Reproductive responses to increased density and global change drivers in a widespread clonal wetland species, Schoenoplectus americanus

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Whigham, Aoi Kudoh, J. Patrick Megonigal, J. Adam Langley, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2814013/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Sep, 2023 Read the published version in Estuaries and Coasts → Version 1 posted 5 You are reading this latest preprint version Abstract The expansion of many wetland species is a function of both clonal and sexual propagation. The production of ramets through clonal propagation enables plants to move and occupy space, while seeds produced by sexual reproduction enable species to disperse and colonize open or disturbed sites. The balance between clonal propagation and sexual reproduction is known to vary with plant density but few studies have focused on reproductive allocation with density changes in response to global change. Schoenoplectus americanus is a widespread clonal wetland species in North America and a dominant plant in a Chesapeake Bay brackish tidal wetland. Long-term experiments on responses of S. americanus to global change provided the opportunity to compare the two modes of propagation under different treatments condition. Seed production increased with increasing shoot density, supporting the hypothesis that factors causing shoot density to increase stimulate sexual reproduction and dispersal of genets. The increase in allocation to sexual reproduction was mainly the result of an increase in the number of ramets that flowered and not an increase in the number of seeds per reproductive shoot, or the ratio between the number of flowers produced per inflorescence and the number of flowers that developed into seeds. Seed production increased in response to increasing temperatures and decreased or did not change in response to increased CO 2 or nitrogen. Results from this comparative study demonstrate that plant responses to global change treatments affect resource allocation and can alter the ability of species to produce seeds. clonal plants wetland global warming seed production sexual reproduction Schoenoplectus americanus Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Global change impacts submersed and emergent plant communities, changes that are already occurring at the global scale (Short et al. 2016 ). To understand the dispersion and establishment of tidal wetland species, it is important to examine their responses to changing climate conditions. Several studies have focused on the responses of tidal wetland species to changes in environmental factors. Gabler et al. ( 2017 ) showed that mangroves respond mostly to temperature, CO 2 , and changes in hydrology. Nehring and Hesse ( 2008 ) suggested that the spread of Spartina angelica , an invasive species, is most likely due to increasing global temperatures. Phragmites australis is an invasive species in many parts of the world that is likely to benefit from global change (Eller et al. 2017 ), including from an extended growing season and increased levels of CO 2 (Caplan et al. 2015 ; Mozdzer et al. 2016 ). While these and other studies have assessed the responses of tidal wetland plant communities and ecosystem-level parameters such as plant biomass or productivity to changing climate conditions, few studies have focused on the reproductive responses of plants to changing environmental conditions. Expansion of wetland species is a function of both clonal and sexual propagation. Clonal propagation is an adaptive strategy by which plants produce genetically identical individuals that are spaced apart from each other and exchange resources through belowground tissues such as rhizomes, roots, and stolons (de Kroon and van Groenendael 1997 ; Cornelissen et al. 2014 ). Clonality is a successful strategy in flowering plants as demonstrated by the enormous diversity of morphological features associated with clonal species (Klimešová 2018 ). It enables plants to move and occupy space, often at the exclusion of species that were already present (Zedler and Herscher 2004). On the other hand, sexual propagation is also an important strategy that enables species to colonize open or disturbed sites (Kettenring et al. 2015 ; Kettenring and Whigham 2018 ). While the benefits of each type of reproduction are clear, it has been difficult to identify the allocation of sexual propagation in ecologically important clonal plants growing in different environmental conditions (Cornelissen et al. 2014 ). Some studies suggested that the allocation of effort to these two propagation modes vary with plant density. It was predicted that sexual propagation should be favored at low plant densities where potential success of sexual propagation is higher (Loehle 1987 ; Newell and Tramer 1978 ) and decreasing reproductive effort in response to increasing density has been reported (Snell and Burch 1975 ; Williams et al. 1977 ; Law et al. 1979 ; Humphrey and Pyke 1998 ). In contrast, there is also evidence that seed production at higher densities is an adaptive trait. Giroux and Bedard (1995) found greater seed production associated with higher shoot densities of Scirpus pungens ( Schoenoplectus pungens ) in brackish tidal wetlands. Similar tendencies were reported in other species such as Tussilago farfara (Ogden 1974 ; Abrahamson 1975 ; Holler and Abrahamson 1977 ; Demetrio et al. 2020 ). Ikegami et al. ( 2012 ) used a lattice modelling approach to predict that the production of seeds at high densities is the most efficient evolutionary strategy. However, the allocation of effort of clonal species to sexual reproduction in response to climate change has not been clarified. In this study, we focused on the allocation of resources to sexual propagation in Schoenoplectus americanus , an abundant wetland clonal C 3 plant in the sedge family that has variable resource allocation in response to differences in shoot density. Ikegami ( 2004 ) showed that S. americanus produced more flowering shoots and increased inflorescence mass in patches with higher shoot densities. Ikegami did not, however, examine aspects of seed productivity in response to differences in density. Neither has the allocation of resources to sexual production been examined as part of long-term experiments to characterize the species response to differences in CO 2 concentration, nitrogen availability, temperature and sea level rise (Arp and Drake 1991 ; Langley and Megonigal 2010 ; White et al. 2012 ; Langley et al. 2013 ; Mozdzer et al. 2016 ; Noyce et al. 2019 ; Lu et al. 2019 ; Pastore et al. 2017 ; Cott et al. 2020 ; Gabriel et al. 2022 ). We focused on two questions related to S. americanus sexual reproduction: (i) Does sexual reproductive effort increase with increasing shoot density under natural and experimental conditions, and (ii) Does sexual reproductive effort vary in response to the global change treatments of elevated CO 2 , temperature, and nitrogen. We examined these topics by analyzing annual shoot density data from three long-term experiments in combination with measurements of density and allocation to sexual reproduction at the long-term experimental sites. Methods Study site and species This study was conducted at the Global Change Research Wetland (GCReW), part of Kirkpatrick Marsh (38° 53’ N, 76° 33’ W), a 23-ha brackish tidal wetland in the Rhode River subestuary of Chesapeake Bay (Fig. 1 ). The wetland is flooded approximately 40% of the time and the dominant species are the C 3 sedge S . americanus and the C 4 grasses Spartina patens (Aiton) Muhl. and Distichlis spicata (L.) Kuntze. Other abundant species are Iva frutescens L., Kosteletzkya virginica (L.) C. Presl ex A. Gray, and Schoenoplectus robustus (Pursh) Soják. The invasive non-native haplotype of Phragmites australis (Cav.) Trin. E Steud. is also present and has increased in abundance in recent decades (Holmquist et al. 2021 ; McCormick et al. 2010 ). S. americanus is distributed in tidal wetlands on the coasts of North and South America (Koyama 1963 ; Tiner and Burke 1995 ) and is a dominant or co-dominant plant in GCReW experiments that focus on species and ecosystem responses to elevated CO 2 , nitrogen (N), and temperature (Drake et al. 1989 ; Erickson et al. 2007 ; Pastore et al. 2016 , 2017 ; Langley et al. 2009a , b ; Lu et al. 2019 ; Noyce et al. 2019 ; Zhu et al. 2022 ; Gabriel et al. 2022 ). CO 2 experiment This experiment, hereafter referred to as the ‘CO 2 experiment’, began in 1987 to investigate plant responses to elevated CO 2 and was established in three different plant communities (Drake et al. 1989 ; Drake 1992 ). One community, hereafter referred to as ‘C 3 ’, was dominated by S. americanus . The second community, hereafter referred to as ‘C 4 ’, was dominated by Spartina patens and Distichlis spicata . Both species are in the Poaceae and use the C 4 photosynthetic pathway (Ehleringer and Cerling 2002 ) that responds minimally to elevated CO 2 compared to C 3 species (Ghannoum et al. 2000 ). A third community, hereafter referred to as ‘Mixed’, had all three species. Each community have deviated from the original composition in 1986, generally increasing in dominance of relatively flood-tolerant S. americanus (Gabriel et al. 2022 ). In each community, there is an equal number (5) of open-top chambers (ca. 1 m diameter) that continuously receive ambient air and five chambers that receive ambient air + 340 ppm CO 2 during treatment periods. There are also five no-chamber controls in each community, hereafter referred to as ‘control plots’. The ambient and elevated treatments run 24 h per day from May 1 to October 31 annually. CO 2 x N experiment A second long-term experiment that also uses open-top chambers was initiated in 2006 to investigate plant and ecosystem responses to elevated CO 2 and N addition (Langley et al. 2009a , b ). The study was established in an area of the Kirkpatrick Marsh that was in the same general location as the CO 2 experiment but where the plant community was dominated by S. americanus . Five chambers (ca. 2 m diameter) receive ambient air, five chambers receive ambient + 340 ppm CO 2 , five chambers receive ambient air and no added nitrogen, five chambers receive elevated CO 2 and nitrogen. Each chamber has an outside non-chambered control area, hereafter referred to as ‘control plots’. Chambers that receive CO 2 are managed using the same protocols as described above for the CO 2 experiment, except that CO 2 is only added during daylight hours. Chambers in the nitrogen addition treatment are fertilized monthly from May to September with NH 4 Cl (5 g N m − 2 month − 1 = 25 g N m − 2 yr − 1 ). Warming experiment In 2016, the Salt Marsh Accretion Response to Temperature eXperiment (SMARTX) was initiated to investigate plant and ecosystem responses to whole-ecosystem warming using infrared lamps and belowground heating cables (Noyce et al. 2019 ). We used the 12 experimental plots in the S. americanus -dominated plant community (a.k.a. C 3 community) treated with four levels of warming (control, + 1.7°C, + 3.4°C, + 5.1°C) at ambient CO 2 , with each treatment replicated three times. Thus, replication in the warming experiment (n = 3) was lower than in the other experiments (n = 5). Schoenoplectus density and sexual production During the 2019 growing season (August-September) we counted the number of vegetative and reproductive shoots in each chamber and control plot for each of the three experiments. We randomly harvested 10 flowering shoots from each chamber and control plot for determination of reproductive effort (described below). If there were fewer than 10 flowering shoots per chamber or control plot, we harvested all of them. Flowering shoots produce a terminal inflorescence composed of 1–15 spikelets. Each spikelet has one or more flowers that can develop into fruits that are a firm, brown achene. In the laboratory, we counted the number of spikelets on each harvested shoot and dissected each spikelet to determine the number of mature and dispersed fruits. The number of dispersed fruits could be determined because each one left a depression on the spikelet rachilla. Immature fruits on each spikelet, always at the terminal end of a spikelet, were also counted. Immature fruits were much smaller than mature fruits, were not black, and were still subtended by a bract that is not present at the base of mature fruits. Data Analysis Statistical analyses were performed using R version 3.6.0 (R Core Team 2019 ). To evaluate seed reproduction, three sexual reproduction variables were used: 1. flowering ratio (the ratio of the number of flowering shoots to the total number of shoots), 2. potential seed production (sum of mature and immature seeds) per reproductive shoot, and 3. ratio of mature to potential seeds in each reproductive shoot. To test the hypothesis that sexual reproductive effort increased with increasing shoot density, we performed a logistic regression model for variables 1 and 3, and a linear regression model for variable 2 to examine the association of shoot density and the three reproduction variables. These analyses were performed with the glm and lm functions. We also calculated the main and interactive effects of a factorial combination of shoot density and treatment. In each experiment, shoot count data from control plots and all treatments were pooled and analyzed with a logistic regression model for variables 1 and 3, and a linear regression model for variable 2. The effects of experimental treatments (CO 2 , N, temperature) on the three reproductive variables were also examined. Data for the three reproductive variables were tested for normality with the Shapiro-Wilk test (Shapiro.test function in the stats R package version 3.6.0) before and after log transformation. Log transformation was accomplished by applying the equation log(x + 1) to each data set. We used untransformed data on the following analysis because the data were not normally distributed either before or after log transformation (Supplemental Table 1). For the CO 2 experiment, the main effects and interactions between treatments (control, ambient CO 2 , elevated CO 2 ) and communities (C 3 , C 4 , Mixed) were analyzed with the Scheirer-Ray-Hare test (scheirerRayHare function in the rcompanion R package version 2.3.25) and means were compared with the Steel-Dwass test. For the CO 2 x N and warming experiments, treatments were compared using Kruskal-Wallis test (kruskal.test function in the stats R package version 3.6.0) and means were compared with Steel-Dwass test. We considered p-values of < 0.50 to be particularly meaningful but this arbitrary p-value threshold was not used as the sole source of inference to judge whether our results were scientifically meaningful (Smith 2020 ). Results Sexual reproductive effort responses to differences in shoot density in non-chambered control plots The flowering ratio increased significantly across spatial and temporal variability as shoot density increased in the control plots of all three experiments (Fig. 2 A, D, G; Table 1 ). In the CO 2 experiment, the number of potential seeds per reproductive shoot and the ratio of mature seeds to potential seeds increased with increasing shoot density in the control plots (Fig. 2 B, C; Table 1 ). In the CO 2 x N experiment, the number of potential seeds was positively related to shoot density, but the relationship was not significant and the ratio of mature to potential seeds was negative in the control plots (Figs. 2 E, F; Table 1 ). There was no relationship between the two seed-related variables and density in the Warming experiment control plots (Fig. 2 H, I; Table 1 ). Table 1 Results of the regression comparisons between shoot density and reproductive variables (flowering ratio, number of potential seeds per reproductive shoot, ratio of mature to potential seeds). A logistic regression model was used for the flowering ratio and ratio of mature to potential seeds. A linear regression model was used for number of potential seeds per reproductive shoot. CO 2 experiment (C 3 + C 4 + Mixed) CO 2 x N expriment (C 3 community) Warming experiment (C 3 community) Control Amb CO 2 Elev CO 2 Control Amb CO 2 Amb CO 2 + N Elev CO 2 Elev CO 2 + N + 0ºC + 1.7ºC + 3.4ºC + 5.1ºC Flowering ratio per plot Coefficients (Intercept) -6.9 -2.1 -3.6 -8 -1 -2.6 -2.6 -2.6 -8.1 -2.3 -3.1 -2.9 Coefficients (Density) 8.10E-03 -1.60E-05 1.40E-03 4.70E-03 -1.10E-03 3.90E-04 2.60E-04 -1.90E-05 7.50E-03 3.50E-04 2.20E-03 2.10E-03 Residual deviance 46.5 319 314.3 68.8 84.8 177.3 114.4 162.9 13.9 42.7 56 56.4 p-value* < 2E-16 0.95 1.10E-07 0.024 0.0022 0.36 0.56 0.96 0.019 0.67 4.70E-05 7.90E-05 Number of potential seeds per reproductive shoot Coefficients (Intercept) 2.1 78.6 22.3 -29.7 54.2 47.7 33 43.9 20.7 47.8 45.6 55.2 Coefficients (Density) 0.02 -0.05 0.02 0.09 -0.003 -0.02 0.02 0.004 0.002 -0.02 -0.007 -0.02 Adjusted R-squared 0.05 0.06 0.007 0.04 -0.005 0.01 0.0008 -0.006 -0.1 -0.004 -0.008 0.005 p-value 0.041 0.0059 0.17 0.22 0.81 0.12 0.29 0.75 0.98 0.42 0.7 0.22 Ratio of mature to potential seeds per reproductive shoot Coefficients (Intercept) -5.8 -3 -4.7 2.8 -1.9 -2 -1.3 -3.6 -2.2 -0.55 -0.031 -1.9 Coefficients (Density) 7.80E-03 2.40E-03 3.60E-03 -6.50E-03 2.70E-04 -2.80E-04 -1.10E-03 1.10E-03 -8.90E-05 -1.10E-03 -1.90E-03 7.70E-04 Residual deviance 288.8 1702.8 1762.6 66.3 2109.2 969.8 1567.9 1130 24.4 971.2 1237.2 1030.6 p-value* < 2E-16 < 2E-16 < 2E-16 1.80E-08 0.048 0.32 6.70E-06 1.60E-06 0.98 2.70E-04 8.60E-09 4.00E-04 * The p-value indicates the significance of a density effect. Sexual reproduction responses under experimental conditions CO 2 experiment The number of potential seeds per reproductive shoot and the ratio of mature to potential seeds differed sharply among treatments (Table 2 ). Means for the flowering ratio were not significantly different but the means were greater in the ambient and elevated chambers (Fig. 3 A). The mean number of potential seeds was higher in the ambient and elevated chambers compared to controls (Fig. 3 B). The mean ratio of mature to potential seeds were also higher in the ambient chambers compared to the controls and elevated chambers, particularly in the elevated chambers (control vs ambient: p = 0.011, ambient vs elevated: p = 5.9E-05 in Steel-Dwass test, Fig. 3 C). The number of potential seeds and the ratio of mature to potential seeds differed among communities (Table 2 ), and treatments and communities interacted to affect the ratio of mature to potential seeds (Table 2 ). The means and standard errors for the three reproductive variables in each plant community are shown in Supplemental Table 2 and Supplemental Fig. 1. Table 2 Results of Scheirer-Ray-Hare test for the CO₂ experiment and the Kruskal-Wallis test for CO₂ x N experiment and warming experiment for four reproductive variables: flowering ratio, the number of potential seed per reproductive shoot, ratio of mature to potential seeds. Flowering ratio Number of potential seeds per reproductive shoot Ratio of mature to potential seeds Statistic H / Chi-squared* p-value Statistic H / Chi-squared* p-value Statistic H / Chi-squared* p-value CO 2 experiment (C 3 + C 4 + Mixed) Treatment 4.9 0.09 47.7 0.0 19.9 4.8E-05 Community 1.6 0.44 16.8 2.3E-04 37.5 0.0 Interaction (Treatment:Community) 3.0 0.55 6.5 0.17 9.7 0.046 CO 2 x N experiment (C 3 community) Treatment 74.4 2.7E-15 35.8 3.2E-07 39.0 6.9E-08 Warming experiment (C 3 community) Treatment 27.7 4.1E-07 8.8 0.031 3.3 0.35 * This column shows the H statistic for the CO₂ experiment and chi-squared for CO₂ x N experiment and warming experiment. CO 2 x N experiment There were significant treatment differences for all reproductive variables (Table 2 ). The means for the flowering ratio were higher in all treatment chambers and highest in ambient chambers (Fig. 3 D). The mean number of potential seeds was higher in the treatment chambers compared to controls, but the means were not significantly different (Fig. 3 E). The mean ratio of mature to potential seeds was more variable, and there were no significant differences between controls and treatment chambers (Fig. 3 F). Means and standard errors for three reproductive variables are in Supplemental Table 2. It is difficult to compare the controls and chambers in this experiment because during the summer of 2019, an outbreak of an unknown insect, not apparent in most years, consumed inflorescences inordinately in the control plots (A. Langley, personal observation). Warming experiment There were significant treatment differences for the flowering ratio and the number of potential seeds per reproductive shoot (Table 2 ). Warming increased the flowering ratio and the mean number of potential seeds (Fig. 3 G, H). The means for the ratio of mature to potential seeds were not significantly different but were greater in all warming plots (Fig. 3 I). Means and standard error for three reproductive variables are in Supplemental Table 2. Effects of experimental conditions on the relationship between seed production and shoot density CO 2 experiment The flowering ratio increased significantly with increasing shoot density in the control plots (Fig. 2 ) but there was no statistically significant relationship with density in the ambient chambers (Fig. 4 A; Table 1 ). There was a significantly positive relationship between the flowering ratio and density in the elevated chambers (Fig. 4 A; Table 1 ). The number of potential seeds increased with increasing shoot density in the control plots (Fig. 2 B). However, the relationship was significantly negative in the ambient chambers and while the relationship was positive in the elevated chambers it was not significant (Fig. 4 B; Table 1 ). The ratio of mature to potential seeds increased in the controls and treatment chambers (Figs. 2 C and 4 C; Table 1 ). CO 2 x N experiment The flowering ratio was significantly related to shoot density in the control plots (Fig. 2 D). The relationship was negative in the ambient chambers (Fig. 4 D; Table 1 ) and there were no significant relationships in the other three treatments (Fig. 4 D, G; Table 1 ). The number of potential seeds was positively related to shoot density, but the relationship was not statistically significant in the control plots (Fig. 2 E). The relationships were not significant in treatment chambers (Fig. 4 E, H). The ratio of mature to potential seeds had a negative relationship to density in the control chambers (Fig. 2 F). However, the ratio increased with increasing shoot density in the ambient and elevated + N chambers but the relationships were not significant (Fig. 4 F, I; Table 1 ). Warming experiment The flowering ratio increased with increasing shoot density in control plots (Fig. 2 G) and the + 3.4°C and + 5.1°C plots but the relationship was not statistically significant in the + 1.7°C plots (Fig. 4 J; Table 1 ). The number of potential seeds had no statistically significant relationship to density in any of the elevated temperature plots (Fig. 4 K; Table 1 ). The ratio of mature to potential seeds had no statistically significant relationship to density in control plots (Fig. 2 I) but the relationships were negative and statistically significant in the + 1.7 and + 3.4°C plots and positive and statistically significant in the + 5.1°C plots (Fig. 4 L; Table 1 ). Discussion In over 35 years of experimental research, S . americanus in the Kirkpatrick Marsh has been temporally dynamic with changes in the relative abundance and morphology (Drake 2014 ; Lu et al. 2019 ). The results of this study demonstrate that changes in shoot density can influence the reproductive effort of S . americanus and the relationship between seed production and shoot density may vary depending on experimental conditions. Schoenoplectus americanus is widespread nationally (Fig. 1 and Smith 2002 ), is a dominant species in Chesapeake Bay wetlands (McCormick and Somes 1982) and will be an important species in coastal wetlands as they respond to changing climatic conditions (Noyce et al. 2023 ; Vahsen et al. 2023 ). It will be an especially important species in influencing wetland response to sea level rise at is has been shown to be most abundant and productive when tidal flooding is frequent (Kirwan and Guntenspergen 2015 ; Holmquist et al. 2021 ). The long-term GCReW experiments have focused on response of plant production and density in S. americanus but none of the prior research focused on seed production, an important component of ecosystem function (e.g., Pearse et al. 2017 ; Solbreck and Knape 2017 ). Our results from the non-chambered control plots demonstrate a positive relationship between shoot density and sexual reproductive effort for S . americanus (Fig. 2 ). The increase of sexual reproductive effort was mainly the result of an increase in the number of ramets that flowered. Studies of other species suggest that the relationship between sexual reproductive effort and density is variable. Winn and Pitelka ( 1981 ) suggested that increased shoot density would result in increased competition between ramets and decreased resource availability for seed production, a view supported by Newell and Tramer ( 1978 ) and Loehle ( 1987 ). In contrast, increased seed production at higher densities may be an adaptive trait in competitive environments as seed production and dispersal would enable species to escape from competitive crowding and colonize new environments (Abrahamson 1975 ; Abrahamson 1980 ; Winn and Pitelka 1981 ; Giroux and Bedard 1995). A feature of clonal species like S . americanus that would support increased seed production, even at higher densities, is division of labor (Roiloa et al. 2014 ), a process that enables physiologically connected ramets of clonal plants to share resources. In environments such as wetlands where clonal herbaceous species are often dominant and where resources are spatially heterogeneous (e.g., Weiss et al. 2014 ; Dong et al. 2015 ), division of labor would enable ramets to secure resources that are locally available with ramets that may be in rooted in areas where resources are less available. Ikegami ( 2004 ) demonstrated that division of labor occurs in S . americanus and that process may be partially responsible for the positive relationships between density in the non-chambered control plots and the two seed production variables in the CO 2 experiment (Fig. 2 B. C) as the sharing of resources can benefit a ramet that is in suboptimal conditions. In the experimental conditions, we found that there was no positive relationship between shoot density and flowering ratio under increased CO 2 , N addition and modest warming (+ 1.7°C) conditions. Multiple studies have shown that plant production of S. americanus is limited under CO 2 increasing. Langley and Megonigal ( 2010 ) showed that N addition enhanced the CO 2 stimulation of plant productivity in the first year of the CO 2 x N experiment. Lu et al. ( 2019 ) included more recent data from the CO 2 experiment and analyzed data from the CO 2 x N experiment. In addition to annual differences in shoot density in both experiments, Lu et al. ( 2019 ) found that the diameter and height of S . americanus shoots decreased in the CO 2 experiment but not in the CO 2 x N experiment. They concluded that N limitation caused by elevated CO 2 was responsible for the morphological changes as added N reversed the effects in the CO 2 x N experiment. The N limitation caused production of smaller individual stems at higher plant density (Lu et al., 2019 ) and may restricted seed production at the higher density (Fig. 4 A, D). Seed production has been predicted or shown to both increase and decrease in crops in response to higher CO 2 (Ziska et al. 2001 ; Allen and Prasad 2004; Lenka et al. 2017 ) and the same has been shown for native plants (Billings and Billings 1983 ; Zangerl and Bazzaz 1984 ; HilleRisLambers et al. 2009 ). For S . americanus , an increase in the percentage of flowering shoots associated with increased shoot density does not, however, necessarily result in increased seed production per individual reproductive shoot when CO 2 and CO 2 x N are environmental factors. A positive relationship between the three variables and plant density in elevated CO 2 (Fig. 4 A, B, C) indicate that as concentrations of atmospheric CO 2 increase, S . americanus will allocate more resources to seed production. When shoot density is not considered, however, the lower means for all three variables in the elevated chambers (Elev in Fig. 3 A B, C) indicate that other factors may limit seed production, especially the maturation of seeds. The number of potential seeds per flowering shoot were statistically the same for ambient and elevated chambers (Fig. 3 B), but the mean ratio of mature to potential seeds was statistically lower in the elevated chambers, indicating that resources may limit seed maturation under elevated CO 2 . Unfortunately, interpretation of the results from the CO 2 x N experiment were complicated by the impacts of an insect outbreak across the GCReW area prior to when we sampled the site. A positive response between shoot density and the number of potential seeds per flowering shoot (Fig. 2 E) and a significant increase in the same variable when density was not considered indicate that CO 2 and N increase the potential for seed production. Resource limitations, however, may have a negative impact of seed maturation (compare Figs. 3 E and F), especially in response to increasing shoot densities (Fig. 4 E-I). Temperature has variable effects on seed production in crops and native species (Klady et al. 2011 ; Caignard et al. 2017 ; Lenka et al. 2017 ; Lippmann et al. 2019 ). Our results demonstrate that temperature will impact sexual reproductive effort in S . americanus . Means for all three variables related to sexual reproductive effort were higher in the + 1.7, + 3.4, and + 5.1°C treatments in the Warming experiment and the differences were significant for two of the variables (Fig. 3 G, H, I). Data from the CO 2 and CO 2 x N experiments also provide evidence of a potential temperature effect on sexual reproductive effort. Higher means for the three variables from the Ambient treatment in the CO 2 and CO 2 x N experiments (Amb in Fig. 3 A, B, C, D, E, F) versus the non-chambered control suggests a chamber effect that we interpret to be due to the cumulative effect of slightly higher temperatures in the ambient chambers over many years. Temperature differences inside and outside of chambers have been reported for experiments that are not designed to evaluate temperature (Vanaja et al. 2006 ; Messerli et al. 2015 ) and Drake et al. ( 1989 ) reported temperatures inside chambers in the CO 2 experiment at GCReW to be 1-2 o C higher than outside temperatures. Noyce et al. ( 2019 ), using plant density as a variable in the calculation of net primary production, also found annual differences in response to the temperature treatments in the first three years of the SMARTX experiment. They also showed that root-to-shoot allocation was changed depending on warming and suggested that modest warming (+ 1.7°C) caused plant demand for N to outpace the soil N supply while extreme warming (+ 3.4 and + 5.1°C) caused the N supply to outpace plant N demand. In this study, there is no positive relationship between flowering ratio and plant density in ambient chambers in CO 2 and CO2 x N experiments, and at + 1.7°C plots in Warming experiment. However, there was a positive relationship at the + 3.4 and + 5.1°C plots in Warming experiment. Seed production of S. americanus may be suppress under modest warming conditions because of N limitation but not be suppressed under extreme warming conditions with little N limitation. Few studies have documented the effects of multiple factors on seed production. Miyagi et al. ( 2007 ) found that elevated CO 2 increased seed production in eleven annual species but concluded that the response was limited by nitrogen availability. Osanai et al. ( 2017 ) examined the interacting effects of elevated CO 2 and temperature on cotton under variable soil conditions. They found, similar to this study, temperature and elevated CO 2 had a positive impact on fruit production, but the temperature response was larger, and the two variables interacted through the effects on nutrient availability, especially nitrogen availability. Increasing nutrient availability, however, may not result in increased seed production in nutrient limited ecosystems. Molau and Shaver ( 1997 ) found that elevating temperatures increased sexual reproductive effort of Eriophorum. vaginatum , a species in the Cyperaceae - similar to S . americanus - in open topped chambers in arctic environments, but adding N and P to experimental plots had no effect on seed number. In summary, results of this comparative study demonstrate that an important wetland clonal species produces more flowering ramets in response to increasing density and that increasing temperature has a positive effect on the allocation of resources to sexual reproduction, perhaps mediated by temperature effects on N supply (Noyce et al. 2019 ). We also demonstrate that when other environmental factors (CO 2 and N) are considered, increased allocation of resources to sexual reproduction may not result in an increase in the production of more mature seeds, even though reproductive ramets may have more flowers (i.e., potential seeds). The results suggest that the adaptative reproductive strategy of S . americanus will be impacted by future climate changes. The study demonstrated that an increased allocation of resources to sexual reproduction but not the production of mature seeds, an important component of the species ecological strategy (Ikegami et al. 2012 ). Future research on this species and other important wetland clonal species, should focus on experiments that include several levels of the key environmental factors. The research should also consider studies of seed dispersal, similar to those conducted by Kudoh and Whigham ( 2001 ) and seedling growth and establishment as the ability of species to colonize suitable habitats will be an important aspect of wetlands in the future. Declarations Acknowledgments A. K. was supported by Kyoto University OMORO Challenge Scholarship Program. The long-term research at the study sites has been supported by the Smithsonian Institution and the National Science Foundation Long-Term Research in Environmental Biology Program (DEB-0950080, DEB-1457100, DEB-1557009, DEB-2051343), the Department of Energy Terrestrial Ecosystem Science Program (DE-FG02-97ER62458, DE-SC0014413, DE-SC0019110, DE-SC0021112), the United States Geological Survey (G10AC00675), the National Science Foundation Long-Term Research in Environmental Biology Program (DEB-0950080, DEB-1457100, DEB-1557009, DEB-2051343), and the Smithsonian Environmental Research Center. References Abrahamson, W. 1975. Reproductive strategies in dewberries. Ecology 56: 721–726. doi: 10.2307/1935508 . Abrahamson, W. 1980. Demography and vegetative reproduction. In Demography and evolution in plant populations , ed. Solbrig O. T., 89–106. Oxford: Blackwell Scientific. Allen, L. H., P. V. Prasad, and R. M. Goodman. 2004. Crop responses to elevated carbon dioxide. 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(A) Black dots shows the distribution of \u003cem\u003eSchoenoplectus americanus. \u003c/em\u003e(B) Enlarged map showing the location of the study area within the Chesapeake Bay. The distribution of \u003cem\u003eS. americanus \u003c/em\u003ewas provided from GBIF Occurrence Download (https://doi.org/10.15468/dl.qv53hc). The map was created with Quantum Geographic Information System (QGIS) software version 3.16.0 (QGIS Development Team, 2022). The base map was obtained from Natural Earth.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2814013/v1/cd57f097ee9b21e3021726fa.png"},{"id":36079855,"identity":"8c2085ac-943e-46b8-961a-26ef05fe6816","added_by":"auto","created_at":"2023-04-20 17:47:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147305,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic and liner regression model relationships between reproductive variables and shoot density in control plots showed that seed production increased with increasing shoot density. The increase in allocation to seed production was mainly caused by an increase in flowering ratio. (A–C) = CO\u003csub\u003e2\u003c/sub\u003e experiment, (D–F) = CO\u003csub\u003e2 \u003c/sub\u003ex N experiment, (G–I) = Warming experiment. (A, D, G) Flowering ratio in each experimental plot. (B, E, H) Number of potential seeds per reproductive shoot. (C, F, I) Ratio of mature to potential seeds per reproductive shoot. The plots with deep color indicate overlapping. Black boxes indicate the community type as described in the Methods.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2814013/v1/ddf337e78ed7d176bad7d6aa.png"},{"id":36079854,"identity":"a2cb7ef3-2f7d-41ed-974a-86bccb041fa8","added_by":"auto","created_at":"2023-04-20 17:47:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":348682,"visible":true,"origin":"","legend":"\u003cp\u003eReproductive variables of seed production increased in response to increasing temperatures but decreased or did not change in response to increased CO2 or nitrogen. (A–C) = CO\u003csub\u003e2\u003c/sub\u003e experiment, (D–F) = CO\u003csub\u003e2 \u003c/sub\u003ex N experiment, (G–I) = Warming experiment. (A, D, G) = flowering ratio, (B, E, H) = Number of potential seeds per reproductive shoot, (C, F, I) = Ratio of mature to potential seeds. The error bars indicate the SE. Means were compared using Steel-Dwass test. Values with the same letter indicate no significant difference at\u0026nbsp;\u003cem\u003eα\u003c/em\u003e = 0.05.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2814013/v1/3474c47202a0d5224cbbce11.png"},{"id":36080338,"identity":"97823a74-bf7b-4214-a950-1ae74ed8e9c1","added_by":"auto","created_at":"2023-04-20 17:55:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":466738,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic and liner regression model relationships between reproductive variables and shoot density in experimental conditions showed that no positive relationship between shoot density and flowering ratio under CO\u003csub\u003e2\u003c/sub\u003e increased, N addition and modest warming (+1.7 \u003csup\u003e°\u003c/sup\u003eC) conditions. (A–C) = CO\u003csub\u003e2\u003c/sub\u003e experiment, (D–I) = CO\u003csub\u003e2 \u003c/sub\u003ex N experiment, (J–L) = Warming experiment. (A, D, G, J) Flowering ratio in each experimental plot. (B, E, H, K) Number of potential seeds per reproductive shoot. (C, F, I, L) Ratio of mature to potential seeds per reproductive shoot. The plots with deep color indicate overlapping. Black boxes indicate the community type as described in the Methods.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2814013/v1/87c2ee08a0c49de7548391f7.png"},{"id":43301346,"identity":"80fa8355-4f53-4b6f-a9ba-03dbdaecd2e0","added_by":"auto","created_at":"2023-09-18 15:10:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1770802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2814013/v1/94bdb9d6-c112-476c-95b7-138b2c070f30.pdf"},{"id":36079852,"identity":"401fb005-9f8b-4b03-87b4-a59c6058894d","added_by":"auto","created_at":"2023-04-20 17:47:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":181842,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableandFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-2814013/v1/94074d9735e6acff38341b40.docx"}],"financialInterests":"","formattedTitle":"Reproductive responses to increased density and global change drivers in a widespread clonal wetland species, Schoenoplectus americanus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal change impacts submersed and emergent plant communities, changes that are already occurring at the global scale (Short et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To understand the dispersion and establishment of tidal wetland species, it is important to examine their responses to changing climate conditions. Several studies have focused on the responses of tidal wetland species to changes in environmental factors. Gabler et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) showed that mangroves respond mostly to temperature, CO\u003csub\u003e2\u003c/sub\u003e, and changes in hydrology. Nehring and Hesse (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) suggested that the spread of \u003cem\u003eSpartina angelica\u003c/em\u003e, an invasive species, is most likely due to increasing global temperatures. \u003cem\u003ePhragmites australis\u003c/em\u003e is an invasive species in many parts of the world that is likely to benefit from global change (Eller et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), including from an extended growing season and increased levels of CO\u003csub\u003e2\u003c/sub\u003e (Caplan et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mozdzer et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While these and other studies have assessed the responses of tidal wetland plant communities and ecosystem-level parameters such as plant biomass or productivity to changing climate conditions, few studies have focused on the reproductive responses of plants to changing environmental conditions.\u003c/p\u003e \u003cp\u003eExpansion of wetland species is a function of both clonal and sexual propagation. Clonal propagation is an adaptive strategy by which plants produce genetically identical individuals that are spaced apart from each other and exchange resources through belowground tissues such as rhizomes, roots, and stolons (de Kroon and van Groenendael \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Cornelissen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Clonality is a successful strategy in flowering plants as demonstrated by the enormous diversity of morphological features associated with clonal species (Klimešov\u0026aacute; \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It enables plants to move and occupy space, often at the exclusion of species that were already present (Zedler and Herscher 2004). On the other hand, sexual propagation is also an important strategy that enables species to colonize open or disturbed sites (Kettenring et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kettenring and Whigham \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While the benefits of each type of reproduction are clear, it has been difficult to identify the allocation of sexual propagation in ecologically important clonal plants growing in different environmental conditions (Cornelissen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome studies suggested that the allocation of effort to these two propagation modes vary with plant density. It was predicted that sexual propagation should be favored at low plant densities where potential success of sexual propagation is higher (Loehle \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Newell and Tramer \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) and decreasing reproductive effort in response to increasing density has been reported (Snell and Burch \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Williams et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Law et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Humphrey and Pyke \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In contrast, there is also evidence that seed production at higher densities is an adaptive trait. Giroux and Bedard (1995) found greater seed production associated with higher shoot densities of \u003cem\u003eScirpus pungens\u003c/em\u003e (\u003cem\u003eSchoenoplectus pungens\u003c/em\u003e) in brackish tidal wetlands. Similar tendencies were reported in other species such as \u003cem\u003eTussilago farfara\u003c/em\u003e (Ogden \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Abrahamson \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Holler and Abrahamson \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Demetrio et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ikegami et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) used a lattice modelling approach to predict that the production of seeds at high densities is the most efficient evolutionary strategy. However, the allocation of effort of clonal species to sexual reproduction in response to climate change has not been clarified.\u003c/p\u003e \u003cp\u003eIn this study, we focused on the allocation of resources to sexual propagation in \u003cem\u003eSchoenoplectus americanus\u003c/em\u003e, an abundant wetland clonal C\u003csub\u003e3\u003c/sub\u003e plant in the sedge family that has variable resource allocation in response to differences in shoot density. Ikegami (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) showed that \u003cem\u003eS. americanus\u003c/em\u003e produced more flowering shoots and increased inflorescence mass in patches with higher shoot densities. Ikegami did not, however, examine aspects of seed productivity in response to differences in density. Neither has the allocation of resources to sexual production been examined as part of long-term experiments to characterize the species response to differences in CO\u003csub\u003e2\u003c/sub\u003e concentration, nitrogen availability, temperature and sea level rise (Arp and Drake \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Langley and Megonigal \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; White et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Langley et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mozdzer et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Noyce et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pastore et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cott et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gabriel et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We focused on two questions related to \u003cem\u003eS. americanus\u003c/em\u003e sexual reproduction: (i) Does sexual reproductive effort increase with increasing shoot density under natural and experimental conditions, and (ii) Does sexual reproductive effort vary in response to the global change treatments of elevated CO\u003csub\u003e2\u003c/sub\u003e, temperature, and nitrogen. We examined these topics by analyzing annual shoot density data from three long-term experiments in combination with measurements of density and allocation to sexual reproduction at the long-term experimental sites.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site and species\u003c/h2\u003e \u003cp\u003eThis study was conducted at the Global Change Research Wetland (GCReW), part of Kirkpatrick Marsh (38\u0026deg; 53\u0026rsquo; N, 76\u0026deg; 33\u0026rsquo; W), a 23-ha brackish tidal wetland in the Rhode River subestuary of Chesapeake Bay (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The wetland is flooded approximately 40% of the time and the dominant species are the C\u003csub\u003e3\u003c/sub\u003e sedge \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e and the C\u003csub\u003e4\u003c/sub\u003e grasses \u003cem\u003eSpartina patens\u003c/em\u003e (Aiton) Muhl. and \u003cem\u003eDistichlis spicata\u003c/em\u003e (L.) Kuntze. Other abundant species are \u003cem\u003eIva frutescens\u003c/em\u003e L., \u003cem\u003eKosteletzkya virginica\u003c/em\u003e (L.) C. Presl ex A. Gray, and \u003cem\u003eSchoenoplectus robustus\u003c/em\u003e (Pursh) Soj\u0026aacute;k. The invasive non-native haplotype of \u003cem\u003ePhragmites australis\u003c/em\u003e (Cav.) Trin. E Steud. is also present and has increased in abundance in recent decades (Holmquist et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; McCormick et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eS. americanus\u003c/em\u003e is distributed in tidal wetlands on the coasts of North and South America (Koyama \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1963\u003c/span\u003e; Tiner and Burke \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and is a dominant or co-dominant plant in GCReW experiments that focus on species and ecosystem responses to elevated CO\u003csub\u003e2\u003c/sub\u003e, nitrogen (N), and temperature (Drake et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Erickson et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Pastore et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Langley et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009a\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Noyce et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gabriel et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCO\u003csub\u003e2\u003c/sub\u003e experiment\u003c/h2\u003e \u003cp\u003eThis experiment, hereafter referred to as the \u0026lsquo;CO\u003csub\u003e2\u003c/sub\u003e experiment\u0026rsquo;, began in 1987 to investigate plant responses to elevated CO\u003csub\u003e2\u003c/sub\u003e and was established in three different plant communities (Drake et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Drake \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). One community, hereafter referred to as \u0026lsquo;C\u003csub\u003e3\u003c/sub\u003e\u0026rsquo;, was dominated by \u003cem\u003eS. americanus\u003c/em\u003e. The second community, hereafter referred to as \u0026lsquo;C\u003csub\u003e4\u003c/sub\u003e\u0026rsquo;, was dominated by \u003cem\u003eSpartina patens\u003c/em\u003e and \u003cem\u003eDistichlis spicata\u003c/em\u003e. Both species are in the Poaceae and use the C\u003csub\u003e4\u003c/sub\u003e photosynthetic pathway (Ehleringer and Cerling \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) that responds minimally to elevated CO\u003csub\u003e2\u003c/sub\u003e compared to C\u003csub\u003e3\u003c/sub\u003e species (Ghannoum et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). A third community, hereafter referred to as \u0026lsquo;Mixed\u0026rsquo;, had all three species. Each community have deviated from the original composition in 1986, generally increasing in dominance of relatively flood-tolerant \u003cem\u003eS. americanus\u003c/em\u003e (Gabriel et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In each community, there is an equal number (5) of open-top chambers (ca. 1 m diameter) that continuously receive ambient air and five chambers that receive ambient air\u0026thinsp;+\u0026thinsp;340 ppm CO\u003csub\u003e2\u003c/sub\u003e during treatment periods. There are also five no-chamber controls in each community, hereafter referred to as \u0026lsquo;control plots\u0026rsquo;. The ambient and elevated treatments run 24 h per day from May 1 to October 31 annually.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCO\u003csub\u003e2\u003c/sub\u003e x N experiment\u003c/h2\u003e \u003cp\u003eA second long-term experiment that also uses open-top chambers was initiated in 2006 to investigate plant and ecosystem responses to elevated CO\u003csub\u003e2\u003c/sub\u003e and N addition (Langley et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009a\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003eb\u003c/span\u003e). The study was established in an area of the Kirkpatrick Marsh that was in the same general location as the CO\u003csub\u003e2\u003c/sub\u003e experiment but where the plant community was dominated by \u003cem\u003eS. americanus\u003c/em\u003e. Five chambers (ca. 2 m diameter) receive ambient air, five chambers receive ambient\u0026thinsp;+\u0026thinsp;340 ppm CO\u003csub\u003e2\u003c/sub\u003e, five chambers receive ambient air and no added nitrogen, five chambers receive elevated CO\u003csub\u003e2\u003c/sub\u003e and nitrogen. Each chamber has an outside non-chambered control area, hereafter referred to as \u0026lsquo;control plots\u0026rsquo;. Chambers that receive CO\u003csub\u003e2\u003c/sub\u003e are managed using the same protocols as described above for the CO\u003csub\u003e2\u003c/sub\u003e experiment, except that CO\u003csub\u003e2\u003c/sub\u003e is only added during daylight hours. Chambers in the nitrogen addition treatment are fertilized monthly from May to September with NH\u003csub\u003e4\u003c/sub\u003eCl (5 g N m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e = 25 g N m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eWarming experiment\u003c/h2\u003e \u003cp\u003eIn 2016, the Salt Marsh Accretion Response to Temperature eXperiment (SMARTX) was initiated to investigate plant and ecosystem responses to whole-ecosystem warming using infrared lamps and belowground heating cables (Noyce et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We used the 12 experimental plots in the \u003cem\u003eS. americanus\u003c/em\u003e-dominated plant community (a.k.a. C\u003csub\u003e3\u003c/sub\u003e community) treated with four levels of warming (control, +\u0026thinsp;1.7\u0026deg;C, +\u0026thinsp;3.4\u0026deg;C, +\u0026thinsp;5.1\u0026deg;C) at ambient CO\u003csub\u003e2\u003c/sub\u003e, with each treatment replicated three times. Thus, replication in the warming experiment (n\u0026thinsp;=\u0026thinsp;3) was lower than in the other experiments (n\u0026thinsp;=\u0026thinsp;5).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSchoenoplectus\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003edensity and sexual production\u003c/span\u003e\u003c/p\u003e \u003cp\u003eDuring the 2019 growing season (August-September) we counted the number of vegetative and reproductive shoots in each chamber and control plot for each of the three experiments. We randomly harvested 10 flowering shoots from each chamber and control plot for determination of reproductive effort (described below). If there were fewer than 10 flowering shoots per chamber or control plot, we harvested all of them.\u003c/p\u003e \u003cp\u003eFlowering shoots produce a terminal inflorescence composed of 1\u0026ndash;15 spikelets. Each spikelet has one or more flowers that can develop into fruits that are a firm, brown achene. In the laboratory, we counted the number of spikelets on each harvested shoot and dissected each spikelet to determine the number of mature and dispersed fruits. The number of dispersed fruits could be determined because each one left a depression on the spikelet rachilla. Immature fruits on each spikelet, always at the terminal end of a spikelet, were also counted. Immature fruits were much smaller than mature fruits, were not black, and were still subtended by a bract that is not present at the base of mature fruits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R version 3.6.0 (R Core Team \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To evaluate seed reproduction, three sexual reproduction variables were used: 1. flowering ratio (the ratio of the number of flowering shoots to the total number of shoots), 2. potential seed production (sum of mature and immature seeds) per reproductive shoot, and 3. ratio of mature to potential seeds in each reproductive shoot.\u003c/p\u003e \u003cp\u003eTo test the hypothesis that sexual reproductive effort increased with increasing shoot density, we performed a logistic regression model for variables 1 and 3, and a linear regression model for variable 2 to examine the association of shoot density and the three reproduction variables. These analyses were performed with the glm and lm functions. We also calculated the main and interactive effects of a factorial combination of shoot density and treatment. In each experiment, shoot count data from control plots and all treatments were pooled and analyzed with a logistic regression model for variables 1 and 3, and a linear regression model for variable 2.\u003c/p\u003e \u003cp\u003eThe effects of experimental treatments (CO\u003csub\u003e2\u003c/sub\u003e, N, temperature) on the three reproductive variables were also examined. Data for the three reproductive variables were tested for normality with the Shapiro-Wilk test (Shapiro.test function in the stats R package version 3.6.0) before and after log transformation. Log transformation was accomplished by applying the equation log(x\u0026thinsp;+\u0026thinsp;1) to each data set. We used untransformed data on the following analysis because the data were not normally distributed either before or after log transformation (Supplemental Table\u0026nbsp;1). For the CO\u003csub\u003e2\u003c/sub\u003e experiment, the main effects and interactions between treatments (control, ambient CO\u003csub\u003e2\u003c/sub\u003e, elevated CO\u003csub\u003e2\u003c/sub\u003e) and communities (C\u003csub\u003e3\u003c/sub\u003e, C\u003csub\u003e4\u003c/sub\u003e, Mixed) were analyzed with the Scheirer-Ray-Hare test (scheirerRayHare function in the rcompanion R package version 2.3.25) and means were compared with the Steel-Dwass test. For the CO\u003csub\u003e2\u003c/sub\u003e x N and warming experiments, treatments were compared using Kruskal-Wallis test (kruskal.test function in the stats R package version 3.6.0) and means were compared with Steel-Dwass test. We considered p-values of \u0026lt;\u0026thinsp;0.50 to be particularly meaningful but this arbitrary p-value threshold was not used as the sole source of inference to judge whether our results were scientifically meaningful (Smith \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSexual reproductive effort responses to differences in shoot density in non-chambered control plots\u003c/h2\u003e \u003cp\u003eThe flowering ratio increased significantly across spatial and temporal variability as shoot density increased in the control plots of all three experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, D, G; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the CO\u003csub\u003e2\u003c/sub\u003e experiment, the number of potential seeds per reproductive shoot and the ratio of mature seeds to potential seeds increased with increasing shoot density in the control plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the CO\u003csub\u003e2\u003c/sub\u003e x N experiment, the number of potential seeds was positively related to shoot density, but the relationship was not significant and the ratio of mature to potential seeds was negative in the control plots (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, F; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There was no relationship between the two seed-related variables and density in the Warming experiment control plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH, I; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the regression comparisons between shoot density and reproductive variables (flowering ratio, number of potential seeds per reproductive shoot, ratio of mature to potential seeds). A logistic regression model was used for the flowering ratio and ratio of mature to potential seeds. A linear regression model was used for number of potential seeds per reproductive shoot.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e experiment\u003c/p\u003e \u003cp\u003e(C\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;Mixed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e x N expriment\u003c/p\u003e \u003cp\u003e(C\u003csub\u003e3\u003c/sub\u003e community)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e \u003cp\u003eWarming experiment\u003c/p\u003e \u003cp\u003e(C\u003csub\u003e3\u003c/sub\u003e community)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmb CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElev CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAmb CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAmb CO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eElev CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eElev CO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e+\u0026thinsp;0\u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e+\u0026thinsp;1.7\u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e+\u0026thinsp;3.4\u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e+\u0026thinsp;5.1\u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFlowering ratio per plot\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficients (Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficients (Density)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.10E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.60E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.70E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.10E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.90E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.60E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.90E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.50E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.50E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.20E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.10E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual deviance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e314.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e177.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e114.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e162.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e56.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.70E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e7.90E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of potential seeds per reproductive shoot\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficients (Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e55.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficients (Density)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRatio of mature to potential seeds per reproductive shoot\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficients (Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficients (Density)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.80E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.40E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.60E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.50E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.70E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.80E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.10E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.10E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-8.90E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-1.10E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-1.90E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e7.70E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual deviance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1702.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1762.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2109.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e969.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1567.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e971.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1237.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1030.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.80E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.70E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.60E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.70E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.60E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003e* The p-value indicates the significance of a density effect.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSexual reproduction responses under experimental conditions\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eCO\u003csub\u003e2\u003c/sub\u003e experiment\u003c/h2\u003e \u003cp\u003eThe number of potential seeds per reproductive shoot and the ratio of mature to potential seeds differed sharply among treatments (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Means for the flowering ratio were not significantly different but the means were greater in the ambient and elevated chambers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The mean number of potential seeds was higher in the ambient and elevated chambers compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The mean ratio of mature to potential seeds were also higher in the ambient chambers compared to the controls and elevated chambers, particularly in the elevated chambers (control vs ambient: p\u0026thinsp;=\u0026thinsp;0.011, ambient vs elevated: p\u0026thinsp;=\u0026thinsp;5.9E-05 in Steel-Dwass test, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The number of potential seeds and the ratio of mature to potential seeds differed among communities (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and treatments and communities interacted to affect the ratio of mature to potential seeds (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The means and standard errors for the three reproductive variables in each plant community are shown in Supplemental Table\u0026nbsp;2 and Supplemental Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Scheirer-Ray-Hare test for the CO₂ experiment and the Kruskal-Wallis test for CO₂ x N experiment and warming experiment for four reproductive variables: flowering ratio, the number of potential seed per reproductive shoot, ratio of mature to potential seeds.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFlowering ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNumber of potential seeds \u003c/p\u003e \u003cp\u003eper reproductive shoot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eRatio of mature to \u003c/p\u003e \u003cp\u003epotential seeds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistic H / \u003c/p\u003e \u003cp\u003eChi-squared*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStatistic H / \u003c/p\u003e \u003cp\u003eChi-squared*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStatistic H / \u003c/p\u003e \u003cp\u003eChi-squared*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e experiment\u003c/p\u003e \u003cp\u003e(C\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;Mixed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.8E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteraction\u003c/p\u003e \u003cp\u003e(Treatment:Community)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e x N experiment\u003c/p\u003e \u003cp\u003e(C\u003csub\u003e3\u003c/sub\u003e community)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.2E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.9E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarming experiment\u003c/p\u003e \u003cp\u003e(C\u003csub\u003e3\u003c/sub\u003e community)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e* This column shows the H statistic for the CO₂ experiment and chi-squared for CO₂ x N experiment and warming experiment.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eCO\u003csub\u003e2\u003c/sub\u003e x N experiment\u003c/h2\u003e \u003cp\u003eThere were significant treatment differences for all reproductive variables (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The means for the flowering ratio were higher in all treatment chambers and highest in ambient chambers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The mean number of potential seeds was higher in the treatment chambers compared to controls, but the means were not significantly different (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The mean ratio of mature to potential seeds was more variable, and there were no significant differences between controls and treatment chambers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Means and standard errors for three reproductive variables are in Supplemental Table\u0026nbsp;2. It is difficult to compare the controls and chambers in this experiment because during the summer of 2019, an outbreak of an unknown insect, not apparent in most years, consumed inflorescences inordinately in the control plots (A. Langley, personal observation).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eWarming experiment\u003c/h2\u003e \u003cp\u003eThere were significant treatment differences for the flowering ratio and the number of potential seeds per reproductive shoot (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Warming increased the flowering ratio and the mean number of potential seeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, H). The means for the ratio of mature to potential seeds were not significantly different but were greater in all warming plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). Means and standard error for three reproductive variables are in Supplemental Table\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEffects of experimental conditions on the relationship between seed production and shoot density\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eCO\u003csub\u003e2\u003c/sub\u003e experiment\u003c/h2\u003e \u003cp\u003eThe flowering ratio increased significantly with increasing shoot density in the control plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) but there was no statistically significant relationship with density in the ambient chambers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There was a significantly positive relationship between the flowering ratio and density in the elevated chambers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The number of potential seeds increased with increasing shoot density in the control plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). However, the relationship was significantly negative in the ambient chambers and while the relationship was positive in the elevated chambers it was not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The ratio of mature to potential seeds increased in the controls and treatment chambers (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eCO\u003csub\u003e2\u003c/sub\u003e x N experiment\u003c/h2\u003e \u003cp\u003eThe flowering ratio was significantly related to shoot density in the control plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The relationship was negative in the ambient chambers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and there were no significant relationships in the other three treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, G; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The number of potential seeds was positively related to shoot density, but the relationship was not statistically significant in the control plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The relationships were not significant in treatment chambers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, H). The ratio of mature to potential seeds had a negative relationship to density in the control chambers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). However, the ratio increased with increasing shoot density in the ambient and elevated\u0026thinsp;+\u0026thinsp;N chambers but the relationships were not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, I; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eWarming experiment\u003c/h2\u003e \u003cp\u003eThe flowering ratio increased with increasing shoot density in control plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG) and the +\u0026thinsp;3.4\u0026deg;C and +\u0026thinsp;5.1\u0026deg;C plots but the relationship was not statistically significant in the +\u0026thinsp;1.7\u0026deg;C plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The number of potential seeds had no statistically significant relationship to density in any of the elevated temperature plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The ratio of mature to potential seeds had no statistically significant relationship to density in control plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI) but the relationships were negative and statistically significant in the +\u0026thinsp;1.7 and +\u0026thinsp;3.4\u0026deg;C plots and positive and statistically significant in the +\u0026thinsp;5.1\u0026deg;C plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eL; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn over 35 years of experimental research, \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e in the Kirkpatrick Marsh has been temporally dynamic with changes in the relative abundance and morphology (Drake \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The results of this study demonstrate that changes in shoot density can influence the reproductive effort of \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e and the relationship between seed production and shoot density may vary depending on experimental conditions. \u003cem\u003eSchoenoplectus americanus\u003c/em\u003e is widespread nationally (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Smith \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), is a dominant species in Chesapeake Bay wetlands (McCormick and Somes 1982) and will be an important species in coastal wetlands as they respond to changing climatic conditions (Noyce et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vahsen et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It will be an especially important species in influencing wetland response to sea level rise at is has been shown to be most abundant and productive when tidal flooding is frequent (Kirwan and Guntenspergen \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Holmquist et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe long-term GCReW experiments have focused on response of plant production and density in \u003cem\u003eS. americanus\u003c/em\u003e but none of the prior research focused on seed production, an important component of ecosystem function (e.g., Pearse et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Solbreck and Knape \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our results from the non-chambered control plots demonstrate a positive relationship between shoot density and sexual reproductive effort for \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The increase of sexual reproductive effort was mainly the result of an increase in the number of ramets that flowered. Studies of other species suggest that the relationship between sexual reproductive effort and density is variable. Winn and Pitelka (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) suggested that increased shoot density would result in increased competition between ramets and decreased resource availability for seed production, a view supported by Newell and Tramer (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) and Loehle (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). In contrast, increased seed production at higher densities may be an adaptive trait in competitive environments as seed production and dispersal would enable species to escape from competitive crowding and colonize new environments (Abrahamson \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Abrahamson \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Winn and Pitelka \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Giroux and Bedard 1995). A feature of clonal species like \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e that would support increased seed production, even at higher densities, is division of labor (Roiloa et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), a process that enables physiologically connected ramets of clonal plants to share resources. In environments such as wetlands where clonal herbaceous species are often dominant and where resources are spatially heterogeneous (e.g., Weiss et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dong et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), division of labor would enable ramets to secure resources that are locally available with ramets that may be in rooted in areas where resources are less available. Ikegami (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) demonstrated that division of labor occurs in \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e and that process may be partially responsible for the positive relationships between density in the non-chambered control plots and the two seed production variables in the CO\u003csub\u003e2\u003c/sub\u003e experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. C) as the sharing of resources can benefit a ramet that is in suboptimal conditions.\u003c/p\u003e \u003cp\u003eIn the experimental conditions, we found that there was no positive relationship between shoot density and flowering ratio under increased CO\u003csub\u003e2\u003c/sub\u003e, N addition and modest warming (+\u0026thinsp;1.7\u0026deg;C) conditions. Multiple studies have shown that plant production of \u003cem\u003eS. americanus\u003c/em\u003e is limited under CO\u003csub\u003e2\u003c/sub\u003e increasing. Langley and Megonigal (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) showed that N addition enhanced the CO\u003csub\u003e2\u003c/sub\u003e stimulation of plant productivity in the first year of the CO\u003csub\u003e2\u003c/sub\u003e x N experiment. Lu et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) included more recent data from the CO\u003csub\u003e2\u003c/sub\u003e experiment and analyzed data from the CO\u003csub\u003e2\u003c/sub\u003e x N experiment. In addition to annual differences in shoot density in both experiments, Lu et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that the diameter and height of \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e shoots decreased in the CO\u003csub\u003e2\u003c/sub\u003e experiment but not in the CO\u003csub\u003e2\u003c/sub\u003e x N experiment. They concluded that N limitation caused by elevated CO\u003csub\u003e2\u003c/sub\u003e was responsible for the morphological changes as added N reversed the effects in the CO\u003csub\u003e2\u003c/sub\u003e x N experiment. The N limitation caused production of smaller individual stems at higher plant density (Lu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and may restricted seed production at the higher density (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, D). Seed production has been predicted or shown to both increase and decrease in crops in response to higher CO\u003csub\u003e2\u003c/sub\u003e (Ziska et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Allen and Prasad 2004; Lenka et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the same has been shown for native plants (Billings and Billings \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Zangerl and Bazzaz \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; HilleRisLambers et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e, an increase in the percentage of flowering shoots associated with increased shoot density does not, however, necessarily result in increased seed production per individual reproductive shoot when CO\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e x N are environmental factors. A positive relationship between the three variables and plant density in elevated CO\u003csub\u003e2\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B, C) indicate that as concentrations of atmospheric CO\u003csub\u003e2\u003c/sub\u003e increase, \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e will allocate more resources to seed production. When shoot density is not considered, however, the lower means for all three variables in the elevated chambers (Elev in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA B, C) indicate that other factors may limit seed production, especially the maturation of seeds. The number of potential seeds per flowering shoot were statistically the same for ambient and elevated chambers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), but the mean ratio of mature to potential seeds was statistically lower in the elevated chambers, indicating that resources may limit seed maturation under elevated CO\u003csub\u003e2\u003c/sub\u003e. Unfortunately, interpretation of the results from the CO\u003csub\u003e2\u003c/sub\u003e x N experiment were complicated by the impacts of an insect outbreak across the GCReW area prior to when we sampled the site. A positive response between shoot density and the number of potential seeds per flowering shoot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) and a significant increase in the same variable when density was not considered indicate that CO\u003csub\u003e2\u003c/sub\u003e and N increase the potential for seed production. Resource limitations, however, may have a negative impact of seed maturation (compare Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and F), especially in response to increasing shoot densities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-I).\u003c/p\u003e \u003cp\u003eTemperature has variable effects on seed production in crops and native species (Klady et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Caignard et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lenka et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lippmann et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our results demonstrate that temperature will impact sexual reproductive effort in \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e. Means for all three variables related to sexual reproductive effort were higher in the +\u0026thinsp;1.7, +\u0026thinsp;3.4, and +\u0026thinsp;5.1\u0026deg;C treatments in the Warming experiment and the differences were significant for two of the variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, H, I). Data from the CO\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e x N experiments also provide evidence of a potential temperature effect on sexual reproductive effort. Higher means for the three variables from the Ambient treatment in the CO\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e x N experiments (Amb in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B, C, D, E, F) versus the non-chambered control suggests a chamber effect that we interpret to be due to the cumulative effect of slightly higher temperatures in the ambient chambers over many years. Temperature differences inside and outside of chambers have been reported for experiments that are not designed to evaluate temperature (Vanaja et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Messerli et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Drake et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) reported temperatures inside chambers in the CO\u003csub\u003e2\u003c/sub\u003e experiment at GCReW to be 1-2\u003csup\u003eo\u003c/sup\u003eC higher than outside temperatures. Noyce et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), using plant density as a variable in the calculation of net primary production, also found annual differences in response to the temperature treatments in the first three years of the SMARTX experiment. They also showed that root-to-shoot allocation was changed depending on warming and suggested that modest warming (+\u0026thinsp;1.7\u0026deg;C) caused plant demand for N to outpace the soil N supply while extreme warming (+\u0026thinsp;3.4 and +\u0026thinsp;5.1\u0026deg;C) caused the N supply to outpace plant N demand. In this study, there is no positive relationship between flowering ratio and plant density in ambient chambers in CO\u003csub\u003e2\u003c/sub\u003e and CO2 x N experiments, and at +\u0026thinsp;1.7\u0026deg;C plots in Warming experiment. However, there was a positive relationship at the +\u0026thinsp;3.4 and +\u0026thinsp;5.1\u0026deg;C plots in Warming experiment. Seed production of S. americanus may be suppress under modest warming conditions because of N limitation but not be suppressed under extreme warming conditions with little N limitation.\u003c/p\u003e \u003cp\u003eFew studies have documented the effects of multiple factors on seed production. Miyagi et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) found that elevated CO\u003csub\u003e2\u003c/sub\u003e increased seed production in eleven annual species but concluded that the response was limited by nitrogen availability. Osanai et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) examined the interacting effects of elevated CO\u003csub\u003e2\u003c/sub\u003e and temperature on cotton under variable soil conditions. They found, similar to this study, temperature and elevated CO\u003csub\u003e2\u003c/sub\u003e had a positive impact on fruit production, but the temperature response was larger, and the two variables interacted through the effects on nutrient availability, especially nitrogen availability. Increasing nutrient availability, however, may not result in increased seed production in nutrient limited ecosystems. Molau and Shaver (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) found that elevating temperatures increased sexual reproductive effort of \u003cem\u003eEriophorum. vaginatum\u003c/em\u003e, a species in the Cyperaceae - similar to \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e - in open topped chambers in arctic environments, but adding N and P to experimental plots had no effect on seed number.\u003c/p\u003e \u003cp\u003eIn summary, results of this comparative study demonstrate that an important wetland clonal species produces more flowering ramets in response to increasing density and that increasing temperature has a positive effect on the allocation of resources to sexual reproduction, perhaps mediated by temperature effects on N supply (Noyce et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We also demonstrate that when other environmental factors (CO\u003csub\u003e2\u003c/sub\u003e and N) are considered, increased allocation of resources to sexual reproduction may not result in an increase in the production of more mature seeds, even though reproductive ramets may have more flowers (i.e., potential seeds). The results suggest that the adaptative reproductive strategy of \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eamericanus\u003c/em\u003e will be impacted by future climate changes. The study demonstrated that an increased allocation of resources to sexual reproduction but not the production of mature seeds, an important component of the species ecological strategy (Ikegami et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Future research on this species and other important wetland clonal species, should focus on experiments that include several levels of the key environmental factors. The research should also consider studies of seed dispersal, similar to those conducted by Kudoh and Whigham (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and seedling growth and establishment as the ability of species to colonize suitable habitats will be an important aspect of wetlands in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eA. K. was supported by Kyoto University OMORO Challenge Scholarship Program. The long-term research at the study sites has been supported by the Smithsonian Institution and the National Science Foundation Long-Term Research in Environmental Biology Program (DEB-0950080, DEB-1457100, DEB-1557009, DEB-2051343), the Department of Energy Terrestrial Ecosystem Science Program (DE-FG02-97ER62458, DE-SC0014413, DE-SC0019110, DE-SC0021112), the United States Geological Survey (G10AC00675), the National Science Foundation Long-Term Research in Environmental Biology Program (DEB-0950080, DEB-1457100, DEB-1557009, DEB-2051343), and the Smithsonian Environmental Research Center.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbrahamson, W. 1975. 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Rising atmospheric carbon dioxide and seed yield of soybean genotypes. \u003cem\u003eCrop Science\u003c/em\u003e 41: 385\u0026ndash;391. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2135/cropsci2001.412385x\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2001.412385x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"clonal plants, wetland, global warming, seed production, sexual reproduction, Schoenoplectus americanus","lastPublishedDoi":"10.21203/rs.3.rs-2814013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2814013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe expansion of many wetland species is a function of both clonal and sexual propagation. The production of ramets through clonal propagation enables plants to move and occupy space, while seeds produced by sexual reproduction enable species to disperse and colonize open or disturbed sites. The balance between clonal propagation and sexual reproduction is known to vary with plant density but few studies have focused on reproductive allocation with density changes in response to global change. \u003cem\u003eSchoenoplectus americanus\u003c/em\u003e is a widespread clonal wetland species in North America and a dominant plant in a Chesapeake Bay brackish tidal wetland. Long-term experiments on responses of \u003cem\u003eS. americanus\u003c/em\u003e to global change provided the opportunity to compare the two modes of propagation under different treatments condition. Seed production increased with increasing shoot density, supporting the hypothesis that factors causing shoot density to increase stimulate sexual reproduction and dispersal of genets. The increase in allocation to sexual reproduction was mainly the result of an increase in the number of ramets that flowered and not an increase in the number of seeds per reproductive shoot, or the ratio between the number of flowers produced per inflorescence and the number of flowers that developed into seeds. Seed production increased in response to increasing temperatures and decreased or did not change in response to increased CO\u003csub\u003e2\u003c/sub\u003e or nitrogen. Results from this comparative study demonstrate that plant responses to global change treatments affect resource allocation and can alter the ability of species to produce seeds.\u003c/p\u003e","manuscriptTitle":"Reproductive responses to increased density and global change drivers in a widespread clonal wetland species, Schoenoplectus americanus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-20 17:47:01","doi":"10.21203/rs.3.rs-2814013/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-04-18T23:57:12+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-04-18T15:12:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Estuaries and Coasts","date":"2023-04-14T13:22:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-04-14T04:51:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Estuaries and Coasts","date":"2023-04-13T13:27:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"84199a60-2f6f-4dc8-8ab6-9d01a8e43e6b","owner":[],"postedDate":"April 20th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-18T15:07:48+00:00","versionOfRecord":{"articleIdentity":"rs-2814013","link":"https://doi.org/10.1007/s12237-023-01249-z","journal":{"identity":"estuaries-and-coasts","isVorOnly":false,"title":"Estuaries and Coasts"},"publishedOn":"2023-09-15 15:01:33","publishedOnDateReadable":"September 15th, 2023"},"versionCreatedAt":"2023-04-20 17:47:01","video":"","vorDoi":"10.1007/s12237-023-01249-z","vorDoiUrl":"https://doi.org/10.1007/s12237-023-01249-z","workflowStages":[]},"version":"v1","identity":"rs-2814013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2814013","identity":"rs-2814013","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}