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PRIMARY RESEARCH ARTICLE
Asymmetric responses of primary productivity to precipitation
extremes: A synthesis of grassland precipitation manipulation
experiments
Kevin R. Wilcox
1
*
|
Zheng Shi
1
*
|
Laureano A. Gherardi
2
|
Nathan P. Lemoine
3
|
Sally E. Koerner
4
|
David L. Hoover
5
|
Edward Bork
6
|
Kerry M. Byrne
7
|
James Cahill Jr.
8
|
Scott L. Collins
9
|
Sarah Evans
10
|
Anna K. Gilgen
11
|
Petr Holub
12
|
Lifen Jiang
1
|
Alan K. Knapp
3
|
Daniel LeCain
5
|
Junyi Liang
1
|
Pablo Garcia-Palacios
13
|
Josep Pe~
nuelas
14,15
|
William T. Pockman
9
|
Melinda D. Smith
3
|
Shanghua Sun
16
|
Shannon R. White
17
|
Laura Yahdjian
18
|
Kai Zhu
19,20
|
Yiqi Luo
1
1
Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA
2
School of Life Sciences, Arizona State University, Tempe, AZ, USA
3
Department of Biology & Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA
4
Department of Integrative Biology, University of South Florida, Tampa, FL, USA
5
U.S. Department of Agriculture, Agriculture Research Service, Fort Collins, CO, USA
6
Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
7
Department of Environmental Science and Management, Humboldt State University, Arcata, CA, USA
8
Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
9
Department of Biology, University of New Mexico, Albuquerque, NM, USA
10
Department of Integrative Biology, Department of Microbiology and Molecular Genetics and Kellogg Biological Station, Michigan State University, Hickory
Corners, MI, USA
11
Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
12
Global Change Research Institute, Czech Academy of Sciences, Brno, Czech Republic
13
Area de Biodiversidad y Conservaci
on, Departamento de Biolog
ıa, Geolog
ıa, F
ısica y Qu
ımica Inorg
anica, Escuela Superior de Ciencias Experimentales y
Tecnolog
ıa, Universidad Rey Juan Carlos, M
ostoles, Spain
14
CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Catalonia, Spain
15
CREAF, Cerdanyola del Vall
es, Catalonia, Spain
16
College of Forestry, Northwest A & F University, Yangling, China
17
Environment and Parks, Government of Alberta, Edmonton, AB, Canada
18
Facultad de Agronom
ıa, Instituto de Investigaciones Fisiol
ogicas y Ecol
ogicas Vinculadas a la Agricultura (IFEVA), Consejo Nacional de Investigaciones
Cient
ıficas y T
ecnicas, Universidad de Buenos Aires, Buenos Aires, Argentina
19
Department of BioSciences, Rice University, Houston, TX, USA
20
Department of Biology, University of Texas, Arlington, TX, USA
Correspondence
Kevin Wilcox, Department of Microbiology
and Plant Biology, University of Oklahoma,
Norman, OK, USA.
Zheng Shi, Department of Microbiology and
Plant Biology, University of Oklahoma,
Norman, OK, USA.
Emails: wilcoxkr@gmail.com and zheng.
shi@ou.edu
Abstract
Climatic changes are altering Earth’s hydrological cycle, resulting in altered precipita-
tion amounts, increased interannual variability of precipitation, and more frequent
extreme precipitation events. These trends will likely continue into the future, hav-
ing substantial impacts on net primary productivity (NPP) and associated ecosystem
*Authors co-led this manuscript.
Received: 7 September 2016
|
Accepted: 23 February 2017
DOI: 10.1111/gcb.13706
Glob Change Biol. 2017;1–10. wileyonlinelibrary.com/journal/gcb ©2017 John Wiley & Sons Ltd
|
1
Funding information
Division of Emerging Frontiers, Grant/Award
Number: 1137293; National Institute of
Food and Agriculture, Grant/Award Number:
AFRI 2016-67012-25169; Cordis, Grant/
Award Number: SyG-2013-610028
IMBALANCE-P; Division of Environmental
Biology, Grant/Award Number: 1027319,
1456597
services such as food production and carbon sequestration. Frequently, experimental
manipulations of precipitation have linked altered precipitation regimes to changes
in NPP. Yet, findings have been diverse and substantial uncertainty still surrounds
generalities describing patterns of ecosystem sensitivity to altered precipitation.
Additionally, we do not know whether previously observed correlations between
NPP and precipitation remain accurate when precipitation changes become extreme.
We synthesized results from 83 case studies of experimental precipitation manipula-
tions in grasslands worldwide. We used meta-analytical techniques to search for
generalities and asymmetries of aboveground NPP (ANPP) and belowground NPP
(BNPP) responses to both the direction and magnitude of precipitation change. Sen-
sitivity (i.e., productivity response standardized by the amount of precipitation
change) of BNPP was similar under precipitation additions and reductions, but
ANPP was more sensitive to precipitation additions than reductions; this was espe-
cially evident in drier ecosystems. Additionally, overall relationships between the
magnitude of productivity responses and the magnitude of precipitation change
were saturating in form. The saturating form of this relationship was likely driven by
ANPP responses to very extreme precipitation increases, although there were lim-
ited studies imposing extreme precipitation change, and there was considerable vari-
ation among experiments. This highlights the importance of incorporating gradients
of manipulations, ranging from extreme drought to extreme precipitation increases
into future climate change experiments. Additionally, policy and land management
decisions related to global change scenarios should consider how ANPP and BNPP
responses may differ, and that ecosystem responses to extreme events might not
be predicted from relationships found under moderate environmental changes.
KEYWORDS
aboveground net primary productivity, belowground net primary productivity, biomass
allocation, climate change, grasslands, meta-analysis, root biomass
1
|
INTRODUCTION
Global warming has intensified many hydrological processes (Hunt-
ington, 2006), and general circulation models predict diverse
responses of the water cycle to climate change. These include
increases or decreases in precipitation amount depending on geo-
graphic region (Hartmann & Andresky, 2013; Zhang et al., 2007),
increased interannual variability of precipitation, and increased fre-
quency of extreme wet and dry years (Easterling et al., 2000;
Jentsch & Beierkuhnlein, 2008; Singh, Tsiang, Rajaratnam, & Diffen-
baugh, 2013; Smith, 2011), all of which will likely have large effects
on primary productivity (Breshears et al., 2005; Del Grosso et al.,
2008; Gherardi & Sala, 2015; Weltzin et al., 2003). It is especially
important to understand the magnitude of these impacts in grass-
lands, most of which are strongly water limited (Knapp, Briggs, &
Koelliker, 2001; Sala, Parton, Joyce, & Lauenroth, 1988), cover a
large proportion of the terrestrial land surface (Chapin, Chapin, Mat-
son, & Vitousek, 2011), and provide valuable ecosystem services
(e.g., forage production, soil C storage: Sala, Yahdjian, Havstad, &
Aguiar, 2017). Observational precipitation studies have shown robust
relationships between climatic context (e.g., mean annual precipita-
tion—MAP) and the sensitivity of ecosystems to altered precipitation
(i.e., the magnitude of change in production standardized by the
magnitude of precipitation change; Huxman et al., 2004; Sala, Gher-
ardi, Reichmann, Jobb
agy, & Peters, 2012; Guo et al., 2012). Yet,
findings from individual experiments often conflict with these broad
patterns (Byrne, Lauenroth, & Adler, 2013; Cherwin & Knapp, 2012;
Koerner & Collins, 2014; White, Cahill, & Bork, 2014; Wilcox, Blair,
Smith, & Knapp, 2016; Wilcox, Fischer, Muscha, Petersen, & Knapp,
2015), highlighting the need for synthesis across experiments (Car-
penter et al., 2009; Knapp et al., 2004).
Most existing knowledge concerning patterns of ecosystem sen-
sitivity to precipitation is based on aboveground net primary produc-
tivity (ANPP) data, even though belowground net primary
productivity (BNPP) represents a large proportion of NPP in many
grasslands (Sims & Sing, 1978). Furthermore, recent evidence sug-
gests that BNPP responses to altered precipitation are often differ-
ent in magnitude from those of ANPP (Byrne et al., 2013; Wilcox
2
|
WILCOX ET AL.
et al., 2015). Existing theory states that plants shift biomass alloca-
tion (above- vs. belowground) depending on soil resource availability
(Bloom, Chapin, & Mooney, 1985; Gao, Chen, Lin, Giese, & Brueck,
2011; Giardina, Ryan, Binkley, & Fownes, 2003). If soil moisture
decreases due to drought, plants may increase allocation of carbohy-
drates to roots to maximize resource uptake, thus minimizing BNPP
loss while exacerbating ANPP loss. Alternately, if soil moisture
increases due to high precipitation levels, plants may allocate growth
aboveground to maximize light capture, resulting in larger responses
above- vs. belowground. Under this framework, we would predict
allocation patterns to offset BNPP increases and decreases under
increased and decreased precipitation, respectively. If generalizable,
these allocation patterns should lead to higher ANPP sensitivity than
BNPP. Empirical evidence for optimal allocation theory concerning
soil nutrients is abundant (McConnaughay & Coleman, 1999; Poorter
& Nagel, 2000; Poorter et al., 2012), whereas a smaller number of
studies have shown such allocation responses under altered soil
moisture (Milchunas & Lauenroth, 2001; Wilcox, Blair, & Knapp,
2016). However, some experimental evidence has shown BNPP to
be more sensitive than ANPP to changes in precipitation (Frank,
2007; Wilcox et al., 2015).
Another critical knowledge gap is whether the sensitivity of net
primary productivity (ANPP+BNPP) differs under precipitation
increases vs. decreases. Knapp and Smith (2001) showed that ANPP
responded more strongly in wet vs. dry years, and they posited that
this was due to drought tolerance mechanisms of resident plants.
Yet, we lack similar information for BNPP responses; currently, our
synthetic knowledge of BNPP responses to altered precipitation in
grasslands consists of a few experiments conducted across 2–3 sites
(e.g., Byrne et al., 2013; Fiala, Tuma, & Holub, 2009; Wilcox et al.,
2015), and portions of two meta-analyses with limited numbers of
studies documenting BNPP responses (Wu, Dijkstra, Koch, Penuelas,
& Hungate, 2011; Zhou et al., 2016). Recently, a number of addi-
tional grassland precipitation studies have reported BNPP responses
in individual ecosystems, and this presents an opportunity to exam-
ine and identify trends of BNPP responses to increased and
decreased precipitation amounts across studies.
As precipitation extremes such as widespread drought (e.g., Mid-
western United States in 2012) and high precipitation years become
more frequent (IPCC, 2013), understanding patterns of ecosystem
responses in extreme wet and dry years will be vital for assessing
future provisioning of ecosystem services. Currently much of our
knowledge comes from ecosystem responses to naturally occurring
climatic variation (Huxman et al., 2004; Knapp, Ciais, & Smith, 2016;
Knapp & Smith, 2001; La Pierre, Blumenthal, Brown, Klein, & Smith,
2016), or from experiments implementing mild-to-moderate alter-
ations relative to the inherent interannual variation at the site (e.g.,
Miranda, Armas, Padilla, & Pugnaire, 2011; Cherwin & Knapp, 2012;
Byrne et al., 2013; Koerner & Collins, 2014; Wilcox et al., 2015; all
sensu Knapp et al., 2015). Extreme precipitation manipulations are
more rare (Evans & Burke, 2013; Hoover, Knapp, & Smith, 2014;
Yahdjian & Sala, 2006), and syntheses of extreme precipitation
experiments are even more uncommon. Understanding whether
ecosystem responses to mild/moderate precipitation change are pre-
dictive of ecosystem responses to larger magnitude precipitation
changes is necessary to assess and update projections of future
ecosystem functioning under climate change scenarios.
We synthesized results from 83 experimental case studies that
measured ANPP and/or BNPP responses to manipulated precipita-
tion amounts to address these knowledge gaps. Precipitation alter-
ations in these case studies ranged in magnitude from –86% to
+431% relative to control plots. We used meta-analytical techniques
with this compiled data set to test the following hypotheses: (1)
BNPP is less sensitive than ANPP to altered precipitation amount;
(2) both ANPP and BNPP have greater sensitivity to increased vs.
decreased precipitation; (3) ANPP and BNPP sensitivities vary across
temperature and precipitation gradients; and (4a) ANPP and BNPP
responses to precipitation change are linear across the magnitude of
precipitation change; or (4b) ANPP and BNPP responses to precipita-
tion change are saturating across precipitation magnitudes. Com-
pared with a linear relationship, a saturating relationship would
indicate larger responses to extreme drought and lesser responses to
extreme precipitation increases (Knapp et al., 2016). Assessment of
these hypotheses is integral for assessing climate change impacts on
ecosystem services across larger spatial scales, as well as identifying
where/when impacts of climatic extremes are likely to be severe.
2
|
MATERIALS AND METHODS
2.1
|
Data compilation
We collected publications that reported on primary productivity
responses to experimental precipitation manipulations in grassland
ecosystems by searching Web of Science. This included both
increased (+PPT) and decreased (PPT) precipitation treatments. We
used the following search terms to obtain papers from January 1st,
1900, to November 14th, 2016: (“plant growth”OR “primary pro-
duct*”OR “plant product*”OR “ANPP”OR “BNPP”) AND (“altered
precipitation”OR “drought”OR “decreased precipitation”OR “in-
creased precipitation”OR “increased summer precipitation”OR “de-
creased summer precipitation”OR “water addition”OR “water
reduction”OR “water treatment*”) AND (“herbaceous”OR “grass*”)
AND (“experiment*”OR “treatment*”). The search resulted in 322
peer-reviewed papers. We then went through these papers and
removed all that did not meet the following criteria:
1. Study described a unique experiment. In the case of multiple
publications of the same responses, we used the latest pub-
lished paper. However, if the newest paper did not present
annual responses, we used the most recent paper presenting
annual data.
2. Plant communities were not artificially constructed, with the
exception of species assemblages planted to approximate com-
munity abundances of a natural study site.
3. Experiment was conducted in the field, or using monolith plots
in a greenhouse.
WILCOX ET AL.
|
3
4. Treatment was consistent in all years.
5. Raw productivity values were reported (not just proportional
change, or biomass with woody species).
6. Productivity was measured <2 months after treatment stopped.
7. Total community productivity was reported (not just species
productivity).
8. Reported primary productivity in mass per area units.
9. A control precipitation treatment was present, and replication
was greater than one.
10. Reported the amount or proportion of precipitation change.
11. Reported the standard deviation or standard error and sample
size.
We also added multiple studies fitting these criteria obtained
via personal communications and from literature cited sections of
published papers. Production responses were excluded when ANPP
incorporated previous year woody growth or if belowground stand-
ing crop root biomass was measured instead of BNPP in all peren-
nial ecosystems. We limited our analyses to results from plots that
solely manipulated precipitation—results from plots receiving pre-
cipitation combined with other resource manipulations were
excluded. We compiled annual means, standard deviations, and
sample sizes of ANPP and BNPP from the literature or directly
from the authors. We also compiled mean annual temperature
(MAT), mean annual precipitation (MAP), and the amount and/or
proportion of precipitation added or subtracted in each year of the
study, obtained from the papers or authors. When studies reported
results from experiments conducted in different locations or having
multiple distinct treatments, these components were treated as
individual case studies. In total, our meta-analysis included 47 pub-
lished papers providing 83 precipitation manipulation case studies.
Most (62 of the 83) of the case studies occurred in North America
and Europe (Table S1). See Table 1 for summary information
regarding the compiled data set and Text S1 for a bibliography of
the papers used.
2.2
|
Calculating sensitivity for meta-analysis
We employed a meta-analytic approach to assess the overall sensi-
tivity of ANPP and BNPP to altered precipitation (Hedges, Gurevitch,
& Curtis, 1999; Luo, Hui, & Zhang, 2006). Sensitivity (Sens) was cal-
culated to represent the magnitude of response relative to the
amount of precipitation change, as previously used by others (e.g.,
Huxman et al., 2004; Knapp et al., 2016; Sala et al., 2012; Smith,
Wilcox, Power, Tissue, & Knapp, 2017; Wilcox, Blair, Smith, et al.,
2016; Wu et al., 2011). The benefit of this calculation is that ecosys-
tem responses are made comparable by standardizing by the magni-
tude of precipitation change:
Sens ¼
Xc
Xt
PPTcPPTt
(1)
where
Xtand
Xcare the productivity means across replicates of
treatment and control groups, respectively, and PPT
t
and PPT
c
are
the precipitation amounts in treatment and control groups, respec-
tively. A variance (v
sens
) associated with sensitivity was approximated
using Equation (2).
vsens ¼1
PPTcPPTt
2
sc2þst2
(2)
where s
t
and s
c
are standard deviations of treatment and control
groups, respectively. We validated our calculated variance using
Monte Carlo simulations (Text S2).
We aggregated sensitivity across studies by calculating a
weighted sensitivity estimate, similar to how previous meta-analyses
have aggregated response ratios (Hedges et al., 1999; Luo et al.,
2006). We calculated the weighted sensitivity (Sens
++
) as:
Sensþþ ¼Pk
i¼1wiSensi
Pk
i¼1wi
(3)
where wis the weighting factor (w¼1
vsens) and kis the number of
studies. Standard error (SE) associated with Sens
++
was computed
using Equation (4).
SE Sensþþ
ðÞ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
Pk
i¼1wi
s:(4)
Significance of Sens
++
was assessed using a mixed-effects meta-
analytic model where individual studies are weighted by the follow-
ing equation:
w¼1
s2þvsens
(5)
where wis the weighting factor for an individual study, sis the
amount of variability not accounted for using the existing parameters
in the model, and v
sens
is the study variance as calculated from Equa-
tion (2). Many of the experiments were conducted over multiple
years, and responses often varied interannually due, in large part, to
year-to-year variation in ambient rainfall. To account for this, case
study was designated as a random effect within the mixed-effects
model to account for pseudoreplication originating from studies
spanning multiple years.
TABLE 1 Summary information for experimental precipitation addition (+PPT) and reduction (PPT) treatments included in the meta-
analysis
Avg.
DPPT (%)
Range
DPPT (%)
Avg. duration
(year)
Range duration
(year)
Avg.
MAP (mm)
Range
MAP (mm)
Avg.
MAT (°C)
Range
MAT (°C)
+PPT 43.1 1.9–431 3.2 1–23 551 161–1526 7.5 4.8–16.3
PPT 48.7 18.1–86.0 2.0 1–4 572 168–1632 10.4 1.6–22.0
All 49.7 86–431 2.7 1–23 554 161–1632 8.7 4.8–22.0
DPPT, percent change of precipitation manipulation relative to control plots; MAP, mean annual precipitation; MAT, mean annual temperature.
4
|
WILCOX ET AL.
2.3
|
Sensitivity vs. climatic factors
To assess patterns of sensitivity across climatic gradients, we aver-
aged sensitivity values across years for ANPP or BNPP under
increased or decreased precipitation treatments within each case
study. This resulted in up to four sensitivity calculations per case
study, which occurred if a study measured ANPP and BNPP and
imposed both precipitation additions and reductions (one sensitivity
value each for PPT+ANPP, PPT- ANPP, PPT+BNPP, and PPT-
BNPP). Linear and various nonlinear models were compared using
AIC values to determine the most appropriate model structure for
correlating sensitivity with MAP and MAT (see Table S2 for identity
and form of relationships tested).
2.4
|
Magnitude of precipitation change vs.
magnitude of ANPP or BNPP response
Because the percentage change of precipitation varies in most stud-
ies from year to year, depending on ambient precipitation, we
assessed relationships between the percentage precipitation change
and the percentage productivity response for each year of each case
study. First, we did this to determine whether relationships between
the magnitude of production response and the magnitude of precipi-
tation change differed for ANPP vs. BNPP. We also determined
whether this relationship differed for precipitation additions vs. sub-
tractions. To this end, we calculated the percentage precipitation
change—%DPPT =(PPT
t
- PPT
c
)/PPT
c
, percentage productivity
response for ANPP—%DANPP =(ANPP
t
- ANPP
c
)/ANPP
c
, and
BNPP—%DBNPP =(BNPP
t
- BNPP
c
)/BNPP
c
. We used percentage
change for this analysis—instead of the raw amount of precipitation
change—because percentage change is comparable across ecosys-
tems spanning climatic gradients, whereas the absolute amount of
precipitation change may have very different implications in dry vs.
wet sites. We constructed a mixed-effects weighted-estimation
metaregression model (van Houwelingen, Arends, & Stijnen, 2002)
(Equation 5), with case study as a random factor, to look for signifi-
cant interaction terms between productivity type (ANPP vs BNPP)
and %DPPT as well as between treatment (increased vs decreased
precipitation) and %DPPT.
Second, we looked at whether these relationships were linear or
saturating. We did this through AIC comparisons of linear and natu-
ral log transformed (Table S4) models relating %DPPT with %DANPP
or %DBNPP. We again weighted the regressions using mixed-effects
weighted-estimation metaregression models. If the more appropriate
model is linear, this suggests that ecosystem responses to precipita-
tion extremes are proportional to their responses to mild or moder-
ate alterations in precipitation (i.e., levels of precipitation change
similar to those commonly found in historical precipitation records;
Knapp et al., 2015). If the more appropriate model is saturating, pri-
mary production responses under precipitation extremes may not
conform to patterns assessed under milder precipitation change.
Intercepts for %DPPT-%DANPP (or %DBNPP) regressions were set
at zero because, in an experimental framework, %DANPP (or %
DBNPP) should be zero when %DPPT is zero. Five outliers were
removed from the increased precipitation vs. %DANPP using a
threshold of a=0.05 (r-student: 6.29, 5.62, 4.36, 3.95, 3.80; all Bon-
ferroni p<.05). Results were qualitatively similar when these points
were included (Table S5). We did not detect publication bias when
examining plots showing the observed effect size and study variance
(funnel plots; Sterne & Egger, 2001).
All analyses were conducted in R(R Core Team, 2016), and
mixed-effects models were conducted using the NLME package
(Viechtbauer, 2010).
3
|
RESULTS
Across all studies, we found that the sensitivity of ANPP and BNPP
to both precipitation increases and decreases was greater than zero
(Figure 1; ANPP+:F
1,71
=32.2, p<.01; ANPP-: F
1,39
=36.7,
p<.01; BNPP+:F
1,25
=5.71, p=.02; BNPP-: F
1,10
=6.97, p=.02).
ANPP sensitivity to increased precipitation was 147% greater than
ANPP sensitivity to decreased precipitation (z=3.0, Tukey-adj.
p=.01). In contrast, BNPP sensitivity to precipitation increases and
decreases was not significantly different (z=1.8, Tukey-adj.
p=.28). Sensitivity to increased precipitation was 118% greater for
ANPP than BNPP (z=3.4, Tukey-adj. p<.01), but sensitivity to
decreased precipitation was not significantly different between
ANPP and BNPP (z=1.5, Tukey-adj. p=.44).
3.1
|
Precipitation sensitivity and background
climate
The broad range of MAP and MAT of sites used in this meta-analysis
(Table 1) allowed us to examine patterns of precipitation sensitivity
FIGURE 1 Sensitivity of aboveground net primary productivity
(ANPP) and belowground net primary productivity (BNPP)
aggregated across experiments simulating increased (filled circles)
and decreased (open circles) precipitation. Sensitivity is calculated as
the amount of productivity response divided by the amount of
precipitation change. Numbers above symbols represent the number
of studies incorporated in each estimate. Different letters represent
different sensitivity at a=0.05, and error bars represent one
standard error from the mean
WILCOX ET AL.
|
5
across large climatic gradients. We first tested whether sensitivity-
MAP and sensitivity-MAT relationships varied between precipitation
increases and decreases. We found that the sensitivity-MAP rela-
tionship was marginally different under precipitation increases vs.
decreases (F
1,85
=3.82, p=.05; different trend lines in Figure 2a).
The sensitivity of ANPP to precipitation additions was higher in arid
sites than in mesic sites. We found no relationship between MAP
and sensitivity of ANPP to precipitation reduction treatments (Fig-
ure 2a, Table S2). BNPP sensitivity-MAP relationships were not dif-
ferent for precipitation increases vs. decreases (F
1,29
=0.18, p=.67),
and sensitivity of BNPP to precipitation manipulations generally
decreased with MAP (Figure 2c). We did not find a significant inter-
action between MAT-sensitivity and precipitation increases vs.
decreases for ANPP (F
1,85
=0.04, p=.84) or BNPP (F
1,29
=0.85,
p=.36). We did not find a significant relationship between MAT
and ANPP sensitivity (Table S2), while BNPP sensitivity was greater
in colder sites (Figure 2b, d). See Table S2 for information about the
form, coefficients, and selection of each regression.
3.2
|
Comparing %DPPT and %DNPP linear vs.
saturating relationships
In our full models comparing the percentage change of productivity
(%DNPP) vs. percentage change of precipitation (%DPPT), we found
the natural log transformed model was a better fit than the linear
model (linear model AIC: 177.7, natural log model AIC: 175.2;
Table S3). Within the full natural log model, we found significant
interactions between %DPPT and precipitation direction
(precipitation increases vs. decreases) and between %DPPT and pro-
ductivity type (ANPP vs BNPP; Table S3). This was due to steeper %
DPPT-%DNPP slopes for ANPP (0.24 0.11; slope standard
error) vs BNPP (0.15 0.07) and for precipitation increases
(0.59 0.17) vs. decreases (0.22 0.07). Additionally, we found
significant interactions between %DPPT and precipitation direction
for both ANPP and BNPP analyzed separately (Table S3). These
interactions indicated that relationships between the magnitude of
productivity response and %DPPT may vary between ANPP and
BNPP as well as under precipitation increases vs. decreases.
We then analyzed %DPPT-%DNPP relationships separately for
ANPP and BNPP under precipitation increases and decreases to
assess whether linear or saturating (natural logarithmic) models bet-
ter fit the data for each category. We found that the saturating
model was a better fit for ANPP under precipitation increases (linear
model AIC 47.1 vs ln model AIC 44.6; Figure 3; Table S4). The bet-
ter fit of the saturating model was maintained even after removal of
the point having very large %DPPT, but the AIC differentiation was
weaker (Table S5). For ANPP under precipitation decreases and
BNPP under precipitation increases and decreases, we found weak
or no evidence for saturating models as a better fit to the data (Fig-
ure 3; Table S4).
4
|
DISCUSSION
Plant growth accounts for a large fraction of the terrestrial carbon
cycle and acts as an important buffer against fossil fuel emissions (Le
FIGURE 2 Relationships between site-
level climate and sensitivity of ANPP (a, b)
and BNPP (c, d) to increased (filled circles)
and decreased (open circles) precipitation
treatments (Trt). Climatic variables tested
were mean annual precipitation (a, c) and
mean annual temperature (b, d). Trendlines
in (a) are split into precipitation increases
and decreases because slopes were
significantly different. Trendlines in (b–d)
represent overall regressions because
sensitivity-MAP or MAT relationships were
not different between increased and
decreased precipitation treatments
(nonsignificant Trt 9MAP or Trt 9MAT
interactions). Relationships without
trendlines and the dotted trendline in (a)
are not significant at a=0.1
6
|
WILCOX ET AL.
Qu
er
e et al., 2015). Making accurate assessments of future carbon
budgets depends upon understanding influences of altered precipita-
tion on primary productivity. Fortunately, many recent precipitation
studies have documented responses of various components of NPP,
allowing for synthesis to identify key general patterns of herbaceous
responses to precipitation change. We found that sensitivity to pre-
cipitation change often differed between ANPP and BNPP, and
depended on whether precipitation was increased or decreased. We
also found that drier and cooler sites had higher sensitivity to precip-
itation change, especially to precipitation additions. Lastly, we found
evidence that productivity responses to increased precipitation may
saturate under very wet conditions. In the paragraphs below, we dis-
cuss implications and potential mechanisms underlying these find-
ings.
4.1
|
Overall ANPP vs. BNPP sensitivity to
precipitation change
BNPP was less sensitive than ANPP to increased precipitation treat-
ments (Figure 1), coinciding with previous work (Wu et al., 2011).
One interpretation is that root:shoot plasticity may be strong under
wet conditions (Knapp, 1984) and may result in decreased root allo-
cation to facilitate greater light capture during periods of high soil
resources (Joslin & Wolfe, 1998). Additionally, saturated soil mois-
ture conditions may limit root development (Kozlowski, 1997). These
changes in allocation may limit increased BNPP under increased pre-
cipitation, as well as heighten ANPP responses, compared with over-
all NPP responses. Secondly, longevity of live roots is typically
greater under moderately wet soil conditions (Facette, McCully, &
Canny, 1999), which may reduce the need/space for increased BNPP
to replace root systems under wetter conditions (Hayes & Seastedt,
1987). However, under extremely wet conditions, root lifespan can
decrease (Kozlowski, 1997), which may result in a threshold
response at a certain magnitude of precipitation increase. The
methodology used to measure roots in most of these studies (root
ingrowth cores) would likely not detect this second mechanism as
competition for space is not typically a factor for roots growing in
root ingrowth cores during much of the growing season. For this
reason, we suggest that plasticity in root:shoot allocation may be the
important factor driving different above vs. belowground productiv-
ity responses to increased precipitation observed in this study. Con-
versely, we found that ANPP and BNPP sensitivities to drought
were similar in magnitude. This may be due to a limitation of carbo-
hydrates available for growth (and thus allocation shifts) during peri-
ods of low soil moisture in drought treatments. In the early portion
of the growing season, soil moisture is often high in both drought
and control conditions due to winter inputs and low evaporation
rates occurring with cooler spring temperatures. However, as soil
moisture is depleted later in the growing season and drought effects
become more evident (Denton, Dietrich, Smith, & Knapp, 2016), car-
bohydrates may be similarly deficient for both root and aboveground
growth, which may limit the potential for changes in allocation
above- or belowground.
4.2
|
PPT sensitivity across climatic gradients
Previous observational studies have assessed patterns of climatic
context (e.g., MAP) vs. the sensitivity of primary production to
altered precipitation amount by examining the slope between pri-
mary production and annual precipitation (Huxman et al., 2004; Sala
et al., 2012). We used a similar sensitivity metric to assess whether
similar patterns exist for ANPP and BNPP based on experimental
data. We found the sensitivity of ANPP and BNPP to altered precipi-
tation was negatively related to MAP (Figure 2a, c), coinciding with
these observational studies (Huxman et al., 2004; Sala et al., 2012).
To our knowledge, this pattern has not been previously identified
through synthesis of experimental findings. Wu et al. (2011) found
no relationship between sensitivity and MAP, potentially because
they limited their analysis to linear regression, a relationship we
found to be substantially less predictive than the negative exponen-
tial relationship shown in Figure 2 (Table S2). The nonlinearity of the
ANPP sensitivity-MAP relationship (Figure 2a, c) highlights the
importance of understanding precipitation impacts in more xeric
FIGURE 3 Relationships between percentage responses of (a)
ANPP or (b) BNPP and the magnitude of experimental precipitation
manipulation (DPPT; increased (filled circles and decreased (open
circles)). Circle sizes are inversely correlated with the estimated
sampling variance of the percentage response of productivity, which
was used to weight points within the metaregression (i.e., larger
circles influence the regression more, see Methods). In panel (a), the
far right point is included in the regression, but results are
qualitatively similar when this point is removed (Table S5)
WILCOX ET AL.
|
7
ecosystems, due to their potential for much higher sensitivity to pre-
cipitation increases than more mesic systems. We found no signifi-
cant relationship between MAP and ANPP sensitivity to drought.
The different sensitivities to precipitation increases vs. decreases in
arid ecosystems may be due to buffering capacity of drought toler-
ant plant traits (Knapp & Smith, 2001) possessed by dominant plant
species in these ecosystems.
We found BNPP was generally less sensitive to precipitation
changes in warmer ecosystems. This may be due to longer residence
times of added soil moisture in cooler sites, resulting in a higher pro-
portion of soil water being utilized by plants vs. being evaporated
directly from the soil and cooler sites having higher water use effi-
ciency (Vermeire, Heitschmidt, & Rinella, 2009). In addition, this
could be driven by deeper rooting profiles in cooler, high latitude
sites. Root growth tends to occur more homogenously throughout
the soil profile due to more homogenous soil moisture levels across
soil depths (e.g., Schenk & Jackson, 2002; Wilcox et al., 2015), so it
may be that the additional soil depths available for root production
in cooler systems leads to greater BNPP sensitivity to water addi-
tions.
4.3
|
ANPP and BNPP responses across magnitudes
of precipitation manipulation
If relationships between %DPPT and %DNPP are nonlinear and satu-
rating, then using linear models from historical precipitation-produc-
tivity regressions will not accurately predict the impacts of extreme
drought or extreme precipitation increases (Knapp et al., 2016). In
our full models, we found that the saturating model relating %DPPT
and %DANPP/BNPP was a better fit to the data than the linear
model (Table S3). Past observational studies have looked for, but
have not been able to identify, nonlinear patterns of primary produc-
tivity and precipitation change through site-level historical records of
ANPP and annual precipitation (Hsu & Adler, 2014). This may stem
from the fact that, by definition, years having extreme precipitation
amounts occur very infrequently in the historical record. For exam-
ple, Hoover et al. (2014) examined a 27-year ANPP-precipitation
data set from the Konza Prairie Biological Station, and in the context
of a 111-year precipitation record from this same area, found only
one year of ANPP data that was linked with extreme precipitation.
This highlights the value of climate change experiments for quantify-
ing future ecosystem responses under novel climatic conditions, as
experimental manipulations are able to push systems beyond histori-
cal climatic limits within sites (e.g., Evans, Byrne, Lauenroth, & Burke,
2011; Zhu, Chiariello, Tobeck, Fukami, & Field, 2016).
The nature of the overall saturating relationship could be driven
by (1) lower magnitude of productivity responses under extreme pre-
cipitation increases, (2) greater magnitude of productivity responses
under extreme drought, or (3) both. When we analyzed %DANPP vs
%DPPT separately for +PPT and –PPT, we only found convincing
evidence for a saturating relationship for +PPT (Table S3; Figure 3).
We did not find that the saturating curve was a substantially better
fit for %DBNPP under +PPT or –PPT. We think this may be due to a
few factors. First, the range of %DPPT was much greater for studies
increasing precipitation and measuring ANPP, so perhaps saturating
relationships are only evident under very extreme changes in precipi-
tation (Knapp et al., 2016). Second, perhaps extreme drought
impacts require multiple successive years of precipitation reductions
to fully develop (Hoover et al., 2014) due to depletion of soil water
or carbohydrate reserves. The majority (51 of 83; Table S6) of our
case studies were only 1–2 years in length, which may be why we
failed to detect logarithmic relationships under drought alone—even
though we included a number of experiments with large drought
magnitudes (Table 1). Third, extreme heat waves often co-occur with
extreme drought during real-world climatic extremes. This is likely to
cause larger productivity responses than typically found in single fac-
tor drought experiments through further depletion of soil moisture
(Hoover et al., 2014).
We found substantial variation surrounding the trends shown in
Figures 2 and 3. Much of the variation seen in these relationships
may stem from cross-site variation of nonclimate characteristics,
such as soil texture, soil fertility, plant species composition, fire
regime, or presence/absence of grazing. For example, nitrogen limita-
tion may constrain a site’s sensitivity to increased precipitation (Lad-
wig et al., 2012), or drought tolerant plant species may reduce
sensitivity of an ecosystem to changes in water availability (Wilcox,
Blair, Smith, et al., 2016). Unfortunately, many studies did not report
sufficient site-level characteristics for robust assessment of these
factors as drivers of ecosystem sensitivity to precipitation change.
We encourage future precipitation studies to report ecosystem char-
acteristics such as soil available nutrients, soil texture, and plant spe-
cies/functional composition. We also see considerable value in
conducting experiments within single sites manipulating a gradient of
precipitation levels—ranging from extreme precipitation increases to
extreme precipitation decreases—while controlling for other vari-
ables that may affect sensitivity (e.g., Gherardi & Sala, 2015; Luo,
Jiang, Niu, & Zhou, 2017).
To provide accurate projections of how ecosystems will respond
to future precipitation scenarios, generalities informing patterns of
precipitation impacts on ecosystem function are needed. Using
meta-analytic methods, we explored overall ANPP and BNPP sensi-
tivity to precipitation change, the climatic context of sensitivity, and
how patterns of primary productivity change as precipitation
changes become extreme. First, we suggest that shifts in allocation
of biomass above- vs. belowground may lower NPP during high rain-
fall years, compared with expectations based on ANPP responses
alone. Second, we identified drier ecosystems as being especially
sensitive to precipitation increases, while cooler ecosystems were
somewhat more sensitive to any changes in precipitation. Lastly, we
found that previously identified asymmetries—showing greater pro-
ductivity responses in wet vs. dry years (Knapp & Smith, 2001)—
may be reversed when precipitation alterations become very
extreme. In the future, we advocate for (1) increased attention to
BNPP responses to extreme precipitation changes, and (2) more
long-term experiments that implement multiple levels of increased
and decreased precipitation amount.
8
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WILCOX ET AL.
ACKNOWLEDGEMENTS
Thanks to B. Hungate and two anonymous reviewers for their
insightful input. Also, we would like to thank all the principal investi-
gators, graduate students, technicians, etc. who contributed to the
precipitation experiments used in this analysis, thus making this
study possible. We would like to acknowledge all the funding agen-
cies supporting past, current, and future climate change experiments
including DEB-1456597, DEB-1027319, ERC SyG-2013-610028
IMBALANCE-P, USDA NIFA-AFRI 2016-67012-25169, and the NSF
Macrosystems Biology funded Extreme Drought in Grasslands
(EDGE) project.
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SUPPORTING INFORMATION
Additional Supporting Information may be found online in the sup-
porting information tab for this article.
How to cite this article: Wilcox KR, Shi Z, Gherardi LA, et al.
Asymmetric responses of primary productivity to precipitation
extremes: A synthesis of grassland precipitation manipulation
experiments. Glob Change Biol. 2017;00:1–10.
https://doi.org/10.1111/gcb.13706
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