Philippa Kaur

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The overall magnitude of changes in SOC, TN, and C:N in response to chemical nitrogen fertilizers reductionThe results showed that chemical nitrogen fertilizers reduction significantly decreased SOC and TN by 2.76% and 4.19% respectively, while increased C:N by 6.11% across all database (Fig. 1). SOC mainly derives from crop residues and secretions which closely related to crops growths, and crops growths were affected by fertilization, especially nitrogen fertilization20,21. The reduction of chemical nitrogen fertilizer led to poor crop growth, which reduced the amount of crop residues return, and then decreased SOC. Similarly, TN from crops was reduced due to poor crop growth. In addition, the reduction of chemical nitrogen fertilizers directly reduced the input of soil nitrogen. The increase of C:N was the result of the decrease of TN being greater than that of SOC. The responses of C:N to chemical nitrogen fertilizers reduction enhanced the comprehension of the couple relationship between SOC and TN, which was beneficial to the evolution of the C-N coupling models. Moreover, the accuracy of C-N coupling models depends on the precise quantification of the responses of SOC and TN to nitrogen fertilization. And our results accurately quantified the difference responses of SOC and TN to different nitrogen fertilization regimes, thus optimizing the C-N coupling models.Figure 1The weighted response ratio (RR++) for the responses to chemical nitrogen fertilizers of soil organic carbon (SOC, a), total nitrogen (TN, b), and their ratios (C:N, c). Bars denote the overall mean response ratio RR++ and 95% confidence intervals (CI). The star (*) indicates significance when the 95% CI that do not go across the zero line. The vertical lines are drawn at lnRR = 0. The value represents independent sample size.Full size imageResponses of SOC, TN and C:N to chemical nitrogen fertilizers reduction magnitudeWhen grouped by chemical nitrogen fertilizers reduction magnitude, SOC showed a significant increase by 6.9% in medium magnitude, while SOC was significantly decreased by 3.10% and 7.26% in high and total magnitude respectively (Fig. 1a). Liu and Greaver22 also stated the reduction of medium nitrogen fertilizer increased the average microbial biomass from 15 to 20%, thereby increasing the SOC content. Previous studies had reported that there were strong positive correlations between soil organic matter and soil microbial biomass in both the agricultural ecosystem and natural ecosystem23,24. Numerous researchers have demonstrated the significance of nitrogen availability in soil for the plant biomass across most ecosystems25,26. Moreover, nitrogen deficient would inhibit the activity of extracellular enzymes and root activities27. Generally, soil degradation caused by continuous rising chemical nitrogen fertilizers application may inhibit the growth of crops and ultimately reduce the SOC28.TN showed no significant difference in low and medium chemical nitrogen fertilizers reduction magnitude (p  > 0.05), while TN in high magnitude and total chemical nitrogen fertilizers reduction magnitude exhibited a decrease with 3.10% and 9.37% respectively (Fig. 1b). Numerous studies described that the amount of nitrogen fertilizers used in China was higher than the demand of N for crop, which caused serious N leaching and runoff29,30. Chemical nitrogen fertilizers in low and medium magnitude would not decrease the TN of soil by reducing N leaching and runoff. However, the residual nitrogen in soil cannot meet the requirement for the sustainable growth of plant with litter or without exogenous nitrogen supplement, which resulted in the decrease of TN in high and total chemical nitrogen fertilizers magnitude. Consequently, optimal nitrogen fertilizers application rates will take into account crops yield and environment friendliness.Additionally, C:N had a significant increase with ranging from 1.82% to 8.98% under the four chemical nitrogen fertilizers reduction magnitude (Fig. 1c), suggesting the relative increase of SOC compared to TN. Previous studies have revealed that C:N had significantly influence on the soil bacterial community structures31. And there were also considerable studies indicated that chemical nitrogen fertilizers have impact on the soil bacterial communities32,33. We may speculate that the change of C:N would bring about the variations of soil bacteria communities under the chemical nitrogen fertilizers regimes.Responses of SOC, TN, and C:N to chemical nitrogen fertilizers reduction durationNegative response of SOC to short-term chemical nitrogen fertilizers reduction was observed in our study, which was consistent with the study of Gong, et al.34 that chemical nitrogen fertilizers reduction decreased SOC by reducing crop-derived carbon by one year. However, SOC was significantly increased by 1.06% and 4.65% at mid-term and long-term chemical nitrogen fertilizers reduction respectively, which was similar with the findings of Ning, et al.11 that SOC was significantly increased under more than 5 years of chemical nitrogen fertilizers reduction treatment. TN was significantly decreased by 1.96% at short-term chemical nitrogen fertilizers reduction duration, while the results converted at mid-term chemical nitrogen fertilizers reduction duration. The effect of long-term chemical nitrogen fertilizers reduction on TN was not significant (p  > 0.05). The divergent response of TN to different chemical nitrogen fertilizers duration was mainly caused by the various treatments. In terms of C:N, a greater positive response was observed at short-term chemical nitrogen fertilizers duration (9.06%) than mid-term and long-term duration (1.99%). Moreover, with the prolongation of the chemical reduction time of nitrogen, the response ratio tends to zero, suggesting that the effect of chemical fertilizers gradually decrease. This may be ascribed to the buffer capacity of soil to resist the changes from external environment, including nutrients, pollutants, and redox substances35.Responses of SOC, TN, and C:N to different chemical nitrogen fertilizers reduction patternsUnder the pattern of chemical nitrogen fertilizers reduction without organic fertilizers supplement, SOC and TN significantly decreased by 3.83% and 11.46% respectively, however, chemical nitrogen fertilizers reduction with organic fertilizers supplement significantly increased SOC and TN by 4.92% and 8.33% respectively. Moreover, C:N significantly increased under the two chemical nitrogen fertilizers patterns (p  0.05), but there was a negative effect on SOC in high and total magnitude (p  0.05). The no significant decrease at mid-term duration might result from the limited information reported in original studies of this meta-analysis36. TN showed no significant response to chemical nitrogen fertilizers without organic fertilizers supplement in the low and medium magnitude (p  > 0.05). However, TN was significantly decreased by 8.62% and 16.7% respectively in the high and total magnitude. When regarding to chemical nitrogen fertilizers reduction duration, TN was significantly reduced at all of the categories, ranging from 3.13% to 13.4% (Fig. 2c). In the pattern of chemical nitrogen fertilizers reduction with organic fertilizers supplement, chemical nitrogen fertilizers reduction at medium, high, and total magnitudes significantly increased SOC by 13.85%, 13.03%, and 5.46%respectively, however, the response of SOC in the low chemical nitrogen fertilizers magnitude was not significant. Chemical nitrogen fertilizers reduction duration significantly increased SOC by 7.01%, 1.71%, and 22.02% in the short-term, mid-term, and long-term respectively. Comparatively, TN showed a significantly increase in most chemical nitrogen fertilizers categories expect for the long-term chemical nitrogen fertilizers duration, with an increasing from 4.90% to 14.69% (Fig. 2d).Figure 2The weighted response ratio (RR++) for the responses to chemical nitrogen fertilizers of soil organic carbon (SOC, a), total nitrogen (TN, b), and their ratios (C:N, c) under the two patterns (with organic fertilizers ; without organic fertilizers). Bars denote the overall mean response ratio RR++ and 95% confidence intervals (CI). The star (*) indicates significance when the 95% CI that do not go across the zero line. The vertical lines are drawn at lnRR = 0. The values represent independent sample size.Full size imageOrganic fertilizers were mainly derived from animal manure or crops straws, which contained large amount of organic matter and nitrogen elements37,38. The application of organic fertilizers increased the input of SOC and TN directly. Moreover, organic fertilizer could promote the growth of crops by releasing phenols, vitamins, enzymes, auxins and other substances during the decomposition process, thus the SOC derived from crops would be increased37,39. In addition, organic fertilizers provide various nutrients for microbial reproduction, which increase the microbial population and organic carbon and total nitrogen content37. More importantly, the application of organic fertilizers could improve organic carbon sequestration and maintain its stability in aggregates, thereby reducing losses of SOC and TN40.C:N showed an increase under all of the chemical nitrogen fertilizers reduction with organic fertilizer supplement. The positive response of C:N to organic fertilizer supplement may be related to the higher C:N of organic fertilizer than soil. The average values of C:N of the commonly used organic fertilizers including animal manure, crop straws and biochar were 14, 60 and 30 respectively, while the C:N of soil was lower than 10 in average according to extensive literature researches41. Therefore, organic fertilizers would be a favorable alternative of chemical fertilizers for the sustainable development of agriculture.The correlation between the response of SOC, TN, and C:N and environmental variablesThe analysis of linear regression was conducted to analyze the environmental variables including mean annual temperature (MAT), mean annual precipitation (MAP), accumulated temperature above 10 °C (MATA), which may exert influence on SOC, TN and C:N. No significant correlation among the lnRR of SOC, TN, C:N and environmental variables were observed among the whole database (p  > 0.05; Fig. S1). Rule out the interference of organic fertilizers supplement, we analyzed the relationship between lnRR of SOC, TN, C:N and environmental variables as the Figures showed in Figs. 3 and 4 respectively. Under chemical nitrogen fertilizers without organic fertilizers supplement, there was a significant negative correlation between lnRR of SOC and MAT (p  More

ChemicalsErbium (III) oxide nanoparticles (Er2O3-NPs) were purchased from Sigma-Aldrich Chemical Company (Saint Louis, USA) with pink appearance and product number (203,238). Powders of Er2O3-NPs with 99.9 trace metals basis were suspended in deionized distilled water to prepare the required concentrations and ultra-sonicated prior use.Cell lineHuman hepatocellular carcinoma (Hep-G2) cells were obtained from Nawah Scientific Inc., (Mokatam, Cairo Egypt). Cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) media supplemented with streptomycin (100 mg/mL), penicillin (100 units/mL) and heat-inactivated fetal bovine serum (10) in humidified, 5% (v/v) CO2 atmosphere at 37 °C.Characterization of Er2O3-NPsThe purchased powders of Er2O3-NPs were characterized using a charge coupled device diffractometer (XPERT-PRO, PANalytical, Netherlands) to determine its X-ray diffraction (XRD) pattern. Zeta potential and particles’ size distribution of Er2O3-NPs were also detected using Malvern Instrument Zeta sizer Nano Series (Malvern Instruments, Westborough, MA) equipped with a He–Ne laser (λ = 633 nm, max 5mW). Moreover, transmission electron microscopy (TEM) imaging was done to detect the shape and average particles’ size of Er2O3-NPs suspension.Sulforhodamine B (SRB) cytotoxicity assaySulforhodamine B (SRB) assay was conducted to assess the influence of Er2O3-NPs on the proliferation of cancerous Hep-G2 cells12. Aliquots of 100 µl of Hep-G2 cells suspension containing 5 × 103 cells were separately cultured in 96-well plates and incubated for 24 h in complete media. Hep-G2 Cells were then treated with five different concentrations of Er2O3-NPs (0.01, 0.1, 1, 10 and 100 µg/ml) incubated for 24 h or (0.1, 1, 10, 100 and 1000 µg/ml) incubated for 72 h. After 24 or 72 h of Er2O3-NPs exposure, cultured cells were fixed by replacing media with 10% trichloroacetic acid (TCA) and incubated for one hour at 4 °C. Cells were then washed five times with distilled water, SRB solution (0.4% w/v) was added and incubated cells in a dark place at room temperature for 10 min. All plates were washed three times with 1% acetic acid and allowed to air-dry overnight. Then, protein-bound SRB stain was dissolved by adding TRIS (10 mM) and the absorbance was measured at 540 nm using a BMG LABTECH-FLUO star Omega microplate reader (Ortenberg, Germany).Cells treatmentCancerous Hep-G2 cells were cultured at the appropriate conditions and dived into control and treated cells. The control cells were treated with an equal volume of the vehicle (DMSO; final concentration, ≤ 0.1%), while the treated cells were treated with the IC50 of Er2O3-NPs. All cells were left for 72 h after nanoparticles treatment and were harvested by brief trypsinization and centrifugation. Each treatment was conducted in triplicate. Cells were washed twice with ice-cold PBS and used for different molecular assays.Estimation of genomic DNA integrityThe impact of Er2O3-NPs exposure on the integrity of genomic DNA in cancerous Hep-G2 cells was estimated using alkaline Comet assay13,14. Treated and control cells were mixed with low melting agarose and spread on clean slides pre-coated with normal melting agarose. After drying, slides were incubated in cold lysis buffer for 24 h in dark and then electrophoresed in alkaline electrophoresis buffer. Electrophoresed DNA was neutralized in Tris buffer and fixed in cold absolute ethanol. For analysis slides were stained with ethidium bromide, examined using epi-fluorescent microscope at magnification 200× and fifty comet nuclei were analyzed per sample using Comet Score software.Estimation of intracellular ROS generationThe effect of Er2O3-NPs exposure on intracellular ROS production in cancer Hep-G2 cells was studied using 2,7-dichlorofluorescein diacetate dye15. Cultured cells were washed with phosphate buffered saline (PBS) and then 2,7-dichlorofluorescein diacetate dye was added. Mixed cells and dye were left for 30 min in dark and spread on clean slides. The resultant fluorescent dichlorofluorescein complex from interaction of intracellular ROS with dichlorofluorescein diacetate dye was examined under epi-fluorescent at 20× magnification.Measuring the expression levels of apoptotic and anti-apoptotic genesQuantitative real time Polymerase chain reaction (RT-PCR) was conducted to measure the mRNA expression levels of apoptotic (p53 and Bax) and anti-apoptotic (Bcl2) genes in control and treated Hep-G2 cells. Whole cellular RNA was extracted according to the instructions listed by the GeneJET RNA Purification Kit (Thermo scientific, USA) (Thermo scientific, USA) and using Nanodrop device purity and concentration of the extracted RNAs were determined. These RNAs were then reverse transcribed into complementary DNA (cDNA) using the instructions of the Revert Aid First Strand cDNA Synthesis Kit (Thermo scientific, USA). For amplification, RT-PCR was performed using the previously designed primers shown in Table 116,17 by the 7500 Fast system (Applied Biosystem 7500, Clinilab, Egypt). A comparative Ct (DDCt) method was conducted to measure the expression levels of amplified genes and GAPDH gene was used as a housekeeping gene. Results were expressed as mean ± S.D.Table 1 Sequences of the used primers in qRT-PCR.Full size tableAnalysis of cell cycle distributionDistribution of cell cycle was analyzed using flow cytometry. Control and treated cancer Hep-G2 cells with IC50 of Er2O3-NPs for 72 h were harvested, washed with PBS and re-suspended in 1 mL of PBS containing RNAase A (50 µg/mL) and propidium iodide (10 µg/mL) (PI). Cells were incubated for 20 min in dark at 37 C and analyzed for DNA contents using FL2 (λex/em 535/617 nm) signal detector (ACEA Novocyte flow cytometer, ACEA Biosciences Inc., San Diego, CA, USA). For each sample, 12,000 events are acquired and cell cycle distribution is calculated using ACEA NovoExpress software (ACEA Biosciences Inc., San Diego, CA, USA).Estimation of apoptosis inductionApoptotic and necrotic cell populations were determined using Annexin V- Fluorescein isothiocyanate (FITC) apoptosis detection kit (Abcam Inc., Cambridge Science Park Cambridge, UK) coupled with two fluorescent channels flow cytometry. After treatment with Er2O3-NPs for 72 h and doxorubicin as a positive control, Hep-G2 cells were collected by trypsinization and washed twice with ice-cold PBS (pH 7.4). Harvested cells are incubated in dark with Annexin V-FITC/ propidium iodide (PI) solution for 30 min at room temperature, then injected via ACEA Novocyte flowcytometer (ACEA Biosciences Inc., San Diego, CA, USA) and analyzed for FITC and PI fluorescent signals using FL1 and FL2 signal detector, respectively (λex/em 488/530 nm for FITC and λex/em 535/617 nm for PI). For each sample, 12,000 events were acquired and positive FITC and/or PI cells are quantified by quadrant analysis and calculated using ACEA NovoExpress software (ACEA Biosciences Inc., San Diego, CA, USA).Statistical analysisResults of the current study are expressed as mean ± Standard Deviation (S.D) and were analyzed using the Statistical Package for the Social Sciences (SPSS) (version 20) at the significance level p  More

General description of the experimental areaThe experiment was performed for two years at the National Key Irrigation Experimental Station located on the Songnen Plain in Heping town, Qing’an County, Suihua, Heilongjiang, China, with a geographical location of 45° 63′ N and 125° 44′ E at an elevation of 450 m above sea level (Fig. 1). This region consists of plain topography and has a semiarid cold temperate continental monsoon climate, i.e., a typical cold region with a black soil distribution area. The average annual temperature is 2.5 °C, the average annual precipitation is 550 mm, the precipitation is concentrated from June to September of each year, and the average annual surface evaporation is 750 mm. The growth period of crops is 156–171 days, and there is a frost-free period of approximately 128 days year−122. The soil at the study site is albic paddy soil with a mean bulk density of 1.01 g/cm3 and a porosity of 61.8% prevails. The basic physicochemical properties of the soil were as follows: the mass ratio of organic matter was 41.8 g/kg, pH value was 6.45, total nitrogen mass ratio was 15.06 g/kg, total phosphorus mass ratio was 15.23 g/kg, total potassium mass ratio was 20.11 g/kg, mass ratio of alkaline hydrolysis nitrogen was 198.29 mg/kg, available phosphorus mass ratio was 36.22 mg/kg and available potassium mass ratio was 112.06 mg/kg.Figure 1Location of the study area. The map and inset map in this image were drawn by the authors using ArcGIS software. The software version used was ArcGIS software v.10.2, and its URL is http://www.esri.com/.Full size imageHumic acid fertilizerHumic acid fertilizer was produced by Yunnan Kunming Grey Environmental Protection Engineering Co., Ltd., China (Fig. 2). The organic matter was ≥ 61.4%, and the total nutrients (nitrogen, phosphorus and potassium) were ≥ 18.23%, of which N ≥ 3.63%, P2O5 ≥ 2.03%, and K2O ≥ 12.57%. The moisture content was ≤ 2.51%, the pH value was 5.7, the worm egg mortality rate was ≥ 95%, and the amount of faecal colibacillosis was ≤ 3%. The fertilizer contained numerous elements necessary for plants. The contents of harmful elements, including arsenic, mercury, lead, cadmium and chromium, were ≤ 2.8%, 0.01%, 7.6%, 0.1% and 4.7%, respectively; these were lower than the test standard.Figure 2Humic acid fertilizer in powder form.Full size imageExperimental design and observation methodsIrrigationIn this experiment, three irrigation practices, namely, control irrigation (C), wet irrigation (W) and flood irrigation (F), were designed (Table 1).Table 1 Different irrigation methods.Full size tableControl irrigation (C) of rice had no water layer in the rest of the growing stages, except for the shallow water layer at the regreen stage of rice, which was maintained at 0–30 mm, and the natural dryness in the yellow stage. The irrigation time and irrigation quota were determined by the root soil moisture content as the control index. The upper limit of irrigation was the saturated moisture content of the soil, the lower limit of soil moisture at each growth stage was the percentage of saturated moisture content, and the TPIME-PICO64/32 soil moisture analyser was used to determine the soil moisture content at 7:00 a.m. and 18:00 p.m., respectively. When the soil moisture content was close to or lower than the lower limit of irrigation, artificial irrigation occurred until the upper irrigation limit was reached. The soil moisture content was maintained between the upper irrigation limit and the lower irrigation limit of the corresponding fertility stage. Under the wet irrigation (W) and flood irrigation (F) conditions, it was necessary to read the depth of the water layer through bricks and a vertical ruler embedded in the field before and after 8:00 am every day to determine if irrigation was needed. If irrigation was needed, then the water metre was recorded before and after each irrigation. The difference between before and after was the amount of irrigation23.FertilizationIn our research, five fertilization methods were applied, as shown in Table 2. In this experiment, the rice cultivar “Suijing No. 18” was selected. Urea and humic acid fertilizer were applied according to the proportion of base fertilizer:tillering fertilizer:heading fertilizer (5:3:2). The amounts of phosphorus and potassium fertilizers were the same for all treatments, and P2O5 (45 kg ha−1) and K2O (80 kg ha−1) were used. Phosphorus was applied once as a basal application. Potassium fertilizer was applied twice: once as a basal fertilizer and at 8.5 leaf age (panicle primordium differentiation stage) at a 1:1 ratio22.Table 2 The fertilizer methods.Full size tableThis study was performed with a randomized complete block design with three replications. Three irrigation practices and five fertilizer methods were applied, for a total of 15 treatments as follows: CT1, CT2, CT3, CT4, CT5; WT1, WT2, WT3, WT4, WT5; FT1, FT2, FT3, FT4, and FT5 (C, W, and F represent control irrigation, wet irrigation, and flood irrigation; T represents fertilizer treatment).Measurements of the samplesA soil temperature sensor (HZTJ1-1) was buried in each experimental plot to monitor the temperature of each soil layer (5 cm, 10 cm, 15 cm, 20 cm and 25 cm depth). The transmission of photosynthetically active radiation was measured from 11:00 to 13:00 by using a SunScan Canopy Analysis System (Delta T Devices, Ltd., Cambridge, UK), and data during the crop-growing season were recorded every day24.Plant measurements were taken during the periods of tillering to ripening on days with no wind and good light. The fluorescence parameters were measured by a portable fluorescence measurement system (Li-6400XT, America). The detection light intensity was 1500 μmol m−2 s−1, and the saturated pulsed light intensity was 7200 μmolm−2 s−1. The functional leaves were dark adapted for 30 min, and then the maximum photosynthetic efficiency of PSII (Fv/Fm) was measured. Photochemical quenching (QP) and nonphotochemical quenching (NPQ) were measured with natural light. Simultaneously, the leaf chlorophyll relative content (SPAD) was monitored using SPAD 502 (Konica Minolta, Inc., Tokyo, Japan). For plant agronomic characteristics, the distance from the stem base to the stem tip was measured with a straight ruler to quantify plant height24.Statistical analysisExperimental data obtained for different parameters were analysed statistically using the analysis of variance technique as applicable to randomized complete block design. Duncan’s multiple range test was employed to assess differences between the treatment means at a 5% probability level. All statistical analyses were performed using SPSS 22.0 for Windows24.
Ethics approvalExperimental research and field studies on plants, including the collection of plant material, comply with relevant institutional, national, and international guidelines and legislation. We had appropriate permissions/licences to perform the experiment in the study area. More

MaterialsWe collected from the literature and available databases a dataset of radiocarbon dates from Europe (West of 60°E and North of 37°N) either obtained from remains of the four analyzed species or from archaeological layers where they have been observed. However, we only considered observations dated between 7500 and 47,000 cal BP: their scarcity before this period may bias the GAMs, and after it, domesticated cattle, pigs and (later) horses arrived in Europe, making it difficult to differentiate them from their wild forms.We excluded any record fitting one or more of the following conditions: unreliable; not in accord with the expected chronology of their archaeological layer; without a reported standard error; available only as terminus ante/post quem.All dates were calibrated with OxCal5 version 4.4 using the IntCal20 curve51, and we further excluded any record for which calibration resulted in an error, resulting in the number of points presented in Table 1 as “Original dataset” (available at the link https://doi.org/10.6084/m9.figshare.20510364).Table 1 Number of observations for each species.Full size tableSDMs based on GAMs need presence/background data, not frequencies; moreover, multiple observations (i.e., presence in different archaeological layers) from the same site and time slice are likely to introduce stronger sample biases linked to chrono-geographically differential sampling efforts. For this reason, we collapsed our observations by keeping only one point per grid cell per time slice for each species, leaving the number of observations reported in Table 1 as “Collapsed datasets”, used for all the analyses presented in this work.To perform all analyses, we used the R package pastclim v. 1.042 to couple each observation from the collapsed datasets to paleoclimatic reconstructions published in8 by setting dataset = “Beyer2020”. These are based on the Hadley CM3 model, include 14 different bioclimatic variables at a spatial resolution of 0.5°, and are available for the whole world every 1000 years until 22 kya and every 2000 years before that date (referred to in the manuscript as “time slices”). Specifically, each observation was associated with the relevant bioclimatic reconstruction based on its average age and spatial coordinates.As already mentioned, the four species analyzed show different preferences regarding temperature, habitat, and altitude. Therefore, for the Species Distribution Modelling, we choose five environmental variables that should be able to capture such differences: two measures of temperature (BIO5, maximum temperature of the warmest month, and BIO6, minimum temperature of the coldest month); two variables to help capture habitat differentiation (BIO12, total annual precipitation, and Net Primary Productivity, NPP), and one measure of topography (rugosity42).High collinearity can be problematic in SDMs; we confirmed that all our variables had a correlation below 0.7, a threshold commonly adopted for this kind of analysis52,53.Whilst the GAMs predicted all time points; we visualized our results by creating an average estimate for the following periods: pre-LGM (from the beginning of the time range analyzed, i.e., 47 kya to 27 kya), LGM (from 27 to 18 kya), Late Glacial (from 18 to 11.7 kya), Holocene (from 11.7 kya to the end of the time range analyzed, i.e., 7.5 kya).MethodsWe generated 25 sets of background points for each species to adequately represent the existing climatic space in our SDMs. Each set was generated by sampling, for each observation, 50 random locations matched by time. This resulted in n = 25 datasets (“repetitions”) of background points and presences (observations) for each species, which we used to repeat our analyses to account for the stochastic sampling of the background. For each dataset, we used GAMs to fit two possible models: a “constant niche” model, which included only the environmental variables as covariates, and a “changing niche” model, that also included interactions of each environmental variable with time (fitted as tensor products).In GAMs, the effect of a given continuous predictor on the response variable (in our case, the logit transformed probability of a presence) is represented by a smooth function; this smooth function can be linear or non-linear and can become highly complex in shape depending on the number of knots selected by the GAM fitting algorithm. The interaction between two covariates is modelled by tensor products54; this approach is equivalent to an interaction term in a linear model but with the added complexity of the smooth function. In our models, we confine tensor products to the interaction between an environmental variable and time; a simple way to think about such a tensor product is that it allows the smooth representation of the relationship between the variable and the probability of a presence to change progressively over time.GAMs were fitted using the mgcv package in R54 using thin plate regression splines (TPNR; bs = “tp”, default in mgcv) for environmental variables and their tensor products with time in the “niche changing” models. The GAM algorithm automatically selects the complexity of the smooth most appropriate to the data that are being fitted; as GAM can have issues with overfitting, we added an additional penalty against overly complex smooths (gamma = 1.4) and used Restricted Maximum Likelihood (REML = TRUE), as recommended by54. It is possible that even with these settings, the complexity of the smooth is not sufficient; we used mgcv::gam.check() to check this, and increased the basis dimension of the smooth, k, to make sure that k-1 was larger than the estimated degrees of freedom (edf). We found the best maximum thresholds for k to be 16 for bio06 and 10 for all other variables.We checked for non-linear correlation among variables using the mgcv::collinearity function and checked the values of estimated concurvity. All estimates were below the threshold of 0.8 in all models, runs and variables except for a few instances for time (Supplementary Figs. 5–8). We consider this not to be worrying: this is most likely a result of sample bias, and GAM is known to be robust to correlation/concurvity55,56.We verified the model assumptions by inspecting the residuals using the R package DHARMa57. Standard tests for deviations from the expected distribution and dispersion were non-significant for all repetitions for all species, as were the tests for outliers. Furthermore, we tested for spatial autocorrelation among residuals by computing Moran’s I; all tests were either non-significant or, when significance was detected, the estimate of Moran’s I was very close to zero, revealing a trivial deviation from the assumptions which should not impact the results (Supplementary Tables 1–4).We performed model choice (Supplementary Tables 5–8) by comparing the constant- and changing-niche models for each combination of species and repetition using the Akaike Information Criterion (AIC). AIC strongly supported the changing-niche model in all species and repetitions, an inference supported by the higher Nagelkerke R2 and expected deviance for those models than for the constant-niche ones (Supplementary Tables 5–8).The model fit for each of the changing niche GAMs was evaluated with the Boyce Continuous Index25,26, designed to be used with presence-only data58,59. We set a threshold of Pearson’s correlation coefficient  >  0.8 to define acceptable models25 (Supplementary Table 9).The relative importance of each environmental variable was quantified for all the models above the BCI threshold of 0.8 in two different ways. Firstly, we computed the total deviance explained by each variable by simply fitting a GAM with only that variable. We then estimated the unique deviance explained by each variable by comparing the full model with one for which that variable was excluded (i.e., we computed the explained deviance lost by dropping that predictor). The difference between the two values represents the deviance explained by a variable which can also be accounted for by other variables (i.e., the deviance in common with other variables).To achieve more robust predictions60, we averaged in two different ensembles the repetitions for the changing niche GAMs with BCI  > 0.8: by mean and median. This step is intended to reduce the weight of models that are highly sensitive to the random sampling of the background60. Then, for each species, we selected the ensemble (either based on mean or median) with the higher BCI as the most supported and used it to perform all further analyses.The effect of different variables through time was visualized by plotting the interactions of the GAMs. For each model with a BCI  > 0.8, we used the R package gratia27 to generate a surface with time as the x-axis, the environmental variable as the y-axis, and the effect size as the z-axis (visualized as colour shades). We then plotted the mean surface for each species, which captures the signal consistent across all randomized background sets.To visualize the prediction for each species, we then transformed the predicted probabilities of occurrence from the ensemble into binary presence/absences by using the threshold needed to get a minimum predicted area encompassing 99% of our presences (function ecospat.mpa() from the ecospat R package61). The binary predictions were then visualized using the mean over the time steps within each major climatic period.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Nearly two years into the United Nations Decade of Ocean Science, research, including some featured in this month’s issue, shows that there is still a wealth of scientific secrets to uncover in the ocean depths.
In many ways, considering the ocean as a single unit is overly broad. The global ocean covers 71% of the planet’s surface, reaches down to depths of over 10 kilometres, includes about 1.35 billion cubic kilometres of water and houses an approximated 2.2 million eukaryotic species. There are distinct regions, with distinct physical properties, and, in turn, there are distinct species. Yet, the world’s oceans do have a level of physical and thematic connectivity.
Credit: Daria Zaseda / DigitalVision Vectors / GettyPhysically, a large part of the connection is related to the presence of large rotating ocean currents that transfer heat across latitudes and contribute to ocean mixing (thermohaline circulation). Some of these currents are warming at alarming rates — up to three times faster than the rest of the ocean, leading to questions about the underlying mechanisms of the warming and expectations for change.Focusing on western boundary currents (WBCs) in the Southern Hemisphere, in an Article in this issue of Nature Climate Change, Li and colleagues answer a long-debated question on the mechanisms of change, showing that temperature-gradient-related instabilities, rather than flow-speed-related instabilities are behind the shifts. In another Article, focusing on the global future changes of eddies (including eddy-rich WBCs), Beech and colleagues report the development of a flexible method that maximizes local model resolution while minimizing computational costs, to reveal the long-term geographical specificities and nonlinear temperature increases expected to 2100 (see also the News and Views article by Yang on these papers).A recent paper1 has demonstrated the important role of large ocean currents in defining plankton biogeography and dynamics, and WBC warming has previously been linked to impacts such as fishery collapses. The tight link between physical processes and biological responses is an underscoring theme of climate change ecology, but is perhaps more apparent in the open ocean, where physical processes can be easily (if imperfectly) linked to primary productivity using remotely sensed phytoplankton pigment absorption, and where life is generally less impacted by geographical, political or disturbance-based boundaries compared with land and freshwater systems. These aspects may facilitate modelling of current and future communities, while also allowing broader assumptions to be made about biological movement and connectivity.Despite these benefits, understanding ocean change comes with its own difficulties. Biological sampling, while easy enough in the surface waters, becomes increasingly difficult at depth. Although future habitats for various organisms have been projected on the basis of their thermal limits in the ocean, these predictions often still rely on temperatures at the surface of the sea. Addressing this, Santana-Falcón and colleagues report in an Article the global mapping of ocean temperature changes to depths of 1,000 metres, and reveal the complex depth-dependent changes in thermal upper and lower bounds that marine organisms will soon be subjected to. In another Article, Ariza and colleagues neatly address the issue of directly monitoring deep-ocean change by compiling a large database of sound-based observations, and subsequently classifying the ocean’s ‘echobiomes’, defined as sound-scattering communities with comparable structural and functional properties (see also the accompanying News and Views article by Hazen). Sound-based methods are also increasingly being used on land2, and represent an exciting tool for monitoring change, particularly in hard-to-reach places such as deep forests, high mountaintops or underground. While the sound reflection method used in the study by Ariza and colleagues has limits in its ability to identify organisms at the individual or species levels, it does provide a community-level focus on change, which remains much needed in the field of global change ecology.At the other end of the spatial spectrum, research by Lee and colleagues reported in an Article also in this issue dives deep into the DNA of a keystone ocean organism (a copepod), to understand the mechanisms that may allow longer-term adaptation to warming and pH stress. The work reveals remarkable adaptation over just a few short generations, which is linked to epigenetic changes. As climate change impacts continue to escalate, the ability of organisms to invoke both shorter- and longer-term adaptations has become an increasingly relevant area of research. Epigenetics has previously been reported as a quick-response method to cope with environmental stress, and may be particularly relevant in defining the adaptation of short-lived animals such as insects and the resilience of the communities they uphold.The five research pieces linked to the oceans in this issue reveal just some of the diversity of topics, methods and scales relevant to understanding global change. Also increasingly relevant are works on ocean conservation3 and on the social and economic impacts of ocean change4,5. Like climate change science, the topic of ocean change is less of a field, and more of a cross-disciplinary theme. More

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Agriculture soils are a source of nitrous oxide and account for 60% of total emissions. It is well established that nitrogen addition via fertilizers drives nitrous oxide emissions during crop growing season. However, little is known about the role of melting snow and thawing surface soil layers during the spring. Limited knowledge of this phenomenon reduces our ability to develop accurate nitrous oxide emissions inventories required under the UN Framework Convention on Climate Change (UNFCCC). More