Diversity of plant nutrient-acquisition strategies ...Diversity of plant nutrient-acquisition...

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Diversity of plant nutrient-acquisition strategies increases during

long-term ecosystem development

Supplementary methods

Site selection. The Jurien Bay dune chronosequence (30°01' to 30°24' S; 114°58' to 115 11'

E) consists of three main dune systems, the Quindalup, Spearwood and Bassendean dunes,

corresponding to sea-level highstands during the Holocene (Quindalup dunes), the Middle

Pleistocene (Spearwood dunes) and the Early Pleistocene (Bassendean dunes)31,32. From the

three main dune systems, six chronosequence stages were delineated using the geographic

information system (GIS), Quantum GIS33 (Supplementary Fig. 1). The first step of the

randomised stratification process was done by combining high-resolution aerial imagery

(obtained from Landgate, http://www.landgate.wa.gov.au), combined with observations in the

field (June and July 2011), including augering and shallow pit excavations, for cross-

validation (ground-truthing). The Quindalup dune system was stratified into the first three

chronosequence stages: (1) recently-stabilised dune sand; (2) well-developed soil further

inland supporting mature vegetation; and (3) well-developed soil of dunes within

approximately 1 km of the Quindalup-Spearwood transition. The Spearwood dunes were

stratified into two stages (4 and 5) based on the degree of soil development: (4) the most-

westerly extreme of the Spearwood dunes, with sand overlying limestone, generally with 1 m

of the soil surface; (5) deep, fully-decalcified Spearwood sand, at least several metres in

depth. The Bassendean dunes comprised stage 6, and were not further delineated due to the

absence of consistent topographic or soil features allowing a clear distinction to be made

within the system. Overall, the study system spanned ~45 km north to south and ~15 km west

to east.

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SUPPLEMENTARY INFORMATION DOI: 10.1038/NPLANTS.2015.50

GIS model. After delineation of the chronosequence stages, the GIS model was augmented

with tracks from which potential field sites could be accessed, together with roads, tracks and

regions, which were excluded from the surveys (because of disturbance). To minimise edge

effects of nearby tracks, and to ensure survey feasibility, buffer zones were created extending

between 40 and 200 m from the nearest track. Chronosequence stages 2 to 6 were intersected

with these buffer zones to produce regions (plot regions) within which the study sites could

potentially be located. However, the first stage had regions digitised by hand, due to its sparse

vegetation cover and due to the dunes often not being in close proximity to tracks.

Study plots. Plot locations within the chronosequence stages were randomly generated as

points in the GIS; each point represented the centre of the 10 × 10 m plot. To allow for

possible rejection of plots (for example, due to disturbance at the site not visible from aerial

imagery), an excess of the desired 10 plots was generated for each stage. For each of the six

stages, 10 plots of 1010 m were located, giving a total of 60 plots for the entire dune

chronosequence (Supplementary Table 1). All plots were located on freely-draining soil and

seasonally-wet areas were excluded. These site selection criteria prevented seasonal water

dynamics and oxygenation from being confounding factors. The mean distance between

neighbouring plots was 2.1 km; we deemed this plot separation to be sufficient to minimise

the effects of spatial auto-correlation among plots.

Subplots. To estimate canopy cover and abundance within each of the 1010 m plots, seven

randomly-positioned 22 m subplots were surveyed within each plot. Species accumulation

curves, calculated with non-parametric estimators using the EstimateS software package34,

were initially used in a particularly species-rich location to determine the optimal number of

subplots that would ensure survey feasibility, while also ensuring that the sampled area was

large enough to be representative of the entire community. Using these species accumulation

curves, it was gauged that seven subplots would give an accurate representation of




community composition and diversity within the 1010 m plot area. This is consistent with a

prior study35 in similar species-rich kwongan shrublands from this region, which

recommended sampling an area of between 15 to 30% of the entire survey area to ensure

adequate sampling. The 28% plot coverage given by the seven subplots fell within the upper

end of this recommended range.

Flora surveys. Surveys of all vascular plants in the plots were done in August, September,

October and November of 2011, and January, February, March and September of 2012. Plots

that were initially surveyed outside the peak-flowering season (approximately August to

November) were resurveyed in September 2012 to ensure that seasonal (annual or geophytic)

species would be observed. All vascular plants were recorded and identified in the field to the

species (or subspecies) level when identification was certain. Field names were given and

specimens taken for later reference for those for which identity was uncertain. The identity of

undetermined species was resolved, whenever possible, at the Western Australian Herbarium.

Species names and family associations followed the APG III classification36. Only species

presence within each 1010 m plot was recorded. By contrast, within each subplot the

number of individuals was recorded for species whose stems were either fully, or partially,

within the subplot; the number of seedlings was also counted. Canopy cover was visually

estimated for all species with vegetation cover within the subplot, irrespective of the location

of their stem, as a percentage of the subplot covered by a vertical projection of the canopy.

Consistency of visual estimation was maintained by the estimation being done by the same

person throughout all surveys. Species known to be clonal were classified as separate

individuals if stems or culms were >20 cm from others belonging to the same species. The

median height and canopy width were also recorded for each species within each subplot.

The cover of bare soil (soil not covered by live vascular plants) was assessed and recorded for

each subplot.




Determination of nutrient-acquisition strategies. The nutrient-acquisition strategies for

several plant families in south-western Australia, for example, the Proteaceae, Ericaceae, and

Orchidaceae, are well known and are summarised on the Mycorrhizal Associations

website37,38. For many other groups, however, knowledge is more limited39. To supplement

our knowledge base, root samples from those plants with unknown strategies and substantial

plot cover were taken in September 2012 and July 2013. The samples were from fine roots,

generally from at least four individuals per plant species. In total, 62 plant species,

representing 27 families, were sampled. The specimens were stored in ethanol (with

minimum concentration of 50%) until later analysed for mycorrhizal colonisation (see next

section for details).

Canopy cover, which was also subsequently normalised against the total vegetation cover to

obtain the relative cover, was used to estimate the relative abundance of each nutrient-

acquisition strategy in the community.

Mycorrhizal analyses. Clearing and staining of root segments were done using KOH and

trypan blue using an adjusted standard procedure40,41. Specifically, after washing off all

excess soil, roots were immersed in tubes containing either 2.5% or 10% (w/v) KOH,

depending on root toughness and thickness. The tubes were then placed in a boiling water

bath for up to 1 h, depending on the darkness of the roots. After removal from the KOH

solution, if the roots were not sufficiently clear, they were rinsed and bleached in 10 vol (3%,

v/v) H2O2 for 10 min. All root segments were then rinsed thoroughly and returned to their

tubes with 2% (v/v) HCl, and left to acidify for at least 1 h. The roots were then rinsed in tap

water and stained with 0.05% (w/v) trypan blue in an acidified glycerol solution by heating in

a boiling water bath for 10 min. After thoroughly rinsing the roots in tap water, excess

staining was removed by immersing and storing the stained roots in acidified glycerol.




Colonisation was assessed using light microscopy (× 200-400 magnification) of slide-

mounted sections of fine roots. For eight of the species, which had at least nine separate

samples, colonisation was quantitatively assessed using the magnified intersects method42;

the other species had their mycorrhizal status qualitatively assessed, based on repeated

presence of structures consistent with each specific type of mycorrhiza. For arbuscular

mycorrhizas (AM) these diagnostic features were arbuscules, Paris coils, or vesicles

combined with morphology of hyphae consistent with AM. For ectomycorrhizas, the

diagnostic features were the presence of both Hartig nets and mantle hyphae38,43.

Soil analyses. Soil from each 2 × 2 m subplot was collected in June 2012, giving a total of

420 soil samples (seven samples from each of 60 plots). Samples were taken at 0–20 cm

depth using a 50 mm diameter sand auger and organic debris was removed using a 2-mm

mesh sieve. The depth of sampling (to 20 cm) was based on the fact that this is the zone

where most nutrients and fine roots are located in these shrublands44. Within 8 h of sampling,

a separate set of subplot samples were homogenised in the laboratory by using 25 g

subsamples, to give 60 plot-level bulked samples.

‘Available’ nitrogen (N) was extracted by shaking 10 g fresh soil in 50 mL 1 M KCl for 1 h

with a rotating tube shaker. KCl extracts were then filtered using Whatman number 40

ashless paper and frozen at -20°C for later analyses. The remaining bulked soil samples were

incubated in sealed plastic bags in complete darkness at room temperature (≈20°C) for eight

days. On day 8 we repeated the KCl extraction procedure (described above) on the incubated

soil as a simple N mineralisation assay. Subsamples (≈20 g) were taken for estimation of

gravimetric soil moisture at the start and end of the incubation. KCl extracts were again

immediately frozen at -20°C. Once all extractions were performed, all KCl extracts were left

to thaw at room temperature and stabilised by adding 50 μL of 96% (v/v) H2SO4. Nitrate

ammonium were determined colourimetrically on a Lachat Quikchem 8500 (Hach Ltd,




Loveland, Colorado, USA) using standard procedures. Total dissolved N was determined by

combustion and gas chromatography on a Shimadzu TOC-TN analyser. Dissolved organic N

was calculated as the difference between total dissolved N and the sum of nitrate and

ammonium.

Total phosphorus (P), calcium, potassium, magnesium, and manganese were determined on

all the 60 bulked samples by nitric acid digestion under pressure in PTFE vessels, with

detection by inductively coupled plasma optical emission spectrometry (Optima 7300DV;

Perkin Elmer, Shelton, CT, USA). Total P was also determined on all 420 subplot samples by

ignition (550°C, 1 h) and acid extraction (1 M H2SO4, 16 h). Readily-exchangeable P (resin

P) was determined on the 60 bulked samples by extraction with anion-exchange membranes

(1 cm × 4 cm; manufactured by BDH, Poole, UK, and distributed by VWR International,

West Chester, PA, USA)45. For both total and resin P, phosphate detection was by automated

molybdate colourimetry on a Lachat Quikchem 8500. Total N was measured on all 420

subplot samples on a Thermo Flash EA112 analyser (CE Elantech, New Jersey, USA).

Carbonate concentration was calculated by mass loss following acid addition46, but only for

the three Quindalup systems, because the other dune systems did not contain measurable

levels of carbonate. Organic carbon (C) was measured as total C remaining after carbonate

removal by automated combustion and thermal conductivity detection on a Thermo Flash

EA112 analyser (CE Elantech, New Jersey, USA).

Soil pH was determined in a 1:2 soil to solution ratio, in either water or in a 10 mM CaCl2,

using a glass electrode. Exchangeable cations (Al, Ca, Fe, K, Mg, Mn, Na) were determined

by extraction in 0.1 M BaCl2 with detection by ICP-OES on an Optima 7300 DV (Perkin

Elmer, Inc, Shelton, CT)47.




Statistical analyses. Modelling of nutrient-acquisition strategy richness, diversity and cover

along the chronosequence used generalised least squares models, implemented using the R

‘nlme’ package48. Model residuals were visually inspected for heteroscedasticity and

appropriate transformations were used if they significantly improved the models. Rarefaction

was based on the minimum number of individuals in each plot and used the ‘rarefy’ function

from the ‘vegan’ R package49. Regression of strategy richness and relative cover against soil

variables used mixed-effect linear models50, implemented using the ‘lmer’ function from the

‘lme4’ R package51. In these analyses, the chronosequence stage was used as a random effect,

allowing the intercept to vary among stages, in order to take into account the hierarchical (i.e.

nested) structure of the sampling design. Selection between competing models was based on

lower Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values.

Goodness-of-fit for the linear mixed-effect models was estimated using an adjusted R2

formula for mixed-effect models52. The null model of functional dispersion used the

‘commsimulator’ function from the vegan49 package, with the ‘quasiswap’ method, to

randomise the observed vegetation relative cover values amongst species, but restricting

randomisation only to species that only occurred within each chronosequence stage. This null

model is conservative in that it considers potential environmental filtering effects at the

chronosequence stage level, while still maintaining species richness and species relative

abundance distribution within each community. We also tested an even more conservative

null model where the relative cover values were randomised only from those species present

in the same plot53, and results were qualitatively similar. The ‘fdisp’ function from the FD

package54 was used to calculate the functional dispersion in the null model for the results

shown; we also used Rao’s quadratic entropy55, which yielded qualitatively similar results.

The standardised effect size of the null model was calculated using the formula (FDobs –

FDexp) / SDexp, where FDobs is the observed functional dispersion, FDexp is the expected (null




model) mean functional dispersion, and SDexp is the expected (null model) standard

deviation. All data analyses were done in R56.




Supplementary discussion

The Jurien Bay chronosequence, like all long-term chronosequences, has experienced

variation in climate, with interglacial periods being relatively wetter and glacial periods

drier57,58. Indeed, it was this variation in climate, causing sea level variations59-61, which

allowed for the formation of these barrier dunes over such long periods. Importantly, no

glaciation has occurred in south-western Australia since the Permian (> 250 Ma), and the

region has likely had a Mediterranean climate since the Early Miocene62. A marine origin for

the parent material of all dunes in the chronosequence is supported by multiple lines of

evidence, as recently outlined by Laliberté et al.63. Ages of the selected dunes in the Jurien

Bay chronosequence have not yet been confirmed; however, several dating techniques have

been used to date the Holocene (Quindalup) dunes and Pleistocene (Spearwood) limestone to

the south of Jurien Bay60,64-68. Ages for the Bassendean (stage 6) sand have been inferred,

based on the underlying marine deposits, to represent deposits from the Early Pleistocene and

perhaps into the Late Pliocene69. Whilst the ages for the stages chosen for this study remain

to be determined more precisely, they represent a clear succession of soil development70.

Surface reworking of the substrate has occurred, and still does occur in some areas68, and it is

possible that the Spearwood stages (4 and 5) correspond to similar depositional ages, but they

do show clear differences in nutrient concentrations consistent with long-term soil

development, thus justifying their separation into separate chronosequence stages70.

A recent study using soil from the Jurien Bay chronosequence with crop species as

phytometers31 showed that phosphorus (P) limitation occurred in the older stages (equivalent

to stages 4 through to 6). Turner et al.70 analysed the fractions of soil P, and found patterns

similar to the model proposed by Walker and Syers71 and other long-term chronosequences72.

Specifically, higher proportions (up to 40%) of the total P was in organic form by stage 6,

with high proportions of P (29 to 50%) being in occluded form. The extremely low




concentrations of P, together with the high proportions in occluded and organic pools, clearly

indicate depletion-driven P limitation73. Soil total P concentrations on a volume basis (kg P

m-3 soil) to 1 m in depth, obtained from soil profile pits, generally change little with soil

depth, and where changes have been observed they take the form of increased nutrient

concentrations (N and P) and organic matter in the surface (0-10 cm) layer70. Therefore, the

changes in surface soil (0-20 cm) P concentrations mirror those observed at greater soil

depths. The absence of any significant aerosol P input74, combined with the generally

uniform soil profile, indicate a lack of any significant P inputs into the system throughout the

duration of soil development in the chronosequence70. The parent material of the Bassendean

dunes (stage 6) likely was lower in carbonate content than for all younger stages31,63. Hence,

total P concentrations in the Bassendean parent material may have been different (and

perhaps lower) than in the other chronosequence stages, potentially contributing to the

extremely low total P concentrations observed in the soils of stage 6.

Plant nutrient acquisition is likely to occur predominantly during the middle part of the year

(approximately June to October), when the soil is adequately hydrated. The extended summer

drought, coupled with extremely high evapotranspiration rates (typically ~10 mm d-1)75

results in the sandy soil drying through the profile to several metres in depth76-78. As a

consequence, the great majority of new root growth, exudate release, soil microbial activity

and nutrient movement through the soil occur during those wetter months79-82. Whilst

hydraulic redistribution does occur83, its effects in the dry season would be localised to the

vicinity of the lateral roots from those plants with deep sinker roots; hence, any microbial

activity would also be locally constrained.

The soils in the Holocene, Quindalup stages (stages 1 to 3) are alkaline, with relatively high

carbonate concentrations. Soil sodium concentrations are very low in all of these stages; by

contrast, exchangeable calcium almost exclusively contributes to the effective cation




exchange capacity70. The high pH of the Quindalup soils likely prevents the successful

colonisation of many species from the regional species pool, as suggested by a recent study84

that used structural equation modelling to simultaneously test several hypotheses about the

drivers of the local plant diversity. Soil P concentrations are highly correlated with pH

throughout the chronosequence; indeed, soil total P concentrations equally well explains the

richness of nutrient-acquisition strategies as does soil pH.

The analyses of species richness and mycorrhizal species cover against soil P used the total

soil P, although we also did analyses with an alternative measure of soil P, resin P. In general,

model fits to data were better using total P than resin P. More importantly, we surmise that

total P is a more representative measure for soil P when soil P concentrations are very low, as

some plant P-acquisition strategies enable access to P pools not extractable with the resin

membrane technique. Although the resin P concentrations were very low, they perhaps were

slightly higher than would be expected, given the extraordinarily low total P concentrations.

One possible explanation is that low amounts of P with a microbial origin were included (due

to the air-drying process), thus raising the proportion of resin P to total P at extremely low

total P concentrations. Nonetheless, given the high proportion of non-mycorrhizal species

with root specialisations well equipped to access both sorbed inorganic and organic P, we

would expect that total P is a more representative measure.

Many nutrient-acquisition strategies in plants have previously been documented and the

hypothesis, presented by Lambers et al.85, proposed changes in the suites of these strategies

in plant communities as soil nutrient concentrations change. The large range of the key

limiting nutrients, especially P, in this dune chronosequence, coupled with the extremely rich

regional flora, enabled this hypothesis to be explored in great detail for the very first time. As

the hypothesis directly related plant nutrient-acquisition strategies to soil nutrients, the key

root trait considered in the study was the nutrient-acquisition strategy (or strategies) rather




than additional root traits, such as specific root length. None of these strategies are novel and

most have been explicitly proposed as part of a handbook of standardised functional traits86.

Rather, the key consideration was how the mix of strategies employed in the community

changed with soil development and nutrients. Aside from the nutrient-acquisition strategy,

the morphology and physiology of the roots for some strategy groups unquestionably confers

additional benefits; however, these were not considered. For example, fine root hairs and/or a

hard, protective sheath, in sand-binding roots likely aids in the prevention of desiccation87,88.

Additionally, our use of the nutrient-acquisition strategy as a belowground trait was confined

to plants and the symbionts within their roots, where known, as indeed was the case for the

hypothesis originally outlined by Lambers et al85. Other soil microbes likely also play a role

in nutrient acquisition, for example, in P acquisition from phytate89, but they were not

explicitly considered as separate traits. The complete picture regarding nutrient acquisition,

therefore, is likely more complex than that presented here; however, we consider only

including plants and their direct symbionts to be a valid approximation.

Morphologically, sand-binding roots (sand-binding strategy) had two distinct forms:

relatively soft, flexible roots with long sand-binding root hairs; and tough roots with a

strongly lignified root epidermis that binds sand tightly in a sheath around the root. Both of

these morphological types were classified under the same strategy grouping of ‘sand-

binding’; physiological differences of these two morphologies were not investigated,

although the second category corresponds to roots reported for the species, Lyginia barbata

R.Br.88,90. Differences in the degree of carboxylate exudation from these root types, as well as

many others, likely could be inferred by leaf manganese (Mn) concentrations; accumulation

of Mn in leaves from different groups of non-mycorrhizal species suggests that they can be

used as a proxy for the release of high concentrations of carboxylates91.




The role of carboxylate release in bursts of relatively high concentrations has been

demonstrated for many non-mycorrhizal species with cluster roots79,82,92,93 and dauciform

roots94,95, whilst leaf Mn concentrations strongly suggest that sand-binding roots exude

carboxylates equally effectively88,91,96. These high concentrations of carboxylates release

sorbed inorganic and organic P from soil particles either by ligand exchange or complexation

of metal ions holding P82. In addition, phosphatases released by the roots converts organic P

to inorganic P82. Together, these exudates greatly enhance P acquisition in these non-

mycorrhizal roots. Although these non-mycorrhizal roots appear to be functionally analogous

in many ways91,94, there is still much to be learned about their physiology; the conservative

approach that we took was to classify each as a separate nutrient-acquisition strategy. Some

ectomycorrhizal fungi have been shown to also increase concentrations of carboxylates –

mainly oxalate and malate – in the soil97-99. The release of these carboxylates has not been

demonstrated to occur in bursts and the resulting concentrations of carboxylates reported are

lower than those for the non-mycorrhizal root types mentioned above. Release of these

carboxylates has been linked to increased P acquisition and implicated in mineral

weathering43. However, the faster rate of carboxylate release, usually in the form of tri-

carboxylates (e.g., citrate), in those specialised non-mycorrhizal roots is substantially more

effective at facilitating the uptake of P from extremely P-impoverished soils82. Carboxylate

exudation, coupled with the substantial input of carbon into the upper layer of soil in the form

of short-lived roots (e.g., cluster roots), likely also contributes to microbial mineralisation of

nutrients100, and perhaps also increased pathogen resistance101. Taken together, non-

mycorrhizal plants with these root and exudative specialisations would appear to be better

able to access the extremely low concentrations of P found in this chronosequence than

mycorrhizal fungi, or, at the very least, would have enhanced competitiveness against many

mycorrhizal species.




Mycorrhizal symbioses have long been known to influence, and usually enhance, the uptake

of poorly mobile nutrients, especially P, but the exact nature of the symbiosis may take many

forms43,102. For example, in the arbuscular mycorrhizal (AM) symbiosis experimentally-

measured plant growth responses may vary, and sometimes even show negative responses;

however, in most cases once colonisation is established the mycorrhizal pathway appears to

be the dominant pathway for the uptake of P, irrespective of whether this is accompanied by

an increase in fitness103,104. Other mycorrhizal forms are also strongly involved in nutrient

acquisition, but their capabilities vary43. For example, ectomycorrhizal fungi release enzymes

that allow access to organic carbon and nitrogen, and phosphatases and phytases, which

facilitate conversion of organic forms of P to orthophosphate105,106. Ericoid mycorrhizas also

release a range of enzymes (including proteases, phosphatases and chitinases) that not only

allow access to organic forms of key nutrients, but also aid in the breakdown of chitin, lignin

and tannins43,107-109. On the other hand, AM fungi do not appear to produce significant

quantities of phosphatases103,110,111. Because of these clearly demonstrated nutrient-

acquisition capabilities, it was deemed appropriate to define each of the mycorrhizal

symbiosis types a priori as nutrient-acquisition strategies in their own right. That said, in our

study the exact nature of the mycorrhizal symbiosis and the degree of mycorrhization for all

the species were not investigated, and may have varied even within the same species growing

on soils with differing nutrient concentrations. Due to the complex nature of these symbioses,

however, such quantification may not necessarily produce clear-cut answers to questions

regarding nutrient acquisition102. The observed reduction in the abundance of mycorrhizal

plants with decreasing soil P, however, supports the hypothesis of a community-wide shift in

plant nutrient-acquisition strategies to a greater abundance of non-mycorrhizal plants with

nutrient-acquisition specialisations85.




Inspection of excavated root systems indicated that the vegetation cover generally was a good

proxy for the extent of the root system. A few species had either substantially greater

belowground than aboveground extents, or vice versa, but we assumed that estimating their

abundance and degree of dominance in the communities via canopy cover is a reasonable

proxy for the extent of the root system. Of all nutrient-acquisition strategies, species with

sand-binding or capillaroid strategies sometimes had root systems with a greater extent than

their canopy cover would indicate, usually because their shoots took the form of thin culms,

rather than leaves. However, these species generally had low absolute cover.

Supplementary notes

We selected models of the rarefied richness of nutrient-acquisition strategies, against both

total soil phosphorus (P) and soil pH (measured in 10 mM CaCl2), using a backward selection

procedure. Prior models of individual variables demonstrated a best fit using the logarithm of

soil P; hence, we used log-transformed soil total P as a predictor variable in the models.

Linear mixed-effect models were used, with the chronosequence stage as a random intercept.

We started with the full model (Supplementary Table 4), both with (modelfull) and without

interactions (modelni) and then eliminated terms that did not significantly improve the models

(based on P > 0.05). The first step in the procedure (Supplementary Table 4) eliminated the

full model with interactions (modelfull) and the second, and final, step showed that modelling

the richness of nutrient-acquisition strategies using either soil total P concentration or soil pH

as the predictor variable provided an equally good estimation as the model with two terms

(Supplementary Table 5). For completeness, we also show that the single-term models

performed similarly and were significantly better than the null (intercept only) model

(Supplementary Table 6). Maximum likelihood estimation was used in the backward

selection process, whilst restricted maximum likelihood (REML) was used to fit the final

model50.




As soil pH, measured in water, is commonly reported, we also performed model analyses

using these pH (H2O) values (summary statistics are shown in Supplementary Table 7). The

use of pH (H2O) did not significantly affect model outcomes.

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Supplementary figures

Supplementary Figure 1 | The Jurien Bay chronosequence. The Jurien Bay chronosequence is part of the broader Swan Coastal Plain and comprises the Holocene Quindalup dunes, which fringe the coastline, the Middle Pleistocene Spearwood dunes, further inland, whilst furthest from the coast are the Early Pleistocene Bassendean dunes, which reach their northern-most extent north of Jurien Bay.




Supplementary Figure 2 | Mean relative cover of all strategy types treating each unique combination of strategies as a distinct strategy. Arbuscular mycorrhizal (AM) species were the most abundant throughout the chronosequence, but the AM strategy often occurred in concert with other strategies. Non-mycorrhizal strategies, such as cluster roots, became increasingly abundant in older chronosequence stages.




Supplementary Figure 3 | Richness of nutrient-acquisition strategies, and indices of functional diversity, increased along the chronosequence and increased with decreasing soil P. a, Richness of nutrient-acquisition strategies doubled along the chronosequence. b, Functional dispersion decreased with increasing total soil phosphorus concentration. c, Shannon’s diversity index. d, Shannon’s diversity index decreased with increasing total soil P. e, Rao’s quadratic entropy. f, Rao’s quadratic entropy also decreased with total soil P. g, Boxplot of the standardised effect sizes showing differences between observed and expected (i.e. null model) functional dispersion for each plot, after 10,000 iterations (see Methods). Mean values ± 95% confidence intervals are shown along with letters indicating groups based on Tukey’s HSD test (P ≤ 0.05). For the boxplot, the box spans the interquartile range, while the whiskers extend to observations at most 1.5 times the interquartile range; extreme values beyond that range are represented as dots.




Supplementary Figure 4 | Rarefied richness of plant species using the 10 least-abundant of the main nutrient-acquisition strategies. Species employing more than one strategy concurrently were counted separately for each strategy they used. Mean values ± 95% confidence intervals are shown along with letters indicating groups based on Tukey’s HSD test (P ≤ 0.05). Mycorrhizal strategies are shaded with light grey bars, and non-mycorrhizal strategies are shaded with darker bars.




Supplementary Figure 5 | Relative cover of plants using the using the 10 least-abundant of the main nutrient-acquisition strategies, each treated in isolation from other strategies. As some species used multiple strategies concurrently, the relative cover values in some chronosequence stages sum to >100%. Mean values ± 95% confidence intervals are shown along with letters indicating groups based on Tukey’s HSD test (P ≤ 0.05). Mycorrhizal strategies are shaded with light grey bars, and non-mycorrhizal strategies are shaded with darker bars.




Supplementary Figure 6 | Contrasting relationships of rarefied richness and relative cover of mycorrhizal and non-mycorrhizal species. a, Rarefied species richness increased for both nutrient-acquisition categories, but more so for non-mycorrhizal species. b, Relative cover decreased for mycorrhizal species and correspondingly increased for non-mycorrhizal species.




Supplementary Figure 7 | Examples of mycorrhizal and endophytic fungal colonisation. a-d are ‘typical’ morphologies. a, Arum-type arbuscular mycorrhizal (AM) arbuscules in Hibbertia hypericoides (DC.) Benth. (Dilleniaceae). b, Arum-type arbuscules and associated hyphae in Senecio pinnatifolius var. latilobus (Steetz) I.Thomps. (Asteraceae). c, Paris-type AM coils in Calytrix strigosa A.Cunn. (Myrtaceae). d, Ectomycorrhizal (EM) Hartig net in Spyridium globulosum (Labill.) Benth. (Rhamnaceae). e-f, Dark septate (DS) endophytic fungal hyphae. e, DS in Lepidosperma squamatum Labill. (Cyperaceae). f, DS in Scaevola crassifolia Labill. (Goodeniaceae). g-h, Fine, arbuscule-forming fungal hyphae (fine Arum) in roots of Sowerbaea laxiflora Lindl. (Asparagaceae). g, Fine hyphae ramifying through root cells. h, Fine, dense arbuscules (arrows). i-l EM-like fungal morphologies lacking either Hartig nets or mantle sheaths, diagnostic features of EM. i, Hyphal strand bundle in Rhagodia baccata (Labill.) Moq. (Chenopodiaceae/Amaranthaceae). j, Mantle sheaths without Hartig nets in Gompholobium tomentosum Labill. (Fabaceae). k, Dense mantle of fine hyphae, without Hartig nets in Desmocladus asper (Nees) B.G.Briggs & L.A.S.Johnson (Restionaceae). l, Structures resembling Hartig nets from an EM-like association, which lack mantle sheaths, in Conostylis candicans subsp. calcicola Hopper (Haemodoraceae).




Supplementary tables

Supplementary Table 1 | Study plot locations within the dune systems of the Jurien Bay chronosequence. The three main dune systems were stratified into six chronosequence stages. Within each stage we positioned 10 plots, from which the location of each plot’s centre point is listed.

Dune system Stage Plot code

Location Latitude Longitude

Young Quindalup 1 Q.Y.1 30° 24' 21.0" S 115° 5' 41.2" E Q.Y.3 30° 15' 11.5" S 115° 2' 22.7" E Q.Y.7 30° 5' 37.7" S 114° 59' 23.4" E Q.Y.8 30° 6' 24.0" S 114° 59' 47.6" E Q.Y.12 30° 10' 51.7" S 115° 0' 13.6" E Q.Y.15 30° 24' 24.8" S 115° 4' 55.2" E Q.Y.16 30° 6' 54.4" S 115° 0' 23.3" E Q.Y.17 30° 2' 43.2" S 114° 57' 45.2" E Q.Y.18 30° 13' 25.0" S 115° 0' 29.4" E Q.Y.20 30° 14' 54.1" S 115° 2' 53.8" E

Middle-aged Quindalup 2 Q.M.7 30° 16' 27.6" S 115° 2' 49.2" E Q.M.8 30° 13' 34.8" S 115° 1' 6.2" E Q.M.18 30° 3' 35.0" S 114° 58' 58.4" E Q.M.23 30° 10' 13.2" S 115° 0' 43.0" E Q.M.25 30° 13' 27.3" S 115° 0' 38.4" E Q.M.26 30° 12' 50.6" S 115° 0' 37.3" E Q.M.30 30° 16' 35.9" S 115° 2' 43.2" E Q.M.31 30° 4' 23.0" S 114° 58' 57.1" E Q.M.32 30° 9' 52.7" S 115° 0' 27.0" E Q.M.33 30° 5' 25.8" S 114° 59' 55.0" E

Old Quindalup 3 Q.O.3 30° 3' 46.8" S 115° 0' 43.4" E Q.O.4 30° 2' 7.5" S 115° 0' 19.9" E Q.O.5 30° 13' 32.4" S 115° 3' 53.6" E Q.O.11 30° 11' 28.2" S 115° 3' 33.6" E Q.O.14 30° 14' 26.2" S 115° 4' 0.9" E Q.O.15 30° 14' 26.2" S 115° 3' 51.5" E Q.O.17 30° 12' 49.2" S 115° 3' 50.0" E Q.O.20 30° 18' 56.6" S 115° 3' 59.7" E Q.O.22 30° 16' 35.7" S 115° 3' 42.7" E Q.O.24 30° 16' 15.2" S 115° 4' 14.9" E

West Spearwood 4 S.W.3 30° 8' 35.6" S 115° 3' 43.6" E S.W.6 30° 5' 52.9" S 115° 3' 28.7" E S.W.8 30° 14' 7.0" S 115° 4' 14.4" E S.W.11 30° 11' 31.5" S 115° 3' 46.9" E S.W.14 30° 4' 57.5" S 115° 3' 8.9" E S.W.17 30° 13' 43.1" S 115° 4' 6.6" E S.W.26 30° 14' 25.4" S 115° 4' 9.5" E S.W.34 30° 1' 39.3" S 115° 2' 20.0" E S.W.35 30° 11' 52.4" S 115° 4' 25.9" E S.W.36 30° 11' 29.3" S 115° 4' 27.4" E

Deep-sand Spearwood 5 S.DS.2 30° 11' 34.1" S 115° 6' 28.7" E S.DS.3 30° 15' 32.1" S 115° 6' 25.7" E S.DS.4 30° 10' 45.0" S 115° 5' 48.7" E S.DS.6 30° 6' 25.6" S 115° 5' 10.4" E S.DS.9 30° 15' 50.6" S 115° 6' 2.2" E S.DS.10 30° 14' 54.7" S 115° 6' 0.4" E S.DS.11 30° 11' 11.2" S 115° 4' 55.8" E S.DS.17 30° 17' 47.7" S 115° 9' 44.4" E S.DS.23 30° 17' 58.3" S 115° 7' 10.7" E S.DS.25 30° 12' 29.6" S 115° 4' 1.4" E

Bassendean 6 B.HR.2 30° 17' 42.3" S 115° 11' 15.5" E B.HR.5 30° 17' 39.2" S 115° 11' 5.7" E B.L.4 30° 11' 12.3" S 115° 6' 32.6" E B.L.5 30° 9' 51.6" S 115° 7' 21.5" E B.L.6 30° 10' 17.1" S 115° 6' 32.6" E B.L.9 30° 10' 5.5" S 115° 6' 20.5" E B.L.10 30° 9' 36.0" S 115° 7' 14.0" E B.L.14 30° 10' 51.1" S 115° 7' 43.5" E B.NL.1 30° 7' 46.5" S 115° 6' 26.8" E B.NL.3 30° 7' 36.0" S 115° 6' 22.7" E




Supplementary Table 2 | The 14 main nutrient-acquisition strategy types found along the chronosequence. Of the 14 strategies, five were mycorrhizal and most of the others were non-mycorrhizal; however, frequently multiple strategies were used simultaneously, e.g., arbuscular mycorrhizal and nitrogen-fixing.

Strategy Description Arbuscular mycorrhizal Mycorrhizal symbiosis characterised by vesicles and/or arbuscules; two main

morphologies, Arum and Paris Ectomycorrhizal Mycorrhizal symbiosis characterised by dense hyphal mantles around root tips and

Hartig nets within roots Ericoid mycorrhizal Mycorrhizal symbiosis restricted to ericaceous plants Orchid mycorrhizal Mycorrhizal symbiosis restricted to orchidaceous plants

Thysanotus mycorrhizal Mycorrhizal symbiosis restricted to plants of the genus Thysanotus Capillaroid roots Non-mycorrhizal roots with long, dense root hairs; exudates are likely released to

enhance nutrient uptake Carnivory Many (or all) nutrients are derived from animals, trapped and consumed by the

plant Cluster roots Non-mycorrhizal roots with loose or dense root clusters; exudates are released to

enhance nutrient uptake Dauciform roots Non-mycorrhizal roots with carrot-shaped swellings; exudates are released to

enhance nutrient uptake Nitrogen fixing Bacterial-plant symbioses that facilitate uptake of atmospheric nitrogen

Non-mycorrhizal Non-mycorrhizal plants with other specialised root physiology (that typically releases exudates) for nutrient acquisition

Parasitism Nutrients (and water) are parasitised from other plants; includes holoparasites and hemiparasites

Sand binding Non-mycorrhizal roots that strongly bind sand with either long or short root hairs Unspecialised Non-mycorrhizal plants without specialised roots structures or physiology for

nutrient acquisition, e.g., ruderal species




Supplementary Table 3 | Species analysed for mycorrhizal and endophytic colonisation. The strategy may consist of multiple individual strategies, with only arbuscular mycorrhizal (AM) and ectomycorrhizal (EM) strategies given a colonisation intensity ranging from low to high. In addition, the morphology of AM hyphae and arbuscules was noted as Paris (Paris coils), Arum, intermediate (i.e. intermediate features between Arum and Paris) and fine Arum (colonisation by fine hyphae with colonisation and arbuscules like Arum-type AM). Colonisation intensity by dark septate (DS) and other endophytic fungi was similarly ranked from low to high. Abbreviations: Str, hyphal strand-forming endophyte; Mtl, mantle-forming endophyte; Hn, Hartig net-like colonisation; FE, fine endophyte; n, number of specimens analysed.

Family Species Strategy DS colonisation Other endophytic colonisation n

Intensity Str Mtl Hn FE Aizoaceae Carpobrotus virescens Non-mycorrhizal low moderate 4 Araliaceae Trachymene pilosa AM (moderate; Paris,

Arum) moderate low 4

Asparagaceae Acanthocarpus preissii AM (low; Paris) low - 4 Lomandra maritima AM (low; Arum, Paris) moderate low + + 3 Sowerbaea laxiflora AM (high; Intermediate,

fine Arum) moderate - 1

Thysanotus patersonii Sand-binding, Thysanotus mycorrhizal

moderate-high low 4

Asteraceae Angianthus cunninghamii AM (moderate-high; Paris)

low - 2

Brachyscome iberidifolia AM (low-moderate; Paris) low low 2 Olearia axillaris AM (moderate; Paris,

Arum) low-moderate low 4

Senecio pinnatifolius var. latilobus

AM (moderate-high; Paris, Arum)

low low 4

Brassicaceae Lepidium rotundum Non-mycorrhizal low low 1 Chenopodiaceae Rhagodia baccata AM (low; Arum) moderate low + + 4

Threlkeldia diffusa Non-mycorrhizal low moderate 4 Colchicaceae Burchardia congesta AM (moderate; Paris) moderate - 2 Cyperaceae Ficinia nodosa Sand-binding low low + 4

Gahnia lanigera Dauciform low-moderate low 3 Lepidosperma aff.

squamatum Dauciform low-moderate low + + 4

Mesomelaena pseudostygia

Non-mycorrhizal low-moderate low (by AM fungus)

+ 5

Schoenus brevisetis Non-mycorrhizal low-moderate low 3 Dasyponaceae Dasypogon obliquifolius Sand-binding low low 9

Dilleniaceae Hibbertia hypericoides AM (moderate-high; Paris, Arum)

low-moderate low + 4

Ecdeiocoleaceae Ecdeiocolea monostachya

AM (low-moderate; Paris); Sand-binding

low-moderate - 5

Ericaceae Astroloma xerophyllum Ericoid mycorrhizal low low 9 Euphorbiaceae Beyeria cinerea AM (moderate; Arum, fine

Arum) moderate low + + 3

Fabaceae Acacia lasiocarpa AM (low-moderate; Arum); N-fixing


Acacia spathulifolia AM (low-moderate; Paris); N-fixing


Bossiaea eriocarpa AM (moderate; Paris, fine Arum); N-fixing; Cluster

roots

moderate - 3

Gompholobium tomentosum

AM (low-moderate; Paris); N-fixing

moderate low-moderate

+ + 4

Jacksonia floribunda AM (low; Paris); EM (low-moderate); N-fixing

moderate-high low 9

Jacksonia hakeoides AM (low; Paris, Arum); N-fixing; Cluster roots

moderate low + + 4

Labichea cassioides AM (moderate; Paris) moderate - 4 Goodeniaceae Scaevola crassifolia AM (low-moderate; Arum,

Paris); EM (moderate) moderate - 4

Scaevola thesioides subsp. thesioides

AM (moderate; Paris, Arum); EM (low)

low low + 4

Haemodoraceae Blancoa canescens Sand-binding moderate-high low 9 Conostylis candicans

subsp. calcicola Sand-binding low low + + 4




Conostylis crassinervia subsp. absens

Non-mycorrhizal low - 9

Loganiaceae Logania spermacocea AM (low; Arum, Paris) low-moderate - 2 Myrtaceae Calothamnus quadrifidus

subsp. quadrifidus EM (moderate) low-moderate low 4

Calytrix strigosa AM (low-moderate; Paris) low-moderate low + 4 Darwinia sanguinea AM (low-moderate; Paris) low-moderate low + 9 Eremaea asterocarpa

subsp. asterocarpa AM (low; Paris); EM (low) low-moderate low 9

Melaleuca leuropoma AM (low-moderate; Paris, fine Arum); EM (low)

moderate low 1

Melaleuca systena AM (low; Paris, Arum); EM (low)

low-moderate low 5

Pileanthus filifolius AM (moderate; Paris); EM (low)

moderate - 1

Scholtzia umbellifera AM (low; Paris) low low-moderate

+ 3

Phyllanthaceae Phyllanthus calycinus AM (moderate-high; Arum, Paris)

low - 3

Poaceae Austrostipa compressa AM (low; Arum, Paris); Sand-binding

low low + 4

Austrostipa elegantissima AM (low; Paris); Sand-binding


Poa porphyroclados AM (low; Paris); Sand-binding

moderate low 4

Proteaceae Stirlingia latifolia Cluster roots low low 9 Strangea cynanchicarpa Cluster roots low low 1

Ranunculaceae Clematis linearifolia AM (moderate-high; Arum)

moderate - 2

Restionaceae Desmocladus asper Sand-binding low-moderate low-moderate; low (Str)

+ + 4

Lepidobolus preissianus Sand-binding, Capillaroid low-moderate low; low (Str)

+ 3

Rhamnaceae Spyridium globulosum AM (moderate; Arum, Paris); EM (moderate)

moderate - 4

Stenanthemum notiale subsp. notiale

AM (low; Arum, Paris); EM (moderate)

low - 4

Rubiaceae Opercularia spermacocea AM (low; Arum, Paris) moderate-high low + 4 Opercularia vaginata AM (low-moderate; Arum) low-moderate low 4

Rutaceae Diplolaena obovata AM (moderate; Paris, Intermediate)

low-moderate - 2

Scrophulariaceae Myoporum insulare AM (low-moderate; Arum, Paris)

low-moderate low + + 3

Solanaceae Anthocercis littorea Non-mycorrhizal low low 4 Stylidiaceae Stylidium

crossocephalum AM (moderate; Paris);

EM (low) moderate - 2




Supplementary Table 4 | Goodness of fit parameters for the two models of rarefied nutrient-acquisition strategy richness, compared in the first step of the backward selection procedure.

Model df AIC BIC Log likelihood χ2 dfχ P(>χ2)

modelni 5 215.07 225.55 -102.54

modelfull 6 217.02 229.59 -102.51 0.0536 1 0.82

The models compared were the full model without interactions of model terms (modelni), and the full model, including interactions (modelfull). The

lower AIC value for modelni and the P-value > 0.05 (0.82) indicated that the interaction term was not needed. The regression formulae for each

model were as follows. modelni: Richness ~ log(P) + pH + (1|stage); modelfull: Richness ~ log(P) + pH + log(P)*pH + (1|stage). The richness

modelled was the rarefied richness of nutrient-acquisition strategies, P was the total soil phosphorus and pH was the soil pH measured in CaCl2.

Abbreviations:df, degrees of freedom; AIC, Akaike information criterion; BIC, Bayesian information criterion.

Supplementary Table 5 | Goodness of fit parameters for stage 2 of the backward selection procedure for models of rarefied nutrient-acquisition strategy richness.


modelP 4 214.88 223.26 -103.44

modelpH 4 215.66 224.04 -103.83 0 0 1

modelni 5 215.07 225.55 -102.54 2.577 1 0.108

Three models were compared, a model using only soil pH as a predictor (modelpH), a model using only soil P as a predictor (modelP) and the full

model without interactions of model terms (modelni). The model using only soil total P (modelP) performed approximately the same as both other

models although it had lower AIC and BIC values. The regression formulae for two new models were as follows. modelP: Richness ~ log(P) +

(1|stage); modelpH: Richness ~ pH + (1|stage). See Supplementary Table 4 for abbreviations and the full definition of model parameters.

Supplementary Table 6 | Goodness of fit parameters for the models from stage 2 of the backward selection procedure in comparison to the null model.


modelnull 3 226.12 232.40 -110.06

modelP 4 214.88 223.26 -103.44 13.23 1 0.0003

modelpH 5 215.66 224.04 -103.83 0 0 1

Comparison of the two models, soil P as sole a predictor (modelP) and soil pH as a sole predictor (modelpH), with the null model (intercept only)

showed that both single-term models significantly outperformed the null model (P = 0.0003) and predicted the response equally well. See

Supplementary Table 4 and Supplementary Table 5 for model formulae, abbreviations and the full definition of model parameters.

Supplementary Table 7 | Soil pH, measured in water, for the six stages of the Jurien Bay chronosequence.

Chronosequence stage 1 2 3 4 5 6

pH 9.3

[9.2, 9.3]

8.6

[8.5, 8.6]

8.6

[8.5, 8.7]

6.6 [6.4, 6.8]

6.4

[6.2, 6.5]

5.9

[5.7, 6.0]

All values are means with associated 95% confidence intervals in square brackets.


Diversity of plant nutrient-acquisition strategies ...Diversity of plant nutrient-acquisition...

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