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    Eucalyptus obliqua tall forest in cool, temperate Tasmania becomes a carbon source during a protracted warm spell in November 2017

    Site descriptionWarra Supersite, (Lat: 43° 5′ 42ʺ S; Long: 146° 39′ 16ʺ E) is located on a floodplain of the Huon River within the Warra Long Term Ecological Research site (https://warra.com/) 60 km southwest of Hobart, Tasmania. The forest at the Supersite is a Eucalyptus obliqua tall forest with a canopy height of 50–55 m, overtopping a 15–40 m tall secondary layer of rainforest and wet sclerophyll tree species. Ferns dominate the ground layer. The forest is very productive with an aboveground biomass of 790 tonnes/ha16 and a leaf area index of 5.7 m2/m247.The Supersite is within the Tasmanian Wilderness World Heritage Area (TWWHA). That part of the TWWHA experiences infrequent, but sometimes intense, wildfire. Except for a small proportion of mature ( > 250 years-old) E. obliqua trees, the current forest resulted from seedling regeneration following the last major wildfire in that part of the landscape in 1898. No timber harvesting has ever been done in the forest at the Supersite.The climate at Warra is classified as temperate, with no dry season and a mild summer48. Mean annual rainfall measured at the nearby Warra Climate Station (Bureau of Meteorology Station 097024) is 1736 mm and the mean daily temperature is 14 °C and 5.6 °C in January and July, respectively. The soil at the site is a Kurosolic Redoxic Hydrosol16.Analysis of historical heatwaves in southern TasmaniaDaily maximum temperature records from the Bureau of Meteorology station at Cape Bruny Lighthouse (station number 94010) were extracted from the Bureau of Meteorology’s online climate data portal (http://www.bom.gov.au/climate/data). Cape Bruny Lighthouse is one of the 112 stations in the ACORN-SAT network of Australia’s reference sites for monitoring climate change49. The station provides a record of daily maximum temperature measurements commencing in 1923 and spanning almost a century. It is the southern-most station in the ACORN-SAT network; is 59 km south-east of the Warra Flux Site; and bounds the south-eastern extent of E. obliqua tall forest in Tasmania.Missing temperature measurements represented less than 0.6% of the 35,795 records collected at Cape Bruny Lighthouse between January 1st 1923 and December 31st 2020. The missing measurements were gap-filled using predicted values calculated from linear regression models constructed from measurements made at nearby Bureau of Meteorology stations (listed in order of proximity to Cape Bruny Lighthouse and priority for gap-filling)—Cape Bruny Automatic Weather Station (1997-present), Hastings Chalet (1947–1987) and Hobart-Ellerslie Road (1892-present).Average, standard deviation and 90th percentiles of daily maximum temperature were calculated for each calendar day of the year. Further analysis of heatwaves was restricted to the period between the beginning of August and the end of February. This period bounds the growing season of the forest at the Warra Supersite when there is normally a net carbon gain by the forest (Wardlaw unpublished data). Heatwaves were identified as three or more consecutive days with maximum temperatures that met or exceeded the 90th percentile value sensu Perkins and Alexander9. For each heatwave event that was identified, the following three statistics were calculated: (1) average daily maximum temperature during the heatwave; (2) summed departures (as standard deviations) from average daily maximum temperature during the heatwave; (3) summed departures (as standard deviations) from average daily maximum temperature of the 21 day period centred on the middle day of the heatwave. The November 2017 heatwave, as described by these three statistics, was ranked against all the other heatwave events identified between 1923 and 2020 at Cape Bruny Lighthouse. In addition, the z-score was calculated to measure the magnitude of the departure of the average daily maximum temperatures during the November 2017 heatwave from the long-term average of this 21-day period. Those statistics were also calculated for the same period in 2016.Weather conditions at Warra Supersite during the 2017 warm spellFour attributes of weather were used to describe the November 2017 warm spell—air temperature, vapor pressure deficit (calculated from temperature and relative humidity), incoming shortwave radiation and soil moisture. Air temperature and relative humidity were measured using an HMP155A probe (Vaisala, Finland) and incoming shortwave radiation was measured using a CNR4 radiometer (Kipp and Zonen, The Netherlands). Both instruments were mounted 80-m above ground level at the top of the Warra Flux tower. Data was processed to 30-min averages and logged onto a CR3000 datalogger (Campbell Scientific, Logan, USA).Soil moisture was measured by time-domain reflectometry using two CS616 soil moisture probes (Campbell Scientific, Logan, USA) each installed at a depth of 20 cm. These probes were installed in two pits approximately 40 m west of the tower. Soil moisture data were processed to 30-min averages and logged onto a CR1000 datalogger (Campbell Scientific, Logan USA).Turbulent fluxes at Warra Supersite during the November 2017 warm spellMeasurement of turbulent fluxes (carbon, water and energy) were done by eddy covariance (EC) using a closed-path infra-red gas analyser (Model EC155, Campbell Scientific Inc., Logan, USA) to measure CO2 and H2O concentrations and a 3-D sonic anemometer (Model CSAT3A, Campbell Scientific Inc, Logan, USA) to measure turbulent wind vectors and virtual air temperature. The sonic anemometer and infra-red gas analyser were mounted at 80-m above the ground at the top of the Warra Flux tower. Storage of CO2 and H2O beneath the forest canopy was measured by a profile system (Model AP200, Campbell Scientific Inc, Logan, USA ), with sampling heights of 2, 4, 8, 16, 30, 42, 54, 70 m. Temperature sensors in aspirated shields (Model 110-ST, Apogee Instruments, Logan, USA) were co-located with the CO2/H2O sample inlets of the profile system. High frequency (10 Hz) measurements of turbulent fluxes were processed to 30-min averages in a datalogger (Model CR3000, Campbell Scientific, Logan USA). High frequency (2 Hz) of CO2 and water concentration measurements were processed to 15-s averages sequentially for each profile sample height in a datalogger (Model CR1000, Campbell Scientific, Logan, USA). Thus, each inlet was sampled for a 15 s interval every 2 min. The rate at which sub-canopy storage of CO2 changed was calculated from changes in the quasi-instantaneous (2-min) vertical profile concentrations beneath the tower at the beginning and end of each 30-min flux averaging period using the method of McHugh50.Soil heat flux (SHF) was measured to enable calculation of energy balance that was needed to partition energy fluxes into latent and sensible heat. SHF was measured using five SHF plates (Model HFP01SC, Hukseflux, Delft, The Netherlands) inserted in the soil at depth 8 cm adjacent to the two pits in which the soil moisture probes were installed. Each of the five SHF plates were allocated to one of the two soil pits in a 2–3 split. Changes in soil temperature was measured by an averaging thermocouple (Model TCAV, Campbell Scientific Inc, Logan, USA) inserted into the soil above each SHF plate at depths of 2 and 6 cm. Soil moisture measurements at 20 cm depth were as described previously. Heat flux, soil temperature and soil moisture data were processed to 30-min averages on a datalogger (Model CR1000, Campbell Scientific Inc, Logan, USA).Raw 30-min flux, CO2 storage and climate data were processed by the standard OzFlux QA/QC processing stream51 using PyFluxPro Version 1.0.2 software. Fluxes (carbon and energy) adjusted for storage were computed at the mid-stage (level 3). At the final stage of data processing (level 6), gap-filled net ecosystem exchange (NEE) data were partitioned into gross primary productivity (GPP) and ecosystem respiration (ER) using the u*-filtered night-time CO2 flux records to calculate ER with the SOLO artificial neural network algorithm as described in51. The standard conventions of the global flux network were adopted in partitioning NEE as described in52.The full period between 10 and 30th November 2017 was defined as the November 2017 warm spell. The climate and fluxes measured during this period were compared with measurements of those made during the same calendar days of the preceding year, 2016. The carbon fluxes measured in the 10 weeks before (1 September–9 November) and the month after (1–31 December) the 2017 warm spell period were also compared with the same periods in 2016. This was done to ascertain whether changes in carbon fluxes during the 2017 warm spell we not due to differences in antecedent weather conditions and, whether or not differences in carbon fluxes arising from the warm spell persisted after the warm spell.Data analysisFor each day of the 10–30 November period, daily sums were calculated for measurements of carbon fluxes and incoming shortwave radiation (Fsd), while daily averages were calculated for air temperature, VPD and soil moisture. Quantile plots, done for Ta and VPD, used 30-min data during daytime hours (when Fsd  > 0). The significance of differences in measurements during the 10–30 November period among the two years of each variable were tested by analysis of variance. Tests were first done to confirm the data for each variable were normally distributed and between-group variances were homoscedastic. Log-transformation was used to correct skewness in the VPD data. Soil moisture data were strongly skewed, and transformation was unable to correct. For this variable, the Kruskal–Wallis method was used to test the significance of differences in medians among the two years. These analyses were repeated for the 10 weeks (1st September–9th November) leading up to the warm spell and the 4 weeks (1st–31st December) following the warm spell to examine antecedent conditions and subsequent recovery from the warm spell, respectively.The energy fluxes were examined for evidence of coupling between GPP, transpiration and canopy conductance. Closure of the energy balance was first determined for the two periods to ensure comparability of the energy fluxes for the 2017 warm spell period and the corresponding period in 2016. This was done by firstly resampling the 30-min data and calculating 2-hourly averages of latent heat flux (Fe), sensible heat flux (Fh), net radiation (Fn) and ground heat flux (Fg), then fitting linear regressions of Fe + Fh against Fn–Fg for dates encompassing the warm spell in each of two years. Peak energy storage of the biomass, Fb, in the forest at Warra was estimated as 40 W m−2 using the method described in17. That estimate used the value of LAI of 5.72 based on the average of periodic measurements of LAI at Warra reported in47 and the value of 22.0 for the quadratic mean radius at breast height (1.3 m) calculated from tree measurements in a 1-ha plot adjacent to the Warra Flux tower (detailed in47). The ratio of energy storage in the biomass and ground heat flux at their respective daily maxima was calculated, assuming their respective diurnal peaks coincided. This ratio was then applied to each 2-h average of ground heat flux measured in the warm spell period in 2017 and the corresponding period in 2016. Available energy was recalculated using the formula Fn–(Fg + Fb). Analysis of variance was used to test the significance of differences between the 2017 warm spell and the corresponding period in 2016 of each component energy fluxes (Fn, Fe, Fh and Fg) for each of the twelve, 2-h periods, in the day. Kruskal–Wallis rank test was used if a variable had a non-normal distribution or exhibited heteroscedasticity. The Bowen ratio, which is the ratio between Fh and Fe, was calculated for each 2-h period during daytime hours. The 2-h average data were non-normal and heteroscedastic so testing the significance of differences in daytime Bowen ratio between the warm spell and comparison period was done using 2-sample t-test with unequal variance.Latent heat flux was converted to evapotranspiration by dividing the measured latent heat flux by the latent heat of vaporisation of water. Evapotranspiration was used as a proxy of transpiration on the assumption that evaporation was a minor component of evapotranspiration in the tall E. obliqua forest at Warra based on measurements of soil and litter evaporation in similar forests by23. An estimate of total canopy conductance of sunlit leaves, Gt, was calculated from transpiration (E) and vapor pressure deficit, VPD, using the Skelton et al.53 adaptation of the method developed by Hogg and Hurdle54, whereby:$${text{G}}_{{text{t}}} = (upalpha /1000){text{E}}/{text{VPD}}$$The atmospheric pressure of water vapor, α, is equivalent to ρwGvTk, where ρw is the density of water (c 1000 kg m−3), Gv is the universal gas constant for water vapor (0.462 m3 kPa kg−1 K−1) and Tk is air temperature (in K = Ta + 273.15). Gt (in mmol m−2 s−1) was calculated for each 2-h period during the 2017 warm spell and the same calendar days in 2016 using each period’s corresponding values of E, VPD and Ta. Records were excluded if rain fell during the 2-h period. The significance of differences in daytime canopy conductance between the 2017 warm spell and the 2016 comparison period was tested using a two-sample t-test as the data were strongly skewed.The diurnal patterns of GPP, ER and canopy conductance were compared with incoming shortwave radiation, air temperature and vapour pressure deficit. Each 30-min record of the six variables was recoded to its corresponding 2-h time interval. Analysis of variance was used to test for significance of differences between the warm spell and comparison period for each of the twelve 2-h diurnal periods of the six variables. Kruskal–Wallis rank test was used in the data were non-normal or heteroscedastic. Time series plots of diurnal 2-hourly averages for each of the six variables were plotted and visually compared. More

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    Effect of contrasting phosphorus levels on nitrous oxide and carbon dioxide emissions from temperate grassland soils

    Site descriptionThis experiment was conducted in two long-term P-trial grassland sites (Site A and Site B) situated in proximity (~ 350 m) to each other in the dairy farm at Johnstown Castle, Wexford, Co. Wexford, Ireland (6°49′ W, 52°29′ N). The sites were grazed permanent grasslands before establishment. When the experiment was established in 1995, 16 (10 m × 2 m) plots were formed in each site in a fully randomised block design with four replicates. The two sites established were selected to represent different soil types and drainage classes. Site A is a moderately drained brown earth and site B is an imperfectly drained gley soil31. Each year in February, each plot received one of the four phosphorous (P) fertilization rates (16% P superphosphate): 0 (P0), 15 (P15), 30 (P30), and 45 (P45) kg P ha−1 year−1. All plots were initially sown with Lolium perenne and reseeded in 2016 with the same species. However, plant species such as Poa trivialis, Agropyron repens, Trifolium repens were present to a lesser extent. Above-ground biomass is harvested each month between February and August followed by 40 kg N ha−1 fertilizer applications. In the year (2019) of this experiment and the years before, SulCAN as a solid was applied at the first or second week of each month during February-August and potassium (K) as muriate of potash (KCl) was applied in February at a rate of 125 kg K ha−1. SulCAN contains 26.7% N in the form of nitric and ammoniacal nitrogen and 5% water soluble Sulphur. For this study plots receiving P0, P15 and P45 at the two field sites were set up to carry out this experiment. The two sites were selected as they had slightly different soil properties and thus there was an opportunity to consider a soil × treatment effect in the experiment.Experimental designFertilizer N and substrate C were applied on 8 May and 12 June in the experiment undertaken between May and July 2019, which represents the main growing season in Ireland. Within each plot, an area of 1 m × 1 m was selected. Following N fertilizer application (40 kg N ha−1) to all plots, carbon substrate [mixture of glucose (40%), sodium acetate (30%) and methanol (30%)] was applied once within the selected area using a sprayer watering can. Labile C available in animal excreta usually contains carbohydrates, volatile fatty acids, and alcohols32; as such different carbon substrates were applied to mimic this. Our review of the literature also indicated that C source types could differentially affect denitrifying communities and consequently denitrification rate. Thus, a mixture of three C sources was used to decrease bias of one microbial group over another as a result of single substrate use. Carbon was supplied to alleviate C-limitations of denitrification and nitrification processes as observed by O’Neill et al.29 in soils from this trial and to ensure equal substrate availability across all soil P levels. Equivalent C input rate of 0.63 g C m−2 day−1 was added to represent a daily rate of plant carbon input from Lolium perenne dominated ecosystem33. Soil samples were collected on eight occasions throughout the experimental period. Soil was sampled from across each selected area to a depth of 10 cm, sieved through 4 mm sieve and analysed for soil mineral N and microbial biomass.Soil properties, plant biomass and climate parametersPhysico-chemical soil properties were characterized by taking samples from 10 cm depth from each plot in the two sites before the commencement of the experiment. Soil pH was measured in water (2:1, water volume:soil mass) using Sally pH Auto analyser Dilution System (Gilson 215, Gilson, Dunstable, England). Soil organic matter (SOM) content was determined from mass loss on ignition at 550 °C for 7 h. Total C and total N concentrations were measured using a TrueSpec C/N analyser (TruSpec, LECO Corporation, Michigan, USA). Plant available P, potassium (K), and magnesium (Mg) were estimated using Morgan’s extraction34 and analysed using a Lachat QuickChem 8500 Series 2 Flow injection Analyzer (Lachat, QuickChem, 5600 Loveland, Colorado, USA). Particle size analysis was performed using the Pipette method35, where 2 mm sieved dry soil (20 g) was pre-treated with 6% H2O2, 3% NH4OH, and 5% sodium hexametaphosphate before separating soil aliquots into particle sizes. Water Holding Capacity (WHC) was determined from the mass difference between water-saturated and then overnight dried (105 °C) soil. Bulk density was determined by dividing weight of oven-dried soil by the total soil volume.To determine the mineral N concentrations, ten gram fresh soil was extracted with 50 mL 2 M KCl (5:1 solution to soil ratio). The supernatant was filtered through Whatman No. 1 filter paper and filtrates were stored in a cold room at 4 °C for about a week until analysis. Ammonium (NH4+) and nitrate (NO3−) concentrations in the extracts were analysed by the Aquakem 600 discrete analyser.Above-ground plant biomass from each plot of both sites was harvested twice during the experiment period (June 10 and July 11, 2019) to a height of ~ 5 cm using a Haldrup plot harvester. The total harvested biomass weight from each plot was recorded and a 100 g sub-sample was taken for dry matter (DM) analysis. Each fresh herbage sub-sample was weighed and placed in an oven at 70 °C for 3 days, and dry weight of the biomass was determined after re-weighing.Rainfall records for the experiment period were obtained from a Met Éireann weather observing station located in Teagasc dairy farm in Johnstown Castle, Co. Wexford., situated within a 100 m distance from the experimental sites. Volumetric soil moisture content and temperature was measured to 5 cm depth on individual plots on each gas sampling occasion using a handheld theta probe (WET-2 WET Sensor, Delta-T Devices, Cambridge, England). Water-filled pore space (WFPS) were calculated from the soil moisture values, bulk density of the soils, and soil particle density (2.65 g cm−3).Microbial biomass, glomalin-related soil protein and potential denitrification activitySoils were analysed for microbial biomass nitrogen (MBN), phosphorus (MBP) and carbon (MBC) using the fumigation extraction method as described respectively in (Brooks et al.36,37, and Vance et al.38). Five gram fumigated (24 h) and non-fumigated soil samples were extracted with 100 mL 0.5 M NaHCO3 and analysed for P colorimetrically using an Aquakem 600 discrete analyser (Thermo Electron OY, Vantaa, Finland). In order to avoid the spike readings by the instrument due to the effervescent nature of NaHCO3, one millilitre of 10% HCl was added to 10 mL extracts and diluted to 50 mL using distilled water. Microbial P was calculated by subtracting the P concentration of non-fumigated samples from fumigated samples, and dividing the result by an extraction factor of 0.437.Microbial biomass C and N were determined similarly using chloroform fumigation method with extraction period of 48 h with 0.5 M K2SO438. The extracts of the fumigated and non-fumigated samples were analysed for total C and N using a TOC-L CPH/CPN analyser (Shimadzu, Tokyo, Japan), and the differences, divided by correction factors of 0.45 and 0.54, were used to estimate the microbial biomass C and N, respectively.Glomalin is a glycoprotein produced by AMF and can be used as an indicator of mycorrhizal colonization in the plant root-soil interface39. Total glomalin-related soil protein (GRPS) was extracted by 90 min of autoclaving (121 °C) of 1 g air-dried soil in 8 mL of 50 mM sodium citrate adjusted to pH 8.0 with HCl40. Three additional sequential extractions were performed with the sodium citrate solution by autoclaving for 60 min until no red-brown color was visible in the last supernatant. After autoclaving, the samples were centrifuged at 10,000 revolutions per minute (rpm) for 5 min. The amounts of glomalin in the extracts were quantified using the Bradford dye-binding assay with bovine serum albumin (BSA) as the standard (2 mg mL−1). In a 96-well plate, replicated 200 µL of standard or extracts and 50 µL of dye reagent were added in each well and mixed using a microplate mixer. The Bradford-reactive substance was determined by measuring absorbance at 600 nm using Microplate Reader (Modulus Microplate Multimode Reader, Turner BioSystems, Sunnyvale, California, USA). Sample concentrations were determined using the standard curve. Potential denitrification activity (PDA) was determined using the acetylene inhibition method, modified from Pell et al.41. Briefly, replicated 20 g fresh soils were added into two identical flasks from a sample of soil. The flasks were then sealed with a rubber stopper and flushed and filled with helium after evacuating the headspace air. In one of the replicas, 10% of the headspaces were removed and replaced by acetylene. All flasks were incubated at 15 °C on an orbital shaker at 175 rpm for 30 min followed by the addition of a nutrient solution containing 75 mmol L−1 KNO3, 37.5 mmol L−1 Na-succinate, 25 mmol L−1 glucose, and 75 mM Na-acetate. Gas samples were taken from the headspace every 1 h for 5 h. N2O concentrations were determined using a gas chromatograph (Bruker, Scion 456-GC, Livingston, Scotland), and PDA was calculated from the rate of change of N2O concentrations over time from acetylene amended flasks.N2O and CO2 flux measurementsGas samples (N2O and CO2 fluxes) were measured before and after the application of N fertilizer and C substrates, with a daily sampling for 10 days directly after C + N additions and 3–4 times a week in the third and fourth week and 2–3 times a week in the subsequent weeks. A rectangular (40 × 40 cm) static collar, made of stainless steel (opaque), was anchored 5 cm deep into the soil within the marked area of 1 m × 1 m in each of the selected plots. During gas sampling, a 10 cm tall chamber lid fitted with two septa on top was placed on the collar lined with neoprene rubber band. To ensure hermetic sealing of the headspace during sampling, the ring area of the collar was half-filled with water, and a 10 kg weight was placed on the top of the lid to compress the seal. Gas samples were collected between 09:30 and 11:30 local time using a 10 mL Luer lock syringe fitted with a hypodermic needle via one of the septa at 0, 20, and 40 min after chamber closure. Prior to transferring the final sample into a pre-evacuated 7 mL glass vial, air in the chamber headspace was mixed by flushing the syringe three times. Gas samples were analysed using a gas chromatograph (Bruker, Scion 456-GC, Livingston, Scotland) fitted with an electron capture detector to analyse for N2O concentrations and a thermal conductivity detector to analyse for CO2 concentrations. Daily Fluxes (F) were calculated for each plot using the following equation:$$ F = left( {frac{Delta C}{{Delta t}}} right) times left( {frac{M times P}{{T times R}}} right) times left( frac{V}{A} right) $$where ∆C is the change in gas concentration in the chamber headspace during chamber enclosure period in ppbv, ∆t is chamber closing period in minutes, so ∆C/∆t is the slope of the gas concentration with time. M is the molar mass of N2O-N (28 g mol−1) and CO2-C (12 g mol−1), P and T are the atmospheric pressure (Pa) and temperature (K). Atmospheric pressure values were obtained from the nearby weather station whereas for T, wet sensor values were used. V is the headspace volume of the closed chamber (m3) and A is surface are of the chamber (m3). R is the ideal gas constant (8.314 J K−1 mol−1). Daily flux for each treatment is reported as the average of the replicates.Cumulative N2O and CO2 emissions were calculated over each application period by multiplying the daily N2O and CO2 fluxes by the number of days between two consecutive measurements. A summation of the cumulative flux of each application period is reported as the total cumulative flux.Statistical analysisANOVAs with repeated measures were used to test for the C + N addition effect on N2O and CO2 emissions, MBC, MBN, MBP, NO3−, and NH4+ with P treatment, site, and day of measurement as fixed effects, and individual plots as a random effect. Two-way ANOVA was applied to test for main and interaction effects of P treatment and site on cumulative N2O and CO2 emissions, soil property parameters (Table 1), plant biomass, and GRSP. Prior to analysis, response variables were checked for normality (sphericity for repeated ANOVA) and homogeneity of variance, and log transformed when required. Tukey’s HSD post-hoc tests were conducted to identify pair-wise comparisons of significant effects at P  More

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    The Black Death devastated parts of Europe — but spared others

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    The fourteenth-century pandemic known as the Black Death might not have been as devastating as was previously thought, an analysis of ancient pollen suggests1.

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    ReferencesIzdebski, A. et al. Nature Ecol. Evol. https://doi.org/10.1038/s41559-021-01652-4 (2022).
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