stuttering -also known by the name stammering/disfluency -has been found to be the most common one [START_REF] Sheikh | Machine learning for stuttering identification: Review, challenges & future directions[END_REF]. Stuttering is a neuro-developmental speech impairment which occurs due to the malfunctioning of sensorimotors, responsible for speech production [START_REF] Smith | How stuttering develops: The multifactorial dynamic pathways theory[END_REF], and usually take the shape of core behaviours: blocks, repetitions and prolongations [START_REF] Guitar | Stuttering: An Integrated Approach to its Nature and Treatment[END_REF].

In conventional stuttering detection (SD) paradigm, the speech or recording of PWS is manually analyzed and monitored by speech therapists. However, this method of detecting stuttering is very arduous and time-intensive, and is also prejudiced towards the subjective belief of speech therapist. Besides, automatic speech recognition (ASR) tools also fail to recognize the stuttered speech [START_REF] Mitra | Analysis and tuning of a voice assistant system for dysfluent speech[END_REF], that makes it unrealistic to easily access virtual assistants like Alexa, Apple Siri, Cortana, etc. for PWS. The SD can be utilized to adapt and enhance ASR towards stuttered speech.

The advancements of deep learning (DL) has shown immense development in various speech domains like speech synthesis [START_REF] Ning | A review of deep learning based speech synthesis[END_REF], ASR [START_REF] Nassif | Speech recognition using deep neural networks: A systematic review[END_REF], emotion detection [START_REF] Akc | Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers[END_REF], speaker identification [START_REF] Li | Speaker-invariant affective representation learning via adversarial training[END_REF], etc, however SI/SD has received minimal attention from the DL paradigm. DL can help in detecting the various types of stuttering by exploiting the presence of various acoustic cues in stuttered speech [START_REF] Sheikh | Machine learning for stuttering identification: Review, challenges & future directions[END_REF]. The recent works have made significant strides in DL based SD [START_REF] Sheikh | Machine learning for stuttering identification: Review, challenges & future directions[END_REF], however, all the existing systems focus only on single task stuttering learning strategy without focusing on the auxiliary tasks. Unfortunately, this is not the approach that we human beings process the speech utterances, rather we simultaneously decode the primary task with other meta contents like speaker characteristics, linguistic content, etc. This multi-task learning (MTL) have been proven effective in computer vision, ASR, emotion detection, by jointly learning several tasks through a learned shared encoding [START_REF] Vandenhende | Multi-task learning for dense prediction tasks: A survey[END_REF]. In this work, we investigate the impact of MTL approach in SD, by jointly learning stuttering and metada information.

SD systems are further affected by source diversity, be that linguistic content, speaker, gender, accent, or encompassing acoustic conditions [START_REF] Sheikh | Machine learning for stuttering identification: Review, challenges & future directions[END_REF]. In this study, in addition to MTL, we also investigate an ADV framework with the aim to learn robust stuttering representations. Among the various source variabilities, we specifically focus on eliminating podcast (meta-data) information with the intention to improve the detection performance of stuttering and its types. Inspired by unsupervised domain adaptation training [START_REF] Ganin | Unsupervised domain adaptation by backpropagation[END_REF] and speaker invariant affective learning [START_REF] Li | Speaker-invariant affective representation learning via adversarial training[END_REF], we explore and provide the first ever deeper analysis by using MTL and ADV in the context of SD.

Our main contributions are: 1) We investigate the effect of applying MTL and ADV in the context of SD. The preliminary results show that the MTL boosts the detection performance of only disfluent classes, and increases the confusion rate for fluent samples. 2) We observed that the ADV framework learns robust stutter features, which are stutter discriminate but at the same time are metada invariant as shown in Fig. 4. 3) To address class imbalance, we use a multi branch training scheme.

II. MOTIVATION AND RELATED WORK

In this section, we mention the related work and describe briefly the motivation for using MTL and ADV frameworks in the context of SD.

A. Motivation

Multi-task learning (MTL) is basically an inductive transfer scheme which simultaneously learn multiple tasks by optimizing several objectives using a shared hidden representation [START_REF] Vandenhende | Multi-task learning for dense prediction tasks: A survey[END_REF], [START_REF] Caruana | Multitask learning[END_REF]. It provides an effective way of improving generalization. MTL aims to exploit the integrated knowledge across multiple domains with the intention of improving the performance of Fig. 1. Robust SD using ADV MTL with red curve indicating gradient reversal, CE: Cross entropy, θ's are the parameters of each module, X: MFCC input R 20×T , Z: encoder output, E(X).

primary task [START_REF] Crawshaw | Multi-task learning with deep neural networks: A survey[END_REF]. MTL has been successfully applied in several tasks in speech domain such as emotion detection, speaker recognition, ASR [START_REF] Vandenhende | Multi-task learning for dense prediction tasks: A survey[END_REF], etc.

A contrasting approach is to presume that robust acoustic representation of stuttering should be invariant to secondary tasks, in particular speaker recognition, as stuttering speech task should not depend on a particular set of speakers. The way to achieve such robust invariances is ADV learning [START_REF] Ganin | Unsupervised domain adaptation by backpropagation[END_REF]. The ADV framework learns invariances to metada characteristics in a min-max fashion that the main branch is trained to maximize the metadata (podcast) classification loss and the speaker branch is trained to minimize its classification loss. Robust representation of speech signals have been studied via ADV training in various speech domains such as emotion detection [START_REF] Li | Speaker-invariant affective representation learning via adversarial training[END_REF] and ASR [START_REF] Meng | Speaker-invariant training via adversarial learning[END_REF]. However, there is no prior study on the application of MTL and ADV frameworks in the domain of acoustic SD.

B. Related Work

Most of the work related to SD employ mel-frequency cepstral coefficients (MFCCs) or spectrograms as feature extraction methods [START_REF] Sheikh | Machine learning for stuttering identification: Review, challenges & future directions[END_REF]. T. Kourkounakis et al [START_REF] Kourkounakis | Detecting multiple speech disfluencies using a deep residual network with bidirectional long short-term memory[END_REF] used ResNet and BiLSTM to solve the problem of SD. The latter was formulated as a multiple binary classification problem, where they trained separate model for each type of stuttering. In another study, they proposed FluentNet for end-to-end identification of stuttered speech [START_REF]Fluentnet: End-to-end detection of stuttered speech disfluencies with deep learning[END_REF]. The networks were trained using the spectrograms, which were the sole input features to the model. The proposed architecture showed promising results on a small subset of speakers (25) of UCLASS dataset, but did not perform well on a large set of speakers [START_REF] Sheikh | Stutternet: Stuttering detection using time delay neural network[END_REF]. In addition, they have mentioned it end-to-end, but they are still using the hand crafted spectrogram features. Recently Lee et al [START_REF] Lea | Sep-28k: A dataset for stuttering event detection from podcasts with people who stutter[END_REF] introduced ConvLSTM model for detecting various types of stuttering: blocks, interjections, word repetitions, and sound repetitions. The architecture takes 40 MFCCs with pitch and articulatory features as an input, and was trained with a batch size of 256 using cross entropy loss function. They also introduced the new stuttering SEP-28k dataset with metadata information, which we exploit in this study. Melanie et al. [START_REF] Jouaiti | Dysfluency classification in stuttered speech using deep learning for real-time applications[END_REF] recently introduced the phoneme based BiLSTM for disfluency detection. Even though the model shows promising results but they have mixed all the datasets together for their case study. We recently introduced a single branch multi-class StutterNet, a time-delay neural network based stuttering classifier, capable of identifying fluent segment and core behaviours [START_REF] Sheikh | Stutternet: Stuttering detection using time delay neural network[END_REF], and, it shows encouraging results compared to state-of-the-art on a large set of speakers. The StutterNet was trained with MFCCs and cross entropy on a set of 104 speakers from UCLASS dataset. A detailed summary of various SD methods can be found in the review paper by Shakeel et al [START_REF] Sheikh | Machine learning for stuttering identification: Review, challenges & future directions[END_REF].

This study is an extension of our previous work Stutter-Net [START_REF] Sheikh | Stutternet: Stuttering detection using time delay neural network[END_REF]. Due to the diversity and uniqueness of stuttering, it continues to be the most demanding and challenging to detect, due to its inherent nature of huge class imbalance across different types of disfluencies [START_REF] Sheikh | Machine learning for stuttering identification: Review, challenges & future directions[END_REF]. Training a model on a such type of dataset will bias the majority class. To this end, we employ a MB StutterNet (baseline), where, we introduce the pseudo temporary labelling of all disfluent classes as one class. The MB StutterNet is a two branched network, with one branch for differentiating between fluent vs disfluent and the other branch differentiates among various disfluent types. In addition, we investigate the effectiveness of MTL in SI/SD domain, by jointly training the primary task i.e, stuttering classification and auxiliary task i.e, podcast classification. The MTL has shown promising results in other domains of speech such as emotion detection, speaker recognition, ASR, etc. [START_REF] Vandenhende | Multi-task learning for dense prediction tasks: A survey[END_REF]. However, no attention has been given regarding the impact of adapting MTL framework in SD. This paper proposes a SI/SD method that utilizes TDNN based MTL and ADV with the intention to improve generalization performance of primary task (SD) by learning jointly the metada information.

III. PROPOSED FRAMEWORK

This section describes the framework for MTL and ADV schemes that we have employed in this case study.

A. Multi-Task Learning

Within the MTL framework, given a training set of D = (X i , s i , d i ) n i=0 , where each sample is comprised of three components: a sequence of R 20×T acoustic MFCC features, a speaker label s i , pseudo fluent label f i (for FluentNet, all different stuttering classes are treated as a single disfluent class by introducing a temporary pseudo-labelling scheme to solve the class imbalance problem) and a stutter label d i , we consider a shared encoder E having parameters θ e and three classifier branches S with parameters θ s , F with parameters θ f and D with parameters θ d for podcast, fluent and stuttering sub type detections respectively. Our goal is to minimize both stutter loss L stutter (θ e , θ d ) and speaker (podcast here) loss L speaker (θ e , θ s ). Therefore, the total objective function in MTL can be represented as:

L(θe, θf, θd, θs) =(1 -λ) * Lstutter(θe, θf, θd) + λ * Lspeaker(θe, θs) Lstutter(θe, θf, θd) =Lfluent(θe, θf) + Ldisfluent(θe, θd) (1)
where, λ ∈ (0, 1), is a weighting parameter between the losses tuned manually.

B. Adversarial Learning

In applying ADV framework to stuttering domain, we aim to learn acoustic representations which are robust and metada invariant, but at the same time is stuttering discriminative. For this purpose, similar to MTL, we consider an encoder E having parameters θ e , which maps the T input acoustic features to representation Z by Z i = E(X i ). This Z i representation is then fed to the DisfluentNet (denoted by D) and FluentNet decoders (denoted by F) to output the stuttering and fluent class probabilities p(d i /Z i ; θ e , θ d ) and p(f i /Z i ; θ e , θ f ) respectively. In addition, this Z i is fed to another branch speaker decoder (denoted by S), to output metadata (podcast) probabilities p(s i /Z i ; θ e , θ s ). To learn metadata-invariant representations, our goal is to minimize stuttering classification loss, L stutter (θ e , θ d ) and to optimize θ e to maximize the speaker classification loss, L speaker (θ e , θ s ) and, at the same time minimize the speaker classification loss by optimizing θ s . The total objective loss becomes:

L(θe, θf, θd, θs) = Lstutter(θe, θf, θd) -λ * Lspeaker(θe, θs)

Lspeaker = - (X i ,f i ,d i ,s i )∈D log(p(si/Zi)) Lstutter = - (X i ,f i ,d i ,s i )∈D log(p(di/Zi)) + log(p(fi/Zi)) (2)
where, λ ∈ (0, 1), is a weighting trade-off hyper-parameter, and s i , f i and d i are the labels. We optimize the parameters using Adam by applying the gradient reversal layer [START_REF] Ganin | Unsupervised domain adaptation by backpropagation[END_REF] in speaker branch during backpropagation as shown by red curve in Fig. 1.

C. Model Architecture

Our SD models are based on time delay neural networks, which has been proven effective in various speech domains [START_REF] Sheikh | Stutternet: Stuttering detection using time delay neural network[END_REF]. The model is fed with mean-normalized 20dimensional MFCC input features extracted on a 20 ms sliding window with a hop length of 10 ms. The proposed ADV model is based on MTL framework with four components including the encoder E, which generates representations, the FluentNet F classifier branch, the stutter classifier module named as DisfluentNet D, and the speaker classifier S as illustrated in Fig. 1. The encoder module is composed of five time delay layers with the first three focusing only on small contextual frames of [t -2, t + 2], {t -2, t, t + 2}, and {t -3, t, t + 3} respectively. The remaining two focuses on the {t} contextual frames. The last layer of encoder is statistical pooling layer (SPL) with mean and standard deviation. The fixed dimension representation from SPL, which is the output of the E, is further passed to the three sub-classifiers: DisfluentNet, FluentNet and SpeakerNet. These three sub modules are comprised of three fully connected layers and a softmax layer. A dropout of 0.2 is applied after first two fully connected layers in each branch. Except SPL, each layer is followed by ReLU non linear activation function and a 1D batch normalization. To address the class imbalance problem, we use two branches FluentNet and DisfluentNet for SD. During inference time, speaker branch S θ is discarded, and FluentNet output is considered, if it predicts fluent, otherwise DisfluentNet predictions are considered.

IV. EXPERIMENTAL SETUP Dataset: For our experimental studies, we have used the SEP-28k stuttering dataset, curated recently by Apple from a set of 385 podcasts across 8 shows [START_REF] Lea | Sep-28k: A dataset for stuttering event detection from podcasts with people who stutter[END_REF]. From each podcast episode, 40-250 three second segments were extracted, resulting in an overall total samples of 28,177. The SEP-28k dataset is having two different types of annotations: stuttering and non-stuttering. The stuttering annotation consists of core behaviours, which include repetitions, prolongations and blocks. In addition to core behaviour labels, it also contains annotation for interjections and fluent segments. The non stuttering annotation is comprised of poor audio quality, music, no speech, unsure, unintelligible and natural pauses. In this experimental study, we used and focused mainly on the stuttering annotations. Out of 28,177 samples, after leaving out non stuttering samples, we used only 23573 speech segments for our case study which constitutes of 19.65 hours of data. The distribution of stuttering data is highly imbalanced, among which the majority of the samples (12419) are fluent segments constitutes 10.35 hours, 3995 are interjections constitutes 3.34 hours, 3286 are repetitions constitutes 2.74 hours, 2103 are blocks constitutes 1.75 hours, and 1770 are prolongations constitutes 1.48 hours of data. For our baseline, we randomly selected 80% of podcasts for training, 10% for validation and remaining 10% for test set which comprises of 309, 37 and 39 podcasts respectively. For MTL and ADV, we mix the train and validation sets from baseline, and then randomly selected 90% samples for train set and 10% samples for validation set from each podcast to have at-least each disfluency from each podcast in both the sets, and the test set remains same in all the cases. In this study, we use podcast ids as meta deta information to learn robust stutter representations.

Evaluation Metrics: To test and evaluate the model performance, we use the widely and standard metrics that are used in stuttering and other speech tasks [START_REF] Sheikh | Machine learning for stuttering identification: Review, challenges & future directions[END_REF] which include recall, precision, F1-score and accuracy. The models are evaluated and compared to our previous work StutterNet [START_REF] Sheikh | Stutternet: Stuttering detection using time delay neural network[END_REF]. The results reported are the average of 10 experiments with 10fold cross validation technique. For ADV, we first train the SpeakerNet for 25 epochs by freezing the other two branches, then we freeze the SpeakerNet and train the FluentNet and DisfluentNet for 25 epochs. From epoch 50-75, we jointly train all the branches by applying gradient reversal layer in the speaker branch during backpropogation, and after epoch 75, we freeze all the weights and fine tune the FluentNet and DisfluentNet branches for the recovery phase until the stopping criteria is achieved.

Implementation: We have used PyTorch library for our implementation purposes. We trained the models using Adam optimizer, cross entropy loss function with a learning rate of 10 -2 . The training was stopped with an early stopping criteria having a patience of 7 on validation loss.

V. RESULTS AND DISCUSSION

Baseline: Tables I and II show the results of our baseline StutterNet along with the proposed methods. In comparison with the single branch (SB), multibranch (MB) outperforms the single branch in all the disfluency class predictions with an overall improvement of 26%. However, the SB shows better performance on fluent class because of the class imbalance issue. After addressing the class imbalance issue via two branch (FluentNet and DisfluentNet) training scheme, we found that the blocks and repetitions are still being confused with fluent speech as shown in Fig. 3. This is intuitive because the repetitions are fluent speech with the same word being repeated more than once. Similarly, the blocks are fluent speech with some small initial pause followed by fluent speech. From the Table II, we also observe that compared to the single task learning approach, our model based on simple MTL approach helps to boost the detection performance for stuttering types but degrades for fluent and prolongation segments, as the confusion rate of fluent and prolongation samples increases with other disfluency pairs in the MTL framework as shown by the red bar plots in Fig. 3. The MTL-based model increases the detection performance of R, B and I's by a relative margin of 10%, 6.78%, and 2% respectively, which results in an overall improvement by 1.6% in the stuttering classes. More interestingly, we found that the ADV improves the detection performance of fluent classes by 3.59% as compared to the baseline. It can be clearly seen from the Fig. 3, that there is a notable drop in the confusion rate of fluent category with other classes, but at the same time, the fluent false positive rate also increases. By adversarial training scheme, the model learns podcast-invariant representations which improves the performance of the fluent class. Effect of lambda: Figure. 2 (left) 1 shows the result of MTL when the value of λ hyper-parameter in the loss eq. ( 1) is varied from 0.1 to 0.9, and it includes the accuracy of all the classes. The results for all classes almost show a similar trend except for fluent class. The λ = 0.4 shows the 1 Initial is disf. class, A: Acc., S2CA: Stutter two class Acc. of FluentNet best accuracy for the fluent class. As the λ value increases, the detection performance of all disfluent classes decrease as well as in fluent class, which is expected because the overall loss function assigns more weight to the podcast identification branch. In addition, we also tried to divide λ by 10 (starting with 1) at each epoch, and we found that it increases the detection performance of blocks slightly and repetitions considerably, with slight reduction in other classes. Figure . 2 (right) shows the result of ADV training, with varying λ. The block class follows a trend that by increasing the λ, its detection performance also improves. For other classes, as the value of λ is increased from 0.1 → 0.9, the identification performance decreases. Following the work in [START_REF] Adi | To reverse the gradient or not: an empirical comparison of adversarial and multi-task learning in speech recognition[END_REF], we also tried varying λ by 2/(1+e pi )-1, where p i is the scaled version of epoch. However, it degrades performance.

In addition, the two class accuracy of disfluent samples in the FluentNet decreases as we increase the value of λ in the MTL framework. The λ acts as a control parameter for the podcast information to flow through the network. As λ increases, it is allowing more and more podcast-related information to flow through the network and thus decreasing the detection performance of stutter samples in the FluentNet. To this contrary, the podcast identification branch in the ADV framework is trying to play a good adversary and tries to remove more irrelevant information as λ value increases. This, in turn, helps in boosting the detection performance of stutter samples in the FluentNet branch. We can conclude that the podcast information is entangled with the stuttering characteristics, and by reducing podcast-related information, the model is able to improve the discrimination between the fluent and disfluent classes, but it fails to generalize within the disfluent classes.

Visualization: from the statistics pooling layer. In MTL scheme, it is evident from the well formed podcast clusters that the model is trying to learn podcast dependent stuttering information. On the other hand, for the adversarial setting, the clusters are not visible and the model is trying to learn this meta-data invariant robust stutter features.

VI. CONCLUSION AND FUTURE WORK Due to the diversity and uniqueness of stuttering, it continues to be the most demanding and challenging to detect due to its inherent nature of huge class imbalance across different types of disfluencies. Training a model on such a type of dataset will bias the majority class. To this end, we employ an MB StutterNet (BL), where, we introduce the pseudo temporary labeling of all disfluent classes as one class. The MB StutterNet is a two-branch network, with one branch to differentiate fluents from disfluent and the other branch to differentiate different types of disfluent types if the prediction in the FluentNet is non-fluent. On top of the baseline, we investigate the MTL and ADV framework by adding one more speaker branch in the context of SD. From the results, we found that the MTL framework improves the detection performance of blocks, repetitions, and interjections. We also found that the confusion rate increases for the fluent and prolongation classes. With the ADV scheme, we found that the fluent detection performance increases with a relative improvement of 3.59%. In addition, we also found that by decreasing the λ hyper-parameter in both the frameworks, the detection rate of disfluent classes increases. There might be issues with the meta-information of the dataset, as there is a possibility of having the same speakers across multiple podcasts.

Future works may include to study and investigate the impact of additional meta-data like language, accent, acoustic conditions in the context of SD. ACKNOWLEDGMENT

Fig. 2 .

 2 Fig. 2. Effect of lambda in MTL (left) and ADV (right) in SD.

Fig. 3 .

 3 Fig.3. in disfluency pairs in BL, MTL and ADV. XasY refers class X identified as class Y. For better visualization, we did not show in percent.

Figure. 4

 4 Fig.4. t-SNE embeddings from the shared encoder MTL (L) and ADV (R). We selected four podcasts for visualisation purposes.

TABLE I STUTTERING

 I DETECTION RESULTS (B: BLOCK, F: FLUENT, R: REPETITION, P: PROLONGATION, I: INTERJECTION, MB: MULTI BRANCH, BL: BASELINE)

					Precision
	Model	Method	R	P	B	I	F
	StutterNet [16]	SB	0.22 0.28	0.02	0.50 0.88
	MB StutterNet	BL	0.35 0.36	0.23	0.58 0.67
	MB StutterNet	MTL	0.32 0.32	0.10	0.59 0.72
	MB StutterNet	ADV	0.27 0.33	0.08	0.56 0.77
					Recall.
	StutterNet [16]	SB	0.42 0.42	0.25	0.69 0.62
	MB StutterNet	BL	0.29 0.38	0.10	0.58 0.74
	MB StutterNet	MTL	0.34 0.36	0.21	0.54 0.67
	MB StutterNet	ADV	0.35 0.37	0.20	0.58 0.66
					F1 Score.
	StutterNet [16]	SB	0.29 0.33	0.04	0.57 0.73
	MB StutterNet	BL	0.31 0.36	0.12	0.57 0.70
	MB StutterNet	MTL	0.32 0.33	0.13	0.56 0.69
	MB StutterNet	ADV	0.3	0.34	0.12	0.57 0.71

This work was made with the support of the French National Research Agency, in the framework of the project ANR BENEPHIDIRE (18-CE36-0008-03). Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several universities as well as other organizations(see https://www.grid5000.fr) and using the EXPLOR centre, hosted by the University of Lorraine.