New Results

Effects of two centuries of global environmental variation on phenology and physiology of Arabidopsis thaliana

Victoria L. DeLeo, Duncan N. L. Menge, Ephraim M. Hanks, Thomas E. Juenger, Jesse R. Lasky
doi: https://doi.org/10.1101/424242

Abstract

Intraspecific diversity arises from genetic and plastic responses to the environment. Immense natural history collections allow comprehensive surveys of intraspecific diversity across a species range through centuries of environmental variation. Using 216 years of Arabidopsis thaliana samples, we tested if traits exhibit coordinated variation and hypothesized adaptive responses to climate gradients. We used spatially varying coefficient models to quantify region-specific trends. Traits generally showed little coordination. However, C:N was low for summer versus spring-collected plants, consistent with a life history-physiology axis from slow-growing winter annuals to fast-growing spring/summer annuals. Collection date was later in more recent years in many regions, possibly because these populations shifted toward more spring (as opposed to fall) germination. δ15N decreased over time across most of the range, consistent with predictions based on anthropogenic changes. Regional heterogeneity in phenotype trends indicates complex responses to spatial and temporal climate shifts potentially arising from variation in local adaptation and plasticity.

Introduction

Fitness critically depends on an organism’s response to environmental variability. Anthropogenic global environmental change is resulting in dramatic changes in phenotypes of many organisms. Despite general patterns of species’ poleward range shifts and advancement of temperate spring phenology (Parmesan & Yohe), populations and individuals often differ in their responses to changing climates (Both et al. 2004; CaraDonna et al. 2014). These diverse responses could be caused by geographic variation in the rate of environmental change or by intraspecific genetic variation, clouding our understanding of climate impacts. Quantifying the spatial patterns of responses to environmental gradients can put temporal trends in context as well as reveal adaptive phenotypic variation across the species range.

Organisms respond to environmental stressors in diverse ways, including life history, phenology, and physiology. In seasonal locations, phenology dictates the environment encountered during vulnerable stages. Plant phenology and development are primarily limited by moisture, temperature, and photoperiod (Wilczek et al. 2009; Burghardt et al. 2015). Warmth usually increases growth rate, however cold temperatures in critical periods can advance flowering time through a process known as vernalization. Rapid development and reproduction can allow plants to escape drought, a strategy employed by some genotypes of our study species Arabidopsis thaliana (hereafter, Arabidopsis, Kenney et al. 2014). Other genotypes exhibit drought avoidance by minimizing water loss (e.g. through stomatal closure) and maximizing water uptake (Ludlow 1989; Kenney et al. 2014). Fast life histories can allow spring or summer annual life cycles, where a plant germinates and flowers within a single season, while slow life histories and vernalization requirements result in a winter annual cycle, where a plant germinates in the fall and flowers the following spring.

Standardized metrics are needed to compare ecologically relevant phenological variation among sites of different climate timing and at different latitudes. Photothermal units (PTU) integrate developmental time under favorable temperatures and light across a growing season and account for much of the environmental influence on flowering dates (Wilczek et al. 2009; Brachi et al. 2010). Thus, PTUs may help capture genetic differences in phenology across environments. Measures of developmental time standardized to environmental conditions can better capture genetic variation in development compared to raw flowering dates in Arabidopsis (Brachi et al. 2010).

Beyond flowering time, plant physiological response to environment is partly reflected in leaf isotope and nutrient composition, traits with high intraspecific genetic and plastic variation (Nienhuis et al. 1994; Chardon et al. 2010). Δ13C which measures discrimination against 13C in photosynthesis, is an indicator of pCO2 within leaves (Ci) relative to atmospheric pCO2 (Ca) (Farquhar et al. 1982). Ci declines when stomata are closed, which may occur under drought, while Ca declines with elevation. Thus, we expect Δ13C to increase in moist environments and decrease with elevation (Farquhar et al. 1982; Diefendorf et al. 2010; Zhu et al. 2010).

Leaf nitrogen physiology is another important aspect of environmental response, since leaf nitrogen is important to photosynthetic capacity (Stocking & Ongun 1962; Evans 1989) and photorespiration (Rachmilevitch et al. 2004). At the community level, leaf N (proportion of mass) generally increases with mean annual temperature (Reich & Oleksyn 2004; Ordoñez et al. 2009), and increases with greater precipitation (Reich et al. 2003). Lower nitrogen concentration leaves (high C:N, low proportion N) are found in drier and in hotter areas, in part because of investment in non-photosynthetic leaf features, especially veins (Blonder et al. 2011; Sack et al. 2012). Leaf δ15N, or the fraction of 15N over total nitrogen in a leaf, can be affected by the same resource acquisition strategies and is related to leaf N (Stock & Evans 2006). However, leaf N and δ15N may also reflect deposition and biogeochemical cycling (e.g. Pardo et al. 2007), complicating biological explanations. In this study, we used the ratio of carbon to nitrogen in the leaf (C:N) to capture leaf nitrogen investments.

The physiological, morphological, or phenological traits plants use to cope with varied environments often are subject to tradeoffs. When resources are abundant (high nutrient, ample water, warmth), functional traits enabling rapid growth and low investment in tissues may be favored, leading to low leaf C:N ratio. When resources are limited, slow growth and costlier, longer-lived tissues may be favored (Reich 2014). Accordingly, the winter annual phenology of northern Arabidopsis ecotypes described above could be explained by a slow growth strategy, while a fast growth strategy could lead to drought escape in arid regions. In addition, this fast growth could lead to higher leaf Δ13C through increased photosynthetic and transpiration rates. The leaf economic spectrum (LES) predicts that multiple dimensions of trait variation are coordinated to reflect a single life history and physiology axis from fast to slow that also corresponds to changes in dominant vegetation types across environments (Wright et al. 2004; Reich 2014), though the association of LES traits with climate may be weaker within species (Wright & Sutton-Grier 2012). We hypothesized that phenotype-environment correlations would follow LES or fast-slow predictions (Table 1).

View this table:
Table 1:

Hypothesized responses of phenotypes to temperature, rainfall, or year. Year trends are predicted due to elevated CO2, nitrogen deposition, or elevated temperatures.

Museum collections offer broadly distributed sampling in space and time to test the relationships between these phenotypes and climate (Willis et al. 2017; Lang et al. 2018). Variation in herbarium collection dates can be a reliable proxy for variation in phenology (Miller-Rushing et al. 2006; MacGillivray et al. 2010; Davis et al. 2015). Here, we leverage the immense fieldwork underlying natural history collections to understand how intraspecific diversity is structured through time and along spatiotemporal climate gradients. We combine these records with global gridded climate data to ask three questions:

  1. Does intraspecific trait variation among wild individuals fall along a single coordinated life history-physiology axis? Alternatively, is trait variation higher dimensional?

  2. Do Arabidopsis life history and physiology vary across spatial environmental gradients, suggesting adaptive responses to long-term environmental conditions?

  3. Have Arabidopsis life history and physiology changed over the last two centuries? In particular, have changes tracked climate fluctuations, suggesting adaptive responses?

Material and methods

Our samples included 3300 Arabidopsis thaliana herbarium and germplasm accessions with known collection date between 1794 and 2010 from the native range of Arabidopsis in Europe, the Middle East, Central Asia, and North Africa (Hoffmann 2002). For each herbarium specimen (n = 3163 of the total 3300) we visually verified species identification, flowering and fruiting reproductive status. Samples that were only fruited/senesced (n = 40) and samples that were only flowering (n = 96) as well as samples that had neither open flowers nor fruits (n = 10) were excluded to select for a more uniform development stage. Since progression of plant development involves reallocation of nutrients, C:N should be assessed at a consistent developmental stage. We also excluded dozens of misidentified specimens. Our effort highlights the risks of using unverified natural history collection data in online databases. Wild-collected germplasm accessions with known collection date and location were included from the Arabidopsis Biological Resource Center (https://abrc.osu.edu/).

We removed and pulverized leaf samples of 470 herbarium specimens and sent them to the UC-Davis Stable Isotope Facility. In total, we obtained δ15N values for 456 accessions, δ13C values for 454 accessions, C:N ratios for 455 accessions, and proportion N values for 455 accessions. Because atmospheric δ13C has changed dramatically over the time period of this study, we converted leaf δ13C to Δ13C using a common estimate of the atmospheric δ13C time series (McCarroll & Loader 2004) from 1850 to 2000, continuing linear extrapolation beyond 2000, using the 1850 value for earlier specimens, and the equation of Farquhar et al. (1989) Embedded Image where δa is the isotope ratio in the atmosphere and δp is the isotope ratio in plant tissue (ratios relative to a standard).

We selected climate variables based on knowledge of critical Arabidopsis developmental times and likely environmental stressors: average temperature in April (AprilMean in the models), minimum temperature in January (JanMinimum), July aridity index (AI) (Hoffmann 2002; Lasky et al. 2012; Fournier-Level et al. 2013; Wilczek et al. 2014). Aridity index was calculated from July precipitation divided by July PET (United Nations Environment Program 1997). These climate gradients were generally not strongly correlated (July Aridity to April Mean Temperature r2 = .09; July Aridity to January Minimum Temperature r2 = .003; January Minimum Temperature to April Mean Temperature r2 = .44 by linear regression). Temperature, precipitation, and PET values came from the Climate Research Unit time series dataset (New et al. 2000).

To estimate accumulated photothermal units (PTU) at date of collection, we used the equation of Burghardt et al. (2015) to model the hourly temperature values for the accumulation of sunlight degree hours between January 1 and dusk on the day of collection at each accession’s coordinate. Daylength was approximated with the R package geosphere (Hijmans 2017). Monthly temperature values for the period 1900-2016 came from the Climate Research Unit time series dataset (New et al. 2000). PTUs were only calculated for specimens collected after 1900. Daily temperatures were interpolated from monthly temperatures using the function splinefun in R on the “periodic” setting.

Arabidopsis displays substantial genetic diversity in environmental response between genotypes from different regions (e.g. Lasky et al. 2018). Thus, we employed a regression model with spatially varying coefficients (generalized additive models, GAMs) to account for regional differences in responses to environment, much of which may have a genetic component (Wood 2006; Wheeler & Waller 2009). GAMs allow fitting of parameters that vary smoothly in space (i.e. parameter surfaces) and can thus capture spatially varying relationships between predictors and the response of interest. To assess how phenotypes have changed across the last few centuries, we first tested a model with a spatially varying intercept (SVI) and a spatially-varying coefficient model for the effect of year, allowing for geographic variation in temporal trends. We refer to these subsequently as “year models”. These models included all specimens with phenotype data.

Next, to assess how temporal fluctuations in climate drive phenotypic change, we fit models with an SVI and standardizing climate conditions from all years (1901-2016) within a grid cell to unit standard deviations and mean zero (“temporal models”). These models only included specimens from after 1900, when we had data on monthly climate from CRU. Embedded Image

In this model, the subscript j denotes location and i denotes year of collection. The SVI is denoted by μi, where the “j” subscript indicates that the intercept varies with location. The errors are assumed to be independent, be normally distributed, and have constant variance.

Third, to study spatial variation in phenotypes in response to spatial climate gradients, we fit models with spatially varying effects of climate, and with scaled long-term, 50-year climate averages at each location and scaled year of collection as covariates (“spatial models”) (Hijmans et al. 2005). In these models, the intercept was not allowed to vary spatially, but was kept constant over space. Covariates were scaled to unit standard deviation. In these spatial models, year of collection can be considered a nuisance variable for our present purposes. These models included all specimens with phenotype data. Embedded Image

In this model, the subscript j denotes location. The errors are assumed to be independent, normally distributed, and have constant variance.

Models were fit in R (version 3.5.0, R Core Team 2011) using the ‘gam’ function in package mgcv (version 1.8-17, Wood 2011). We allowed the model fitting to penalize covariates to 0 so that covariates weakly associated with phenotypes could be completely removed from the model; thus, using the mgcv package we can achieve model selection through joint penalization of multiple model terms. Coefficients in spatially varying coefficient models represent the individual relationship between each term and phenotype at each geographic point, which we visualized by plotting the estimated coefficients on a map. Each cell in the 100×100 grid model rasters corresponded to 106 km East/West at the lowest latitude (28.16°) and 44km North/South, calculated using Vincenty ellipsoid distances in the geosphere package. Model predictions farther than 200km from a sampled accession were discarded when visualizing results.

We considered two other spatially-varying environmental variables of interest: elevation and N deposition. However, the N deposition estimates we tested (Dentener 2006) were spatially coarse and correlated with year (r2 = .209). Elevation was not obviously correlated with year (r2 = .004), but the smooth term for elevation had an estimated concurvity greater than .9 in both the temporal and spatial models, which indicates that it could be approximated from the smooth terms of our other variables. We left elevation and nitrogen deposition covariates out of the final models because inclusion resulted in instability in the numerical routines the GAM software (mgcv) used to estimate parameters and approximate Hessian matrices needed for confidence intervals. When elevation and nitrogen deposition were included in the model as covariates, the Hessian matrices were not positive definite, and thus could not be used to obtain confidence intervals. We include results in the supplement with elevation but not year for both temporal and spatial models (Figures S13 and S14). Including only variables the three climate covariates and year resulted in numerically stable estimates. In addition, scaling of year and climate variables tended to reduce the concurvity of variables and increase stability.

Finally, we considered how traits covary by fitting GAMs with spatially varying intercepts and measured phenotypes as both response and predictor variables to observe how the correspondence of traits changes through space.

Code for all the models and plots will be included as a supplement and will be available on github.

Results

Distribution of samples through time and space

Samples were broadly distributed, with dense collections in Norway/Sweden, the Netherlands, and Spain (reflecting major herbaria used in the study), and sparser collections in the East.

Figure 1:
Figure 1:

A) Locations of collections used in our analysis. Color of circle corresponds to year of collection. B) Distribution of years of collection. C) Sample herbarium record from Nepal, 1952, with a Δ13C of 21.9, a δ15N of 3.6, and a C:N ratio of 23.4.

Correlations among phenotypes

We found generally weak correlations among phenotypes of Arabidopsis individuals. The first two principal components explained only 36.3% and 24.1%, respectively, of the variance in the phenotypes of Δ13C δ15N date of collection, C:N, and PTU (N = 397). The first principal component corresponded to a negative correlation between C:N and day of collection (bivariate r = −0.194). Inspecting the relationship between collection date and C:N further, it had a triangle shape (Figure 2B), i.e. there were no late-collected individuals with high C:N. ANOVA showed the slopes of the regression of the 25th and 75th percentiles to be significantly different (p = .00153). The second PC corresponded to a negative correlation between Δ13C and δ15N (bivariate r = -.218). C:N and leaf N are highly correlated (bivariate r = −0.815), so we focus on C:N in the text. Leaf N results can be found in the supplement.

Figure 2:
Figure 2:

(A) Variation in phenotypes across the native range of Arabidopsis for Δ13C, δ15N, C:N, collection date, and photothermal units (PTU) at collection. Color indicates the fitted mean value of the phenotype. Collection date is earlier in the southwest and Turkey in comparison to other regions; however, PTUs are higher in the southwest and in the northeast. δ15N is variable across the range. C:N shows a gradient of increasing to the southeast and southwest. Δ13C increases along a northeasterly gradient. (B) Correlations between phenotypes in this study. There is a positive trend between PTU and date of collection. PCA of phenotypes showed an inverse relationship between C:N and PTU and an inverse relationship between Δ13C and δ15N along the first and second principal components (C). Arrows represent correlation of phenotypes with principal components.

Spatial variation in long-term average phenotypes

We visualized spatial diversity in phenotypes by plotting the intercept surfaces in the year only models (Figure 2A). All phenotypes showed significant spatial variation (all GAM smooth terms significant). Δ13C was lower in the Iberian Peninsula and higher in Russia (GAM smooth term, p = .0002). δ15N varied across the range, but with less pronounced spatial gradients (GAM smooth term, p = .003). C:N was higher in the Iberian Peninsula and the East and lower in Russia (GAM smooth term, p = 8.24e-05). Collection day was earlier along the Atlantic coast and Mediterranean (GAM smooth term, p = <2e-16). Despite this, PTU at collection still was higher in the Mediterranean region as well as at far northern, continental sites (GAM smooth term, p = <2e-16).

Temporal change in phenotypes

Several phenotypes have changed significantly over the study period (1794-2010, Figure 3 above). For example, C:N ratio increased in later years in much of Southwestern Europe. δ15N decreased significantly throughout most of the range. Collection date and PTUs became significantly later in many regions from the Mediterranean to Central Asia, although collection date became significantly earlier in the extreme south (Morocco and Himalayas). There was no significant temporal trend in Δ13C (not shown).

Figure 3:
Figure 3:

previous page: Change in phenotypes due to year for collection date (A), photothermal units (B), delta nitrogen (C), and C:N (D). Color indicates the value of the coefficient for year in the model excluding climate variables. Day of collection and photothermal units have increased over time in most of the range, but with some exceptions for day of collection in the south. Change in collection day was uneven across regions, with greater shifts in the Aegean than in the Scandinavian Peninsula. δ15N decreased across most of the range, and C:N increased, most notably in the southwest. Gray shading indicates regions where estimated coefficient is not significantly different from 0. Inset scatterplot in A shows the significant increase in collection date with year for samples in the boxed Mediterranean region. Plots to the left of A show the density of collection dates through the year remains stable through time for Scandinavian collections within the boxed region (top) but shift toward more collections late in the year in the boxed Mediterranean collections (bottom).

Temporal change in phenotypes after accounting for climate anomalies

The temporal trends in phenotypes across the study period were likely partly related to underlying climate variation. However, many of the phenotypes were still significantly associated with year of collection even when accounting for temporal anomalies in climate from 1901-2010. There was a general delay in collection through time in much of the eastern range (Figure S1, S2). However, Iberian collections were significantly earlier in later years. Across most of Europe, later years of collection also were associated with significantly greater C:N ratio (Figures S9, S10). Similarly, we observed δ15N decreasing in later years across much of Arabidopsis’ range, as expected with elevated CO2 and increased nitrogen deposition. There was still no significant temporal trend in Δ13C.

Phenotype associations with spatiotemporal climate gradients

Date of collection

In years (temporal models) with a relatively warm January or April plants were collected significantly earlier (Figure 4A). Similarly, in locations (spatial models) with warmer temperatures plants were collected earlier, though in many regions these coefficients were non-significant and some exhibited reversed signs. We also tested associations with July aridity index (precipitation/PET) and found that plants were collected significantly earlier in years (temporal models) with dry summers in central/eastern Europe, suggesting a drought escape strategy, but later following dry summers in Central Asia, suggesting a drought avoidance strategy (Figure 4B).

Figure 4:
Figure 4:

Association between collection day of Arabidopsis thaliana temporal mean April temperatures (A) and July Aridity Index (B) anomalies (compared to 50-year average). Color indicates the value of the coefficient of the April mean temperature or July Aridity Index term. In years where April was warmer, plants were collected earlier. In wetter years, plants were collected later in Eastern Europe but earlier in Asia. Shading indicates regions where estimated coefficient is not significantly different from 0. Scatterplots of phenotype measures for individuals within the boxed areas show a decreasing collection date with mean April temperature and increasing collection date with July aridity index in Eastern Europe and decreasing collection date in Central Asia.

Leaf C:N

Leaf C:N was significantly different among locations (spatial models) differing in April mean temperature and January minimum temperature, although the direction of these trends differed among regions. In this model, warmer winters were associated with higher C:N in southwestern Europe but lower C:N in central Asia (Figure S10B) and warmer temperatures in April predicted lower C:N in Iberia. For the temporal model, plants collected in wetter years in Iberia had significantly higher C:N ratios.

Photothermal units

To standardize spatiotemporal variation in developmental periods, we also modeled climate associations with PTUs. As expected if PTUs account for most of the environmental control on development, there were few areas where temperature anomalies were significantly associated with PTUs (Figure S3). However, in some areas, accumulated PTUs at collection changed significantly in association with spatial temperature gradients, suggesting greater complexity in phenology beyond PTU models (Figure S4). Locations with warmer Aprils were collected at more PTUs around the Baltic sea and fewer PTUs around the Aegean. In the temporal model, accessions from Western Europe, Greece, and Central Asia, had lower PTUs in wetter years and accessions in Central Europe had higher PTUs wetter years. In the spatial model, accessions from wetter areas in the south had lower PTUs, but this pattern was reversed in the North (Figure S3, S4).

Δ13Carbon

Although we expected moisture limitation would lead to stomatal closure and lower Δ13C, the coefficient for summer aridity was not significantly different from 0 anywhere in either the temporal or spatial models of Δ13C. However, there was regional significance in the spatial relationship between Δ13C and long-term mean April temperature (smooth term significance: p = 1.58e-5). Specifically, in Central Asia areas with warmer Aprils had higher Δ13C than collections from areas with cooler Aprils (Figure S6A). In Iberia and Northeastern Europe, the reverse was true (though the local relationships were not significant).

Including elevation caused estimation of confidence intervals to be unreliable, so elevation was left out of the final models, but elevation tended to have a negative relationship with Δ13C as expected due to declining Ca at high elevation (Körner et al. 1988) (Figures S13 and S14). This was significant in the temporal model across much of Asia.

δ15Nitrogen

δ15N was positively significantly related to temporal anomalies in July Aridity Index in a number of regions (Figure S7), but this relationship was heterogenous across space. The relationship was positive in Iberia, Asia, and Central Europe, but negative in the North of France. Temperature was only significantly related to δ15N in the case of spatial variation in minimum January temperatures around the North Sea.

Covariance of phenotypes

In a GAM where C:N was a function of date or accumulated photothermal units, we found both measures of phenology were negatively correlated with C:N across the Arabidopsis native range, but this was insignificant at the 95% confidence interval when the effect of year was included (Figure S20). Δ13C was likewise insignificantly correlated with date of collection and accumulated photothermal units when accounting for year (Figure S20).

Discussion

Widely distributed species often exhibit considerable phenotypic diversity, a large portion of which may be driven by adaptive plastic and evolutionary responses to environmental gradients. The existence of genetic variation in environmental responses among populations suggests that responses to temporal environmental shifts may differ dramatically among populations. Previous studies of intraspecific trait variation in response to environment have tended to focus on genetic variation of environmental responses in common gardens (e.g. Wilczek et al. 2009; Kenney et al. 2014), temporal trends in phenology from well-monitored sites (CaraDonna et al. 2014), or field sampling of individuals from a small number of sites (Jung et al. 2010) Here, we build upon this work to study change in traits across an entire species range over two centuries, giving us a window into drivers of intraspecific diversity and regional differences in global change biology. From the accumulated work of field biologists contained in natural history collections, we found later flowering times and higher accumulated photothermal units over the study period across most of the range and lower δ 15nitrogen and higher C:N in more recent collections. Additionally, we observed distinct regional differences in phenology, Δ 13carbon, and C:N in response to rainfall and temperature. However, we observed insignificant covariance among phenotypes in space.

Phenology

We found strong gradients in two measures of phenology suggesting adaptive responses to climate drive intraspecific phenotypic diversity. Years and locations that were warmer than average in either April or January corresponded to significantly earlier collection dates, consistent with temperature’s positive effect on growth rate (Wilczek et al. 2009). The fact that these relationships were spatially variable, and insignificant in some regions, may indicate areas of contrasting phenological response, perhaps due to lost vernalization signal or variable effects on germination (Burghardt et al. 2016). Alternatively, Arabidopsis is known to complete a generation within a growing season, climate permitting, and warmer climates allow for fall flowering (Wilczek et al. 2009; Fournier-Level et al. 2013). If warmer temperatures enable a greater number of spring or summer germinants to flower before winter in regions such as Central Europe, we would expect to see later collection dates in more recent years (Burghardt et al. 2015). In this case, regions that have earlier collection dates with warmer temperature may be limited in generational cycles due to another environmental factor, such as summer drought or short growing seasons. Our models provided support that some phenological variation did reflect seasonality of moisture availability. We found Arabidopsis was collected significantly earlier in years with dry summers in central Europe and at significantly lower PTU in regions of wet summers around the Mediterranean, suggesting drought escape or avoidance strategies could be important in those regions. Alternatively, later collections in wetter years could be the result of multiple successful generations due to the extra rainfall.

Our findings of later collection dates through the study period (1798-2010) may surprise some readers due to previously observed acceleration of temperate spring phenology (Parmesan & Yohe 2003). However, we modeled changes in mean phenological response to environment, which can be weakly related to phenology of extreme individuals (e.g. first-flowering individuals) (CaraDonna et al. 2014). Why might Arabidopsis flower later even as global temperatures rise? First, non-climate environmental changes (e.g. in land use) may drive phenology. Second, changes in climate or atmospheric pCO2 may favor alternate life histories. As described above, later collections in more recent years might represent an increasing proportion of fast-growing spring or summer annuals as opposed to winter annuals.

Physiology, leaf economics spectrum

We found little evidence for tight coordination among studied phenotypes, fitting with some past surveys that found weak to no support for a single major axis in intraspecific trait variation in response to environment (e.g. Albert et al. 2010; Wright & Sutton-Grier 2012). Common garden experiments often find substantial genetic covariation between these traits possibly due to pleiotropy or selection maintaining correlated variation (Kenney et al. 2014). The massively complex environmental variation organisms experience in the wild may combine with genotype-by-environment interactions to generate high dimensional variation among individuals in nature.

Nevertheless, we found that plants collected later in the year have low leaf C:N, indicative of a fast-growing resource acquisitive strategy. This strategy may be adaptive for rapid-cycling plants germinating and flowering within a season (spring/summer annuals), contrasted with slower-growing genotypes known to require vernalization for flowering over a winter annual habit. Indeed, Des Marais et al. (2013) found that vernalization requiring (likely winter annual) Arabidopsis genotypes had lower leaf N than genotypes not requiring vernalization for flowering, the latter of which could also behave as spring or summer annuals.

However, the Leaf Economic Spectrum and fast/slow life history frameworks do not well explain our C:N models in response to climate variables, which were insignificant across regions for both spatial and temporal climate gradients of temperature and aridity. This may be due to the intraspecific nature of our study, as opposed to the interspecific basis for LES. In multispecies analyses, phenotype correlations with climate may be influenced by community composition changes across the environment or may not represent the physiology or climate niche of a given species (Albert et al. 2010; Elmore et al. 2017). However, as additional environmental processes drive leaf nitrogen in the wild, our study may have lacked power to differentiate the effects of nitrogen acquisition and efficiency traits from the effects of geochemistry. This may be especially true for δ15N, which neither decreased with rainfall nor responded to temperature as expected, but did decrease with year as previously reported in multi-species surveys (Craine et al. 2009; McLauchlan et al. 2010).

Similarly, we did not see strong relationships between aridity and Δ13C. Δ13C was expected to be closely related to environmental conditions of rainfall and temperature due to stomatal gas exchange dynamics (Farquhar et al. 1989; Diefendorf et al. 2010). While surprising, Δ13C patterns may be weaker than expected for a couple of reasons. First, we observed both positive and negative trends for aridity and date of collection, consistent with the hypothesis that Arabidopsis exhibits both drought escaping and drought avoiding genotypes. The phenological responses to moisture might limit Δ13C responses by allowing consistent favorable conditions during growth periods. Second, gas exchange and carbon assimilation depend in part on leaf architecture and physiology traits like venation, specific leaf area (SLA), and leaf N (Schulze et al. 2006; Brodribb et al. 2007; Easlon et al. 2014), which could mitigate the Δ13C response we observe. For instance, as SLA increases in areas of high rainfall (expected according to LES (Reich et al. 2003)), the photosynthetic rate could be impacted. In addition, genetic variation in these traits may affect δ13C, and thus in turn Δ13C differently in spring and winter annuals (Easlon et al. 2014). Furthermore, water use efficiency, for which Δ13C serves as an indicator, is not limited to leaf traits but could also reflect root investment or other traits unmeasured here. The lack of Δ13C patterns could also be the effect of elevated atmospheric CO2 (Drake et al. 2017), although limiting the model to observations before 1950 (when atmospheric CO2 was much lower) suggested, surprisingly, that Asian accessions growing in locations of high Aridity Index may have had decreased Δ13C. Investigating at a smaller scale the patterns we found could clarify mechanisms leading to the phenotype-climate associations or lack thereof. Smaller scales would benefit in addressing populations known to be genetically unique, as in Iberian relicts (Alonso-Blanco et al. 2016), that may cause unexpected regional differences in phenotype trends (Figure S1D).

Our approach, technical problems to surmount in future studies

Understanding how spatiotemporal environmental variation drives the intraspecific diversity that exists in broadly distributed species has been challenging due to the scale of the problem. However, advances in digitization of museum specimens and the generation of global gridded spatiotemporal environmental data are opening a new window into large scale patterns of biodiversity. One challenge of herbarium specimens is that they are typically mature (reproductive) individuals. Thus, these specimens contain limited information on phenology and physiology at earlier life stages, which can have subsequently strong impacts on later observed stages. Use of developmental models (Burghardt et al. 2015) might allow one to backcast potential developmental trajectories using herbarium specimens and climate data, to make predictions about phenology of germination and transition to flowering.

Generalized additive models are a flexible approach to model phenotype responses to environment that might differ spatially among populations (MacGillivray et al. 2010). Herbarium records represent imperfect and biased samples of natural populations, and future efforts may benefit from additional information that might allow us to account for these biases. Here, we sampled a very large number of specimens across continents and decades and so we deem it unlikely that most of the patterns we observed were driven by biases associated with specific collectors. Nevertheless, as museum informatics advance it may become possible to explicitly model potential sources of bias, for example those arising from collecting behavior of specific researchers.

Conclusion

Widely distributed species often harbor extensive intraspecific trait diversity. Natural history collections offer a window into this diversity and in particular allow investigation of long-term responses to anthropogenic change across species ranges. Here we show that spatiotemporal climate gradients explain much of this diversity but nevertheless much of the phenotypic diversity in nature remains to be explained.

Acknowledgements

Hundreds of botanists collected the specimens studied here. The staff at herbaria at Oslo Natural History Museum, Kew Gardens, Real Jardin Botanico, Komarov Botanical Garden, and the British Museum of Natural History gave permission for tissue sampling. Michelle Brown provided essential assistance in collecting herbarium tissue. Jason Bonette helped coordinate sample preparation. Major assistance in digitizing of specimens was provided by Patrick Herné. Eugene Shakirov aided in translating Russian specimen labels. Data from the MNHN in Paris were obtained thanks to the participatory science program Les Herbonautes” (MNHN/Tela Botanica) which is part of Infrastructure Nationale e-RECOLNAT: ANR-11-INBS-0004. Additional volunteers from the Atlas of Living Australia helped with digitization and georeferencing. Funding was provided by an Earth Institute fellowship to JRL.

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Posted September 23, 2018.
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Effects of two centuries of global environmental variation on phenology and physiology of Arabidopsis thaliana
Victoria L. DeLeo, Duncan N. L. Menge, Ephraim M. Hanks, Thomas E. Juenger, Jesse R. Lasky
bioRxiv 424242; doi: https://doi.org/10.1101/424242
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