Ecology

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We carried out monitoring of ECM wintering at a roost in Gibraltar that consists of a series of caves alongside each other at sea level, primarily during the autumn–winter period of 2019-2020. The monitoring consisted of weekly counts of birds returning to roost, and of regular ringing and measuring sessions. The ringing data for 2019–2020 were augmented with data collected at the site between 2016 and 2018.The eight caves at the site are all micro-sites within a single ECM roost and three of them, which lie just above the current sea level and are the only ones accessible from land, were studied: Gorham’s Cave (36° 07′ 13.86″ N 5° 20′ 32.57″ W UTM 30 N 289190.5 3999856.5), Vanguard Cave (36° 07′ 18.89″ N, 005° 20′ 31.62″ W UTM 30 N 289218.0 4000011.0) and Cave F (36° 07′ 19.8″ N, 005° 20′ 30.088″ W UTM 30 N 289257.0 4000038.1) (Figs. 4 and 5).Figure 4The position of Gorham’s Cave (1), Vanguard Cave (2) and Cave F (3) at the Gorham’s Cave Complex UNESCO World Heritage Site.Full size imageFigure 5Plan map of Gorham’s Cave, Vanguard Cave and Cave F at the Gorham’s Cave Complex UNESCO World Heritage Site, indicating key roosting areas for ECM and the position of the mist nets at each cave.Full size imageTwenty-three weekly evening counts were conducted of birds returning to the roosting site during the period 4th October, 2019 to 12th March, 2020, from a fixed point overlooking the study site. We attempted to space these evenly in time, but adjustments were made due to unfavorable weather (mean number of days between counts = 6.96 ± 1.45 SE). The approach of the birds as they return to roost is described elsewhere12. It occurs along a fixed trajectory and our vantage point optimized the viewing of these movements. All ECM returned to the site along a common trajectory and birds only broke up and headed towards the different caves once at the site, so that in principle, every bird had equivalent opportunities to access each cave on arrival to the site.We used a combination of the results of the counts and the Schnabel Index for mark-release-recapture data from a series of dates33 to estimate the total roosting population size of ECM at the site during the 2019–2020 period. The latter was achieved by estimating the number of birds roosting at each cave and then combining these for a total population size, although we recognize that birds also use other micro-sites7; (pers. obs.). Due to differences in sample sizes of birds recaptured, 95% confidence limits for the estimated roosting population size at each cave were drawn from the t-distribution for Gorham’s Cave and the Poisson distribution for Vanguard Cave and Cave F33.Trapping and ringing were carried out at the three caves at least once a week. All licences required under the laws of Gibraltar were obtained and protocols were approved by the Ethics Committee of the University of Gibraltar. Ringing and handling of birds was carried out under the auspices of the Gibraltar Ornithological & Natural History Society (GONHS), which carries out its bird ringing under licence from the Ministry for the Environment, HM Government of Gibraltar, under the 1991 Nature Protection Act. Gibraltar-based ringers are licensed by the British Trust for Ornithology (BTO), and we adhered closely to the technical and ethical standards of the BTO for handling and ringing birds34. Routinely, birds are released without ringing when their condition is poor. One bird was captured in a condition that was too poor for it to be ringed. The reporting recommendations of the ARRIVE guidelines35 were followed.The majority of the data used in this study were collected between October 29th 2019 and March 4th 2020. In addition, trapping and ringing had taken place intermittently at Vanguard and Cave F during the winter since 2016, and trapping took place at the site throughout autumn 2020. We used the BTO A-sized rings, in accordance with guidelines for other European hirundines34. Due to the different dimensions of the caves, we used different mist net sizes at each one. A 6m-length net was used at Vanguard Cave, 12 m and 3 m nets at Cave F, and 3 × 6 m nets mounted vertically on triple high poles at Gorham’s Cave.The number of trapping sessions, and the range of dates of these at each cave during the 2019–2020 autumn-winter season, was: 10 Gorham’s Cave (29/10/2019–04/03/2020; mean number of days between sessions 14.11 ± 2.23 SE), 11 Vanguard Cave (13/11/2019–04/03/2020; mean number of days between sessions 11.20 ± 1.81 SE), 11 Cave F (13/11/2019–04/03/2020; mean number of days between sessions 11.20 ± 1.81 SE). Seven extra trapping sessions took place at Vanguard Cave and Cave F before the 2019-2020 autumn-winter season, on: 01/28/2016, 02/16/2016, 02/13/2018, 02/21/2018, 12/04/2018, 01/08/2019 and 02/21/2019. There were eight additional trapping sessions during the autumn of 2020, on: 10/29/2020, 11/02/2020, 11/12/2020, 11/15/2020, 11/19/2020, 11/24/2020, 12/02/2020 and 12/03/2020. 1511 different birds were processed between 2016–2020, of which 156 were captured at least twice. 796 individuals were processed during the 2019–2020 autumn-winter season, the period for which most of our analyses are based: 369 at Gorham’s Cave, 221 at Vanguard Cave and 206 at Cave F. Of the birds recaptured that had been ringed at the site during previous seasons, eighteen were from the 2019–2020 season (ten ringed at Gorham’s Cave, two at Vanguard Cave, seven at Cave F), fifteen were from the 2018–2019 season (eight at Vanguard Cave, six at Cave F), four were from the 2017–2018 season (three at Vanguard Cave, one at Cave F), and one was from the 2015–2016 season (from either Vanguard Cave or Cave F; unspecified and excluded from the analysis). A bird was recaptured that had been ringed elsewhere in Gibraltar (the GONHS Jews’ Gate Field Centre) on the 14/01/2014, 2233 days before it was captured again on the 25/02/2020.Biometric measurement of all birds was carried out by a single person (CP) in order to maximize consistency. We followed the standard processing procedure of the BTO34, which includes recording the weight of birds in grams (g) to 0.1 g and length of wing in millimeters (mm) to 0.5 mm. Birds were aged whenever this was possible but ageing of ECM became increasingly difficult towards the end of the winter period, increasing the possibility of confusion with adults9. For this reason, age was excluded from most of the analyses. Birds could not be sexed because sexes are similar in appearance, including size4,36. We captured birds only during the evening, to ensure that condition of birds was not a factor of weight-loss whilst roosting, since ECM at the site are known to weigh less during mornings than the evenings13. Birds captured were roosted in boxes and released at the site the following morning.Although Elkins & Etheridge12 assumed that movement of birds between different parts of the roost at Gibraltar is considerable, this was never tested. The proximity of different parts of the roost from each other means that all micro-sites are potentially equally accessible to ECM using the site. It is expected that they should be able to use micro-sites interchangeably, given especially their approach during evenings along a fixed narrow route. Any fidelity to micro-sites must thus be explained by factors other than distance between individual micro-sites. The multiple cavities at the roosting site, and the ease with which we were able to access these, allowed a unique opportunity to test whether individual birds repeatedly used the same micro-sites within the roost, both within and between winters. We used the data gathered to test the following hypotheses: (1) that a degree of fidelity to different spaces within the roost (‘micro-sites’) exists among ECM, with individuals more likely to be recaptured at the same cave than in a different cave, (2) that any fidelity observed will translate to a difference in quality of roosting sites, as indicated by differences in condition of birds according to micro-site, and (3) that the incidence of recapture should be highest at the cave at which birds are in the best condition.Statistical analyses followed Sokal & Rohlf37 and were carried out on SPSS statistical software (IBM). We used a binomial Z test to analyze whether recaptured birds that were initially ringed during the 2019–2020 season were returning to the cave where they were first trapped/ringed, (1) within the 2019–2020 season and (2) between this and separate seasons. We also used a 3 × 2 Fisher’s exact test to test for differences, between caves, in the frequency with which birds ringed at one cave were captured at another. Multiple recaptures of birds were excluded from all of these analyses on fidelity in order to avoid bias.We explored the relationship between wing length and weight using linear regression analysis, to control for the possible effect of body size on weight—on the basis that wing length provides a good measure of body size in passerines38—using only data collected during the 2019–2020 season. For individual birds that were trapped more than once, we used wing length and weight on the date of first capture. We then grouped, by cave, the residuals of the regression and used a one-way ANOVA to explore differences in mean condition of birds between caves, with condition expressed as the relationship between wing length and weight. We also used linear regression to explore the relationship between daily recapture rate at all caves and the number of days from the first day of trapping at each cave, with the latter as the explanatory factor. Again, we segregated the residuals of the regression by cave and used a one-way ANOVA to explore differences in recapture rates between caves. We used Pearson’s chi-squared test with Yates’s correction for small sample sizes39 to explore differences in the likelihood of recapture of birds on more than one occasion at each cave. Because differences in weight and wing length have been recorded between adult and juvenile ECM in Gibraltar13, we used Pearson’s chi-squared test to explore the relationship between age and use of the different micro-sites for all the birds that we were able to age (n = 395 of 796 birds processed), to see whether this was consistent with our other findings. More

Study sitesWe sampled ponds in the following five sites representing different regional-scale permafrost integrity: Toolik, Alaska, USA; Qeqertarsuaq, Disko Island, Greenland, Denmark; Whapmagoostui-Kuujjuarapik, Nunavik, Quebec, Canada; Abisko, Sweden and Khanymey, Western Siberia, Russia (Online-only Table 1). The aim was to include representatives of different stages of permafrost thaw in order to understand whether responses can be generalized across different geographic and environmental conditions.The sampling site in Alaska is located in a continuous permafrost area, mostly dominated by moss-tundra characterized by tussock-sedge Eriophorum vaginatum and Carex bigelowii, and dwarf-shrub Betula nana and Salix pulchra15. The average depth of the active layer in 2017 was ~50 cm16. Records of surface air temperature from 1989 to 2014 showed no significant warming trend, and there was no significant increase in the mean maximum thickness of the active layer or maximum thaw depth17.The sampling site in Greenland is located in the Blæsedalen Valley, south of Disko Island, and is characterized as a discontinuous permafrost area. From 1991 to 2011, Hollensen et al.18 observed an increase of the mean annual air temperatures of 0.2 °C per year in the area, while Hansen et al.19 highlighted that sea ice cover reduced 50% from 1991 to 2004. Soil temperatures recorded by the Arctic Station from the active layer of the coarse marine stratified sediments also showed an increase over the years18. The sampling site is comprised of wet sedge tundra, and the dominating species are Carex rariflora, Carex aquatilis, Eriophorum angustifolium, Equisetum arvense, Salix arctophila, Tomentypnum nitens and Aulacomnium turgidum20.The Canadian site is located within a sporadic permafrost zone, in a palsa bog, in the valley of Great Whale river, close to the river mouth to Hudson Bay. The vegetation consists of a coastal forest tundra, dominated by the species Carex sp. and Sphagnum sp.21 Since the mid-1990s, there has been a significant increase in the surface air temperature of the region for spring and fall, which has been correlated to a decline of sea ice coverage in Hudson Bay22. This area has experienced an accelerated thawing of the permafrost over the past decades, resulting in the collapse of palsas and the emergence of thermokarst ponds as well as significant peat accumulation21,23. In this specific site, thermokarst ponds at different development stage can be found, from recently emerging to older, mature thermokarstic waterbodies. The stage of the ponds was estimated based on the distance between the pond and the edge of the closest palsa, as well as based on satellite images14. The edges of the emerging ponds reached a maximum of 1 m from the closest palsa and were less than 0.5 m deep, whereas the edges of the developing ponds had a maximum distance of 2–3 m to the closest palsa and were ~1 m deep. Mature ponds were identified based on satellite images and were up to 60 years old.The Swedish site is located in a discontinuous permafrost zone at the Stordalen palsa mire, on an area of collapsed peatland affected by active thermokarst. The region has experienced an increase in mean annual air temperature and active layer thickness since the 1980s, which has been followed by a shift to wetter conditions24. The vegetation found on the surface of the palsa depressions of Stordalen mire is dominated by sedges (Eriophorum vaginatum, Carex sp.) and mosses (Sphagnum sp.)24,25.The Russian site is located in a discontinuous permafrost area in Western Siberia Lowland, near Khanymey village. The sampling site is a flat frozen palsa bog with a peat depth no more than 2 m, and is affected by active thermokarst, resulting in the emergence of thermokarst ponds26,27. The vegetation is dominated by lichens (Cladonia sp.), schrubs (Ledum palustre, Betula nana, Vaccinium vitis-idaea, Andromeda polifolia, Rubus chamaemorus) and mosses (Sphagnum sp.)28.Sample collectionAt all sites, water from the depth of 10 cm was collected from 12 ponds, totaling 60 ponds for the full dataset. Unfiltered water samples were collected for total P analysis. For analyzing Fe, various dissolved anions and cations, DOC concentrations, and perform optical and mass spectrometry analyses on DOM, water was filtered through GF/F glass fiber filters (0.7 μm, 47 mm, Whatman plc, Maidstone, United Kingdom). Moreover, water samples were collected in order to measure GHG (CO2 and CH4) concentrations. Water, detritus and sediment samples were also collected from ponds for fungal community analyses. Water samples were collected and filtered sequentially first through 5 µm Durapore membrane filter (Millipore, Burlington, Massachusetts, USA) and then through a 0.22 µm Sterivex filter (Millipore) to capture fungal cells of different sizes. The samples were filtered until clogging or up to a maximum of 3.5 liters (filtered volume ranging from 0.1 l to 3.5 l). Surface sediments were sampled from each of the ponds, with the exception of the Canadian site, where only one emerging and three developing ponds were sampled for sediments. From the sites in Alaska, Greenland, and Sweden, also detritus samples (dead plant material) were collected. The detritus was washed in the lab using tap water, followed by overnight incubation in 50 ml tap water to induce sporulation. The use of tap water may have added fungal spores to the samples, which should be kept in mind when using the detritus data. After the incubation, the water was filtered through a 5 μm pore size filter and the filter was stored at −20 °C.All the samples for DNA extraction were transported to the laboratory frozen, with the exception of the Alaskan samples, which were freeze dried prior to transportation. The samples transported frozen were freeze dried prior to DNA extraction to ensure similar treatment of all samples. The samples for nutrient and carbon measurements were transported frozen with the exception of samples for DOC and fluorescence analyses, which were transported cooled.Chemical analysesAll chemical, optical and mass spectrometry results are provided in OSF29. DOC quantification was carried out using a carbon analyzer (TOC-L + TNM-L, Shimadzu, Kyoto, Japan). Accuracy was assessed using EDTA at 11.6 mg C/l as a quality control (results were within + − 5%) and the standard calibration range was of 2–50 mg C/l. Fe(II) and Fe(III) were determined by using the ferrozine method30, but instead of reducing Fe(III) with hydroxylamine hydrochloride, ascorbic acid was used31. Absorbance was measured at 562 nm on a spectrophotometer (UV/Vis Spectrometer Lambda 40, Perkin Elmer, Waltham, Massachusetts, USA). The samples were diluted with milli-Q water if needed. The concentration of total P was determined using persulfate digestion32. The anion NO3− was measured on a Metrohm IC system (883 Basic IC Plus and 919 Autosampler Plus; Riverview, Florida, USA). NO3− were separated with a Metrosep A Supp 5 analytical column (250 × 4.0 mm) which was fit with a Metrosep A Supp 4/5 guard column at a flow rate of 0.7 ml/min, using a carbonate eluent (3.2 mM Na2CO3 + 1.0 mM NaHCO3). SO4 was analyzed using Metrohm IC system (883 Basic IC Plus and 919 Autosampler Plus, Riverview), NH4+ spectrophotometrically as described by Solórzano33, and NO2− and DN as in Greenberg et al.34.For the gas analyses, samples from Alaska and Canada were taken as previously described in Kankaala et al.35, except that room air was used instead of N2 for extracting the gas from the water. Shortly, 30 ml of water was taken into 50 ml syringes, which were warmed to room temperature prior to extraction of the gas. To each syringes 0.5 ml of HNO3 and 10 ml of room air was added and the syringes were shaken for 1 min. Finally, the volumes of liquid and gas phases were recorded and the gas was transferred into glass vials that had been flushed with N2 and vacuumed. For Greenland, Sweden and Russia 5 ml of water was taken for the gas samples with a syringe and immediately transferred to 20 ml glass vials filled with N and with 150 µL H2PO4 to preserve the sample. All gas samples were measured using gas chromatography (Clarus 500, Perkin Elmer, Polyimide Uncoated capillary column 5 m x 0.32 mm, TCD and FID detector respectively).Optical analysesIn order to characterize DOM, we recorded the absorbance of DOM using a UV-visible Cary 100 (Agilent Technologies, Santa Clara, California, USA) or a LAMBDA 40 UV/VIS (PerkinElmer) spectrophotometer, depending on sample origin. SUVA254 is a proxy of aromaticity and the relative proportion of terrestrial versus algal carbon sources in DOM36 and was determined from DOC normalized absorbance at 254 nm after applying a corrective factor based on iron concentration37. S289 enlights the importance of fulvic and humic acids related to algal production38 and were determined for the intervals 279–299 nm by performing regression calculations using SciLab v 5.5.2.39We also recorded fluorescence intensity on a Cary Eclipse spectrofluorometer (Agilent Technologies), across the excitation waveband from 250–450 nm (10 nm increments) and emission waveband of 300–560 nm (2 nm increments), or on a SPEX FluoroMax-2 spectrofluorometer (HORIBA, Kyoto, Japan), across the excitation waveband from 250–445 nm (5 nm increments) and emission waveband of 300–600 nm (4 nm increments), depending on sample origin. Based on the fluorometric scans, we constructed excitation-emission matrices (EEMs) after correction for Raman and Raleigh scattering and inner filter effect40. We calculated the FI as the ratio of fluorescence emission intensities at 450 nm and 500 nm at the excitation wavelength of 370 nm to investigate the origin of fulvic acids41. Higher values (~1.8) indicate microbial derived DOM (autochthonous), whereas lower values (~1.2) indicate terrestrial derived DOM (allochthonous), from plant or soil42. HIX is a proxy of the humic content of DOM and was calculated as the sum of intensity under the emission spectra 435–480 nm divided by the peak intensity under the emission spectra 300–445 nm, at an excitation of 250 nm. Higher values of HIX indicate more complex, higher molecular weight, condensed aromatic compounds43,44. BIX emphasizes the relative freshness of the bulk DOM and was calculated as the ratio of emission at 380 nm divided by the emission intensity maximum observed between 420 and 436 nm at an excitation wavelength of 310 nm45. High values ( >1) are related to higher proportion of more recently derived DOM, predominantly originated from autochthonous production, while lower values (0.6–0.7) indicate lower production and older DOM42,44.High resolution mass spectrometry50 ml water samples were collected from each of the ponds and were filtered with a Whatman GF/F filter for mass spectrometry analyses. For each sample, 1.5 ml of water was dried completely with a vacuum drier, and was then re-dissolved in 100 µL 20% acetonitrile, 80% water with three added compounds as internal standards (Hippuric acid, glycyrrhizic acid and capsaicin, all at 400 ppb v/v). Samples were filtered to an autosampler vials and injected at 50 µL onto the column. In order not to overload the detectors, some of the higher concentration samples were injected at a lower volume, to give a maximum of 20 µg carbon loaded.High-performance liquid chromatography – high resolution mass spectrometry (ESI-HRMS) was conducted as described in Patriarca et al.46 using a C18-Evo column (100 × 2.1 mm, 2.6 µm; Phenomenex, Torrance, California, USA). The ESI-HRMS data was averaged from 2–17 min to allow formula assignment to a single mass list. Formulas considered had masses 150–800 m/z, 4–50 carbon (C) atoms, 4–100 hydrogen (H) atoms, 1–40 oxygen (O) atoms, 0–1 nitrogen (N) atoms and 0–1 13 C atoms. Formulas were only considered if they had an even number of electrons, H/C 0.3–2.2 and O/C ≤ 1. The data are presented as a number of assigned formulas and weighted average O/C ratio, H/C ratio and m/z.The analysis was run in two batches (36 and 24 samples per run, respectively) and to the latter run, three samples of Suwannee River fulvic acid (SRFA, reference material) were added. At the moment of the run, the DOC concentration of these samples was unknown, so 50 µL was injected. From high resolution mass spectrometry, average H/C and a number of assigned formulas were obtained. The H/C can be used as a proxy of DOM aliphatic content; higher H/C values (  > 1) indicate more saturated (aliphatic) compounds, whereas values lower than 1 indicate more unsaturated, aromatic molecules47.DNA extraction, ITS2 amplification and sequencingAll samples for molecular analyses (water and detritus filters and sediments) were extracted using DNeasy PowerSoil® kit (Qiagen, Hilden, Germany), following the manufacturer’s recommendations for low input DNA. Extracts were eluted in 100 µl of Milli-Q water and DNA concentrations were measured with Qubit dsDNA HS kit. The fungal ribosomal internal transcribed spacer 2 (ITS2) sequences were amplified using a modified ITS3 Mix2 forward primer from Tedersoo48, named ITS3-mkmix2 CAWCGATGAAGAACGCAG, and a reverse primer ITS4 (equimolar mix of cwmix1 TCCTCCGCTTAyTgATAtGc and cwmix2 TCCTCCGCTTAtTrATAtGc)14. Each sample received a unique combination of primers containing identification tags generated by Barcrawl49. All tags had a minimum base difference of 3 and a length of 8 nucleotides. Both forward and reverse primer tags were extended by two terminal bases (CA) at the ligation site to avoid bias during ligation of sequencing adaptors, and the forward primer tag also had a linker base (T) added to it50. The list of primers and tags is found in Supplementary Table S1. PCR reactions were performed on a final volume of 50 µl, with an input amount of DNA ranging from 0.07 ng to 10 ng, 0.25 µM of each primer, 200 µM of dNTPs, 1U of Phusion™ High-Fidelity DNA Polymerase (Thermo Fisher Scientific, Waltham, Massachusetts, USA), 1X PhusionTM HF Buffer (1X buffer provides 1.5 mM MgCl2, Thermo Fisher Scientifics) and 0.015 mg of BSA. PCR conditions consisted of an initial denaturation cycle at 95 °C for 3 min, followed by 21–35 cycles for amplification (95 °C for 30 sec, 57 °C for 30 sec and 72 °C for 30 sec), and final extension at 72 °C for 10 min. In order to reduce PCR bias, all samples (in duplicates) were first submitted to 21 amplification cycles. In case of insufficient yield, the number of cycles was increased up to 35 cycles (see the records on the number of cycles for each of the samples in Supplementary Table S2).The PCR products were purified with Sera-MagTM beads (GE Healthcare Life Sciences, Marlborough, Massachusetts, USA), visualized on a 1.5% agarose gel and quantified using Qubit dsDNA HS kit. The purified PCR products were randomly allocated into three DNA pools (20 ng of each sample), which were purified with E.Z.N.A.® Cycle-Pure kit (Omega Bio-Tek, Norcross, Georgia, USA). Nine of the samples (4 water, 1 sediment and 4 detritus) were left out of the pools because of too little PCR product, giving a total of 203 samples for sequencing (Online-only Table 1). Negative PCR controls were added to each pool, as well as a mock community sample containing 10 different fragment sizes from the ITS2 region of a chimera of Heterobasidium irregular and Lophium mytilinum, ranging from 142 to 591 bases, as described by Castaño et al.51. The size distribution and quality of all the pools were verified with BioAnalyzer DNA 7500 (Agilent Technologies), and purity was assessed by spectrophotometry (OD 260:280 and 260:230 ratios) using NanoDrop (Thermo Fisher Scientific). The libraries were sequenced at Science for Life Laboratory (Uppsala University, Sweden), on a Pacific Biosciences Sequel instrument II, using 1 SMRT cell per pool. This PacBio technology allows the generation of highly accurate reads ( >99% accuracy) which are produced based on a consensus sequence after a circularization step.Quality filtering of reads, clustering and taxonomy identification of clustersThe sequencing resulted in a total of 1071489 sequences, ranging from 397 to 9184 sequences per sample (average on 2551 sequences per sample). The raw sequences were filtered for quality and clustered using the SCATA pipeline (https://scata.mykopat.slu.se/, accessed on May 19th, 2020). For quality filtering, sequences from each pool were screened for the primers and tags, requiring a minimum of 90% match for the primers and a 100% match for the tags. Reads shorter than 100 bp were removed, as well as reads with a mean quality lower than 20, or containing any bases with a quality lower than 7. After this filtering, 582234 sequences were retained in the data. The sequences were clustered at the species level by single-linkage clustering at a clustering distance of 1.5%, with penalties of 1 for mismatch, 0 for gap open, 1 for gap extension, and 0 for end gaps. Homopolymers were collapsed to 3 and unique genotypes across all pools were removed. For a preliminary taxonomy affiliation of the clusters, hereafter called OTUs (Operational Taxonomic Units), sequences from the UNITE + INSD dataset for Fungi52 database were included in the clustering process. After the clustering, the data included 518128 sequences, divided among 8218 OTUs. For taxonomical annotation, all OTUs with a minimum of ten total reads in the full dataset were included, retaining 3108 OTUs and 498414 sequences in the taxonomical analysis. More

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Active ingredients detected in bee collected pollenAll 188 pollen samples had at least 12 active ingredients detected in each sample, with a maximum of 31 AIs and an average of 22.0 ± 0.3 per sample. Over both years, 80 of the 259 screened pesticide active ingredients were detected in the pollen. These included 28 fungicides, 26 insecticides, 21 herbicides, two miticides, and one rodenticide. We also detected one synthetic antioxidant and one pesticide synergist (Table S1). We detected approximately twice as many AIs in pollen collected by honey bees (68 AIs) in 2019 than in pollen collected by bumble bees (32). All AIs detected in pollen from bumble bees were also collected by honey bees in either 2018 or 2019. Honey bee collected pollen also had significantly more AIs on average detected at each site (35.0 ± 0.9 S.E. AIs per site) compared to bumble bees (18.6 ± 0.6) in 2019 (R2m = 0.73; X2 = 68.2, df = 1, p  More

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