Furthermore, the δ13C (–27 ~ –24‰) and δ34S values (–10 ~ 15‰) of OM indicated that most of OM were originated from terrestrial sources. Thus, the temperature-driven glacial melting could release more OM originating from the meltwater or terrestrial materials, which are available for THg binding in the European Arctic fjord ecosystems.Deglaciation has accelerated the transport of minerals as well as modern and ancient organic matter from land to fjord sediments in Spitsbergen, Svalbard, in the European Arctic Ocean. Consequently, such sediments may contain significant levels of total mercury (THg) bound to terrestrial organic matter. The present study compared THg contents in surface sediments from three fjord settings in Spitsbergen: Hornsund in the southern Spitsbergen, which has high annual volume of loss glacier and receives sediment from multiple tidewater glaciers, Dicksonfjorden in the central Spitsbergen, which receives sediment from glacifluvial rivers, and Wijdefjorden in the northern Spitsbergen, which receive sediments from a mixture of tidewater glaciers and glacifluvial rivers. Our results showed that the THg (52 ± 15 ng g These increasing levels have been proposed to be caused by the long-distance transport of anthropogenic mercury (Hg) from industrialized countries to the Arctic, given its long atmospheric lifetime of 0.3 to 2 years4. In addition, it has been shown that ‘Atmospheric Mercury Depletion Events’ (AMDE) in the spring can lead to high Hg deposition in the Arctic Ocean. Reactive Hg (Hg2+) is rapidly formed through in situ oxidation of gaseous Hg° by halogens , which are generated by solar irradiation through O3 destruction7. However, previous measurements of atmospheric Hg deposition in the Arctic regions have revealed relatively low deposition rates of <5 µg m−2 yr−1 8. Recent studies have suggested that natural processes governing Hg release via riverine transport9, soil runoff, and/or discharge of water coming from thawing permafrost10 could be important sources of Hg to the Arctic environments.Increasing temporal trends of total mercury (THg) concentration in the sediments of the Arctic continental shelves and lakes have recently been reported11. Previous studies have focused on the effects of local and/or long-range transport, oceanic currents, and long-term sediment deposition rates 17 on spatial distribution and the extent of accumulation of Hg in the fjord sediment. Recent studies have shown that fjords are effective sequesters of organic carbon among other marine environments19. Given that Hg demonstrates a strong binding affinity towards organic matter (OM), we investigated the influence of OM on the spatial distribution of THg in the fjord systems. To address this research, we collected 35 surface sediment samples from three fjords in Spitsbergen that are affected by various glacial influences: Wijdefjorden in the northern Spitsbergen,  Dicksonfjorden in the central Spitsbergen and Hornsund in the southern Spitsbergen. Each study area is characterized by distinct glacier dynamics, bedrock lithology, and oceanic currents with meltwaters. The THg, total organic carbon (TOC), total nitrogen (TN), and total sulfur (TS) contents, and their isotope values were analyzed in the surface sediments. Six trace metals  and twelve proxy tracers, which indicate the influence of weathering and redox processes, were also analyzed to examine the potential controlling factors for the spatial distribution of THg in fjord surface sediments. Our study examines the sources and factors that influence the deposition , and the spatial distribution of Hg in fjord sediments in the Svalbard archipelago.Fjords on Svalbard, the main island of the Svalbard archipelago, is among the most studied fjord systems in the world24. However, sedimentation rates can exceed several tens of cm yr−1 in the vicinity of terrestrial sources26.The bedrock mainly represents Paleozoic strata in Wijdefjorden and Dicksonfjorden, and Mesozoic strata in Hornsund Table . Therefo−1, n = 21), followed by Wijdefjorden , and Dicksonfjorden  (Table −2 yr−1) was higher than those of Wijdefjorden (1.2 ± 0.4 μg m−2 yr−1) and Dicksonfjorden (17 ± 6 μg m−2 yr−1) in Svalbard. The THg mass accumulation rate in Hornsund was comparable to that of the Huang He River (100 ± 15 μg m−2 yr−1), which is well known for high sedimentary Hg accumulation rates due to industrial and riverine input, but much higher compared to East China (36 ± 2 μg m−2 yr−1) and Yellow seas (29 ± 2 μg m−2 yr−1) (Table The THg concentrations in the surface sediments were the highest in Hornsund (52 ± 15 ng g8) Table . The THgThe THg concentration in Wijdefjorden showed a decreasing trend from terrestrial to coastal (offshore) regions. The THg concentrations in Dicksonfjorden were relatively constant throughout the study area Fig. . Other tn = 21) and TS  and TOC/TN ratios  were the highest in Hornsund, followed by Wijdefjorden , and Dicksonfjorden  , followed by Dicksonfjorden , and Hornsund   compared to other sites   Table . The ave1) Table . The ave) Tables .sample and [TOC]sample) were normalized to mean fjord values ([THg]Fjord mean and [TOC]Fjord mean)  Fig. . The TOCan) Fig. . Interesan) Fig. .Figure 313C (‰) values similar to the endmembers for terrestrially-derived OM, while the Wijdefjorden and Dicksonfjorden had higher δ13C (‰) values, characterized by marine-derived OM  endmembers for terrestrial and marine sources   decreased with increasing latitude, suggesting that TOC in the Wijdefjorden sediments was predominantly composed of marine OM   values, which were within the endmember of terrestrially derived-OM  Fig. . The reles) Fig. . In addi-OM Fig. .29. Our results showed positive relationships between the concentrations of THg, Zr, Ti and Hf across all sites  on THg concentrations, THg was plotted with detrital and lithogenic proxy metals such as Al, Zr, Ti, Rb, Th and Hf, which are often used as indicators of these influences31. Since the 1990s, the Hornsund glaciers have retreated rapidly in Svalbard (~70 m yr−1 on average) and had a large annual volume of loss glaciers (0.44 km3 yr−1 on average) due to higher summer temperature (by 2~3 °C) compared to other fjords31. Therefore, Hornsund with tidewater glaciers is particularly susceptible to the influence of glacier melting, which releases fresh meltwater and drifting ice to fjords. These processes have shown to supply modern and ancient OM, trace metals, and nutrients both directly by releasing glacier-trap resources and indirectly by exposing terrestrial landscape prone to runoff to fjord ecosystems33. In fact, high sediment (0.17 ~ 0.66 cm yr−1) and organic carbon (OC) accumulation (9.3 ~ 49.4 g m−2 yr−1) rates were observed in Hornsund. The THg concentrations (52 ± 15 ng g−1) and THg mass accumulation rates (THg MAR) in Hornsund (94 ± 27 μg m−2 yr−1) were comparable to those of Huang He River  and higher than East China  and Yellow Seas , which are characterized by substantial coastal runoff and the largest local anthropogenic Hg emission in the world , and thiolates (RS−) in OM behave as ligands to form a transition metal thiolate complex, which leads to a strong binding affinity with trace metals including Hg36. These associations among OM, thiolates, and THg contents can explain the positive relationships among them as observed in the Svalbard fjords  and a heavier δ13C value (> −25‰)46. Our results suggest that OM in the Hornsund sediments, which display relatively high TOC/TN ratios and relatively depleted δ13C values, is mainly derived from modern and ancient terrestrial OM  using the δ13C (‰) end members derived from terrestrial and marine sources39. Due to the complex lithology of the Svalbard fjord sediments, which can lead to an extensive range of δ13C end members for terrestrial OM, we used measured δ13C end members from Spitsbergen (Hornsund and Adventfjord) for terrestrial sources, and sites west of Svalbard for marine sources , and calculated a mixing model with C and N isotopes, and TOC/TN ratios48. The results indicated that the Hornsund sediments were composed of both modern and ancient terrestrial OM (78%), which is consistent with earlier studies48. The contribution of OM derived from marine sources was relatively higher in the Wijdefjorden (65%) and Dicksonfjorden (37%) sediments  50. Thus, δ34S values can be a conservative tracer accounting for terrestrially- and marine-derived OM51. Generally, the terrestrially-derived OM has lower δ34S values (6.3‰) compared to marine-derived OM (18.1‰)50. In our results, Hornsund had notably lower δ34S isotopic values  than those of Wijdefjorden  and Dicksonfjorden , indicating that the surface sediment of Hornsund was mainly composed of terrestrial sources  Fig. . The δ34n by 1 ~ ‰50. Thuses Table . In part−2 yr−1) have also shown particularly low values compared to other regions of the world (East China and Yellow seas: 86 ~ 133 ug m−2 yr−1) but the THg concentration of the Svalbard fjord sediments are comparable to several remote regions  compared to the Devonian rock in the Wijdefjorden and Dicksonfjorden (ranging from 5 to 30 ng g−1)52. To assess the contribution of weathering or erosion, we analyzed the detrital metals degraded from bedrocks , which are the most abundant components of minerals, less immobile in sediments and exclusively derived from natural sources 53.Observed positive relationships between THg and some detrital proxies  suggest that weathering or erosion may have liberated Hg, which is then sequestered and transported by terrestrially-derived OM to the sediment , industrial runoff, and riverine input. The fjord sediments in Svalbard are atypical such that there are few major point sources of anthropogenic Hg. The recent records of atmospheric Hg deposition in the Arctic region >5 ug m− yr−1 hav54 has suggested that high Hg concentrations observed in lake sediments located near Ny-Ålesund are caused by increased phytoplankton biomass due to global warming. Phytoplankton carcasses can take up Hg from the water column and subsequently deposited Hg directly into the sediment54. Based on the relatively heavier δ13C values and lower TOC/TN ratios43, which are indicators of the influence of marine phytoplankton, it is possible that algal scavenging processes have influenced the Wijdefjorden and Dicksonfjorden sediments in recent periods. This process, however, does not explain the elevated sediment THg concentration in Hornsund, which is mainly consisted of terrestrial OMGiven the absence of local or regional anthropogenic disturbances (which are described in the supplementary information in detail), another natural Hg source to the Svalbard fjord sediments is by sinking of particulate OM. A recent study−1) and TOC (1.68 ± 0.55%), compared to the Wijdefjorden and Dicksonfjorden fjords. Our study suggests that the sedimentary THg contents of all three fjords are mainly derived from weathered and deposited minerals, and both modern and ancient OM released from melting glaciers act as the primary transport mechanism for Hg. Thus, the large amount of THg released from the terrestrial to the fjord system is likely to affect Hg cycling in the water column as well as the sediments of the Svalbard fjords.In summary, Hornsund experiencing rapid glacial retreat rates and a wide calving front showed the highest concentrations of THg , Dicksonfjorden  and Hornsund  during three marine geological cruises on R/V Helmer Hanssen in September 2015, July 2016 and July 2017. The samples were collected using a box corer (W50 cm × H50 cm × D50 cm)  between the 1 cm and 0–5 cm depth samples from Hornsund, indicating that samples of 0–5 cm depth have similar properties to the surface sediments. All subsamples were freeze-dried for two days and then ground to a powder using a mortar and pestle before further analyses.Surface sediment samples were collected from 35 stations located in Wijdefjorden (m) Table . Sub-samFour bedrock samples of phyllite sandstone, calcareous shale, and black mudstone were obtained from an archive of Svalbard bedrocks that were collected during field campaigns led by the Korea Polar Research Institute from 2013 to 2015, in the Isfjorden area and central Spitsbergen. The analyzed rocks have similar compositions to the rocks surrounding our study sites  concentrations for surface sediments and bedrock samples were measured at the Library for Marine Samples (LIMS), KIOST and the Gwangju Institute of Science and Technology, using an automatic Hg analyzer, which undergoes thermal decomposition followed by catalytic reduction, amalgamation, desorption, and an atomic absorption module . The THg concentrations were calibrated against marine sediment reference materials (MESS-3) for trace metals and other constituents . The analytical accuracies were determined to be <5%, based on replicate analyses of certified reference materials, and the analytical precision of the THg concentration measurement was determined to be <10%, based on replicate measurements of standard materials and sediment samples. To determine the concentrations of Al, Zr, Ti, Rb, Hf, Fe, Mn, Cr, V, Mo and U, the powdered samples were dissolved with a standard reference material (MAG-1) in a mixture of hydrofluoric and perchloric acids, and the elemental concentrations were measured using inductively coupled plasma atomic emission spectroscopy  at the Korea Basic Science Institute. The analytical accuracy and precision ranged from 5 to 10%, and the concentrations were calibrated against Marine Sediment-1 reference material 2 coulometer  for TIC , at the Library for Marine Samples (LIMS), KIOST. The accuracy and precision in the analysis of these elements were <5%, based on analysis of standard reference materials . TOC concentrations were calculated by subtracting TIC from TC59.The concentrations of total carbon (TC), total nitrogen (TN), total sulfur (TS) and total inorganic carbon (TIC) in the dried and ground sediment samples were measured using a Thermo Electron Corporation Flash EA 1112 Series NC Soil Analyzer for TC and TN, an organic elemental analyzer for TS , and a CO3, while the δ15N samples were not acid-treated. The δ13C and δ15N values were measured using a CN elemental analyzer  coupled with an isotope ratio mass spectrometer  at the Korea Polar Research Institute60. The measurement error was <0.2‰ for carbon and <0.3‰ for nitrogen, and the δ13C and δ15N values were reported relative to the δ13C value for PDB (Pee Dee Belemnite) and air, respectively. The PDB standard and air were calibrated relative to Indiana University Acetanilide #1, USGS40, USGS41, Urea, and Thermo Soil Stand.For the carbon stable isotope analyses of OM, all sediment samples were treated with a 1 M HCl solution to remove CaCO34S) were measured with bulk sediment samples using a CNS elemental analyzer  coupled with a stable isotope ratio mass spectrometer  at the Korea Institute of Geoscience and Mineral Resources (KIGAM). Replicated analyses had a precision of better than 0.6%. The Vienna-Canyon Diablo Troilite (VCDT) was used for standardizing the material, and it was calibrated with NBS127 as reference.The sulfur (TS) contents and its isotopic value  than marine OM , because C13C of −26.8‰ for the terrestrial OM endmember48, and −20.6‰ for the marine OM in the Spitsbergen fjords48. Using these C isotope endmember values, we calculated the relative contribution of terrestrial OM (Fterr) to fjord sediments using the following equations:45Fterr (%) is the percentage of terrestrial organic carbon in the samples, δ13Corg(sample) is δ13C measured in the samples, and δ13Corg and δ13Corg(marine) are the endmember values for terrestrial and marine OM as provided above.For the calculations, we used a δThe distribution of THg in Fig. Supplementary Information.


Lichenothelia and Saxomyces, that have been suggested to be early diverging lineages. We assembled phylogenomic datasets from newly sequenced (4) and previously available genomes (238) of 242 taxa. We explored the influence of tree inference methods, supermatrix vs. coalescent-based species tree, and the impact of varying amounts of genomic data. Overall, our phylogenetic reconstructions provide consistent and well-supported topologies for Dothideomycetes, recovering Lichenothelia and Saxomyces among the earliest diverging lineages in the class. In addition, many of the major lineages within Dothideomycetes are recovered as monophyletic, and the phylogenomic approach implemented strongly supports their relationships. Ancestral character state reconstruction suggest that the rock-inhabiting lifestyle is ancestral within the class.Dothideomycetes is the most diverse fungal class in Ascomycota and includes species with a wide range of lifestyles. Previous multilocus studies have investigated the taxonomic and evolutionary relationships of these taxa but often failed to resolve early diverging nodes and frequently generated inconsistent placements of some clades. Here, we use a phylogenomic approach to resolve relationships in Dothideomycetes, focusing on two genera of melanized, extremotolerant rock-inhabiting fungi,  Dothideomycetes is the largest and most diverse fungal class of ascomycetes, comprised of c. 20,000 species  generate a genome-scale phylogeny of Dothideomycetes to resolve the phylogenetic placement of still unsupported lineages and in particular clarify that of Lichenothelia and Saxomyces and their relationships with other RIF lineages within the class, (ii) assess whether and to which extent the amount of genetic information, the alignment processing and the phylogenomic reconstruction method impact the final phylogenetic inference, and (iii) assess the minimum amount of genomic information needed to generate a topology that agrees with the phylogeny generated with the entire set of genes.Here, we present a phylogenomic study on the evolutionary relationships of Lichenothelia and Saxomyces species were available from previous culture isolations reported by Muggia et al. ; Lichenothelia intermixta L2282 (LMCC0543); Saxomyces alpinus CCFEE5470 (CBS135222); and Saxomyces americanus L1853 . Strains were sub-cultured on malt-yeast medium  according to the manufacturer’s protocol. The quality of the genomic DNA was checked by gel electrophoresis on 0.8% agarose gel, which showed a sharp genomic DNA band with a small amount of degraded nucleic acid. DNA concentration was 7–11 ng/μl (175–275 ng of DNA), and the nucLSU rDNA was successfully sequenced to confirm the identity of the strains (100% sequence identity). The four genomic DNA extractions were sent to the University of Illinois at Chicago sequencing facility for library preparation (Nextera XT) and sequencing on an Illumina MiSeq platform. The strain of A bioinformatic pipeline consisting of several programs was generated to extract single-copy genes from whole-genome assemblies and create individual gene alignments and phylogenies Fig. . Fastq fThe phylogenetic reliability of the generated dataset was tested with different numbers of genes, alignment filtering methods and tree reconstruction methods. Five individual datasets were constructed: (i) a dataset of genes longer than 1000 bp after Gblocks trimming (“> 1 kb Gblocks” dataset) and (ii) one after GUIDANCE trimming with less than 50% of gaps (“> 1 kb GUIDANCE” dataset); (iii) the complete set of retrieved genes including both genes longer and shorter than 1 kb after Gblocks filtering (“Complete Gblocks” dataset) and (iv) GUIDANCE filtering (“Complete GUIDANCE” dataset); (v) a dataset without any missing data for estimating the impact of missing data on the tree, hence reduced both in gene number and taxa (“No missing” dataset). The individual gene alignments of each dataset were either used for individual gene tree calculations, or concatenated into a supermatrix with FasCONcat 1.0 .As the analysis of a genome-based supermatrix with bootstrap support can be highly computationally demanding, an alternative, customized, resampling strategy was tested in the analyses on the “> 1 kb Gblocks” concatenated alignment. Thirty runs of IQTree were carried out on reduced concatenated matrices made of an increasing, randomly selected number of columns from this alignment. The phylogeny resulting from the complete supermatrix was taken as reference and used to compute RF distances in RAxML 8.2 . Both Maximum Parsimony and Maximum Likelihood approaches were tested using “Trace character History” option in Ancestral State Reconstruction package of MESQUITE 3.6  and 664 genes longer than 1000 bp after Gblocks trimming. The total alignment length is 1.1 Mb. The “Complete Gblocks” dataset comprises the same samples, but it includes all the 2998 genes which are single copy, not predicted in multiple version and not empty after Gblocks trimming. As Gblocks does not allow gaps and it selects only perfectly aligned regions, many genes were drastically shortened. Hence the final length of the alignment is only twice as long as (2.2 Mb) the “>1Kb Gblocks” dataset, which is a subset of the total. The presence of samples characterized by relevant events of gene duplication or low-quality assemblies .Changing both the starting dataset, (“Complete Gblocks” or “1Kb Gblocks”) and the reconstruction method  generated the most diverse topologies  and markers (63), produced highly similar results when considering the phylogeny obtained from the concatenation ; conversely, increasing the resampling effort from 20 to 30% did not produce a significant shift of the distances from the reference topology.Phylogenetic analyses based on the randomly resampled, increasingly bigger alignment from “1Kb Gblocks” produced topologies which progressively approached the reference Fig. . The resMaximum parsimony analysis highlighted the rock-inhabiting lifestyle as the most parsimonious state for the basal node of the phylogeny Table . The MaxNot applicable.Lichenothelia and Saxomyces. Exploring different combinations of markers, alignment filtering and phylogenetic reconstruction methods helped to find support among the overarching phylogenetic relationships. Our data suggest that the rock-inhabiting lifestyle is highly likely to be the ancestral state within this diverse class of fungi.The phylogenomic analyses presented here provide robust insight into evolutionary relationships within Dothideomycetes, with focus on the RIF genera Phylogenies are increasingly inferred from datasets comprising unprecedented numbers of samples and genetic markers. However, the number of samples and genetic loci, which both drive the accuracy of phylogenetic inferences, are often far from being comprehensive. Further, which of the two contributes more than the other in the phylogenetic accuracy remains debated, although empirical evidence tends to support the importance of extensive species sampling  were included, we showed that a subset of the whole supermatrix, corresponding to about 20% of our dataset, provides a phylogenetic accuracy which did not improve significantly by further increasing the amount of data Fig. . MoreoveStrict alignment filtering criteria have been shown to worsen single-locus inference  and other laboratories which investigate hidden fungal biodiversity, are filling this gap, sequencing fungal genomes from the least known families. This effort, from a phylogenetic point of view, will make the taxon sampling more comprehensive and, therefore, the phylogenetic inference in Dothideomycetes more accurate.Concatenation approaches have been shown to produce highly supported but wrong topologies when sufficiently short branch lengths are generated in relation to effective population size  or the use of amino acid alignments instead of nucleotide sequences. The latter would exploit the redundancy of the genetic code to better “preserve” phylogenetic signal . We anticipated that this drastic reduction of genetic markers would likely have a greater effect on the resulting phylogeny than the missing data. However, we recovered largely the same topology with the dataset of 664 concatenated genes. Only minor differences concerning the placement of single samples were noted, but these did not involve the most basal nodes. In accordance with other studies which tested real and simulated data  “> 1 kb Gblocks” IQTree run 1; (1) “> 1 kb Gblocks” IQTree run 2; (2) “> 1 kb Gblocks” IQTree run 3; (3) “> 1 kb Gblocks” ASTRAL; (4) “Complete Gblocks” IQTree; (5) “>1Kb GUIDANCE” IQTree; (6) “>1Kb GUIDANCE” ASTRAL.Additional file 3: Table S3. RF distances and normalized RF distances from the reference topology of the 30 resampled matrices for each resampling effort value (0.1–30%).Additional file 4: Table S4. Average values and standard deviation (SD) of RF distances and normalized RF distances among the 30 phylogenies generated from the randomly resampled matrix with the same resampling effort.Additional file 5: Table S5. Taxa and associated lifestyle used in ancestral character state reconstruction .Additional file 6: Figure S1. Assembly completeness on the base of 3156 Pezizomycotina orthologs evaluated by BUSCO and expressed as the percentage of complete (green), duplicated (pink) and fragmented or missing (grey) genes. Distribution outliers are highlighted with “*”.Additional file 7: Figure S2. Phylogeny generated from 2998 concatenated genes of the “Complete Gblocks” dataset with IQTree.Additional file 8: Figure S3. Phylogenies generated from 63 genes of the “No missing” dataset; (a) ML tree generated with IQTree on concatenated genes; (b) ASTRAL III species tree.Additional file 9: Figure S4. Phylogeny generated from 1260 concatenated genes of the “>1Kb GUIDANCE” dataset with IQTree.Additional file 10: Figure S5. Phylogeny generated from 1260 concatenated genes of the “>1Kb GUIDANCE” dataset with ASTRAL.


There is no universal definition of rurality due to the heterogeneity in what makes a place “rural” or “urban”. This study explored how elements of rurality are related to each other, and how the elements that define rurality vary by region. Data were abstracted for all 1948 non-metropolitan counties in the contiguous 48 states on rurality. K-means cluster analyses (k=4-8) were conducted to examine classification structures among component variables examining regional differences. In the South region, the majority (51.2%) were “Type 2” counties: low population size and density but higher urbanized population. The Midwest had a majority of “Type 3” counties (56.4%): intermediate for population size and density, but higher distances to metro areas. These exploratory findings underscore the heterogeneity and regional variability in rurality and how those measures are structurally related to each other, and essential to understanding those factors that truly drive rural-urban health disparities for older adults. Part of a symposium sponsored by the Rural Aging Interest Group.


Carcharodon carcharias) emerged in central California. These records contradicted the species established life history, where juveniles remain in warmer waters in the southern California Current. This spatial shift is significant as it creates potential conflicts with commercial fisheries, protected species conservation, and public safety concerns. Here, we integrate community science, photogrammetry, biologging, and mesoscale climate data to describe and explain this phenomenon. We find a dramatic increase in white sharks from 2014 to 2019 in Monterey Bay that was overwhelmingly comprised of juvenile sharks < 2.5 m in total body length. Next, we derived thermal preferences from 22 million tag measurements of 14 juvenile sharks and use this to map the cold limit of their range. Consistent with historical records, the position of this cold edge averaged 34° N from 1982 to 2013 but jumped to 38.5° during the 2014–2016 marine heat wave. In addition to a poleward shift, thermally suitable habitat for juvenile sharks declined 223.2 km2 year−1 from 1982 to 2019 and was lowest in 2015 at the peak of the heatwave. In addition to advancing the adaptive management of this apex marine predator, we discuss this opportunity to engage public on climate change through marine megafauna.During the 2014–2016 North Pacific marine heatwave, unprecedented sightings of juvenile white sharks ( Even though all species and taxa are influenced3, it has been argued that prioritizing attention to marine megafauna may help increase public concern and engagement to combat climate change4. However, the swift pace of changes in the ocean has driven a corresponding rapid shift in the distribution of marine megafauna6. As a result, a combination of conventional techniques as well as innovative and adaptive strategies for monitoring these species are needed. Such advances may also serve to help understand the broad and cascading consequences of our rapidly warming ocean to local ecosystems, communities and economies.Marine ecosystems are exhibiting major ecological realignments in response to climate change7, adult white sharks (Carcharodon carcharias) are apex predators throughout their distribution8. Most juvenile sharks 11 reside in coastal nursery areas12. In the northeastern Pacific this demographic consists of neonates (< 1.50 m TBL), young-of-year , and other juveniles (< 2.5 m TBL) which primarily use coastal waters of northern Mexico and southern California14. A lack of confirmed observations beyond this region suggests the Southern tip of the Baja peninsula approximates their southernmost range. Within this region, juveniles remain in a relatively narrow temperature band, a pattern which is consistent across distinct white shark populations15. Even though white sharks are endothermic, greater surface-to-volume ratios in these juveniles may challenge their thermal inertia and ability to thermoregulate especially at the cold limit of their thermal habitat. As a result, the thermal range of juvenile white sharks is likely a major driver of the habitats they occupy, and this appears to be sensitive to climatic shifts during El Niño events15.Except for rare instances17. A warm mass of surface water described both as the Pacific Warm Anomaly and “the Blob” entered southern California in the fall of 201416 and resulted in persistent warm-water conditions that endured to the 2015–2016 and 2018–2019 El Niño events18. Record-high sea surface temperature (SST) was observed in the area between Point Conception  and San Miguel Island , where nearshore SST reached a peak of 6.2 °C above the historical average in September 201519. The multiyear warm anomalies caused widespread species shifts21, epizootics22, multiple unusual mortality events and dieoffs26, and broad impacts to commercial fisheries27 across the region.Ecosystems in the Northeast Pacific experienced extreme conditions over the past decade, highlighted by anomalously warm conditions from 2013 to 201628, neonate, YOY, and other juveniles < 2.5 m TBL sharks in the Monterey Bay remained non-existent or extremely rare until 201415. Partly in response to these events, and out of concern for public safety at beaches, in 2018 the California State Senate passed funding measures (SB 840) to expand population monitoring of white sharks at popular recreation areas29. In addition to such traditional and official wildlife monitoring programs, community science initiatives are becoming a viable source of in situ data that can document and reflect changes in realized species distributions31. As nearshore monitoring programs have only recently been implemented, incorporating community science data streams can improve our emerging understanding of the ecological relationship between climate and juvenile white sharks in the Northeast Pacific.Concurrent with these ecologically significant warm-water events, local communities around the Monterey Bay 36.8° N, 121.95° W) began reporting small white sharks < 2.5 m TBL in nearshore Monterey Bay proximate human activities  and adult  white sharks are common residents of central California and Monterey Bayted Fig.  since thn depths between tagged sharks reveals a common break at 20 m and suggests that records from 0 to 20 m broadly constitute near-surface behavior  of the continuous thermal suitability model varied considerably from 1982 to 2019. This value had its lowest position in 2008 at 32.0° N, and most northerly position in 2015 at 38.5° N 14. Point Conception is a terrestrial headland and is considered a marine biogeographic boundary that sharply separates the warmer waters of the southern California bight, from the northern remnant of the California Current Ecosystem. From 2014 to 2020, the mean position moves 240 km north of Point Conception to 36.3° N. Regression analyses revealed that the Jan-Apr tlat played a statistically significant role in determining the Jan-Dec tlat  and northern Baja California , emergent (− 2 km2 year−1), and future (+ 58.3 km2 year−1) habitat regions of juvenile white sharks vary  mortality from white shark bites39.White sharks appear to exhibit an ontogenetic shift at the age of 3–4 years, at 2.5–3 m TBL, resulting in habitat as well as dietary changes41, sea otters have not substantially overlapped with juvenile white sharks for a century or longer42. However, the extensive record of sea otter strandings with shark bite wounds today represents the single greatest mortality source facing the threatened species44. This is significant as it poses a risk to the ecosystem benefits that sea otters provide to nearshore ecosystems46 as well as to sea otter conservation programs that are actively working to recover otter populations and restore their ecosystem functions48. Since 2010, shark related otter strandings have been on the rise and initially the seasonal pattern of their occurrence overlapped with, and implicated, adult white sharks arriving in Central California for their annual fall foraging migration49. Since 2012, however, sea otters are being bitten by white sharks throughout the calendar year39, including the spring and summer months when adults white sharks are largely absent from the Central California coast49. At the same time, the significant loss of Macrocystis and Nereocystis kelp forest canopies in California has removed sheltering habitats and exposed otters to ambush predators like white sharks41.As the Southern sea otter population today is distributed from 34 to 37° N39. Therefore, sea otters are not a food source for any life stage of white sharks and present no mechanism to explain juvenile white shark range expansion. The second is that while California sea otter population grew substantially from 1989 to 2014 and expanded their geographic range, the sea otter population density in northern Monterey Bay (where we documented the recent increase in juvenile white sharks) was essentially unchanged during this period41. Rather, our tag-derived calculation of the cold edge of the juvenile white shark thermal habitat, and its poleward shift since the 2014–2016 heatwave provide a more compelling explanation of the poleward shift in these endotherms at a younger age than previously recorded.While these factors working together might explain the increase in shark bites to otters throughout their range, it does not explain why juvenile white sharks, including YOYs, are now in Monterey Bay Fig. . To supp12  1,300 km south of Monterey Bay. Though juvenile white sharks < 2.5 m TBL have a clearly defined thermal affinity  and maximum (max) lengths for outliers. As a standard metric of shark abundance, we calculated from these records the total number of white sharks observed per trip.We compiled white shark abundance and length observations from field surveys and by curating a community science project. From 2009 to 2019, one of us (EM) kept detailed logbook records of white shark observations during 198 recreational trips in Monterey Bay. With minor variations, all trips used the same vessel , began and ended in the same port  and followed a similar transect path to a shipwreck reef  during daylight hours. From 2009 to 2014, as sharks were uncommon, 1–2 observers manually counted and photographed sharks as they basked near the surface. From 2015 to 2019, observers used a boat-based aerial drone  operating at < 120 m altitude (FAA# FA3T3FRLF7) to photograph and count sharks and estimate body lengths near the shipwreck reef. Logbooks recorded the total number of sharks sighted per trip, and when available listed minimum (μ and σ. Given our (EM) anecdotal observations that 80% of the sharks during the study measured 2.1–2.7 m, we ran 800 of the simulations with μ = 2.3 and σ = 1, and the remaining simulations with σ = 1 and where μ was equally split between min and max recorded lengths. On 9 trips in 2018–2019, we validated shark length estimates with photogrammetry, using drone surveys to capture reference lengths placed alongside surface basking sharks. We plotted the modeled length density (n = 403 sharks) against the photogrammetry-validated length density (n = 65 sharks), comparing both to accepted length-stage categories for Northeast Pacific white sharks15.To understand size composition, we developed a bootstrapping procedure to expand the sparse logbook records using random variates to account for measurement error. For each shark length measurement, this routine generated 1000 random variates from a normal distribution with parameters 64, we individually curated user-submitted, geo-referenced images that had previously been taxonomically verified by the iNaturalist user community. From a total of 72 contributed observations excluded subadult and adult (> 3 m TBL) demographics, as well as sharks occurring north of Monterey Bay. This yielded 53 quality-checked and vetted observations from 2012 to 2019 (available at: https://bit.ly/2Px15zo). We report abundances as observations year−1, rescale both these and the logbook abundance data from 0 to 1, and fit a single loess model61 to both data series as an ensemble index of white sharks in Monterey Bay.As an independent measure of juvenile white shark abundance in Monterey Bay, we sought public observations through a community science project. Using the iNaturalist platform60. Juvenile white sharks experience a wide temperature range (6–26 °C) throughout the water column while occupying a more specific band (16–20 °C) of preferred temperatures15. We quantified observed species occupancy-temperature relationships using both binomial and continuous thermal suitability models. We used temperature data from pop-up archival tags  deployed on 14 white sharks deployed from 2001 to 2015 , but sharks subsequently ventured throughout their historical range, broadly reflecting the thermal preference of this demographic in the Northeast Pacific.A two decade biologging program provided a robust data series of the three-dimensional movements and the thermal affinity of juveniles , and coefficient of variation (CV) among individuals for each depth and compared the form of these curves across depths. Natural breaks in the mean, SD and CV curves determined the depths considered to include near-surface behavior.To capture accurate behaviors, we applied a number of routines to the raw tag data. First, we excluded all tag records both before tag deployment and after tag detachment. Next we applied a correction to the depth series to account for drift in the mechanical pressure sensor following previous studiesn = 2.28 × 107) in 0.5° C bins over the recorded temperature gradient , yielding more discriminating and precise preferences across all temperatures.We used the empirical temperature records observed at these tag depths to generate a thermal suitability. Tag deployments were seasonally variable and duration less than one year (mean ± SD: 117.5 ± 80 days). To avoid individual and seasonal biases, we equally weighted observations from each month and each tag, aggregating the observations  for juvenile white sharks by calculating the latitudinal center of gravity71 of the cold edge of the realized thermal niche through time, using the formula:lati are for OISST.v2 grid i; ti denotes a calculated binary cold-edge index (0 or 1) at OISST.v2 grid i, and k is the total number of OISST.v2 cells (n = 657) in the raster grid. As 2020 records were incomplete at the time of this analysis, we extrapolated its value (tlat_2020) based on the partial (Jan-Apr) to full (Jan-Dec) year relationship of tlat from 1982 to 2019. We average tlat_2020 from two methods—the mean annual value of the Jan-Apr tlat / Jan-Dec tlat quotient and a simple linear regression.We tracked the northern range limit (2), summing all raster values. This provides a daily assessment of available habitat that is thermal suitability-weighted according to tag-derived occupancy. We summarized available habitat in three relevant regions of the California Current Large Marine Ecosystem (LME). These areas reflect the historical range , the emergent range , and perhaps the future range . All analyses and visualizations were conducted in the R environment72.We combined the continuous thermal suitability model and the OISST.v2 data to calculate the availability of juvenile white shark habitat over time. For each daily OISST.v2 raster, we converted the SST values in each cell with the model derived thermal suitability. We rescaled the rasters to 0–1 and multiplied the resulting value by cell area (range 522–710 kmSupplementary Information.


The development of accurate and efficient image reconstruction algorithms is a central aspect of quantitative photoacoustic tomography (QPAT). In this paper, we address this issues for multi-source QPAT using the radiative transfer equation (RTE) as accurate model for light transport. The tissue parameters are jointly reconstructed from the acoustical data measured for each of the applied sources. We develop stochastic proximal gradient methods for multi-source QPAT, which are more efficient than standard proximal gradient methods in which a single iterative update has complexity proportional to the number applies sources. Additionally, we introduce a completely new formulation of QPAT as multilinear (MULL) inverse problem which avoids explicitly solving the RTE. The MULL formulation of QPAT is again addressed with stochastic proximal gradient methods. Numerical results for both approaches are presented. Besides the introduction of stochastic proximal gradient algorithms to QPAT, we consider the new MULL formulation of QPAT as main contribution of this paper. Photoacoustic tomography (PAT) is an emerging imaging modality, which combines the benefits of pure ultrasound imaging (high resolution) with those of pure optical tomography (high contrast); see ,2. The bOne important reconstruction problem in PAT is recovering the initial pressure distribution , which is commonly considered as a very accurate model for light transport in tissue (1)Fi-th forward operator evaluated at Tikhonov type as well as iterative regularization methods can both be formulated as finding a solution of the optimization problemmin12∑i=1N equations in  is already recovered from acoustic measurements (5)∀(5)∀Φ:Ω×The photon density RTE; see  for a moTheorem 1 (Well-posedness of the RTE).For every nary RTE  admits aProof. See . ☐Definition 1 (Solution operator for the RTE).The solution operator for the RTE is defined bywhere Φ denotes the unique solution of .Theorem 1 guarantees that the operator Due to the spatially varying absorption of photons, the tissue is locally heated and emits an acoustic pressure wave. The acoustic source is proportional to the amount of absorbed photons, the light intensity and the so-called Grüneisen parameter refer to . TherefoDefinition 2 (Heating operator).The heating operator is defined byIf one introduces the averaging operator R, and acoustic data are collected on a subset of The local heating causes an acoustic pressure wave, where the initial pressure distribution equation:(∂t2−Δ)p.(1)The operators (2)For every Proof. See . ☐Equations  and (14)Theorem 3 (Adjoint of Let with Proof. See . ☐Given data Theorem 4 (Lipschitz continuity of ∇FI)For any data Proof. v. Then, for any Without loss of generality, we assume ution of  with vThere are, at least, two different strategies to address such an inverse problem: Tikhonov type regularization on the one and iterative methods on the other hand. In this section, we give an overview of such methods. In particular, we describe proximal stochastic gradient methods , which seem particularly well suited for multi-source QPAT but have not been investigated yet for that purpose.In this section, we consider a quadratic Tikhonov regularization term for solving . LetL:DTheorem 5 (Well-posedness and convergence).(1)For any (2)Let ■Every sequence ■The limit of every weakly convergent subsequence of ■If the Proof. See . ☐F is smooth and G is convex . HerProximal gradient algorithm: The proximal gradient algorithm is a splitting method that iteratively computes explicit gradient steps for F and implicit proximal steps for G. In our context, we take F as the data fidelity term andi-th data fidelity term computed in Theorem 3.■ nctional  reads andOne can also incorporate a block-iterative (or mini-batch) strategy in the stochastic gradient method, meaning that a small subset of ods, see ,40,41,42An alternative class of algorithms to address nonlinear inverse problems are iterative techniques. The most basic iterative method for solving the nonlinear inverse problem Equation ), and PDAT readsμk+1=PDμkteration  for mini mapping  reduces i-th data fidelity term computed in Theorem 3 and k and, therefore, in this special situation, the iteration becomes In a similar manner, one can also use a stochastic version of the projected Landweber iteration. Using the loping strategy of ,44,45 inμk+1=PDμkt method  with λ=0found in ,44.Since the RTE is time-consuming to solve, we are looking for a suitable reformulation of the inverse problem in multi-source QPAT avoiding computation of a solution of the RTE in each iterative step. In this paper, we propose to write  as a muli indicates the i-th illumination, and Recall the forward problem of QPAT governed by the RTE . With th in QPAT  then canuch thatM(μ)Φi=qiIn the case that the data ution of . For thaution of  by the ction of (z):=ution of , we minihe formsJ(z)=∑i=1N be the number of illuminations, write For solving QPAT in the novel MULL formulation , we use mulation .MULL-projected stochastic gradient algorithm: Here, we consider the form . For any iteration index ■ lgorithm –(28).For better scaling, in our actual numerical implementation, we replace the scalar step sizes mulation  and its mulation .For the Tikhonov approach to multi-source QPAT, the radiative transfer equation is numerically solved by a streamline diffusion finite element method. Solving the RTE is required to evaluate the forward operator h and Employing a finite element scheme, we derive the weak formulation of Equation  by integθ) as in . Here, te obtain∫Ω∫S1.radients –(48) reqate ruleμΦiHik+1=ate ruleμΦiHik+1=als; see . The posRemark 1.Note that in the stochastic gradient methods for the novel MULL formulation of QPAT calculating the matrix vector product mulation  requiresThe reconstruction times for the final reconstructions using all methods described above are shown in In In this paper, we developed efficient proximal stochastic gradient methods for image reconstruction in multi-source QPAT. We used the RTE as an accurate model for light transport and employed the single stage approach for QPAT introduced in . One cla


The geometric morphometrics method (GMM) is a technique to study scale and shape relationships of structures using Cartesian geometric coordinates rather than linear, areal (of area), or volumetric variables. GMM has been of great value in many biological studies, but does not appear to have been used to examine equine skulls.N = 9), 6–15 years old (N = 10) and > 16 years old (N = 10) were examined.In this exploratory study, twenty-nine normal equine heads of three different age groups: <5 years old (Computed tomography (CT) bone window DICOM images were reconstructed into isosurfaces , onto which landmarks were added using Stratovan Checkpoint® software. Data from 29 landmarks were analysed using MorphoJ analysis, which applies a Procrustes fit, prior to reducing data dimensionality through principal component (PC) analysis. PCs with and without allometry were considered. Allometric shape described by PC1 accounted for 27% of variance. Loading pertaining to: the pterygoid process, bilaterally; caudal aspect of hard palate; tip of nasal bone; ethmoid sinuses, bilaterally; caudal aspect of the ventral conchal bulla, bilaterally and caudal aspect of the vomer bone suggest that these anatomical structures are predictive of age group. When allometric effects (shape variation explained by size) were removed, PC1 was unable to distinguish horses by age group. Allometric shape differences could distinguish the youngest versus the two older age groups. The potential applications of GMM in equine diagnostic imaging are wide ranging and include the investigation of changes in the equine skull with respect to genetics and characterisation of conformation-related diseases affecting the teeth, jaws and sinonasal compartments. The study of anatomical shape and its variation is a major topic of medical research. The use of shape analysis is one approach to understanding the causes of variation and the morphological transformation of animals due to evolution, age, disease and selective breeding Zelditch . GeometrGMM was developed to address the shortcomings of simple linear, volume or area-based methods that do not preserve the geometric relationships between the measurements, leading to difficulties in the interpretation of spatial relationships  changes that occur in normal equine skulls by using landmarks-based geometric morphometric methods in horses of three different age groups.Our hypothesis was that there would be ontogenic difference between horses of difference age, with young horses having more skull variability than adult or old horses.This study was approved by the University of Edinburgh, Ethical Review Committee.This exploratory study included the following samples:Equine heads were obtained from two sources:The heads of 54 horses were freshly obtained from an abattoir. Each head was of unknown history and grossly similar in size to an adult Thoroughbred horse head. Heads were positioned on their mandibles and imaged using a multislice CT scanner  in a helical scan mode using a 512 × 512 Matrix, 120 Kv, 300 mA, at a slice thickness of 1.5 mm.Bone windows (H70) were used to review the images at a WW of 4000 Hounsfield Unit (HU) and WL of 1000 (HU). After image acquisition, the heads were frozen (− 20 °C) and then sectioned transversely at 5 cm intervals with a band saw. After thawing, both sides of each section were examined grossly for sinonasal or significant dental abnormalities.Anatomical and CT images were similarly obtained from 36 equine heads with unknown histories that had been freshly obtained from an abattoir by Justin Perkins . CT images were acquired using a 4th generation, Universal Medical System CT scanner, GE light speed ultras, Highland Heights, Ohio, USA, at 1.25 mm slice thickness, 120 kV, 300 mA. The heads and CT images were examined for the presence of sinonasal disease or significant dental abnormalities.Twenty-six of 54 heads in Group A had clinical and/or imaging evidence of dental or sinonasal disease and were thus excluded from the study, leaving 28 heads available in this group. Four of the 36 heads in Group B had clinical and/or imaging evidence of dental or sinonasal disease and were also excluded, leaving 32 heads in this group .The Computed tomographic images were reviewed by a Dipl.ECVDI (TL) and the clinical examination of each head by a Dipl.ECVD (equine) (PMD).a and MorphoJb . The analysis including statistical test was mainly conducted by the first author (TL) using software designed for GMM analysis N = 4]; 6–15 years old [N = 4]; >16 years old [N = 4]) that had undergone CT head imaging for clinical reasons other than sinonasal disorders were measured to obtain their head dimensions.In order to compare the head sizes in the current study population with head sizes of known breeds, CT images of 12 Thoroughbreds of known age  was measured from CT sagittal reconstructions; head “width”  was measured from CT dorsal reconstructions and head “height”  was measured using CT sagittal reconstructions. These three measurements were multiplied together to produce a measurement of head “volume” N = 15), 6–15 years old (Group 2: N = 21) and > 16 years old (Group 3: N = 24).The 60 heads were aged by clinical incisor examination using standard guidelines along with CT measurements of cheek teeth reserve crown and root length. Heads were placed into three age groups: <5 years old (Group 1: www.osirix-viewer.com) imaging software which was used to perform multiplanar image reconstructions and to evaluate the image quality. Isosurfaces of each skull were generated from their constituent DICOM images using Stratovan CheckPoint® software which allows collection of x, y and z coordinates from landmarks placed on skull images. Twenty-nine heads were selected for this study: 0–5 years old (Group 1: N = 9), 6–15 years old (Group 2: N = 10) and > 16 years old (Group 3: N = 10) based on the accurate identification of all anatomical landmarks (see below) after the isosurface was generated from the DICOM images.Bone window CT data of the 60 heads were transferred as DICOM images to Osirix®  were used in each skull Fig.  A-D. EacPlacement of landmarks was performed on sagittal, dorsal or transverse slice windows or on the isosurface.Cartesian landmark coordinates were reformatted using custom R scripts and loaded into MorphoJ  analyses were considered in this study.16; in this study between the landmarks for each age group and the log-centroid size. A total of 10,000 random permutations were conducted and the multivariate regression result was highly significant .In order to correct for the effect of size on shape, a regression analysis on log- transformed centroid size with a permutation test was performed. This analysis uses the null hypothesis of complete independence between the dependent and independent variablesA Discriminant Function Analysis (DFA) was also considered in the data evaluation to determine any variable present between the three different groups: Group 1 vs Group 2, Group 1 vs Group 3 and Group 2 vs Group 3.p = 0.196), width (p = 0.942), height (p = 0.086) or volumes (p = 0.829) between the cadaver heads used in this study and the 12 control Thoroughbred horses (p < 0.05).No significant difference was found between head length , demonstrating minimal overlap between Groups 1 and 2 and no overlap between Groups 1 and 3. However, the overlap between Groups 2 and 3 was substantial. These results could be interpreted as showing a high variability in skull shape in young horses , while the skull form of adult  and old horses  is comparatively fixed. A further explanation for the difference between age groups is provided by the large morphospace the young group occupies in PC1, in relation to Groups 2 and 3   in different anatomical orientations .Two landmarks  showed no variability between Groups 1 and 3, whilst three landmarks  showed no variability between Groups 1 and 2. Eight landmarks  ethmoidal sinus caudal to 211 and 111; caudal aspect of VCB (left and right) and caudal aspect of vomer bone showed no variability between Groups 2 and 3. These results highlight the variability of the landmarks between age groups, with horses in Group 1 showing most variability in landmarks and only 2/29 landmarks not showing any variability.This appears to be the first publication to utilise GMM to investigate ontogenic shape differences in normal equine skulls in horses of different age and showed shape changes that were related to skull growth. These changes within the growing equine skull were not uniform with the allometric shape of skulls of horses under 5 years old differing substantially from older age groups.In the current study, PCA graphic representation showed a larger morphospace occupied by young horses (Group 1) in comparison to the older horses Groups 2 and 3)  vs Groups 2 and 3 (>5 y.o.) horses, with lesser variability found between Groups 1 and 2, and Groups 2 and 3. In particular, the results revealed that PC loadings between Groups 1 and 3 were located at the pterygoid process bilaterally, while PC loadings between Groups 1 and 2 were located at the pterygoid process bilaterally and also at the caudal aspect of the hard palate. PC loadings between Groups 2 and 3 were located at multiple locations .These findings showed that the variation in equine skull shape between age groups is not uniform, but more pronounced in specific anatomical areas. However, the pterygoid bone showed minimal variation and appeared to be at a constant location within the three groups. Vidic  examinedFuture GMM studies could investigate changes in equine skulls of defined known age, breed and gender in relation to their genotype to characterize breed-related disorders affecting teeth and sinonasal compartments. For example, miniature horses are predisposed to dental overcrowding due to their small skull and large teeth conformation, predisposing them to severe dental and sino-nasal problems.Overjet/overbite (“Parrot mouth”), a rostral malocclusion of the upper incisors in relation to the lower incisors is the most common equine congenital craniofacial abnormality .Conceptualization: Tiziana Liuti.Methodology: Tiziana Liuti, P.M Dixon.Formal Analysis and Investigation: Tiziana Liuti.Writing-original draft preparation: Tiziana Liuti, P.M Dixon.Writing-review and editing: Tiziana Liuti, P.M Dixon.Funding Acquisition; N/A.Resources: N/A.Supervision: P.M Dixon.


GBA1 gene is one of the most common genetic risk factors to develop Parkinson’s disease (PD). Here the serendipitous finding is reported of a polymerase dependent allelic imbalance when using next generation sequencing, potentially resulting in false-negative results when the allele frequency falls below the variant calling threshold (by default commonly at 30%). The full GBA1 gene was sequenced using next generation sequencing on saliva derived DNA from PD patients. Four polymerase chain reaction conditions were varied in twelve samples, to investigate the effect on allelic imbalance: (1) the primers (n = 4); (2) the polymerase enzymes (n = 2); (3) the primer annealing temperature (Ta) specified for the used polymerase; and (4) the amount of DNA input. Initially, 1295 samples were sequenced using Q5 High-Fidelity DNA Polymerase. 112 samples (8.6%) had an exonic variant and an additional 104 samples (8.0%) had an exonic variant that did not pass the variant frequency calling threshold of 30%. After changing the polymerase to TaKaRa LA Taq DNA Polymerase Hot-Start Version: RR042B, all samples had an allele frequency passing the calling threshold. Allele frequency was unaffected by a change in primer, annealing temperature or amount of DNA input. Sequencing of the GBA1 gene using next generation sequencing might be susceptible to a polymerase specific allelic imbalance, which can result in a large amount of flase-negative results. This was resolved in our case by changing the polymerase. Regions displaying low variant calling frequencies in GBA1 sequencing output in previous and future studies might warrant additional scrutiny.A variant in the  GBA1 gene are, apart from the GWAS risk loci, the most common risk factor known to date to develop Parkinson’s disease (PD)2. Sequencing of the GBA1 gene is known to be challenging, due to the highly homologous nearby pseudogene GBAP14. GBAP1 is not transcribed, but is in close proximity to GBA1 and the exonic region of GBAP1 shares 96% sequence homology with the coding region of the GBA1 gene. False positive results are a well-known complication if highly homologous pseudogenes are not accounted for during sequencing. This can be overcome by using primers specific for the functional GBA1 gene, long range amplification of the entire gene and by masking the pseudogene during alignment4.Variants in the GBA1 gene in 3638 patients with Parkinson’s disease from the Netherlands, based on a next generation sequencing (NGS) protocol5. The pseudogene was accounted for by use of NGS with long-range polymerase chain reaction (PCR) and a primerset unique to the GBA1 gene.We recently performed a large-scale screening of the 5. We noted that a GBA1 variant that was previously detected in a patient in another study6, could not be confirmed by our sequencing method. Upon further investigation, the previous finding turned out to be a true positive result, while in our NGS method the variant was present, but it did not pass the default variant calling filter (heterozygous variant detected in more than 30% of the reads). A heterozygous allele should have a variant calling frequency of approximately 50% for both variants and a homozygous allele should have a variant calling frequency of approximately 100%, with very little noise using modern techniques7. Upon experimental lowering of this variant calling filter to 2%, the total GBA1 variant hit-rate almost doubled, primarily driven by the relatively common NM_000157.3:c.1093G > A;p.(Glu365Lys)  variant.Here we report the serendipitous finding of an initially significant number of false negative results in our study, which could be readily solved by changing the polymerase enzyme. The corrected results were used for the previously published manuscripta), amounts of DNA input and two different polymerases.This paper describes how changing the polymerase enzyme normalized all variant frequencies, thereby uncovering the false negative results, by using a structured assessment of different primers, PCR primer annealing temperatures  had an exonic variant with a variant frequency higher than 30%. An additional 104 samples (8.0%) had an exonic variant with a variant frequency lower than 30%, see Fig. The pattern of normal and abnormal variant frequencies is depicted per exonic variant in Fig. Based on intronic variants, including known benign variants, many samples without exonic variants were also imbalanced. Due to uncertainty of sequencing in GC-rich and repeat regions, some intronic variants with a low frequency could be sequencing or mapping errors as opposed to imbalanced amplification. Therefore, allelic balance could not be assessed for all samples without an exonic variant (data not shown). An overview of all intronic and exonic variant frequencies of all samples sequenced by Q5 polymerase is given in Supplementary Fig. a for Q5 and TaKaRa using all four primer sets can be seen in Fig. a of 62 °C for Q5 and 63 °C for TaKaRa was chosen.PCR yield of human control DNA per TGBA1 gene. Samples amplified using TaKaRa polymerase showed balanced variant frequencies, including the initially imbalanced samples using Q5 polymerase. PD sample 1 had a low yield after PCR using primer set 1 and TaKaRa polymerase and PD sample 9 had a low yield after PCR using primer set 3 and Q5 polymerase, therefore these two samples could not reliably be analyzed.The PCR product increased with increasing DNA input  of the human control samples. Using the PD samples, primer set 2 had the lowest yield. Samples using primer set 2, control samples with 4 ng DNA input and negative controls were omitted from library preparation and sequencing. Variant frequencies per polymerase and per primer set of control samples with varying DNA input can be seen in Table GBA1 variant based on Q5 polymerase (variant frequency ranges: 2.0–23.7% (n = 58), 39.6–57.4% (n = 144), 81.4–94.5% (n = 11) and 99.8–100% (n = 3)), were confirmed using TaKaRa polymerase (variant frequency ranges: 44.8–57.4% (n = 213) and 99.4–99.8% (n = 3)), see Fig. GBA1 variant over the reference while using Q5 polymerase.All samples with an exonic 5 was based on correct data after this artifact was detected and corrected. Considering this finding, we strongly advise to further explore the lower frequency regions of GBA1 sequencing output in previous and future cohorts. Similar allelic imbalance in sequencing studies can potentially have a major impact on the prevalence of GBA1 variants reported in other populations.This paper describes the serendipitous finding of a polymerase dependent allelic imbalance when sequencing the GBA1 gene, resulting in a high number of false negative results. In our cohort, the variant hit rate increased by 93% after changing the polymerase, primarily driven by the relatively common c.1093G > A;p.(Glu365Lys) (E326K) variant and the [c.535G > C];[c.1093G > A];p. (D140H + E326K) complex allele . This artifact was initially disguised by the commonly used variant calling filter set at a frequency of 30%. Our previous publication11. A different variant in the primer annealing site was excluded as a cause due to equivalent results when using different primer sets. No specific intron or exon variant could be detected that differentiated between balanced and imbalanced samples. At this point, it remains unclear what causes this imbalance. Similarly, it is unclear how this translates to other polymerases or whether this could be resolved using modified PCR conditions. Concentration of the Q5 polymerase enzyme could not be varied, because this is provided in a mastermix solution.Preferential engagement of the polymerase to a specific allele can have multiple causes, like differences in GC content between alleles, heterozygous variants in the primer region, methylation status or altered folding10. Amplification is not prevented by methylation, but it is rather driven preferentially toward the unmethylated allele when the methylation status of the two alleles is distinct11. Similarly, altered DNA strand folding, like hairpin and G-Quadruplex structures, can also interfere with PCR13. Coadjuvants to improve DNA denaturation, and performing PCR amplification in a KCl free buffer have been suggested to circumvent these problems12. Histone modifications are known to be involved in epigenetic modification9, but these are typically removed during DNA purification, so an effect on PCR seems unlikely.Methylation status can alter the DNA secondary structure and melting properties14, but this seems unlikely in the current report, considering the widespread and reproducible imbalances reported.A previous assessment of allele dropout showed a majority to be caused by nonreproducible PCR failures rather than sequence variantsMost exonic variants, if abnormal in read frequency, displayed a decrease in frequency to below 30%. Some exonic variants however, like c.1223C > T;p.(Thr408Met) (T369M) and c.1226A > G;p.(Asn409Ser) (N370S), only showed normal or abnormally high read frequencies. Samples with a variant frequency up to 94.5% turned out to be heterozygous (correct frequency ~ 50%) after changing to TaKaRa polymerase. This shows that also marginally abnormal results should be interpreted with caution.Most exonic variants that occurred in an imbalanced frequency in certain samples, were also seen in a normal frequency in other samples (using Q5 polymerase). This implies that the exonic variant is not exclusively responsible for the imbalance. Conversely, exonic variants that were only seen in a normal frequency, are not precluded from a potential imbalance, because these variants were less prevalent and may therefore have only been detected with balanced frequencies by chance. Using the TaKaRa polymerase, the allelic imbalance was eliminated for all exonic variants and most intronic variants, but the imbalance could still be seen for some remaining intronic variants  (E326K) variant was initially not foundGBA1 pathway, more and more people with Parkinson’s disease will be screened for GBA1 variants. Both for counseling purposes and for adequate enrollment in upcoming clinical trials, a reliable sequencing method is essential.Considering the ongoing drug development targeting the 5. This study was approved by an Independent Ethics Committee of the Foundation ‘Evaluation of Ethics in Biomedical Research’ (Stichting Beoordeling Ethiek Biomedisch Onderzoek), Assen, The Netherlands. Written informed consent was obtained from all participants according to the Declaration of Helsinki.PD patients were included in the Netherlands as described previously5. The GBA1 gene was unambiguously amplified using primers unique to the functional gene; these primers were used previously20. Initially, the Q5 Hot Start High-Fidelity 2X Master Mix (New England BioLabs Inc.) was used. After the imbalanced variant frequencies were detected, a structured assessment of various PCR conditions was conducted.Saliva was obtained from patients using Oragene DNA OG-500 tubes (DNA Genotek). DNA isolation, next generation sequencing (NGS) and data analysis was performed by GenomeScan B.V., Leiden, the Netherlands, as described previouslyDNA was isolated according to standardized procedures using the QIAsymphony DSP DNA Midi Kit (Qiagen). The DNA concentration of the samples was determined using Picogreen (Invitrogen) measurement prior to amplification. Afte PCR, the long-range PCR product was fragmented using the Bioruptor Pico (Diagenode) to an average size of 300–500 bp before sequencing on an Illumina sequencer. Library preparation was performed using the NEBNext Ultra II DNA Library Prep kit (New England Biolabs E7370S/L). End repair/A-tailing, ligation of sequencing adapters and PCR amplification was performed according to the procedure described in the NEBNext Ultra DNA Library Prep kit instruction manual. The quality and yield of the library preparation was determined by Fragment Analyzer analysis. Clustering and DNA sequencing (paired-end 150 bp) using the Illumina cBot and HiSeq 4000 was performed according to the manufacturer's protocols. Image analysis, base calling and quality check was performed with the Illumina data analysis pipeline RTA v2.7.7 and Bcl2fastq v2.17.21, including instructions for raw data quality control, adapter trimming, quality filtering, alignment of short reads, and frequency calculation. During the alignment step (using Burrows-Wheeler Aligner v0.7.4) to the human reference (hg19), the GBAP1 gene was masked due to the high homology with GBA1. By masking GBAP1, mapping quality of GBA1 reads increased, especially at the 3′-prime of the gene, where the homology is the highest.Data analysis was performed using a standardized in-house pipeline developed by GenomeScan B.V., based on the Genome Analysis Toolkit’s (GATK) best practice recommendationsGBA1 variants are described based on the amino acid position excluding the 39-residue signal sequence at the start , which is used historically in GBA1 research (format: E326K). The recommended nomenclature by the Human Genome Variation Society (HGVS) is also given (format: p.(Glu365Lys)) and further variant details can be found in our previous publication5. The used NCBI Reference Sequence is NM_000157.3, NP_000148.2, assembly GRCh37.a) specified for the used polymerase; and (4) the amount of DNA input.To investigate the cause of the variant frequency imbalance, four PCR conditions were varied: (1) the primers (n = 4); (2) the polymerase enzymes (n = 2); (3) the primer annealing temperature  and six samples with an abnormal variant frequency, see Table Twelve samples with a 22. TaKaRa LA Taq DNA Polymerase combines Taq DNA polymerase and a DNA-proofreading polymerase, with 3′ → 5′ exonuclease activity23.The four primers investigated can be found in Table a) was assessed for both polymerases, using all four primer sets and 100 ng commercial human DNA. A standard three-step PCR cycle was performed according to the instructions of the supplier. For the Q5 polymerase, a Ta of 58–66 °C with increments of 2 °C was investigated and for the TaKaRa polymerase, a Ta of 60–68 °C with increments of 2 °C was investigated. Concentrations of the PCR products were measured using Picogreen (Invitrogen). The size of the PCR products was determined by Fragment Analyzer analysis.First, the optimal primer annealing temperature (Ta per polymerase, the four primers were assessed using the twelve DNA samples from PD patients, commercial human control DNA and a No Template Control. The commercial human DNA was used to vary the DNA input, using 4 ng, 20 ng and 100 ng. For all PD samples, 100 ng was used. See Table Using the determined TGBA1 exonic variant, either balanced or imbalanced, were rerun in the new conditions based on the above mentioned assessment.All samples with a Supplementary Figures.


The measured data is an extension to previous two year investigations (June 2017 – June 2019), which was published in the research article “Application of Stable Water Isotopes to Improve Conceptual Model of Alluvial Aquifer in the Varaždin Area” The dataset in this article consists of one year of monthly observations of water stable isotopes in precipitation (July 2019 - May 2020). The samples were collected by rain gauge installed in the Hrašćica village in the Varaždin area, NW Croatia. The data presented in the article are raw isotope data of precipitation supplemented by calculated  In addition, the relationship between ratios δ•Researchers working on hydrology/hydrogeology related problems can use the data for identifying the origin of precipitation, groundwater and surface water and determination of their interrelationships.•The data can be used as input for regional studies, e.g. investigation of isotope effects  in modern precipitation 118O and δ2H) of monthly composite precipitation collected by rain gauge in the village Hrašćica, near the town Varaždin, NW Croatia  in the period from July 2019 to May 2020. The location of the sampling point is shown in The data presented include water stable isotope ratios , 10 measurements from this sampling period (July 2019 – May 2020) and 24 measurements from previous sampling period (June 2017 – June 2019). Measured δ18O and δ2H are plotted on dual isotope diagram, together with calculated local meteoric water lines and their equations .Meteo station.zip – ESRI shapefile with exact location of the meteorological station Varaždin. Coordinate reference system is the official coordinate system of the Republic of Croatia (HTRS96/TM).Stable_isotopes_Hrascica.xlsx – table with values of measured data, associated d-excess values and monthly precipitation  were collected using by a special designed rain gauge for collection of precipitation samples for stable isotope analysis, which was installed in the village Hrašćica , near the town Varaždin - Varaždin County, NW Croatia. Field sampling was conducted on a monthly basis in duration of one year, from July 2019 to May 2020. The special designed rain gauge (which is used by IAEA) collected precipitation into 3 L HDPE container which is installed in the gauge. The last day of the month, composite sample was poured into 1000 mL or in smaller or bigger HDPE plastic bottles with a tight-fitting cap (depending on how much sample was collected during the month), and preserved at 4 °C in the portable refrigerator until analysis. To remove impurities, all samples were filtered through 0.45 μm sterile syringe filters (Chromafil Xtra PET-45/25) before isotope analyses. Analyses were done upon arrival of the sample into the laboratory.Monthly composite precipitation samples , which were checked periodically against the International Atomic Energy Agency (IAEA) standards: Vienna Standard Mean Ocean Water 2 (VSMOW2) and Standard Light Antarctic Precipitation 2 (SLAP2). To achieve accurate measurements, elimination of “bad” samples or injections based on H2O concentrations due to syringe or septa underperformance, between-sample ‘’memory-effect’’, determination δ amount dependence on H2O amount, determination and application of residual memory carryover correction, correction for linear or non-linear instrumental drift, and normalization of all data to the VSMOW/SLAP scales, IAEA guidance was used d = δ2H – (8 × δ18Ο) Stable isotope ratios in precipitation were analysed in the Hydrochemical Laboratory of the Croatian Geological Survey, using Cavity Ring-Down Spectrometer Picarro L2130-i Isotope analyser The local meteoric water line (LMWL) has been calculated using Local Meteoric Water Line Freeware The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.