Leaf nutrients, but not genome size, modulate plant photosynthesis

Leaf nutrients, but not genome size, modulate plant photosynthesis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 January 2025 V1 Latest version Share on Leaf nutrients, but not genome size, modulate plant photosynthesis Authors : Xin Song , Helena Vallicrosa , Pol Fernández , Joan Garcia-Portaf , Alba Anadon-Rosell , Daijun Liu , Guille Peguero , and Marcos Fernández-Martínez Authors Info & Affiliations https://doi.org/10.22541/au.173753375.50501603/v1 714 views 251 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The extreme variability of genome size (GS) across plant species results in morphological and physiological constraints leading to ecological and evolutionary consequences. Previous studies pointed out that plants with larger GS have lower photosynthetic rates. Plants with larger GS, however, also have higher foliar concentrations of nitrogen (N) and phosphorus (P), which positively correlate with photosynthetic rates following the assumptions of the leaf economics spectrum. Nonetheless, the interplay between GS, leaf photosynthetic rates (Amax ), N and P concentrations across a relevant phylogenetic scale remains elusive. We address this question by compiling a global dataset of GS, Amax , leaf concentrations of N and P and environmental information for 376 plant species. Our results indicate that the evolutionary history is a direct factor affecting GS, Amax , foliar N and P. Larger GS were found in plants with high foliar P and living over acidic soils. Amax was higher in P and N-rich plants, but we did not find evidence suggesting that photosynthetic capacity is constrained by their GS. Our results suggest that GS-driven evolutionary limitation does not pivot around a constraint imposed on the photosynthetic capacity of plant species. INTRODUCTION The genome contains the complete set of genetic instructions that controls the growth, development, functioning, and reproduction of all living organisms, making it of profound biological significance across all taxa 1 . One of the most striking features of the genome is its extreme size variability 2,3 . The differences between the smallest and largest genomes in angiosperms is 2400-fold 4,5 . Recently, the discovery of Tmesipteris oblanceolata , with a genome size (GS) of 160.45 Gbp/1C, has expanded the range of the known variation in eukaryotic GS over 61,000-fold 6 . In general, polyploidization, rediploidization, and the accumulation and deletion of repetitive DNA, along with the influence of natural selection or neutral evolutionary processes, are the primary mechanisms driving genome size variation 7–9 . However, such huge differences across plant species in their GS are hypothesized to impose important morphological and physiological constraints in plants, which should eventually lead to ecological and evolutionary consequences. Large-scale comparative analyses have shown that GS is positively correlated with cell size 10–12 and the size of the stomata 10,13 , although the strength of the correlation varies depending on cell type, cellular activity, and developmental stage. Among most terrestrial plants, the size of stomata regulates the exchange of CO 2 , water, and nutrients by altering stomatal closure, thereby influencing water use efficiency, photosynthesis, and primary production 14 . In order to adapt to various environmental conditions, plants need to adjust their stomatal size and density through their evolutionary history 15,16 . Vascular plants with larger genomes tend to have fewer but larger stomata in their leaves (due to larger nuclei) 15 , which constrains their capacity to regulate water and CO 2 exchange with the environment and, hence, this may potentially reduce their photosynthetic capacity as compared to those species with smaller GS (Path 1 in Fig. S1). Moreover, GS can impose nutrient trade-offs that may further constrain organism functioning. DNA and RNA can account for as much as 15% of nitrogen (N) and 9% of phosphorus (P) in dry weight 17 , but a large proportion of the genome is non-coding DNA with no clear biological function 18 . Hence, an important proportion of N and P will be permanently sequestered in the DNA implying a high nutrient cost because cell replication will need those N and P-rich molecules for the new cells to be built out of the whole cell nutrient budget 19 . We would then expect species with larger GS to present larger concentrations of N and P, which, as shown by the leaf economics spectrum 20 , would tend to present higher photosynthetic rates (Path 2 in Fig. S1). This hypothesis would contradict the above-mentioned observation indicating a negative relationship between GS and photosynthesis. Therefore, the three-way relationship between the GS, elemental composition and the photosynthetic capacity of plants remains elusive. GS variation may be influenced by climate and other environmental variables. For instance, factors such as latitude 21,22 , altitude 23,24 , temperature 25–27 , precipitation 28 , ultraviolet-B radiation 29 , P availability 30 , cell and seed size 31 , all have been suggested to contribute to GS evolution. The impact of environmental drivers in GS is strongly dependent on species and its location and a general agreement has not been reached yet. Thus, it is essential to explore how climatic and environmental variables influence GS at a species level. In this study, we conducted a comparative analysis on a global scale to investigate the relationship between GS and photosynthetic capacity across species. The maximum photosynthetic rate (A max ) was considered as an indicator of photosynthetic capacity. Due to the phylogenetic correlation between species’ trait values, the data points cannot be considered statistically independent 32,33 , making it essential to incorporate phylogenetic information in cross-species analyses 10 . We analyzed the factors influencing GS and A max and their interrelationship with foliar N and P concentrations using a global database encompassing GS, environmental variables, and leaf traits for 376 species, within a phylogenetic framework. Our main aim is to disentangle the role of GS on foliar N and P concentrations and its effect on leaf A max . We hypothesize that species with larger GS will, on average, have larger N and P concentrations but will present lower A max compared to species with the same N and P concentrations but smaller genomes. Our work contributes to clarifying the role of GS in evolution and the interactions between GS and environmental variables on plant growth. Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex RESULTS Our analyses indicated a very strong phylogenetic signal in GS (Pagel’s λ = 0.98) indicating that most of the variability in GS among species can be explained by their evolutionary history. A max , foliar N, and P also showed phylogenetic signal, though much smaller than that of GS, scoring λ values of 0.43, 0.62, and 0.50, respectively (Fig. 1). The phylogenetic linear model indicated that A max is mediated by both environmental variables and evolutionary processes (Table 1, Fig. 2). Mean annual temperature (MAT), soil C/N ratio, soil pH, aridity, SLA, foliar N, and foliar P are all positively related to A max and collectively account for 45% of the variance in A max (Table 1, Fig. 2). The variables of interest explained only 7% of GS variance (Table 1, Fig. 2). Nonetheless, we found that MAT, aridity and foliar P were positively correlated to GS at the global scale, while plants growing in climates with high precipitation were more likely to have smaller GS (Table 1). When considering only GS, A max , foliar N, and foliar P, A max was significantly influenced by both foliar N and P, while GS was significantly impacted by foliar P. No interaction effects were observed between the variables (Table 2). Our analyses using phylogenetically-informed structural equation models indicated that soil pH and foliar P directly affect GS, with effects of -0.09±0.04 and 0.07±0.04, respectively (Fig. 3). Additionally, MAT influences GS indirectly through soil pH, and foliar P. Foliar P has a positive total effect on GS with an effect size of 0.07, while soil pH exerts a negative total effect of -0.08 (Fig. 3). A max is directly affected by MAT, aridity, foliar N, and foliar P, being foliar nutrients the strongest predictors (0.40±0.05, and 0.39±0.04, respectively for N and P) (Fig. 3). Furthermore, MAT and aridity also indirectly influence A max by affecting foliar N, foliar P, and soil pH. Aridity, foliar N, foliar P, MAT, and soil pH all have a positive total effect on A max with effect sizes of 0.24, 0.57, 0.39, 0.06 and 0.18, respectively. No direct relationship was observed between GS and A max (Fig. 3) confirming previous results using phylogenetic linear models (Tables 1 and 2). Role of foliar phosphorus and temperature in genome size We found a strong phylogenetic signal in GS variation across species, and to a lesser extent, in A max , foliar N, and foliar P, indicating that the environmental controls on genome size are much weaker, or act at much longer evolutionary scales, than those on plant functions such as photosynthesis or their elemental composition (Fig. 1). Partly consistent with our hypothesis that species with larger GS will have larger N and P concentrations, we found that higher concentrations of foliar P, but not foliar N, are associated with larger genome sizes (Table 1, Fig. 3). This result is not consistent with some previous studies conducted in specific regions or with experimental approaches and limited sets of plant species. For instance, an analysis of the flora present in a karst region found that foliar N is a limiting factor for the size of genome in 99 species of Gesneriaceae 34 . In contrast to this study, Pellicer et al. (2010) found that GS was not affected by either foliar or soil P in Mediterranean plants 35 . In a long-term P addition experiment, Šmarda et al. (2013) found that soil P availability favored plant species with larger GS 30 . These different and somewhat contrasting results may be caused by the focus on specific species and soil or foliar P availability. Our global database, including 376 plant species, covering all major families and phylogenetic lineages within the plant kingdom, may capture a larger variation across species, thus showing more reliable relationships between GS and leaf nutrients. It is estimated that 43% of the natural terrestrial land area (glacial, urban, and cropland not included) is significantly limited by phosphorus 36 . The competition for P between DNA synthesis and physiological processes, such as photosynthesis, may influence the evolution of GS, making environmental P availability a key limiting factor in GS growth in a particular lineage. Indeed, small genomes are prevalent in tropical regions 37,38 , which are typically P-limited, indirectly supporting our results. In tropical regions, while temperature and UVB radiation damage can contribute to reducing GS 25,39 , nutrient scarcity—particularly P limitation—tends to be more severe than in other areas 40 . This scarcity potentially reduces the competitiveness of large genomes 30 , thereby the abundance of large genome species. Combined with the evolutionary history and climate change, with an imbalance of global N and P deposition 41,42 , P limitation induced by N deposition 43 , might exert a greater pressure on genomes to drive the selection for smaller genome species. The relationship between temperature and GS is probably not linear and debated at regional scales, displaying positive 23,44 , negative 23 , or no relationship 38 . In our results, temperature showed a significant positive correlation with GS (Table 1, Fig. 2). However, within the phyloSEM analysis, MAT can only explain a very small proportion (2%) of the variability in GS (Fig. 3), suggesting a weak macro-ecological trend. The indirect effect of MAT on GS could be through its positive effect on soil pH, which subsequently affects foliar P concentration (Fig. 3). A recent large-scale study, which constructed the largest current genome database including 16017 angiosperm species, found that temperature can explain 40% of the global distribution of angiosperm GS 25 . In contrast to our database, where woody plants make up 97.6% of the species, woody plants accounted for only 30% in their dataset. Hence, GS of plants from different growth forms may respond differently to MAT. Role of temperature, aridity, soil pH, and foliar nutrients in maximum photosynthetic rate This study demonstrates that MAT, aridity, soil pH, and foliar N and P concentration all positively influence A max , either through direct or indirect pathways (Tables 1 and 2, Figs. 2 and 3). In previous studies, the impact of temperature on plant photosynthesis has been found to be positive 45–47 , negative 48,49 , or neutral 50,51 . On one hand, warming temperatures may stimulate photosynthesis, while on the other, warming could induce drought stress, thereby inhibiting photosynthesis 49 . Therefore, the effect of temperature likely depends on water availability, aridity and the species composition of the plant communities 46,52 . Maire et al. (2015) analyzed environmental and soil factors influencing photosynthetic rates using data from 288 locations and 1,509 species. Their findings indicated that in arid soils with higher pH levels, plant light-saturated photosynthetic rates tend to be higher 53 . Soil pH is generally associated with processes that affect nutrient availability and enzyme activity 54 . Across a wide range of soil types, higher pH values often imply greater nutrient availability in organic matter reducing the cost of nutrient acquisition and the biochemical processes needed for photosynthesis, thereby maintaining a higher photosynthetic rate 53 . Although our dataset did not account for soil P availability, we found that soil pH has a substantial indirect impact on photosynthetic rates by positively affecting leaf P concentration (Fig. 3). In line with our findings, A max is often positively correlated with foliar N 55,56 and P concentration 57,58 due to the critical role of N-rich compounds, particularly ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), in the biochemical fixation of CO 2 59 . How is the relationship between genome size and maximum photosynthetic rate? Contrary to our expectations, species with larger genomes do not exhibit lower photosynthetic rates compared to species with similar foliar N and P concentrations but smaller genomes (Figs. 3 and 4). Our results were confirmed by two models, which take only foliar N, foliar P, and their interaction into consideration (Table 2). The relationship between GS and A max remained unaltered even when the ploidy level was included (Table S1 and S2). Furthermore, the findings were consistent across the three databases we generated. Previous studies have shown a weak negative relationship between GS and A max , with one conducted at the genus level 60 and another at the species level without accounting for the phylogenetic signal 61 . Beaulieu et al. (2007) reported that the variability in GS was not related with changes in metabolic rates 62 . The weak support for the relationship between GS and A max may stem from attempts to predict variation in A max based on mass without considering the cost of construction 61 . Our results indicate that whether using mass or area-based maximum photosynthetic rate, the relationship between GS and A max remains consistent (Table S3 and S4; Fig. S4), suggesting that the metric used (mass or area-based) does not influence the results We propose several possible explanations to explain the lack of relationship between GS and A max . The GS has both adaptive and apparently non-adaptive components 63,64 , enabling A max to respond to environmental changes without alterations in GS. This complexity suggests that the relationship between GS and A max is not straightforward or linear (Fig. 5). Moreover, average environmental conditions may fail to fully account for the extensive variation observed among species 60 , as the variation in GS and A max are too substantial. In our results, it looks like species with larger GS tend to have a lower A max , even though that trend could emerge from a lower availability of species with large genomes (Fig. 5). However, our data analysis did not capture a statistically significant tendency. Further, this relationship might be obscured, particularly among species with minor differences in GS. This could be due to the plasticity in nuclear size, which is influenced by factors such as chromatin condensation levels, histone acetylation, DNA methylation, and transcriptional activity 65 . The lack of a clear effect of GS on A max could result from a trade-off between different adaptive pathways (Path 1 and 2 in Fig. S1). Resource scarcity may prioritize cellular construction over photosynthetic processes 37 . Perhaps having a large GS is a burden to plants struggling to survive in suboptimal conditions in nature, where they cannot reach their maximum photosynthesis capacity because of limiting nutrients, light, water, competition or pathogens and herbivores. CONCLUSIONS Our results indicate that GS variation has a strong evolutionary component, but it does not constrain the photosynthetic capacity of plants. Our analyses also suggest that foliar P concentration and soil pH directly influence GS, while MAT, aridity, and foliar N concentration affect GS indirectly, either through foliar P concentration or soil pH. The A max was primarily affected by foliar N and P concentration, aridity, and soil pH. Our study advances the understanding of how plant GS responds to climatic and other environmental variables, as well as functional traits, revealing the pace of GS evolution. Additionally, our results offer a new insight into how GS might evolve under global change with high N deposition rates and an increasing P deficiency. Future studies should also consider the size of the plastid genome in addition to the nuclear genome, as the numerous copies of plastid DNA per cell may significantly contribute to the overall variation in an organism’s total DNA content. METHODS Genome size and functional traits We collected a comprehensive dataset consisting of 7,059 data points on plant genome size (GS) from the following sources: Kew Garden’s database (https://cvalues.science.kew.org/) 3 and the GOAT webpage (https://goat.genomehubs.org/) 2 . These data points correspond to 378 plant species distributed globally across various latitudes and ecosystems (Fig. S2). We specifically included only those species with data from more than three locations to ensure the robustness of our dataset. The dataset references a total of 120 scientific publications. Overall, the dataset consists of 378 species (236 trees, 133 shrubs, 5 herbs, and 2 vines), distributed across various biomes (e.g., tropical, subtropical desert, temperate, boreal, and tundra) (Fig. S3). The mean annual temperature (MAT) for the locations of the species included ranges from -13.9 to 29.7 °C, while mean annual precipitation (MAP) ranges from 39 to 6743 mm. The elevation spans from -2 to 5271 m. The GS of ~75% species is less than 1600 Mbp/1C (range:257 – 16963 Mbp/1C). The genera Viburnum , Artemisia , Cistus , and Chamaedorea exhibited large GS values. Additionally, we obtained data on foliar nitrogen (N) and phosphorus (P) content per dry weight, specific leaf area (SLA), and area-based and mass-based maximum photosynthetic rate (A max ) from the TRY Plant Trait Database v6 (https://www.try-db.org/TryWeb/Home.php) 66 . The traits’ ID in TRY database are 14, 15, 3117, 53, and 40, respectively. SLA was utilized for conversions between A max measured on a mass basis and A max measured on an area basis: A mass = SLA * A area where A area is the net carbon assimilation rate per unit leaf area and A mass is the photosynthetic rate per unit leaf biomass. Hereafter, A max represents mass-based maximum photosynthetic rate. Climate and soil data The climatic data utilized in this study was sourced from the WorldClim 2.0 database 67 , with a resolution of 30 arc seconds (~1 km 2 ) at the equator: mean annual temperature (MAT), mean diurnal range, isothermality, temperature seasonality, annual temperature range, mean annual precipitation (MAP), mean precipitation seasonality. These climate variables were derived from a long-term meteorological time series spanning 1970–2000. The data were interpolated from various meteorological stations across the region and adjusted to account for local topography. The aridity index (AI) was calculated using WorldClim data as AI = MAP/ mean annual evapotranspiration 68 . Soil parameters such as soil pH, soil organic carbon, soil total nitrogen, soil moisture and soil clay content were obtained from the USDA Global Soils Region Map (https://www.nrcs.usda.gov/conservation-basics/natural-resource-concerns/soils/global-soil-map ), which also provides a resolution of 1 km 2 at the equator. Both climatic and soil data were accessed using the “raster” package in R 69 , and then integrated with the genomic and foliar databases for comprehensive analysis by using latitude and longitude. Data analysis We calculated the mean values of all variables for each species in our database. Species names were cross-checked and corrected using The Plant List database through the R package “U.Taxonstand” 70 , ensuring that all species names were accurate and up to date. After correcting the species name, we used S.PhyloMaker to generate a phylogeny of our species with an existing PhytoPhylo megaphylogeny as a backbone provided by Qian and Jin 71 . Out of the 378 species initially in our database, 376 species had corresponding matches in the phylogenetic tree. Therefore, only these were used for further analyses, ensuring that our analyses were based on reliable and accurate species information. To explore whether and how phylogenetic relatedness affects GS, A max , foliar N, and foliar P, we used the function of phylosig in ”phytools” package in R to estimate the phylogenetic signal 72 . The phylogenetic signal was assessed using the λ (lambda) metric. Pagel’s λ is a widely used method for quantifying the strength of phylogenetic signal in trait data, particularly useful for understanding trait evolution in phylogenetic studies 73 . The mean λ value remains consistent regardless of the number of species in the phylogeny, making it robust for large datasets with more than 50 species 32 . The range of λ value is from 0 to 1. A λ value of 1 indicates that trait variation is fully explained by phylogeny (i.e., traits are perfectly phylogenetically conserved). A λ value of 0 suggests that trait variation is independent of phylogenetic relationships (i.e., traits are evolving independently) 74 . Their phylogenetic signal was then visualized using the phylogenetic tree with continuous mapping ( contMap function in the ”phytools” package in R). Given that the phylogenetic signal was detected in foliar N, P, SLA and A max , we used the impute function in “funspace” package in R to fill in the gaps in those variables to have a complete dataset 75 . The missing rates for these variables are 14.6%, 27.3%, 16.2%, and 52.5%, respectively. As suggested in the guideline of “funspace” package, the phylogenetic tree was used in the imputation process. This incorporates species phylogenetic relatedness in conjunction with trait-trait relationship information 76 . Several phylogenetic eigenvectors derived from the tree (as specified by the nEigen argument, defaulting to 10) are incorporated into the original trait matrix. This expanded dataset is then utilized in the random forest-based procedure implemented in the missForest R package to impute the trait matrix 77 . Since GS exhibited a skewed distribution, we applied a log transformation to the data prior to analysis to ensure that the assumptions of the statistical models were met. All variables were then standardized for subsequent analyses. After the database and phylogenetic tree were well established, we first tested whether GS was related to climate, soil condition, and foliar N and P concentrations. The correlation between the variables was examined to eliminate those with high autocorrelation. The phylogenetic linear models were fitted in which the response variable was GS, and the predictors were foliar N and P concentrations, SLA, MAT, MAP, isothermality, temperature seasonality, precipitation seasonality, and aridity for climate, and soil pH, carbon, nitrogen, and C/N for soil condition. The same predictors mentioned above, combined with GS, were employed to explain variability in A max models. We used the phylostep function in the R “phylolm” package to conduct stepwise regression to explore the best fitted model 78 . In our phylogenetic linear regressions, we used the Pagel’s λ method (argument model = lambda , in phylolm function) to assess the strength of the phylogenetic signal. The number of independent bootstrap replicates was set to 100. The final model was achieved by removing the least relevant terms from the full model, in both direction process until the lowest AICc was achieved. To further test our hypothesis, we performed models in which the response variable was GS or A max , the predictors were foliar N, foliar P, A max (GS for A max ), and their interaction. The package “visreg” in R was employed to visualize the model results. The relationships between variables in our database are complex, as their influences on traits of interest can be both direct and mediated by other variables. To uncover these intricate relationships among GS, A max , MAT, aridity, soil pH, and foliar N and P concentrations, we employed the “phyloSEM” package in R 79 . Since structural equation modeling (SEM) is a confirmatory factor analysis, we first developed a conceptual SEM (hypothetical model) based on the results of the phylogenetic linear model and our current ecological knowledge. And then, the phylogenetic tree explained above was used to link the phylogenetic signal to the conceptual SEM. The root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) were employed to evaluate the reliability of our SEM. The SRMR measures the average discrepancies between observed and estimated variances and covariances, with values less than 0.05 indicating an adequate fit 80 . Additionally, a non-significant χ2 test ( P > 0.05) suggested slight differences and thus a good fit 81 . Only statistically significant ( P < 0.05) paths were kept in our final model. The results were visualized using the “semPlot” and “ggplot2” packages in R. To ensure the robustness of our results, two alternative databases were used to perform the same analyses. In one, missing data were imputed using the “Rphylopars” package in R 82 , while the other was the original database without data imputation. The results of both additional approaches did not change the interpretation of our results. DATA AVAILABILITY The data and R code used in this study are available at Figshare: https://doi.org/10.6084/m9.figshare.27257463 DECLARATION OF INTERESTS The authors declare no conflict of interest. Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex ACKNOWLEDGEMENTS This study was supported by European Research Council project ERC-StG-2022-101076740-STOIKOS; the project ETRAITS (PID2022-141972NA-I00), funded by the MICIU/AEI/10.13039/501100011033 and by FEDER, EU; Juan de la Cierva (JDC2023-051504-I) funded by the MICIU/AEI/10.13039/501100011033 and by FSE+; Ramón y Cajal fellowship (RYC2021-031511-I); Juan de la Cierva fellowship (IJC2019-041908-I) funded by the MICIU/AEI/10.13039/501100011033; FPU Fellowship (FPU21/03564); the FWF Austrian Science Fund (Lise Meitner Programme M2714-B29), and the program “Atracción de Talento Investigador Modalidad I” from the Spanish Comunidad de Madrid (2022-T1/AMB-24171). We thank Dr. Marc Riera for his invaluable assistance during the data analysis. REFERENCES 1. Bennett, M. D. & Leitch, I. J. 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Estimate StdErr t.value lowerbootCI upperbootCI P value Intercept -0.15 0.24 -0.62 -0.52 0.37 0.54 MAT 0.12 0.05 2.50 0.01 0.20 <0.05 Soil C/N 0.15 0.04 3.50 0.06 0.23 <0.001 Soil pH 0.18 0.07 2.79 0.06 0.31 <0.01 Aridity 0.19 0.06 3.16 0.09 0.29 <0.01 SLA 0.08 0.05 1.79 -0.01 0.16 0.07 Foliar N 0.36 0.05 7.41 0.27 0.44 <0.001 Foliar P 0.38 0.04 8.91 0.30 0.47 <0.001 GS R 2 = 0.07, λ = 0.92 Intercept 1.09 0.67 1.63 -0.18 2.41 0.10 MAT 0.22 0.09 2.41 0.06 0.39 <0.05 MAP -0.28 0.09 -2.95 -0.45 -0.11 <0.01 Temp_Season 0.12 0.06 1.89 0.02 0.27 0.06 Soil C/N 0.11 0.04 2.68 0.04 0.18 <0.01 Soil pH -0.10 0.06 -1.53 -0.20 0.02 0.13 Aridity 0.23 0.08 2.86 0.07 0.40 <0.01 SLA -0.07 0.04 -1.66 -0.15 0.00 0.10 Foliar P 0.07 0.04 1.68 -0.01 0.14 0.09 Models based on 376 plant species. MAT: mean annual temperature, MAP: mean annual precipitation, A max : mass-based maximum photosynthetic rate, GS: genome size, N: nitrogen, P: phosphorus, Temp_Season: temperature seasonality, SLA: specific leaf area. Table 2 Phylogenetically-informed linear models assessing the relationships between mass-based maximum photosynthetic rate (A max ) and genome size (GS) with foliar nitrogen (N) and phosphorus (P) concentrations and their interactions. A max R 2 = 0.09, λ = 0.35 Estimate StdErr t.value lowerbootCI upperbootCI P value Intercept -0.06 0.23 -0.24 -0.52 0.30 0.81 Foliar N 0.37 0.05 7.82 0.27 0.45 <0.001 Foliar P 0.43 0.04 9.58 0.36 0.51 <0.001 GS 0.03 0.05 0.68 -0.04 0.10 0.49 Foliar N* P -0.02 0.03 -0.77 -0.08 0.02 0.44 Foliar N* GS 0.02 0.04 0.46 -0.05 0.09 0.65 Foliar P* GS 0.03 0.04 0.83 -0.06 0.11 0.41 Model specifications: GS~ Foliar N+ Foliar P+ A max + Foliar N* A max + Foliar P* A max GS R 2 = 0.02, λ = 0.91 Intercept 1.09 0.67 1.62 -0.29 2.13 0.11 Foliar N -0.06 0.05 -1.07 -0.16 0.03 0.28 Foliar P 0.09 0.05 1.94 0.01 0.19 0.05 A max 0.01 0.05 0.25 -0.08 0.12 0.80 Foliar N* P -0.02 0.04 -0.47 -0.10 0.08 0.64 Foliar N* A max 0.04 0.04 0.94 -0.03 0.10 0.35 Foliar P* A max -0.05 0.04 -1.33 -0.15 0.03 0.19 Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex Fig. 1. Distribution of genome size (GS), maximum photosynthesis per mass (A max ), foliar nitrogen (N), and foliar phosphorus (P) across plant phylogeny. The plant silhouettes were retrieved from PhyloPic (http://phylopic.org). Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex Fig. 2. Partial residuals plots showing the relationships between mean annual temperature (MAT), aridity, and foliar phosphorus (foliar P) with GS or maximum photosynthetic rate. All data were standardized. β±SE represents standardized coefficients ± the standard error of each coefficient according to phylogenetically-informed linear models (see data analyses section for further information). The gray-shaded area represents the 95% confidence band. Fig. 3. Phylogenetically-informed structural equation model showing the directly and indirectly influences of MAT, soil pH, Aridity, foliar nitrogen, and foliar phosphorus on genome size and maximum photosynthetic rate (χ2>0.05, RMSEA=0, SRMR<0.001). All variables were observed. The numbers besides the arrows are standardized path coefficients and standard errors. “*”, “**” and “***” besides lines represent significant difference at P < 0.1, P < 0.05 and P < 0.01, respectively. Blue color indicates positive effects, red color indicates negative effects. The thickness of the arrows is the symbols of effect size. Alongside each response variable, the proportion of variance explained (R 2 ) is presented. MAT: mean annual temperature, A max : mass-based maximum photosynthetic rate, GS: genome size, N: nitrogen, P: phosphorus. The bar graph at the bottom indicates the total effect of each variable. Fig. 4. The phylogenetically-informed relationship between maximum photosynthetic rate (A max ), foliar nitrogen (N), and foliar phosphorus (P). The blue color of the points represents the log-transformation genome size. The gray-shaded area represents the 95% confidence band. Fig. 5. Relationship between genome size (GS) and maximum photosynthetic rate for the species included in our database. The gray-shaded area represents the 95% confidence band. Information & Authors Information Version history V1 Version 1 22 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords genome size leaf nitrogen leaf phosphorus photosynthesis phylogenetics Authors Affiliations Xin Song CREAF View all articles by this author Helena Vallicrosa Swiss Federal Institute for Forest Snow and Landscape Research WSL View all articles by this author Pol Fernández Institut Botànic de Barcelona View all articles by this author Joan Garcia-Portaf Complutense University of Madrid View all articles by this author Alba Anadon-Rosell CREAF View all articles by this author Daijun Liu View all articles by this author Guille Peguero Universitat de Barcelona View all articles by this author Marcos Fernández-Martínez CREAF View all articles by this author Metrics & Citations Metrics Article Usage 714 views 251 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xin Song, Helena Vallicrosa, Pol Fernández, et al. Leaf nutrients, but not genome size, modulate plant photosynthesis. Authorea . 22 January 2025. 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