Revisiting the role of mean annual precipitation in shaping functional trait distributions at a continental scale

Abstract

Mean annual precipitation (MAP) varies substantially across the globe, impacting the spatial distribution and structure of vegetation (Schimper, 1898). However, evidence for consistent relationships between MAP and the functional traits of the organisms in these ecosystems is equivocal. Indeed, while some early global-scale analyses reported MAP as a key predictor of plant traits (Wright et al., 2005; Moles et al., 2009; Ordoñez et al., 2009), more recent analyses have found relatively weak relationships between traits and MAP (among other broad-scale climatic variables; Moles et al., 2014; Maire et al., 2015; Bruelheide et al., 2018), seemingly at odds with the important role that water availability is predicted to play in determining plant success from first principles (McDowell et al., 2008, 2022). The independent contribution of MAP in shaping the spatial distribution of species traits, at least at the global scale, has therefore remained unclear. An emerging observation is that trait–environment patterns become more pronounced when focussing on regional scales, or within habitat types (Chelli et al., 2019; Guerin et al., 2022; Kambach et al., 2023). Here, focussing on the continent of Australia, we empirically test our theoretical understanding of the relationship between traits and MAP using an unprecedented database, AusTraits – the largest harmonised continent-specific collection of georeferenced trait values globally. Australia represents the ideal laboratory to test trait–MAP relationships for several reasons. First, MAP and mean annual temperature (MAT) are orthogonal in Australia (r = 0.02; Fig. 1a). As such, associations between water availability and traits can be isolated from the effect of MAT, by design. Australia also spans an extraordinary precipitation gradient, encompassing the 22nd (79 mm) and 99th (7625 mm) quantiles of the global distribution of MAP, thereby representing all but the very driest regions of the globe (Fig. 1b). Finally, although Australia is a major land-based carbon sink (accounting for c. 60% of the global terrestrial carbon sink in some years; Poulter et al., 2014), there is also significant uncertainty regarding the effect of precipitation on carbon uptake in this region, which is proposed to emerge from a number of factors including poor representation of drought-adaptation within the highly endemic and structurally distinct vegetation of Australia and significant variation within model ensembles in the simulated or prescribed fraction of woody and herbaceous cover (Teckentrup et al., 2021). Altogether, a re-examination of the relationship between plant traits and MAP in Australia would not only improve our fundamental understanding of the evolution of trait distributions but also yield a timely assessment of the embedded processes in dynamic vegetation models (DVMs) used to simulate ecosystem processes (Teckentrup et al., 2021). We selected eight key functional traits widely considered to capture important physiological processes in vascular plants, and for which sufficient data were available, and generated hypotheses for how each would respond to spatial variation in MAP. Hypotheses were derived from published eco-evolutionary theories explicitly relating traits to MAP or soil moisture and, if these were not available, we inferred predictions from theories based on other moisture-related environmental drivers including vapour pressure deficit (VPD) and site productivity. The selected theories invoke a range of processes including, for example, tissue damage due to leaf overheating, optimisation of plant construction to maximise net carbon uptake and stand-based competition (Table 1). We used bivariate linear regressions to test each of these hypotheses (according to the sign of the relationship) with our overarching hypothesis being that traits with direct theoretical links to MAP would have the strongest correlations. To establish a clearer picture of macroclimatic control on traits in Australia, we also quantify the extent to which trait–MAP relationships are mediated by MAT via interaction (Wright et al., 2017). To account for potential variation in trait responses to the environment due to woodiness, we tested whether observed patterns differed when species were classified as woody or nonwoody. We expected relationships would be stronger in woody taxa because long-lived individuals must function in challenging environmental conditions whereas nonwoody species often avoid dry conditions by surviving as seed. VPD1 Soil moisture2 Wang et al. (2017)1 Paillassa et al. (2020)2 Time to reproductive maturity ↑ Access to light ↑ Productivity1 Moisture stress2 Competition for light greater in mesic environments1 Moisture stress places upper limit on plant height2 Falster et al. (2017)1 Jensen & Zwieniecki (2013)2 Construction cost ↑ Water transport rate ↑ Construction cost ↑ Leaf turnover rate ↓ Photosynthetic rate ↑ Respiration and construction cost ↑ Number of offspring ↓ Competitive ability of offspring ↑ Construction cost ↑ Growth-dependent mortality ↓ For woody taxa, MAP was an excellent predictor (r2 ≥ 30%) of the variation in leaf Δ13C (Δ13C; C3 plants only), leaf mass per area (LMA), maximum plant height (MH) and leaf nitrogen per area (Narea), with LMA and Narea decreasing and MH and Δ13C increasing with MAP (Table 2; Fig. 2). In addition, MAP was a moderate predictor (r2 ≥ 20%) of leaf area (LA) and wood density (WD), being positively and negatively correlated with MAP, respectively. However, MAP was a weaker predictor (r2 ≥ 10%) of the Huber value (SA : LA) and seed mass (SM), being negatively and positively correlated, respectively. Regardless of correlation strength, in all cases the direction of the fitted correlation was consistent with our predictions (Table 1). Combining woody and nonwoody observations tended to cause the amount of variance explained by MAP to decline for the nonwood-related traits (see the Materials and Methods section), with the exception of LA, which increased slightly. Most notably, we observed a reduction in the variance explained for Narea from r woody 2 = 31 % $$ {r}_{\mathrm{woody}}^2=31\% $$ to r overall 2 = 26 % $$ {r}_{\mathrm{overall}}^2=26\% $$ , and a much larger decrease in the variation of LMA explained ( r woody 2 = 38 % $$ {r}_{\mathrm{woody}}^2=38\% $$ vs r overall 2 = 2 % $$ {r}_{\mathrm{overall}}^2=2\% $$ ), to the extent that MAP and LMA were now virtually uncorrelated. These outcomes emerged because, for the most part, trait–MAP relationships were weaker in nonwoody taxa, and in the case of LMA, there was a bimodal distribution in the observations at low MAP due to a greater representation of herbaceous annuals (Fig. 2). In general, including MAT as an interacting predictor with MAP had minimal impact (Δ < 5 percentage points) on the variation explained in each trait for woody or nonwoody taxa, as well as when combined (Table S1). However, there was an c. 10 percentage point increase in r2 in the interaction model compared with the MAP-only model for LA, SM (but not nonwoody) and MH (overall only) which, for LA and MH, mostly reflected a steepening of the MAP slope in warmer climates, and for SM, a mean increase towards warmer climates driven by the main effect of MAT (Figs S1–S3; Table S2). Alone, for woody taxa, MAT was an excellent predictor of MH, a moderate predictor of SM, a weak predictor of LA and SA : LA, and was uncorrelated (r2 ≤ 10%) with the remaining traits (Table S2). In contrast to recent global analyses (Moles et al., 2014; Maire et al., 2015; Bruelheide et al., 2018), we found MAP was a strong predictor of several key functional traits, in a manner consistent with predictions based on the theoretical literature (Table 1). Specifically, as MAP increased, we observed a systematic shift from resource-conservative to resource-acquisitive values for most traits. This response was most prominent in woody taxa, in partial support of our hypothesis, but we also found important exceptions where the explanatory power of MAP was equivalent between growth forms. These findings support an emerging phenomenon that trait responses to macroclimatic gradients become stronger when focussing on distinct regions or within habitats (Buzzard et al., 2019; Chelli et al., 2019; Kambach et al., 2023), as has been shown in Australia for plot-based community-weighted analyses of LA, SM and MH in response to climate (Guerin et al., 2022). Importantly, the rapid expansion of the AusTraits trait database since Guerin et al. (2022) has permitted the significant advancement upon our understanding of Australian trait ecology that we present here by incorporating observations from naturally occurring plant communities across all but the most extreme ranges of the Australian rainfall gradient (including tropical rainforest) for a much greater range of traits, including plant-construction traits which are highly relevant for simulating ecosystem processes in DVMs such as SA : LA, WD and LMA (Sakschewski et al., 2015). The strongest trait–MAP relationship we observed was the positive correlation between plant height and MAP for woody taxa, which was followed closely by when all taxa were analysed simultaneously. This outcome is qualitatively similar to a previous global-scale study of plant height, which found that precipitation in the wettest quarter, and then MAP, were the strongest predictors of plant height (Moles et al., 2009), although MAP explained a substantially greater amount of the total variation in the data in the present study (50% compared to 21%). MH may increase with MAP for several reasons. Maximum achievable height may be biophysically constrained by water availability such that, all else being equal, taller plants can survive in wetter sites (Jensen & Zwieniecki, 2013). An alternative explanation is that on average, natural selection favours taller plants in wetter sites because the benefit of having a higher position in the canopy outweighs the drawback of increased stem construction costs and delayed reproduction as competition for light intensifies (Falster et al., 2017). Similar to MH, we also found that leaf Δ13C and MAP were strongly and positively correlated across all C3 taxa, consistent with a recent, global-scale empirical analysis (Cornwell et al., 2018). Δ13C is a measure of the long-term average of the ratio of the partial pressure of CO2 in the intercellular spaces (Ci) and the atmosphere (Ca). According to all else being equal, the cost of water for in et al., 2014), to and plants to more in to water transport et al., 2017). Indeed, in with this we also observed that which is to et al., was greater in sites with MAP of growth in contrast to et al. the of the correlation between Δ13C and MAP was strong for woody and nonwoody taxa, that the processes are to MAP in growth forms. the trait data for woody and nonwoody species also important in response to MAP. For example, in with our based on LMA was found to be strongly and negatively correlated with MAP r2 for woody LMA is to decline as site because the more rapid height growth by and therefore greater to for leaf turnover costs with the leaf of low LMA (Falster et al., 2017). In addition, higher LMA is to be in more sites to leaf function at more leaf water et al., 2023). our are consistent with et al. found a relationship r2 10%) between LMA and MAP at the global scale, but only for The that MAP explained such a of the variation in LMA in the study to the global is In Australia, woody and are mostly that the between LMA and leaf is not by a shift to being However, the data for vs taxa, et al. found MAP only explained at most of the variation in The observed relationship to the important role of MAP in selection on leaf for woody taxa across strong rainfall gradients in Australia, and LMA and MAP were virtually uncorrelated when the nonwoody taxa and this reflected the for trait–environment relationships to be weaker within this functional or trait which the relationships we observed in woody taxa, may not be present in the nonwoody taxa For example, an analysis of trait using data from the database that the direct relationship between leaf and LMA, which is across all taxa and woody taxa, is not present for nonwoody taxa et al., weaker trait–environment relationships may emerge in nonwoody species because this functional a of to or more such as relatively in the or as a in the seed as is the case for of the annual analysed here & in DVMs has been by the of which trait values to be predicted based on climatic conditions et al., 2015). analysis using a trait database evidence for a role of MAP in shaping broad-scale trait gradients in Australia and, for these relationships to be the of of this However, this on our ability to the processes that cause these relationships to To this we that traits with strong with MAP LMA, MH, tended to theoretical hypotheses to precipitation or soil being driven by site or (Table 1). An is the extent to which the strong trait–MAP that we observed are to other or are the outcome of distinct processes occurring only within Australia et al., 2023). is to this we that although the selection moisture gradients are present in all other processes may For example, while Australia is a continent with minimal recent and the of much global trait in the Maximum As such, between species distributions and climate may be greater in these associations & et al., 2021). relationships may also be more in Australia because, in with the a relatively fraction of the continent & plant growth is not by the effect of water availability on plant We not temperature as a of the effect of MAP on traits in Australia, Indeed, we have that for traits LA and MH, trait–MAP patterns may become more in warmer as has been shown for LA at the global (Wright et al., 2017). The analysis that we here also of trait–environment using this unprecedented for Australian First, while our of as independent data represents the of trait variation across sites emerging from species turnover and not the contribution of these to the observed patterns & the of this variation would have important for our understanding of how patterns have emerged and how communities be to et al., while our analysis trait responses to MAP of selection on traits such that to can be of et al., analyses using this to how traits varies across environmental gradients et al., would yield how these to plant conditions (Fig. In a similar the variation that we observed for some traits across species within sites to a significant contribution of species to trait the of which may be on climate et al., 2021). For example, although we observed a mean response of WD to MAP, there was also a strong in the data where taxa in mesic sites had a much greater range of WD from very light to very wood (Fig. 2). of within a climatic also some to the very weak response of SM to precipitation that we and (Moles et al., have The for theoretical is not to the of trait distributions et al., Wang et al., et al., but also the of which may a climatic linear and by predictions from the we have that for woody taxa, MAP is strongly with key functional traits. focussing the of this study on Australia, we not only trait–environment patterns for a highly endemic and continent et al., 2023), but also the potential for analyses to as natural by for distinct with other environmental factors the between MAP and MAT in To this we for the of analyses to regions where observations strong gradients in the of of other environmental et al., 2023). AusTraits Falster et al., is a harmonised trait database of functional traits for taxa occurring in Australia (including and is therefore the largest of trait data for plants in Australia and for We the AusTraits database for eight functional traits which of plant are widely across Australia and for which predictions the effect of MAP be derived from the literature (Table 1). These traits were the leaf carbon ratio ( $$ $$ ), maximum height Huber value (SA : leaf area leaf mass per area (LMA), leaf nitrogen per area (Narea), dry seed mass and wood density is in trait data such as AusTraits which from with and et al., and, in this our analysis which across climatic gradients in Australia et al., Fig. for all traits considered the of the rainfall gradient is not by and we are that the of relationships at a across outweighs the costs of potential in the we were in how traits respond to variation in we the data to only observations which had In to trait observations from we also the of our study to trait observations on individuals occurring in In other this most data from laboratory and and Finally, we the trait data to observations which were as across or values to that variation within species across if was these observations had minimal impact on the total number of observations for each trait < of the with the exception of MH, for which c. of observations were at the or species This reflected the that some in AusTraits the maximum height observed across or from published to all observed individuals within the the outcome of the analysis for MH when these observations were (Fig. was consistent with when were although the variation explained by MAP was greater in the for LA on species differed between which considered LA at the vs the The theoretical for the response of LA to the environment considered in the present study is based on the rate of the which, for is at the (Wright et al., 2017). to variation in the LA data with the of we first which LA on taxa and for which LA was at the leaf scale, or for which the was within these we used the data in which taxa as being or to taxa as having or taxa to be LA observations for taxa with or for which was within these were then In c. of the independent LA were this We used a number of to for in the First, to for we compared the data distribution for a the distribution of all other combined for each trait and this for for taxa with with observations from we the range of values that a taxa to This to the of a number of data = being which had or low values to other observations of that taxa but also to the distribution of the trait Finally, we the data for of trait values for a between which may in cases where the data was from a data although data were for this To our hypothesis regarding the of plant growth on the relationship between MAP and functional traits, we also the AusTraits database for the using a by et al. This trait taxa based on the and extent of and taxa In the case of this we not plant growth as a response per we taxa with growth being or For the we selected taxa with an and a stem based on the in the This was the largest in the of the taxa for which was the nonwoody a more of growth including the most nonwoody but also and taxa other such as and can To test whether of these taxa a the outcome of our we our analysis including taxa with the first the and the was a increase in model for the woody and nonwoody in the we the of this of although there was in the of our in case our was to the relationship between traits and MAP, we also selected a range of other climatic which are considered to soil and moisture In addition, we also selected MAT to how temperature responses to MAP. data were from a of as all MAP, mean precipitation in the wettest quarter, mean precipitation in the driest quarter, mean precipitation in the quarter, mean precipitation in the quarter, mean precipitation and MAT were from at a of at the mean annual potential from at a of and mean data from to from at a of For the mean we the values across to a mean annual Moisture was by MAP by annual potential We then climate data to the with trait In regions where trait observations were climate data were not In these we used the function from the & to these observations with climate data from the within 10 this not yield climate these observations were not in observed relationships tended to be similar or weaker when MAP with other (Table S2). The most exceptions were that SA : LA and MH more strongly to precipitation in the This was the case for MH when all taxa were which is explained by the that this annual rainfall from (Fig. there was some evidence to that Narea more negatively to and LA more positively to precipitation in the wettest and compared with MAP in woody and nonwoody taxa when as well as when although we these in the we not on these analyses considered that trait variation may at spatial being variation and Here, a site is the across which broad-scale climatic gradients trait within sites is to be due to other factors including environmental as well as of functional Here, we trait observations to sites by a on the to the of the climate where each represents a we found the mean in the case of for each for each on the trait and growth In other for a each site has a number of observations equivalent to the number of taxa in This that across observations from data and Importantly, this for the effect of on analyses by to observation per we trait and climate data to the of for linear Specifically, we all traits, MAP, precipitation in the wettest and quarter, moisture and precipitation and precipitation in the driest To first test trait responses to MAP across all taxa, we the values of each functional trait MAP using We then the correlation as a measure of the and direction of as well as the r2 value to the percentage of variation explained by MAP. the in this for weak are to be we not To the explanatory power of MAP compared with other moisture-related we each trait as a bivariate linear function of each of the remaining climatic To test whether the relationship between traits and MAP is mediated by we also fitted each trait as an interaction between MAP and MAT and the increase in variation explained by the more model as a measure of model To test our hypothesis that woody taxa stronger responses to the climate nonwoody taxa, we then the as for the woody and nonwoody In all taxa with growth were from analyses to between patterns when taxa were combined and when by growth To the of in the relationship between traits and we all of the models with an and the r2 value to whether including the model For the most part, model were not substantially However, there was some evidence that Narea had a relationship with MAP there was a decline in Narea from to mesic regions c. rainfall where increasing (Fig. 2). with and the analysis and of the This was by a by Australia using functional traits to species distributions and responses to environmental to and and by the Australian to and We also to The AusTraits from the Australian The is by the by of as of the of via the of Australian and the with of the by and the of trait analysis with from the of the and with the of the of the and are on AusTraits is at Fig. of the annual precipitation relationship with mean annual temperature for all Fig. of the annual precipitation relationship with mean annual temperature for woody Fig. of the annual precipitation relationship with mean annual temperature for nonwoody Fig. trait correlation for woody Fig. distribution of functional trait data based on Fig. distribution trait observations with to the Australian climate and Fig. of the relationship between maximum height and mean annual precipitation when and observations are vs in the Fig. of the relationship between maximum height and mean annual precipitation vs precipitation in the Table of model in mean annual precipitation models vs annual temperature interaction Table for trait–environment linear regressions when taxa are with woody Table for trait–environment linear regressions when taxa are with nonwoody Table for trait–environment is not for the or of by the be to the The is not for the or of by the be to the for the

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