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    Crop–livestock integration enhanced soil aggregate-associated carbon and nitrogen, and phospholipid fatty acid

    Aggregate size distributionAs hypothesized, the improved soil aggregation was observed under ICL, which is attributed to the presence of animals resulting in higher organic matter contents of total C and N fractions that can significantly enhance soil health over time32. Moreover, well-aggregated soils as observed under ICL ( > 4 mm) at site 1 and NE (2–4 mm) at site 2 have a greater potential of retaining their structure and may have higher macropores, which facilitate sustained root growth than soils with low aggregation such as under CNT (corn–soybean without grazing or CC) in this study. It also explains the significance of ICL systems with no-tillage and undisturbed grassland, where the formation of stable macroaggregates may occur as a result of incorporation of plant residues, stimulation of root exudates and increased biological activity. Furthermore, it was noticed that ICL system not only enhanced the macroaggregates but accentuated the presence of microaggregates due to persistent binding agents, which are critical in SOC protection against microbial decomposition. When integrating grazing livestock into crop rotation, soil aggregation is typically improved under moderate and controlled grazing than the high intensity grazing systems33. Compared to the long-term sites ( > 30 years), short-term site 4 did not result in discernible effects of grazing or CC on soil aggregation. However, within this short-term study, grazed pasture mix was able to enhance aggregation of 1–2 and 2–4 mm sized aggregates compared with oats, oats with CC, oats with CC and grazing. To observe the influence of CC and grazing on  > 4 mm or  4 mm) under ICL at site 1 resulted in 1.3–1.5 times significantly higher SOC concentration than NE and CNT. The greater concentration of SOC and N in ICL and NE is attributed to the lack of soil disturbance, crop residue retention, and rhizodeposition, which reduces macroaggregate turnover rate14. At site 3, NE enhanced aggregate-associated C and N concentrations and performed significantly better than both ICL and CNT treatments. The higher C and N accrual in the NE than ICL and CNT, especially at site 3, can be due to massive root systems, long-term establishment and absence of cultivation, which contributes to enhanced soil quality, while reducing nutrient vulnerability to loss by oxidation18,36. For short-term study at site 4, insignificant differences in aggregate-associated SOC suggested that longer study period of at least  > 5 years is required for SOC to respond to grazing and cover crop management. The higher total N under ICL and NE can also be due to the presence of legumes, and brassicas in CC, which are effective at recycling N and may have helped in scavenging N.An overall increase in C and N cycling under ICL and NE systems has been attributed to ingested pasture being converted into urine and manure. Under these systems, livestock catalyze nutrient cycling by breakdown of complex plant molecules, greater soil incorporation and decomposition of plant residues and soil organic matter, which can maintain or even improve soil fertility by production of organic acids such as fulvic and humic acids6,8,19. Moreover, grazing stimulates the carbohydrate exudation from grass roots, which is mostly composed of polysaccharides, a C-O alkyl source37. The enhanced C concentration under ICL and NE can also be associated with higher MWD. Integrated system cool-season pasture and winter CC had significantly higher total C and N than the non-integrated continuous corn in previous study6. The results from another integrated system study7 showed that soybean and oat-Italian ryegrass CC increased total C (1.16 Mg ha−1 yr−1) and N stocks (0.12 Mg ha−1 yr−1) under 7 year study period. It is previously reported that ICL system contains labile organic matter pools10,38, subsequently showing higher C stocks and greater root densities near soil-surface, which promotes aggregate-associated C stabilization18,39,40, higher infiltration rates, thus providing likely benefits to soil function linked to erosion control and soil water relations41.Soil microbial community compositionTotal bacterial biomass, AM fungi, and PLFA were enhanced under NE, which can be result of accumulation of organic residues and higher pasture root mass7,32, pasture being grazed can promote exudation of organic compounds by roots, serving as energy sources for microorganisms. The consistent increase in microbial population under NE can also be result of increased SOC and N, however, the same does not hold true for ICL system, where despite observing greater SOC and N, a significant decrease in the microbial population at site 2 was noticed. The enhanced total PLFA under NE system at site 2 is due to concomitant increase in AM fungi, gram (−), fungal/bacterial ratio, and total bacterial biomass compared to ICL. The fungal to bacterial ratio was reduced under ICL compared to NE at sites 1 and 2, pertaining to relatively low abundance of the fungal fatty acid 18:2ɷ6 in grazed system as compared to unmanaged grassland. This finding corroborates the notion that livestock-grazing systems contain bacterial-based decomposition channels and are mostly dominated by gram (+) bacteria and that the fungal population is comparatively more important in decomposer food-webs of native grasslands. These results coincide with previous studies42,43. Moreover, the increase in fungal to bacterial ratios under NE system in contrast to ICL at sites 1 and 2 can relate to modifications in soil health with C sequestration, as fungal populations incline towards higher C assimilation proficiencies and greater storage of metabolized C than bacterial populations9,44. The grazing intensity also plays a significant role in bacterial and fungal presence. It is previously reported that high grazing intensity had greater bacterial PLFA concentration than the low grazing counterparts in grassland systems45. It is considered that under heavily grazed sites in grasslands, bacteria-based energy channels of decomposition dominate other microbial communities, while fungi can successfully enable decomposition in both slightly grazed and non-grazed systems43. Grazed pasture mix at short-term study site 4 showed 12–21% higher total PLFA than the oats, oats with CC, oats with CC + grazing systems. It is also possible that this increased total PLFA at site 4 under grazed pasture mix contributed to enhancing the 1–2 and 2–4 mm sized aggregates compared to other treatments. It indicated that though physicochemical properties can take longer ( > 8–15 years) in significantly responding to changes in management systems, soil microbial community and structure may show a rapid response (~ 3 years), thus it can be used as an early indicator while assessing the variations in soil health18,46.Overall, NE exemplified the undisturbed grazed mixture with a greater microbial population at sites 1, 2, and 3, when compared to other agricultural systems. Our findings coincide with previous studies where pasture systems performed better than the agricultural soil, in terms of, showing greater microbial biomass and fatty acid signatures related to bacterial and fungal populations, which is mostly attributed to greater surface coverage and absence of tillage practices in pasture systems9,47,48. Lower soil microbial communities under ICL system than native Cerrado pasture have been found previously because of reduced soil porosity and macropore continuity resulting in restricted gas diffusion and water movement18.Although the AM fungi abundance was not significant for sites 3 and 4, and significantly lower for ICL system than NE at sites 1 and 2, it should be taken into consideration that FAME analysis cannot reflect species-level changes for fungi and/or bacteria and the variations in microbial community structure for ICL system can be due to changes in abundance and distribution among microbial groups. For example, in a previous study9, while increased bacterial population was observed for continuous cotton compared to the ICL system, however, pyrosequencing for bacterial diversity assessment demonstrated disparities between both systems, where greater Proteobacteria was seen under ICL system than continuous cotton. Numerous factors such as degree of disturbance, pH level, bulk density, porosity, soil water content, C and N distribution, and residue positioning regulate the amount of bacterial and fungal biomass in agroecosystems18,49. Arbuscular mycorrhizal fungi are responsible for formation of macroaggregates ( > 0.25 mm) by producing a glycoprotein called glomalin, which is present abundantly in natural and agricultural systems. However, increased grazing intensity, use of excess fertilizers and fungicides can directly or indirectly reduce mycorrhizal population by influencing soil organisms accountable for converting soil organic matter into plant nutrients38. Animals may also cause moderate soil compaction affecting the fungal biodiversity and soil pore space6,38.Relationship among measured soil propertiesBased on PCA, it is derived that integrated crop–livestock and natural ecosystem of native grassland can provide substrate for the microbial composition and enhance aggregate-associated C and N fractions. A positive correlation between SOC and microbial communities suggested the inclination of microbes to affect SOC and N turnover and vice-versa through interaction with crop–livestock grazing, vegetation, and soil properties. Fungi exhibited insignificant responses to changes in soil pH and bulk density than bacteria because chitinous cell walls make fungi more resistant and resilient to variations in soil conditions50,51. A reduction in gram (−) bacteria may indicate the presence of stressed soil conditions due to pH and increased bulk density, which has previously been observed in other studies52,53. A significant negative correlation between bulk density and SOC, N, gram (−), actinomycetes, total bacteria, and total PLFA indicated that the microorganisms influenced the soil compaction and related SOC and N. Moreover, positive correlation between SOC and microbial composition suggested that microbes can influence C sequestration in the soil via a shift in community structure. Microbial composition is influenced by soil C, whereas N is a critical biogenic element that improves microbial growth and their ability to utilize soil C54. More

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    Tropical forest restoration under future climate change

    Tropical forest restoration areaTo determine the geographic distribution of land available for tropical forest restoration, we used a widely applied global forest restoration map2. This dataset limits potential restoration area to regions that are biogeophysically suitable for forest, and excludes croplands. To define the tropics, we masked the potential restoration map with the following three ecoregions from the Ecoregions2017 vegetation map34: ‘Tropical and Subtropical Moist Broadleaf Forests’, ‘Tropical and Subtropical Dry Broadleaf Forests’, and ‘Tropical and Subtropical Coniferous Forests’. The resulting restoration mask includes all tropical and subtropical forest ecoregions with some that are outside the tropical latitudes, but excludes wetlands and high mountain areas (Extended Data Fig. 4). The restoration mask was converted from a presence–absence raster at its native ~350 m resolution to a 0.5° geographical grid by aggregating to the fraction of each 0.5° grid cell available for restoration. Any uncertainties in the allocation of restorable area, distinguishing crop and pasture, and forest to non-forest classification from the original forest restoration map were also implicitly included in our restoration extent. While the resulting restoration area is relatively small, its spatial distribution is representative for most of the humid tropics.To prioritize for carbon uptake capacity, we selected all grid cells with restoration area greater than 1 ha and ranked these by carbon storage density (above ground and below ground; g m−2) at 2100 under the default scenario. We then selected the top n grid cells with greatest carbon density until cumulatively 64 Mha of restored area was reached. Similarly, for cost we calculated the restoration cost for each grid cell following ref. 27 and sorted the grid cells by their cost, beginning with the lowest value, until 64 Mha were reached. To consider the combined impact of carbon uptake and restoration costs, we divided our restoration cost layer by the total carbon uptake per grid cell from restoration and ranked the cost per carbon uptake from cheapest to most expensive, selecting the n grid cells with the lowest values until 64 Mha were reached. We then used the selected grid cells to mask carbon uptake under the various climate change and CO2 fertilization scenarios. To factor in climate change in the prioritization process, we used the same restoration cost layer but used the carbon density and total carbon uptake layers with climate change impacts in CO22014 for the year 2100.Vegetation modelWe used the LPJ-LMfire DGVM19, a version of the Lund-Potsdam-Jena DGVM (LPJ)35. LPJ-LMfire is driven by gridded fields of climate, soil texture and topography at 0.5° resolution, and with a time series of atmospheric CO2 concentrations (see Supplementary Information). To simulate land use, LPJ-LMfire separates grid cells into fractional tiles of ‘unmanaged’ land that has never been under land use, ‘managed’ land, and areas ‘recovering’ from land use36. Restoration removes land from the ‘managed’ tile and transfers it to the ‘recovering’ tile; land is never reallocated to the ‘unmanaged’ tile. The tiles are treated differently with respect to wildfire: on the ‘unmanaged’ and ‘recovering’ tiles, lightning-ignited wildfires are not suppressed, while fire is excluded from ‘managed’ tiles. For our analysis of total carbon (above and below ground), we only used the ‘recovering’ tile.Climate dataClimate forcing used to drive LPJ-LMfire comes from the output of 13 GCMs in simulations produced for the CMIP6 Supplementary Table 2 (refs. 37,38). For each GCM, we obtained simulations for the historical period (1850–2014) and four future SSPs (SSP1-26, SSP2-45, SSP3-70 and SSP5-85 covering 2015–2100). We used only GCMs that archived all seven climate variables needed to run LPJ-LMfire: 2 m temperature (tas, K), precipitation (pr, kg m−2 s−1), convective precipitation (prc, kg m−2 s−1), cloud cover (clt, %), minimum and maximum daily temperature (tmin, tmax, K), and 10 m surface wind speed (sfcWind, m s−1) (Supplementary Fig. 2). For each model, we concatenated the historical simulation with a future scenario, calculated anomalies with respect to 1971–1990 and added those to observed 30 year climatologies to create bias-corrected monthly climate time series covering 1850–2100 (see Supplementary Information). Where multiple ensemble members were available from a GCM, we chose the first simulation.Simulation protocolWe drove LPJ-LMfire with the GCM simulations described in the previous section, and the same atmospheric CO2 concentrations and land use boundary conditions as those used in the CMIP6 simulations. All forcings cover the historical period (1850–2014) and the individual future SSPs (2015–2100). Each LPJ-LMfire simulation was initialized for 1,020 years with 1850 atmospheric CO2 and land use, and the 1850s climatology of each CMIP6 GCM. This was followed by simulations with transient climate from 1850 to 2100 for each CMIP6 GCM under each of the four SSPs. For each the 13 CMIP6 GCMs running each of the SSP scenarios, we conducted two CO2 experiments (CO22014 and CO2free) and two fire experiments. In total, we ran 221 vegetation model simulations covering the range of future climate, CO2 and fire scenarios.Atmospheric CO2 in these simulations either followed the CMIP6 historical and SSP trajectory for the entire 1850–2100 run (CO2free), or followed the historical CMIP6 trajectory until 2014, and was then fixed at 2014 concentrations for the remainder of the simulation (CO22014). This allowed us to test the vegetation response to future climate change in the absence of additional CO2 fertilization of photosynthesis. Our simulations ended with the standard SSP projections in 2100, 80 years after restoration begins. We therefore could not assess the fate of restored carbon beyond that point. On the basis of the trends in the multi-model mean carbon uptake rates, we estimated that only under severe climate change will carbon storage be reduced shortly after 2100 in CO22014.In control simulations, land use followed the historical CMIP6 trajectory until 2014, after which it was fixed under 2014 conditions until 2100. Land use after 2014 was fixed at 2014 levels because it is the last year with common land use between all scenarios, which allowed us to identify future climate change impacts on restoration permanence and avoid influences from land abandonment and expansion prescribed in the different SSP scenarios.In the restoration experiments, land use also followed the historical CMIP6 trajectory until 2014, but then diverged: cropland extent remained at 2014 levels until 2100, while pasture (or non-cropland land use) remained constant from 2014 to 2020 and was then linearly reduced by the restoration area from 2020 to 2030. From 2030, land use remained constant at that lower level until 2100. The amount of restoration in a grid cell was limited by the pasture area, that is, once all of the available pasture area had been restored, no additional restoration took place. Because it is highly unlikely to be practical to restore the entire target area of tropical forest at once, we linearly increased the restoration area from 2020 to 2030, which caused an expansion-driven increase in carbon uptake over the 11 year period (Extended Data Fig. 1). This means that two factors controlled carbon uptake over time in our experimental design: first the expansion of the restoration area, accounting for approximately 19.7 Pg C, and second the long-term effect of carbon accumulation (Extended Data Fig. 5).Primary climate change impacts, such as drought and heat stress that reduce carbon uptake, were implicitly included in the climate forcing data, while secondary climate change impacts from wildfire were simulated by LPJ-LMfire on the basis of climate. To quantify the contribution of wildfire on the carbon storage from restoration, we repeated the simulations described above with fires turned off in LPJ-LMfire.Restoration opportunity indexWe created a restoration opportunity index to evaluate the suitability of locations for restoration on the basis of the ability for restoration to result in net carbon uptake over 2020–2100 and to store this carbon without episodes of major loss. For each of the 13 realizations of the four SSPs in the CO22014 experiment, we identified all restoration grid cells (1) that had a net carbon uptake by 2100 relative to 2030, and (2) where temporal reductions in total carbon storage over 2030–2100 were More

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    Photosynthetic performance of symbiont-bearing foraminifera Heterostegina depressa affected by sunscreens

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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