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Pollen collection and RNA extractionPollen is a microscopic and notoriously resistant plant product. Thus, methods to collect a sufficient and roughly equivalent volume of pollen per species, and to ensure RNA was collected from viruses both internal and external to pollen grains, were developed specifically for this work. At each of the four regions, we identified visually asymptomatic plants species that were in full flower and in high enough abundance to achieve our pollen sample minimum. Many of the pollen samples were collected from public roadsides. However, some from the California Grasslands were collected from the University of California’s McLaughlin Natural Reserve, and some from the Eastern Deciduous Agro-forest Interface were collected from the University of Pittsburgh’s Pymatuning Laboratory of Ecology. We had permission to sample in both places. In addition, we obtained permission from the USDA Forest Service to sample in the Till Ridge Cove area of the Chattahoochee-Oconee National Forest for sampling in Central Appalachia. None of the sampled plants displayed classic viral symptoms (e.g., leaf yellowing, vein clearing, leaf distortions, growth abnormalities). To achieve the broadest representation of plant species, we selected species in different families, where feasible. Also when possible, we focused primarily on perennial species to avoid any effects of life history variation. From these, we collected 30 to 50 mg of pollen from newly dehiscing anthers (3–967 fresh hermaphroditic flowers from 1–27 plants per species; Supplementary Table 3) in situ using a sterile sonic dismembrator (Fisherbrand Model 50, Fisher Scientific, Waltham, MA, USA) with a frequency of 20 Hz. We removed non-pollen tissues (e.g., anther debris) with sterile forceps. In addition to removing non-pollen debris that was visible to the naked eye in the field at the time of pollen sample collection, we conducted microscopic and gene expression analyses to confirm the purity of the pollen samples in the lab (Supplementary Methods). Visibly pure pollen from a single species was transferred to a 2-mL collection tube with Lysing Matrix D (MP Biomedicals, Irvine, CA, USA) and kept on dry ice until transported to and stored at −80°C at the University of Pittsburgh (Pittsburgh, PA, USA).Before extracting the total RNA, we freeze-dried the pollen samples (FreeZone 4.5 Liter Benchtop Freeze Dry System, Labconco Corporation, Kansas City, MO, USA) and lysed with a TissueLyser II (Qiagen, Inc., Germantown, MD, USA) at 30 Hz with varying times for different plant species (Supplementary Table 3). We confirmed via microscopy that this protocol resulted in the breakage of ≥50% of the pollen grains in a sample. The total RNA, including dsRNA, was extracted using the Quick-RNA Plant Miniprep Extraction Kit (Zymo Research Corporation, Irvine, CA, USA), following the full manufacturer’s protocol, including the optional steps of in-column DNA digestion and inhibitor removal.RNA sequencingWe assessed the quantity and quality of the total RNA extracted from each pollen sample with a Qubit 2.0 fluorometer (Invitrogen, ThermoFisher Scientific, Waltham, MA, USA) and with TapeStation analyses performed by the Genomics Research Core (GRC) at the University of Pittsburgh. Only samples with an RNA integrity value of ≥1.9 were used (Supplementary Table 3). Stranded RNA libraries were prepared by the GRC using the TruSeq Total RNA Library Kit (Illumina, Inc., San Diego, CA, USA), and ribosomal depletion was performed using a RiboZero Plant Leaf Kit (Illumina, Inc., San Diego, CA, USA). At the GRC, we pooled depleted RNA libraries from six species on a single lane of an Illumina NextSeq500 platform.Pre-virus detection stepsA sequencing depth of 117–260 million 75 bp paired-end reads was achieved per sample (Supplementary Table 3). Sequences were demultiplexed and trimmed of adapter sequences. We used the Pickaxe pipeline42,60,61 to detect known and novel pollen-associated viruses. First, Pickaxe removes poor-quality raw reads42,60,61 and aligns the quality-filtered reads using the Bowtie2 aligner with default parameters62 to a subtraction library. Each customized subtraction library contained the host plant species genome or the most closely related plant genomes in the National Center for Biotechnology Information (NCBI) database, if the host plant genome was not available (Supplementary Table 7), as well as other possible contaminant genomes (e.g., the human genome)42,60,61. The subtraction libraries with 1–8 closely related plant genomes, a bioinformatically tractable amount, were used to remove plant sequences, which allows for a conservative estimate of the viruses associated with pollen to be made. The size of the subtraction libraries did not influence the number of identified viruses, as there was no correlation between library size and either estimate of virus richness (conservative: r = 0.08, P = 0.75; relaxed: r = 0.06, P = 0.77). After subtraction, only non-plant reads remained and were used for viral detection.Known RNA virus detection, identity confirmationWith Pickaxe, we used the Bowtie2 aligner with default parameters62 (v2.3.4.2-3) to align viral non-plant reads to Viral RefSeq42,60,61 (hereafter, VRS; Index of /refseq/release/viral (nih.gov)). Each known virus reflects the top hit of an alignment to VRS42,60,61. Following Cantalupo et al.42, we considered a known virus to be present if the viral reads covered at least 20% of the top hit and aligned to it at least ten times. For viruses with segmented genomes, at least one segment was required to meet these criteria.Contig annotation and extension; novel RNA viral genome detection, identity confirmationViral reads were assembled into contigs using the CLC Assembly Cell (Qiagen Digital Insights, Redwood City, CA, USA), and Pickaxe was used to remove repetitive, short ( More

Female plants must not only allocate resources to flowering but also to producing seeds as well as fruits and/or cones. This suggests that the costs of reproduction are higher for female than male plants, or for female function of hermaphrodite plants. Some dioecious plants (i.e. separate male and female plants) are vegetatively very different (i.e. dimorphic) between the sexes, such as females having larger branch and leaf sizes. Differences between male and female resource allocation to reproduction and the possible consequences of this for vegetative dimorphism in dioecious plants, is a central issue in plant evolution but it is a controversial and difficult topic1,2. In the most highly cited paper on this topic, Obeso1 notes it is practically impossible to measure the direct costs of male and female allocation to sexual reproduction. For example, most vascular plant species (about 95%) are hermaphrodites which makes measuring direct allocation by the two sexes, difficult. Thus Paterno et al.3 used an indirect allometric method to measure sexual allocation in hermaphroditic inflorescences and concluded that larger flowers represent greater relative allocation to male function.The problem of shared sexual allocation to inflorescences is avoided in dioecious plants making them important tests for ideas of sexual allocation in plants. However, they are both rare as species and as individuals and are typically large, forest trees. For example, there are relatively few dioecious individual trees in the very large Barro Colorado forest tree data set4. Again, this large size makes direct measurement, such as of allocation to reproductive structures, difficult. The Cape Floral Region is a useful place to investigate sexual allocation in plants and its consequences, because dioecy is relatively common and vegetative dimorphism between the sexes can be extreme. Also, Cape plants are amenable to research being short (about 2–5 m), rapidly mature and short-lived (about 5–20 years). Thus, the large (about 85 spp.) Cape genus Leucadendron (Proteaceae) is probably the most researched genus globally for male and female differences5,6,7,8,9,10,11,12,13,14.Even in these dioecious plants it is difficult to directly measure allocation to male and female function because of the difficulty of finding a common currency to compare allocation. For example, comparing allocation differences in attractiveness, nectar, seeds, pollen, cones and fruits and differences in the timing of producing these structures1. In Leucadendron males are generally more visually attractive than females. This is achieved by the loss of photosynthetic capacity in floral leaves and bracts12,15. It would be difficult to directly compare this photosynthetic loss in males, with for instance, female allocation to cones and seeds. Despite the difficulties in directly measuring and comparing allocation to reproduction, the consensus is that female allocation to sexual reproduction typically exceeds male allocation1, including in Leucadendron2,9.Greater female allocation to reproduction is one of the suggested reasons for vegetative dimorphism between the sexes2. The three main hypotheses for sexual vegetative dimorphism are (i) greater female sexual resource allocation requires this to be balanced by having a more efficient physiology (resource use efficiency hypothesis), or (ii) greater female allocation requires females to be in the more optimum habitats (the sexual site dimorphism hypothesis) and this facilitates vegetative differences, such as larger female leaves in the more mesic habitats. Finally, (iii) vegetative dimorphism may be a consequence of selection on reproductive traits (reproductive traits hypothesis). In support of the resource use efficiency hypothesis in Leucadendron, Harris and Pannell9 argue that supplying water to live, closed cones in the canopy of serotinous Leucadendron females is a form of maternal care that non-serotinous species and males do not incur. To keep these cones from opening they need always to be hydrated and therefore serotinous females need to be more efficient in their water use than their males. They argued that fewer and thicker branches in females provides a hydraulic advantage. However, the data in Midgley8 and Roddy et al.14 showed no support for sexual differences in water use efficiency. Clearly, there are opposing views as to whether females allocate more to reproduction than males and whether females are eco-physiologically more efficient than males.The sexual site dimorphism hypothesis has not been tested for Leucadendron presumably because males and females co-occur on a small spatial scale16 but is tested in the present analysis of Aulax umbellata and A. cancellata. In support of the reproductive trait’s hypothesis, it was argued5 that in Leucadendron, vegetative dimorphism is an allometric consequence of selection for smaller male inflorescences. Smaller inflorescences are then associated with more, but narrower, stems and thus smaller leaves via Corners Rules5. Besides the evolutionary relevance for understanding sexual differences in allocation, it may also have conservation implications. For example, Hultine et al.17 argued that dioecious plants are under more threat than hermaphrodites because dioecious females are presumed to allocate more resources to reproduction than males. As global change progresses, females may suffer greater mortality and thus dioecious populations may have lower reproductive potential if they become more male biased.One way around the measurement problem of determining direct allocation to sexual reproduction is to use indirect methods based on trade-offs1 such as the influence of allocation to sexual reproduction, on sex ratios and sizes of co-occurring male and female plants. If for example, males allocated less to reproduction than co-occurring females, they should be relatively larger or live longer and this would impact size and sex ratios, especially as plants age and competition intensifies.
The Cape is uniquely suitable to consider allocation differences between the sexes because populations of dioecious Cape species are often large ( > 1000’s of plants ha−1) and with males and females co-existing at a fine spatial scale. The Cape Proteaceae grow in a stressful summer dry Mediterranean climate with nutrient-poor soils18. This provides strong selection on reproductive allocation to seeds (such as large size and high nutrient concentrations) to produce seedlings large enough to survive their first summer. The Cape Proteaceae are strongly fire-adapted. For example, many species are serotinous (canopy storage of seeds in live, closed cones which mainly open after fire)19. This too requires high female sex allocation to maintaining cones in the canopy. Most Cape Proteaceae species are post-fire re-seeders19 in that all plants die in fire. This results in single-aged populations of single-stemmed non-clonal individuals; adults die in fires and dense patches of seedlings establish in the first winter after the fire and die in the next fire. Co-occurring males and females have the same age and thus differences in size or sex ratios will mostly reflect allocation differences and competition rather than age or habitat. Also, because seedlings in the Cape grow up in an open post-fire environment, woody plants do not need to allocate specifically to height growth, to achieve full light. They are in full light their whole lives and therefore any sexual architectural differences do not reflect differences in habitat shadiness. Here we focused on Aulax umbellata, but also present sex ratios and size metrics for the congeneric A. cancellata. These are two common, single-stemmed strongly serotinous Cape species in the Proteaceae which are highly vegetatively dimorphic. Although both Leucadendron and Aulax are dioecious, a rare trait in the family, this represents independent evolution as the two genera are not close phylogenetically20. We test the hypothesis that vegetative sexual dimorphism in Aulax umbellata and Aulax cancellata can be explained by differences in allocation to growth. We predicted that co-occurring males and females would occur in equal sex ratios and be equal in size due to equal growth, despite vegetative dimorphism. More

Study site and experimental designThe study site, experimental design, and annual sampling protocol have been described in previous publications15,22,47 but a summary will be provided here. The experiment was conducted in a remote study site approximately 20 km northeast of Kangerlussuaq, Greenland, at 67.11° N latitude and 50.34° W longitude, approximately 160 km inland from Baffin Bay. Annual growing season (May through July) mean temperature and total precipitation at the study site during the duration of this experiment (2002–2017) were 8.62 ± 0.20 °C and 43 ± 6.78 mm, respectively47. The surrounding area has functioned as an important caribou (Rangifer tarandus) migration corridor, calving ground, and Indigenous Peoples hunting site for at least approximately 4000 years48, and was designated as a UNESCO World Heritage Site, Aasivissuit—Nipisat, by the United Nations in 2018. Caribou are present in greatest numbers seasonally, with most of the animals that use the site migrating into it during late winter and early spring and migrating out of it in mid to late summer; some male caribou remain at the site through winter. Muskoxen (Ovibos moschatus) are present at the site year-round. Arctic hares (Lepus arcticus) and rock ptarmigan (Lagopus muta) occupy the site in low numbers. In contrast to other locations in the Arctic where they are important herbivores, this site does not harbor voles or lemmings.In June 2002 we erected six exclosures constructed of woven wire fencing material supported by steel t-posts; each exclosure was circular and measured 800 m2. Adjacent to each exclosure, and separated from it by approximately 20–50 m, we located a comparable control site. Exclosure sites and adjacent control sites covered a range of elevations from approximately 275–300 m above sea level. In early May 2003, prior to onset of the plant growing season, we installed passive, open-topped warming chambers constructed of UV neutral glazing material on three plots inside and three plots outside of one exclosure site and three plots inside and four plots outside of a second exclosure site. In early May 2004, we added three warming chambers inside and three warming chambers outside one of the sites equipped in 2003, and we installed an additional three warming chambers on plots inside and three warming chambers on plots outside of a third exclosure site, thus resulting in a total of 12 warmed plots distributed among three exclosure sites and 13 warmed plots distributed among three control (grazed) sites. An ambient (control) plot was located near, but not closer than 2 m to, each warmed plot, thus resulting in 25 warmed plots and 25 ambient plots distributed among three exclosures and adjacent grazed sites. No plot was located closer than 2 m to the edge of any exclosure. Warming chambers were constructed according to the International Tundra Experiment (ITEX) protocol49, were 1.5 m in basal diameter, and encompassed 1.77 m2. Warming chambers were installed in early May each year, anchored to plots using metal garden stakes, and removed annually at the time of vegetation sampling, which was intended to coincide with peak aboveground abundance at mid to late July in most years (except in 2006, when sampling was conducted in mid-June, and in 2003 and 2011 when sampling was conducted in mid-August)47. Warming chambers significantly elevated near surface temperature by approximately 1.5–3.0 °C, and resulted in a non-significant reduction of soil moisture22,50.Vegetation samplingVegetation sampling was conducted non-destructively using a square Plexiglas tabletop point frame on adjustable aluminum legs. The point frame measured 0.25 m2 and was centered within each plot for sampling. The corners of each plot were equipped with hollow aluminum tubes sunk into the soil surface at the cardinal directions, and the legs of the point frame were inserted into these tubes to ensure consistent orientation and location of the frame during sampling. Once the frame was positioned, a steel welding pin was lowered through each of 20 randomly located holes in the point frame tabletop, and each encounter by the tip of the pin with vegetation was recorded until the pin struck soil, litter, or rock. In 2003 and 2004, vegetation was recorded at the species level for deciduous shrubs (Betula nana and Salix glauca) and at the functional group level for graminoids (including grasses, rushes, and sedges of the genera Calamagrostis sp., Poa sp., Festuca sp., Hierochloë sp., Trisetum spicatum, Luzula sp., Carex sp., and Kobresia sp.), forbs, mosses, lichens, and fungi. Beginning in 2005, vegetation was recorded at the species level for forbs, in addition to deciduous shrubs, and at the genus level for lichens (Peltigera sp.), fungi [Calvatia sp.; most likely C. cretacea51], and mosses (Aulacomnium sp.). Graminoids were not resolved to the genus or species levels due to concerns about consistent identification. All taxa were identified in the field by the authors on the basis of visual inspection of live individuals in consultation with reference guides52,53,54,55. In adherence with the Guidelines for Professional Ethics established by the Botanical Society of America, sampling and identification were done non-destructively, and no voucher specimens were collected.Commonness estimationEcologically meaningful estimation of commonness is inherently relative; a taxon is only common or rare in relation to other taxa5. While there exist a considerable array of quantitative indices of commonness56, we opted for one that integrates abundance and occurrence by assigning equal weight to each. Using annual abundance sums obtained during point frame sampling, we calculated commonness for each taxon as the product of its proportional abundance across all plots within each treatment and its proportional occurrence across all plots within each treatment. Hence, the commonness (C) of an individual taxon, i, in a given year, t, can be expressed as the product of its proportional abundance (A) and proportional occurrence (O) in that year:$$C_{it} = A_{it} *O_{it}$$
(1)
in which proportional abundance of taxon i in year t is the sum of point frame pin intercepts, h, for that taxon in that year across all plots sampled that year divided by the total number of point frame pin intercepts, H, of live vegetation biomass recorded across all plots sampled that year:$$A_{it} = h_{it} /H_{t}$$
(2)
and in which proportional occurrence of taxon i in year t is the sum of the number of plots, p, on which point frame pin intercepts of taxon i were recorded in year t divided by the total number of plots, P, sampled in year t:$$O_{it} = p_{it} /P_{t}$$
(3)
This index was used to estimate taxon-specific commonness within each experimental treatment combination (i.e., exclosed ambient, exclosed warmed, grazed ambient, and grazed warmed treatments), as well as across the entire site (sitewide commonness) for derivation of baseline commonness. To derive baseline commonness for subsequent analysis of its contribution to taxon-specific trends in commonness over the course of the experiment, we used sitewide commonness of each taxon in the year 2006. As described above, greater taxonomic resolution beyond functional group was not widely applied in our sampling until the third year of the experiment, 2005. However, we decided against using 2005 as a baseline for commonness at the site because it also happened to be the final year of a two-year outbreak of caterpillar larvae of a noctuid moth, Eurois occulta, that reduced aboveground abundance of nearly all taxa on our plots22,57. Except for the fungus C. cretacea, all taxa, whether recorded by pin intercepts during point-frame sampling or not, were observed on at least one plot under each of the four experimental treatment combinations. The rarest forb in this study, Pyrola grandiflora, was observed on a single plot under each of the exclosed ambient, exclosed warmed, and grazed warmed treatments, and on two plots under the grazed warmed treatment, but was not recorded during point frame sampling of exclosed ambient or grazed ambient plots. Hence, any conclusions about the effects of warming on this species must be limited. Similarly, the lichen Peltigera sp., which was also very rare in this study, was recorded during point frame sampling on plots under each treatment combination, but was not detected by sampling on exclosed warmed plots after 2005 even though it was observed on one exclosed warmed plot after that. This might be considered corroboration of the negative effect on this genus of warming under herbivore exclusion reported in the Results, but caution may also be warranted. The fungus C. cretacea first appeared under the grazed ambient treatment in 2008 and then under the exclosed ambient treatment in 2012, but was not recorded under the grazed warmed or exclosed warmed treatments. This might in and of itself suggest a negative effect of warming on the establishment or occurrence of this species, or fungi in general, and might be consistent with limiting effects of reduced moisture availability under warming. However, we urge caution with this interpretation because fungi may not form fruiting bodies every growing season, and such fruiting bodies may emerge aboveground in different locations from one growing season to the next, thereby potentially confounding repeated detection by sampling methods such as ours.Analysis of experimental treatment effects on plant functional group abundanceWe used a Gaussian generalized linear model (GLM) with an identity link function to analyze variation in functional group abundance among experimental treatment combinations. This GLM included total annual abundance, for the period 2003–2017, of deciduous shrubs (comprising summed abundances of Betula nana and Salix glauca leaf and stem point frame pin intercepts), graminoids (comprising all grass, rush, and sedge tissue point frame pin intercepts), forbs, mosses, lichens, or fungi, in separate models with the two experimental treatments (warming and herbivore exclusion) and their interaction as factors, year as a factor, and day of year of sampling as a continuous covariate. Significance of individual treatment effects of warming and herbivore exclusion, as well as their interaction, was determined based on Wald Chi-square statistics and associated two-tailed P-values (with significance indicated at P ≤ 0.05).Analysis of experimental treatment effects on commonnessAnalyses of commonness data were performed at higher taxonomic resolution than were analyses of abundance data, and so were limited to analysis of data from the last 12 years of the experiment, 2006–2017. Using Eq. (1), commonness was estimated for 14 taxa, including two species of deciduous shrubs, Betula nana and Salix glauca; graminoids, comprising at least eight non-distinguished genera of grasses, rushes, and sedges listed above in the sub-section Vegetation sampling; eight species of forbs, including Equisetum arvense, Stellaria longipes, Cerastium alpinum, Bistorta vivipara, Draba nivalis, Campanula gieseckiana, Viola canina, and Pyrola grandiflora; one genus of moss, Aulacomnium sp.; one genus of fungus, Calvatia sp.; and one genus of lichen, Peltigera sp.We first investigated general characteristics of and treatment effects on commonness across the study site. We examined the skewness of commonness to determine whether the distribution of the 14 focal taxa was significantly right-skewed, indicating greater numbers of rare than of common taxa2. We obtained an estimate of skewness and its standard error across pooled data for the period 2003–2017, derived a 95% confidence interval, and compared it to zero. Next, we examined experimental treatment effects on sitewide commonness. To do this, we used a Gaussian GLM with identity link function to analyze pooled commonness of all taxa for the period 2006–2017, with commonness as the dependent variable and the two experimental treatments and their interaction as factors, year as a factor, taxon as a factor, and day of year of sampling as a covariate. We determined significance of individual treatment effects and their interaction by examining Wald Chi-square statistics, with significance indicated if the two-tailed P ≤ 0.05. We then tested for experimental treatment effects on individual taxa using the same analytical approach, but with taxon-specific commonness as the dependent variable, and treatment and year as factors, with day of year of sampling as a covariate.Analysis of trends in commonness and skewness of commonness over the last 12 years of the experimentWe next investigated whether common and rare taxa displayed different trends in commonness over the course of the last 12 years of the experiment. This was motivated by a presupposition that warming and/or herbivore exclusion might have differentially altered commonness of common vs. rare species. We first examined linear trends in sitewide commonness of all 14 taxa pooled across experimental treatments by testing for significance of linear regressions of taxon-specific commonness vs. year for the period 2006–2017. We then conducted the same analysis for each taxon individually under each experimental treatment combination to determine whether our experimental manipulations contributed to trends differentially in common vs. rare taxa. We then investigated whether the distribution of commonness across the 14 focal taxa displayed directional change over the course of the final 12 years of the experiment, and whether it might have done so differently in relation to experimental treatment combinations. To do this, we tested for significance of linear regressions of treatment-specific skewness of commonness vs. year for the period 2006–2017. Finally, we examined whether trends in commonness were related to baseline commonness for the 13 taxa resolved to the genus or species level, excluding graminoids because this group comprised multiple unresolved genera. This analysis was motivated by interest in determining whether taxa that were common at the beginning of the experiment tended to become more common and taxa that were rare at the beginning of the experiment tended to become rarer, thus indicating that degree of commonness itself might be an important driver of changes in commonness over the course of a multi-annual experiment such as ours. To do this, we fit a non-linear regression model using a von Bertalanffy equation to quantify the relationship between taxon-specific commonness trend (standardized coefficient from the regression of commonness vs. year, ranging between − 1 and 1) and baseline commonness by treatment. This equation took the form:$$Y = 1 – left( {1 – a} right)e^{ – bX}$$
(4)
In which Y = taxon- and treatment-specific commonness trend, estimated in this case using the standardized coefficient from a linear regression of commonness of taxon i under a given experimental treatment combination vs. year; a = the Y-intercept; b = the slope; and X = baseline commonness of taxon i under the same treatment combination in 2006. Significance of regressions for each treatment was determined by calculating an F-statistic using corrected model sums of squares, error sums of squares, model degrees of freedom, and error degrees of freedom. Non-linear regression models were considered significant if the F-associated P ≤ 0.05. More

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