MATERIALS AND METHODS
Propagation
Accessions (population level seed samples) of O. eriopoda were
sourced from the National Seed Bank of the Australian National Botanic
Gardens Canberra, The Australian Botanic Gardens Mount Annan, The Royal
Tasmanian Botanical Gardens, and Royal Botanic Gardens Victoria and
germinated as described in Satyanti et al. (2019). We included seedlings
from 16 populations (Figure S1 and Table S1) that represented the range
of germination strategies expressed in this species. We aimed for a
balanced representation of germination strategies based on early assay
results, however there were many populations with postponed germination
so we included more of those to be representative. Consequently, we had
three populations with an immediate germination strategy, three with a
staggered strategy, seven postponed and three with a postponed-deep
germination strategy (Table S1). Seeds from each population were
germinated in two batches so that germination of early (autumn) and late
(spring) seedlings coincided and seedling age was consistent regardless
of germination strategy (Appendix S1). This design addressed the
potentially confounding factors of age, size and starting time of the
experiment and allowed us to manage these factors within the logistical
constraints of a blocked design (Lu et al., 2016). Seeds germinating on
agar were transplanted within 1-2 weeks to soil media in pots and grown
under temperatures conducive to growth. Seedlings were grown until they
were 18 – 20 weeks old when they had 8 ± 3.6 (SD) leaves and we
inferred that the establishment stage was complete (Appendix S1). AsO. eriopoda is slow growing, this establishment step was
important to ensure that plants across all populations were large enough
to withstand transplant stress and thus to avoid confounding soil
warming effects.
Experimental design
Soil temperatures are particularly important for alpine species because
given their low stature, soil temperatures determine the microclimate to
which plants are exposed more than the air temperatures do (Körner,
2003; Reinhardt & Odland, 2012; Scherrer, Schmid, & Körner, 2011;
Wolkovich et al., 2012). We therefore focused our study on the effect of
soil warming on plant traits.
For each population, ten individuals were randomly allocated to each of
the two soil warming treatments. We assessed initial seedling size and
confirmed that there were no a-priori differences in the size of plants
between ambient and warm soil treatments (linear mixed model with
germination strategy as a random term; p = 0.286 and 0.52 for
leaf number and leaf length, respectively). For the staggered
germination strategy, both early (autumn) and late (spring) seedlings,
were placed in the respective block, thus, these populations had 20
individuals/soil warming treatments. One representative population of
each putative germination strategy was assigned to each of the four
blocks. The imbalance of populations for each germination strategy was
not an impediment for the analyses. There were five populations for
which we did not have 20 seedlings and for these we assigned half of the
available number to each warming temperature. Extra plants were placed
in the empty spaces on the bench to maintain homogeneous spacing across
the experiment but were not included in analyses (number in grey, Figure
S1).
Warm and ambient benches were set up in a glasshouse with air
temperature set to follow the seasonal changes and natural photoperiod
(Fig. S2). The targeted air temperature day/night sequence was 20/10 °C
(autumn), 5/5 °C (winter), 20/10 °C (spring), 25/15 °C (summer), and
finally 20/10 °C (autumn). Soil warming was achieved by placing a
heating mat (Electronic Foil Panel with Thermostat, ADLOHEAT, Victoria)
on a given bench set to be continuously ~ 5 °C warmer
than the set glasshouse air temperature throughout the experimental
period, including winter. The 5 °C soil temperature increase is based on
Australian alpine mean air temperature predictions for 2050, i.e. an
increase of +0.6 to +2.9 °C (Hennessy et al., 2003) and that the maximum
soil temperatures in Australia are to increase by almost double that of
air temperature by the end of 21st century (Ooi, Auld, & Denham, 2009).
The 5 °C, thus, falls between the predicted +1.2 to 5.8 °C soil
temperature increase by 2050. The ambient treatment was located on
benches that had mats but no heat, paired one each with the four heated
benches. A frame of 6 mm-thick PVC sheet was placed around each bench,
17 cm above the mat, and a 5 cm thick sheet of polystyrene foam was
placed on the top of this to insulate the soil. Square openings in the
polystyrene matched the pot size and held these in place in the frame.
Temperature at plant level (15 cm above bench; Fig. 1) and soil (2, 8
and 14 cm below surface) was monitored during the course of the
experiment with i-Button data-loggers (Thermochron DS1921G, Temperature
Technology, Adelaide) in each block (32 in total, Fig 1). We analysed
the temperatures at plant and soil level to determine efficacy of the
design using ANOVA and found significant warming differences for each of
the soil and air depths/height. The temperature difference between
ambient and warm soil at 8-cm below the surface (where most roots were
located) was approximately 6 °C during the day and 9 °C during the night
(Fig. 1), in agreement with climate patterns which show that night-time
temperatures have increased more than day-time temperatures (Donat &
Alexander, 2012; Easterling et al., 1997). With warmer air temperature
and reduced snow cover, the soil in the Australian Alps becomes warmer,
evidenced by a snow removal experiment (Slatyer, 2016). Thus, we also
increased the soil temperature during winter for the warmer soil
scenario. In summer soil was warmed to ~35 °C which is
realistic for the Australian mountains where bare dark soils can easily
exceed 45 °C on sunny days and the difference to the nearby vegetated
soils can be over 30 °C (Slatyer, 2016).
The irrigation system was set to keep plants adequately-watered. We used
an automatic Water-Pro vapour pressure deficit (VPD) to provide an
automatic watering system (MicroGrow Green House Systems, Temecula,
California), with each plant being watered individually by a dripper at
soil level. Drippers were calibrated to a standard flow rate that was
checked at the beginning of the experiment. Watering events were
triggered when pre-set VPD targets were reached. Plants received
~ 90 ml per watering which was enough to saturate the
soil at the start of the experiment. As the plants grew, we adjusted the
VPD point, based on the two sensors for ambient and warm soils, so that
the drippers delivered water sufficiently more frequently to the plants
in the warmed treatment to keep all plants healthy.
Trait measurement
Leaf number and the length of the longest leaf were recorded for all
plants at the start of the experiment, and at the end of autumn (day
38). Those traits were recorded for a subset of plants (4 individuals
per soil temperature and population) in early spring (day 124). Leaf
increment rate at the early vegetative stage was calculated from the
difference of leaf number at the end of autumn from the start divided by
38 (number of days from planting). Leaf increment at the transition to
reproductive stage was the difference between total leaf number at day
38 and day 124, divided by 86 (number of days between measurement).
Specific Leaf Area (SLA) is an indicator of resource allocation and
ecological strategy. We expected that plants growing faster in warmed
conditions would have higher SLA. SLA was measured for every individual
at the start of winter (day 52). SLA was measured by acquiring the
youngest fully expanded leaf for each individual. The leaf was then
scanned on a flatbed scanner, dried at 60 °C for 72 hrs and weighed. The
SLA was calculated as area/weight
(cm2g-1).
In early spring, a random sample (four plants, even numbered
individuals, per population per soil treatment) was photographed with a
reference scale over a white Styrofoam board to determine total canopy
area. Each plant image was then converted to an 8-bit graphic file. The
threshold was adjusted so that only the actual canopy area was detected
for selection and using the known distance of the reference scale we
calculated the plant area. The image analysis of the canopy area was
performed in ImageJ (Schneider, Rasband, & Eliceiri, 2012).
Plants were monitored every 1 – 2 days for phenology. The date at which
the first inflorescence with closed buds emerged from the plant base was
recorded as first flowering. All infructescences were collected at the
point of natural seed dispersal when all seeds were brown or
purple-brown and easily dislodged, and the date of collection was
recorded for each infructescence. Infructescences were stored at 15% RH
and 15 °C. Total number of inflorescences and infructescence were
determined for each plant. Further, we measured individual seed mass for
five, even numbered, individuals per population per soil treatment by
weighing three replicates of 25 seeds (Figure S1). The date of
collection of the last infructescence was recorded as the end of seed
dispersal, and the time between the first and the last seed harvest was
defined as the duration of seed production. Some plants naturally
senesced over the course of the experiment and the date of senescence
(all leaves browned, no new leaves emerging) was recorded. At the end of
the experiment, aboveground biomass was harvested for all plants, dried
at 60 °C to constant weight, and weighed. Plants that died before
harvest were sampled for biomass within a week of senescence.
Transgenerational effect on germination
traits
To minimise potential genetic influences that may have occurred from
cross-pollination of populations, populations were separated by organza
fabric sheeting supported by a plastic frame. Seed was collected from
plants at the time of natural dispersal and stored separately at the
plant level.
Seeds from parent plants of three germination strategies: immediate,
staggered, and postponed were used to test whether there was an effect
of soil temperature during development on the germination strategy and
response to germination temperature of progeny. Ten populations were
selected to represent the three germination strategies, three immediate,
three staggered and four postponed strategies (Figure S1). For each
population and soil temperature we selected 50 seeds produced in the
peak seed production period from each of five fruiting individuals per
warming treatment. Twenty-five seeds each were sown on each of two petri
dishes, and randomly allocated to one of two germination chambers set at
25/15 °C and 30/20 °C 12/12 hours photoperiod for 9 weeks. Subsequently
the seeds were transferred to 5 °C for 8 weeks and then returned for 17
weeks to the same temperature regimes they were initially allocated to
(25/15 °C or 30/20 °C). Germination was scored weekly as the seeds moved
through the temperature regimes (beginning week 1 until week 34), to
develop germination curves for each population and warming treatment. At
the end of the experiment a cut-test was performed to determine whether
ungerminated seeds were empty, dormant, or dead. Each chamber consisted
of five blocks (shelves). For a given population, one maternal plant was
represented in each block.
Statistical analyses
Mixed models were selected for the analysis of plant traits. Models
included terms for germination strategy and soil temperature and the
interaction thereof as fixed factors, and populations, nested within
blocks, were assigned as random factors. Some exceptions were made in
the random model where either block or population was used as the random
factor because inclusion of population nested in block resulted in
convergence failure (see Table S2). Vegetative and reproductive traits
that were discreet (number of leaves, number of inflorescences, number
of inflorescences, day to flower, day to seed dispersal, seeding
duration, and day to senesce) were analysed using Generalized Linear
Mixed Models (GLMM), setting the distribution family as Poisson and the
link function as natural logarithm (Bolker et al., 2009). Leaf number
(but not length of the longest leaf) at the start of the experiment
significantly varied across germination strategies (Table S2). Hence, we
used leaf number at the start of the experiment as a covariate for
corresponding traits, i.e. leaf number, leaf increment rate, SLA, plant
area, aboveground biomass. For proportion data (survival and proportion
of plants producing seed), GLMM were used with the distribution family
as binomial, the link function as logit, and the dispersion parameter
set to be estimate. Responses that were continuous (longest leaf, leaf
increment rate, individual seed mass, SLA, and aboveground biomass) were
analysed with Linear Mixed Models (Restricted Maximum Likelihood, REML.
Leaf increment rates, plant area, seed mass, and aboveground biomass
were log-transformed prior to fitting to REML (Table S2).
Repeated measures analyses were run for the leaf number and leaf length
using germination strategy, soil temperature and measurement time as
fixed factors. Population nested in block was used as the random model
and leaf number at the start of the experiment as covariates for the
analysis of the leaf number. However, the results were the same as when
we performed the analyses for each measurement time point and thus, we
present the results from the two measurement points as they provide
clearer visual inference.
To assess transgenerational effects on germination strategy, we analysed
the final germination, non-dormant seed fraction (germination before
spring), and time to reach 50% germination of the F1 seeds. The final
germination and non-dormant seed fraction were analysed using GLMM,
assigning germination temperature, soil temperature, and germination
strategy as the fixed factors, population and individual plants nested
on the incubator shelf were set as the random factor, and we set the
distribution family as binomial and the link function as logit. Time to
reach 50% germination was derived by examination of cumulative
germination for each dish to the closest 0.25 week and treated as a
continuous variable and analysed using Linear Mixed Models (REML) with
fixed and random factors as for the non-dormant fraction analyses. GLMM
and REML were performed in Genstat 19th Edition.