Legacies of past land use persist today in the form of incised, single-threaded stream channels with dramatically different hydrologic functions of pre-colonial stream valleys. Restoration practices that aim to return lost hydrologic functions by re-establishing floodplain and groundwater connections should result in stream habitat and biological assemblages that differ from modern, single-threaded channels. The aim of this case study was to identify attributes of macroinvertebrate assemblages that might serve as biological indicators of improved hydrologic functions following the restoration of a stream-wetland complex, similar to a Stage 0 restoration, of a headwater valley in the Western Allegheny region of the USA. We monitored hydrologic functions and macroinvertebrate assemblages from stream reaches of a restored and unrestored site over multiple years during the early years following restoration. Reduced bed mobility and increased flow duration indicated improved hydrologic functions from the restored site. Aggregate metrics that capture functional attributes of macroinvertebrate assemblage  were consistently greater from the restored site. EPT biomass from restored pools was 3–4 × greater than amounts from the unrestored site as a result of consistently greater mayfly abundance. Restored pools also supported a subassemblage of taxa with life history attributes that are aligned with habitat conditions created from improved hydrologic functions. Results from this case study demonstrate the importance of habitat-specific sampling designs that report the absolute abundance of potential biological indicators. Findings from this case study should help guide the development of rapid biological indicators of improved hydrologic functions.The online version contains supplementary material available at 10.1007/s10661-023-10983-7. The morphology of headwater streams influences the transport of water and sediment, and consequently, the morphology and habitat for biota are the product of those same fluxes. Prior to the European settlement of North America, this complex interaction was largely influenced by beaver. Beaver-induced wood inputs maintained floodplain connections that promoted sediment and organic matter retention and resulted in a pre-colonial landscape dominated by beaver pools and connected wetlands  are greatly needed to guide ecologically sustainable restoration practices  for water quality assessment and monitoring; rather, they return the hydrologic functions upon which stream ecosystem structure and function depend. While valley floors buried by legacy sediments, subfossils , predominately forested watersheds within the Daniel Boone National Forest 1). The sThe region experienced extensive logging and farming in the early to mid-1900s. Evidence of hydrologic impairment from historical land use exists throughout the national forest and includes the presence of historic farm fields in valleys, straight single-threaded channels relocated to the edge of the valleys, vertical eroding stream banks, channel beds dominated by exposed weathering shale bedrock and valley lag deposits, intermittent annual flow patterns, and incising stream networks from frequent headcutting. Test pits from the historical floodplains of the study sites revealed post-settlement alluvium that varied in thickness from less than a few centimeters to about 1.5 m. Disconnections from the floodplain and aquifer resulted in prolonged and extensive channel drying during late summer and fall. Channel velocities were sufficiently high to cause frequent mobilization of bed sediments during flood flows. Ultimately, these alterations resulted in single-threaded channels with homogenized, frequently disturbed habitat and limited refuge for the aquatic organisms Fig. a, c, d.FThe goal of the restoration project at Slabcamp Creek was to recreate the hydrologic functions that would have been present in the pre-settlement streams. Specifically, the restoration aimed to build a channel connected to the historical valley aquifer within the groundwater aquifer to increase the duration of baseflow, build a wide, low floodplain that would be frequently inundated during flooding, and reduce velocities on the channel during flood events. This restoration was achieved by removing the majority of the post-settlement alluvium, which was placed along the hillside. A wide (12–18 m) inset floodplain was created with a small channel with low banks (< 0.3 m) within that floodplain. The elevation of the baseflow channel was based on the elevation of the gravel layer identified during the geotechnical examination of the floodplain. The post-settlement alluvium removed to create the floodplain was placed against the hillsides to form ponds that collect overland flow from adjacent hillslopes and are connected to the groundwater during periods of the year when the water table is high. Tree trunks, root wads, and large limbs removed during the exaction process were installed within the channel to establish and maintain grade control and to provide additional habitat for fish and macroinvertebrates. The total linear distance of the restoration was 3.8 km and construction was largely complete by December 2011. A variety of new aquatic habitats was available for macroinvertebrate colonization upon completion of the restoration, including a main channel , side channels, woody debris dams, and depressional ponds in the floodplain.Post-restoration hydrologic and biological monitoring was conducted in a 150-m stream reach of Slabcamp Creek  through 6th-year post-restoration (July 2017). The water stage was continuously measured at a 5-min interval in each reach using pressure transducers. Channel pressure transducer locations, floodplain, and channel topography were surveyed using a total station to determine periods of drying and flooding. Changes to the channel and floodplain topography were monitored during repeat visits to the site throughout the period of biological monitoring.The mobility of coarse sediments in excess of 2 mm was assessed through the installation of traps which were vertically oriented and open at the channel surface. Traps were paired with a continuously recording seismic impact sensor to provide an improved measurement of the movement of coarse sediments during floods Park, . Sedimen2, 243-µm mesh). Only riffles, pools, and stretches of bedrock with the shallow flow during high-baseflows were available for sampling at the unrestored site. We monitored macroinvertebrate assemblages only from riffles and pools because they were the only comparable habitats between the study sites. Contents from the Hess were rinsed into plastic bags and preserved with 95% ethanol and transported to the laboratory for analysis. Our habitat-specific sampling program resulted in a total of 130 benthic samples throughout the study .Quantitative benthic samples were collected from a 150-m reach of each study site on eight separate occasions during the 3rd , 4th , and 5th (16 March 2016) years post-restoration. During each sampling event, five replicate samples were taken from randomly selected riffles and from five random pool channel units  in the laboratory. Invertebrates were sorted under a dissecting microscope from both fractions, but time-prohibitive samples from the 250 µm fraction were split with a volumetric sample splitter. Invertebrates were identified to the lowest practical taxonomic level, enumerated, and measured to the nearest 0.5 mm in order to estimate standing stocks of invertebrate biomass from published length-mass regressions  to reduce heteroscedasticity. These analyses were completed in R , stream (unrestored vs. restored), and channel unit  were the categorical fixed effect variables. The individual samples per channel unit were treated as the random effect variable. The fixed effect variables influence the mean of our response variables  invertebrate abundance data. Following the ordination analysis, we tested the null hypotheses of no differences in assemblage structure (absolute) among groups with a non-parametric multi-response permutation procedure (MRPP), which provides a p-value and an A-value. A-values from MRPP comparisons are a measure of effect size and greater values indicate stronger differences in assemblages. Finally, an indicator species analysis  with the Bray–Curtis similarity coefficient was used to explore potential differences in assemblage structure in four a-priori channel unit groups . Replicates from each channel unit on each sampling date were combined and considered a sample unit, and taxa with low frequency (< 4%) were not included in the analysis. The final ordination matrix included 28 samples (rows) × 100 taxa (columns). First, we explored similarity in community structure and performed the ordination by calculating the Bray–Curtis similarity coefficients from the log The unrestored site dried repeatedly during the late spring through late fall, with the earliest drying occurring in May of 2015 and the latest occurring in December of 2016. Drying events ranged from a few hours to weeks Fig. a. PeriodCoarse sediment texture was similar between the two sites, with a higher median diameter for the unrestored site due to the greater presence of cobble and boulder material from exposed valley lag deposits Table . In the We identified 112 total taxa and 58 EPT taxa throughout the entire study Table . A totalWe collected 32,309 total individuals  and 1574 mg AFDM total invertebrate biomass (873 mg AFDM EPT biomass) from the restored site and 13,594 individuals  and 725 mg AFDM total invertebrate biomass (397 EPT biomass) from the unrestored site Table . CollectEphemera, Caenis, and Stenacron  compared to within site differences from the unrestored site . MRPP comparisons also revealed greater differences between pools of the sites  rather than riffles . Axis 1 of the ordination  to rheophilic taxa typical of riffles in headwater reference reaches in the region  . Axis 1 (48%), Axis 2 (19%) and Axis 3 (15%) collectively explained 82% of the variation in the data. Visual examination of the ordination plots revealed differences in assemblages between sites, greater dispersion (variation) in the assemblage from the unrestored site, and more distinct differences between riffles and pools of the restored site than the unrestored site Fig. . MRPP co6) Pond, , 2012.Fip < 0.05) indicator taxa (Table Ephemera) or sprawl (Caenis) — were near-perfect  indicators of restored pools. Other strong indicators  of restored pools included the collector-gathering ostracods and Dubiraphia (a beetle). The mayfly Eurylophella was the only relatively weak indicator  of unrestored pools. The top indicators  of restored riffles were collector-filterers , predaceous flies , and one scraping beetle (Stenelmis). The top indicators of unrestored riffles included collector-filtering caddisflies , a predaceous caddisfly (Rhyacophila), and the collector-gathering and scraping mayflies, Diphetor and Epeorus.Results from the ISA produced 35 significant  when many headwaters in our region experience periods of channel drying, including the unrestored site. Second, flood velocities were reduced, which led to a reduction of coarse sediment movement and increased the retention of organic matter in the channel.We detected differences in macroinvertebrate assemblages between the restored and unrestored sites, but due to our inability to sample prior to the restoration, we cannot be certain the differences were due solely to the restoration. However, empirical evidence from hydrologic monitoring coupled with our years of experience with macroinvertebrate assemblages from forested, single-threaded headwaters in the region and wetlands throughout the southeastern USA, we are confident that the magnitude of difference between the assemblages from the study sites was largely a result of environmental change following restoration rather than inherent differences in assemblages before the restoration and throughout the monitoring period.2) to the total amount of wetted area covered by riffles and pools during the monitoring period indicates macroinvertebrate biomass from riffles and pools of the restored reach (34–147 g AFDM) was 2–4 × greater than the amounts from unrestored reach (14–34 g AFDM). These rough estimates are especially conservative for the restored reach because we observed aquatic insects on habitats and substrates that were not included in our sampling design. Preliminary sampling and consistent observations throughout the monitoring period indicated few, if any, macroinvertebrates  on bedrock sheets from the unrestored site.Quantitative collections from pools and aggregate functional metrics  revealed the greatest differences in macroinvertebrate assemblages between the restored and unrestored sites. Our estimates of macroinvertebrate biomass indicate potential food available for insectivorous fish per square meter of stream bottom at time points throughout the study. Habitat-weighted estimates are particularly useful here because they allow for comparison among reaches with vastly different habitats. A quick extrapolation of our sample-scale biomass estimates , and richness of fish from the restored site . Additionally, aerial stages of aquatic insects are known to subsidize riparian food webs , while taxa with a wider tolerance to temperature were associated with the restored site.Because the aquatic biota from forested headwaters may be limited by habitat and bottom-up factors , the physical and biological changes brought about by canopy removal, the new flow regime, and habitat (structure and stability) probably had complex and perhaps confounding effects on the assemblage from the restored site. Without a rigorous study design that incorporates basal food resources, nutrients, and physical–chemical factors , we cannot effectively partition the relative influence of hydrologic improvement and canopy removal on the assemblage from the restored site. Some attributes of the restored assemblages (greater densities and biomass) may indicate subsidy effects and an altered temperature regime as a result of canopy removal. For example, indicator taxa from riffles of the unrestored site might suggest unfavorable conditions for rheophilic, oxygen-loving taxa associated with cool water (Ephemera) were particularly important because they drove the differences in EPT abundance and biomass and were among the strongest indicators of restored pools. Burrowing mayflies excavate burrows in finer sediments where they exist and collect organic matter, which aligns with greater channel habitat stability, sediment, and organic matter retention in the restored site. They are also among the longest-lived mayflies (up to 2 years) and develop slowly over multiple seasons . Our direct measures of sediment analysis and movement provide evidence of homogenized habitat and biota that has been widely reported from channelized streams  and richness of macroinvertebrate taxa from a composite of samples collected from multiple riffles or other habitats along a reach. Taken together, the important mayfly assemblages driving the functional differences between sites would not have been detected if the riffle and pool samples were combined, and we did not consider the absolute abundance of taxa from these habitats. We agree with recent calls from others to consider habitat-specific abundance and biomass of macroinvertebrates in post-restoration assessments  from pools of the restored site. Results from our post-restoration biological monitoring demonstrate the importance of habitat-specific sampling and suggest metrics based on functional (density and biomass) rather than structural (taxonomic richness) attributes of invertebrate assemblages may be robust indicators of improved hydrologic functions following restoration. Collective results from various geographic regions and watershed contexts might reveal similar assemblage attributes that can be developed into scientifically sound, robust bioindicators of hydrologic improvements following the restoration of stream-wetland complexes. We encourage restoration practitioners, regulatory agencies, and the scientific community to think carefully about expected biological endpoints from restored stream-wetland complexes before applying RBP methods that were not designed to assess ecological responses to restored hydrologic functions.Supplementary file1 (DOCX 40 kb)Below is the link to the electronic supplementary material.


Off-ice fitness and on-ice skating tests were performed. Camp 2 consists of on-ice tests and match performance with players selected by coaching staff at Camp 1. Camp 2 data came from official matches performance and a combination of skating tests and intrasquad matches. Hockey Quebec’s selection process consists of going from an entire pool of athletes to a team of twenty-five players, through several camps spread over two years. Correlation analyses were conducted to verify associations between anthropometric measures and performances. Results: In Camp 1, heavier-taller male athletes displayed better performances in most off-ice fitness tests, while heavier female athletes tended to display poorer performance. Camp 2 showed no significant correlations in on-ice tests and match performance. However, some tendencies were observed: heavier male players were less involved in blocked shots, and taller females were more agile. Conclusions: Despite off-ice advantages, the taller-heavier prototype does not translate directly to better performance on the ice among elite adolescent players. Coaches should analyze anthropometric characteristics with caution, and place more focus on match-specific abilities to identify talent.In the field of ice hockey, body mass and height have always played a role in team selection. This study aims to analyze the associations between anthropometry and multiple dimensions of performance among elite adolescent ice hockey players. Methods: 187 adolescent players (males:  Ice hockey is a high-intensity team sport that requires sport-specific technical skills , as wellConsidering the importance of physical dominance in ice hockey, evaluators have always placed considerable importance on this facet of talent appraisal . The mosIn ice hockey, performance is measured according to several components: functional off-ice testing or “off-ice fitness” ; on-ice For young players, national team talent evaluation camps are opportunities to measure performance indicators in various contexts. These events, organized by the federations, serve, among other things, to periodically evaluate athletes with the purpose of getting more information about their development. Selection camps help federations select athletes who will represent the country in the national youth teams (under 18 and under 20) . These uRegarding the potential associations between anthropometry and performance in ice hockey, a few studies have investigated the links between off-ice functional fitness, on-ice fitness, and their potential to link it with performance ,29,34,50Camp 1, a total of 199 players between the ages of 14 and 16 years  were invited to the Team Quebec evaluation camp. The criteria for invitation were based on players’ regular season performances as well as evaluation from the province’s hockey program directors. Indeed, both prospect  camps serve as an important stage to determine those who will represent Quebec in national competitions and potentially be invited to Team Canada evaluation camps in the following years. A week before each camp, players were informed about the research project during an online information meeting. Those who agreed to participate were asked to sign a consent form . The project was approved by the ethics board of the researchers’ institution (CER-21-278-07.09). The full protocol has been published in a previous article , according to gender. For the male cohort, 45 athletes (selected from Camp 1) were invited to take part in a provincial tournament-showcase. For the female cohort, no competitions were organized. The provincial ice hockey federation decided to organize a second evaluation camp  in which 48 female athletes took part. Camp 2 was an opportunity to evaluate whether anthropometry plays a role among the first draft of selected players. In both cohorts, goaltenders were removed from the study, due to their specific tasks. This study was developed in collaboration with researchers and the governing bodies of Hockey Quebec, the province’s ice hockey federation. The present investigation refers to a two-phase selection process that occurs through a 5-month period. Anthropometric measures are used with the objective of body composition assessment of ice hockey players ,31,32,33Measures were selected in collaboration with the federations’ stakeholders and align with specific components identified as potential determinants of performance in ice hockey. For anthropometry, height and mass were assessed. For off-ice fitness, nine tests were selected and divided into three categories: (1) lower limb power, (2) upper limb strength-power-endurance, (3) running speed, agility, and VO2max. On-ice tests were used to measure skating speed and agility. As shown on ® platform [Male players were only involved in a tournament-showcase in which they played three matches (3 × 3 (20 min) periods) at Camp 2. No on-ice testing was conducted with this group. Upon arrival at the competition, we assessed athletes’ body mass and height. All matches were video-recorded, and match performance data were collected by using the InStatplatform . We chos® platform was only in charge of male analytics official matches. As a consequence, we collected match performance manually, by using an observational grid, for female intra-squad matches. We chose four performance markers, which were most similar to those analyzed by the Instat® platform, that can be performed by both forwards and defense players: (1) physical implication, (2) puck recoveries, (3) offensive actions, and (4) shot attempts. Physical implication was defined as every action in which players were involved in puck battles . Puck recoveries refers to every situation in which a player recovers a loose puck and maintains possession. Offensive actions were defined as any action that contributed to puck possession such as passes to teammates and zone entries. Shot attempts were defined as all of a player’s shots towards the opponent’s net. Finally, blocked shots were removed from the database because these actions were only collected for players who played on defense . To assure reliability, two evaluators were involved in the coding process. Video-recorded matches were observed two times, until inter-evaluator agreement was congruent (≥90% of agreement).For the female evaluation camp, we assessed body height–mass upon arrival at the camp. On day 1, we tested on-ice skating abilities during the first on-ice session. We applied Camp 1 protocol in which we measured skating speed-acceleration and agility; in addition to that we added the 44.8-m backward skating test, following the same procedure as the forward skating sprint test. Match performance was assessed during intra-squad matches. Due to the context  of Camp 2, Instatp > 0.50), except for two match performance indicators . However, the skewness values of these markers were still under 1.0 which is deemed acceptable for correlational analyses [All statistical analyses were conducted with SPSS software . We verified for normality assumptions for each variable under study, using Kolmogorov–Smirnov (K-S) test for normality and verified skewness and kurtoses values for each distribution. No violation of normality was observed , produced more absolute force and power during vertical jump , and had higher eccentric and concentric impulse capacities during jumping . They also had better absolute grip strength . Taller players were also faster in the 30 m sprint test . Similar patterns were also observed regarding the associations between body mass and off-ice fitness. Heavier players demonstrated higher performance in broad jump , produced more absolute force and power during vertical jump , and had higher eccentric and concentric impulse capacities during vertical jumping . They also developed more absolute grip strength  and were also faster .p < 0.05) and had higher eccentric and concentric impulse capacities during vertical jump . However, taller female players demonstrated poorer performance when we focused on other parameters, such as relative maximal power  and vertical jump flight time . Heavier female players demonstrated lower performance in the broad jump and vertical jump , as well as relative maximal power  and vertical jump flight time . Regarding speed capacity, results showed that heavier female players were slower in sprint time , slower in change of direction time during 5-10-5 pro-agility test , and also demonstrated that they had less VO2max , However, when we focused on upper body absolute force output, heavier players had higher performance results , and also displayed better absolute grip strength .For female players, results revealed that taller players displayed higher performance in absolute grip strength . A similar pattern was observed for heavier players, regarding the association between body mass and on-ice agility test . Results from the female cohort revealed no correlation between height and on-ice performance. However, heavier female players displayed poorer on-ice acceleration and forward skating sprints .p < 0.05).Tendencies on shot blocking and anthropometric body mass. Since the only significant association was between shot blocking and body mass, we took a more specific approach to this association. p < 0.01). These results suggest that heavier players displayed less efficiency in that category of defensive action.p < 0.10).Tendencies of associations among the height-body mass classified sub-groups. Since we found only one significant association (at p < 0.10) between anthropometric measures and skating performance, we verified the tendencies for associations between height and on-ice skating performance at Camp 2 by considering the lesser level of variability among the selected players from Camp 1. p < 0.05).In ice hockey, talent identification and selection camp processes are complex. Professionals evaluate physical fitness, on-ice skills, and match performance. According to Ransdell et al. , anthropThe first part of our study examined the follow-up selection camp processes and anthropometric evolution over a five-month period. Our results suggest a height and body mass increase in male hockey athletes during this short period of time. Body mass results were corroborated by Leiter et al. and Cordingley et al. ,39, who The second part of the present study was related to relationships between anthropometric measurements and performances. Our results suggest the relative importance of anthropometric variables in the early phases of detection and selection at Camps  on off-ice fitness parameters and on-ice performance, more than in the latter phases . In the first phases of selection, anthropometry could have played an important role on off-ice performance. Taller and heavier male youth ice hockey players demonstrated better performance in many components , where some results were not significant . Our significant results corroborated with previous studies ,49,58 whOur study illustrated the limitations of the relationship between anthropometry and match performance. Indeed, we showed that only the shot blocking parameter was related to body mass in young male athletes, heavier athletes were less capable of blocking shots from their opponent. With the sub-group analysis, we observed the tendency of lighter athletes to block more shots. The lack of correlation between anthropometry and ice hockey match performance has already been highlighted by several studies ,29,58. B® analytics data, unlike the male cohort tournament. In this regard, we decided to collect data manually for female athletes, trying to get as close as possible of Instat® match performance indicators used for male cohort. We knew it would lead to a limitation when comparing associations between anthropometry and match performance between gender. Further analysis could consider match performance indicators with InStat® platform for both female and cohort, to better understand gender differences. We also decided to remove blocked shots indicator from female match performance analysis, which another limitation, while keeping this indicator for male cohort. Results suggested that this parameter was related to body mass in male athletes, so we could imagine it could also be linked to anthropometric characteristics in female athletes. More accurate objective on-ice and match measures, such as global or local positioning system technologies , could contribute to better understand the role of anthropometry on precise match performance indicators .Despite its contribution, the present investigation also has its limitations. The first concerns the assessment protocol for anthropometric measures. In order to be time-efficient during these camps, we chose to evaluate only body mass and height, as other measures were prioritized and took more time to assess . Despite the financial and time costs of DEXA or skinfolds methods, these are both more accurate than traditional evaluation. Indeed, our simple method is time-group-camp-effective, but less precise, especially for body mass assessment. We believed that with a more precise evaluation tool, results would have provided a more precise picture of the associations between body composition and performance . Body faThis article highlights the importance of evaluating anthropometrics in selection camps for young elite ice hockey athletes. It helps us to better understand the relationship between anthropometry and functional performance in off-ice, on-ice, and match situations during talent identification camps. Correlations exist, but after the second phase of the selection process, the effect was reduced. As young ice hockey athletes complete the selection process, the relative importance of an “optimal” anthropometric profile beyond performance in off-ice fitness, on the ice, and match performance, seems to decrease. The assessment of anthropometric characteristics should not take precedence during these adolescent growth phases, as other qualities are more important to evaluate, such as game intelligence or technical capacities on the ice. Following the divergent results obtained, we invite organizations and federations to be cautious when analyzing anthropometrics, and their relation to performance in adolescents or the importance given to the physical qualities of a player.


Whether continuing increased ice loss will accelerate further, and by how much, remains contentious9. A main contributor to future ice loss is the Northeast Greenland Ice Stream (NEGIS), Greenland’s largest basin and a prominent feature of fast-flowing ice that reaches the interior of the GrIS12. Owing to its topographic setting, this sector is vulnerable to rapid retreat, leading to unstable conditions similar to those in the marine-based setting of ice streams in Antarctica20. Here we show that extensive speed-up and thinning triggered by frontal changes in 2012 have already propagated more than 200 km inland. We use unique global navigation satellite system (GNSS) observations, combined with surface elevation changes and surface speeds obtained from satellite data, to select the correct basal conditions to be used in ice flow numerical models, which we then use for future simulations. Our model results indicate that this marine-based sector alone will contribute 13.5–15.5 mm sea-level rise by 2100  and will cause precipitous changes in the coming century. This study shows that measurements of subtle changes in the ice speed and elevation inland help to constrain numerical models of the future mass balance and higher-end projections show better agreement with observations.Over the past two decades, ice loss from the Greenland ice sheet (GrIS) has increased owing to enhanced surface melting and ice discharge to the ocean Analysis of global navigation satellite system observations and satellite data shows that frontal changes in 2012 of the North-East Greenland Ice Stream led to speed-up and thinning at least 200 km inland. To detect how far the ice flow acceleration has propagated upstream, we use GNSS data28 and recent improved Sentinel-1 ice velocity products29. We use GNSS data from three stations located between 90 and 190 km inland; these stations recorded data from 2016 to 2019. The GNSS receivers were installed on the glacier in a configuration that followed the centre of the main trunk. Owing to deep crevasses, NEG3 was installed 10 km south-east of the main trunk. We use these GNSS stations to validate ice flow accelerations from Sentinel-1 data. We then use Sentinel-1 flow accelerations combined with satellite altimetry to fine-tune our ice flow model to improve the response of the upstream sector to changes at the terminus.Over the past decade, the NEGIS has been rapidly speeding up, but the uncertainty of the flow speeds obtained from satellite interferometry has hampered the detection of ice flow acceleration more than 100 km upstream of the terminus30. We calculate the position of the GNSS receivers at 15-s intervals and derive mean daily velocities for each site by fitting a trend to the position estimates using east, north and up coordinates  to create a time series of the speed for each grid point. We then remove outliers and fit a trend to each grid point to estimate the flow acceleration  flights from April 2011 to April 2019 (ref. 32) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data from October 2018 to April 2021 (refs. 34). We find a thinning of more than 30 m from 2011 to 2021 near the ZI calving front  and captured the observed ice speed changes and elevation changes, but only in the lower region of the NEGIS close to the ice margin. This basal friction parameterization, however, does not capture the observed acceleration in the flow speed deep inland that was detected by satellite data  while forcing the ice-front retreat based on observed terminus positions. We found that the friction laws that are almost plastic are able to reproduce deep inland acceleration and thinning with remarkably good agreement  anomalies from MAR 43.We then use these numerical ice flow models for forecast simulations, using forcings similar to those used in ref. 45. The MAR simulations are carried out with a fixed ice surface elevation and, therefore, we apply a correction for the SMB anomalies using the gradient method to account for the elevation-SMB feedback.We model changes in the SMB and ocean forcing until 2100 using the output from three general circulation models (GCMs), MIROC5, CanESM2 and NorESM1, from the Coupled Model Intercomparison Project Phase 5 (CMIP5), based on Representative Concentration Pathway 4.5 (RCP4.5) and RCP8.5 scenarios, respectively2, experiences a thinning rate of 2–4 m year−1 . According to our new ice flow models, which are able to reproduce deep-inland observed accelerations and thinning, the mass loss increases by a factor of 5 to between 13.5 and 15.5 mm  drains 6% of the GrIS drainage area and contributed 4.2 ± 0.5 mm to sea-level rise from 1875 to 2012 (ref. 48). The glacier retreated more than 40 km, with a large collapse of the floating tongue in 2000 (ref. 48). Similar to ZI, JI is retreating along a negative bed slope (a bed that deepens inland)50. Recent model projections for JI have been calibrated using observations from the lower sectors49. We posit that these projections could be underestimations. Taking into account the propagation of inland thinning more completely in the simulations would increase the estimated future contributions of Greenland and Antarctica to sea-level rise and would markedly reduce uncertainty in future sea-level-rise estimates.Our study shows that ice flow accelerations in the interior of an ice sheet (>100 km inland) obtained from in situ GNSS observations or satellite data greatly improve the predictive skills of models used to project future sea-level rise. The use of GNSS observations and satellite data to detect inland flow accelerations could be crucial for other large glacier systems, such as Pine Island Glacier or Thwaites Glacier in Antarctica52 from 2000 to 2021 . This collapse has been followed by a steady retreat of about 700 m year−1. NG lost large sections of its floating tongue from 2002 to 2004; after this, calving events of the main floating tongue have been less frequent. However, in 2020, the northern section of NG completely collapsed, releasing more than 120 km2 of floating shelf ice into the ocean . We use GIPSY-OASIS version 6.4, which was developed at the JPL30. We use JPL final orbit products, which include satellite orbits, satellite clock parameters and Earth orientation parameters. The orbit products take the satellite antenna phase centre offsets into account. The atmospheric delay parameters are modelled using the Vienna Mapping Function 1 (VMF1) with VMF1 grid nominals54. Corrections are applied to remove the solid Earth tide and ocean tidal loading. The amplitudes and phases of the main ocean tidal loading terms are calculated using the Automatic Loading Provider , which is applied to the FES2014b ocean tide model including correction for the centre of mass motion of the Earth owing to the ocean tides. The site coordinates are computed in the IGS14 frame55. We convert the Cartesian coordinates at 15-s intervals into local up, north and east coordinates for each GNSS site monitored at the NEGIS surface. An example of a 15-s solution is shown in Extended Data Fig. We process the GNSS data using the GIPSY-OASIS software package with high-precision kinematic data processing methods−2 at NEG1, 4.5 ± 0.3 m year−2 at NEG2 and 4.9 ± 0.3 m year−2 at NEG3 (corrected for downhill acceleration).We use the 15-s solution to estimate daily solutions of the ice surface speed  and associated standard deviation of the underlying shift maps generated by the offset tracking to create a time series of speed for each grid point  experience almost the same noise. Thus, when estimating the difference between two neighbouring time series, the noise is reduced to ±0.3 m year−2. To estimate the downhill correction of the flow acceleration, we fit a trend to the time series of the differences between two pixels.We use mosaics based on ESA Sentinel-1 to estimate the downhill correction of the flow acceleration at GNSS stations. We estimate the time series of the ice speed at two pixels on a velocity map on a 0.5 × 0.5-km grid. Pixel 1 is located at the GNSS starting position (when the station was deployed) and pixel 2 is located at the GNSS end-point position. The difference between the two time series is used to estimate the downhill correction for the flow acceleration. The uncertainty level in Extended Data Fig. −2 at NEG1, 0.14 ± 0.29 m year−2 at NEG2 and 0.12 ± 0.30 m year−2 at NEG3.Our corrections of the flow acceleration, which compensate for the downhill movement of the GNSS stations, are as follows: 0.06 ± 0.18 m yearC. For each grid point, we select CryoSat-2 data with coordinates P, with a maximum distance of 1,000 m from C. The CryoSat-2 data points with coordinates P have an elevation hi measured at time ti. The index i denotes the ith data point. We use all available CryoSat-2 data measured between July 2010 and July 2021 to create surface elevation time series at each grid point C. Previous studies have used a third-order polynomial equation to describe changes in the elevation and a third-order polynomial equation to describe the shape of the surface57. However, to describe surface changes over 10 years, we fit a polynomial with a degree of 7 to describe changes in the elevation and we use a third-order polynomial equation to describe the shape of the surface. In addition, we fit a seasonal term to account for the annual surface changes. For each grid point with a centre C, we find the nearest data point within 1,000 m and fit a seventh-order polynomial H(ti)poly, a third-order surface topography polynomial Htopo and an annual term H(ti)Annual:To estimate elevation changes over the ice surface, we use a regular grid with a resolution of 500 × 500 m that covers the NEGIS. We denote the centre of each grid point as H(t).Extended Data Fig. H(t). P1 is a point closest to the ice margin and is located at about 590 m elevation. Extended Data Fig. Htopo (black error bars) and a combination of our best-fitting seventh-order polynomial H(t)poly and the annual term H(t)Annual (red curve). The annual term at this elevation has an amplitude of 1.41 ± 0.10 m. Extended Data Fig. Extended Data Fig. Extended Data Fig. We use the best-fitting seventh-order polynomial  Release 004, which was retrieved from https://nsidc.org/data/atl06 (ref. 58).We use ICESat-2 data from October 2018 to June 2021 and estimate elevation changes over the ice surfaceWe estimate elevation changes using the same method described in the previous section. However, to describe surface changes over about 2.5 years, we fit a third-order polynomial  and we also use a third-order polynomial equation to describe the shape of the surface and a seasonal term to account for the annual surface changes. Extended Data Fig. H(t). P3 is a point close to the ice margin and is located at about 267 m elevation . The ATM flights are mainly concentrated along the margin of the GrIS. To estimate elevation changes, we take the height difference between overlapping points from two different campaigns, that is, we take the height differences between the 2011 survey and the 2012 survey, between the 2012 survey and the 2013 survey etc. However, it should be noted that no survey was conducted in spring 2020. Extended Data Fig. We estimate elevation changes using NASA’s ATM surveys in Greenland from spring 2011 to spring 2019  with the Green’s functions derived by Wang et al.62 for the elastic Earth model iasp91 with a refined crustal structure taken from CRUST 2.0. For each grid point, we estimate the elastic uplift rate dhelas and the associated uncertainty σelas. The elevation change rate for each grid point isWe correct the observed ice surface elevations for bedrock movement caused by elastic uplift owing to present-day mass changes and long-term past ice changes ). We correct for GIA using the GNET-GIA empirical modelExtended Data Fig. 63, a finite-element ice flow model, to model the NEGIS. The horizontal mesh resolution varies from 200 m near the ice front to 20 km inland, and it is vertically extruded into four layers. The model is based on a 3D higher-order approximation of the full Stokes model to include vertical shear for the stress balance; this is a good approximation for both fast-moving and slow-moving regions65. We use the surface and bed geometry from BedMachine Greenland50 (version 3). We infer the ice viscosity parameter over floating ice and basal conditions under grounded ice using inversions. To infer the initial basal conditions, we use the Budd friction law and invert for the friction coefficient using surface velocities from 2007 to 2008 (ref. 66). We then analytically calculate the friction coefficient for a regularized Coulomb friction law that produces the same basal stress. The friction coefficient is kept constant in time during the simulations. Previous studies have shown that the choice of friction law can have a considerable impact on simulations70. However, here we select the friction law based on observations.We use the Ice-sheet and Sea-level System Model (ISSM)72. We calibrate the stress threshold of the calving law to match the observed ice front retreat from 2007 to 2017. We use the same stress threshold values, 1 MPa for grounded ice and 150 kPa for floating ice, used by Choi et al.24. The calibrated stress thresholds for two glaciers (NG and ZI) are assumed to be constant for all simulations.The model uses the level-set-based moving boundaries to track ice front positions. We use the von Mises tensile stress calving law to calculate the calving rate24, we calibrate the calving law to qualitatively match the observed front changes from 2007 to 2017 (ref. 72). For hindcast simulation, the model is forced by monthly SMB data from the Regional Atmospheric Climate Model (RACMO) version 2.3 (ref. 73). We apply ocean forcing that considers melting under floating ice for ZI and NG74, along with the undercutting at the ice front of the terminus of ZI once it becomes grounded76. The details of the ocean forcing parameterizations can be found in Choi et al.8. We keep the ice temperature constant, as it is not affected by the surface temperature on the timescales considered in this study77. We find that the model with a regularized Coulomb law yields much better agreement with observed accelerations . Extended Data Fig. Extended Data Fig. Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-022-05301-z.Peer Review FileSupplementary Video 1Animation of modelled frontal positions from 2007 to 2100 using the regularized Coulomb friction law, the GCM NorESM1 and RCP4.5. A Landsat-8 image from 2017 is used as the background. The colour denotes the surface speed. The animation was prepared using MATLAB R2021a software.


It is well known that carbohydrates are the main source of calories in most diets. However, by inhibiting carbohydrases, intake of calories is reduced and weight loss is improved. α-amylase is an enzyme that hydrolyses α-1,4 glycosidic linkages of α-linked polysaccharides, resulting in low-molecular-weight products such as glucose, maltose and maltotriose, while α-glucosidase catalyzes the hydrolysis of nonreducing α-1,4-linked glucose moieties from disaccharides or oligosaccharides. Currently, one of the most common nutritional disorders in the world is hyperglycemia. One of the new therapeutic approaches to treat this disease is the application of natural inhibitors, such as polyphenols, that control starch digestion and regulate blood glucose level. Dietary polyphenols showed potential inhibitory activity against α-amylase and α-glucosidase and this review summarizes the recently published literature that studied inhibition mechanisms and the structure–activity relationship between individual dietary polyphenols and mentioned digestive enzymes. It is known that higher binding interactions cause higher inhibitory activities; thus, different polyphenols can affect different steps in the digestion of polysaccharides. The aim of this review is to clarify these mechanisms and to introduce polyphenol-rich functional foods as potential tools for the inhibition of α-amylase and α-glucosidase. Currently, type II diabetes is reaching alarming rates across the world. There are different causes of this disease and are divided into modifiable risk factors, such as increased calorie intake, sedentary lifestyle and stress, and nonmodifiable risk factors, such as genetics and age. Various socioeconomic and cultural aspects cause overweight and obesity, which are consequently leading to increased risk for the disease’s development ,2. Type Polyphenols are secondary plant metabolites and are introduced into the body through plant-based foods. Enzymes can react with polyphenols and these reactions are the subject of numerous recently published studies ,16,17,18Polyphenols commonly occur in various fruits, vegetables, spices and seeds. Their structural diversity and different health-promoting properties make them the subject of numerous studies ,21,22,23Actinoplanes species that competitively inhibits α-amylase and α-glucosidase [Diabetes mellitus is a multifactorial metabolic disorder in which either the concentration of the blood glucose is above 200 mg/dL or the fasting plasma glucose level is higher than 126 mg/dL ,28. Thercosidase ,37. Strucosidase .It is important to consider the bioavailability of polyphenols when discussing their effectiveness in the prevention of diseases . The pha50 value, which represents the concentration of an inhibitor that has exhibited 50% inhibition of enzyme activity, inhibition kinetics, molecular docking, fluorescence quenching, etc. However, it is difficult to directly determine interactions between polyphenols and enzymes since it is difficult to calculate the exact amount of enzymes reaching the small intestine and the amount of the enzymes secreted from pancreas. Other substances present in the digestive tract, such as polysaccharides and proteins, affect their interactions [During the digestion of foods, salivary enzymes make the first contact in the digestive systems with food components. These enzymes are mainly hydrolases that degrade carbohydrates, lipids and proteins . Carbohyractions . Main reractions . Amyloseractions ,56. It wractions .α-amylase is an enzyme present in plants, animals, bacteria and fungi . It hydr50 values or the percentage of inhibition) of polyphenols against α-amylase.Due to polyphenols’ instability, delivery systems are often prepared to preserve them from environmental effects using different techniques. In the encapsulated form, polyphenols retain their ability to inhibit α-amylase. A study by Lo Piparo et al.  showed tMethylation and methoxylation caused reductions in the inhibitory activity and also a decrease in the hydrogen bond acceptor/donor numbers as hydrogen bonds are an important factor affecting the binding of flavonoids to α-amylase. It also caused a decrease in the polarity and improved the capacity of penetrating into the tryptophan-rich regions of proteins that are hydrophobic and are placed in the interior of the folded regions of proteins . The met3 at -OH in their structure are not so effective as inhibitors against α-amylase as those with -OH substituents [In the study by Xiao et al. , it was tituents .50 values than luteolin-7-O-β-glucoside and luteolin-4′-O-β-glucoside. These results confirmed that luteolin is a stronger inhibitor than luteolin glucoside. The same was observed for kaempferol and its glycoside forms. Increasing molecular size, polarity and nonplanar structure caused the decrease in inhibition activity. With the substitution of a hydroxyl group with glycoside, the binding of flavonoids and α-amylase was weak since steric hindrance occurred [Flavonoids present in nature are often in the form of β-glycosides and mostly as the 3- and 7-O-glycosides. Quercetin has stronger inhibition activity than rutin. Monoglycoside forms (quercitrin and hyperin) of quercetin are stronger than the polyglycoside form (rutin). In addition, it was reported that luteolin had lower ICoccurred .In the study by Sun and Miao , it was The hydrogenation of the C2=C3 double bond of flavonoids decreased the binding activity for α-amylase. By comparing the inhibition activity of two flavonoids, apigenin and naringenin, it was observed that hydrogenation of the C2=C3 double bond of apigenin caused a decrease in the inhibitory effect. From these results, it can be concluded that planarity of the C ring is important for the inhibition of α-amylase, as saturated C2-C3 bonds permitted more twisting of the B ring in relation to the C ring. Near-planar structure of the molecules enabled easier entering into the hydrophobic pockets of enzymes . HydrogeThere is a lack of consistency between the structure–affinity relationship and the structure–activity relationship of quercetin and its glycosides as α-amylase inhibitors. Hydrophobic forces cause the binding between flavonoids and α-amylase, while hydrogen bonds are not the main force that cause the binding of flavonoids to α-amylase. This occurs due to the backbone structure of flavonoids and the hydrophobic catalytic center of α-amylase. In addition, methods for determining the interactions between polyphenols and α-amylase and studying the docking model of α-amylase–polyphenol should be improved as they have a lot of drawbacks .Sun et al.  reportedAcidic environments are commonly favorable for most proanthocyanidins and anthocyanidins, although they are usually unstable in alkaline conditions. For determination of inhibition effects in vitro, it was proposed that a simulated digestion system be formed for anthocyanins and proanthocyanidins by using pH buffers, digestive enzymes and digestive juices. The inhibitory activity of polyphenols before and after digestion can be determined after that . It is kProanthocyanidins have polymeric structures, and, by the evaluation of α-amylase inhibition activity, it was observed that polymers have stronger inhibition activity than oligomers . DietaryBy studying the inhibitory activity of cyanidin and its glycosides, it was observed that cyanidin and cyanidin-3-glucoside have synergistic effects when combined with a low concentration of acarbose. Cyanidin-3-glucoside had higher inhibition activity than cyanidin and cyanidin-3-galactoside, while cyanidin-3,5-diglucoside did not show inhibition activity . CyanidiRemoving tannins from berry extracts caused a weakening in the inhibition of α-amylase . In the The most effective components from berry polyphenols that inhibited α-amylase were ellagitannins and proanthocyanidins. In the study by Boath et al. , inhibitThe inhibitory effects of hydroxycinnamic and phenolic acids followed the order: caffeic acid > tannic acid > chlorogenic acid = quinic acid . Chlorog50 values depended on the substrate concentration. Results showed that malvidin-3-glucoside and epigallocatechin gallate had significantly stronger inhibition effects than other investigated polyphenols. Epigallocatechin gallate showed a highly uncompetitive nature, malvidin showed a competitive character and phlorizin showed a weak mixed inhibition. For chlorogenic acid, pure noncompetitive inhibition was observed. It was concluded that IC50 values depended on the α-amylase type, specific reaction conditions and substrate concentration. Therefore, it is inconvenient to compare the obtained data with previously published literature. However, IC50 values could be a useful tool for the comparison of results within a measurement series, but not with other values obtained using different assays [In the study by Kaeswurm et al. , effectst assays .Saccharomyces cerevisiae α-glucosidase showed high similarity to Bacillus cereus α-glucosidase in the amino acid sequences. Conserved amino acid sequences of α-glucosidases families I and II, which were determined according to their primary structures, are clarified in the study by Chiba [Hydrolytic reaction and release of the α-glucose from the nonreducing ends of aryl -glucosides, disaccharides or oligosaccharides is catalyzed by α-glucosidase . Althougby Chiba .Two α-glucosidase isoforms are found in the small intestine. One isoform is a maltase–glucoamylase and the other is a sucrose–isomaltase. Each isoform has a different activity: the amino-terminal subunit serves as a maltase, while the carboxyl-terminal subunit acts as a glucoamylase. The maltase–glucoamylase enzyme has a higher hydrolytic activity than the sucrose–isomaltase isoform. The amino-terminal subunit of the maltase–glucoamylase isoform is labeled as the main α-glucosidase .Fungi produce commercial α-glucosidases that are currently used. However, they all have limitations in the terms of slow catalytic activity, high acidic pH requirements, moderate thermostability and creation of byproducts. To improve glucose production and decrease production costs, it is important to find novel α-glucosidases. In the study by Zhai et al. , it was Zhang et al.  reportedVarious studies have come to different conclusions regarding the inhibition of α-glucosidase by flavonoids. This occurred due to different structures of enzymes being used in the particular assays .Flavones without a hydroxyl group on the 5, 6 or 7 position of the A ring did not show inhibition activity against α-glucosidase. It was concluded that 5,6,7-trihydroxylflavone structure was the decisive factor affecting inhibition of α-glucosidase. Increases in inhibitory activity could be achieved with the introduction of electron-donating or electron-withdrawing groups at position 8 of 5,6,7-trihydroxylflavone. In addition, hydroxylation on the B ring of 5,6,7-trihydroxylflavone contributed to the increase in inhibitory activity. That was proven by comparing the inhibitory activities of luteolin and apigenin, in which it was observed that hydroxylation at C-3′ of the apigenin, vitexin and isovitexin increased the inhibitory activity against α-glucosidase. The quercetin analogues with the caffeoyl structures of C-2, -3, -4 and -1′-6′ in the B/C rings of the flavonoid had higher α-glucosidase inhibition than the equivalent kaempferol derivatives. Hydroxylation of the C ring, on the other hand, caused a decrease in the inhibitory activity . GenisteThe methylation on 5-OH and methoxylation on C-4′ or C-3′ flavones improved the inhibition activity. This activity was reduced by the methylation on 3′-OH of flavone. The methylation on the A ring of kurarinone and the B ring of sophoraflavanone caused a decrease in inhibition activity because of decreases in the hydrogen bond acceptor/donor numbers and the weakening of the polarity. Moreover, improvements were observed in the diffusion into the tryptophan-rich regions of proteins, which occur in the inside of the folded protein. Methoxylation at the C-3 also reduced the inhibitory activity against α-glucosidase. Flavones with 4′-methoxy groups had lower activities than 3′,4′-dimethoxyflavones. Flavones without a substitute group at the B ring also had lower inhibition activities. These results showed that inhibitory activity of 7-methoxyflavone was enhanced with the methylation of the groups at the 5′-, 3′- and 4′-positions .Saccharomyces cerevisiae α-glucosidase by flavonols: quercetin > isoquercetin = rutin, while it followed the order: isoquercetin = quercetin > rutin for Bacillus subtilis α-glucosidase. It was reported that flavones  inhibited α-glucosidase. Luteolin, luteolin-7-glucoside and amentoflavone had the highest inhibitory activity [Glycosylated forms of flavonoids, such as rutin, hesperidin, hyperin, linarin, baicalin, pectolinarin and isorhamnetin 3-O-rutinoside, did not prove to be potential inhibitors of α-glucosidase. For luteolin and its glycosylated forms, the following order was observed: luteolin (monoglycoside) > luteolin-7-O-glucoside > lonicerin (polyglycoside). These results confirm that monoglycosides were stronger inhibitors of α-glucosidase than polyglycosides. The same was observed for quercetin, as this flavonoid proved to be a stronger inhibitor than isoquercetin and rutin. In blocking the enzyme, caffeoyl moiety was critical and with the substitution of the sugar moiety by a phenolic acid, α-glucosidase inhibitory activity could be enhanced . The folactivity . Increasactivity .Weakening of the inhibitory activity occurred after hydrogenation of the C2=C3 of flavones. Apigenin showed higher inhibition activity than narigenin. It was concluded that the planarity of the C ring in flavonoids was important for binding interactions with proteins. Molecules that had saturated the C2-C3 bonds had more twisting of the B ring in relation to C ring. Near-planar structures enabled easier entering into the hydrophobic pockets in enzymes .Saccharomyces cerevisiae α-glucosidase has a hydrophobic active center. However, by increasing the hydrophilicity, the affinity for this center would not increase. The IC50 value of polycondensate of catechin with glyoxylic acid decreased compared to catechin. Polycondensate of catechin with glyoxylic acid could interact with amino acid residues outside of the main pocket. In this way, it could effectively hinder substrate access as a result of its large size [For theaflavins, it was observed that the galloylation improved the inhibition activity. Their activity was closely associated with the presence of a free hydroxyl group at the 3′-position of the theaflavins . Saccharrge size .By studying the inhibitory activity of proanthocyanidins and anthocyanins, it was observed that oligomers had higher inhibitory activity than polymers . A studyThe glycosylation of stilbenes weakened the inhibitory activity as trans-resveratrol had higher inhibitory activity than rumexoid and glucosylated stilbene piceid. The hydroxylation of the B ring of stilbenes also caused a decrease in the inhibitory activity against α-glucosidase .It was observed that chlorogenic acid (3-O-caffeoylquinic acid) and 5-O-caffeoylquinic acid, which is its structural isomer, strongly inhibited α-glucosidase, while insignificant inhibitory activity was observed for gallic acid, ferulic acid, m-hydroxycinnamic acid and p-hydroxycinnamic acid. The increase in the length of the alkyl chains caused an increase in the inhibitory activity of mono- and diketal derivates of chlorogenic acid up to the 5-nonanone derivates in the monoketals and diketals, while further increases in the alkyl chain length did not cause changes in the inhibition potency .Inhibition mechanisms of tannins against α-glucosidase were similar to synthetic inhibitors such as acarbose and voglibose, which are used therapeutically to control noninsulin-dependent diabetes mellitus. Tannic acid and tannin-rich compounds of red wine caused reductions in the glucose levels after consumption of meals rich in starch .Chalcones are precursors of flavonoids and isoflavonoids. These compounds showed potent inhibitory activity against α-glucosidase. Aminochalcones showed strong inhibition activity against α-glucosidase, while nonaminochalcones showed a lack of this property. The monoglycosylation of iriflophenone caused a decrease in the inhibition potency .To sum up, In the present study, structures of polyphenols were evaluated for how they can affect their inhibitory activity against α-amylase and α-glucosidase. To summarize, the hydroxylation of flavonoids, the galloylation of catechins and the presence of caffeoyl moieties improved the activity against both enzymes. Glycosylation of flavonoids, on the other side, caused a decrease in inhibitory activity against α-amylase and α-glucosidase. Polymerization of proanthocyanidins enhanced the inhibitory activity against α-amylase, while it caused decrease in the inhibitory activity against α-glucosidase. Inhibitory activity of dietary polyphenols against α-amylase and α-glucosidase enables retarded starch digestion and the alleviation of postprandial hyperglycemia. Hence, these compounds may be developed as functional foods to prevent and treat type II diabetes. Further research should be focused on the assessing the nature, isolation, purification and analysis of the individual polyphenols that are responsible for the positive effects. Further research should also focus on the potential synergistic effects that polyphenols have with different metabolites. The additional clinical investigations are also required in order to make clear findings concerning the efficacy and safety of both the short-term and long-term administration of polyphenols in people with type II diabetes. There is a need for the development of novel foods enriched with polyphenols and these phytochemicals could be included in a variety of foods, such as bread, baked products or beverages. It is suggested that more research be conducted focusing on the concentrations of polyphenols that should be added to foods that would not affect texture, organoleptic and nutritional properties, as these characteristics are deciding factors in determining acceptability of novel foods on the market.


Molecular dynamics simulations are used to study collisions between amorphous ice nanoparticles consisting of CO, CO In these contexts, the outcome of a collision between dust particles is of interest as it determines the evolution of the size distribution of dust particles in the population10; in the context of protoplanetary disks, such collisions govern the early stages of planet formation11.Collisions between dust particles are ubiquitous in space. Examples are provided by protoplanetary dust disks12. The sequence of ices is governed by the so-called ‘ice lines’; that is the distance to the star beyond which a particular ice species will condense from the gas phase and form ice particles or mantle other, more refractory, particles. The position of the ice lines is influenced by the temperature-dependent sublimation pressures of the ices, the radial pressure distribution in the protoplanetary disk, the outward diffusion of gas and the inward drift of condensed ice grains. Among the most important species are silica and water, but also carbon-containing species such as in particular CO and CO12. For the example of the protosolar system, it is estimated that the water ice line is at 2 AU followed by CO14. The position of the CO ice line is further outside, at around 30 AU17.The composition of these dust particles differs depending on the type of protostar and on the distance to it20 and simulations22. But also collisions between water ice particles were investigated26 and it was found that water ice is considerably more sticky than silica27. In atomistic simulations, it was shown that it is in particular the larger plastic deformability of water ice and its ability to melt under the collision that give rise to the large differences in the collisional behavior of silica and water ice28. While even nanosized silica particles may bounce from each other at sufficiently large collision velocities, ice particles will stick for all velocities apart form high-energy collisions which fragment the colliding grains.Collisions between dust particles have mostly been studied for silica particles, both with experiments14 performed collisions between 100 μm-sized CO29 review available data on collisions between CO30. They find that H31 argue that the high stickiness of water ice is caused by a melt film of molecular thickness—the so-called quasi-liquid layer32 or premelting33—that surrounds ice particles even at extremely low temperatures.Collisions between dust nanoparticles (NPs) consisting of other ice species—such as CO and CO35. This idea was later criticized29 since according to macroscopic theories of bouncing36, the bouncing velocity is mainly influenced by the surface energy, but this quantity is of similar magnitude for HOther authors proposed that the vanishing dipole moment of COIn the present paper, we address the question of how collisions between ice nanoparticles consisting of CO, COThe four ices studied here differ strongly in their properties. Some of the relevant characteristics are assembled in Table m, and the triple-point temperature In order to be able to compare these materials, we introduce units that are based on the molecular mass, 37; for crystalline LJ solids, it is somewhat higher, 38. The triple-point and critical temperature of LJ material are 41; the latter value depends strongly on the cut-off radius of the potential42. For the convenience of the reader, we note that for the amorphous LJ solids used here, the specific surface energy amounts to 37.We use these triple-point data, since the NPs are below or around the triple point even if collision-induced heating is taken into account. The density for the liquid phase is used, since data seem to be more easily available for this phase and also because the structure of our amorphous NPs more closely resembles that of the liquid state. Note that the density of amorphous LJ material is The values of entclass1pt{minima43; it is based on a two-center LJ potential augmented by dipole and quadrupole potentials. A similar potential is used for CO44, in which the dipole term is of course missing. These potentials are part of the open access database MolMod45; it treats the molecules as rigid, since at the low temperatures encountered, excitation of molecular vibrations is excluded. For water, we use the so-called monatomic water potential designed by Molinero and Moore46 since it gives a good representation of amorphous water ice48 and also the thermodynamic properties of other water phases49.Ar NPs are used as a reference case; Ar atoms interact with a LJ potential, Eq. . For CO,51 to a final temperature of 50 K. Spherical NPs with radius For all these potentials, amorphous solids are prepared by quenching from the melt52 and our previous results on water-ice NPs28. We therefore present here only the results of a central collision with a relative velocity of 2, and this applies also to most laboratory experiments20. At these small velocities, central collisions between NPs will be sticking, since the kinetic energy available after the collision does not allow the collision partners to overcome the attractive interactions and to separate from each other.For the collision simulation, the amorphous NP is duplicated and put outside of the cut-off radius of the interaction of the original NP. Only central collisions were considered. We found that for all velocities in the range of 150–100,000 m/s, collisions between COentclass1pt{minima53. Atomistic snapshots are generated with OVITO54.The molecular dynamics simulations are performed with the LAMMPS codeWe study the effect of the initial temperature Figure For Ar, a large number of atoms is ejected during the collision for the higher temperatures; this is in line with the larger sublimation rate of Ar as compared to Hcollision zone as a slab of width 1 nm at the center of mass of the collision system. The temperature of this slab—corresponding to the kinetic energy of all atoms in the collision zone—is then shown in Fig. The material deformation under the collision is connected to the collision-induced temperature increase in the collision zone. We plot the temperature in the collision zone as a function of time in Fig. With the exception of the smallest mentclass2pt{minimFurther quantitative information on the processes occurring under the collision are provided in the SM where the evolution of the contact area and of the pressure in the collision zone are displayed. The pressure shows little variation with mentclass2pt{minimIn this section, we study the collision of various ice NPs of the same temperature, Figure The Ar and CO NP collisions generate to ejecta, 530 atoms and 306 molecules for Ar and CO, respectively. Most of the ejecta are single atoms or single molecules. There are only 3 dimers for Ar and 2 for CO. Water and COmentclass2pt{minimFigure mentclass2pt{minimFigure Note that intramolecular vibrational excitation is not included in any of the molecular models used since these were considered rigid. This assumption appears justified at the small temperatures encountered after the collision. All intermolecular vibrations are of course fully taken into account in the simulation.Only water features a double peak in the temperature evolution. The first peak at around 10 ps coincides with the maximum of the pressure in the collision zone, see the SM, and is therefore immediately induced by the collision. The second peak, at around 30 ps, shows up when the pressure is already decreasing. It is caused by heating at the expanding rim. We display the temperature distributions in the collision zone at the time of 30 ps in Fig. Heating is caused by friction during the radial expansion flow. As radial velocities are largest close to the rim, heating is largest there as well. The kinetic energy in the radial expansion flow is comparable to the temperature increase in the collision zone. For the example of water, the average expansion velocity amounts to around 360 m/s for water, see Fig. We studied the amount of material intermixing between the two colliding NPs in the collision zone, see Fig. These numbers demonstrate that collision-induced mixing may be a strong effect even in cases of minor NP deformation. Such mixing processes may be relevant for instance for NPs whose surfaces are contaminated by adsorbates; these would mix and might react in the collision zone redistributing the adsorbates to the interior of merged clusters and providing new molecular species as reaction products.Nanoparticles of the size considered here—a few ten nm—are always sticking under collisions since energy dissipation in the collision is strong.For higher collision velocities, they are strongly deformed under the collision. The deformation is plastic.The temperature at which the collision occurs strongly influences the collision outcome. Partial melting of the merged cluster in the collision zone contributes to energy dissipation and deformation.The high plasticity is caused by the high elastic and plastic softness of these cryogenic ices.The number of ejecta emitted during the collision follows the trend in triple-point temperatures and increases exponentially with the NP temperature.In the collision zone, material is mixed between the two colliding NPs. Mixing increases if the local temperature approaches or surpasses the triple-point temperature.Even at comparable collision conditions, the collision outcomes of the ices investigated here—Ar, CO, COThe amorphous 20-nm sized ice NPs did not show any bouncing, in contrast to experiments on larger (μm-sized) CO24. We argue that there may be several reasons for this difference. i.55 for the case of silica particles, where the effect of adsorbates on the surface energy may reach up to two orders of magnitude. A reduction of the NP surface energy will increase the tendency for bouncing.Our ice particles are clean, while in experiments, the surface of the colliding NPs may be contaminated. Such contaminations reduce the surface energy of the NPs; this effect has been discussed in great detail by Kimura et al.ii.Also, real surfaces will be rough; in addition, the NP shape may deviate from a sphere. Both effects will influence the bouncing behavior.iii.56. This is because of the competition for a given NP radius, between adhesion, which increases restitution coefficient as velocity increases, and dissipation, which has the opposite effect30. We note that a similar argument for crystalline NPs was recently published58.It has been shown that both surface adhesion and viscous dissipation processes lead to dissipation of kinetic energy, and experiments and simulations for micro-scale impacts show that the bouncing velocity threshold increases as the NP radius decreasesiv.59.Finally, our present study focused on central collisions, while in experiment and in nature large impact parameters will occur. Large impact parameters decrease the tendency for sticking as only parts of the colliding NPs come into close contact and experience their mutual attractionRecently, it was shown that energy dissipation in granular aggregates of water ice leads to different aggregation and fragmentation outcomes60. The present work indicates that phase transformation should be considered as a possible energy dissipation channel also for aggregate collisions.We studied the collision behavior of amorphous cryogenic ice NPs and found the following features. In future work, it will be interesting to extend the present results to larger NPs in order to see whether the differences in the bouncing behavior observed experimentally for microscopic grains start to evolve for NPs.Supplementary Information.


Balaena mysticetus) face threats from diminishing sea ice and increasing anthropogenic activities in the Arctic. Passive acoustic monitoring is the most effective means for monitoring their distribution and population trends, based on the detection of their calls. Passive acoustic monitoring, however, is influenced by the sound propagation environment and ambient noise levels, which impact call detection probability. Modeling and simulations were used to estimate detection probability for bowhead whale frequency-modulated calls in the 80–180 Hz frequency band with and without sea ice cover and under various noise conditions. Sound transmission loss for bowhead calls is substantially greater during ice-covered conditions than during open-water conditions, making call detection ~ 3 times more likely in open-water. Estimates of daily acoustic detection probability were used to compensate acoustic detections for sound propagation and noise effects in two recording datasets in the northeast Chukchi Sea, on the outer shelf and continental slope, collected between 2012 and 2013. The compensated acoustic density suggests a decrease in whale presence with the retreat of sea ice at these recording sites. These results highlight the importance of accounting for effects of the environment on ambient noise and acoustic propagation when interpreting results of passive acoustic monitoring.Bowhead whales ( PAM allows marine mammal population density estimation to help with stock assessment and management8. Density estimation requires knowledge of the distance between the source  and the receiver (PAM sensor)9, but environmental factors can substantially alter the effective listening area around a recording location. Factors in the environment that can influence the propagation of underwater sounds include water column and seabed physical properties, bathymetry, sea state and sea ice coverage. These influences on acoustic propagation are specific to a hydrophone location and affect the detectability of recorded signals by altering the signal-to-noise ratio. Accounting for these factors is important for analysis of PAM detection time series and for understanding their relationships with habitat or other ecological variables.Passive acoustic monitoring (PAM) is an important tool for studying marine mammal seasonal distribution, migration, and behavior, especially in remote locations such as polar regions, where sea ice cover and light limitations prevent ship access and visual surveys for much of the year11. For example, the estimated probability of detecting humpback whale calls in the 0.2–1.8 kHz frequency range within a 20 km radius of three recording locations off California differed by up to 60% due to site-specific effects of bathymetry and seabed properties on sound transmission10. At one location, detection probability was also reduced by > 50% within the 20 km radius due to an increase in ocean noise levels by 10 dB12. When propagation modeling is combined with measurements of ambient noise and instrumental self-noise levels, it is possible to compensate for time-varying acoustic detection probability and thereby to improve estimates of acoustic density12.Acoustic propagation modeling and ocean noise levels have been used previously to account for environmental effects on marine mammal sound detection13. Bowhead whales  are closely associated with sea ice and have been the subject of decades of research efforts14, especially in the Bering-Chukchi-Beaufort (BCB) Seas where bowheads are harvested for subsistence by Inupiat communities15 and where exploration and development of offshore petroleum has raised concerns about underwater noise impacts16. Reductions in Arctic sea ice have the potential to significantly impact bowhead whales, both by changing their foraging environment and by exposing them to anthropogenic activities such as ship traffic and commercial fishing17.In Arctic waters, rapid decreases in sea ice and increases in human activities, such as commercial shipping and mineral extraction, motivate the need to monitor marine mammal populations19. These relatively simple bowhead calls have been used to indicate their seasonal presence in PAM-based studies across much of the BCB bowhead range21, contributing to knowledge of their seasonal movements and distribution.Bowhead underwater sound production includes a great variety of sounds, but often consists primarily of simple, narrow-band, frequency-modulated calls , usually less than 200 Hz Fig. 123. Sea ice strongly scatters sound in ways that are dependent upon ice thickness and underside roughness, and upon the frequency of the sound. Across a 100 Hz frequency band that includes most bowhead FM calls (80–180 Hz), there is substantially higher attenuation when sea ice cover is present than when compared to open-water conditions26. Previous experiments in the Arctic have found scattering from sea ice cover to contribute 10 to 15 dB of attenuation at frequencies between 70 and 200 Hz with sound paths at surface grazing angles of 10–20°29.In the Arctic, the presence of sea ice influences acoustic propagation and ambient noise30. At a site on the Beaufort-Chukchi continental slope (receiver depth 328 m), airgun pulses from a deep-water survey (> 3000 m water depth) were recorded more than 500 km away, whereas from a shallow-water survey (< 100 m water depth) they were detected only up to ~ 100 km and experienced transmission losses much greater than the typical spherical spreading, due to interaction with the seafloor31.Interaction with the sea bottom is another factor influencing the propagation of sound, with bottom interactions increasing for a given frequency as water depth of the source or receiver decreases34. Mean ambient noise levels in the bowhead whale FM call frequency band (80–180 Hz) are highest during fall open-water conditions and lowest during spring ice-covered conditions33. Sound pressure spectrum levels increase with wind speed (u) by ~ 20 log10(u) in open-water35 but by only ~ 5 log10(u) with 75–100% ice-cover33. Episodic events in the sea ice layer  also create underwater noise36. Seismic surveys can increase noise levels significantly; for example, by 3–8 dB in the 80–180 Hz band during extended seismic survey periods33. Ocean currents can also cause flow-induced oscillations of the hydrophone (strum) in the 1–500 Hz band, resulting in instrumental noise, rather than true ocean ambient noise37.Arctic ambient noise levels may vary over time due to sea ice, wind, currents and surface waves changing on seasonal and shorter time scalesIn this study, we use underwater acoustic modeling to develop a site-specific and time-dependent compensation applied to time series of bowhead whale acoustic detections at recording sites in the northeast Chukchi Sea outer shelf and continental slope Fig. . The comSound transmission losses in the frequency band of bowhead whale FM calls are substantially higher in the presence of ice-cover than for open-water conditions Fig. . This suAveraged across all azimuths at distances from 0 to 20 km, propagation losses are greater at the slope site than at the shelf site in both ice states Table , partly At longer distances of 20–40 km from both sites during ice cover, sounds traveling upslope toward shallow water have higher predicted propagation loss than sounds traveling downslope toward deeper water Table . This spAverage detection probabilities for calls follow similar patterns to sound transmission loss Fig. . DetectiAveraging detection probability across azimuth also gives detection probability as a function of distance for each noise level and ice state Fig. . The pro2) to less than 5% at the highest noise levels. With ice cover, the effect of additional noise on detection probability is greatest at distances 5–20 km, while the effect of added noise is greatest at > 20 km distance in open water. For example, at distance 10 km on the slope site, increasing noise from 60 to 80 dB reduces detection probability from 80 to ~ 5%. In open water, this noise increase reduces detection probability at a 10 km radius from > 95 to ~ 60%. Overall, adding noise results in substantially lower detection probabilities and a decreased detection area at shorter radial distances with ice cover and longer radial distances in open water.Mean detection probability is modeled as a function of noise level for each site and ice state within the 40 km radius in Fig. 2 spectrum level in the 80–180 Hz band, detection probabilities in open water are > 90% for calls originating upslope from both sites and for sounds originating downslope from the shelf site  were ~ 7 dB lower than those at the shelf (70 ± 7 dB re 1 μPa2) site due to current-induced strumming of mooring hardware at the shelf recorder.Ambient noise levels in the 80–180 Hz frequency band were higher by ~ 10 dB during periods with little or no ice coverage at both the slope Fig.  sites. IThe detection probability for bowhead FM calls was significantly higher during open-water than during ice-cover at both sites Figs.  and 9. ABowhead whale FM calls were detected at both sites in the fall, during the time of ice formation, and in the spring, well before and following ice breakup Figs.  and 9. CCompensating the detection time series using Eq. , yielding a bimodal distribution of detection probability over the course of a year, whereby calls are ~ 3 times more likely to be detected during open-water conditions. Without compensating for the effects of ice-cover on sound propagation, acoustic presence is overestimated during open-water periods and underestimated when sea ice is present. This is despite the increase in ambient noise levels during open-water. The impact of sound attenuation by ice-cover on call detection is greater than the decrease in signal-to-noise owing to higher open-water noise levels. The compensation method presented here, therefore has the effect of normalizing the estimates of detection density across periods of varying ice-conditions, as well as between the two sites, including differences in environmental and instrumental characteristics. This presents the possibility of comparing detection densities between sites to provide quantitative estimates of bowhead presence.In the Arctic, seasonal sea ice cover dramatically decreases the range over which bowhead whale FM calls can be detected. With the presence of ice-cover, sound attenuates to undetectable levels over much shorter distances than in open-water. This has a large effect on call detection probability29. Diachok25 found TL of 95 dB at 200 Hz from 40 km with sea ice, compared with a theoretical prediction of 76 dB with open-water conditions. For the slope site, TL was 105 dB at 40 km with ice cover and 76 dB in open-water. The shelf site had higher TL of 110 dB at 40 km range for ice-cover and TL of 68 dB in open-water. The increased TL for the shelf site with sea ice-cover is likely due its shallower depth, resulting in more reflections off the seafloor which then interact with the underside of the sea ice at the surface. The low up-slope and along-slope TL at the shelf site may also be related to less interaction with the sea bottom and the surface. Due to the sound speed profile and deeper water, the slope site is located in an acoustic waveguide during open-water31.The presence of a sea ice layer dramatically increases transmission loss (TL), consistent with previous studies of Arctic long-range sound propagation38. Our model uses an RMS roughness of 1.38 m for the sea surface in open-water, which would correspond to typical wind speeds (~ 5 m/s) observed in the study area33. Higher wind speeds do occur during open-water (> 15 m/s) and would result in greater surface roughness and increased ambient noise levels. At higher wind speeds, scattering strength increases for bowhead whale call frequencies at grazing angles up to 25°39. The effect of surface roughness might reduce the high detection probability we predict for open-water conditions and should be incorporated into future models. Scattering of acoustic energy is also affected by the number and depth of ice keels under pressure ridges25 and in the marginal ice zone40. Future models should develop additional parameters to account for these factors in acoustic propagation.In the arctic, increasing sound speed with depth results in rays that are upward refracted and interact with the surface, making surface scattering an important parameter for sound propagation. Surface scattering strength increases as a function of wind speed during open-water conditions3km2/day within a 40 km radius.Applying the detection density function Eq.  to uncom41.Examining bowhead acoustic density with respect to sea ice, the annual patterns of presence are similar at both sites, with whales first appearing in April during full ice-cover and again approximately two weeks before the onset of ice formation through freeze-up. A sustained period of acoustic density occurs at the shelf site prior to freeze up in the fall and increases during ice formation. This fall density of occurrence does not appear as pronounced at the slope. Both sites exhibit peaks in acoustic density in June and July, which decreases around the onset of sea ice break-up. This pattern does not appear in the uncompensated acoustic detection time series and is consistent with satellite telemetry results showing seasonal movements of bowheads extending to areas of their range outside of previous survey coverage for their abundance and distribution30. Optimum frequencies decrease in the Arctic due to the upward refracting sound speed profile, increasing sound transmission loss through surface interactions31. Loss to the seafloor becomes greater at lower frequencies30 and is expected to be significant in the thick sediment layers of the Chukchi Sea. This may increase the lower end of the optimum frequency band relative to other areas. The combined effects of these factors may act to narrow the bandwidth of frequencies that propagate best within the study area, presumably corresponding to the 80–180 Hz band used for bowhead FM calls.How might bowhead whales have optimized the propagation of their FM calls based on the environment of the Arctic? The frequency for optimum (minimum loss) acoustic propagation in shallow open-water is dependent on depth. In temperate oceanographic conditions, optimum propagation frequencies are about 220–50 Hz for depths from 100 to 300 m, respectively42. Thus, frequencies below 80 Hz should be evaluated for inclusion in sound propagation modeling for bowhead acoustic density. While this study does not distinguish between call categories beyond a focus on non-song FM calls, some call categories such as “complex” calls may be shifting to lower frequencies to a larger degree than others42. Where call categories have different frequency characteristics, the proportion of calls by category may also be important for improving future detection probability and density estimation.Characteristics of bowhead whale calls may change over time or with environmental conditions and ambient sound levels; and this potential variability may need to be incorporated into future development of acoustic density estimation for bowhead whales. Minimum and peak frequencies of bowhead calls in the Beaufort Sea decreased during 2008–2014, with an increasing proportion of calls below 75 HzDetection of acoustic signals from bowhead whales in Arctic shelf and slope waters are subject to environmental factors that affect the transmission of the sounds and factors that affect the levels of ambient noise. To help interpret results from passive acoustic monitoring detections, it is necessary to address these factors and account for them. Applying this compensation method to multi-year time series of acoustic data will substantially improve the use of passive acoustic monitoring in estimating abundance and distribution of marine species and to investigate ecological relationships with environmental factors, such as sea ice.http://teledynebenthos.com) with a combined sensitivity of − 193.5 dB re: V/µPa and preamplifier gain of 50 dB, and a high-frequency stage with a single spherical transducer  with a relatively flat (± 2 dB) sensitivity of − 200 dB re: V/µPa from 1 to 100 kHz43. All acoustic recordings were converted into an adapted wav file format (XWAV) for analysis. To minimize computational requirements, XWAV files were decimated by a factor of 20 using an eighth-order Chebyschev type I filter (10–5000 Hz). Analyses were conducted using the Triton program, based on MATLAB , to calculate and display long-term spectral averages (LTSA) and spectrograms, to perform audio playbacks, and to log call detections43.High-frequency Acoustic Recording Packages (HARPs) were deployed at two locations along the northeast Chukchi Sea shelf (site D) and slope (site C) between October 2012 and October 2013  for bowhead whale FM calls, using previously described acoustic behaviorFigure 44. Detection probability for calling whales can be estimated using a Monte Carlo method45 with adaptation for site-specific sound propagation12. Detection probability ,p-p received level during the call and NL is the p-p level one second prior to the call. The detection threshold was estimated from the SNR distributions. A single threshold of 2 dB was chosen because 95% of the actual detected calls had a measured SNR above 2 dB. We assume that calls with RL less than 2 dB above the background level were not reliably detectable.To choose a signal-to-noise threshold appropriate for detection probability simulation and modeling, an additional analysis was performed for a subset of calls from each site using every detectable call for: (1) the last one-week period before sea ice breakup with mean weekly ice concentration greater than 90% (ice-covered), (2) the week centered on the last day with 50% sea ice concentration before open-water , and (3) the last full week with sea ice no greater than 10% (open-water). The peak-to-peak (p-p) and root mean square (RMS) received sound pressure level for each call were calculated as follows:49. Sea ice is primarily first-year with interannually variable proportions of thicker multi-year ice50. The water column characteristics in the NE Chukchi Sea slope vary seasonally and are strongly influenced by sea ice, extreme low air temperatures, sea ice melt, and Atlantic water present at depth23. A two-layer profile of temperature and salinity is often present, with a cold (~ − 2° C) relatively fresh upper layer during periods with ice cover, saltier relatively warmer underlying water column, and a weak thermocline52. The seafloor consists of relatively thick fine silt, sand, and clay, especially on the shallow shelf53.The outer shelf and continental slope of the northeast (NE) Chukchi Sea is a transition zone between the shallow Chukchi Sea shelf (avg. depth ~ 50 m) and abyssal Canada Basin (avg. depth ~ 3600 m). The region is covered by seasonal sea ice, typically present from October through August, with substantial interannual variability in timing of ice formation and breakup54 in May, 2013, and during open-water from the USCGC Healy in October, 2013. Sound speed profiles  and processed using Windows Image Manager (WIM) and Windows Automation Module (WAM) software60 to produce time series of mean daily Sea Ice Concentration (SIC) within 40 km of each recording site. WAM software was used to compute the daily arithmetic mean, variance, and median of the sea ice concentration as a percent of the total mask area. The 40 km radius was selected as a conservative detection range for bowhead whale calls based on previous studies18.Daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice maps61, which provides a Matlab-based graphical user interface for the Acoustics Toolbox . The RAMGEO parabolic equation model62 was used since it is well suited for shallow water environments and for frequencies < 1 kHz63 and can incorporate water column, seabed, sea surface and sea ice input parameters. Sound speed profiles were selected from hydrographic data collected in ice-covered and open-water conditions  grids were created using the freely-available Acoustics Toolbox User Interface and Post-processor (AcTUP v 2.2L)ons Fig. . The sea63 and a 100-point moving average was applied with range, to smooth each transect. Using linear interpolation between transect points, transmission loss was estimated within a 1 km resolution grid centered on each recording site. Grid locations were mapped to geographic coordinates using a polar stereographic projection, yielding 40 km radius transmission loss grids for each site and ice condition. An additional range-dependent loss term of − 0.26 dB/km was applied to all locations in the transmission loss grids to approximate excess loss observed for the Chukchi Shelf 31.Transmission loss models were calculated along transects at 22.5° angular intervals originating at the recording site. Models were executed in one Hz steps from 80 to 180 Hz with source depth 26 m below the sea-air surface and receiver depth 10 m above the seafloor. The models were incoherently averaged across frequency 2 in 5 dB steps. For each noise level, two simulations were run using the modeled transmission loss grids for open-water and ice-covered states. For each combination of site, noise level, and ice state, the simulation randomly selected 100,000 bowhead call source level values and placed them within the 40 km radius grid. Source levels were randomly generated from a probability distribution fit to source level estimates for bowhead calls reported by Cummings and Holliday46 . Each 1 km by 1 km cell in the detection grid was scored with the proportion of detected calls, those having a received level at least 2 dB above the noise level. The detection probability, P̂, was calculated as the proportion of detected to total simulated calls originating from that location. The mean detection probability for each site, ice condition, and noise level was calculated by weighted average of the detection probability values for all locations in the 40 km radius grid. Finally, a spline interpolation was used to fit a curve to detection probability as a function of noise level for both ice conditions.A simulation method was used to estimate call detection probability for different ocean noise levels and ice states. Seven ambient noise levels were used from 50 to 85 dB re μPaTo examine patterns in sound transmission loss and detection probability with changing bathymetry and ice state, we identified azimuths for each site to represent upslope and downslope propagation. True bearings between 22.5 and 67.5° from both sites were defined as downslope and bearings between 202.5 and 247.5° were defined as upslope. Since the shelf site was located directly upslope from the slope site, these azimuthal definitions of up and downslope propagation were the same for both recording locations. Transmission losses and detection probabilities were averaged for all model locations within the up and downslope sectors for distances from 0 to 20 km and 20–40 km to compare relatively long and short-range estimates for the locations with and without sea ice.T. To evaluate uncertainty in Using the acoustic data recorded in the northeast Chukchi Sea Fig. , the det