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Climate Change and Reproductive Phenology: Context-
Dependent Responses to Increases in Temperature and
Implications for Assisted Colonization
by
Susana Wadgymar
A thesis submitted in conformity with the requirements for the degree of Doctorate of Philosophy
Graduate Department of Ecology and Evolutionary Biology University of Toronto
Table 2.3 A hurdle model demonstrating the effects of flowering onset, fruiting onset, and final
plant size on survival and seed production in populations of Chamaecrista fasciculata planted in
both ambient and artificially warmed conditions. This two-part analysis first models the
probability of surviving to produce seed using a generalized linear model with a binomial
distribution and logit link (zero component). Seed production, excluding zeros, is then modeled
by a negative binomial generalized linear model with log link (count component). Chi-squared
values are reported for fixed effects in the final, optimized model.
df Survival Seed number
Flowering onset 1 NS 3.90*
Fruiting onset 1 -- 21.61***
Biomass 1 7.96** 100.94***
Temperature 1 13.98*** 0.48
Population 3 58.42*** 22.48***
Flowering onset*Temperature 1 NS 6.99**
Fruiting onset*Temperature 1 -- NS
Biomass*Temperature 1 6.77** NS Den. df: Zero 198-203, Count 318-321 Significance: NS: Not Significant, --: not included in model, p<0.1†, p<0.05*, p<0.01**, p<0.001***
38
Table 2.4 Estimates of direct and total phenotypic linear selection coefficients +/- SE for
Chamaecrista fasciculata planted in ambient and artificially warmed conditions. Coefficients
were derived from a hurdle model that examined relationships between phenotypes and seed
number (negative binomial distribution with log link) separately from those of phenotypes and
survival (binomial distribution with logit link). Fruiting onset was not included in the survival
analysis, as survival was scored as the production of at least one fruit. Significant differences in
the strength of selection between treatments are indicated in Table 3. For reference, selection
gradients and differentials derived by multiple regression per Lande and Arnold (1983) are also
reported.
Hurdle model Multiple Regression Survival Seed Number Seed Number
Direct Selection Ambient Heated Ambient Heated Ambient Heated
Flowering onset -0.80 (0.65)
-1.10 (0.84)
-0.45* (0.23)
-0.19 (0.21)
-0.89*** (0.19)
-0.13 (0.21)
Fruiting onset -- -- -0.69*** (0.14)
-0.51** (0.17)
-0.45** (0.15)
-0.58** (0.18)
Biomass 0.25 (033)
1.72** (0.64)
1.00*** (0.14)
0.90*** (0.10)
1.21*** (0.18)
0.81*** (0.10)
Survival Seed number Seed number
Total Selection Ambient Heated Ambient Heated Ambient Heated
Flowering onset -1.19 (0.63)
-0.79 (0.64)
-0.68*** (0.25)
-0.35 (0.25)
-0.70*** (0.10)
-0.32** (0.11)
Fruiting onset -- -- -0.69*** (0.17)
-0.45** (0.16)
-0.50*** (0.13)
-0.33** (0.12)
Biomass 0.37 (0.31)
1.61** (0.51)
0.77*** (0.13)
0.97*** (0.09)
-0.04 (0.12)
0.52*** (0.52)
Significance: NS: not significant, †P<0.1, *P<0.05, **P<0.01, ***P<0.001
39
Figure 2.1 A map of the eastern U.S. showing the northern range limit of Chamaecrista
fasciculata (dashed line), as well as the seed collection sites in Minnesota (MN), Pennsylvania
(PA), Missouri (MO) and North Carolina (NC). The experimental relocation took place in
southern Ontario at the Koffler Scientific Reserve at Joker’s Hill (KSR). The distribution of C.
fasciculata was estimated from herbarium specimens, field observations, communications with
other researchers, and the PLANTS database maintained by the United States Department of
Agriculture.
MN
MO
NC
PA
KSR
N
0 200 400 600km
40
Figure 2.2 Reaction norms showing the mean ± 2 standard errors of (a) reproductive
phenological traits and (b) growing degree day accumulations upon the expression of those traits
in the Minnesota (MN), Pennsylvania (PA), Missouri (MO), and North Carolina (NC)
populations in ambient (A) and artificially heated (H) conditions.
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egre
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41
Figure 2.3 The mean ± standard error of (a) the log of above ground biomass and (b) the number
of seeds produced in the Minnesota (MN), Pennsylvania (PA), Missouri (MO), and North
Carolina (NC) populations in ambient and artificially warmed conditions.
0
2
4
log V
eget
ative
biom
ass (
g)
AmbientHeated
(a)
MN PA MO NC0
350
700
Numb
er o
f see
ds
AmbientHeated
(b)MN PA MO NC
42
Figure 2.4 Logistic regressions portraying the probability of surviving to produce seed in heated
and ambient conditions as a function of (a) flowering onset or (b) aboveground vegetative
biomass, scaled to a mean of 0 and a standard deviation of 1, per the zero component of the
hurdle model (Table 4). Histograms depict the trait values of individuals that survived (upper
panels) or died (lower panels) from each population.
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rtality
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43
Figure 2.5 Relationships between seed number and (a) flowering onset, (b) fruiting onset, and
(c) the log of vegetative biomass per the partial regression coefficients obtained from the count
component of the hurdle model in Table 4. Note that these relationships are linear on a log scale,
and the response variable was log transformed for ease of viewing and comparison.
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44
Figure 2.6 (a) Estimates of the degree of temporal isolation, ρ, between populations of C.
fasciculata in ambient (below diagonal) and artificially heated (above diagonal) conditions, and
population and treatment differences in (b) average flowering duration and (c) total flower
production ± standard error. Estimates of ρ span from 0 (random mating between populations)
to 1 (populations are reproductively isolated). We constructed 95% confidence intervals via
bootstrapping 1000x with replacement. Estimates marked with an asterisk lie outside of the
interval range of the corresponding population comparison in the opposing thermal regime.
MN PA MO NC
NC
MO
PA
MN
0.978 0.950* 0.754
0.675* 0.379* 0.675*
0.341* 0.213* 0.915*
0.469* 0.603* 0.969
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45
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Appendix A
Supplemental Information for Chapter 3
Figure A1 (a) Individual-‐level display schedules from experiment 1 not included in the main text. Individuals from the Pennsylvania (PA) and North Carolina (NC) populations in both heated and ambient treatments are staggered along the y-‐axis in order of flowering onset date. The size of the circles corresponds to the proportion of total flowers produced by an individual on a given day. (b) Population-‐level display schedules for each of the same population and treatment combinations. The height of these lines reflects the proportion of total flowers produced by that group on a given day.
0
20
40
60
80
100
212 232 252 272 2920
0.1
Julian date
Indiv
idual
(a)
(b)
Prop
ortio
n of
flowe
rs pr
oduc
ed
88
Figure A2 The display schedules of (a) MN Heated, (b) MN Ambient, (c) PA Heated, (d) PA Ambient, (e) MO Heated, (f) MO Ambient, (g) NC Heated, and (h) NC Ambient populations and treatments of experiment 1, with deployment schedules highlighted in dark grey and retained flowers highlighted in light grey. Deployment schedules were estimated by accounting for temperature-‐mediated variation in floral longevity (see main text). For reference, average daily temperatures are shown in panel (i).
0.0
0.1(a)
0.0
0.1(b)
0.0
0.1(c)
0.00
0.14 (d)
0.0
0.1(e)
0.00
0.12(f)
0.00
0.08 (g)
0.00
0.06(h)
212 232 252 272 2925
30 (i)
Julian date
Pro
porti
on o
f flo
wers
pro
duce
d da
ilyTe
mpe
ratu
re (°
C)
RetainedDeployed
89
Figure A3 The display schedules of (a) MN Pollinated, (b) MN Unpollinated, (c) PA Pollinated, (d) PA Unpollinated, (e) MO Pollinated, (f) MO Unpollinated, (g) VA Pollinated, (h) VA Unpollinated, (i) NC Pollinated, and (j) NC Unpollinated populations and treatments of experiment 2, with deployment schedules highlighted in dark grey and retained flowers highlighted in light grey. Deployment schedules were estimated by accounting for temperature-‐mediated variation in floral longevity (see main text). For reference, average daily temperatures are shown in panel (k).
0.00
0.12 (a)
0.00
0.04 (b)
0.00
0.12 (c)
0.00
0.06 (d)
0.0
0.1 (e)
0.00
0.06 (f)
0.00
0.06 (g)
0.00
0.08 (h)
0.00
0.06 (i)
0.00
0.06 (j)
212 222 232 242 252 262 272 28210
25 (k)
Prop
ortio
n of
flow
ers
prod
uced
dai
lyTe
mpe
ratu
re (°
C)
Julian date
RetainedDeployed
90
Figure A4 Heatmaps summarizing the cross-‐correlation coefficients between population-‐level (a) display schedules or (b) deployment schedules and average daily humidity for populations and treatment combinations in experiments 1, 2, and 3. The color of a specific box reflects the sign and magnitude of the correlation coefficient, with significant correlations marked with an S.
Lag0
Lag1
Lag2
Lag3
Lag4
Lag5
S SS S
S S
SS
S SS
Lag0
Lag1
Lag2
Lag3
Lag4
Lag5
SSS
S SS
S S
SS S
SS
MN LateNC EarlyMN Early
NC UnpollinatedNC Pollinated
VA UnpollinatedVA Pollinated
MO UnpollinatedMO Pollinated
PA UnpollinatedPA Pollinated
MN UnpollinatedMN PollinatedNC AmbientNC Heated
MO AmbientMO HeatedPA AmbientPA Heated
MN AmbientMN Heated
!0.5 !0.25 0 0.25 0.5(a) (b)
91
Figure A5 Heatmaps summarizing the cross-‐correlation coefficients between population-‐level (a) display schedules or (b) deployment schedules and total daily precipitation for population and treatment combinations in experiments 1, 2, and 3. The color of a specific box reflects the sign and magnitude of the correlation coefficient, with significant correlations marked with an S.
Lag0
Lag1
Lag2
Lag3
Lag4
Lag5
S
S
SS
SS
Lag0
Lag1
Lag2
Lag3
Lag4
Lag5
S SS
SS
SS
NC UnpollinatedNC Pollinated
VA UnpollinatedVA Pollinated
MO UnpollinatedMO Pollinated
PA UnpollinatedPA Pollinated
MN UnpollinatedMN PollinatedNC AmbientNC Heated
MO AmbientMO HeatedPA AmbientPA Heated
MN AmbientMN Heated
!0.5 !0.25 0 0.25 0.5(a) (b)
92
Figure A6 Population-‐level display schedules for the constant watering treatment (solid), one-‐week watering treatment (dashed), and two-‐week watering treatment (dotted) for the (a) MN early, (b) NC early, and (c) MN late populations in experiment 3.
0.0
0.1 (a) ConstantOne weekTwo week
0.0
0.1
Prop
ortio
n of
flowe
rs pr
oduc
ed
(b)
209 219 229 239 249 259 2690.0
0.1
Julian date
(c)
93
Figure A7 Heatmaps summarizing the cross-‐correlation coefficients between population-‐level (a) display schedules or (b) deployment schedules and the volumetric water content measured within different replicates of the watering treatments for the MN early cohort of experiment 3. The color of a specific box reflects the sign and magnitude of the correlation coefficient, with significant correlations marked with an S.
Lag0
Lag1
Lag2
Lag3
Lag4
Lag5
Two Week 4
Two Week 3
Two Week 2
Two Week 1
One Week 4
One Week 3
One Week 2
One Week 1
Constant 4
Constant 3
Constant 2
Constant 1
S
S
S
S
S
S
S
Lag0
Lag1
Lag2
Lag3
Lag4
Lag5
S
S
!0.5 !0.25 0 0.25 0.5(a) (b)
94
Figure A8 The population-‐level display schedules for the 29 flowering species naturally occurring at the Koffler Scientific Reserve at Joker’s Hill. See Table S1 for full species names.
95
Table A1 Estimates of population synchrony (Sp), average within-individual synchrony (Si ± standard error of the mean), and the strength of phenological assortative mating (ρ) for each population and treatment combination of experiments 1-3 and for the 29 species naturally occurring at the Koffler Scientific Reserve at Joker’s Hill. Population synchrony was calculated per Weis et al. (2014), individual synchrony per the metric presented in the main text, and the strength of assortative mating per Weis and Kossler (2004).
Mating opportunities between flowers on a given individual, i, are minimized when open flowers are distributed evenly across days and maximized when all flowers are open at once. Thus, uniform display schedules (where the variance in flowers produced per day = 0) should have the lowest levels of synchrony regardless of flowering duration or the total number of flowers produced (Si =0). This property is captured by the coefficient of variation, CVi, measured as the standard deviation of flower number across days (SDi) divided by the mean number of flowers across days ( i); however, this measure scales with the duration of flowering, Di. Standardizing the coefficient of variation by the square root of the flowering duration ensures that Si is largely insensitive to flowering duration (Fig. A9).
Display schedules with short flowering durations are sensitive to the distribution of flowers among days (Fig. A9). Specifically, as Di approaches 0, Si becomes increasingly lower as display schedules become more uniform (i.e. as SDi approaches 0). We suggest caution when applying this metric to display schedules that are 2-‐5 days in length, especially when the distribution of flowers among days is nearly uniform.
Figure A9 Si as a function of the duration of flowering for four example display schedules. In each schedule, all flowers are produced on the first and last date of flowering with zero flower counts on all other days. The
display schedules differ in the proportion of total flowers produced on the first versus the last day, represented by the different line types above.
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Si is technically undefined when Di=1. Individuals that produce all flowers in a single day are assigned a synchrony of 1. Individual synchrony is always 0 for uniform display schedules, regardless of flowering duration (Fig. A10A vs. A10B) or the total number of flowers produced (Fig. A10A vs. A10C). Si will be equivalent for display schedules that allocate identical proportions of flowers among days (Fig. A10D vs. A10F and A10E vs. A10G) irrespective of schedule shape (Fig. A10F vs. A10H).
Figure A10 Eight different hypothetical flowering schedules for individuals A-‐H, along with the corresponding values of Si, Di, and Ti.
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SA = 0DA= 5TA = 50
SC = 0DC= 5TC = 500
SE = 0.975DE= 5TE = 50
SG = 0.951DG= 5TG = 25
SB = 0DB= 10TB = 50
SD = 0.157DD= 10TD = 30
SF = 0.157DF= 10TF = 60
SH = 0.157DH= 10TH = 60
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Si can be rewritten as follows:
where SSi is the sum of squares for individual i and Ti is the total number of flowers produced. This form of the equation can be easily adjusted to accommodate flower counts that occur at equal intervals throughout the growing season:
where Ii equals the census interval (e.g. Ii = 3 if flowers are counted every 3 days) and Ci equals the total number of census days. Here we assume that the patterns observed during census days are representative of the data that were not sampled, so that the duration of flowering is equal to Ii*Ci, the total number of flowers produced is equal to Ii*Ti, and the sample sum of squares is equal to Ii*SSi.
Figure A11 shows how well this metric captures true levels of synchrony as interval lengths increase. We used four sample individual display schedules from experiment 3, and we estimated Si when Ii=1 (the true value) up to an interval length of 6. Estimates start to deviate from the true value when I>4, but adequately descries Si when Ii is three or less.
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Figure A11 Estimates of Si for different display schedules (A-‐D) as the census interval increases. The true Si is shown when I=1.
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Chapter Four
The Influence of Competition on Phenotypic Responses to Warming
This chapter resulted from collaboration with Benjamin Gilbert, Matthew N. Cumming, Marc W.
Cadotte, Caroline M. Tucker, and Arthur E. Weis. Susana M. Wadgymar carried out the
experiment, performed the analyses, and wrote the manuscript. MNC assisted with fieldwork,
BG lent advice on statistical analyses, BG and MWC assisted with funding, CMT helped develop
the motivation for the study, all coauthors contributed to experimental design, and AEW
contributed to manuscript editing.
Abstract
Global warming has influenced the timing of life history traits in many plant species.
The extent of shifts in reproductive phenological traits has been observed to vary according to a
species’ developmental position within a community of plants, with early flowering species
advancing more often, and by a larger degree, than those flowering later. Species may also
experience temporal variation in competition as the surrounding community changes in density
and composition throughout the growing season. Warming-induced plasticity in reproductive
phenology may vary among species in magnitude, direction, or adaptive value if phenotypic
shifts alter the degree of overlap with competing species. In this way, phenological responses to
warming may vary among species occupying distinct yet overlapping temporal niches, and may
depend on the presence, abundance, and species-specific responses of the heterospecific
competitors.
To investigate the influence of competition on phonological responses to warming among
phonologically distinct species, we manipulated competitive and thermal regimes for 3 weedy
plant species that differ in growth and development: Sinapis arvensis (early flowering),
Chamaecrista fasciculata (intermediate flowering), and Ambrosia artemisiifolia (late
flowering). We constructed communities that varied in the form and strength of competition,
with each species planted individually in monoculture and together in polyculture at both low
and high densities. These communities were then exposed to either ambient or elevated
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temperatures via artificial warming in a field setting. We monitored plasticity in flowering onset
date (a temperature-sensitive trait) and plant size (a trait representative of competitive ability) for
each species in each environment, and we used total reproductive biomass to estimate patterns of
selection across treatments.
For all traits and species, differences in community composition and plant density did not
interact with thermal treatment to influence flowering onset dates or final plant sizes. Planned
contrasts revealed that increased temperatures only significantly influenced flowering onset date
in two of the four competitive environments in the early flowering S. arvensis, indicating that
competitive regimes can sometimes constrain potential phenotypic responses to warming. In all
cases, plasticity in flowering onset date was adaptive and selection regimes did not differ
significantly between treatments. The patterns of selection imposed by warming on final plant
size were dependent on culture type for C. fasciculata, but were otherwise similar across
treatments for S. arvensis and A. artemisiifolia.
Our results demonstrate that phenotypic responses to warming and subsequent patterns of
selection are species and trait-specific. In general, variation in the competitive environment may
not act to constrain potential responses to increases in temperature in cases where competing
species are phenologically distinct, and other ecological or evolutionary processes may be
contributing to species-level differences in responses to warming.
Introduction
The widespread advances in plant reproductive phenology observed over the past few
decades are viewed as indicators of global climate change (Fitter & Fitter 2002; Parmesan 2007;
Menzel et al. 2006). However, variation in the responses of species remains largely unexplained,
even when accounting for phylogenetic non-independence (Willis et al. 2008). Some have
observed that earlier-flowering species are advancing more than those flowering later in the
growing season (Fitter & Fitter 2002; Menzel et al. 2006; Bertin 2008), suggesting that species
occupying distinct temporal niches are experiencing contrasting abiotic and biotic conditions that
may act in concert with increases in temperature to influence flowering onset dates. Biotic
factors, including competition for resources, can also vary seasonally and have the potential to
independently influence life history traits (Dyer & Rice 1999; Elzinga et al. 2007; McGoey &
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Stinchcombe 2009; Wright et al. 2015), or to act synergistically or antagonistically with the
effects of climate change (Kareiva et al. 1993). Observational and experimental work aimed at
documenting phenotypic responses to increasing temperatures often cannot distinguish between
the cumulative effects of the abiotic and biotic environment, leading to reduced predictive power
and misidentifications of the factors promoting phenological change (Gilman et al. 2010; Van
der Putten et al. 2010).
Long-term monitoring studies are invaluable because they relay phenological responses
in natural plant assemblies and under natural settings (Fitter & Fitter 2002; Forrest et al. 2010;
Willis et al. 2008). Such studies, however, are unable to differentiate between plastic and
genetic changes in flowering time. Generally, it is difficult to partition the effects of warming
from those of other uncontrolled factors, including biotic interactions (Gienapp et al. 2008;
Merilä & Hendry 2014). For example, species may vary in competitive abilities, and it is
possible that the response of a species to warming observed in one community is constrained in
another because of the presence of a superior competitor (Goldberg & Barton 1991; Weiner
1988). Additionally, we frequently do not have fitness data to accompany these observations,
resulting in speculation on the fitness consequences of any phenological shifts (Merilä & Hendry
2014).
In contrast, phenological data collected from manipulative warming experiments control
for many other factors that may also influence the timing of flowering, and by design distinguish
between plastic shifts in flowering onset within a growing season and evolutionary shifts
between seasons (Dunne et al. 2004; Anderson et al. 2012). However, these manipulations are
typically applied to natural communities where phenotypes and fitness are not followed on
individual plants (Price & Waser 1998; de Valpine & Harte 2001; Sherry et al. 2007), or where
competition is not quantified (Post et al. 2008). Other experiments have manipulated
temperatures for plant populations constructed to resemble monocultures with constant densities
(Wadgymar et al., in press). As such, we have little idea of how plasticity in growth and
development may be restricted by aspects of the biotic environment, including spatial or
temporal overlap with competitors. On account of this, many of the effects seen in these
experiments might only be roughly indicative of potential outcomes in more natural settings
(Wolkovich et al. 2012).
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Although relatively unexplored, it is plausible that warming-induced shifts in life history
traits, whether plastic or genetic, can be influenced by the competitive dynamics within
communities. The timing of flowering onset can influence patterns of resource allocation
between growth and reproductive functions (Bazzaz et al. 1987). Plant size is often indicative of
competitive ability (Gaudet & Keddy 1988), and species-specific shifts in reproductive timing
may influence the degree of temporal overlap with conspecifics (Price & Waser 1998; Sherry et
al. 2007; Aldridge et al. 2011), potentially altering competitive dynamics between species for
pollinator access, resources for fruit maturation, or future seedling establishment (Ågren &
Fagerström 1980; Forrest et al. 2010). Consequently, fitness may be influenced directly by shifts
in phenology or indirectly through associated changes in plant size or growth rate (Weiner 1988).
Ultimately, the magnitude of warming-induced phenological shifts, and their adaptive value, may
depend on the presence and responses of competing species within the same community.
At the community level, the growth and reproductive development of species are
staggered throughout the growing season at temperate latitudes (Rabinowitz et al. 1981; Herrera
1986; Weis et al. 2014). Species that differ in phenological traits may occupy distinct, yet
overlapping, temporal niches, where early- and late- flowering species experience competition
from those developing later or earlier, respectively, and intermediate-flowering species
experience competition from both groups (Kareiva et al. 1993; Pau et al. 2011). While empirical
evidence and support for temporal niche occupation is rare (Dante et al. 2013; Zhang et al.
2014), the potential for temporally asymmetric competition to differentially constrain the
potential responses of species to warming or alter patterns of selection on growth or phenological
traits remains unexplored.
To investigate the potential for competitive regimes to enhance or repress phenological
responses to climate change differentially among phonologically distinct species, we exposed
plant communities of different composition to either ambient or elevated temperatures via
artificial warming. We monitored the growth and flowering phenology of three annual species
planted from seed: early-flowering Sinapis arvensis, intermediate flowering Chamaecrista
fasciculata, and late-flowering Ambrosia artemisiifolia. We examined the effects of intra- verses
inter-specific competition by planting these species with conspecifics in monocultures or with
each other in polycultures at both low and high densities. We applied a factorial combination of
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three treatments: two thermal environments, two culture types, and two densities. We ask (1)
Does the competitive environment alter phenotypic responses to warming? and (2) Does the
competitive environment alter the selection pressures imposed by warming?
Methods
Study organisms
We selected three weedy, summer annual species that differed in patterns of growth and
phenology to create simple communities with varied competitive dynamics. Seeds of early
flowering Sinapis arvensis (Brassicaceae), or wild mustard, were collected in 2009 from a large
population in the margin of an agricultural field near Honfleur, Québec (46.6560°N,
70.8788°W). Growth is determinate in this species, with plants growing vegetatively as rosettes
until bolting and the formation of an indeterminate inflorescence (Mulligan & Bailey 1975).
Flowers are hermaphroditic and are pollinated by a wide variety of species in the orders
Hymenoptera and Diptera (Mulligan & Kevan 1973).
Seeds of the intermediate flowering Chamaecrista fasciculata (Fabaceae), or partridge
pea, were collected in 2009 from a naturalized population in the Grey Cloud Dunes south of
Minneapolis, Minnesota (44.8011°N, 92.9647°W). This species has indeterminate growth and
flowering, with a branching, semi-woody morphology (Garish & Lee 1989). Flowers are
hermaphroditic and are exclusively buzz pollinated (Thorp & Estes 1975).
Seeds of the late flowering Ambrosia artemisiifolia (Asteraceae), or common ragweed,
were collected in 2005 from various established populations around Mississauga, Ontario
(43.5890°N, 79.6441°W). This wind-pollinated species also has indeterminate growth, growing
and maturing seeds until first frost (Bassett & Crompton 1975). Ambrosia artemisiifolia is
monoecious, with distinct male and female flowers that differ in average onset date (Deen et al.
1998).
Experimental design
This study was conducted in the Experimental Climate Warming Arrays at the Koffler
Scientific Reserve at Joker’s Hill (44.0300°N, 79.5275°W), where plants were exposed to either
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present-day or projected future thermal regimes (per OMNR 2007). Each warming array
consisted of six infrared heaters mounted on a steel triangular structure 1.25 meters above soil
level (design per Kimball et al. 2008). Heaters were angled inward and down from horizontal,
creating a uniform circular heat shadow of 3 meters in diameter. Six arrays, or plots,
experienced ambient temperatures while another six were heated to 1.5˚C above ambient during
the day and 3˚C above ambient at night (per Easterling et al. 1997). Plants were otherwise
exposed to natural conditions. Plot-level temperatures were monitored in three plots per
treatment using infrared radiometers (SI-111 infrared radiometer, Campbell Scientific,
Edmonton, Canada).
Competition treatments were applied at the subplot level, with each plot divided into
eight equally sized, wedge-shaped subplots (~0.69 m2) using 6-inch edging buried at soil level to
minimize belowground plant contact between subplots. Each species was planted in
monoculture and polyculture communities to distinguish between the effects of intra- versus
interspecific competition. These mono- and polycultures were then replicated at low and high
densities (18 vs. 150 total seeds per subplot, respectively) to manipulate the strength as well as
the type of competition. Plots were cleared of all natural vegetation prior to planting and were
weeded periodically throughout the growing season.
All seeds were stratified for 8 days prior to planting, and C. fasciculata seeds were also
scarified. Seeds were scattered at random in their appropriate subplots on June 8th, 2012. After
approximately two weeks, we measured the distance to, and identity of, the first and second
nearest neighbors for a subset of focal plants to confirm desired differences in density and
community composition. We periodically surveyed focal individuals for survival and the date of
first flower. For A. artemisiifolia, we monitored the onset of male flowering when pollen was
presented and female flowering when a stigma was first observed to protrude from the flower.
We collected all fruit when matured, and fecundity was estimated as total mass of seeds and
fruit. Upon first frost, plants were harvested at soil level to measure aboveground vegetative
biomass.
Statistical Analyses
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We confirmed that temperature differences between heated and ambient plots were
maintained throughout the experiment using a repeated measures linear mixed model with
thermal treatment, day, and their interaction as fixed effects and plot as a random effect. To
account for any autocorrelation in temperature measurements among days, we incorporated an
auto-regressive error structure of order 1, nested within plot (Zuur et al. 2009), using the nlme
package (Pinheiro et al. 2014) in R (R Development Core Team, 2014). We verified density
differences between low- and high-density treatments using the average distance between our
focal plants and their first and second nearest neighbors as the response variable in a linear
mixed model with density, culture, species, and their interaction as fixed effects and subplot
nested within plot as a random effect, again using the nlme package in R. With these models,
and with those subsequently described, variance heterogeneity among treatments or species was
corrected using error variance covariates, if necessary (Zuur et al. 2009). We selected the
random terms and error covariates by minimizing AIC values, and optimized fixed effects via
maximum likelihood estimations.
We examined treatment effects on the average date of flowering onset and final
aboveground vegetative biomass for each species using linear mixed models, and also analyzed
differences in reproductive biomass using a generalized linear mixed model with a gamma
distribution and log link (via the lme4 package in R, Bates et al. 2014). All three treatments, and
their interactions, were included as fixed effects, while subplot nested within plot was included
as a random effect. Treatment effects on the onset of male and female flowering in A.
artemisiifolia were assessed separately. We analyzed the log of vegetative biomass +1 in order
to meet assumptions of residual normality, and we define reproductive biomass as the mass of
seeds and fruit. Gamma distributions exclude zero; accordingly, we added 0.01 to reproductive
biomass estimates. A significant interaction between the warming treatment and either or both
of the density or culture treatments indicates that the competitive environment has modified a
species’ response to warming. We present the results from the final, optimized models selected
via log likelihood ratio tests.
The analyses described above will reveal whether treatment combinations yielded
differences in average phenotype and fitness. We used planned contrasts from least-squares
means to identify the specific competitive environments in which phenotypic responses to
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warming were statistically significant. We standardized flowering onset date and vegetative
biomass within a species to a mean of zero and a standard deviation of 1. This allows for
comparisons among species, treatments, and traits in the degree and direction of plasticity.
Within each competitive environment, we performed a pairwise contrast between the
standardized trait values in heated and ambient conditions using the lsmeans package in R (Lenth
& Hervé 2015). Error rates were Tukey HSD adjusted and contrasts yielded standard errors of
the differences in means between thermal treatments.
Selection analyses
To examine whether responses to thermal or competitive environments were adaptive, we
performed phenotypic selection analyses to estimate patterns of selection on the onset date of
flowering and on final plant size. We analyzed the covariance between each trait and fitness
using a generalized linear mixed model with a gamma distribution and log link. While this
methodology yields statistically sound estimates of direct selection on traits, they are not on a
linear scale, and are thus not directly comparable to selection gradients calculated via multiple
regression (Lande & Arnold 1983).
There were no clear differences among treatments in the proportion of individuals
surviving to produce seed, with an average of 88% survival across species and treatments (data
not shown). We thus focus our selection analyses on the mass of fruit and seeds produced.
Within a species, traits were mean standardized, and we calculated relative fitness for each
individual as the total reproductive biomass divided by the average reproductive biomass
produced by all individuals of the same species. Gamma distributions exclude zero, and again
we added 0.01 to all individual fitness values prior to calculating relative fitness. For A.
artemisiifolia, we could not include the male and female flowering onset dates due to a high
degree of collinearity, so we assessed selection on each trait separately, with vegetative biomass
included in both models. Due to a limited sample size, we were unable to calculate patterns of
selection for A. artemisiifolia planted in low-density monocultures.
To determine whether selection regimes differed among thermal or competitive
environments, we included all three treatments in interactions with each trait in a separate
analysis. An interaction between trait and treatment(s) indicates that the magnitude or direction
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of selection on that trait is dependent on variation in the treatment. We report chi-squared values
from analyses of deviance for the final, optimized model.
Results
Treatment differences
Artificially warmed plots were maintained at a higher temperature than ambient plots
throughout the duration of the growing season (1.97°C average difference; Treatment F1,
692=15.30, p=0.001; Day F1, 692=156.89, p<0.001; Treatment*Day F1, 692=0.48, p>0.5). Density
treatments were also effectively constructed, with the average distance to the first and second
nearest neighbors significantly lower in the low-density treatment than in the high, regardless of
culture type (Density, F1, 77=45.55, p<0.001). However, within the low-density treatment, S.
arvensis had closer neighbors, on average, than C. fasciculata or A. artemisiifolia (15.01 vs.
17.22 vs. 20.29 cm, respectively, Density*Species, F2, 852=3.25, p<0.05), reflecting a potential
difference in the intensity of competition among species planted at low-density.
Phenotypic responses to warming
Species varied in their phenological responses to thermal and competitive environments.
Increased temperatures marginally advanced flowering onset in the early-flowering S. arvensis,
strongly accelerated flowering onset in the intermediate-flowering C. fasciculata, and did not
affect the onset of male or female flowering in the late-flowering A. artemisiifolia (Thermal
term, Table 4.1, Figs. 4.1a-c). The responses of flowering onset to warming were not influenced
by culture treatment for any of the three species (Thermal*Culture term, Table 4.1), and shifts in
phenology due to thermal treatment were only marginally modified by density for the onset of
female flowering in A. artemisiifolia (Thermal*Density term, Table 4.1). These results suggest
that species vary in their phenological sensitivity to changes in temperature, and that any plastic
responses elicited by increasing temperatures may be largely unaffected by competitive
dynamics.
Increased temperatures slightly decreased aboveground vegetative biomass in A.
artemisiifolia, but had no influence on size in the earlier-developing S. arvensis or C. fasciculata
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(Table 4.1, Figs. 4.1d-f). Furthermore, temperature did not interact with either culture or density
treatments for any species, again suggesting that competition may not play a large role in
managing phenotypic responses to warming.
Phenotypic responses to competitive dynamics
For all three species, the onset of flowering responded to variation in the competitive
environment. For S. arvensis, both density and culture treatments had interacting effects, with
the greatest degree of plasticity in low-density, monocultures and in high-density, polycultures
(Table 4.1, Fig. 4.1a). This effect was predominantly driven by the delay of flowering in low-
density polycultures, where this early-flowering species experienced the least competition
amongst the slower-growing species at low density. For C. fasciculata, culture had a weaker and
additive effect with temperature, with flowering onset delayed in polyculture conditions relative
to monoculture regardless of thermal treatment (Fig. 4.1b). The onset of male and female
flowering was delayed in polyculture communities in A. artemisiifolia (Fig. 4.1c), and the onset
of female flowering was further delayed in ambient, low-density treatments. Both C. fasciculata
and A. artemisiifolia experience competition from earlier-developing species, and our results
imply that the presence of interspecific competitors, and not planting densities, is the most
influential component of the competitive environment for these species.
Final plant size was also affected by competitive regimes in all species. For the early-
developing species, S. arvensis, high-density conditions reduced plant size only in monoculture
communities, where competition was expected to be strongest (Table 4.1, Fig. 4.1d). In contrast,
for C. fasciculata, high-density conditions reduced plant size in polyculture communities at high
densities (Fig. 4.1e). For the later developing species, A. artemisiifolia, high densities
consistently reduced plant size regardless of culture type, although increased temperatures
seemed to ameliorate the degree of decline (Fig. 4.1f).
Differences in reproductive biomass largely reflected treatment effects on plant size
(Table 4.1, Figure 4.2), with fecundity reduced at high densities in monoculture conditions in S.
arvensis and in polyculture conditions in C. fasciculata. Ambrosia artemisiifolia performed the
most poorly at high densities, although this decline was alleviated in elevated thermal conditions.
Clearly, larger plant size results in more-fit individuals, and competitive regimes govern the most
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variation in this trait. However, phenotypic selection analyses can account for variation in size
and reveal whether plasticity in flowering onset confers additional fitness effects.
Modified responses to warming and subsequent patterns of selection
The results of the mixed models reported previously suggest that phenotypic responses to
warming are not influenced by variation in the strength and form of competition; that is, the
slopes of the reaction norms are not distinct from one another. We applied planned contrasts
within competition and density treatment combinations to reveal whether flowering onset date
and final plant size differed between thermal environments. A significant difference would
indicate that the slope of an individual reaction norm is different than zero, even if it was not
distinct from those of the other competitive environments.
Planned contrasts revealed that competitive regimes modified the response of flowering
onset to warming in one of the three species. For S. arvensis, flowering onset was significantly
advanced in only two of the four competitive environments, demonstrating that competitive
dynamics may moderate phenological responses to warming (Fig. 4.3a). Selection gradients did
not differ between thermal regimes (Table 4.2), however early flowering was favored in the
polyculture communities, but was only weakly favored or neutral in monoculture conditions
where competition was likely strongest (Fig. 4.3d, Table 4.4).
For C. fasciculata, flowering onset advanced in response to warming in the same manner
across all competitive environments (Fig. 4.3b). Selection on flowering onset date was more
varied. In the monoculture communities, selection only favored early flowering when plants
were exposed to ambient temperatures (Table 4.3). In high-density conditions, the shift to earlier
flowering alleviated the strength of selection on flowering onset date (Fig. 4.3e). This suggests
that warming-induced shifts to earlier flowering were adaptive in C. fasciculata.
In contrast to the two earlier-developing species, the onset of male flowering was not
affected by thermal or competitive conditions in A. artemisiifolia (Fig. 4.3c), and selection on
this trait was consistently neutral (Fig. 4.3f, Table 4.3). Selection on the onset of female
flowering was also neutral in all cases but one; later flowering was strongly favored in low-
density, polyculture communities when warmed (Table 4.3).
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Aboveground vegetative biomass was not significantly affected by either temperature or
competitive environment in any of the three species (Fig. 4.4a-c). However, in S. arvensis, plant
sizes tended to be smaller in heated conditions when compared to ambient for all but the
monoculture communities planted at high density (Fig. 4.4a). This may be due, in part, to the
associated shifts to earlier flowering in the elevated temperature treatment (Fig. 4.3a). This
warming induced trend towards smaller size resulted in an increase in the strength of selection
for larger plant size (Table 4.3, Fig. 4.4d). For C. fasciculata, selection gradients did not differ
between treatments, although selection was strongest in the polyculture communities when low
densities are heated and when high densities experience ambient temperatures (Table 4.3, Fig.
4.4e). Selection for larger size was consistent in all treatments for A. artemisiifolia (Table 4.3,
Fig. 4.4f).
Discussion
Global warming has prompted shifts in life history traits across a wide array of taxa
(Parmesan 2007), yet our ability to predict a given species’ response to warming is limited by
unaccounted for evolutionary or ecological processes contributing to phenotypic variation. Here,
we explored the potential for the competitive environment to modify the phenotypic responses of
developmentally distinct species to increases in temperature. For all three focal species,
differences in community composition and plant density did not interact with temperature to
influence flowering onset dates or final plant sizes. Planned contrasts revealed that increased
temperatures only significantly influenced flowering onset date in two of the four competitive
environments in the early flowering S. arvensis, indicating that competitive regimes can
sometimes constrain potential phenotypic responses to warming. However, we saw no evidence
of this for plasticity in final plant size or in the other species examined.
In all cases, plasticity in flowering onset date was adaptive and selection regimes did not
differ significantly between treatments. Patterns of selection imposed by warming on final plant
size were dependent on culture type for C. fasciculata, but were otherwise similar across
treatments for S. arvensis and A. artemisiifolia. Cumulatively, our results demonstrate that
phenotypic responses to warming may generally be insensitive to variation in competitive
dynamics, and that the degree of plasticity induced by warming, and subsequent patterns of
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selection, are species and trait-specific. Below we briefly review among-species variability in
the response of flowering onset to warming, and then discuss whether competition may be a
contributor to unexplained phenological variation.
Variation in phenological responses to warming
Proposed explanations for variation in responses to warming include differences in
mating system, pollination mechanism, geographical distribution, phylogenetic history, life form,
and flowering time relative to other members of the community (Fitter & Fitter 2002; Peñuelas et
al. 2002; Menzel et al. 2006; Sherry et al. 2007; Bertin 2008; Willis et al. 2008). For example,
in a survey of changes in flowering onset for 385 British plants, it was demonstrated that annuals
were more likely to have shifted their flowering onset dates than perennials, and that larger shifts
are found in insect-pollinated species than in wind-pollinated species, particularly if they were
already relatively early flowering (Fitter & Fitter 2002). The latter suggestion is supported here,
as we observed an advancement of flowering when warmed for the insect-pollinated S. arvensis
and C. fasciculata, but not for the wind-pollinated A. artemisiifolia. The magnitude and
direction of phenological responses to climate change can be phylogenetically conserved, with
the most responsive species belonging to a nonrandom assortment of plant families (Willis et al.
2008). Some have proposed that earlier-flowering species are more sensitive to change in
temperature than those flowering later in the season, with larger shifts in the onset of
reproduction seen in early-developing species relative to late (Fitter & Fitter 2002, Menzel et al.
2006; Sherry et al. 2007; Bertin 2008; but see Peñuelas et al. 2002).
Regardless of the potential causes of variation in responses to warming, it has been
demonstrated that non-responsive species are suffering negative demographic consequences
(Willis et al. 2008). Thus far, most examinations of the mechanisms proposed to explain
variation in responses to climate change have been correlative and have focused on the outcomes
of evolutionary processes (e.g. pollination mechanism). Below we discuss how short-term
ecological processes, like competition, may be more influential than currently appreciated.
Competition and phenology
114
Competitive dynamics can elicit variable physiological, morphological, and
developmental responses among species, with the effects of intra- and interspecific competition
potentially acting in opposing directions (Connell 1983; Linhart 1988; Stoll & Prati 2001). The
occurrence and strength of competition may depend on the life history stages of species at the
time of interaction (Callaway & Walker 1997) or on habitat quality (Aerts 1999). Community
composition changes through space and time as species emerge and senesce, producing transient
competitive regimes that are unique to each species present (Connell 1983). If developmentally
distinct species experience competition in a systematic way, the effects of competitive
interactions should be predictable based on the presence, abundance, and development times of
heterospecifics.
In our experiment, we competed species that varied drastically in growth and
development times, creating the potential for asymmetric competitive pressures across species.
While we found little evidence that competitive regimes constrain phenotypic responses to
increases in temperatures, we did find that competitive dynamics on their own strongly
influenced plasticity in flowering onset date and final plant size, and that each of our focal
species was affected by different aspects of the competitive environment.
If we consider effects on final plant size as proxies for the intensity of competition, we
see that final plant size was reduced in S. arvensis when intraspecific competition was strong
(high density monocultures), whereas strong interspecific competition (high density
polycultures) was more detrimental for the intermediately flowering C. fasciculata. The last
species to flower, A. artemisiifolia, had suppressed growth at high densities irrespective of the
composition of the surrounding community (Fig. 4.1d-f). With the relative importance of intra
and interspecific competition across species in mind, a reexamination of phenological responses
reveals that the strongest competitive dynamics accelerated flowering onset date in S. arvensis,
delayed onset in C. fasciculata, and had little to no influence on onset dates in A. artemisiifolia
(Table 4.1, Fig. 4.1a-c). While there are differences in treatment effects on plasticity among
species, we see general trends in phenotypic selection among species. For all three species, the
competitive environment influenced patterns of selection on plant size (Fig. 4.4), but not on
flowering onset date (Fig. 4.3). Future work should focus on whether the species-specific
competitive effects observed here are a consequence of each species’ temporal sequence within
115
the community, and whether this may add predictive power to explanations of phenological
variation in nature.
The trends observed here might be contingent on whether our focal species are generally
representative of early, intermediate, and late flowering species and whether they naturally co-
occur and compete. Including additional phenologically distinct species in this experiment
would have come at the expense of replication, and instead we chose three annual species whose
growth and development times made predictions about responses to warming and the symmetry
of competition possible. These species are typically found in similar, disturbed habitats, making
them plausible natural competitors. We observed that C. fasciculata and A. artemisiifolia coexist
in plant communities along the eastern United States (data not shown). While S. arvensis’
distribution overlaps substantially with those of the other two species, it is most often found in
the margins of agricultural fields and may be less likely to naturally coexist with either C.
fasciculata or A. artemisiifolia. While we feel that our species selection was representative of
annual plants common to temperate regions, we caution that the outcome of this experiment may
not be broadly observed across other combinations of phenologically distinct species.
Summary
Overall, our results suggest that the effects of competition on phenotypic responses to
warming are largely additive and may be predictable when taking in to account the
developmental sequence of species within a community. The lack of evidence for an interaction
between thermal and competitive treatments is encouraging for studies where competition is not
quantified. Further explorations of the relative importance of increasing temperatures and
competition should investigate how the degree of phenological separation among species affects
phenotypic plasticity and selection. Additionally, the overwintering of seeds may introduce
important variation in emergence dates, both within and between species, which can influence
competitive dynamics later in life. Variation in reproductive phenology has the potential to
influence population demographics, community composition, and evolutionary trajectories, and
rapidly accelerated increases in temperature mandate the continued exploration of potential
contributors to species’ responses to climate change.
Table 4.1 Analyses of the responses of flowering onset date, aboveground vegetative biomass, and reproductive biomass to thermal (ambient vs. heated), density (low vs. high), and culture (mono-‐ vs. poly-‐) treatments. For A. artemisiifolia, we separately analyzed the onset of male and female flowering. Flowering onset and vegetative biomass were analyzed using linear mixed effects (lme) models, while reproductive biomass was analyzed using a generalized linear mixed (glm) model with a Gamma distribution and log link. We report F-‐values and (for lme models) or Chi-‐squared values from analyses of deviance (for glm models) and associated p-‐values for the fixed effects in the final, optimized models. F-‐values where p<0.05 are in bold.
S. arvensis C. fasciculata A. artemisiifolia
Flowering Onset
Vegetative Biomass
Reproductive Biomass
Flowering Onset
Vegetative Biomass
Reproductive Biomass
Male: Flowering
Onset
Female: Flowering
Onset
Vegetative Biomass
Reproductive Biomass
Thermal 3.93
p=0.08 NS NS 22.13
p<0.001 NS NS NS 0.59
p=0.44 4.87
p=0.05 NS
Density 0.53
p=0.47 19.94
p<0.001 18.45
p<0.001 NS 35.37
p<0.001 20.41
p<0.001 NS 0.34
p=0.56 17.30
p<0.001 10.33
p=0.001
Culture 4.80
p=0.04 20.22
p<0.001 10.14
p=0.001 6.66
p=0.01 32.42
p<0.001 17.56
p<0.001 5.40
p=0.02 6.84
p=0.01 NS NS
Thermal:Density NS
NS NS NS NS NS NS 3.54
p=0.06 NS NS
Thermal:Culture NS
NS NS NS NS NS NS NS NS NS
Density:Culture 7.40
p=0.01 NS NS NS 12.28
p=0.002 6.92
p=0.009 NS NS NS NS
Thermal:Density:Culture NS
NS NS NS NS NS NS NS NS NS
116
117
Table 4.2 Generalized linear mixed effects analyses of the influence of flowering onset date, aboveground vegetative biomass, and experimental treatments on reproductive biomass. Significant interactions between a trait and a treatment signify that patterns of selection on that trait are dependent on treatment level. All higher order interactions were not significant and we report chi-‐squared and p-‐values from analyses of deviance for the final, optimized models. For A. artemisiifolia, we separately analyzed selection regimes when considering the onset date of male and female flowering.
S. arvensis C. fasciculata A. artemisiifolia Male
A. artemisiifolia Female
Flowering onset 36.59 p<0.001
10.36 p=0.001
NS NS
Vegetative biomass 470.04 p<0.001
168.59 p<0.001
263.64 p<0.001
263.64 p<0.001
Temperature NS
NS NS NS
Density NS
3.48 p=0.06
NS NS
Culture NS
0.16 p=0.69
NS NS
Flowering onset*Temperature NS
NS NS NS
Flowering onset*Density NS
NS NS NS
Flowering onset*Culture NS
NS NS NS
Biomass*Temperature NS
NS NS NS
Biomass*Density NS
NS NS NS
Biomass*Culture NS
4.30 p=0.04
NS NS
135
Table 4.3 Estimates of direct phenotypic linear selection coefficients (± S.E.) and p-‐values for species planted in thermal (ambient vs. heated), density (low vs. high), and culture (mono-‐ vs. poly-‐) treatments. For A. artemisiifolia, we examined selection on the onset date of male and female flowering separately to avoid issues of multicolinearity. Coefficients where p<0.05 are shown in bold.
Figure 4.1 Average shifts in (A-‐C) flowering onset date and (D-‐F) above ground vegetative biomass ± S.E. for (A, D) S. arvensis, (B, E) C. fasciculata, and (C, F) A. artemisiifolia in response to thermal (ambient vs. heated), density (low vs. high), and culture (mono vs. poly) treatments. Responses are shown for treatments of significant effects or to facilitate comparisons among species (see Table 1). For A. artemisiifolia, treatment effects are similar between the onset of male and female flowering, and panel (C) only portrays variation in male flowering onset date.
�
24
25
26Ju
lian
date
A High, PolyHigh, MonoLow, PolyLow, Mono
0
1
2
3
log V
eget
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biom
ass (
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Mono Poly
D
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Julia
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te
50
54
58 B PolyMono
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2
3
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ass (
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60
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Ambient Heated Low density High Density0
1
2
3
4
5
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eget
ative
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ass (
g) AmbientHeated
FPolyMono
120
Figure 4.2 Average reproductive biomass ± S.E. for (A) S. arvensis, (B) C. fasciculata, and (C) A. artemisiifolia in thermal (ambient vs. heated), density (low vs. high), and culture (mono-‐ vs. poly-‐) treatments.
0
11
22
Repr
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bioma
ss (g
) AmbientHeated
A
0
11
22
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bioma
ss (g
) B
Repr
oduc
tive
bioma
ss (g
)
Mono Poly Mono PolyLow Density High Density
0
17
34C
121
Figure 4.3 (A) Average flowering onset (in units of sd) in heated conditions relative to ambient for various competitive regimes. Error bars were obtained by planned contrasts of responses between thermal treatments within a particular competitive regime, with stars indicating a significant response to warming. Negative and positive shifts reflect the acceleration or delay of flowering onset when heated, respectively. (B) Selection gradients ± S.E. reflecting the strength of direct selection on flowering onset date in heated and ambient conditions for each competitive environment (see Table 4.2). Negative and positive gradients reflect direct selection for earlier and later flowering, respectively. Due to a small sample size, we were unable to calculate the strength of selection for A. artemisiifolia in low density, monoculture conditions. In panels (C) and (F), we convey the results concerning variation in male flowering onset date in A. artemisiifolia.
!2 !1 0 1 2
Poly, High
Poly, Low
Mono, Low
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Poly, High
Poly, Low
Mono, Low
Mono, High
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Mono, High
122
Figure 4.4 (A) Average above ground vegetative biomass in heated conditions relative to ambient for various competitive regimes. Error bars were obtained by planned contrasts of responses between thermal treatments within a particular competitive regime. Negative and positive shifts reflect increases or decreases in plant size when heated, respectively. (B) Selection gradients ± S.E. reflecting the strength of direct selection on vegetative biomass in heated and ambient conditions for each competitive environment (see Table 4.2). Positive gradients reflect direct selection for later size. Due to a small sample size, we were unable to calculate the strength of selection for A. artemisiifolia in low density, monoculture conditions. In panel (F), we portray the selection coefficients from the model including male flowering onset date in A. artemisiifolia (see Table 4.2).
!2 !1 0 1 2
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0.40 0.65 0.90 1.15 1.40
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Poly, Low
Mono, High
122
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Chapter 5
Concluding discussion
Global temperatures are increasing at unprecedented rates. Uncertainty in how species
will fare in warmer environments warrants research on the limitations of phenotypic plasticity in
traits under selection and of the feasibility of active management programs for vulnerable
species. In my thesis, I investigated the contexts under which plasticity in reproductive
phenological traits are adaptive and whether plasticity adequately relieves the selection pressures
imposed by warmer temperatures. In this final chapter, I review current evidence for how
climate change is eliciting plastic and evolutionary responses in the life history traits of plants
and how the results reported here expand this understanding. I then synthesize the findings of
each chapter to discuss the successful application of assisted colonization and assisted gene flow.
Lastly, I suggest potential areas for future research.
Phenotypic plasticity and evolution in response to warming
The sessile nature of plants suggests a crucial role for phenotypic plasticity in
ameliorating the immediate selection pressures imposed by climate change. While only the
underlying heritable component of phenotypic variation will directly determine responses to
selection, selection itself is acting on an organism’s phenotype as a whole, making any other
contributions to phenotypic variation important in a population’s evolutionary trajectory
(Scheiner 1993). The ecological and evolutionary consequences of plasticity have been
extensively explored in well-understood traits (Stearns 1989, Pigliucci 2001), yet we know little
about the relative roles of plasticity and evolution in governing the responses of reproductive
phenological traits to climate change.
Are all species advancing their phenologies?
The majority of plant species are accelerating their reproductive phenologies as
temperatures warm (Parmesan 2007). When looking at the average shifts in first flowering dates
across the growing season, some have observed that earlier flowering species are more
responsive to increases in temperature than those flowering later in the season (Fitter and Fitter
2002, Menzel et al. 2006, but see Peñuelas et al. 2002). In chapter 4, we hypothesized that
128
asymmetric competition among species may be driving these patterns. While the few species
included in our experiment preclude any generalizations about the responses of phenologically
distinct species to warming, our results do fall in line with this trend, with the early- and
intermediate-flowering Sinapis arvensis and Chamaecrista fasciculata responding to warming
while the later flowering Ambrosia artemisiifolia did not. For S. arvensis, the degree of plastic
response of flowering onset to warming depended on the competitive environment. However, in
no case did the effects of competition significantly interact with thermal treatment to influence
flowering onset date or final plant size.
Previous efforts to characterize species-specific responses to warming have focused on
differences between species that arise over evolutionary timescales, including differences in
pollination mechanism, phylogenetic history, mating system, and life cycle (Fitter and Fitter
2002, Peñuelas et al. 2002, Menzel et al. 2006, Bertin 2008, Willis et al. 2008). We are among
the first to explore the potential for a short-term ecological process (e.g. competition) to modify
phenological responses to warming. The lack of evidence for variation in the competitive
environment to differentially affect species’ responses to warming is encouraging for those who
study single species in isolation, who rely on observational data collected from citizen science
programs, or who are otherwise unable to account for spatial or temporal differences in
community composition or density.
Population-level differences in phenotypic responses to warming may contribute to
patterns of species-level variation. Populations can vary drastically in genetic variation for life
history traits (Loveless & Hamrick 1984; Geber & Griffen 2003) and may also differ in their
genetic variation for plasticity in those traits (Stearns 1989). Site-specific differences in
historical abiotic or biotic environments among populations could generate variation in the
capacity of populations to respond to increases in temperature. Data that are collected from
single populations or sites may not be representative of a species’ average response to climate
change. In chapter 2, we exposed geographically distinct populations of C. fasciculata to the
same ambient and heated thermal treatments and monitored differences in reproductive
phenological traits. We detected little to no significant variation in the population-level
responses of budding and flowering onset date to warming, while plasticity in fruiting onset was
highly variable among populations. These data serve to illustrate how our assessments of
129
species-level differences in responses to environmental change may depend on the focal trait
under consideration as well as the population sampled.
Are all advances in phenology adaptive?
A recent meta-analysis concluded that selection generally favors early flowering,
particularly in temperate latitudes (Munguía-Rosas et al. 2011). The majority of observational
studies have found that flowering plants are accelerating their flowering onset dates as
temperatures and atmospheric CO2 concentrations increase (Fitter and Fitter 2002, Menzel et al.
2006, Parmesan 2007). Together, these findings imply that shifts to earlier flowering onset dates
may typically be adaptive. Manipulative experiments have also commonly found shifts towards
early flowering to be adaptive, but caution that plasticity is insufficient to assuage strong
negative directional selection pressures (Etterson and Shaw 2001, Haggerty and Galloway 2011,
Anderson et al. 2011). Only some of the results reported in this thesis align with these findings,
with exceptions explained by differences in responses and selection pressures among species.
Species-specific differences in the selection pressures imposed by warming could
contribute to variable phenotypic shifts among species. The meta-analysis mentioned above was
largely composed of estimates of the strength of selection on flowering onset date in perennials
(Munguía-Rosas et al. 2011). The few annual species included in the analyses experienced
stronger negative directional selection, and more variable selection pressures among-species,
than did perennials. Annual species have one season in which to mature seeds, and variation in
the strength of selection on flowering time may result from the potential trade off between
flowering time and plant size, where earlier flowering guarantees reproductive success before
season’s end while later flowering allows more time for growth and the accumulation of
resources for the production of offspring (Dorn & Mitchell-Olds 1991; Weis et al. 2014). The
results from chapter 4 indicate that the selection regimes imposed by increased temperatures vary
by species, with early flowering strongly favored in the early-flowering species and with
flowering onset selectively neutral in the last species to flower. Our results, combined with the
scarcity of studies that measure the fitness consequences of plasticity in flowering onset date,
demonstrate the need for caution when making general assumptions about the adaptive nature of
phenological responses to warming.
130
Our results also illustrate that the adaptive value of plasticity may depend on the
ecological context in which it was assessed. Chapters 2 and 4 of this thesis together demonstrate
that selection only favors early flowering in C. fasciculata when low-density monoculture
communities experience ambient temperatures. Under these conditions, warming-induced
advances in flowering onset are adaptive. However, when competitive dynamics are stronger,
through either increases in density or the presence of heterospecifics, selection on flowering
onset date is neutral regardless of thermal environment. Furthermore, the results from chapter 2
reveal that selection analyses are dependent on the component of fitness under consideration,
with differences in selection on flowering onset date between thermal environments only
detected when seed production was used as a proxy for fitness. Our results serve to illustrate
how interpretations of the adaptive nature of plasticity can be contingent on the ecological
circumstances or fitness measures from which we evaluate the relationship between phenotype
and fitness.
Do individual traits respond to warming independently or in a correlated manner?
Individuals are made up of a suite of traits with varying degrees of plasticity and adaptive
value, and these characters may respond to environmental conditions independently or in an
integrated manner. The detection of individual plastic responses among traits indicates that
selection may act independently and effectively on each trait. With many species encountering
novel selection pressures as the climate changes, we must determine whether the ‘mosaic nature
of plasticity’ (Ghalambor et al 2007) fosters adaptive developmental responses across the life
cycle.
The independent responses of sequential reproductive life history traits to warming have
been found in several systems (Post et al. 2008; Haggerty & Galloway 2011), suggesting that
flexibility in associations between phenological traits may be common. In chapter 2, we found
that the onset dates of budding and flowering did not respond to increases in temperature
independently of previously expressed traits (emergence and budding onset dates, respectively).
In contrast, the greatest and most variable degree of independent plasticity was for the onset date
of fruiting, a trait that is largely ignored in studies of climate change (but see Peñuelas et al.
2002). In chapters 2 and 4, we saw that advances in first flowering date did not correspond with
131
shifts towards smaller plant size, a constraint commonly found in many plant species (Dorn &
Mitchell-Olds 1991). These studies reveal adaptive trait combinations in C. fasciculata that
could be achieved through adaptive evolution, and also identify scenarios where correlations
between traits might be genetic and challenge evolutionary processes.
Plasticity in the onset date of flowering is often regarded as a representation of how plant
reproduction as a whole is expected to respond to changes in the environment. The consideration
of flowering onset as a proxy for patterns of reproductive phenology explicitly, and perhaps
naively, assumes that the schedule of flower deployment will follow in suite after the first flower
blooms. In chapter 3, we showed that the shape of display schedules is plastic and independent
of plasticity in flowering onset date. Furthermore, plasticity in flower deployment and floral
longevity seems to be governed by seasonal variation in temperature, which in itself is projected
to be modified by climate change (Stocker et al. 2013). The novel experiments presented in this
thesis demonstrate the dynamic nature of plasticity, and the value of insights gained by adopting
a cumulative life cycle view of phenotypic responses to warming.
Future directions and implications for assisted colonization
In this thesis, I provide evidence that interpretations of warming-induced phenotypic
plasticity and subsequent fitness effects are dependant on the species or populations under
investigation, the traits or trait combinations being examined, the fitness components being
considered, and the competitive environment in which data are being acquired. The context-
dependent nature of phenotypic responses to warming warrants further investigation in order to
improve predictive abilities and maximize the effectiveness of conservation strategies.
This work is among the first to experimentally test hypotheses concerning general factors
that may limit the success of assisted colonization programs. The very limited previous
empirical work on this topic has varied in scope, objective, and motivation (Hewitt et al. 2011;
Pedlar et al. 2012). We need a standardized framework for assessing the successful
establishment of relocated populations and for integrating general inferences among separate
experimental trials. The ecological and evolutionary factors limiting range expansion have been
well explored in a wide array of species (Sexton et al. 2009; Hargreaves et al. 2014), and a
comprehensive review of this body of literature in the context of assisted colonization may reveal
132
specific circumstances that may jeopardize or augment the success of future relocations. We
would benefit from studies that explore the feasibility of relocations within natural communities,
that monitor population growth rates over long time frames, and that assess the necessity and
feasibility of simultaneous relocations of mutualist species pairs. Experiments further assessing
the repercussions of relocating species that rely on photoperiodic and thermal cues for
development may be of particular interest for any considering relocations across latitudes.
Assisted gene flow, often termed genetic rescue, is implemented with the goal of
combining genotypes to expand genetic variation and facilitate evolutionary adaptation in
response to changing environmental conditions (Aitken & Whitlock 2013). In plants, the success
of this program is dependent upon the degree of phenological overlap and subsequent
opportunities for pollen exchange between populations. Currently, we can employ measures of
phenological synchrony to gauge the potential for mating opportunities between groups, as we
did in chapter 2 of this thesis. However, many factors contribute to differences between
predicted and realized gene flow, including pollinator behavior, plasticity in expected patterns of
flower deployment or longevity, and variation in fruit initiation and abortion within individuals.
In chapter 3, we demonstrated the potential for plasticity in floral display schedules to reduce the
strength of phenological assortative mating within populations. That is, mating opportunities
between early- and late-flowering individuals are closer to random than what we observe in other
species. The success of assisted gene flow may be greater in species with populations that
express similar plastic responses in patterns of flowering phenology, as seen in C. fasciculata.
Many plant species exhibit a decline in fruit set probability as plants age (Austen et al.
2015), which indicates that flowering overlap alone is not a guarantee the successful genetic
exchange between populations. Gene flow for loci contributing to flowering phenology may be
non-random (Weis 2015), and this bias can work with or against selection, depending on
phenological differences between populations and the local optimum flowering time. Efforts are
underway to gauge the success of assisted gene flow by refining estimates of realized mating
opportunities between the C. fasciculata populations studied here. This work will incorporate
daily flowering schedules and declines in fruit-set probability to estimate the degree of symmetry
in pollen exchange between populations (Wadgymar & Weis, in preparation). This work will
133
provide a framework for those wishing to assess realized gene flow rates when attempting to
genetically rescue a local population with phenologically divergent migrants.
In this thesis, we present the results of a preliminary investigation of how early and late-
flowering species differ in their plastic responses to warming, and whether distinct populations
of a single species exhibit varying degrees of plasticity. We could expand our awareness of the
effects of climate change on phenological traits if more studies monitored traits and fitness at the
individual level, which would allow for phenotypic selection analyses and assessments of
adaptive plasticity. Future work could further investigate variations in plasticity and selection
regimes among populations and species. We advocate for the continued exploration of the
potential for competition to mediate the effects of increases in temperature. For instance, in
chapter 4, we found that competition did not modify phenotypic responses to warming in
communities where species markedly varied in patterns growth and development. Competition
theory predicts that the intensity of competition increases with species similarity, and
experiments that focused on competition intensity (instead of competitive asymmetry) may yield
different results.
References cited
Aitken SN, Whitlock MC (2013) Assisted Gene Flow to Facilitate Local Adaptation to Climate
Change. The Annual Review of Ecology, Evolution, and Systematics, 44, 367-388.
Aldridge G, Inouye DW, Forrest JRK, Barr WA, Miller-Rushing AJ (2011) Emergence of a mid-
season period of low floral resources in a montane meadow ecosystem associated with
phenotypic plasticity and the potential for contemporary adaptation in new environments.
Functional Ecology, 21, 394-407.
Haggerty BP, Galloway LF (2011) Response of individual components of reproductive
phenology to growing season length in a monocarpic herb. Journal of Ecology, 99, 242-253.
Hargreaves AL, Samis KE, Eckert CG (2014) Are species’ range limits simply niche limits writ
large? A review of transplant experiments beyond the range. The American Naturalist, 183,
157-173..
Hewitt N, Klenk N, Smith AL, Bazely DR, Yan N, Wood S, MacLellan JI, Lipsig-Mumme C,
Henriques I (2011) Taking stock of the assisted migration debate. Biological Conservation,
144, 2560-2572.
Loveless MD, Hamrick JL (1984) Ecological determinants of genetic structure in plant
populations. Annual Review of Ecology and Systematics, 15, 65-95.
Menzel A, Sparks TH, Estrella N, et al. (2006) European phenological response to climate
change matches the warming pattern. Global Change Biology, 12, 1969-1976.
Munguía-Rosas MA, Ollerton J, Parra-Tabla V, De-Nova JA (2011) Meta-analysis of phenotypic
selection on flowering phenology suggests that early flowering plants are favored. Ecology
Letters, 14, 511-521.
Parmesan C (2007) Influences of species, latitudes and methodologies on estimates of
phenological response to global warming. Global Change Biology, 13, 1860-1972.
Pedlar JH, McKenney DW, Aubin I, et al., (2012) Placing Forestry in the Assisted Migration
Debate. BioScience, 62, 835-842.
135
Peñuelas J, Filella I, Comas P (2002) Changed plant and animal life cycles from 1952 to 2000 in
the Mediterranean region. Global Change Biology, 8, 531-544.
Pigliucci M (2001) Phenotypic plasticity: beyond nature and nurture. JHU Press.
Post ES, Pedersen C, Wilmers CC, Forchhammer MC (2008) Phenological sequences reveal
aggregate life history response to climatic warming. Ecology, 89, 363-370.
Scheiner SM (1993) Genetics and evolution of phenotypic plasticity. Annual Review of Ecology
and Systematics, 24, 35-68.
Sexton JP, McIntyre PJ, Angert AL, Rice KJ (2009) Evolution and ecology of species range
limits. The Annual Review of Ecology, Evolution, and Systematics, 40, 415-436.
Stearns SC (1989) The evolutionary significance of phenotypic plasticity. BioScience, 39, 436-
445.
Stocker TF, Qin D, Plattner GK, et al. (2013) IPCC, 2013: climate change 2013: the physical
science basis. Contribution of working group I to the fifth assessment report of the
intergovernmental panel on climate change.
Weis AE (2015) On the potential strength and consequences for non-random gene flow caused
by local adaptation in flowering time. Journal of Evolutionary Biology (in press)
Weis AE, Nardone E, Fox GA (2014) The strength of assortative mating for population date and
its basis in individual variation in flowering schedule. Journal of Evolutionary Biology, 27:
2138:2151.
Willis CG, Ruhfel B, Primack RB, et al. (2008) Phylogenetic patterns of species loss in
Thoreau’s woods are driven by climate change. Proceedings of the National Academy of
Sciences, 105, 17029-17033.
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WILEY OPEN ACCESS TERMS AND CONDITIONS
Wiley Publishes Open Access Articles in fully Open Access Journals and in Subscriptionjournals offering Online Open. Although most of the fully Open Access journals publishopen access articles under the terms of the Creative Commons Attribution (CC BY) Licenseonly, the subscription journals and a few of the Open Access Journals offer a choice ofCreative Commons Licenses:: Creative Commons Attribution (CC-BY) license CreativeCommons Attribution Non-Commercial (CC-BY-NC) license and Creative CommonsAttribution Non-Commercial-NoDerivs (CC-BY-NC-ND) License. The license type isclearly identified on the article.
Copyright in any research article in a journal published as Open Access under a CreativeCommons License is retained by the author(s). Authors grant Wiley a license to publish thearticle and identify itself as the original publisher. Authors also grant any third party the rightto use the article freely as long as its integrity is maintained and its original authors, citationdetails and publisher are identified as follows: [Title of Article/Author/Journal Title andVolume/Issue. Copyright (c) [year] [copyright owner as specified in the Journal]. Links tothe final article on Wiley�s website are encouraged where applicable.
The Creative Commons Attribution License
The Creative Commons Attribution License (CC-BY) allows users to copy, distribute andtransmit an article, adapt the article and make commercial use of the article. The CC-BYlicense permits commercial and non-commercial re-use of an open access article, as long asthe author is properly attributed.
The Creative Commons Attribution License does not affect the moral rights of authors,including without limitation the right not to have their work subjected to derogatorytreatment. It also does not affect any other rights held by authors or third parties in thearticle, including without limitation the rights of privacy and publicity. Use of the articlemust not assert or imply, whether implicitly or explicitly, any connection with, endorsementor sponsorship of such use by the author, publisher or any other party associated with thearticle.
For any reuse or distribution, users must include the copyright notice and make clear toothers that the article is made available under a Creative Commons Attribution license,linking to the relevant Creative Commons web page.
To the fullest extent permitted by applicable law, the article is made available as is andwithout representation or warranties of any kind whether express, implied, statutory orotherwise and including, without limitation, warranties of title, merchantability, fitness for aparticular purpose, non-infringement, absence of defects, accuracy, or the presence orabsence of errors.
The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use,distribution and reproduction in any medium, provided the original work is properly citedand is not used for commercial purposes.(see below)
The Creative Commons Attribution Non-Commercial-NoDerivs License (CC-BY-NC-ND)permits use, distribution and reproduction in any medium, provided the original work isproperly cited, is not used for commercial purposes and no modifications or adaptations aremade. (see below)
Use by non-commercial users
For non-commercial and non-promotional purposes, individual users may access, download,copy, display and redistribute to colleagues Wiley Open Access articles, as well as adapt,translate, text- and data-mine the content subject to the following conditions:
The authors' moral rights are not compromised. These rights include the right of"paternity" (also known as "attribution" - the right for the author to be identified assuch) and "integrity" (the right for the author not to have the work altered in such away that the author's reputation or integrity may be impugned).
Where content in the article is identified as belonging to a third party, it is theobligation of the user to ensure that any reuse complies with the copyright policies ofthe owner of that content.
If article content is copied, downloaded or otherwise reused for non-commercialresearch and education purposes, a link to the appropriate bibliographic citation(authors, journal, article title, volume, issue, page numbers, DOI and the link to thedefinitive published version on Wiley Online Library) should be maintained.Copyright notices and disclaimers must not be deleted.
Any translations, for which a prior translation agreement with Wiley has not beenagreed, must prominently display the statement: "This is an unofficial translation of an
article that appeared in a Wiley publication. The publisher has not endorsed thistranslation."
Use by commercial "for-profit" organisations
Use of Wiley Open Access articles for commercial, promotional, or marketing purposesrequires further explicit permission from Wiley and will be subject to a fee. Commercialpurposes include:
Copying or downloading of articles, or linking to such articles for furtherredistribution, sale or licensing;
Copying, downloading or posting by a site or service that incorporates advertisingwith such content;
The inclusion or incorporation of article content in other works or services (other thannormal quotations with an appropriate citation) that is then available for sale orlicensing, for a fee (for example, a compilation produced for marketing purposes,inclusion in a sales pack)
Use of article content (other than normal quotations with appropriate citation) byfor-profit organisations for promotional purposes
Linking to article content in e-mails redistributed for promotional, marketing oreducational purposes;
Use for the purposes of monetary reward by means of sale, resale, licence, loan,transfer or other form of commercial exploitation such as marketing products
Print reprints of Wiley Open Access articles can be purchased from:[email protected]
Further details can be found on Wiley Online Library http://olabout.wiley.com/WileyCDA/Section/id-410895.html
Other Terms and Conditions:
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