Ecology

Sampling and incubationFour rock samples were collected from the 3.7 km-deep Auka vent field in the Southern Pescadero Basin (23.956094N, 108.86192W)20,23. Sample NA091.008 was collected in 2017 on cruise NA091 with the Eexploration vessle Nautilus and incubated as described previously34. Samples 12,019 (S0200-R1), 11,719 (S0193-R2) and 11,868 (S0197-PC1), the latter representing a lithified nodule recovered from a sediment push core, were collected with Remotely operated vehicle SuBastian and Research vessel Falkor on cruise FK181031 in November 2018. These samples were processed shipboard and stored under anoxic conditions at 4 °C for subsequent incubation in the laboratory. In the laboratory, rock samples 12,019 and 11,719 were broken into smaller pieces under sterile conditions, immersed in N2-sparged sterilized artificial sea water and incubated under anoxic conditions with methane, as described previously for NA091.008 (ref. 34). Additional sampling information can be found in Supplementary Table 1. Mineralogical analysis by X-ray Powder Diffraction (XRD) identified barite in several of these samples, collected from two locations in the Auka vent field, including on the western side of the Matterhorn vent (11,719, NA091.008), and one oil-saturated sample (12,019) recovered from the sedimented flanks from the southern side of Z vent. Our analysis also includes metagenomic data from two sediment cores from the Auka vent field (DR750-PC67 and DR750-PC80) collected in April 2015 with the ROV Doc Ricketts and R/V Western Flyer (MBARI2015), previously published (ref. 23).Fluorescence in situ hybridizationSamples were fixed shipboard using freshly prepared paraformaldehyde (2 vol% in 3× Phosphate Buffer Solution (PBS), EMS15713) at 4 °C overnight, rinsed twice using 3× PBS, and stored in ethanol (50% in 1× PBS) at −20 °C until processing. Small pieces ( More

Identification of Elovl genes from E. tetradactylum and E. rhadinumTotally, we successfully identified 9 Elovl genes, including elovl1a, elovl1b, elovl4a, elovl4b, elovl5, elovl6, elovl6l, elovl7a, and elovl8b, both from E. tetradactylum and E. rhadinum genome (Table 2). In E. rhadinum, the shortest and the longest putative CDS length among all Elovl genes was 810 bp and 2019 bp, respectively. Their encoded protein size ranged from 269 amino acids to 672 amino acids. The theoretical molecular weight of Elovl proteins varied from 31061.48 to 75051.42 Da, with the theoretical isoelectric points (pI) ranging from 7.86 to 9.59. Most of the Elovl proteins were characterized as stable and hydrophilic proteins. Signal peptide prediction analysis showed that the elovl1b, elovl5, and elovl6 contained signal peptide sequences. In addition to elovl8b, all Elovl proteins contained transmembrane domains ranging from 5 to 7. Almost all Elovl proteins were predicted to be endoplasmic reticulum-located except elovl8b, predominantly localized in the nucleus.Table 2 Basic information for the Elovl gene family members.Full size tableIn E. tetradactylum, the putative CDS length of Elovl genes ranged from 810 to 1824 bp, and their encoded protein size ranged from 269 amino acids to 409 amino acids. The molecular weight of Elovl proteins varied from 31049.42 to 68750.14 Da, with the pI ranging from 8.72 to 9.64. Like Elovl proteins in E. rhadinum, most elovl proteins were predicted to be stable and hydrophilic. Signal peptide prediction analysis revealed that elovl1a, elovl5, and elovl6 had signal peptide sequence, which was different from E. rhadinum that elovl1b contained signal peptide sequence, but elovl1a did not. In addition, seven members showed the same number of transmembrane structures with E. rhadinum, while the elovl8b contained three and elovl4b contained seven transmembrane structures in contrast to E. rhadinum. The elovl8b was predicted to be localized in nuclear, while other members were localized in the endoplasmic reticulum, similar to E. rhadinum.Evolution of divergence and conservation of Elovl genesDivergence and conservation accompany the process of species evolution. To elucidate the phylogenetic relationship of Elovl genes among different species, a maximum like-hood tree was constructed on the basis of 18 Elovl genes in E. tetradactylum and E. rhadinum and 106 publicly available Elovl protein sequences. As shown in Fig. 1, these Elovl genes can be divided into eight subfamilies, including elovl1a/1b, elovl2, elovl3, elovl4a, elovl5, elovl6/6 l, elovl7a/7b, elovl8a/8b. However, 6 subfamilies were presented in the Eleutheronema genus, and there was only one subtype for elovl7 (elovl7a) and elovl8 (elovl8b) in E. tetradactylum and E. rhadinum. The elovl3 was mainly identified in mammalians such as Homo sapiens and Mus musculus, while a recent study reported a full repertoire of Elovl genes in the Colossoma macropomum genome, including elovl330. The loss of elovl2 occurred in the vast majority of marine fish lineages, which was only presented in a few fish species, such as C. carpio, D. rerio, S. salar, and S. grahami.Figure 1Phylogenetic tree for 18 Elovl proteins from E. tetradactylum and E. rhadinum, and 106 publicly available Elovl proteins from other species. All these proteins were aligned using ClustalW and then subjected to MEGAX for phylogenetic tree construction using the maximum like-hood method with 1000 replicates.Full size imageWe further performed the gene structure analysis to visualize the exon–intron structure of each gene, and the results revealed that the elovl8b had the largest intron number, while the elovl6/6 l subfamily genes contained three introns (Fig. 2a). Except for elovl8, Elovl genes belonging to the same subfamily shared a similar gene structure. Additionally, we identified ten motifs in Elovl genes, and the conversed motif types, numbers, and distributions in Elovl proteins were much more similar except for the elovl8b (Fig. 2b, TableS1). Two conserved motifs were found in the Elovl gene family except for elovl8b in E. rhadinum, which were related to the ELO domain via SMART evaluation analysis (Fig. 2c and d). Gene structural variation is important for gene evolution. In E. tetradactylum and E. rhadinum, Elovl genes showed similar gene structure, and the proteins shared similar motif compositions, indicating that the Elovl genes were highly conserved in the Eleutheronema genus.Figure 2Gene structure and conserved motifs diagram of Elovl genes. (a) Gene structure of Elovl genes. Exons were represented by pink boxes and introns by black lines; (b) Conserved motifs of Elovl proteins; (c and d) Logo representations of the ELO domains, motifs 1 and 2, respectively.Full size imageIn the process of evolution via natural selection, adaptation to certain environmental conditions likely drove the changes in endogenous capacity for LC-PUFA biosynthesis between marine and freshwater fishes31. The Elovl gene family has been functionally studied and characterized in a variety of fish species, and the member of the Elovl gene family of each species varied greatly. In the present study, for a comprehensive analysis of Elovl genes in the Eleutheronema genus, the Elovl gene ortholog clusters of mammals and various teleosts with different ecological niches and habitats were collected. The results showed that only seven Elovl genes (one gene for each subtype) were observed in mammals; however, more members were variably presented in teleosts, which might be related to the teleost-specific duplication. A previous study revealed that Sinocyclocheilus graham and C. carpio possessed the highest number of Elovl genes, containing 21 members of subtypes, resulting from an extra independent 4th whole-genome duplication event32, 33. Interestingly, only 9 Elovl genes were observed in Eleutheronema genus, the same as T. rubripes, possibly due to gene loss and the asymmetric acceleration of the evolutionary rate in one of the paralogs following the whole-genome duplication in some teleost fishes34. Additionally, the elovl2 and elovl3 were absent, but a novel subtype, elovl8, was present in most marine fishes. The elovl8, the most recently identified and novel active member of the Elovl protein family member, has been proposed to be a fish-specific elongase with two gene paralogs (elovl8a and elovl8b) described in teleost35. In Eleutheronema, we also found that the elovl8b was presented in E. tetradactylum and E. rhadinum, indicating the important roles in the LC-PUFAs biosynthesis of Eleutheronema fish. Similar results were also observed in rabbitfish and zebrafish20. The Elovl gene family member number in Eleutheronema genus is the same as T. rubripes, but less than I. punctatus (10), Gadus morhua (10), D. rerio (14), S. salar (18), and C. carpio (21), which might be due to the differential expansion events during the evolutions of fish species.Predicting the protein structure is a fundamental prerequisite for understanding the function and possible interactions of a protein. In the present study, the secondary structures as well as three-dimensional structures of Elovl proteins in both E. tetradactylum and E. rhadinum were predicted using the SOPMA and Phyre2 programs, respectively. The protein structures of all the candidate Elovl proteins were modeled at  > 90% confidence. The secondary structures of these proteins in E. tetradactylum revealed 40.86–50.30% alpha helixes, 28.10–28.10% random coil, 13.75–20.67% extended strand and 2.38–4.47% beta turn, while these ratios were predicted to be 47.55–53.27, 30.00–36.01, 6.99–18.12 and 2.38–4.75%, respectively, in E. rhadinum (Table 3). High ratio of alpha helixes and random coil in the Elovl protein structure might play important roles in fatty acids biosynthesis in fish, in accordance with the literature for the order Perciformes in Perca fluviatilis36. Additionally, the secondary structure pattern of Elovl proteins in the candidate E. tetradactylum and E. rhadinum species were highly similar (Fig. 3), indicating the probable similar biological functions as well as highly evolutionarily conserved Elovl genes in Eleutheronema species.Table 3 Properties of the secondary structures of Elovl proteins.Full size tableFigure 3The secondary structure pattern, including alpha helix (blue color), random coil (purple color), extended strand (red color), and beta turn (green color), of Elovl proteins in E. tetradactylum and E. rhadinum.Full size imageThe 3D model results showed that all predicted Elovl proteins had complex 3D structures, composing of multiple secondary structures including alpha-helices, random coils, and others (Fig. 4). The Elovl proteins of different subfamilies showed different 3D configurations. The 3D structures of Elovl proteins also revealed the presence of the conserved domain in each Elovl protein, which showed a typical three-dimensional frame comprising of various parallel alpha-helixes. To assay the quality and accuracy of the predicted 3D model for the candidate Elovl proteins, the Ramachandran plot analysis was employed (Figure S1). In model validation, the qualities of the Elovl proteins model varied from 90 to 98% based on the Ramachandran plot analysis, suggesting the reasonably good quality and reliability of the predicted 3D models. These results indicated that the predicted 3D model of Elovl proteins could provide valuable information for the further comprehensive studies of molecular function in the fatty acids biosynthesis in Eleutheronema species. Additionally, the comparisons between these structures in E. tetradactylum and E. rhadinum suggested that the Elovl proteins encompassed the conserved structures. In addition, gene duplication resulted in obvious 3D structural variation in the duplicated genes, such as Elovl4 (elovl4a and elovl4b), Elovl6 (elovl6 and elovl6l). The ascertained variations were revealed in duplicated Elovl proteins, and the diversities in these proteins structure may reflect their different obligations in the fatty acid biosynthesis and other biological processes.Figure 4Three-dimensional modeling of Elovl proteins in E. tetradactylum and E. rhadinum. All models have confidence levels above 90%.Full size imageTo explore the functional selection pressures acting on Elovl gene family, Ka, Ks, and Ka/Ks ratios were calculated for each gene. Generally, Ka/Ks  1 indicates positive selection. In this study, we found that all the Ka/Ks ratios for each gene were less than 0.5, suggesting that they were subjected to strong purifying selection during evolution, and their functions might be evolutionarily conserved (Fig. 5). Therefore, theoretically, the Elovl genes in the Eleutheronema genus had eliminated deleterious mutations in the population through purification selection. Similar results were also observed in Elovl gene family of Gymnocypris przewalskii that no positive selection trace was detected in most members except elovl211. Moreover, elovl6l and elovl8b showed a higher average Ka/Ks ratio than the other seven members, indicating that the evolution of elovl6l and elovl8b might be much less conservative and thereby could provide more variants for natural selection in Eleutheronema species.Figure 5The evolutionary rates of the Elovl genes in (a) E. tetradactylum and (b) E. rhadinum. The Ka, Ks, and Ka/Ks values were demonstrated in boxplots with error lines.Full size imageChromosomal location, collinearity, and protein–protein interaction network analysis of Elovl genesAs shown in Fig. 6a and b, Elovl genes were randomly and unevenly distributed on seven chromosomes in both E. tetradactylum and E. rhadinum, including Chr5, Chr6, Chr8, Chr10, Chr11, Chr13, and Chr25. The Chr5 and Chr6 harbored two Elovl genes (elovl1b and elovl8b in Chr5, elovl5 and elovl6l in Chr6), while other chromosomes each carried a single Elovl gene. Collinearity relationship analysis was performed to further investigate the gene duplication events within the Elovl gene family. The results revealed that a pair of segmental duplication genes (elovl4a/4b) showed collinear relationships. A chromosome-wide collinearity analysis also showed that the chromosomes were highly homologous between E. tetradactylum and E. rhadinum, including the Elovl gene family (Figure S2). To infer the protein interaction within Elovl gene family, we constructed the protein–protein interaction (PPI) network of the Elovl proteins based on the interaction relationship of the homologous Elovl proteins in zebrafish. The results showed that Elovl genes had close interaction with other members except for the elovl4a/4b and elovl8b (Fig. 6c), which suggested that they might participate in diverse functions by interacting with other proteins. Thus far, elovl4a and elovl4b were widely identified in most fish, which could effectively elongate PUFA substrates37. In addition, the elovl4a/4b were identified to be homologous proteins of zebrafish, indicating that the elovl4 subtype was highly conserved during evolution and played important roles in the biosynthesis of LC-PUFA in Eleutheronema.Figure 6Chromosomal location and collinearity analysis of Elovl gene family in (a) E. tetradactylum and (b) E. rhadinum. Colored boxes represented chromosomes. Segmental duplication genes are connected with grey lines; (c) a protein–protein interaction network for Elovl genes based on their orthologs in zebrafish.Full size imageExpression patterns of ELOVL genes in different tissuesIn the present study, we aimed to determine the expression patterns and gained insights into the potential functions of Elovl genes in the brain, eye, gill, heart, kidney, liver, muscle, stomach, and intestine. The expression patterns of Elovl genes in different tissues and species were distinct, suggesting the diverse roles during fish development (Fig. 7a and b). In our present study, the elovl1a and elvovl1b were expressed in a relatively narrow range of tissues, including the liver, stomach, and intestine. Some Elovl genes had much higher relative expression rates, e.g., elovl1a and elovl7a. The elovl4a was primarily distributed in the brain and eye, slightly expressed in gills while hardly detectable in other tissues, consistent with previous studies37, 38, which might play an important role in endogenous biosynthesis of LC-PUFA in the neural system of fish. In contrast to elovl4a, elovl4b was ubiquitously, instead of tissue-specific, expressed in most tissues while hardly examined in the heart and kidney. The elovl4a and elovl4b were two commonly paralogues in evolutionarily diverged fish species, and the striking difference in expression patterns between elovl4a and elovl4b might be due to the potential functional divergence of these two paralogues. In addition, elovl8b, the novel active member of the Elovl protein family, was expressed in several tissues, suggesting the essential roles in LC-PUFAs biosynthesis of teleost as indicated by a previous study20. Moreover, the differences in expression patterns among different Elovl genes indicated that these genes might possibly undergo functional divergence during evolution in the Eleutheronema genus. Overall, our present study firstly provided the preliminary organ-specific expression data of the Elovl gene family in E. tetradactylum and E. rhadinum, which could provide the foundation for further clarifying the function of these genes in the evolutionary development of the Eleutheronema genus.Figure 7qPCR assessment of tissue distribution of elovl1a, elovl1b, elovl4a, elovl4b, elovl5, elovl6, elovl6l, elovl7a, and elovl8b gene expression in (a) E. tetradactylum and (b) E. rhadinum for various tissues including the brain, eye, gill, heart, kidney, liver, muscle, stomach, and intestine.Full size image More

Study site descriptionThe study was carried out at the edge of an arid region in mature plantations dominated by P. halepensis, of age of 40-50 years (Pinus halepensis), and their adjacent non-forested ecosystems. The sites were distributed across a climatic gradient from arid and semi-arid to dry sub-humid (Fig. 1, Supplementary Table S1). Three selected paired sites included: (1) An arid site at Yatir forest (annual precipitation, P: 280 mm; aridity index, AI: 0.18; elevation: 650 m; light brown Rendzina soil, and forest density: 300 trees ha−1), where a permanent flux tower has been operating since the year 2000 (http://fluxnet.ornl.gov). Note that an AI of 0.2 marks the boundary between arid and semi-arid regions. Yatir, with AI = 0.18, is formally within an arid zone, but on the edge of a semi-arid zone. (2) An intermediate semi-arid site in Eshtaol forest (P: 480 mm; AI: 0.37; elevation: 350 m; light brown Rendzina soil, and forest density: 450 trees ha−1). (3) A dry-subhumid site in northern Israel at the Birya forest (P: 770 mm; AI: 0.64; elevation: 755 m; dark brown Terra-Rossa and Rendzina soil, and forest density: 600 trees ha−1). Non-forest ecosystems were sparse dwarf shrublands, dominated by Sarcopoterium spinosum in a patchy distribution with a wide variety of herbaceous species, mostly annuals, that grew in between the shrubs during winter to early spring, and then dried out. All non-forested sites had been subjected to livestock grazing (exposing soils). Finally, an additional site that was characterized as Oak-forest vegetation was added. The site was dominated by two oak species, Quercus calliprinos and Quercus ithaburensis (P: 540 mm; AI: 0.4; see previous publication for more details on the oak site53). All sites were under high solar radiation load, with an annual average of approximately 240 Wm−2 in the arid region and only 3% lower in the northern site in the dry-subhumid region (Table 1).Mobile laboratoryMeasurements were conducted on a campaign basis using a mobile lab with a flux measurement system at all sites except the Yatir forest, where the permanent flux tower was used (http://fluxnet.ornl.gov;54). Repeated campaigns of approximately two weeks at each site, along the seasonal cycle, were undertaken during 4 years of measurements, 2012–2015 (a total of 6-7 campaigns per site, evenly distributed between the seasons) the 4 years of measurements were found to be representative of previous 70 years of precipitation record (Supplementary Figure S1 and Table S2). The mobile lab was housed on a 12-ton 4 × 4 truck with a pneumatic mast with an eddy-covariance system and provided the facility for any auxiliary and related measurements. Non-radiative flux measurements were undertaken using an eddy-covariance system to quantify CO2, sensible heat (H), and latent heat (LE) fluxes using a 3D sonic anemometer (R3, Gill Instruments, Hampshire, UK) and an enclosed-path CO2/H2O infrared gas analyzer (IRGA; LI-7200, LI-COR). Non-radiative flux measurements were accompanied by meteorological sensors, including air temperature (Ta), relative humidity (RH), and pressure (Campbell Scientific Inc., Logan, UT, USA), radiation fluxes of net solar- and net long-wave radiation (SWnet and LWnet, respectively), and photosynthetic radiation sensors (Kipp & Zonen, Delft, Holland). Raw EC data and the data from the meteorological sensors were collected using a computer and a CR3000 logger (Campbell Sci., Logan, UT, USA), respectively. The EC system was positioned at the center of each field site with the location and height aimed at providing sufficient ‘fetch’ of relatively homogeneous terrains. For detailed information on the use of the mobile lab and the following data processing of short and long-term fluxes see previous publications29,55,56.Data processingMean 30-min fluxes (CO2, LE, and H) were computed using Eddy-pro 5.1.1 software (LiCor, Lincoln, Nebraska, USA). Quality control of the data included a spike removal procedure. A linear fit was used for filling short gaps (below three hours) of missing values due to technical failure. Information about background meteorological parameters, including P, Ta, RH, and global radiation (Rg), was collected from meteorological stations (standard met stations maintained by the Israel Meteorological Service, https://ims.gov.il/en). The data were obtained at half-hourly time resolution and for a continuous period of 15 years since 2000.Estimating continuous fluxes using the flux meteorological algorithmEstimation of the flux-based annual carbon and radiation budgets was undertaken using the short campaign measurements as a basis to produce a continuous, seasonal, annual, and inter-annual scale dataset of ecosystem fluxes (flux meteorological algorithm). The flux meteorological algorithm method was undertaken based on the relationships between measured fluxes (CO2, LE, H, SWnet, and LWnet) and meteorological parameters (Ta, RH, Rg, VPD, and transpiration deficit, a parameter that correlated well with soil moisture, see main text and supplementary material of previous publication29. A two-step multiple stepwise regression was established, first between the measured fluxes (H, LE, and the ecosystem net carbon exchange) and the meteorological parameters measured by the mobile lab devices, and then between the two meteorological datasets (i.e., the variables measured by the Israel meteorological stations) for the same measurement times. Annual fluxes were calculated for the combined dataset of all campaigns at each site using the following generic linear equation:$$y={{{{{rm{a}}}}}}+{Sigma }_{i}{b}_{i}{x}_{i}$$
(1)
where, y is the ecosystem flux of interest, the daily average for radiative fluxes (LWnet and SWnet), non-radiative fluxes (H and LE), and daily sum for net ecosystem exchange (NEE), a and ({b}_{i}) are parameters, and ({x}_{i}) is Ta, RH, Rg, vapor pressure deficit, or transpiration deficit. The meteorological variables (({x}_{i})) were selected by stepwise regression, with ({b}_{i}=0) when a specific ({x}_{i}) was excluded.Based on this methodology, ecosystem flux data were extrapolated to the previous 7–15 years (since 2000 in the dry-subhumid and arid sites, since 2004 in the semi-arid sites, and since 2008 in the Oak-forest site) using all the available continuous meteorological parameters from the meteorological stations associated with our field sites. The long-term annual sums and means of extrapolated ecosystem fluxes were averaged for multi-year means of each site for the period of available extrapolated data. In the first, previously published phase of this study29, the extrapolation method was extensively tested, including simulation experiments at the arid forest site, where continuous flux data from the 20 years old permanent flux tower were available. Five percent of the daily data were selected by bootstrap (with 20 repetitions), a stepwise regression was performed for this sample, and then, the prediction of fluxes using Eq. 1 above was performed for the entire observation period. In the current phase of the study additional flux measurements are included with the R2 coefficients for the additional measurements ranging between 0.4-0.9, see Supplementary Table S3.The aridity index of the Oak-forest was in between those of the semi-arid and dry-subhumid Pine-forests (0.4 compared to 0.37 and 0.67, respectively). Therefore, to compare the Oak-forest with Pine-forest and non-forest sites, the average results from the semi-arid and dry-subhumid paired sites were used.Radiative forcing and carbon equivalence equationsTo compare the changes in the carbon and radiation budgets caused by forestation, we adopted the approach of Myhre et al. 30, and used Eq. 2:$${{RF}}_{triangle C}=5.35{{{{mathrm{ln}}}}}left(1+frac{triangle C}{{C}_{0}}right),left[W,{m}^{-2}right]$$
(2)
where land-use changes in radiative forcing (RFΔC) are calculated based on the CO2 reference concentration, C0 (400 ppm for the measured period of study), and ΔC, which is the change in atmospheric CO2 in ppm, with a constant radiative efficiency (RE) value of 5.35. Here, ΔC is calculated based on the annual net ecosystem productivity (NEP; positive carbon gain by the forest, which is identical to net ecosystem exchange (NEE), the negative carbon removal from the atmosphere) as the difference between forested and non-forested ecosystems (ΔNEP) multiplied by a unit conversion constant:$$triangle C={left[{overline{{NEP}}}_{F}-{overline{{NEP}}}_{{NF}}right]}_{[gC{m}^{-2}{y}^{-1}]}cdot k ,[{ppm}]$$
(3)
where, k is a unit conversion factor, from ppm to g C (k = 2.13 × 109), calculated as the ratio between the air molar mass (Ma = 28.95; g mol−1), to carbon molar mass (Mc = 12.0107; g mol−1), and total air mass (ma = 5.15 × 109; g).Etminan et al. 57 introduced an updated approach to calculate the RE as a co-dependent of the change in CO2 concentration and atmospheric N2O:$${RE}={a}_{1}{left(triangle Cright)}^{2}+{b}_{1}left|triangle Cright|+{c}_{1}bar{N}+5.36,left[W,{m}^{-2}right]$$
(4)
where, (triangle {{{{{rm{C}}}}}}) is the change in atmospheric CO2 in ppm resulting from the forestation, as calculated in Eq. 3, (bar{{{{{{rm{N}}}}}}}) is the atmospheric N2O concentration in ppb (323), and the coefficients a1, b1, and c1 are −2.4 × 10−7 Wm−2ppm−1, 7.2 × 10−4 Wm−2 ppm−1, and −2.1 × 10−4 Wm−2ppb−1, respectively.Combining Eqs. 2 and 4 with an airborne fraction of (zeta =0.44)58, we obtain Eq. 5:$${{RF}}_{triangle C}={RE}cdot {{{{mathrm{ln}}}}}left(1+frac{zeta cdot triangle C}{{C}_{0}}right),left[W,{m}^{-2}right]$$
(5)
Next, the annual average radiative forcing due to differences in radiation flux was calculated as follows:$${{RF}}_{triangle R}=frac{triangle Rcdot {A}_{F}}{{A}_{E}},left[W,{m}^{-2}right]$$
(6)
where, ΔR is the difference between forest and non-forest reflected short-wave or emitted long-wave radiation (ΔSWnet and ΔLWnet, respectively), assuming that the atmospheric incoming solar and thermal radiation fluxes are identical for the two, normalized by the ratio of the forest area (({A}_{F})) to the Earth’s area (({A}_{E}=5.1times {10}^{14},{m}^{2})).As forest conversion mostly has a lower albedo, the number of years needed to balance (‘Break even time’) the warming effect of changes in radiation budget by the cooling effect of carbon sequestration is calculated by combining Eqs. 5 and 6:$$^prime{Break},{even},{time}^prime=frac{{{RF}}_{triangle alpha }}{{{RF}}_{triangle C}},[{{{{{rm{years}}}}}}]$$
(7)
The multiyear averages of NEP for each of the three paired sites (forest and non-forest) were then modeled over a forest life span of 80 years. This was done based on a logarithmic model, modified for dryland, which takes as an input the long-term averages of NEP ((overline{{NEP}})) as in Eq. 3:$${{NEP}}_{t}=overline{{NEP}}(1-{exp }^{bcdot t}),left[g{{{{{rm{C}}}}}},{{{{{{rm{m}}}}}}}^{-2}{{yr}}^{-1}right]$$
(8)
where annual carbon gain at time t (NEPt) is a function of the multiyear average carbon gain ((overline{{{{{{rm{NEP}}}}}}})), forest age (t), and growth rate (b). Parameter b is a constant (b = −0.17) and is calculated based on the global analysis of Besnard et al. 59, limiting the data to only dryland flux sites60. Note that this analysis indicates NEP reaching a steady state and was used here to describe the initial forest growth phase, while growth analyses indicate that carbon sequestration peaks after about 80 years, followed by a steep decline50. This is consistent with the time scale for forest carbon sequestration considered here.In contrast to the one-year differences presented in Table 1 (ΔNEP), the net sequestration potential ((triangle)SP) for each of the paired sites was calculated as the accumulated ecosystem ΔNEPt along with forest age ((triangle) is the difference between forest and non-forest sites):$$triangle {SP}=mathop{sum }limits_{t=0}^{{age}}{triangle {NEP}}_{t}/100,left[{{{{{rm{tC}}}}}},{{{{{{rm{ha}}}}}}}^{-1}{{age}}^{-1}right]$$
(9)
The ΔSP growth model was compared with previously published data of long-term carbon stock changes in arid forests (i.e., cumulative NEP over 50 years since forest establishment, t = 50), demonstrating agreement within ± 10%27.For comparison with previous studies, the carbon emission equivalent of shortwave forcing (EESF) was calculated using an inverse version of Eqs. 5 and 6 based on Betts1:$${EESF}={C}_{0}left({e}^{frac{{{RF}}_{triangle R}}{zeta cdot {RE}}}-1right)cdot k/100,left[{{{{{rm{tC}}}}}},{{{{{{rm{ha}}}}}}}^{-1}{{age}}^{-1}right]$$
(10)
where, C0 is the reference atmospheric CO2 concentration (400 ppm, the average atmospheric concentration for the past decade), RFΔR is the multiyear average change in radiative forcing as a result of the change in surface albedo (Eq. 6 W m−2), RE is the radiative efficiency (Eq. 4, W m−2), ζ is the airborne fraction (0.44 as in Eq. 5), and k is a conversion factor, from ppm to g C (2.13 × 109 as in Eq. 3). Equation 10 was also used to calculate the emission equivalent of longwave forcing (EELF) with the RFΔR as the multiyear average change in radiative forcing as a result of the change in net long-wave radiation (ΔLWnet).Finally, the net equivalent change in carbon stock due to both the cooling effect of carbon sequestration and the warming effect due to albedo change (net equivalent stock change; NESC), was calculated by a simple subtraction:$${NESC}=triangle {SP}-{EESF},left[{{{{{rm{tC}}}}}},{{{{{{rm{ha}}}}}}}^{-1}{{age}}^{-1}right]$$
(11)
A comparison of the ΔSP (Eq. 9), the EESF (Eq. 10), and NESC (Eq. 11) with the same metrics as those used in other studies1,14,61 was done when appropriate. An exception was made for Arora & Montenegro (2011), where only carbon stock changes (ΔSP) were available in carbon units, and biogeophysical (BGP) and biogeochemical (BGC) effects were expressed as temperature changes. To overcome this metric difference, we converted the biogeophysical to carbon equivalent units (EESF + EELF) by multiplying the carbon stock changes (ΔSP) by the ratio between the BGP and BGC effects on temperature (EESF + EELF = ΔSP × BGP/BGC).Statistical and data analysesThe paired t-test was used to compare multi-annual averages of all variables between forested and adjacent non-forested sites and between sites across the climatic gradient. The variables of interest that were detected for their significant differences were albedo, net radiation and its longwave and shortwave components, latent heat fluxes, sensible heat fluxes, and net ecosystem productivity. All statistical and data analyses were performed using R 3.6.0 (R Core Team, 2020)62.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

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Research areaThe YREB covers Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Guizhou, and Yunnan. It includes the Yangtze River Delta urban agglomerations (YRDUA), Yangtze River midstream urban agglomeration (YRMUA), and Chengdu-Chongqing urban agglomeration (CCUA). With a regional area of 2.05 million km2, the YREB runs through the eastern, central and western regions in China32. In 2019, the total GDP of YREB is 45.8 trillion yuan, accounting for 46.2% of the national GDP. The YREB plays a pivotal strategic support and leading role in the overall situation of stable economic growth in China33. At the same time, the contradiction between the shortage of land resources and economic growth in the YREB is very prominent. Therefore, this paper selects 107 cities in YREB as the research sample. The specific geographic locations are shown in Fig. 2. This article uses ARCGIS 10.2 version to draw the map. The URL link is http://demo.domain.com:6080/arcgis/services.Figure 2The geographic location of the YREB in China.Full size imageResearch methodsGlobal Malmquist–Luenberger indexUGLUE refers to the effective utilization degree of land elements under certain input of other elements. The green utilization of urban land mainly comes from three aspects: first, improve the utilization intensity of the existing actual input land, that is, increase the input intensity of other elements of the unit land area. Second, reduce the input of land in the production process to avoid excessive waste of land. Third, promote the optimal allocation of land elements among production units. Technical efficiency refers to the maximum degree that all factor inputs need to expand or shrink in equal proportion when all production units reach the production frontier. However, for production units with high technical efficiency, the factor allocation structure may not be reasonable. The land factors may still have the problem of under-input or over-input, resulting in the reduction of UGLUE.Pastor and Lovell34 proposed a global index, which uses all the inspection periods of each decision-making unit as a benchmark to construct the production frontier. According to the current benchmark construction period t, the production possibility set reference set is defined as follows:$$P_{C}^{t} (x^{t} ) = left{ {left. {(y^{t} ,b^{t} )} right|x^{t} {kern 1pt} can{kern 1pt} , produce{kern 1pt} , b^{t} ,y^{t} } right}$$
(1)
The global benchmark is defined as: (P_{G} = P_{C}^{1} , cup ,P_{C}^{2} , cup , cdots ,P_{C}^{t}), The subscripts C and G represent the current benchmark and the global benchmark respectively. The ML index of decision-making unit i is calculated based on the current reference benchmark:$$ML^{S} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{C}^{S} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{C}^{S} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}}$$
(2)
Among them, the superscript S indicates two adjacent periods, t period and t + 1 period. The subscript C indicates the current benchmark, which is a simplified directional distance function. (ML^{s} > 1), indicates that the productivity increases. (ML^{s} < 1), indicates that the productivity decreases.According to Hofmann et al.35, the GMLI is defined as follows:$$GMLI^{t,t + 1} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}}$$ (3) Among them, (D_{G}^{T} (x,y,b) = max left{ {alpha |(y - alpha y,b - alpha b) in P_{G} (x)} right}). (GMLI^{t,t + 1} > 1) indicates that the productivity has increased. (GMLI^{t,t + 1} < 1) indicates that the productivity decreases. The GMLI is further broken down as follows:$$begin{aligned} & GMLI^{t,t + 1} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}} \ & quad = frac{{1 + D_{G}^{t} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{t + 1} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}} times left[ {frac{{(1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} ))/(1 + D_{C}^{T} (x^{t} ,y^{t} ,b^{t} ))}}{{(1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} ))/(1 + D_{C}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} ))}}} right] \ & quad = frac{{TE^{t + 1} }}{{TE^{t} }} times left( {frac{{BPG_{t + 1}^{t + 1} }}{{BPG_{t}^{t + 1} }}} right) = EC_{t}^{t + 1} times BPC_{t}^{t + 1} \ end{aligned}$$ (4) Among them, TE is the change of technological progress. EC is the change of technological efficiency. The change of technological progress reflects the change of the highest technical level. The improvement of the highest technical level often requires the introduction and innovation of advanced technology, which often requires a large amount of investment. The change of technical efficiency reflects the gap with the highest technical level. Narrowing the gap with the highest technical level often requires improvements in internal management and governance structures. (BPG_{t}^{t + 1}) is the “best practitioner gap” between the current period and overall technological frontier. (BPC_{t}^{t + 1}) measures the changes in the “best practitioner gap” between two periods (technological changes). (BPC_{t}^{t + 1} , > , 1 ,) indicates technological progress. (BPC_{t}^{t + 1} < 1) indicates technology regress.Econometric techniques of industrial agglomeration on UGLUEIn recent years, many scholars used the traditional SPM for empirical analysis, which is a basic measurement model suitable for panel data. Therefore, this article firstly uses the traditional SPM to analyze the impact of industrial agglomeration on UGLUE. The formula is:$$begin{aligned} ln UGLUE_{it} & = alpha_{0} + alpha_{1} ln RZI_{it} + alpha_{2} ln RZI_{it} *ln RZI_{it} + alpha_{3} ln RDI_{it} + alpha_{4} ln EC_{it} \ & quad + alpha_{5} ln GDP_{it} + alpha_{6} ln TEC_{it} + alpha_{7} ln ROAD_{it} + alpha_{8} ln GOV_{it} + varepsilon_{it} \ end{aligned}$$ (5) Among them, ε is the disturbance term. i represents the city, i in this paper involves 107 cities in YREB. t represents the time, and the range of t in this paper is from 2007 to 2016. UGLUE is the explained variable, which represents the UGLUE. RZI and RDI are explanatory variables, representing industrial specialization agglomeration and industrial diversification agglomeration. EC is the industrial structure. GDP is the level of economic development. TEC is the level of technology. ROAD is the level of infrastructure. GOV is the degree of government intervention. (alpha_{1}) to (alpha_{8}) is the coefficient of each variable.Formula (5) assumes that the UGLUE changes with the changes of various influencing factors in the current period. That is, there is no time lag effect. But in reality, land use often has a time lag effect. The previous level has a non-negligible impact on the current results. Therefore, this paper selects the dynamic panel model for empirical analysis. However, there is often a two-way causal relationship between industrial agglomeration and UGLUE, which may cause endogenous bias. For example, cities with higher UGLUE levels tend to have higher levels of economic development, which promotes industrial agglomeration in this city. Therefore, this paper adopts the method of system GMM for regression analysis of dynamic panel model. Compared with mixed OLS, system GMM can make full use of sample information, select appropriate lag terms as instrumental variables36. It can effectively solve the endogeneity problem between industrial agglomeration and UGLUE. Based on the above analysis, this paper introduces the first-order lag term of UGLUE on the basis of formula (5). The DPM is as follows:$$begin{aligned} ln UGLUE_{it} & = beta_{0} + tau ln UGLUE_{i(t - 1)} + beta_{1} ln RZI_{it} + beta_{2} ln RZI_{it} times ln RZI_{it} + beta_{3} ln RDI_{it} \ & quad + beta_{4} ln EC_{it} + beta_{5} ln GDP_{it} + beta_{6} ln TEC_{it} + beta_{7} ln ROAD_{it} + beta_{8} ln GOV_{it} + varepsilon_{{{text{it}}}} \ end{aligned}$$ (6) Among them, (tau) is the first-order lag coefficient of UGLUE, reflecting the time lag effect of UGLUE.Variable descriptionExplained variableThe GMLI is used to measure the UGLUE of 107 cities in YREB. According to existing research37, the following core evaluation index of UGLUE are selected (see Table 1). Regarding input indicators, we mainly choose land element input M, labor element input L, and capital element input K as input indicators. Regarding output indicators, we choose the added value of the secondary and tertiary industries in the municipal area as the expected output, and use the GDP deflator to convert it into a comparable value. At the same time, pollution indicators such as industrial wastewater emissions, industrial sulfur dioxide emissions, and industrial smoke (dust) emissions are selected as undesired output. Since the GMLI reflects the growth rate of UGLUE, this paper assumes that the GMLI in 2006 is 1, and then multiplies the calculated GMLI year by year to obtain the development level of UGLUE in each city from 2007 to 2016.Table 1 Input and output index.Full size tableExplanatory variablesIndustrial specialization index ZI is usually used to measure the specialization level of urban industries. The specialization index is represented by the share of the employment of the industry in the total employment of the city:$$ZI_{i} = Max_{j} (S_{ij} )$$ (7) Nextly, we use the relative specialization index to make a horizontal comparison of the specialization level between different cities:$$RZI_{i} = Max(S_{ij} /S_{j} )$$ (8) The most common measure of the level of industrial diversification is the Herfindahl–Hirschman Index (HHI). For city i, the HHI is the sum of the square sum of employment shares of all industries in the city. The diversification index is the reciprocal of the HHI:$$DZ_{i} = frac{1}{{sumlimits_{j} {S_{ij}^{2} } }}$$ (9) The expression of relative diversification index is as follows:$$RDI_{i} = {1 mathord{left/ {vphantom {1 {sumlimits_{j} {left| {S_{ij} - S_{j} } right|} }}} right. kern-0pt} {sumlimits_{j} {left| {S_{ij} - S_{j} } right|} }}$$ (10) Among them, Sij is the employment proportion of j industry in city i, and Sj is the proportion of the total employment of the national j industry. The greater value of RZI and RDI, the higher level of industrial specialization and diversification.Control variablesRegarding control variables, we choose the following variables as control variables.Industrial structure (EC): The continuous optimization of industrial structure promotes the improvement of UGLUE through three aspects: saving land, increasing land income and promoting the optimal allocation of land resources. This paper selects the added value of the tertiary industry as a percentage of GDP (take the logarithm) to express.Technological level (TEC): The higher the technological innovation level of a city is, the more it promotes the use of input elements and the transformation of innovation results, thereby improving the UGLUE. This paper selects the proportion of science and technology expenditure to fiscal expenditure (take the logarithm) to represent.Economic development level (GDP): The continuous economic development promote the rational allocation of various production factors and increase the level of urban land output, thereby improving the UGLUE. This paper selects GDP per capita (take the logarithm) to express.Road infrastructure level (ROAD): The continuous improvement of infrastructure reduces transportation costs and transaction costs, and promotes communication externalities between producers, consumers, and between producers and consumers. This paper selects the average road area per capita (take the logarithm) to express.Government behavior (GOV): Fiscal expenditure is an important means for the government to carry out macro-control. Appropriate fiscal expenditure makes up for market shortages, improves factor flow and resource allocation efficiency, and realizes positive economic externalities. This paper selects the proportion of fiscal expenditure to GDP (take the logarithm) to express. We can see the meaning of specific variables from Table 2.Table 2 The descriptive statistics of variables.Full size tableData sourceThe object of this thesis is the 107 cities in YREB from 2007 to 2016. The urban construction land area data comes from the "China Urban Construction Statistical Yearbook", and the rest of the index data all come from the "China City Statistical Yearbook". The URL link is https://www.cnki.net/. In order to maintain the integrity of the data, this article uses the average method to fill in the missing values. In addition, because Chaohu City began to be under the jurisdiction of Hefei City in 2011, Bijie City and Tongren City in Guizhou Province only became prefecture-level cities in 2011. The three cities and Pu'er City are taken from the sample to maintain the continuity of data. More