Ecology

The Willmott similarity indices, over the first 4 months, for outside and inside temperatures, in sunny and shaded environments, with values close to 1, confirm that A. sexdens colonies were exposed to significantly different temperatures. The lowest values of this index for irradiance, differing between the sunny and shaded environments for internal temperature (in the initial chamber), indicates variation in this parameter between environments. Irradiance values close to 0 between environments represent differences between sunny and shaded areas, but the temperature of the initial chamber did not vary. The selection pressure from irradiance and, consequently, temperature, defines the ideal depth of the initial chamber for the founding queens16 to keep this parameter at values adequate for the fungus garden and the offspring8 in the field. The similar temperature, in the nests in the shaded environment, agrees with that reported for this leaf-cutting ant, of 24°C3, but this varies between species of these insects, being 25–28 °C for A. sexdens17, 27.5 °C for A. vollenweideri18 and 27 °C for A. heyeri19. This adaptation of an ideal depth to construct the initial chamber allows A. sexdens to adapt to different habitats in full sun and shade. The nesting process, habits and foraging strategies differ between A. bisphaerica and A. sexdens, with the former normally establishing their nests in full sun in areas with predominantly grass forages and the latter in shaded areas where it cuts dicot leaves14. Furthermore, differences in nest depth between these species may be related to soil temperature, which is lower in shaded areas7. For this reason, fungal chambers in exposed nests in pastures are deeper than in shaded areas in forests, as soil temperature is negatively correlated with depth7. Soil humidity and temperature act simultaneously due to the thermo preference of workers, resulting in the construction of shallower nests in cold soils and deeper nests in warmer ones7. Soil moisture also varies according to depth, affecting the nest-digging behavior of leaf-cutting ants7,15.The temperature in the shaded environment was higher than that reported for A. sexdens rubropilosa, from 24.82 ± 3.14 to 24.11 ± 1.30 °C at a depth of 5–25 cm underground, for optimal offspring development and, consequently, reduction in the lipid content of queens at high temperatures, without affecting their survival20. This is because the depth of the initial chamber excavated by the queen is adequate for colony success8,16.The different habitats occupied by the ant A. sexdens21, from dense forests to cerrado and caatinga may explain the greater depth and volume of the initial chamber of the nests in shaded than in sunny environments. However, the depth of the initial nests varies between Atta species with 7.5–12 cm, 6.5–13 cm, 15–25 cm, 10–30 cm, 10–15 cm, 11–34 cm, and 9–15 cm for A. colombica, A. cephalotes, A. texana, A. sexdens rubropilosa, A bisphaerica, A capiguara, and A. insulares, respectively1,13,15,22,23,24. The initial chamber volume is within the expected range for A. sexdens1,13 in both environments, with a chamber volume of 24.88 cm3 in a shaded area with eucalyptus plantation13. Different excavation efforts with the removal of small soil particles by the founding queen using her jaws in repeated biting motions25, subsequently discarded outside the nest8,16,26 may explain the greater volume of the initial chamber in the shaded area. The greater solar irradiation in sunny areas increases the temperature, with the higher soil temperature generating greater excavation effort and oxidative damage27,28, in addition to water loss, as described for seed-collecting ants29,30. Further, humid soils are easier to dig, which explains the greater volume of the initial chamber in the shaded area as found for the excavation behavior by Atta spp. in soils with different densities and moistures15,16,31.The higher mass of A. sexdens queens in the first month of the claustral phase than in the fourth, in both sunny and shaded environments, stems from a reduction in their body mass, due to the metabolism of the lipid content during the first 6 months following the flight, but with recovery in the subsequent months1. The mass loss is due to the use of body reserves by the queens to prepare and maintain the colony in the claustral phase, as stored lipids are important in the evolutionary history of the Attini tribe, from semi-claustral to claustral foundation32. The queen, with a claustral foundation, does not feed, remaining enclosed in the nest and rearing her initial offspring by metabolizing her own body reserves33, as reported for this ant species1. The selection pressure on the evolution of claustral foundation tends to minimize risk during foraging33 with a more viable adaptation being the storing of reserves in the body, as observed in our study. The greater biomass of the fungus garden in the fourth month is due to growth, but its values were lower than those reported for 4-month-old A. sexdens nests, from 2000 to 3000 mg1.The lower number of A. sexdens eggs in the first than in the third month is similar to that observed in laboratory colonies of this ant8. The irregularity in the egg production by the queen is due to hormone fluctuations regulated by the endocrine system, and is correlated with the activity cycle of the corpora allata during the 3 or 4 months of colony life34. This gland synthesizes the juvenile hormone, which acts in the oviposition of founding queens, as verified for females that underwent alatectomy34. This hormone acts in the fat body, initiating the synthesis of vitellogenin (glycolipophosphoproteins, with lipids and carbohydrates in its composition) to be deposited in the oocyte35. Thus, the production of offspring depends on body reserves (fatty body lipids and muscle mass protein), as the queen does not feed during the foundation period (claustral foundation). The lower production of small and medium workers in the first month than in the second, third or fourth months, agrees with that reported in A. sexdens nests1. The similar number of larvae over the 4 months is due to the duration of the larval period of A. sexdens, of around 25 days8, with new immature individuals produced monthly with overlapping generations, common in social insects. This overlap begins with larvae in the first month of nests and pupae, usually in the second month of ant nests in the laboratory8.The lower values of fungus biomass and number of eggs, larvae, pupae and small and medium workers of A. sexdens in nests in the sunny environment may be due to a higher incidence of solar irradiance increasing the variation in the internal temperature of the initial chamber. This agrees with reports that a lower incidence of solar irradiance improved the stability of the internal temperature of the initial chamber, favoring A. sexdens with narrow thermal tolerance range as it is a thermally protected underground species11. However, frequent heat peaks, with habitat-specific physiological consequences for subterranean ectothermic animals, are common in sunny areas11. The queen’s body mass, similar between environments, indicates a similar reduction of this parameter between them and their tolerance to temperature variations in this type of foundation. A reduction in the mass of A. sexdens queens is expected from the nuptial flight to the end of the claustral phase. The energy expenditure of A. sexdens queens, in carbohydrates and body lipids for the nuptial flight and nest excavation, was estimated at 0.58 J36 and during the claustral phase, they metabolize body lipids and proteins to survive and form the initial colony26,37.The higher mortality of A. sexdens nests in the sunny environment, during the claustral foundation, is due to a higher incidence of solar irradiance, increasing the variation in the internal temperature of the initial chamber and, consequently, the excavation effort and oxidative damage to the founding queens27,28, in addition to water losses as reported for seed-collecting ants29,30. This mortality may also be related to entomopathogens, unsuccessful symbiotic fungus regurgitation, excavation effort, density and soil moisture1,9,38,39.Atta sexdens founder queens were exposed to sunny and shaded environments with greater solar irradiance and, consequently, a greater variation range in the internal temperature of the initial chamber in the first environment. The shaded environment, with lower incidence of solar irradiance and greater stability of the internal temperature of the initial chamber, was more favorable for colony development, as confirmed by the biological parameters and greater survival of A. sexdens queens.
Atta sexdens female collection methods- after the nuptial flightAtta sexdens queens were collected at the Experimental Farm Lageado in Botucatu, Brazil in 2019 (22°50′37.3″S 48°25′38.3″W) on sunny days after heavy rains from late October to early November. Two hundred queens were collected using tweezers. They were stored separately in 250 ml pots with 1 cm wet plaster for 60 min prior to use. We had permission to collect Atta sexdens queen specimens.Experimental areasThe A. sexdens queens were individualized in two experimental areas: sunny—an open area exposed to Global Horizontal Irradiation with exclusive coverage of Paspalum notatum Flügge grass (N = 100) and shaded – an area exposed to Diffuse Horizontal Irradiation (50% shade screen—1.50 × 50 MT), in a plowed environment (N = 100). The soil is a superficial horizon of oxisols.The founding A. sexdens queens were individualized in the center of a square of land (50 × 50 cm) covered with a transparent bottle measuring 20 cm in diameter by 12 cm in height, delimiting the space to be drilled in the soil by the ant queens per experimental area of 25 m2 (Fig. S1).Development of early nests during the claustral phaseAtta sexdens queens were evaluated over 4 months following nest foundation, to monitor its development. A total of 25% of the successfully established nests were excavated per month by removing the colony with a gardening shovel. The number of eggs, larvae, pupae and adults was counted and the mass of the queen and the biomass of the fungus garden determined. The depth, width, length, and height of each nest were measured with the aid of a caliper. The estimated volume of each fungus chamber was based on a cylinder. A correction factor was used to calculate the volume of the chamber because they are rounded: V = πr2 (ch + r0.67), in which ‘r’ is the chamber base radius and ‘ch’ the cylinder height, measured by subtracting the maximum height of the chamber from its radius, ch = h − r40. Queen mortality was evaluated during the excavation of their nests.Temperature and radiation measurementThe temperatures of the external and internal environments (15 cm deep), in each area, were measured for 4 months, with Data loggers (Testo), after the foundation of the nest by the leaf-cutting ant. Global Horizontal Irradiation (GHI) was measured using an Eppley PSP Pyranometer and Diffuse Horizontal Irradiation by a Kipp & Zonen CM3 Pyranometer (Table 3). Solar measurements were obtained over a five-minute time scale (mean of 60 readings with scanning time every five seconds) in W/m2 by a CR300041 model data logger and stored in an ASCII file.Table 3 Instruments used to measure solar irradiance in nests of Atta sexdens (Hymenoptera: Formicidae) in sunny and shaded environments.Full size tableThe measurements were submitted to a quality control procedure to verify if their values were in accordance with pre-defined solar irradiance thresholds. This procedure consists of a series of checks on physically possible limits per component measured (Table 4). These checks were carried out according to the process created by the International Commission on Illumination (CIE) discarding erroneous measures to avoid compromising the processes of numerical integration or data processing.Table 4 Physically possible minimum and maximum values for each measurement of solar irradiance.Full size tableMeasures accepted as possible were those above 0 W/m2 and lower than the maximum stipulated limit, per component, according to the extraterrestrial solar irradiance (IE) (Eq. 1). This represents the maximum value reaching the top of the atmosphere, without attenuation by atmospheric elements (clouds, particles, among others). Values measured at the earth’s surface are lower than those at the top of the atmosphere. However, the phenomenon of multireflection when scattered clouds near the apparent location of the sun reflect part of the solar irradiance onto the sensor, increase the value measured even higher than the extraterrestrial irradiance over short periods45. For this reason, the global irradiance value can be up to 20% higher than that of the extraterrestrial one.The 1361 of Eq. (1), to calculate the extraterrestrial irradiance, represents the solar constant in W/m246, R the relation of the average dimensionless distance between the Earth and the Sun (Eq. 2) and Z the zenithal angle of the Sun (Eq. 3) in degrees47.$${text{I}}_{{text{E}}} = {1361}left( {text{1/R}} right) ,{{cos}}, left( {text{Z}} right)$$
(1)
$$begin{aligned} {text{R}} & = {1} – 0.000{9467};{text{sen}} left( {text{F}} right) – 0.0{1671};{text{cos}},left( {text{F}} right) – 0.000{1489}left( {{text{2F}}} right) \ & quad – 0.0000{2917};{text{sen}}left( {{text{3F}}} right) – 0.000{3438};{text{cos}},left( {{text{4F}}} right) \ end{aligned}$$
(2)
$${text{Z}} = {text{sen}} ,left(updelta right);{text{sen}}left(upphi right) + cos left(updelta right);cos left(upphi right);cos left(upomega right)$$
(3)
F, in Eq. (4), is the angular fraction of the date of interest in degrees, δ, at 5, the solar declination in degrees, Φ, at 6, the geographic latitude of the location in degrees (22.85) and ω, at 6, the clockwise angle in degrees.$${mathbf{F}} = {36}0^circ ;{text{D/365}}$$
(4)
$$begin{aligned} {{varvec{updelta}}} & = 0.{3964} + {3}.{631};{text{sen}}left( {text{F}} right) – {22}.{97};{text{cos}}left( {text{F}} right) + 0.0{3838};{text{sen}}left( {{text{2F}}} right) – 0.{3885};{text{cos}};left( {{text{2F}}} right) \ & quad + 0.0{7659};{text{sen}};left( {{text{3F}}} right) – 0.{1587};{text{cos}}left( {{text{3F}}} right) – 0.0{1}0{21};{text{cos}}left( {{text{4F}}} right) \ end{aligned}$$
(5)
$${{varvec{upomega}}} = left( {{12} – {text{Hd}}} right){15}$$
(6)
The d, in the previous expression, represents the day of the year, from 1 to 365 and the Hd, the hour and the tenth of an hour in degrees of the moment of interest.The values were numerically integrated, after applying the measurement quality control procedure, obtaining a solar irradiation value for the day in MJ/m2 representing the total energy received daily, on a horizontal surface of 1 m2.Statistical analysisThe null hypothesis that the mortality proportions (probabilities of success) of the founding queens of both groups are the same was submitted to the test of equal proportions.The null hypothesis that a data sample came from a normally distributed population was submitted to the Shapiro–Wilk test.Data structure fitting the ANOVA (completely randomized factorial scheme) assumptions were submitted to this analysis and to Tukey’s test for multiple comparisons of means. The Scheirer Ray Hare test is a nonparametric test used for a two-way completely randomized factorial design49. This procedure is an extension of the Kruskal–Wallis rank test allowing for calculation of the interaction effects and linear contrasts and were used for data structure that did not fit the ANOVA assumptions. Dunn’s test50 of multiple median comparisons was performed with a correction (the false discovery rate method) to control the experiment-wise error rate.The Willmott’s Index of Similarity (d) is a standardized measure of the degree of similarity between two data series ranging from 0.0 to 1.0 with the value 1.0 indicating a perfect match (two identical data sets), and 0 no agreement at all51. As an example, in the sunny environment, the indoor and outdoor temperature data were identical, which results in 1.0. The more identical, close, and concordant two data sets are, the closer to 1.0 they will be. The calculation of the index is presented with A and B representing two data sets whose agreement is to be evaluated.$$begin{aligned} A^{prime}_{i} & = A_{i} – overline{B} \ B^{prime}_{i} & = B_{i} – overline{B} \ d & = 1 – frac{{sumnolimits_{i = 1}^{N} {left( {A_{i} – B_{i} } right)^{2} } }}{{sumnolimits_{i = 1}^{N} {left[ {left| {A_{i} } right| – left| {B_{i} } right|} right]^{2} } }} \ end{aligned}$$The R companion package, ggplot252, FSA53, tidyverse54, and hydroGOF55 used is a free software environment for statistical computing and graphics R version 4.0.456. More

Study area and dateIn the north-west of France, the macrotidal Bourgneuf Bay (1°-2° W, 46°-47° N; total area ~340 km2; Fig. 1) has an intertidal zone largely dominated by mudflats (exposed surface area ~100 km2). Bourgneuf Bay is situated south of the Loire estuary and is open to the sea along 12 km from the west to the north-west. C. gigas aquaculture here is of national importance and wild C. gigas reefs can account for over double the biomass of their farmed conspecifics65. Analysis of satellite observations covering 30 years of MPB biomass in the bay confirmed the co-occurrence of high MPB biomass with wild oyster reefs and cultivated stocks16 (Supplementary Methods: Wider Situation of the Reefs). Two small (each  > 750 m2) wild C. gigas reefs and their immediate surroundings (10–100 m) in the north of Bourgneuf Bay were deemed suitable for experimental manipulation (yellow and orange regions in Fig. 1). Méléder et al.18 described MPB biomass as mostly concentrating around the 2 m isobath, the Falleron river channel (closest point ~400 m NNE from the eastern reef), and oyster farms. Covering this isobath, we superimposed a 350 * 350 m grid (12.25 hectares) to cover the two wild oyster reefs, orientated so that the ‘Y’ axis runs parallel to the slope of bathymetry (Fig. 1). The grid was split regularly into 49 ‘grand-cells’ of 50 * 50 m (i.e., n = 49) and each of those split into 25 cells of 10 * 10 m (i.e., n = 1225; Fig. 1). Four cells per grand-cell were chosen randomly for the sampling of meiofauna, granulometry, OM (see Table 3 for specific methodology), and macrofauna. Of these cells, only every second cell was processed for meiofauna because of time constraints in assessing their abundance.Table 3 Summary of study variables and their sampling methodologies.Full size tableAlthough there were only two oyster reef complexes (‘reefs’, hereon) in this study, multiple sampling cells fell on, or in close proximity to, each reef, so that each reef had many potential (though not independent) distance decay transects running from it capturing natural variation in spatial structure66. Comparing the ecological change following the experimental burning of oyster reefs (described below) against ecological change occurring at these two reefs over the previous 25 years16 also allowed us greater confidence to disentangle the treatment effects from typical variation. Through the centres of five grand-cells to the south of the extent, a transect forming an ‘L’ shape (Fig. 1) was sampled every 10 m for in situ MPB pigment composition and biomass. We used these data to complete the remote sensing approach for MPB biomass estimation (see below, Microphytobenthos). The western reef was slightly larger than the eastern reef and contained a large rock, ‘Roche Bonnet’, rising 0.5–1 m from the sediment. Outside the grid, another larger (200 * 80 m) wild oyster reef lies WSW at ~260 m distance from the western reef. The grid was sampled for the variables listed in Table 3 during the winter MPB low and early autumn peak seasons (see also ground-truthing in16), on the dates 18-19th September 2013 and 17-18th March 2014, before treatment, and on 7-8th October 2014 after treatment.MicrophytobenthosWe mapped MPB biomass by satellite remote sensing, following the method described in detail in Echappé et al. (2018). We used the same long-term record of high-resolution satellite images to analyse the spatial distribution of the normalised difference vegetation index (NDVI), a proxy of MPB chlorophyll a concentration at the sediment’s surface18,67, before and after treatment (individual image details in captions of Fig. 2 and Supplementary Figs. S4–S7). After atmospheric correction (FLASH and US40 aerosol model), the satellite-derived NDVI was validated against associated field measurements (r2 = 0.85, root-mean-square deviation, RMSE = 0.04, n = 57, P 20°) was limited (Supplementary Results: Additional MPB Images). An optimal image was chosen as representative of MPB biomass patterns per season16. The study area would ideally be tidally uncovered for ~2 h before the image was taken, whereupon MPB biomass is concentrated at the sediment surface. The optimal images met this condition (i.e., Fig. 2).To complete NDVI maps, in situ MPB pigment composition and biomass were retrieved by HPLC analysis from the 25 triplicates of sediment. These had been sampled using contact-cores to freeze the top 2 mm of sediment in situ with liquid nitrogen, with a metal surface 56 mm in diameter. Biomass was expressed by Chl a concentration (mg m−2), and dominance of MPB taxa was broadly assessed by ratio of pigment sources to Chl a: Fucoxanthin (Fuco), Diadinoxanthin (DD), Diatoxanthin (DT) and Chl c for diatoms. The ratio of unknown carotenoids (interpreted as by-products due to the low resolution of their absorption spectra) to Chl a was also analysed for ecological purposes (dominant taxa), whereas grazing pressure was investigated using the ratio of pheophorbid a to Chl a (methodological discussion in28).Sediment variablesFor laser granulometry, we sampled two depths, 0–5 cm and 5–10 cm, in triplicate at each cell. Each of the triplicate samples was put in a vial with water and sonicated. The particle size distribution was determined on a Mastersizer 3000 with a reporting range 50 nm to 3 mm. We also determined sediment percentage OM at two depths by mass loss on ignition in comparison to the oven-dried original (procedure as described for Macrofauna, also Table 3).MacrofaunaWe sampled macrofauna by a single 200 * 200 mm (depth * diameter) core per cell. Contents were placed into labelled buckets and sieved onshore (1 mm mesh). Soft-bodied polychaetes were picked out with forceps and preserved in buffered formalin during sieving. All material left on the sieve was bagged and preserved in formalin at the laboratory. Individuals were counted and measured by the longest axis (accuracy 0.1 mm, calipers); the deep-burrowing polychaete Diopatra biscayensis was counted by the presence of visible tubes above the sediment. Calibration curves from length to mass per species per season were built by identifying size classes by Sturges rule. Multiple individuals per size class (ideally n = 100) were measured to estimate mean organic mass per individual of each size class. Shell matter was physically separated from tissue, before both being dried in aluminium foil cups for 48 h at 60 °C and weighed (g) for tissue dry mass using a mass balance. Dry mass was then incinerated for four hours at 450 °C and reweighed (g; decrease in mass of the aluminium cup was also accounted for), the difference giving the organic matter mass (including residue in the shell matter), or ash free dry weight (AFDW). This number was divided by number of individuals. Calibration curves per species used first order polynomial curves for bivalves, unless numbers of size classes and individuals were small (1% of the total abundance. All mapping and analyses were performed in the statistical computing environment R (v4.0.2)73.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Climate change is affecting arctic ecosystems through temperature increase1, hydrological changes2, earlier snowmelt3,4, and the associated increase in growing season length5. Annual arctic air temperature has been increasing at more than double the magnitude of the global mean air temperature increase1, and terrestrial snow cover in June has decreased by 15.2% per decade from 1981 to 20194. Warming is the main driver of the earlier start of the growing season, and the greening of the Arctic6,7,8. Arctic greening is associated with enhanced vegetation height, biomass, cover, and abundance9. However, the complexity of arctic systems reveals an intricate patchwork of landscape greening and browning8,10,11, with browning linked to a variety of stresses to vegetation8 including water stress12,13. The interconnected changes in temperature, soil moisture, snowmelt timing, etc. can have important effects on the carbon sequestered by arctic ecosystems14. The reservoir of carbon in arctic soil and vegetation depends on the interaction of two main processes: (1) changes in net CO2 uptake by vegetation; and (2) increased net loss of CO2 (from vegetation and soil) to the atmosphere via respiration. Therefore, defining the response of both plant productivity and ecosystem respiration to environmental changes is needed to predict the sensitivity of the net CO2 fluxes of arctic systems to climate change.An earlier snowmelt, and a longer growing season, do not necessarily translate into more carbon sequestered by high latitude ecosystems5. There is a large disagreement on the response of plant productivity and the net CO2 uptake to early snowmelt in tundra ecosystems15,16,17,18,19. A warmer and longer growing season might not result in more net CO2 uptake if CO2 loss from respiration increases16, particularly later in the season, and surpasses the CO2 sequestered by enhanced plant productivity in northern ecosystems16,20. Moreover, snowmelt timing and the growing season length greatly affect hydrologic conditions of arctic soils21, as well as plant productivity22. Longer non-frozen periods earlier in the year23 and earlier vegetation greening can increase evapotranspiration (ET), resulting in lower summer soil moisture24,25,26. The complexity in the hydrology of tundra systems arises from the tight link between the water drainage and the presence and depth of permafrost. The presence of permafrost reduces vertical water losses, preventing soil drainage in northern wetlands during most of the summer despite low precipitation input27. Increasing rainfall28 and increased permafrost degradation can increase soil wetness in continuous permafrost regions2. Further permafrost degradation (e.g. ice-wedge melting) can increase hydrologic connectivity leading to increased lateral drainage of the landscape and subsequent soil drying2,29.Given the importance of soil moisture in affecting the carbon balance of arctic ecosystems, and its links with snowmelt timing, in this study, we investigated the correlation between summer fluxes of CO2 (i.e., net ecosystem exchange (NEE), gross primary productivity (GPP) ecosystem respiration (ER)), ET, and environmental drivers such as soil moisture and snowmelt timing, while controlling for the other most important drivers of photosynthesis and respiration (i.e. solar radiation and air temperature). We expected earlier snowmelt to be correlated with larger ET and lower soil moisture, particularly during peak and late season, consistent with drying associated with a longer growing season. The lower soil moisture with earlier snowmelt should result in a negative correlation between snowmelt timing and GPP, particularly during the peak and late season (when we expect the most water stress), and in a positive correlation between snowmelt timing and ER during the entire growing season. This soil moisture limitation to plant productivity should result in lower net cumulative CO2 sequestration during the entire summer, because of lower plant productivity if these ecosystems are water-limited due to lower soil moisture with earlier snowmelt.Testing the impact of snowmelt timing on the carbon dynamics and hydrology of tundra ecosystemsThe 11 sites were selected as among the longest-running tower sites in the circumpolar Arctic (including 6 to 19 years of fluxes per site and a total of 119 site-years of summer (June to August) eddy covariance CO2 flux data, Table S1). All sites lie in the zone of continuous permafrost. The sites are representative of dominant tundra vegetation classes (wetland, graminoids, and shrub tundra), together accounting for 31% of all tundra vegetation types (Fig. 130 and Supplementary Information). Given the complex interactions among different variables (many covarying together), we used a variety of statistical analyses to identify the association between standardized anomalies of NEE, GPP, ER, and ET, and standardized anomalies of the main environmental controls during different periods of the summer corresponding to various stages in seasonal phenology (early season: June, peak season: July, and late season: August). We used a partial correlation analysis to identify if the timing of the snowmelt associates with anomalies of ET, soil moisture, NEE, GPP, ER, atmospheric vapor pressure deficit (VPD), or the Bowen ratio (the ratio between Sensible Heat (H) and Latent Heat (LE)) while statistically controlling for the main meteorological forcing such as air temperature and solar radiation (Methods). Identifying the correlation between ET (and the Bowen ratio) and snowmelt timing is a way to assess water limitation to ecosystems (in addition to testing their response to soil moisture changes), as H, and therefore, the Bowen ratio, are expected to increase with surface drying31,32. To identify the association between snowmelt timing, the main environmental variables (i.e., air temperature and solar radiation), and NEE, GPP, ER, and ET over time, we performed a maximum covariance analysis (MCA) on the monthly median standardized anomalies from 2004 to 2019 (a time period when data for most of the sites were available). MCA allowed us to find patterns in two space–time datasets that are highly correlated using a cross-covariance matrix26. We retained sites as the unit of variation (i.e., by estimating the standardized anomalies by site for each of the indicated variables, see “Methods”), hence the results of the MCA integrated the site level relationships between each of the variables over time). The goal of this analysis was to identify the most important environmental drivers associated with NEE, GPP, and ER across all the sites over time. MCA is particularly appropriate for this study as it can handle data with gaps and unequal lengths in the datasets. We also tested the relative importance of the abovementioned environmental drivers on the monthly median GPP, ER, and NEE using a linear mixed effect model, including site as a random effect to account for the site-to-site variability. The MCA and the mixed model analyses were conducted to test the relative importance of snowmelt and other variables at different times of the season. Finally, to evaluate the water balance at different times of the season, we estimated the difference between Potential Evapotranspiration (PET) and the actual ET, and the difference between precipitation (PPT) and ET for each of the sites, years, and months (e.g. June, July, and August). This study did not attempt to describe the long-term temporal changes in the anomalies of snowmelt and carbon fluxes, given the short data record available for some of the sites (i.e. less than 10 years, Table S1), but instead focused on understanding the association between environmental variables and the carbon balance at different times of the season. More details of these analyses are included in the Methods.Figure 1Study sites. Locations of the 11 eddy covariance flux tower sites used in this study. Light blue regions delineate the total Circumpolar Arctic Vegetation Map (CAVM), green regions delineate the subset of CAVM vegetation types represented in this study (including all the vegetation types listed in Table S1). This map was created using QGIS.org, 2020, QGIS 3.10. Geographic Information System User Guide. QGIS Association: https://docs.qgis.org/3.10/en/docs/user_manual/index.html. The dataset used in the map was the CAVM map: CAVM Team. 2003. Circumpolar Arctic Vegetation Map. (1:7,500,000 scale), Conservation of Arctic Flora and Fauna (CAFF) Map No. 1. U.S. Fish and Wildlife Service, Anchorage, Alaska. ISBN: 0-9767525-0-6, ISBN-13: 978-0-9767525-0-9.Full size imageInfluence of snowmelt timing on NEE, GPP, ER, and hydrological status of tundra ecosystemsOnce statistically controlling for solar radiation and air temperature (in the partial correlation analysis, see “Methods”), we observed a significant positive relationship between the snowmelt timing anomalies and NEE anomalies (i.e. earlier snowmelt was associated with a higher net CO2 sequestration) in June and July, but a negative correlation in August (Fig. 2a, Table 1). A significant relationship was also found between snowmelt date anomalies and GPP anomalies, with more positive GPP anomalies (i.e. higher plant productivity) with earlier snowmelt in June and July, and more negative GPP anomalies with earlier snowmelt in August (Fig. 2b, Table 1). Earlier snowmelt was associated with significantly higher ER in both June and July, but there was no significant relationship in August (Fig. 2c, Table 1), suggesting that the late-season correlation between NEE and snowmelt timing was mostly driven by the lower GPP and with earlier snowmelt in August. The MCA analysis showed that the anomalies in snowmelt timing had the highest squared covariance fraction (SCF) with the monthly median anomalies of GPP, NEE, and ER in June and July, and the lowest in August over the 2004–2019 period (Fig. 3). A similar result was observed in the linear mixed effect model, which showed a significant relationship between snowmelt date and GPP, and NEE, in all summer months, higher ({R}_{m}^{2}) between the snowmelt date and GPP in June and July, and no significant relationship between snowmelt date and ER in August (Table S3). In late season, other environmental variables had a higher covariance with the GPP, NEE, and ER anomalies than the snowmelt timing (Fig. 3, Table S3).Figure 2Relationships between the indicated median monthly anomalies using partial correlation analysis accounting for solar radiation and air temperature anomalies (retaining site as the unit of variation). Given that the interaction term between “month” and snowmelt timing was significant, we included the correlation coefficients and P of the regressions for each of the indicated months separately in each panel (also included in Table 1). Negative values indicate CO2 uptake and positive values CO2 release into the atmosphere, and shaded areas are 95% confidence intervals.Full size imageTable 1 Significance (P) and Pearson’s correlation coefficient (r) of the relationships between the indicated monthly median standardized anomalies for June, July, and August retaining site as a unit of variation.Full size tableFigure 3Squared covariance fraction (SCF) of each couple of the indicated variables for the maximum covariance analysis (MCA) of the monthly median anomalies of GPP, ER, and NEE in June, July, and August. The first pair of singular vectors are the phase-space directions when projected that have the largest possible cross-covariance. The singular vectors describe the patterns in the anomalies that are linearly correlated. A higher SCF indicates a stronger association over time between the indicated variables.Full size imageOur results are consistent with the discrepancy between the observed increase in the maxNDVI over the last four decades and the time-integrated (TI) NDVI which instead has plateaued in the last two decades and even decreased over the last 10 years in several northern arctic ecosystems33. TI-NDVI considers the length of the growing season and phenological variations34 and, therefore, better integrates vegetation development during the entire growing season. Moisture was shown to be important for the NDVI trends33,35. Given the potential water limitation to summer carbon uptake in northern ecosystems12,23,24,25, we tested if an earlier snowmelt was associated with a decrease in soil moisture, which would affect GPP and NEE. We only observed a significant correlation between soil moisture anomalies and snowmelt date anomalies in June (i.e. higher soil moisture with earlier snowmelt, Fig. S1a, Table S2), but no significant correlation in July and August (Fig. S1a, Table S2). The higher soil moisture with earlier snowmelt in June is consistent with surface inundation after snowmelt36,37 and earlier soil thawing resulting in higher soil moisture (i.e., soil moisture is low while soils are frozen). A similar result was observed for the ET anomalies. Higher ET with earlier snowmelt in June (Fig. S1b) could be the result of surface inundation after snowmelt32. The standardized NEE anomalies were significantly correlated with the soil moisture anomalies in each of the summer months (Fig. S1d, Table S2). However, the relationship between the GPP (and ER anomalies) and soil moisture anomalies was only significant in June (Fig. S1e,f, Table S2) suggesting an earlier activation of the vegetation with earlier soil thaw (and the associated higher soil moisture). A higher water loss from ET in early season (Fig. S1b) could have resulted in the drying of the surface moss layer with the progression of the summer, which would have been consistent with the observed lower GPP and the lower net CO2 sequestration with earlier snowmelt observed in August (Fig. 2a,b, Table 1). A potential moisture limitation to plant productivity might have been consistent also with the higher SCF of NEE, or GPP and VPD anomalies in August than in June and July (Fig. 3). However, no significant relationship between ET (or soil moisture) and snowmelt date anomalies was observed in July and August (Fig. S1a,b) contrary to what would be expected if drying occurred following earlier snowmelt. No significant relationship was found between VPD anomalies and snowmelt date anomalies in any of the summer months (P = 0.14 in a partial correlation considering air temperature and solar radiation anomalies). Finally, surface drying should result in an increase in the Bowen ratio anomalies with the progression of the summer, given that H increases with a decrease in water table and surface drying32,38. However, the Bowen ratio showed no correlation with the standardized snowmelt date anomalies in any of the summer months (Fig. S1c, Table S2), and presented similar values in all the summer months (Fig. S2a). The lack of correlation between the soil moisture, VPD, Bowen ratio, and snowmelt date anomalies suggests that an earlier snowmelt did not result in significant surface drying in the sites of this study. The median PET-ET and PPT-ET for all years and sites included in this analysis (Fig.S2 b,c) was slightly higher in August, similar to reports by others for the Russian arctic tundra38,39, further supporting a lack of soil moisture limitation in late season. Although these analyses do not consider runoff, which can be significant21,26, overall our results do not suggest that an earlier snowmelt resulted in a water stress that significantly limited plant productivity in these arctic ecosystems over continuous permafrost.The correlation between the anomalies in the August GPP and snowmelt timing is consistent with earlier senescence in northern plant species (e.g. Eriophorum vaginatum, a dominant species across these tundra types) compared to southern species growing in the same location in a common garden experiment40. The phenotypic variation was shown to be persisting for decades41, and ecotypes may be unable to extend their effective growth period or take advantage of a longer growing season40. Several studies across different plant functional types have shown that once plant growth is initiated after the snowmelt in northern ecosystems, it continues only for a fixed number of days until the occurrence of senescence42,43,44. Therefore, the lower GPP in August with earlier snowmelt might not be linked to water limitation on photosynthesis later in the season, but rather to an earlier senescence arising from the endogenous rhythms of growth and senescence, that plant functional types living in these extreme conditions have developed over decades. On a broader scale, earlier senescence with an earlier start of the growing season after snowmelt in northern ecosystems is also consistent with an earlier spring zero-crossing date and an earlier autumn zero-crossing date of the mean detrended seasonal CO2 variations at Barrow, AK, USA (NOAA ESRL: https://www.esrl.noaa.gov/gmd/ccgg/obspack/) during 2013–2017 compared to 1980–19845. The spring and autumn zero-crossing date is the time when the detrended seasonal CO2 variations intersect the zero line in spring and autumn respectively and can be used as an indicator for the start and end of the net CO2 uptake by vegetation45,46. On the other hand, NDVI measurements show both an earlier start of the season and a later end of season for 2008–2012 compared to 1982–19865. The disagreement between the detrended seasonal atmospheric CO2 concentration showing an earlier autumn zero-crossing date and the NDVI measurements showing a later end of the season has been explained by the increase in respiration in the fall20. The disagreement between atmospheric CO2 concentration trends (showing an earlier autumn zero-crossing date), and NDVI (showing a later end of the season,5 may also be explained by the challenges in using NDVI as a proxy for plant productivity in these arctic systems. The relationship between NDVI and CO2 flux and plant productivity is highly variable and non-linear in arctic ecosystems47. While some arctic ecosystems have shown that NDVI was strongly correlated with GPP (explaining 75% of the variation in GPP48, other studies showed that NDVI was either not significantly correlated with GPP and NEE49 or was only able to explain a minor fraction (maximum of 25%) of the variation in NEE and GPP in some arctic tundra ecosystems after accounting for the seasonal variation50,51.In conclusion, earlier snowmelt was associated with greater net CO2 uptake and higher GPP in early and peak seasons, but with less net CO2 uptake and lower GPP later in the summer, in the studied arctic tundra ecosystems. We did not find evidence of a late-season water limitation to GPP with earlier snowmelt. Although several hypotheses can be forwarded to explain the link between snowmelt and late season declines in plant productivity and carbon uptake, the current literature does not provide a definitive explanation (schematic Fig. 4). Future studies should investigate the potential interaction of different processes explaining the response of the carbon dynamics in the Arctic to earlier snowmelt and reconstruct the temporal changes in the carbon balance from these systems. The link between the long-term changes in the CO2 fluxes and NDVI in tundra ecosystems needs closer examination. Studies should investigate if higher NDVI is definitively associated with higher net CO2 uptake. Greening of the Arctic might not necessarily translate into more net CO2 uptake, as early and peak season carbon gains might be offset by a late-season CO2 loss, and respiration might counterbalance the increase in plant productivity. A better understanding of the processes driving these temporal changes is a fundamental step in advancing our prediction of the response of the arctic CO2 balance to changing climate.Figure 4Schematic of the effect of earlier snowmelt on NEE, GPP, and ER at different times of the season. Earlier snowmelt results in an earlier activation of the vegetation, higher plant productivity, and higher net carbon uptake in June and July. This earlier activation could result in more carbon loss and lower plant productivity with earlier snowmelt in August, potentially related to either environmental stress, or to earlier senescence. Photo credit: Donatella Zona.Full size image More

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A combined DNA metabarcoding/in-situ macro photography approach to reliably analyse carnivorous plant prey spectraResults indicate that DNA metabarcoding allows for reliable analysis of prey spectra composition in carnivorous plants at a taxonomic resolution and level of completeness unachievable by traditional morphology-based approaches (as performed, for example, by4,5,6,7,9,10,11). Even in remote tropical northern Western Australia, where many (if not most) arthropod species have not yet been accessioned into the BOLD or GenBank barcode reference libraries, this method identified over 90% of obtained OTUs from our sample set; most of them at family-level, but 41% to genus-level, and 17% even down to species rank (Supplementary Data S1). Lekesyte et al.27 were able to identify 80% of the analysed prey items found on D. rotundifolia in England to species-level. However, their sampling was performed in western Europe, whose entomofauna is comparatively well studied taxonomically and has an excellent coverage in the BOLD reference library of DNA barcodes41. New insect barcodes are regularly added to the BOLD library through large-scale initiatives such as the international Barcode of Life Project (iBOL; https://ibol.org/) and its Australian node Australian Barcode of Life Network (ABOLN), hence accuracy of future metabarcoding research performed in Australia can be expected to increase to similar levels soon.In-situ macro photography was found to provide a valuable plausibility control tool for the prey taxa identified by metabarcoding. While many of the smaller prey taxa detected by metabarcoding were impossible to identify in the in-situ macro photographs due to their tendency to quickly degenerate after digestion into small, shapeless “crumbs”8, this control method considerably reduced the amount of prey taxa detected which were not actually present as prey in the Drosera samples. This flaw of metabarcoding is most commonly a consequence of procedural errors resulting in cross-contamination within the DNA extraction procedure27, usually resulting in low read numbers. However, in-situ macro photographs may also fail to detect species if prey captured by the sundew escaped from the trap33,42, or was stolen by larger animals. In both cases, a DNA imprint left on the Drosera leaves as excretions, detached scales, hairs or, frequently, as autotomised (shedded) body parts42 could have been detected by metabarcoding. Additionally, some barcoding-detected taxa may not constitute prey if they were associated with another captured prey taxon (either as part of its diet, or as a parasite). The latter may explain some barcode hits for taxa not immediately apparent from the in-situ macro photographs, as they are (endo)parasites of captured prey taxa. This was likely the case in the detected Strepsiptera (stylops) which are frequently contained as larvae and adult females in their hymenopteran and orthopteran hosts43. However, insect endoparasites and other non-obvious prey taxa were by default not excluded by the very conservative approach of pictorial plausibility control. Additionally, in the case of endoparasites, these organisms would also contribute to plant nutrition as “bycatch” after being digested together with their host, despite not having been actively attracted to the carnivorous traps. Finally, the control method tested in this study showed that even heavily digested prey items in the samples had sufficient amounts of intact (mitochondrial) DNA present to be detected by metabarcoding, as we found no instance of any prey item being clearly identifiable in the macro photographs but not present in the barcoding data.Prey spectra composition of the studied Drosera speciesThe analysed prey spectra of the three studied species from D. sect. Arachnopus most commonly contained flying insects (especially of the orders Diptera and Hemiptera, both present in 100% of the samples; Fig. 3), thus confirming earlier in-situ macro photography-based studies of closely-related D. sect. Arachnopus species by Krueger et al.8. All members of D. sect. Arachnopus are characterised by a large, erect growth habit and thread-like aerial leaves which usually do not contact the ground8,32, thereby excluding most ground-dwelling arthropods as prey. This result is also similar to other prey spectra studies of erect-leaved Drosera from different geographic areas, where flying insects (particularly Diptera) unanimously comprised almost the entire recorded prey5,11,44. Furthermore, this study confirmed the result of Krueger et al.8 that Hemiptera—and within this order especially the Cicadellidae—are exceptionally common in the prey spectra of D. sect. Arachnopus compared with all other, previously studied Drosera. A possible explanation for this may be the relatively high abundance of Cicadellidae in tropical habitats45 compared to subtropical or temperate habitats where the above-mentioned previous Drosera prey spectra studies were conducted.Of the five most commonly detected orders, Lepidoptera generally comprised the largest prey items in terms of body size or wingspan, respectively. This prey order was exceptionally common in D. finlaysoniana, being present in 100% of samples and also visually conspicuous in the in-situ photographs. Since this Drosera species had by far the largest trapping leaves among the three species studied with an average leaf length of 10.4 ± 0.6 cm (Suppl Appendix S7), and exhibits the largest leaves in D. section Arachnopus32, this may represent an example of large prey items being more easily captured by species with larger trapping leaves33. Additionally, the sampled population of D. finlaysoniana was huge and dense (see Supplementary Figure S1), probably attracting larger prey and enabling capture of larger prey items by “collective” trapping46. Alternatively, Fleischmann30 suggested that captured Lepidoptera themselves could attract further individuals of the same species by pheromone release, potentially explaining the very high numbers of this insect order observed in D. finlaysoniana.Differences among observed prey spectraComparison of prey spectra between the three studied Drosera species revealed significant differences at arthropod family-level but not at the higher level of arthropod orders, indicating that at a coarse taxonomic resolution, the same five arthropod orders (Diptera, Hemiptera, Hymenoptera, Lepidoptera and Thysanoptera) generally comprise most of the prey in D. sect. Arachnopus, regardless of given Drosera species or habitat. However, as strong differences were discovered in the ANOSIM comparison at family-level, it can be concluded that differences might likely increase with finer taxonomic resolution of prey taxa, a conclusion also reached by the carnivorous plant prey spectra meta-analysis of Ellison & Gotelli47. While these differences may be partially attributed to different morphological traits of the three species such as leaf scent8,30 or eglandular appendages31, the very high ANOSIM R-values returned and the large number of prey families contributing nearly equally to dissimilarity (Table 2) indicate that the most likely explanation is very different available prey spectra at the three study sites. Indeed, significant differences among different study sites, even within the same species, were previously reported for Drosera rotundifolia by Lekesyte et al.27 and for four species from D. sect. Arachnopus by Krueger et al.8. Notably, the three study sites feature different habitat types and climate regimes (Supplementary Fig. S1).Analyses indicate that there is likely little specialisation in prey capture by the three studied Drosera species. For example, the relatively high detection rate of Lepidoptera in the samples of D. finlaysoniana and D. hartmeyerorum compared to D. margaritacea may be explained by the lake margin habitats of the former two species, while the latter species was found in a completely dry drainage channel lacking any nearby waterbodies (Supplementary Fig. S1). Lepidoptera are likely to occur in much higher concentrations near water sources, especially during the dry season (May to November) when the surrounding areas are lacking other water sources (G. Bourke in Fleischmann30).Estimating prey quantityIn addition to providing a plausibility control for the compositional prey analysis by metabarcoding, the in-situ macro photography method facilitated an estimation of prey quantity per sample. Metabarcoding by itself is currently not a reliable tool for prey quantification due to the lack of a linear relationship between the number of sequence reads and organism biomass26,27.In contrast to Krueger et al.8, who generally found more prey items on larger trapping leaves in species of D. sect. Arachnopus (even when values were compared as per cm of trapping leaf length), the species with the largest leaves studied here (D. finlaysoniana) captured significantly less prey items than the smaller-leaved species D. margaritacea and D. hartmeyerorum (Fig. 4). However, while Krueger et al.8 was able to compare sympatric species (thus minimising any potential effects of the habitat or region on prey spectra), the three species in this study were studied at three different, geographically distant sites. While it is possible that overall prey abundance in the habitat was much lower at the D. finlaysoniana study site (Site 1), it can be hypothesised that the low total prey capture observed in this species may be due to the very large and extremely dense population resulting in strong intraspecific competition for prey (see Supplementary Fig. S1). This effect of population structure on prey capture has also been observed by Gibson48 and Tagawa and Watanabe46 who found a significant negative correlation between total prey capture and population density in different species of Drosera.Conclusions and outlookOur study is the first to employ a DNA metabarcoding approach supported by controls for species presence to analyse carnivorous plant prey spectra. When combined with in-situ macro photography, this method is clearly superior in terms of taxonomic resolution and completeness for analysis of environmental bulk samples (containing different organisms in highly variable states of preservation), as used here for the reconstruction of prey spectra of carnivorous plants. The capability of this method increases with new reference barcodes being regularly added to DNA barcode libraries (such as BOLD and NCBI GenBank) and it thus has the potential to become the standard methodology for future carnivorous plant prey spectra research.Additional studies are needed to test this method for other carnivorous plant species and genera, especially those possessing different trap types. Within Western Australia, three additional trap types occur: snap traps (Aldrovanda), suction traps (Utricularia) and pitfall traps (Cephalotus). In particular, it might be expected that in-situ macro photography will not work as well for the extremely small, typically submerged traps of Aldrovanda and Utricularia (which also completely enclose their captured, microscopic prey items49), potentially necessitating usage of alternative control methods for metabarcoding data. Furthermore, even within Drosera (adhesive traps) some species may require adjustments to the methodology presented here as they accumulate captured prey in a central point via tentacle movement (e.g., many climbing tuberous Drosera) or their leaves may be very difficult to place on paper sheets with the sticky side facing upwards (e.g., all pygmy Drosera). The latter problem may be solved by using reverse action forceps and photographing the leaves while held in place by the forceps.Extensive sampling of sites with co-occurring species from D. sect. Arachnopus is clearly required to better understand the ecological role of trap scent and eglandular appendages in this section. For example, manipulation experiments involving the removal of all yellow blackberry-shaped appendages of D. hartmeyerorum (which have been hypothesised to function as visual prey attractants31) and subsequent metabarcoding prey spectra comparisons of mutilated plants lacking emergences with control plants are proposed. Potential effects of population density on prey spectra (as hypothesised here for D. finlaysoniana) could be studied by comparing prey spectra of individual plants from within mass populations with more exposed-growing individuals of the same population. More

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