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Overall frameworkCells in our model are arranged along independent radial files, with each cell in one of either the proliferation, enlargement-only, wall thickening, or mature zones, depending on the distance of the cell’s centre from the inside edge of the phloem and the time of year. Only cells that contribute to the formation of xylem tracheids are treated explicitly. A daily timestep is used, on which cells in the proliferation and enlargement-only zones can enlarge in the radial direction if these zones are non-dormant, and on which secondary-wall thickening can occur in the wall thickening zone. Cells in the proliferation zone divide periclinally if they reach a threshold radial length. Cell-size control at division is intermediate between a critical size and a critical increment22. Mother cells divide asymmetrically, with the subsequent relative growth rates of the daughters inversely proportional to their relative sizes. Size at division and asymmetry of division are computed with added statistical noise22, and therefore the model is run for an ensemble of independent radial files with perturbed initial conditions.Equations and parametersCell enlargement and divisionCells in the proliferation and enlargement-only zones, when not dormant, enlarge in the radial direction at a rate dependent on temperature and relative sibling birth size. A Boltzmann-Arrhenius approach is used for the temperature dependence30:$$mu={mu }_{0}{e}^{frac{{E}_{a}}{k}left(frac{1}{{T}_{0}}-frac{1}{T}right)}$$
(1)
where μ is the relative rate of radial cell growth at temperature T (μm μm−1 day−1), μ0 is μ at temperature T0 (=283.15 K), Ea is the effective activation energy for cell enlargement, k is the Boltzmann constant (i.e. 8.617 x 10−5 eV K−1), and T is temperature (K). μ0 was calibrated to an observed mean radial file length at the end of the elongation period dataset23 (Table 1; see “Observations”), and Ea was calibrated to an observed temperature dependence of annual ring width dataset31 (Table 1; Supplementary Fig. 4; see “Observations”).Table 1 Model parameters calibrated to observationsFull size tableRadial length of an individual cell then increases according to:$${{Delta }}{L}_{r}={L}_{r}({e}^{epsilon mu }-1)$$
(2)
where ΔLr is the radial increment of the cell (μm day−1), Lr is the radial length of the cell (μm), and ϵ is the cell’s growth dependence on relative birth size, given by22:$$epsilon=1-{g}_{asym}{alpha }_{b}$$
(3)
where gasym is the strength of the dependence of relative growth rate on asymmetric division (Table 2; unitless), and αb is the degree of asymmetry relative to the cell’s sister22 (scalar):$${alpha }_{b}=frac{{L}_{r}{,}_{b}-{L}_{r}{,}_{b}^{sis}}{{L}_{r}{,}_{b}+{L}_{r}{,}_{b}^{sis}}$$
(4)
where Lr,b is the radial length of the cell at birth (μm) and ({L}_{r}{,}_{b}^{sis}) is the radial length of its sister at birth (μm), which are calculated stochastically22:$${L}_{r}{,}_{b}={L}_{r}{,}_{d}(0.5-{Z}_{a})$$
(5)
$${L}_{r}{,}_{b}^{sis}={L}_{r}{,}_{d}(0.5+{Z}_{a})$$
(6)
where Lr,d is the length of the mother cell when it divides (μm) and Za is Gaussian noise with mean zero and standard deviation σa (Table 2; −0.49 ≤Za≤ 0.49 for numerical stability).Table 2 Parameters used in the model that are taken directly from literatureFull size tableLength at division is derived as22:$${L}_{r}{,}_{d}=f{L}_{r}{,}_{b}+{chi }_{b}(2-f+Z)$$
(7)
where f is the mode of cell-size regulation (Table 2; unitless), χb is the mean cell birth size (Table 3; μm), and Z is Gaussian noise with mean zero and standard deviation σ (Table 2).Table 3 Parameters used in the model that are calculated from observationsFull size tableThe first cell in each radial file is an initial, which produces phloem mother cells outwards and xylem mother cells inwards. It grows and divides as other cells in the proliferation zone, but on division one of the daughters is stochastically assigned to phloem or xylem, the other remaining as the initial. The probability of the daughter being on the phloem side is fphloem (Table 3).Cell-wall growthBoth primary and secondary cell-wall growth are influenced by temperature, carbohydrate concentration, and lumen volume. A Michaelis-Menten equation is used to relate the rate of wall growth to the concentration of carbohydrates in the cytoplasm:$${{Delta }}M=frac{{{Delta }}{M}_{max}theta }{theta+{K}_{m}}$$
(8)
where ΔM is the rate of cell-wall growth (mg cell−1 day−1), ΔMmax is the carbohydrate-saturated rate of wall growth (mg cell−1 day−1), θ is the concentration of carbohydrates in the cell’s cytoplasm (mg ml−1), and Km is the effective Michaelis constant (mg ml−1; Table 1).The maximum rate of cell-wall growth, ΔMmax, is assumed to depend linearly on lumen volume (a proxy for the amount of machinery for wall growth), and on temperature as in Eq. (1):$${{Delta }}{M}_{max}=omega {V}_{l}{e}^{frac{{E}_{aw}}{k}left(frac{1}{{T}_{0}}-frac{1}{T}right)}$$
(9)
where ω is the normalised rate of cell-wall mass growth (i.e. the rate at T0; Table 1; mg ml−1 day−1), Vl is the cell lumen volume (ml cell−1), and Eaw is the effective activation energy for wall building (eV; Table 1). ω and Km were calibrated to an observed distribution of carbohydrates23 (see next section). Eaw was calibrated to an observed temperature dependence of maximum density31 (Table 1; see “Observations”).Lumen volume is given by:$${V}_{l}={V}_{c}-{V}_{w}$$
(10)
where Vc is total cell volume (ml cell−1) and Vw is total wall volume (ml cell−1). Cells are assumed cuboid and therefore Vc is given by:$${V}_{c}={L}_{a}{L}_{t}{L}_{r}/1{0}^{12}$$
(11)
where La is axial length (μm; Table 2) and Lt is tangential length (μm; Table 3). Vw is given by:$${V}_{w}=M/rho$$
(12)
where M is wall mass (mg cell−1) and ρ is wall-mass density (Table 2; mg[DM] ml−1).Cells in the proliferation and enlargement-only zones only have primary cell walls. ΔMmax (Eq. (9)) is therefore given the following limit:$${{Delta }}{M}_{max}=min ({{Delta }}{M}_{max},rho {V}_{{w}_{p}}-M)$$
(13)
where ({V}_{{w}_{p}}) is the required primary wall volume:$${V}_{{w}_{p}}={V}_{c}-({L}_{a}-2{W}_{p})({L}_{t}-2{W}_{p})({L}_{r}-2{W}_{p})/1{0}^{12}$$
(14)
where Wp is primary cell-wall thickness (Table 3; μm).Carbohydrate distributionThe distribution of carbohydrates across each radial file is calculated independently from the balance of diffusion from the phloem and the uptake into primary and secondary cell walls. The carbohydrate concentration in the phloem is prescribed at the mean value observed across the three observational dates in23, as described below in “Observations”, and the resulting concentration in the cytoplasm of the furthest living cell from the phloem is solved numerically. The inside wall of this cell is assumed to be impermeable to carbohydrates and therefore provides the inner boundary to the solution. It is assumed that the rate of diffusion across each file is rapid relative to the rate of cell-wall building, and therefore concentrations are assumed to be in equilibrium on each day. Carbohydrate diffusion between living cells is assumed to be proportional to the concentration gradient:$${q}_{i}=({theta }_{i-1}-{theta }_{i})/eta$$
(15)
where qi is the rate of carbohydrate diffusion from cell i − 1 to cell i (mg day−1) and η is the resistance to flow between cells (calibrated to the observed distribution of carbohydrates23, see next section; Table 1; day ml−1).As it is assumed that carbohydrates cannot diffuse between radial files, at equilibrium the flux into a given cell must equal the rate of wall growth in that cell plus the wall growth in all cells further along the radial file away from the phloem. From this it can be shown that the equilibrium carbohydrate concentration in the furthest living cell from the phloem in each radial file is given by:$${theta }_{n}={theta }_{p}-eta mathop{sum }limits_{i=1}^{n}mathop{sum }limits_{j=i}^{n}{{Delta }}{M}_{j}$$
(16)
where θp is the concentration of carbohydrates in the phloem (Table 3; mg ml−1) and n is the number of living cells in the file (phloem mother cells are ignored for simplicity). The rate of wall growth in each cell depends on the concentration of carbohydrates (Eq. (8)), and therefore θn must be found that results in an equilibrium flow across the radial file. This is done using Brent’s method41 as implemented in the “ZBRENT” function42.Zone widthsThe widths of the proliferation, enlargement-only, and secondary wall thickening zones vary through the year, and are fitted to observations on three dates23 (see Supplementary Fig. 2 and “Observations”). Linear responses to daylength were found, which are therefore used to determine widths for the observational period and later days:$${z}_{k}={a}_{k}+{b}_{k}{{{{{{{rm{dl}}}}}}}};{{mathrm{DOY}}}ge 185$$
(17)
where zk is the distance of the inner edge of the zone from the inner edge of the phloem (μm), k is proliferation (p), secondary wall thickening (t), or enlargement-only (e), ak and bk are constants (Table 3), dl is daylength (s), and DOY is day-of-year. The proliferation zone width on earlier days when non-dormant was fixed at its DOY 185 width (assuming this to be its maximum, and that it would reach its maximum very soon after cambial dormancy is broken in the spring). During dormancy, the proliferation zone width is fixed at its value on DOY 231 (the first day of dormancy23). The enlargement-only zone width prior to DOY 185, the first observational day, is assumed to be a linear extension of the rate of change after DOY 185. The wall-thickening zone width plays little role prior to DOY 185 at the focal site, and so was set to its Eq. (17) value each earlier day. On all days the condition zt≥ze≥zp is imposed, and zone widths cannot exceed their values at 24 h daylength (necessary for sites north of the Arctic circle). Supplementary Figure 2 shows the resulting progression of zone widths through the year, together with the observed values.DormancyProliferation was observed to be finished by DOY 23123, and so the proliferation and enlargement-only zones are assumed to enter dormancy then. Release from dormancy in the spring is calculated using an empirical thermal time/chilling model33. It was necessary to adjust the asymptote and temperature threshold of the published model because the heat sum on the day of release calculated from observations in Sweden (see “Observations”) was much lower than reported for Sitka spruce buds in Britain in the original work:$${{{{{{{{rm{dd}}}}}}}}}_{req}=15+4401.8{e}^{-0.042{{{{{{{rm{cd}}}}}}}}}$$
(18)
where ddreq is the required sum of degree-days (°C) from DOY 32 for dormancy release and cd is the chill-day sum from DOY 306. The degree-day sum is the sum of daily mean temperatures above 0 °C, and the chill-day sum is the number of days with mean temperatures below 0 °C. Dormancy can only be released during the first half of the year.Simulation protocolsEach simulation consisted of an ensemble of 100 independent radial files. Each radial file was initialised by producing a file of 100 cells with radial lengths χb(1+Za), allowing these to divide once, ignoring the second daughter from each division, and then limiting the remaining daughters to only those falling inside the proliferation zone on DOY 1. Values for ϵ (the relative growth of daughter cells) and Lr,d (the radial length at division) were derived for each cell. The main simulations were conducted at the observation site in boreal Sweden (64.35°N, 19.77°E) over 1951–1995 to capture the growth period of the observed trees23. Results are mostly presented for 1995 when the observations were made. Simulations for calibration of the effective activation energies (i.e. Ea and Eaw) were performed at 68.26°N, 19.63°E in Arctic Sweden over 1901–200431. Daily mean temperatures for both sites were derived from the appropriate gridbox in a 6 h 1/2 degree global-gridded dataset43.ObservationsObservations of cellular characteristics and carbohydrate concentrations23 were used to derive a number of model parameters, and to test model output (model calibration and testing were performed using different outputs). According to the published study we used, samples were cut from three 44 yr old Scots pine trees growing in Sweden (64°21’ N; 19°46’ E) at different times during the growing season. 30 μm thick longitudinal tangential sections of the cambial region were made, and the radial distributions of soluble carbohydrates measured using microanalytical techniques23. Cell sizes, wall thicknesses, and positions in their Fig. 123, an image of transverse sections on three sampling dates, were digitised using “WebPlotDigitizer”44. These, together with the numbers of cells in each zone and their sizes given in the text of that paper, were used to estimate zone widths, which were then regressed against daylength to give the parameters for Eq. (17) (Table 3), mean cell size in the proliferation zone on the first sampling date (used to derive χb; Table 3), mean cell tangential length (Table 3), and final ring width (used to calibrate μ0; Table 1). The thickness of the primary cell wall (Table 3) was derived by plotting cell-wall thickness against time and taking the low asymptote.The distributions of carbohydrates along the radial files on the last sampling date for “Tree 1” and “Tree 3” (results for “Tree 2” were not shown for this date) shown in Fig. 2 of the observational paper23 were calculated. The masses for each of sucrose, glucose, and fructose in each 30 μm section were digitised using the same method as for cell properties and then summed and converted to concentrations, with the results shown in Supplementary Figure 5. Mean observed carbohydrate concentrations and cell masses at four points were used to calibrate values for the η, ω, and Km parameters in Table 1. Calibration was performed by minimising the summed relative error across the observations.The calibration target for the effective activation energy for wall deposition (i.e. Eaw) was the observed relationship between maximum density and mean June-July-August temperature over 1901-2004 in northern Sweden31 (Supplementary Fig. 3), and for the effective activation energy for cell enlargement the relationship between ring width and temperature (i.e. Ea) target was the same study (Supplementary Fig. 4). These observations were made on living and subfossil Scots pine sample material from the Lake Tornesträsk area (68.21–68.31°N; 19.45–19.80°E; 350–450 m a.s.l.) using X-ray densitometry for maximum density, and standardised to remove non-climatic information31.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

National negotiators inked a deal to protect nature in the early hours of 19 December in Montreal.Credit: Julian Haber/UN Biodiversity (CC BY 2.0)

Despite earlier signals of possible failure, countries around the world have cemented a deal to safeguard nature — and for the first time, the agreement sets quantitative biodiversity targets akin to the one that nations set seven years ago to limit global warming to 1.5–2 ºC above pre-industrial levels.In the early hours of 19 December, more than 190 countries eked out the deal, known as the Kunming-Montreal Global Biodiversity Framework, during the COP15 international biodiversity summit in Montreal, Canada. A key target it sets is for nations to protect and restore 30% of the world’s land and seas globally by 2030, while also respecting the rights of Indigenous peoples who depend on and steward much of Earth’s remaining biodiversity. Another target is for nations to reduce the extinction rate by 10-fold for all species by 2050.
10 startling images of nature in crisis — and the struggle to save it
Steven Guilbeault, the Canadian environment minister, described COP15 as the most significant biodiversity conference ever held. “We have taken a great step forward in history,” he said at a plenary session where the framework was adopted.At several points during the United Nations summit, which ran from 7–19 December, arguments over details threatened to derail a deal. In the final hours of negotiations, the Democratic Republic of the Congo (DRC) objected to how the framework would be funded. Nonetheless, Huang Runqiu, China’s environment minister and president of COP15, brought the gavel down on the agreement.Negotiators from several African countries, which are home to biodiversity hotspots but say they need funding to preserve those areas, thought that China’s presidency strong-armed the deal. Uganda called it “fraud”. A source who spoke to Nature from the African delegation, and who asked not to be named to maintain diplomacy, said the negotiating process was not equitable towards developing countries and that the deal will not enable significant progress towards stemming biodiversity loss. “It was a coup d’état,” they say. However, a legal expert for the Convention on Biological Diversity — the treaty within which the framework now sits — told COP15 attendees that the adoption of the framework is legitimate.Concerns and disappointmentsScientists and conservation groups have welcomed the deal, emphasizing that there has never been an international agreement to protect nature on this scale. Kina Murphy, an ecologist and chief scientist at the Campaign for Nature, a conservation group, says, “It’s a historic moment for biodiversity.”

Huang Runqiu, China’s environment minister and president of COP15, brought the gavel down on the biodiversity deal, despite objections from representatives of the Democratic Republic of the Congo.Credit: Julian Haber/UN Biodiversity (CC BY 2.0)

But some concerns and disappointments remain. For one, the deal lacks a mandatory requirement for companies to track and disclose their impact on biodiversity. “Voluntary action is not enough,” says Eva Zabey, executive director of Business for Nature, a global coalition of 330 businesses seeking such a requirement so that firms can compete on a level playing field. Nevertheless, it sends a powerful signal to industry that it will need to reduce negative impacts over time, says Andrew Deutz, an environmental law and finance specialist at the Nature Conservancy, a conservation group in Arlington, Virginia.In addition, the deal is weak on tackling the drivers of biodiversity loss, because it does not specifically call out the most ecologically damaging industries, such as commercial fishing and agriculture, or set precise targets for them to put biodiversity conservation at the centre of their operations, researchers say.
Can the world save a million species from extinction?
“I would have liked more ambition and precision in the targets” to address those drivers, says Sandra Diaz, an ecologist at the National University of Córdoba, in Argentina.The deal is not legally binding, but countries will have to demonstrate progress towards achieving the framework’s goals through national and global reviews. Countries failed to meet the previous Aichi Biodiversity Targets, which were set in 2010 and expired in 2020; scientists have suggested that this failure occurred because of a lack of an accountability mechanism.With the reviews included, the framework “is a very good start, with clear quantitative targets” that will allow us to understand progress and the reasons for success and failure, says Stuart Pimm, an ecologist at Duke University in Durham, North Carolina, and head of Saving Nature, a non-profit conservation organization.A long time comingScientists have estimated that one million species are under threat because of habitat loss, mainly through converting land for agriculture. And they have warned that this biodiversity loss could threaten the health of ecosystems on which humans depend for clean water and disease prevention, and called for a new international conservation effort.
Crucial biodiversity summit will go ahead in Canada, not China: what scientists think
The new agreement took 4 years to resolve, in part because of delays caused by the COVID-19 pandemic (the summit was supposed to take place in Kunming, China, in 2020), but also because of arguments over how to finance conservation efforts. Nations finally agreed that by 2030, funding for biodiversity from all public and private sources must rise to at least US$200 billion per year. This includes at least $30 billion per year, contributed from wealthy to low-income nations. These figures fall short of the approximately $700 billion that researchers say is needed to fully safeguard and restore nature, but represents a tripling of existing donations.Low- and middle-income countries (LMICs), including the DRC, had called for a brand-new, independent fund for biodiversity financing. Lee White, environment minister from Gabon, told Nature that biodiversity-rich LMICs have difficulty accessing the Global Environment Facility (GEF), the current fund held by the World Bank in Washington DC, and that it is slow to distribute funds.But France and the European Union strongly objected to a new fund, arguing it would take too long to set up. The framework instead compromises by establishing a trust fund by next year under the GEF. The final agreement also calls on the GEF to reform its process to address the concerns of LMICs.Progress without drastic changeAnother sticking point during negotiations was how to fairly and equitably share the benefits of ‘digital sequence information’ — genetic data collected from plants, animals and other organisms. Communities in biodiversity-rich regions where genetic material is collected have little control over the commercialization of the data, and no way to recoup financial or other benefits from them. But countries came to an agreement to set up a mechanism to share profits, the details of which will be worked out by the next international biodiversity summit, COP16, in 2024.Overall, the deal marks progress toward tackling biodiversity loss, but it is not the drastic change scientists say they were hoping for. “I am not so sure that it has enough teeth to curb the activities that do most of the harm,” Diaz says. More

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Empirical dataWe applied our theory to two datasets (Table 1): the plant survey dataset and the biodiversity-manipulated experiment dataset. The plant survey dataset contains nine sites of long-term grassland experiments across the United States (see also Hallett et al.10, and Zhao et al.23). Five of nine sites are from the Long Term Ecological Research (LTER) network (see Table 1). Plant abundances were measured either as biomass or as percent cover. In percent-cover cases, summed values can exceed 100% due to vertically overlapping canopies. All sites were sampled annually and were spatially replicated. We only used data of the plant survey dataset from unmanipulated control plots. Methods for data collection were constant over time.The biodiversity-manipulated experimental dataset comprises two long-term grassland experiments, BigBio and BioCON, at the Cedar Creek Ecosystem Science Reserve. Both experiments directly manipulated plant species number (1, 2, 4, 8, 16 for BigBio; and 1, 4, 9, 16 for BioCON). BioCON also contains different treatment levels for nitrogen and atmospheric CO2, but here only data from the ambient CO2 and ambient N treatments were used. We excluded plots with only one species. BigBio comprises 125 plots over 17 years, and BioCON comprises 59 plots over 22 years (Table 1).TheoryLet xi(t) denote the biomass of species i = 1, …, S at time t = 1, …, t and let μi = mean (xi (t)), σi = ({{mbox{sd}}})(xi (t)), and ({v}_{i}={sigma }_{i}^{2}) be the mean, standard deviation and variance of species i, computed through time. Let vij = cov (({x}_{i}left(tright),, {x}_{j}left(tright))) be the covariance, through time, of the dynamics of species i and j. Let xtot (left(tright)={sum }_{i}{x}_{i}(t)), ({mu }_{{{mbox{tot}}}}={sum }_{i}{mu }_{i}), ({v}_{{{mbox{tot}}}}={sum }_{i,j}{v}_{{ij}}), and ({{{{{{rm{sigma }}}}}}}_{{{{{{rm{tot}}}}}}}=sqrt{{v}_{{{{{{rm{tot}}}}}}}}). When population time series are uncorrelated, ({v}_{{{{{{rm{tot}}}}}}}={sum }_{i}{v}_{i}).As defined previously10,15, community stability is the inverse coefficient of variation of ({x}_{{{mbox{tot}}}}left(tright)), ({S}_{{{{{{rm{com}}}}}}}={mu }_{{{{{{rm{tot}}}}}}}/{sigma }_{{{{{{rm{tot}}}}}}}). Population stability is the inverse of weighted-average population variability9, ({sum }_{i}frac{{mu }_{i}}{{mu }_{{{{{{rm{tot}}}}}}}}{{CV}}_{i}={sum }_{i}frac{{mu }_{i}}{{mu }_{{{{{{rm{tot}}}}}}}}frac{{sigma }_{i}}{{mu }_{i}}={sum }_{i}frac{{sigma }_{i}}{{mu }_{{{{{{rm{tot}}}}}}}}), i.e, ({S}_{{pop}}={mu }_{{{{{{rm{tot}}}}}}}/{sum }_{i}{sigma }_{i}). The ratio of community stability over population stability is the Loreau-de Mazancourt asynchrony index14, Φ = ({sum }_{i}{sigma }_{i}/{sigma }_{{{{{{rm{tot}}}}}}}), so that$${S}_{{{{{{rm{com}}}}}}}=varPhi {S}_{{{{{{rm{pop}}}}}}}.$$
(1)
Now we suppose a hypothetical community with the same species-level variances and means as the original community but with species covariances equal to zero. Then, (1) becomes Scom_ip = (SAE)Spop, where ({S}_{{{{{{rm{com}}}}}}_{{{{{rm{ip}}}}}}}=frac{{mu }_{{{{{{rm{tot}}}}}}}}{sqrt{{sum }_{i}{v}_{i}}}=frac{{mu }_{{{{{{rm{tot}}}}}}}}{sqrt{{sum }_{i}{sigma }_{i}^{2}}}) is the value of community stability in the case of uncorrelated or independent populations and SAE is the component of Φ due to statistical averaging (here, “ip” stands for “independent populations”). The equation Scom_ip = (SAE)Spop can be interpreted as a definition of SAE. We then have$$SAE=frac{{S}_{{{{{{rm{com}}}}}}_{{{{{rm{ip}}}}}}}}{{S}_{{{{{{rm{pop}}}}}}}}=frac{{sum }_{i}{sigma }_{i}}{sqrt{{sum }_{i}{sigma }_{i}^{2}}}.$$
(2)
The compensatory effect is then the rest of Φ, i.e.,$$CPE=frac{{S}_{{{{{{rm{com}}}}}}}}{{S}_{{{{{{rm{pop}}}}}}}times SAE}=frac{{sum }_{i}{sigma }_{i}}{{sigma }_{{{{{{rm{tot}}}}}}}left({sum }_{i}{sigma }_{i}/sqrt{{sum }_{i}{sigma }_{i}^{2}}right)}=frac{sqrt{{sum }_{i}{sigma }_{i}^{2}}}{{sigma }_{{{{{{rm{tot}}}}}}}}.$$
(3)
Considering the classic variance ratio ({{{{{rm{varphi }}}}}}=frac{{V}_{{{{{{rm{tot}}}}}}}}{{sum }_{i}{V}_{i}}=frac{{sigma }_{{{{{{rm{tot}}}}}}}^{2}}{{sum }_{i}{sigma }_{i}^{2}}), our CPE is (1/sqrt{varphi }). Values CPE  > 1 (respectively, More

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In the autumn (November), L. dispar egg masses were collected at two sites: unpolluted and polluted forest. The first was a mixed oak forest at Kosmaj Mountain, 40 km south-east of Belgrade (coordinates 44°27′56″N 20°33′56″E). These woods are regarded as unpolluted because they are far from direct pollution and are part of the system of protected green areas around Belgrade, where the construction of industrial facilities and traffic infrastructure with potential negative effects on the environment is prohibited by legal regulations. The second site was Lipovica Forest (coordinates 44°38′11″N 20°24′12″E), with mixed Quercus frainetto and Quercus cerris trees, considered a polluted forest since it is located along the border of State Road 22, one of the most frequently used IB-class roads in Serbia.Collected egg masses were kept in a refrigerator at 4 °C until spring (March) when 200 eggs for each experimental group were set for hatching. After hatching in transparent Petri dishes (V = 200 mL), 10 first instar larvae were transferred and reared together at 23 °C with a 12:12 h light: dark photoperiod and relative humidity of 60%, until the third larval instar. Then, five 3rd instar larvae were reared together in the same Petri dish. After molting into the 4th instar, each larva was kept individually until the third day of the 5th instar, when they were sacrificed. Larvae were fed on an artificial diet designed for L. dispar42, and food was replaced every 48 h. Each experimental group contained between 50 and 60 larvae (Fig. 7).Figure 7A schematic figure of the experimental treatments.Full size imageThe optimal temperature for L. dispar larval development is 23 °C, and the control group was reared at this temperature. The highest summer temperature (2007–2010) measured in Serbian Quercus forests at a similar elevation was 28.4 °C, and the lowest 19.6 °C, while the average summer temperature was 26.3 °C43. Thus, we established variable temperature regimens that included brief (24 h) and daily (72 h) exposures to 28 °C. The control group of larvae were reared through the whole experiment on optimal 23 °C. Results of Huey et al.44 indicate that short term (daily) exposure to higher temperatures during development can increase both optimal temperature and maximal growth rate at the optimum, an example of beneficial thermal acclimation. In our previous research we found that induced thermotolerance modifies the activity of detoxifying enzymes in larvae originating from the polluted forest. We exposed L. dispar larvae in several experimental groups to that regime at 4th larval instar, with intention of analyze the effects of induce thermotolerance on observed parameters (ALP, ACP, hsp 70) in 5th instar larvae reared on optimal or elevated temperature28.At sacrifice on the third day of the 5th instar, the caterpillar midguts were dissected out on ice (n = 8–11 larval midguts per group for each enzyme assay). Midgut from single larvae was weighed and homogenized in insect physiological saline, as insect fluids have buffer values similar to vertebrates45. Homogenization was performed in ice-cold 0.15 M NaCl (final tissue concentration was 100 mg/mL in each sample), for 3 intervals of 10 s with a 15 s pause between them, at 5000 rpm, using Ultra Turrax homogenizer (IKA-Werke, Staufen, Germany). The homogenates were centrifuged for 10 min at 10,000 g at 4 ℃, and supernatants were used for enzyme assays and NATIVE gel electrophoresis. This protocol ensured that supernatants would contain cytosol and lysosomes.On the third day of the 5th instar, larval brain tissues were dissected out on ice and weighed. Pooled brain tissue (n = 30 brain tissues per experimental group) was diluted with 0.9% NaCl (1:9/w:V) and homogenized on ice at 5000 rpm during three 10 s intervals, separated by 15 s pauses (MHX/E Xenox homogenizer, Germany). Homogenates were centrifuged at 25,000 g for 10 min at 4 °C in an Eppendorf 5417R centrifuge (Germany). The supernatants were used for Western blotting and indirect non-competitive enzyme-linked immunosorbent assay (ELISA). Protein concentrations samples were determined using BSA as the standard46.A modified method by Nemec and Socha47 was used to determine the activity of ALP. The reaction mixture contained 0.1 M Tris HCl buffer pH 8.6, 5 mM MgCl2, midgut homogenate, and 5 mM p-nitrophenyl phosphate. During 30 min of incubation time at 30 ℃, the hydrolytic release of p-nitrophenol from p-nitrophenyl phosphate (pNPP) occurred under alkaline conditions.The reaction was stopped with 0.5 M NaOH, and the absorbance of p-nitrophenol was measured at 405 nm. Blank and non-catalytic probes were included. One unit of enzyme activity was defined as the amount of enzyme that released 1 mmol of p-nitrophenol per minute under the assay conditions.The same modified method of Nemec and Socha47 was employed to determine ACP activity, but under acidic conditions (0.1 M citrate buffer pH 5.6 was found optimal for L. dispar ACP), with a prolonged incubation time of 60 min. One unit of enzyme activity was defined as the amount of enzyme that released 1 μmol of p-nitrophenol per minute per mg of total protein. Total ACP activity determined in the midgut samples came from lysosomal ACP that ended up in the cytosol and non-lysosomal ACP, typically localized in the cytosol.Lysosomal ACP were detected indirectly48, under the same conditions, in a mixture containing the specific enzyme inhibitor NaF (50 mM). The absorbance determined at 405 nm is proportional to the activity of the non-lysosomal fraction of total ACP. The activity of the lysosomal fraction was obtained by subtracting not inhibited non-lysosomal acid phosphatases from the total phosphatase activity. Specific activities of ACP are given in mU per mg of total protein.A modified method by Allen et al.49 was used to detect ALP isoforms after native PAGE. Using 12% polyacrylamide gel, 10 μg protein aliquots per well were separated at 100 V and 4 ℃. The ALP isoform activity was visualized by soaking the gel in an incubation mixture consisting of 0.13% α-naphthyl phosphate, 100 mM Tris–HCl buffer (pH 8.6), and 0.1% Fast Blue B. The gels were incubated at room temperature until bands appeared.For ACP phosphatase detection, the same method of Allen et al.49 was also modified. After electrophoresis, the gel was washed with deionized water and equilibrated in 100 mM acetate buffer (pH 5.2) at 30 ℃. The nitrocellulose membrane was pre-soaked in 0.13% α-naphthyl phosphate dissolved in the same acetate buffer for 50 min at room temperature. The gel was covered with the membrane and incubated in a moist chamber for 60 min at 30 ℃. The membrane was soaked in 0.3% Fast Blue B stain dissolved in acetate buffer until bands became visible.Gels were scanned with a CanoScan LiDE 120 (Japan). The intensities of enzyme bands in the regions of ALP and ACP activities were analyzed using the ImageJ 1.42q software (U. S. National Institutes of Health, Bethesda, Maryland, USA).An indirect non-competitive ELISA was used to quantify the concentration of hsp70 in L. dispar brain tissue. Samples were diluted with carbonate-bicarbonate buffer (pH 9.6) and coated on a microplate (15 μg of tissue/well) (Multiwell immunoplate, NAXISORP, Thermo Scientific, Denmark) overnight at 4 °C, in the dark. The indirect non-competitive ELISA for L. dispar hsp70 was performed according to general practice: samples were first incubated with monoclonal anti-Hsp70 mouse IgG1 (dilution 1:5000) (clone BRM-22, Sigma Aldrich, USA) for 12 h at 4 °C, and then for 2 h at 25 °C with secondary anti-mouse IgG1 (gamma-chain)-HRP conjugate (dilution 1:5000) antibodies (Sigma Aldrich, USA). Chromogenic substrate 3, 3’, 5, 5’-Tetramethylbenzidine (TMB) was used as a visualizing reagent. Absorption was measured on a microplate reader (LKB 5060-006, Austria) at 450 nm. To enable statistically valid comparisons of experimental groups across multiple microplates, each microplate contained serial dilutions of standard hsp70 (recombinant hsp70, 50 ng/mL), used for the hsp70 standard curve, and homogenized brain tissues pulled by each treatment that were loaded on the microplates in a matched design, ensuring that each data point represented the mean of three replicates from each experimental group.Western blots were used to detect the presence of heat-shock protein 70 isoforms. Brain tissue homogenates were separated by SDS PAGE electrophoresis on 12% gels, according to Laemmli50. Protein transfer from the gel to the nitrocellulose membrane (Amersham Prothron, Premium 0.45 mm NC, GE Healthcare Life Sciences, UK) was left overnight at 40 V and 4 °C. Monoclonal anti-hsp70 mouse IgG1 (1:5000 dilution, clone BRM-22, Sigma Aldrich) and secondary mouse anti-mouse Hsp70 horseradish peroxidase conjugate antiserum (1:10,000 dilution, Sigma-Aldrich) were used for detection of hsp70 expression patterns in L. dispar larval brain tissue. Bands were visualized using chemiluminescence (ECL kit, Amersham).This study identified the hsp70 concentration in brain tissue and specific activities of total ACP and ALP in the larval midgut as the most promising biomarkers, which are sensitive and have consistent responses to thermal stress. These three biomarkers were combined into an IBR analysis according to Beliaeff and Burgeot51. The value of each biomarker (Xi) was standardized by the formula Yi = (Xi − mean)/SD, where Yi is the standardized biomarker response, and mean and SD were obtained from all values of the selected parameters. The next step was describing Zi as Zi = Yi or Zi = − Yi, depending on whether the temperature treatment caused induction or inhibition of the selected biomarkers. After finding the minimum value of Zi for each biomarker (min), the scores (Si) were computed as Si = Zi + |min|. Scores for biomarkers were used as the radius coordinates of the studied biomarker in the star plots. Star plot areas for the three-biomarker assembly, positioned in successive clockwise order—Hsp70, total ACP, and ALP, were obtained from the following formulas: ({A}_{i}=frac{{S}_{i}}{2*mathrm{sin}beta }left({S}_{i}*mathrm{cos}beta + {S}_{i+1}*mathrm{sin}beta right)), (beta = {mathrm{tan}}^{-1}left(frac{{S}_{i+1}*mathrm{sin}alpha }{{S}_{i}-{S}_{i+1}*mathrm{cos}alpha }right)),(alpha =2pi /n) radians (n is the number of biomarkers). The IBR values were calculated as follows:(IBR= sum_{i=1}^{n}{A}_{i}), where Ai is the area represented by two consecutive biomarkers on the star plot. Excel software (Microsoft, USA) was used to calculate IBR values and generate star plots.Statistical analyses were conducted in GraphPad Prism 6 (GraphPad Software, Inc., USA). Mean values ± standard errors of mean values (SEM) were calculated for the activity of enzymes, larval midgut mass, and the hsp70 concentration in brain tissue. D’Agostino-Pearson omnibus and Shapiro–Wilk tests were used to check the normality of data distribution. The effects of thermal treatments and their interaction on the variance of analyzed biomarkers in larvae from the polluted and the unpolluted forest were tested using two-way ANOVA with thermal treatments as fixed factors. For all comparisons, the level of significance was set at p  More