\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,greek,english]{babel}



\usepackage{float}








\begin{document}

\title{Nitrogen rather than water availability limits aboveground primary
productivity in an arid ecosystem: substantial differences between
grasses and shrubs}



\author[1]{Sandra  Bucci}%
\author[1]{Luisina Carbonell Silletta}%
\author[1]{Fabian Scholz}%
\author[1]{Antonella Burek}%
\author[2]{Virginia Diaz Villa}%
\author[1]{Agustín Cavallaro}%
\author[3]{Javier O. Askenazi}%
\author[1]{Nadia Arias}%
\author[4]{Guangyou Hao}%
\author[2]{Guillermo  Goldstein}%
\affil[1]{Instituto de Biociencias de la Patagonia (INBIOP) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and Universidad Nacional de la Patagonia San Juan Bosco (UNPSJB}%
\affil[2]{Laboratorio de Ecología Funcional Instituto de Ecología Genética y Evolución de Buenos Aires}%
\affil[3]{Centro de Investigación y Transferencia del Golfo San Jorge (CONICET – UNPSJB}%
\affil[4]{Institute of Applied Ecology Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Changes in water and nitrogen availability can affect the structure and
function of arid ecosystems. How these resources affect aboveground
primary productivity (ANPP) remains far from clear. We examined the N
and water limitation of ANPP from the species to the community level and
the response of ANPP to annual precipitation in a Patagonian steppe. We
conducted a 7-year field experiment with water addition (+W), nitrogen
addition (+N), and +NW. Destructive methods for grasses and allometric
relationships for shrubs were used to assess ANPP and vegetation indices
(NDVI and MSAVI2) to estimate community ANPP. An increase in ANPP of one
grass species ( \emph{Papposstipa humilis}) and a decrease of the grass
\emph{Poa ligularis} under +N were observed. Some shrubs species
exhibited mortality under nitrogen addition. Nitrogen exerted a positive
effect on grass ANPP and amplified the sensitivity of grass ANPP to
annual precipitation. However, +N had not effects on the shrub ANPP and
shrub ANPP-precipitation relationship. Water addition by itself had no
effect on ANPP for either shrubs or grasses. However, shrubs responded
positively to an unusually wet year regardless of treatment and were
also more sensitive to changes in annual precipitation than grasses.
Total ANPP increased significantly in +N relative to the C and +W, but
without changes in the sensitivity to annual precipitation. The results
suggest that the responses of grasses and shrubs to water inputs is
driven by soil moisture redistribution and rooting depth and that grass
and community ANPP is more limited by N than by water.%
\end{abstract}%



\sloppy


Nitrogen rather than water availability limits aboveground primary
productivity in an arid ecosystem: substantial differences between
grasses and shrubs

Short title: Aboveground primary productivity in response to nitrogen
and water addition

Luisina Carbonell-Silletta\textsuperscript{ab}, Fabian Gustavo Scholz
\textsuperscript{ab}, Antonella Burek \textsuperscript{ab}, Virginia
Diaz Villa\textsuperscript{c}, Agustin Cavallaro \textsuperscript{ad},
Javier Oscar Askenazi \textsuperscript{e}, Nadia Soledad
Arias\textsuperscript{ab}, Guang-You Hao \textsuperscript{f}, Guillermo
Goldstein \textsuperscript{cg}, Sandra Janet Bucci \textsuperscript{ab}

a Grupo de Estudios Biof\selectlanguage{ngerman}ísicos y Ecofisiológicos (GEBEF), Instituto de
Biociencias de la Patagonia (INBIOP), Consejo Nacional de
Investigaciones Científicas y Técnicas (CONICET) and Universidad
Nacional de la Patagonia San Juan Bosco (UNPSJB), (9000) Comodoro
Rivadavia, Argentina

b Facultad de Ciencias Naturales y Ciencias de la Salud, UNPSJB, (9000)
Comodoro Rivadavia, Argentina

c Laboratorio de Ecología Funcional. Instituto de Ecología, Genética y
Evolución de Buenos Aires. UBA -- CONICET (1425) Buenos Aires, Argentina

d INTA EEAf Esquel, Esquel, Chubut, Argentina

e Centro de Investigación y Transferencia del Golfo San Jorge (CONICET
-- UNPSJB), (9000), Comodoro Rivadavia, Chubut

f CAS Key Laboratory of Forest Ecology and Management \& Key Laboratory
of Terrestrial Ecosystem Carbon Neutrality, Liaoning Province, Institute
of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

g Department of Biology, University of Miami, Coral Gables, Florida, USA

Author for correspondence: Sandra Janet Bucci

Email: sj\_bucci@yahoo.com

Funding: This study was funded by Fondo para la Promoción Científica y
Tecnológica (FONCyT; grant PICT 2010-960, PICT 2013-2426, PICT
2016-3019, PICT 2019-1306) and CONICET (PUE INBIOP 0033).

Acknowledgment: We thank to staff of Instituto Nacional de Tecnología
Agropecuaria (INTA) for allowing the access and assistance in the Rio
Mayo Experimental Field. This work complies with Argentinian law.

Data and code availability: The datasets generated and analyzed and the
R code generated during this study are available from
https://osf.io/cmj4b/.

Author Contributions: FGS, SJB and GG conceived and designed the
experiments. LCS, FGS, SJB, AB, AC and JOA conducted fieldwork. VDV
performed imaging analysis. LCS performed statistical analyses. LCS, SJB
and GG wrote the manuscript. GYH and NSA contributed to the discussion
of the results.

Abstract

Changes in water and nitrogen availability can affect the structure and
function of arid ecosystems. How these resources affect aboveground
primary productivity (ANPP) remains far from clear. We examined the N
and water limitation of ANPP from the species to the community level and
the response of ANPP to annual precipitation in a Patagonian steppe. We
conducted a 7-year field experiment with water addition (+W), nitrogen
addition (+N), and +NW. Destructive methods for grasses and allometric
relationships for shrubs were used to assess ANPP and vegetation indices
(NDVI and MSAVI2) to estimate community ANPP. An increase in ANPP of one
grass species (\emph{Papposstipa humilis} ) and a decrease of the
grass\emph{Poa ligularis} under +N were observed. Some shrubs species
exhibited mortality under nitrogen addition. Nitrogen exerted a positive
effect on grass ANPP and amplified the sensitivity of grass ANPP to
annual precipitation. However, +N had not effects on the shrub ANPP and
shrub ANPP-precipitation relationship. Water addition by itself had no
effect on ANPP for either shrubs or grasses. However, shrubs responded
positively to an unusually wet year regardless of treatment and were
also more sensitive to changes in annual precipitation than grasses.
Total ANPP increased significantly in +N relative to the C and +W, but
without changes in the sensitivity to annual precipitation. The results
suggest that the responses of grasses and shrubs to water inputs is
driven by soil moisture redistribution and rooting depth and that grass
and community ANPP is more limited by N than by water.

Keywords: anomalous precipitation, water addition, nitrogen addition,
Patagonian steppes, rooting depth, soil water distribution

\subsection*{Introduction}

{\label{introduction}}

Aboveground net primary productivity (ANPP) is an attribute that
integrates key aspects of ecosystem functioning. The ANPP is related to
carbon and water cycling and energy fluxes. In arid and semiarid
ecosystems, it has been generally observed that ANPP is strongly limited
by water and nutrient availability (Guo et al., 2022; Lü et al., 2014;
Ma et al., 2020; X. Zhang et al., 2021). Several studies have indicated
a positive linear relationship between ANPP and annual precipitation
across multiples sites (Bai et al., 2008; A. K. Knapp \& Smith, 2001;
Sala et al., 2012). At a specific site, the response of ANPP to
precipitation can be linear (Sala et al., 2012; Wang et al., 2014;
Wilcox et al., 2017; Yahdjian \& Sala, 2006) or non-linear, with
positive (Ahlström et al., 2015; Felton et al., 2019; L. Zhang et al.,
2022; T. Zhang et al., 2020) or negative effects (Hou et al., 2021; Wu
et al., 2018). These inconsistent relationships between ANPP and
precipitation may be due to differences in species composition, the
range of precipitation considered, or biogeochemical factors affecting
the sensitivity of species to changes in precipitation (Deng et al.,
2021; Felton et al., 2019; Knapp et al., 2017). For example, in some
cases nutrient limitations restrict the effect of water on ANPP (Austin
\& Sala, 2002; Harpole et al., 2007; X. Zhang et al., 2021). On the
other hand, increased soil N content alone usually stimulates primary
productivity (Austin \& Sala, 2002; Harpole et al., 2007; LeBauer \&
Treseder, 2008; Tang et al., 2017; Xia \& Wan, 2008; Yue et al., 2020).
Furthermore, many studies indicate that the ANPP of arid ecosystems is
co-limited by water and nutrients (Guo et al., 2022; Harpole et al.,
2007; Lü et al., 2014),

In addition to the typical water and nutrient restrictions to which arid
ecosystems are exposed, it should be considered that these ecosystems
are sensitive to climate change and other global changes (Li et al.,
2021; Niu \& Wan, 2008; Rudgers et al., 2018). Changes in the amount and
temporal pattern of precipitation are predicted for arid regions, with
increases or decreases depending on the region, but with a significant
increase in extreme events (Drumond et al., 2019; Giorgi et al., 2019;
Saurral et al., 2017). Moreover, global N deposition is increasing as a
consequence of intensive anthropic activities (agriculture, oil, gas
production, among others) even in arid and semiarid regions (Bobbink et
al., 2010; Dentener et al., 2006; Gruber \& Galloway, 2008). Increased N
deposition and altered precipitation regimes can have significant
impacts on carbon, water and nutrient cycling (Gruber \& Galloway, 2008;
Harpole et al., 2007; Nielsen \& Ball, 2015), through changes in
vegetation structure and ANPP (Song et al., 2019; Stevens et al., 2015).
Some studies (Ahlström et al., 2015; c et al., 2020; Poulter et al.,
2014) have shown that semiarid regions play a key role, more than other
ecosystems, in regulating intra- and interannual variability of the
global carbon cycle. These changes in N and water inputs to which
ecosystems are exposed may occur at the same time, and their interaction
may not be directly predicted by the additive effects of individual
resources. Some studies have shown that the effects of water and N
amendment on primary productivity are additive (Gong et al., 2011; X.
Zhang et al., 2021), while others show non-additive effects (Gao et al.,
2011; Niu et al., 2009).

In addition to changes in ANPP due to changes in water and nutrient
availability, species composition, species interaction, and stability of
dominant species may be modified with the addition of limiting resources
(Guo et al., 2022; Lannes et al., 2016; Yue et al., 2020; X. Zhang et
al., 2021). The addition of water usually increases species composition
and ecosystem functioning (Hu et al., 2022; Weltzin et al., 2003; Yue et
al., 2020). Plant diversity can be affected by N addition through
competition exclusion, species invasion, or soil nutrient imbalances
(Liu et al., 2019; Tian et al., 2016). However, plant community
responses to N addition vary between ecosystems and, although the most
common response is the reduction of species diversity by competitive
exclusion, the opposite effect has been found in resource-poor
ecosystems (Bai et al., 2010; Chalcraft et al., 2008; Ladwig et al.,
2012). Long-term N addition can decrease soil pH and affect the growth
and survival of species sensitive to soil acidification caused by
excessive N input, thus reducing plant diversity (Fang et al., 2012; Ke
et al., 2023; Wallace et al., 2007).

The Patagonia region of southern Argentina is mainly represented by
arid/semiarid ecosystems with pronounced interannual precipitation
variability (Jobbágy \& Sala, 2000; Paruelo et al., 1998) and a low pool
of available soil N (Carbonell Silletta et al., 2019; Yahdjian et al.,
2014). Climate change predictions indicate a reduction in precipitation
in Patagonia, except in some areas such as the northwest of Chubut,
where there is a tendency to increase rainfall (Barros et al., 2015;
Saurral et al., 2017). Also, although N deposition in arid areas is
lower than in other ecosystems, it is expected to increase, depositing
mostly on natural vegetation (Dentener et al. 2006; Gruber \& Galloway
2008). Several studies have evaluated the patterns and controls of
aboveground production in relation to climatic variables and vegetation
structure in the Patagonian steppe at both spatial and temporal scales
(Austin \& Sala, 2002; Fernández et al., 1991; Gaitán et al., 2014;
Jobbágy \& Sala, 2000; Sala et al., 2012;Yahdjian \& Sala, 2010). Other
studies have evaluated the ecosystem response to reduced water inputs
(Yahdjian \& Sala, 2006), increased water inputs following previous
years of water restriction (Yahdjian \& Sala, 2006), or a single
relatively large experimental rainfall event applied during the summer
(Golluscio et al., 1998). These studies have been short-term (1 to 2
years) and have evaluated the immediate plant response to changes in
water inputs. However, there are no studies that have evaluated the
longer-term effects of both water and nitrogen additions, as well as the
interaction of the addition of both resources.

In this research, we explore how soil N additions and water inputs and
their interaction affect the structure of vegetation and aboveground
primary productivity in a Patagonian steppe. We hypothesized that N and
water addition increases ANPP and that this effect is higher with the
co-addition of both resources, and higher in species with shallow root
systems (grasses) than in higher in species with deeper root systems
(shrubs). Furthermore, we hypothesized that at the temporal scale ANPP
is positively correlated with precipitation, a relationship that is
strengthened with increased nutrient availability. The last hypothesis
proposes that nitrogen addition, alone or together with water addition,
induces an increase in the abundance of nitrophilous grasses. We carried
out a field experiment with water and nitrogen addition during
2013-2019. We also took advantage of the large interannual variability
in precipitation observed between 2015 and 2019 to test the second
hypothesis. In March 2017, a large part of the Patagonian steppe
experienced an extreme rain event that added about 80\% more water than
the mean annual precipitation of the last four decades in the study
area. This was a unique opportunity to assess the ecosystem response to
an extreme precipitation event.

\subsection*{Materials and Methods}

{\label{materials-and-methods}}

\emph{Study site and plant species}

The study site is located at the Río Mayo Experimental Field Station of
INTA (Instituto Nacional de Tecnología Agropecuaria) in South Central
Patagonia (45º24\selectlanguage{english}'11\selectlanguage{english}"S 70o17\selectlanguage{english}'37\selectlanguage{english}"W, 500 m a.s.l.). The historical mean
annual temperature (1982-2018) is 9.4degC, with a mean monthly temperature
in January of 15degC and in July of 2degC. The historical mean annual
precipitation (1982-2018) is 130 mm, with an annual range between 50 and
250 mm. Most precipitation occurs in the autumn and winter, while summer
precipitation events are isolated and unpredictable. Most of the
precipitation events are less than 5 mm (88\% of the total), and 55\% of
them are less than 1 mm (Cavallaro et al., 2020). Soils are coarse
textured (sandy), have a high gravel content and a calcareous layer at a
depth of 45-150 cm. Soil nitrogen content is low, around 0.05\%
(Carbonell Silletta et al., 2022). Soil water content varies seasonally
in the shallowest layers down to 1 m depth, increasing from about 5 to
20\% between May and September, while remaining relatively constant and
close to 10\% throughout the year at higher depths (Pereyra et al.,
2017).

The study site corresponds to a grass-shrub steppe in the southern end
of the Western District of the Patagonian Province. The major land use
in the region is sheep grazing (Golluscio et al., 1998). The Patagonian
steppes are arid to semiarid ecosystems characterized by a low plant
species diversity and a low plant density, with a vegetation cover of 30
to 40\% (Pereyra et al., 2017). The vegetation is distributed in patches
composed of shrubs and grasses on a bare soil matrix (Soriano et al.,
1994). The dominant shrub species in the study area are \emph{Azorella
prolifera} (Cav.) G.M. Plunkett \& A.N. Nicolas (ex \emph{Mulinum
spinosum} )\emph{, Adesmia volckmannii} Phil. and \emph{Senecio
filaginoides} DC. Dominant grass species, which are C3,
include\emph{Pappostipa speciosa} (Trin. \& Rupr.) Romasch.,
\emph{Pappostipa humilis} (Cav.) Romasch. and \emph{Poa ligulari} s Nees
ex Steud (Table 1). The dominant life forms of the Patagonian steppe
have root systems that collectively explore the entire soil profile down
to at least 2 m depth (Bucci et al., 2011) (Table 1).

\emph{Experiment design}

The experiment consists of 20 plots of 625 m\textsuperscript{2} each in
a 4 ha. enclosure, 5 plots per treatment: control (C), N addition (+N),
water addition (+W) and addition of both nitrogen and water (+NW). The
experiment started in 2013 and continues to the present. The
delimitation of the plots was established semi-randomly, taking into
account that those were homogeneous in terms of plant cover. Between
each plot, at least 10 m buffer area was set to avoid interference among
plots. The plots with water addition treatment are equipped with a
semi-automatic system of sprinkler irrigation in which the amount of
historical annual precipitation for the site is increased by
approximately 25\%, distributed in events of 5 mm each during the
growing season. Freshwater extracted from a well located next to the
closure was used to water addition treatment (see Fig. S1 for more
details of experimental design). Water addition is canceled during the
winter, which is the wettest period and to avoid also damage to the
irrigation system by freezing water. Nitrogen is added twice a year by
applying 100 kg/ha/yr in the form of urea and diammonium phosphate (160
kg and 154 kg, respectively). One of the additions is performed at the
beginning of winter and the other at the end of winter, to favor the
dissolution of the fertilizer in the soil during the wet season. The
amount of N added per year was similar to that applied in manipulative
experiments in other arid ecosystems (Bai et al., 2010; Ladwig et al.,
2012; Reichmann et al., 2013). The addition of diammonium phosphate was
performed to also add phosphorus in order to avoid any possible
limitation of this nutrient that interferes with the effect of the
addition of N (Luo et al., 2022).

\emph{Environmental variables}

Temperature and precipitation data were obtained from a meteorological
station located 2 km from the study site (http://sipas.inta.gob.ar/).
The volumetric soil water content was continuously determined by sensors
using the dielectric constant of the surrounding soil medium (ECH2O,
Dielectric aquameter, Decagon Devices, Inc). In each plot, sensors were
installed at 10, 40, 100 and 200 cm depth. The sensors were connected to
CR1000 automatic data acquisition systems (Campbell Scientific Inc.)
powered by batteries charged by a solar panel. Data were recorded every
30 min. Soil psychrometers (PST-55, Wescor, Logan, UT) were used to
continuously monitor soil water potential at 40 cm depth, which was
measured every 30 min and data were recorded with a datalogger (CR-7,
Campbell Scientific Inc.).

Data for pH, total nitrogen, inorganic nitrogen, available phosphorus,
and carbon content in the upper soil layer were obtained in November
2018. Three soil cores (5 cm diameter, 5 cm depth) were randomly taken
from each plot and mixed well to obtain a composite sample (n = 5). The
pH was determined in a 1:2.5 solution of distilled and deionized
H\textsubscript{2}O. Soil organic matter, total nitrogen and available
phosphorous were analyzed by the Soil Laboratory (INTA Chubut,
Argentina). Total nitrogen was determined by the Kjeldahl method
(Bremner, 1996), inorganic nitrogen by the distillation method (Bremner
\& Keeney, 1965), phosphorus by the Olsen method (Olsen et al., 1954),
and organic matter by the wet oxidation method (Walkley \& Black, 1934).
The C content of the organic matter was then determined using the factor
proposed by Van Bemmelen, of 1,724, which assumes that 58\% of the
organic matter is composed of C (Tabatabai, 1996). The C/N ratio was
also calculated by dividing the carbon content by the nitrogen content
(Bremner, 1996).

\subsubsection*{\texorpdfstring{\emph{Vegetation cover and leaf nitrogen
content}}{Vegetation cover and leaf nitrogen content}}

{\label{vegetation-cover-and-leaf-nitrogen-content}}

Changes in vegetation cover between 2012 (before the start of the
experiment) and 2019 (i.e., 6 years after the start of the experiment)
were determined using the intercept point method (Mueller\selectlanguage{english}-Dombois \&
Ellenberg, 1974). For this, the plant interception was determined every
one meter along the two diagonals of each plot. Subsequently, the
percent cover of each species and bare soil in each plot was calculated
annually by dividing the number of positive touches for each species or
bare soil by the total number of touches. It should be noted that
ephemeral herbaceous species, mainly present during October and
November, were not taken into account in the vegetation cover analysis.

Leaves were collected in December 2017 for analyses of nitrogen
concentrations. Fully expanded and healthy leaves from different
individuals of each species and in each plot were collected to obtain a
composite sample per plot and species. Total leaf N content was measured
using the Kjeldahl technique.

\subsubsection*{}

{\label{section}}

\emph{Tussock size and annual grass productivity}

To determine tussock size of \emph{P. ligularis} and \emph{P. speciosa}
, aboveground volume of the tussocks was calculated using the equation
of Derner et al. (2012):

\emph{V} = 1/3 \selectlanguage{greek}Π\selectlanguage{english}\emph{h} (\emph{r} \textsuperscript{2} + \emph{rR}
+\emph{R} \textsuperscript{2})

where \emph{V} is the volume, \emph{h} is the distance from the soil
surface to the top of tussock (height), \emph{r} is the radius of the
basal area occupied by tussock, and \emph{R} is the radius of the area
at top of tussock. We used this equation because the circumference of
tussocks increases from the base to the top. Since a high number
of\emph{P. humilis} tussocks was observed in the +N and +NW plots four
years after the start of the experiment, this species was also included
in this study. However, in C and +W plots we did not find an adequate
number of \emph{P. humilis} tussocks to consider sampling and further
statistical analysis. Determinations were made on randomly selected
tussocks in December 2019.

To determine the annual grass productivity, a 50 x 50 cm quadrat was
randomly selected in each plot, taking into account that if the quadrat
fell on a shrub individual, it was selected again to avoid shrubs in the
samples. This is due to the difficulty of estimating annual shrub growth
using this method, as there are few shrub individuals per unit area,
which would overestimate the calculated ANPP. All aboveground plant
biomass was cut at the soil level and transferred to the laboratory,
where green biomass, dry biomass, and litter were separated. The plant
biomass was then oven-dried at 70 \selectlanguage{ngerman}°C for 72 h. Cacti and herbaceous
dicotyledons were excluded from the analysis due to their low
representativeness in the samples (\textless{}1\%). Subsequently,
biomass was weighted and ANPP was determined as the biomass of each type
divided by the quadrat area in each year and expressed as g
m\textsuperscript{-2} yr\textsuperscript{-1}. Productivity was measured
once a year during peak green grass biomass (December) from 2013 to
2019. We chose to harvest in this month because both temperature and
soil moisture are favorable for growth, while later (summer) the
temperature is higher, but soil water availability decreases and
atmospheric water demand increases. Moreover, a study applying eddy
covariance techniques in the same Patagonian steppe shows the peak of
gross primary productivity and ecosystem net carbon exchange in December
(Burek et al. 2023, under revision). Sala \& Austin (2000) determined
that the method for ANPP estimation that introduces the least error in
grasslands with marked seasonality is that consisting of a single annual
harvest.

\subsubsection*{\texorpdfstring{\emph{Shrub size and annual shrub
productivity}}{Shrub size and annual shrub productivity}}

{\label{shrub-size-and-annual-shrub-productivity}}

Three individuals of each of the most abundant shrub species at the site
(\emph{A. prolifera} , \emph{A. volckmannii} , and \emph{S.
filaginoides} ) were selected and targeted per plot in 2015. In December
of each year from 2015 to 2019, the height and major and perpendicular
diameter of each selected individual were measured. To determine the
major and perpendicular diameter of each, photographs were taken of the
top of the canopy including a scale, and the images were analyzed with
the ImageJ program (Ferreira \& Rasband, 2012). The biomass of each
individual was estimated by applying allometric relationships estimated
by Oñatibia et al. (2010) for the same species and in an area close to
that of the present study. The equations used were the following:

\emph{b} = 0.000003 x \emph{dh} \textsuperscript{3.9321} for \emph{A.
prolifera}

\emph{b} = 0.0002 x \emph{dh} \textsuperscript{3.1402} for \emph{A.
volckmannii}

\emph{b} = 0.0002 x \emph{dh} \textsuperscript{3.1876} for \emph{S.
filaginoides}

where \emph{b} is the estimated aboveground biomass (g) and \emph{dh} is
the average diameter between the major and perpendicular plus plant
height (cm). Annual growth was determined as the change in estimated
biomass between consecutive years and expressed per area. ANPP was
estimated as annual growth per area multiplied by the percent cover of
each species at the site. When dead individuals were observed among
those marked, this was recorded and the number of dead plants per year
was determined. In that case, new individuals were marked and measured.
If plants considered dead sprouted in subsequent years, they were
removed from the dead plant category.

\emph{MSAVI2 and NDVI}

The MSAVI2 and NDVI were used as a proxy for ANPP at a larger spatial
scale than the ANPP estimated at the plant or quadrat scale. Five field
NDVI spectral reflectance sensors (SRS sensors, Decagon Inc, Pullman,
WA, USA) were used to estimate NDVI. An upward-looking hemispherical
sensor provided reference values of sky irradiance against which the
canopy radiance values of the four downward-looking sensors (one sensor
per treatment) were normalized. The SRS sensors were positioned above
the plots at a height of approximately 9 m above the ground, covering an
area of about 80.6 m\textsuperscript{2}. The downward-looking
hemispherical sensors were installed facing the canopy at a 45° angle. A
data logger (Em50, Decagon Inc, Pullman, WA, USA) recorded data from
each sensor every 1 min and expressed as 30-min averages. We calculated
NDVI as:

NDVI = (\selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{NIR} - \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{red}) /
(\selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{NIR} + \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{red}) =
(R\textsubscript{n}/I\textsubscript{n} -
R\textsubscript{r}/I\textsubscript{r}) /
(R\textsubscript{n}/I\textsubscript{n} +
R\textsubscript{r}/I\textsubscript{r})

where \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{NIR} and \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{red} are the percent
reflectance in the near infrared (NIR) and red respectively,
R\textsubscript{n} and R\textsubscript{r} are the radiation reflected by
the canopy in the NIR and red, and I\textsubscript{n} and
I\textsubscript{r} are the incident radiation in the NIR and red,
respectively. We assumed that the percent reflectance is the ratio of
reflected radiation to incident radiation in the specified waveband.

We calculated daily averages, using values acquired only during the
noon hour, to reduce data variability due to changes in
sun-sensor-surface illumination geometry throughout the day. The data
were recorded from July 2019 to July 2020. A technical problem with the
downward-looking hemispherical sensor placed in the +N plot prevented
obtaining the data for this treatment.

The Copernicus Sentinel-2 mission consists of two polar-orbiting
satellites providing multi-spectral imaginery with high resolution (10 m
to 60 m) and revisiting time (5 days). Twenty-three Level 1C from July
2019 to June 2020 (image dates are given in Fig. S2) corresponding to
tile T19GCK were obtained from the Copernicus Open Access Hub. Images
were visually inspected to avoid cloud coverage over the study area.
Level 1C products consists of atmospherically corrected TOA
(top-of-atmosphere) reflectance images, projected in the UTM WGS84
system and Images converted to BOA (bottom-of-atmosphere).

For each image, the MSAVI version 2 index (MSAVI2) was estimated (Qi et
al., 1994). This index is a better alternative to widely used NDVI for
arid and semiarid environments as it reduces the effect of soil signal
when vegetation cover is sparse.

MSAVI2 =\(\frac{\left[2\ \times\ \rho_{b8}+1-\ \sqrt{\left(2\ \times\ \rho_{b8}+1\right)^{2}-8\ \times\ \left(\rho_{b8}-\ \rho_{b4}\right)}\right]}{2}\)

where \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{b8} and \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{b4} are the at-the-ground
reflectance of bands 8 and 4, respectively. Then, for each date, data
for all pixels corresponding to field plots were extracted, including
between 5 and 7 pixels for each plot (the central coordinates of each
plot are shown in Table S1). All the image processing and data
extraction for Sentinel-2 imaginery was done in the SNAP software (ESA,
European Union). The Sen2Cor plugin was used for the TOA to BOA
correction, and the MSAVI2 processor for index estimation.

\subsubsection*{\texorpdfstring{\emph{Statistical
analysis}}{Statistical analysis}}

{\label{statistical-analysis}}

To evaluate the effect of the treatments on each soil variable (total
nitrogen, inorganic nitrogen, available phosphorus, carbon content, C/N
ratio and pH), ANOVAs using linear models (LM) were used. ANOVAs were
also tested to assess the effect of the treatments on the percent cover
of each species and leaf nitrogen content. To evaluate the effect of the
interaction between treatments and years on the number of dead plants, a
generalized linear model (GLM) with binomial distribution and
test-\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} was tested. Differences in tussock size
between treatments were evaluated with an ANOVA for each
species.\emph{P. humilis} was excluded from this analysis due to the
lack of data in the C and +W. Generalized least squares models (GLSs)
were used to evaluate the effect of the interaction between treatments
and years on the ANPP of each shrub species. In this model, a temporal
correlation structure between years was used for each sample (corAR1).
Then, the shrub ANPP, integrating the ANPP of the three species of
shrubs, the grass ANPP and the total ANPP (shrubs + grasses) were
evaluated with ANOVAs, using LMs, to test the effect of the treatments
in the different years. Multiple regression analysis, using LMs, were
performed to evaluate the effect of the treatments on the relationship
between annual precipitation, or precipitation plus irrigation in the
case of +W and +NW treatments, and ANPP (shrub ANPP, grass ANPP and
total ANPP). Generalized additive models (GAMs) were used to adjust the
vegetation indices (NDVI and MSAVI2) through year for each treatment. In
addition, an ANOVA with LM test was used to test the effect of the
treatments on daily NDVI.

All statistical analyses were performed with R software version 4.1.2 (R
Development Core Team, 2023). The GLSs were carried out using the
``gls'' function of the R package ``nlme'' version 3.1-162 (Pinheiro et
al., 2023; Pinheiro \& Bates, 2000). GLMs were performed using the
``glm'' function of the R package ''lme4'' version 1.1-15 (Bates et al.,
2015). GAMs were carried out using the ``gam'' function of the R package
``mgcv'' version 1.8-31 (Wood, 2004). When necessary, all models were
adjusted using variance models. Model selection was based on the
Akaike's information criterion (corrected for small sample size, AICc)
(Burnham \& Anderson, 2002). Tukey's post-hoc analysis were used for
multiple comparisons of LMs and GLSs models when the F-test or
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} tests were significant, using the ``glht'' function
of the R package ``multcomp'' version 1.4-8 (Hothorn et al., 2008). The
multiple comparisons of GAMs models were performed with the ``emmeans''
function of the R package ``emmeans'' version 1.8.6 (Lenth, 2023). The
``visreg'' function of the R package ``visreg'' version 2.7.0 (Breheny
\& Burchett, 2017) and the ``ggplot'' function of the R package
``ggplot2'' version 3.3.5 (Wickham, 2016) were used to graphically
represent the results.

Results

\emph{Environmental variables}

Precipitation, air temperature, and soil water content data for
2015-2019, years for which we have data of both grass and shrub ANPP,
are shown in Fig. 1. The mean daily air temperature for 2015-2019 was
9\selectlanguage{ngerman}°C, and the maximum and minimum temperatures were about 25°C (December
2018) and -9°C (June 2017), respectively (Fig.1A). Annual precipitation
for the period of 2015-2019 ranged from 92 to 233 mm, with 2019 being
the driest and 2017 the wettest year. Between March 29 and April 02,
2017, an extreme precipitation event of 100 mm was recorded (Fig. 1A).
Precipitation in 2013 and 2014, years for which information of grass
productivity is available, was 152.5 and 99.5 mm, respectively. Soil
volumetric water content showed a marked seasonal pattern in the top
soil layer (10 cm), ranging from 5\% in summer to 15-20\% in winter and
early spring (Fig. 1B). Water addition events were identifiable at that
depth, with transient increases in soil water content. At 40 cm depth,
soil water content had a similar pattern to that of the upper layer, but
water addition events were not visible at that depth (Fig. 1C). The
extreme precipitation event in autumn 2017 resulted in a higher increase
in water content throughout the soil profile compared to other years,
but there was a delay in such increase in deeper soil layers. At 100 and
200 cm soil depth, water content was relatively constant, between 6 and
9\% in all treatments except in 2017 (Fig. 1D, 1E). A soil water
retention curve indicated that soil water potential at 40 cm depth was
near 0 MPa when moisture was higher than 12\% and was higher than -1 MPa
when moisture was between 9 and 12\% (Fig. S3).

Inorganic N and available P content were higher in the +N and +NW
treatments than in the control (Table 2). The addition of nitrogen alone
increased the inorganic N content about three times more than the
co-addition of nutrients and water (\emph{p} \textless{}0.001).
Available P content increased about three times in +N and +NW treatments
compared with the control (\emph{p} \textless{}0.05). Total N and C and
N/C ratio were not significantly different between treatments (Table 2).
Soil pH was lower in the +N and +NW treatments than in the control,
decreasing from 7.15 in the control to 6.40 and 6.48 in those
treatments, respectively (Table 2).

\emph{Vegetation cover, plant mortality and leaf nitrogen content}

The vegetation cover before the start of the experiment was similar
among plots (\emph{p} \textgreater{}0.05 for all species, Table S2).
After 6 years of starting the experiment, shrub cover did not change
(Table 3), but it tended to be lower in the treatments with nitrogen
addition compared to the control (Fig. 2A). The +N and +NW treatments
increased the percentage of dead shrub (\emph{p} =0.02 and \emph{p}
=0.007 with respect to the control, respectively), being higher in the
year prior to the extreme rain event of 2017 compared to the following
years (\emph{p} \textless{}0.01) (Table 3, Fig. 2B).

The grass cover after 6 years under resources addition (water and
nitrogen) was similar among treatments, however, nitrogen addition
modified grass species composition (Table 3, Fig. 3). The +N and +NW
treatments resulted in a decrease in \emph{P. ligularis} grass cover
(\emph{p} \textless{}0.0001) and an increase in \emph{P. humilis} grass
cover (\emph{p} \textless{}0.0001), with no change in \emph{P.
speciosa}cover (p\textgreater{}0.05), although this species also tended
to decrease in the +N and +NW plots (Fig. S4). \emph{P. ligularis}
tussock size in the treatments with nitrogen addition was also
significantly reduced compared to the control (\emph{p} =0.03) (Fig. S5,
Table S3). The +W treatment had no effect on tussock size
(p\textgreater{}0.05) or grass cover (\emph{p} \textgreater{}0.05). The
+NW treatment had no differential effect with respect to the effect of
adding nitrogen alone (\emph{p} \textgreater{}0.05).

All studied species (shrubs and grasses) significantly increased the
leaf nitrogen content in the nitrogen addition treatments (Table
S4).\emph{Adesmia volckmannii} and \emph{P. ligularis} were the species
with the highest and the lowest leaf nitrogen content, respectively, in
the control plots. The higher change in leaf nitrogen content was
observed in \emph{P. ligularis} (1.08\% in C and 2.41\% in +N) and the
lower change was found in \emph{A. volckmannii} (2.36\% in C and 2.81\%
in +N).

\emph{Shrub and grass ANPP}

The ANPP of each shrub species varied differently among treatments and
years. In the wet year (2017), ANPP of \emph{A. prolifera} in the
nitrogen addition plots doubled ANPP in the control plots (\emph{p}
=0.04) (Fig. S6). Moreover, the ANPP of \emph{A. prolifera} was higher
in 2017 than in 2016 in the +N treatment (\emph{p} \textless{}0.001) and
was higher in 2017 than in the others studied years in the +NW treatment
(\emph{p} \textless{}0.05). The water addition treatment only affected
\emph{A. volckmannii} such that its ANPP increased in the +W plots
(\emph{p} =0.005) and, in addition, this species increased ANPP during
the wet year, being more than double in 2017 than in the rest of the
years (\emph{p} \textless{}0.01) (Fig. S6). The ANPP of \emph{S.
filaginoides} decreased in +NW compared to C (\emph{p} =0.001) and was
higher in 2017 than in 2019 in all treatments, with intermediate values
in 2016 and 2018 (\emph{p} \textless{}0.05) (Fig. S6).

Total shrub ANPP in the +N, +W and +NW treatments were not significantly
different compared to the control (Table 3, Fig. 4A). Shrub ANPP was
about two folds higher in 2017 as a consequence of the anomalous
precipitation in late March of that year than in the other study years,
regardless of treatments (p\selectlanguage{english}[?]0.01) (Table 3, Fig. 4A). Grass ANPP was not
affected by the treatments until two years after the start of the
experiment (Fig. 4B). From 2015 to 2019, grass ANPP was higher in +N and
+NW than in C (\emph{p} \textless{}0.02) (Table 3, Fig. 4B). There was
no difference between grass ANPP in +W and C (\emph{p}
\textgreater{}0.5). When comparing within-treatment grass ANPP across
years, we found no interannual differences in the control (Fig 4B).
However, grass ANPP in nitrogen addition plots was higher in 2017 and
2018 than in 2014 (\emph{p} \textless{}0.02) and ANPP in +NW treatment
was higher in 2017 than in 2014 (\emph{p} =0.03) (Fig. 4B).
Community-level ANPP (shrubs + grasses) increased significantly in the
treatments with nitrogen addition (\emph{p} \textless{}0.001) compared
to the control (Table 3, Fig. 4C). The total ANPP was higher in the wet
year (2017) than in 2016 (\emph{p} =0.001) and 2019 (\emph{p}
\textless{}0.001) (Fig. 4C).

\emph{MSAVI2 and NDVI}

The MSAVI2 vegetation index obtained from Sentinel 2 images showed
values between 0.023 and 0.096 (Fig. 5). The maximum values were found
in November and the minimum in May. The GAM fitted to the data had a
significant treatment effect (Table 4). During the analyzed period,
MSAVI2 in +W was not different from C except during summer when it was
higher than in C (Fig. 5A). MSAVI2 in +N and +NW was higher than in C
throughout the year (Fig. 5A).

The ground based daily NDVI values recorded from July 2019 to July 2020
ranged from 0.05 to 0.32, showing the maximum value in November and the
minimum value in June (Fig. 6A). The results of the GAM fitting
throughout the period (Table 4, Fig. 6A), as well as the results of the
ANOVA tested with all daily values (Table 3, Fig. 6B), indicated
significant effects of the treatments on NDVI. The treatments
significantly increased NDVI compared to the control (\emph{p}
\textless{}0.0001), the effect being higher in +NW (\emph{p}
\textless{}0.0001). It should be noted that the maximum difference in
NDVI between +W and C occurred in spring while it was much lower in
autumn and winter (Fig. 6A).

\emph{Relationships between ANPP and precipitation (ambient +
experimental)}

The interannual variability in water inputs ranged from 92 to 233 mm in
the plots without experimental water addition and from 122 to 263 in
plots with water addition. A positive relationship with the same
adjusted slope (0.21) was found between annual water input and shrub
ANPP in all treatments (Fig. 7A). The intercept of the ANPP-water input
relationship was lower in +NW than in C (\emph{p} =0.004), while in +N
and +W there was no difference compared to C (\emph{p}
\textgreater{}0.05) (Fig. 7A). Grass ANPP also was positively and
linearly correlated to water input (Fig. 7B), although the nitrogen
addition (i.e., +N and +NW treatments) had a higher positive effect
(p\selectlanguage{english}[?]0.004) (Fig. 7B). The +W treatment did not affect the response of
grass ANPP to water input compared to C (\emph{p} \textgreater{}0.5)
(Fig. 7B). The relationship between total ANPP and water input was
significantly modified by the treatments. The +N and +NW treatments
enhanced the intercept of the function fitted to the ANPP-water input
relationship (p\textless{}0.001), while the +W treatment decreased it
(\emph{p} =0.03) (Fig. 7C).

\subsection*{}

{\label{section-1}}

\subsection*{Discussion}

{\label{discussion}}

Seven-years monitoring of a large-scale field nitrogen and water
addition experiment in a Patagonian steppe showed that plant
productivity, cover and mortality responded differentially to soil
nitrogen addition and water addition. This response also differed
between and within functional groups (grasses and shrubs). Although
water availability is considered the most important environmental factor
limiting productivity in drought-prone ecosystems, our results indicate
that addition of 25\% more water than historical annual precipitation
resulted in a total ANPP similar to the ANPP in the control plots, while
the nitrogen addition had a positive effect on ANPP.

The total ANPP in control plots reported in our study was similar to
that estimated for other semiarid steppes (e.g., Bai et al., 2010; Gao
et al., 2011). The range of ANPP values obtained in our study in the
control plots (41-98 g m\textsuperscript{-2} yr\textsuperscript{-1}) is
consistent with that reported by Jobbagy \& Sala (2000) for a similar
Patagonian steppe (21-75 g m\textsuperscript{-2}yr\textsuperscript{-1}).
We determined the ANPP in early December, while that in previous studies
developed in the Patagonian steppes (e.g., Gherardi \& Sala, 2015;
Jobbagy \& Sala, 2000; Yahdjian \& Sala, 2006), ANPP has been estimated
in January. Despite the similar range of ANPP between our study and the
others, we consider late November or early December as the most accurate
measurement period to estimate ANPP since the maximum NDVI and MSAVI2
were observed in this period. Therefore, measurements done in January
instead at the end of November or early December could result in
relatively low ANPP estimates in the Patagonian steppe.

\emph{Response of plant species to water and nitrogen addition: plant
cover and mortality}

The large increase in the abundance of \emph{P. humilis} observed after
7 years of soil nitrogen addition supported our hypothesis that N
enrichment favors the growth of nitrophilous species. On the other hand,
nitrogen addition had a significant negative effect on \emph{P.
ligularis} by almost completely reducing its presence in those plots,
while persistent tussocks were smaller. A plausible explanation for
these results may be that \emph{P. humilis} is a better competitor on
nitrogen-rich soils that \emph{P. ligularis} and \emph{P. speciosa}which
perform better in poor soils. \emph{Pappostipa humilis} has been
positively associated with the shrub \emph{A. volckmannii} , a N-fixing
species (Armas et al., 2008). The leaves of this shrub species had a
higher leaf N content than the other shrub species in our study, and
therefore a higher amount of N in the soil next to plants of this
species is consistent with the hypothesis that high soil N levels favor
the establishment of \emph{P. humilis} .

On the other hand, the response of \emph{P. ligularis} and \emph{P.
speciosa} could be a consequence of the combination of acidification and
ammonium toxicity. In a previous study at the same site, Carbonell
Silletta et al. (2022) found a lower soil pH and a large increase in the
NH\textsubscript{4}-N/NO\textsubscript{3}-N ratio in the +N plots
compared to the C and +W plots. Several studies have shown that these
soil changes are detrimental to plant germination, growth and survival
and have been proposed as some of the potential mechanisms explaining
biodiversity loss in response to N enrichment (Bai et al., 2010; Liu et
al., 2019; Y. Zhang et al., 2014). Some responses of grasses found in
our study contradict those found by Campana et al. (2022). These authors
observed that \emph{P. ligularis} responded positively to nitrogen
addition increasing its biomass, while \emph{P. humilis} was not
affected and showed a tendency to decrease aboveground biomass. The
discrepancy between these two studies could be due to the different
amount and frequency of N addition.

We hypothesized that grasses are more sensitive to water addition than
shrubs due to differences in root distribution (Bucci et al., 2011).
However, the +W treatment did not affect any of the grass species
studied here. \emph{Poa ligularis} has been previously mentioned as a
more plastic species than \emph{P. speciosa} , increasing its biomass
under optimal water conditions (Couso \& Fernandez, 2012). On the other
hand, Golluscio et al. (1998) observed short-term physiological
responses to summer water addition in \emph{P. speciosa} . In our study,
the only species that responded to water treatment was the shrub\emph{A.
volckmannii} which substantially increased its productivity. Our results
indicate a low plasticity of Patagonian steppe species to water
addition. Although in a recent study in the same area Cavallaro et al.
(2023) observed that these species are relatively plastic to water
addition, the magnitude of plasticity was higher in functional traits
than in morphological traits. Increased leaf carbon assimilation under
the +W treatment observed by Cavallaro et al. (2023) was not reflected
in higher productivity in the present study. Photoassimilates were
probably allocated to roots or other functions (e.g., anti-herbivory
defenses) rather than to increase ANPP.

Soil nitrogen addition also caused mortality of some shrub individuals,
mainly \emph{S. filaginoides} , which resulted in a lower ANPP for this
species. Soil N addition in excess of plant nutritional N demand could
trigger a nitrogen saturation response (Wallace et al., 2007).
Therefore, the advantage of N addition on plant productivity may
decrease with continuous N enrichment (Tian et al., 2016). This response
is species-specific and, therefore, species differences to N saturation
could explain the similar total shrub ANPP found in this study, despite
increased mortality in some species. Across global grasslands, the mean
ANPP saturation threshold has been estimated at 150 kg
ha\textsuperscript{-1} yr\textsuperscript{-1} (Peng et al., 2020),
higher than that applied in the present study.

\emph{Plant functional groups and plant community ANPP responses to
experimental water and nitrogen addition}

Several studies indicate that arid ecosystems are more restricted by
water than nutrients or are co-limited by both (Guo et al., 2022; Hall
et al., 2011; Lu et al., 2014; Robertson et al., 2009). However, our
results contradict this idea, at least for grasses, due to the lack of
response of ANPP to water addition. This response was also found by
Swindon et al. (2019) in another semiarid steppe. However, in that
study, ANPP increased when water addition was combined with nitrogen
addition. We observed non co-limitation between these resources, as
grass ANPP increased in similar magnitude under +N and +NW. A similar
water content in the shallow soil layers over the years, regardless of
water inputs (natural or experimental), could explain the lack of
response of grasses in our study. According to soil water release
curves, the shallow soil layers, where grasses have most of their roots
(Bucci et al., 2011), had water potentials close to 0 MP during a large
part of the growing season of all study years. We suggest that grasses
in this steppe are not limited by water until December, which is the
peak of the growing season.

The increase in grass ANPP in response to nitrogen addition has also
been observed in other studies (Gherardi \& Sala, 2015; Henry \& Aherne,
2014; Kowaljow et al., 2010; Tang et al., 2017; Yahdjian et al., 2014).
Decreases in abundance and tussock size of \emph{P. ligularis}
and\emph{P. speciosa} were offset by changes in \emph{P.
humilis}abundance, such that grass ANPP was higher in the +N and +NW
plots relative to the other treatments. Although grass ANPP increased
significantly with nitrogen addition, this response can be interpreted
as a negative effect for this grassland where livestock grazing is the
main land use (Adler et al., 2004) and a highly palatable species
as\emph{Poa ligularis} is replaced by a species of low forage value
(\emph{P. humilis} ) (Cenzano et al., 2013; Onatibia \& Aguiar, 2016).

The lack of response of shrubs to the +W treatment is probably due to
the amount of water applied in each irrigation pulse. Apparently, it was
not enough to increase soil moisture below the 10 cm layer where the
shrubs explore most of the soil profile. Shrub ANPP only increased
significantly during the wet year, regardless of the treatments. These
findings suggest that shrubs, more than grasses, may be restricted by
soil water availability and only respond to higher precipitation when it
causes an increase in water content in deeper soil layers. Although in
this study site less than 10\% of the roots are below 1 m depth, they
contribute about 35\% of the total water use (Pereyra et al., 2017).
Therefore, the higher shrub ANPP during the wet year could be correlated
with soil water recharge, root distribution, and hydraulic efficiency of
deeper shrub roots. Consistent with our findings, Burek et al. (2023,
under revision) found at the same study site, using the eddy covariance
method, a higher net carbon exchange in 2017 (283.9 g C
m\selectlanguage{english}\textsuperscript{-2} yr\selectlanguage{english}\textsuperscript{-1}) than in 2015 (218.6 g C
m\selectlanguage{english}\textsuperscript{-2} yr\selectlanguage{english}\textsuperscript{-1}), which was related to
higher soil water content throughout the soil profile (0-2 m depth).

Unlike grasses, shrub ANPP was insensitive to nitrogen addition. These
results are in agreement with studies in similar steppes (Fernandez et
al., 2018; Reichmann et al., 2013; Yahdjian et al., 2014). Although ANPP
did not change under the +N treatment, shrubs increased N uptake under
nitrogen addition resulting in a large increase in leaf N content. These
leaf changes did not scale up to alter the biomass production of woody
species because nitrogen use efficiency substantially decreased in all
nitrogen addition treatments. Similar results were reported by Lu et al.
(2014) in temperate semiarid steppes of China. The results of
community-level ANPP determinations using destructive methods and
allometric relationships were consistent with the findings obtained
using field spectral reflectance sensors placed at 9 m height and
satellite imagery. Both approaches indicated that soil nitrogen
enrichment had a positive effect on aboveground plant productivity. The
higher NDVI in the +W plots relative to the C plots could be explained
by the presence of forbs and some minor species which were not sampled
for ANPP determination using destructive methods and allometric
relationships.

\emph{Relationship between aboveground productivity and annual water
inputs (ambient precipitation and experimental water addition)}

Arid and semiarid ecosystems are considered highly sensitive to
precipitation changes (Haverd et al., 2016; Huxman et al., 2004; Maestre
et al., 2012). Several studies show a linear relationship between
interannual ANPP and precipitation (Sala et al., 2012; Wang et al.,
2014). Recently, however, Knapp et al. (2017) proposed that with the
inclusion of precipitation extremes, the common linear relationship may
change to a nonlinear one, reflected in ``concave up'' or ``concave
down'' ANPP-precipitation relationships. Consistent with the linear
model, we found symmetric relationships with ANPP of each functional
group and total ANPP under all treatments, even when our data set
included an anomalous year. In previous studies in a similar Patagonian
steppe (Fernandez et al., 1991; Jobbagy \& Sala, 2000), at least grass
ANPP was not associated with precipitation.

The sensitivity of grass ANPP to annual water inputs was higher in the
+N and +NW treatments than in the C and the +W treatment. Therefore, our
results suggest that increasing N availability can enhance ANPP response
to increased precipitation, as shown by other studies (Ma et al., 2020;
Meng et al., 2022; Zhang et al., 2021). When water availability is not a
limiting factor for plant growth, as was observed for grasses in our
study, plants often exhibit increased biomass production in response to
improved soil N availability. Differences in the slopes of the
ANPP-water input relationships suggest that grasses in the control plots
were generally less sensitive to annual precipitation than shrubs. It
provides evidence that soil water redistribution during wet years
benefits deep-rooted species more than grasses, as grasses are less
water-limited because shallow soil layers are recharged during the fall
and winter seasons.

At the community level, although N enrichment increased productivity, it
did not improve the sensitivity of ANPP to precipitation, probably due
to the combined effects of precipitation and nitrogen addition on shrubs
and grasses, highlighting the importance of community structure. The
lower intercept value of the function fitted to the +W treatment
compared to the control suggests higher nutrient limitation by
increasing plant N demand or N losses (Mudge et al., 2017; Ren et al.,
2017). However, in previous works in the Patagonian steppe, Carbonell
Silletta et al. (2022) found no change in soil nutrient availability
with water addition and Cavallaro et al. (2023) found increased rates of
transpiration and photosynthesis in +W plots. This suggests that by
increasing water input without nitrogen addition, more photosynthates
are allocated to other functions rather than aboveground growth.

\subsection*{}

{\label{section-2}}

\subsection*{Conclusions}

{\label{conclusions}}

Our results show that nitrogen addition rather than water addition
improves community ANPP, mainly due to the effects on the grasses and to
the shift in the dominance of \emph{P. ligularis} to \emph{P. humilis} .
Our study revealed that the relationship between ANPP and annual
precipitation can be described by linear models across dry and wet
years. Grasses and shrubs responded differently to increased
precipitation, with shrubs being more sensitive than grasses to wet
years. However, under increased nutrient availability, the response of
grass ANPP to annual water input was amplified (i.e., steeper slope of
the ANPP-water input relationship), whereas shrubs showed no change in
ANPP with nitrogen addition. Therefore, the higher sensitivity of shrub
ANPP to increased precipitation compared to that of grasses suggests
that under climate change scenarios with increased precipitation, shrubs
could dominate the community, whereas if this change occurs with high N
deposition, an encroachment of grasses by a single species may take
place in the long term.

References

Adler, P. B., Milchunas, D. G., Lauenroth, W. K., Sala, O. E., \& Burke,
I. C. (2004). Functional traits of graminoids in semi-arid steppes: A
test of grazing histories. Journal of Applied Ecology, 41(4), 653--663.
https://doi.org/10.1111/j.0021-8901.2004.00934.x

Ahlstrom, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A.,
Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A.
K., Kato, E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang,
Y. P., Wiltshire, A., Zaehle, S., \& Zeng, N. (2015). The dominant role
of semi-arid ecosystems in the trend and variability of the land CO2
sink. Science, 348(6237), 895--899.
https://doi.org/10.1126/science.aaa1668

Armas, C., Pugnaire, F. I., \& Sala, O. E. (2008). Patch structure
dynamics and mechanisms of cyclical succession in a Patagonian steppe
(Argentina). Journal of Arid Environments, 72, 1552--1561.
https://doi.org/10.1016/j.jaridenv.2008.03.002

Austin, A. T., \& Sala, O. E. (2002). Carbon and nitrogen dynamics
across a natural precipitation gradient in Patagonia, Argentina. Journal
of Vegetation Science, 13, 351--360.
https://doi.org/https://doi.org/10.1111/j.1654-1103.2002.tb02059.x

Bai, Y., Wu, J., Clark, C., Naeem, S., Pan, Q., Huang, J., Zhang, L., \&
Han, X. (2010). Tradeoffs and thresholds in the effects of nitrogen
addition on biodiversity and ecosystem functioning: evidence from inner
Mongolia Grasslands. Global Change Biology, 16, 358--372.
https://doi.org/10.1111/j.1365-2486.2009.01950.x

Bai, Y., Wu, J., Xing, Q., Pan, Q., Huang, J., Yang, D., \& Han, X.
(2008). Primary production and rain use efficiency across a
precipitation gradient on the Mongolia Plateau. Ecology, 89(8),
2140--2153. https://doi.org/https://doi.org/10.1890/07-0992.1

Barros, V. R., Boninsegna, J. A., Camilloni, I. A., Chidiak, M., Magrin,
G. O., \& Rusticucci, M. (2015). Climate change in Argentina: Trends,
projections, impacts and adaptation. Wiley Interdisciplinary Reviews:
Climate Change, 6(2), 151--169. https://doi.org/10.1002/wcc.316

Bates, D., Machler, M., Bolker, B., \& Walker, S. (2015). Fitting Linear
Mixed-Effects Models Using lmer4. Journal of Statistical Software,
67(1). https://doi.org/10.18637/jss.v067.i01

Bobbink, R., Hicks, K., Galloway, J., Spranger, T., Alkemade, R.,
Ashmore, M., Bustamante, M., Cinderby, S., Davidson, E., Dentener, F.,
Emmett, B., Erisman, J. W., Fenn, M., Gilliam, F., Nordin, A., Pardo,
L., \& De Vries, W. (2010). Global assessment of nitrogen deposition
effects on terrestrial plant diversity: a synthesis. Ecological
Applications, 20(1), 30--59.
https://doi.org/https://doi.org/10.1890/08-1140.1

Breheny, P., \& Burchett, W. (2017). Visualization of Regression Models
Using visreg. The R Journal, 9(2), 56--71.

Bremner, J. M. (1996). Nitrogen-total. In D. L. Sparks, A. L. Page, P.
A. Helmke, R. H. Loeppert, P. N. Soltanpour, M. A. Tabatabai, C. T.
Johnston, \& M. E. Sumner (Eds.), Methods of soil analysis:Part 3
Chemical methods, 5.3 (pp. 1085--1121). SSSA-ASA.
https://doi.org/10.2136/sssabookser5.3

Bremner, J. M., \& Keeney, D. R. (1965). Steam distillation methods for
determination of ommonium, nitrate and nitrite. Analytica Chimica Acta,
32, 485--495.

Bucci, S. J., Scholz, F. G., Iogna, P. A., \& Goldstein, G. (2011).
Economia del agua de especies arbustivas de las Estepas Patagonicas.
Ecologia Austral, 21, 43--60.

Burnham, K. P., \& Anderson, D. R. (2002). Model Selection and
Multimodel Inference (D. R. A. Kenneth P. Burnham (ed.)). Springer.

Byrne, B., Liu, J., Bloom, A. A., Bowman, K. W., Butterfield, Z.,
Joiner, J., Keenan, T. F., Keppel-Aleks, G., Parazoo, N. C., Yin, Y., \&
Byrne, B. (2020). Outsized contribution of the semi-arid ecosystems to
interannual variability in North American ecosystems. Global
Biogeochemical Cycles 1, 1--17.
https://doi.org/10.1002/essoar.10502484.1

Campana, S., Reyes, M. F., \& Aguiar, M. R. (2022). Arid community
responses to nitrogen and carbon addition depend on dominant species
traits and are decoupled between above- and below-ground biomass.
Journal of Vegetation Science, 33(5), 1--8.
https://doi.org/10.1111/jvs.13153

Carbonell Silletta, L., Cavallaro, A., Kowal, R., Pereyra, D. A., Silva,
R. A., Arias, N. S., Goldstein, G., Scholz, F. G., \& Bucci, S. J.
(2019). Temporal and spatial variability in soil CO2 efflux in the
patagonian steppe. Plant and Soil, 444, 165--176.
https://doi.org/10.1007/s11104-019-04268-7

Carbonell Silletta, L., Cavallaro, A., Pereyra, D. A., Askenazi, J. O.,
Goldstein, G., Scholz, F. G., \& Bucci, S. J. (2022). Soil respiration
and N-mineralization processes in the Patagonian steppe are more
responsive to fertilization than to experimental precipitation increase.
Plant and Soil, 479, 405--422.
https://doi.org/10.1007/s11104-022-05531-0

Cavallaro, Agustin, Carbonell-Silletta, L., Askenazi, J. O., Goldstein,
G., Bucci, S. J., \& Scholz, F. G. (2023). Phenotypic plasticity in leaf
traits in response to experimental precipitation increase: Wettability,
foliar water uptake and gas exchange. Ecohydrology, e2573.
https://doi.org/10.1002/eco.2573

Cavallaro, Agustin, Carbonell Silletta, L., Pereyra, D. A., Scholz, F.
G., \& Bucci, J. (2020). Foliar water uptake in arid ecosystems:
seasonal variability and ecophysiological consequences. Oecologia, 193,
337--348. https://doi.org/10.1007/s00442-020-04673-1

Cenzano, A. M., Varela, M. C., Bertiller, M. B., \& Luna, M. V. (2013).
Effect of drought on morphological and functional traits of Poa
ligularis and Pappostipa speciosa , native perennial grasses with wide
distribution in Patagonian rangelands , Argentina. Australian Journal of
Botany, 61, 383--393. https://doi.org/https://doi.org/10.1071/BT12298

Chalcraft, D. R., Cox, S. B., Clark, C., Cleland, E. E., Suding, K. N.,
Weiher, E., \& Pennington, D. (2008). Scale-dependent responses of plant
biodiversity to nitrogen enrichment. Ecology, 89(8), 2165--2171.
https://doi.org/10.1890/07-0971.1

Couso, L. L., \& Fernandez, R. J. (2012). Phenotypic plasticity as an
index of drought tolerance in three Patagonian steppe grasses. Annals of
Botany, 110(4), 849--857. https://doi.org/10.1093/aob/mcS147

Deng, L., Peng, C., Kim, D., Li, J., Liu, Y., Hai, X., Liu, Q., Huang,
C., Shangguan, Z., \& Kuzyakov, Y. (2021). Drought effects on soil
carbon and nitrogen dynamics in global natural ecosystems. Earth-Science
Reviews, 214(December 2020), 103501.
https://doi.org/10.1016/j.earscirev.2020.103501

Dentener, F., Drevet, J., Lamarque, J. F., Bey, I., Eickhout, B., Fiore,
A. M., Hauglustaine, D., Horowitz, L. W., Krol, M., Kulshrestha, U. C.,
\& Lawrence, M. (2006). Nitrogen and sulfur deposition on regional and
global scales\selectlanguage{english} : A multimodel evaluation. Global Biogeochemical Cycles,
20. https://doi.org/10.1029/2005GB002672

Derner, J. D., Briske, D. D., \& Polley, H. W. (2012). Tiller
organization within the tussock grass schizachyrium scoparium: A field
assessment of competition-cooperation tradeoffs. Botany, 90(8),
669--677. https://doi.org/10.1139/B2012-025

Drumond, A., Stojanovic, M., Nieto, R., Vicente-Serrano, S. M., \&
Gimeno, L. (2019). Linking anomalous moisture transport and drought
episodes in the IPCC reference regions. Bulletin of the American
Meteorological Society, 100(8), 1481--1498.
https://doi.org/10.1175/BAMS-D-18-0111.1

Fang, Y., Xun, F., Bai, W., Zhang, W., \& Li, L. (2012). Long-Term
Nitrogen Addition Leads to Loss of Species Richness Due to Litter
Accumulation and Soil Acidification in a Temperate Steppe. PLOS ONE,
7(10), 1--8. https://doi.org/10.1371/journal.pone.0047369

Felton, A. J., Zavislan-Pullaro, S., \& Smith, M. D. (2019). Semiarid
ecosystem sensitivity to precipitation extremes: weak evidence for
vegetation constraints. Ecology, 100(2), 1--12.
https://doi.org/10.1002/ecy.2572

Fernandez, M. E., Pissolito, C. I., \& Passera, C. B. (2018). Water and
nitrogen supply effects on four desert shrubs with potential use for
rehabilitation activities. Plant Ecology, 219(7), 789--802.
https://doi.org/10.1007/s11258-018-0834-2

Fernandez, R. J., Sala, O. E., \& Golluscio, R. A. (1991). Woody and
herbaceous aboveground production of a Patagonian steppe. Journal of
Range Management, 44(5), 434--437.

Ferreira, T., \& Rasband, W. (2012). ImageJ User Guide IJ 1.46r (p.
185). http://rsbweb.nih.gov/ij/docs/guide/user-guide.pdf

Gaitan, J. J., Oliva, G. E., Bran, D. E., Maestre, F. T., Aguiar, M. R.,
Jobbagy, E. G., Buono, G. G., Ferrante, D., Nakamatsu, V. B., Ciari, G.,
Salomone, J. M., \& Massara, V. (2014). Vegetation structure is as
important as climate for explaining ecosystem function across patagonian
rangelands. Journal of Ecology, 102(6), 1419--1428.
https://doi.org/10.1111/1365-2745.12273

Gao, Y. Z., Chen, Q., Lin, S., Giese, M., \& Brueck, H. (2011). Resource
manipulation effects on net primary production , biomass allocation and
rain-use efficiency of two semiarid grassland sites in Inner Mongolia ,
China. Oecologia, 165, 855--864.
https://doi.org/10.1007/s00442-010-1890-z

Gherardi, L. A., \& Sala, O. E. (2015). Enhanced precipitation
variability decreases grass- and increases shrub-productivity.
Proceedings of the National Academy of Sciences of the United States of
America, 112(41), 12735--12740. https://doi.org/10.1073/pnas.1506433112

Giorgi, F., Raffaele, F., \& Coppola, E. (2019). The response of
precipitation characteristics to global warming from climate
projections. Earth System Dynamics, 10(1), 73--89.
https://doi.org/10.5194/esd-10-73-2019

Golluscio, R., Sala, E., \& Lauenroth, K. (1998). Differential use of
large summer rainfall events by shrubs and grasses: a manipulative
experiment in the Patagonian steppe. Oecologia, 115, 17--25.
https://doi.org/https://doi.org/10.1007/s004420050486

Gong, X. Y., Chen, Q., Lin, S., Brueck, H., Dittert, K., Taube, F., \&
Schnyder, H. (2011). Tradeoffs between nitrogen- and water-use
efficiency in dominant species of the semiarid steppe of Inner Mongolia.
Plant and Soil, 340(1), 227--238.
https://doi.org/10.1007/s11104-010-0525-9

Gruber, N., \& Galloway, J. N. (2008). An Earth-system perspective of
the global nitrogen cycle. Nature, 451(7176), 293--296.
https://doi.org/10.1038/nature06592

Guo, X., Zuo, X., Yue, P., Li, X., Hu, Y., Chen, M., \& Yu, Q. (2022).
Direct and indirect effects of precipitation change and nutrients
addition on desert steppe productivity in Inner Mongolia, northern
China. Plant and Soil, 471(1--2), 527--540.
https://doi.org/10.1007/s11104-021-05206-2

Hall, S. J., Sponseller, R. A., Grimm, N. B., Huber, D., Kaye, J. P.,
Clark, C., \& Collins, S. L. (2011). Ecosystem response to nutrient
enrichment across an urban airshed in the Sonoran Desert. Ecological
Applications, 21(3), 640--660. https://doi.org/10.1890/10-0758.1

Harpole, W. S., Potts, D. L., \& Suding, K. N. (2007). Ecosystem
responses to water and nitrogen amendment in a California grassland.
Global Change Biology, 13(11), 2341--2348.
https://doi.org/10.1111/j.1365-2486.2007.01447.x

Haverd, V., Smith, B., \& Trudinger, C. (2016). Dryland vegetation
response to wet episode, not inherent shift in sensitivity to rainfall,
behind Australia's role in 2011 global carbon sink anomaly. Global
Change Biology, 22(7), 2315--2316. https://doi.org/10.1111/gcb.13202

Henry, J., \& Aherne, J. (2014). Science of the Total Environment
Nitrogen deposition and exceedance of critical loads for nutrient
nitrogen in Irish grasslands. Science of the Total Environment,
470--471, 216--223. https://doi.org/10.1016/j.scitotenv.2013.09.047

Hothorn, T., Bretz, F., \& Westfall, P. (2008). Simultaneous Inference
in General Parametric Models. Biometrical Journal, 50(3), 346--363.
https://doi.org/10.1002/bimj.200810425

Hou, E., Litvak, M. E., Rudgers, J. A., Jiang, L., Collins, S. L.,
Pockman, W. T., Hui, D., Niu, S., \& Luo, Y. (2021). Divergent responses
of primary production to increasing precipitation variability in global
drylands. Global Change Biology, 27(20), 5225--5237.
https://doi.org/10.1111/gcb.15801

Hu, Y., Li, X., Guo, A., Yue, P., Guo, X., Lv, P., Zhao, S., \& Zuo, X.
(2022). Species diversity is a strong predictor of ecosystem
multifunctionality under altered precipitation in desert steppes.
Ecological Indicators, 137(March), 108762.
https://doi.org/10.1016/j.ecolind.2022.108762

Huxman, T. E., Snyder, K. A., Tissue, D., Leffler, A. J., Ogle, K.,
Pockman, W. T., Sandquist, D. R., Potts, D. L., \& Schwinning, S.
(2004). Precipitation pulses and carbon fluxes in semiarid and arid
ecosystems. Oecologia, 141(2), 254--268.
https://doi.org/10.1007/s00442-004-1682-4

Jobbagy, E. G., \& Sala, O. E. (2000). Controls of grass and shrub
aboveground production in the Patagonian steppe. Ecological
Applications, 10(2), 541--549.
https://doi.org/https://doi.org/10.1890/1051-0761(2000)010{[}0541:COGASA{]}2.0.CO;2

Ke, Y., Yu, Q., Wang, H., Zhao, Y., Jia, X., Yang, Y., Zhang, Y., Zhou,
W., Wu, H., Xu, C., Sun, T., Gao, Y., Jentsch, A., He, N., \& Yu, G.
(2023). The potential bias of nitrogen deposition effects on primary
productivity and biodiversity. Global Change Biology, 29(4), 1054--1061.
https://doi.org/10.1111/gcb.16530

Knapp, A. K., \& Smith, M. D. (2001). Variation among biomes in temporal
dynamics of aboveground primary production. Science, 291(5503),
481--484. https://doi.org/10.1126/science.291.5503.481

Knapp, Alan K., Avolio, M. L., Beier, C., Carroll, C. J. W., Collins, S.
L., Dukes, J. S., Fraser, L. H., Griffin-Nolan, R. J., Hoover, D. L.,
Jentsch, A., Loik, M. E., Phillips, R. P., Post, A. K., Sala, O. E.,
Slette, I. J., Yahdjian, L., \& Smith, M. D. (2017). Pushing
precipitation to the extremes in distributed experiments:
recommendations for simulating wet and dry years. Global Change Biology,
23(5), 1774--1782. https://doi.org/10.1111/gcb.13504

Kowaljow, E., Mazzarino, M. J., Satti, P., \& Jimenez-Rodriguez, C.
(2010). Organic and inorganic fertilizer effects on a degraded
Patagonian rangeland. Plant and Soil, 332, 135--145.
https://doi.org/10.1007/s11104-009-0279-4

Ladwig, L. M., Collins, S. L., Swann, A. L., Xia, Y., Allen, M. F., \&
Allen, E. B. (2012). Above- and belowground responses to nitrogen
addition in a Chihuahuan Desert grassland. Oecologia, 169, 177--185.
https://doi.org/10.1007/s00442-011-2173-z

Lannes, L. S., Bustamante, M. M. C., Edwards, P. J., \& Olde Venterink,
H. (2016). Native and alien herbaceous plants in the Brazilian Cerrado
are (co-)limited by different nutrients. Plant and Soil, 400(1--2),
231--243. https://doi.org/10.1007/s11104-015-2725-9

LeBauer, D., \& Treseder, K. (2008). Nitrogen Limitation of Net Primary
Productivity. Ecology, 89(2), 371--379.
https://doi.org/https://doi.org/10.1890/06-2057.1

Lenth, R. V. (2023). emmeans: Estimated Marginal Means, aka
Least-Squares Means. R package version 1.8.6.
https://cran.r-project.org/package=emmeans

Li, H., Wu, Y., Liu, S., \& Xiao, J. (2021). Regional contributions to
interannual variability of net primary production and climatic
attributions. Agricultural and Forest Meteorology, 303(March), 108384.
https://doi.org/10.1016/j.agrformet.2021.108384

Liu, J., Li, X., Ma, Q., Zhang, X., Chen, Y., Isbell, F., \& Wang, D.
(2019). Nitrogen addition reduced ecosystem stability regardless of its
impacts on plant diversity. Journal of Ecology, 107(5), 2427--2435.
https://doi.org/10.1111/1365-2745.13187

Lu, X.-T., Dijkstra, F. A., Kong, D.-L., Wang, Z.-W., \& Han, X.-G.
(2014). Plant nitrogen uptake drives responses of productivity to
nitrogen and water addition in a grassland. Scientific Reports, 4(4817),
1--7. https://doi.org/10.1038/srep04817

Luo, M., Moorhead, D. L., Ochoa-Hueso, R., Mueller, C. W., Ying, S. C.,
\& Chen, J. (2022). Nitrogen loading enhances phosphorus limitation in
terrestrial ecosystems with implications for soil carbon cycling.
Functional Ecology, 36(11), 2845--2858.
https://doi.org/10.1111/1365-2435.14178

Ma, Q., Liu, X., Li, Y., Li, L., Yu, H., Qi, M., Zhou, G., \& Xu, Z.
(2020). Nitrogen deposition magnifies the sensitivity of desert steppe
plant communities to large changes in precipitation. Journal of Ecology,
108(2), 598--610. https://doi.org/10.1111/1365-2745.13264

Maestre, F. T., Salguero-Gomez, R., \& Quero, J. L. (2012). It is
getting hotter in here: Determining and projecting the impacts of global
environmental change on drylands. Philosophical Transactions of the
Royal Society B: Biological Sciences, 367(1606), 3062--3075.
https://doi.org/10.1098/rstb.2011.0323

Meng, Y., Li, T., Liu, H., Li, S., Xu, Z., \& Jiang, Y. (2022). Legacy
effects of nitrogen deposition and increased precipitation on plant
productivity in a semi \selectlanguage{english}- arid grassland. Plant and Soil.
https://doi.org/10.1007/s11104-022-05550-x

Mudge, P. L., Kelliher, F. M., Knight, T. L., O'Connell, D., Fraser, S.,
\& Schipper, L. A. (2017). Irrigating grazed pasture decreases soil
carbon and nitrogen stocks. Global Change Biology, 23(2), 945--954.
https://doi.org/10.1111/gcb.13448

Mueller\selectlanguage{english}-Dombois, D., \& Ellenberg, H. (1974). Aims and methods of
vegetation ecology. John Wiley and Sons.

Nielsen, U. N., \& Ball, B. A. (2015). Impacts of altered precipitation
regimes on soil communities and biogeochemistry in arid and semi-arid
ecosystems. Global Change Biology, 21(4), 1407--1421.
https://doi.org/10.1111/gcb.12789

Niu, S., \& Wan, S. (2008). Warming changes plant competitive hierarchy
in a temperate steppe in northern China. Journal of Plant Ecology, 1(2),
103--110. https://doi.org/10.1093/jpe/rtn003

Niu, S., Yang, H., Zhang, Z., Wu, M., Lu, Q., Li, L., Han, X., \& Wan,
S. (2009). Non-additive effects of water and nitrogen addition on
ecosystem carbon exchange in a temperate steppe. Ecosystems, 12(6),
915--926. https://doi.org/10.1007/s10021-009-9265-1

Olsen, S. R., Cole, C. V., Watanabe, F. S., \& Dean, L. A. (1954).
Estimation of available phosphorus in soils by extraction with sodium
bicarbonate. Circular No. 939. US Department of Agriculture.

Onatibia, G. R., \& Aguiar, M. R. (2016). Continuous moderate grazing
management promotes biomass production in Patagonian arid rangelands.
Journal of Arid Environments, 125, 73--79.
https://doi.org/10.1016/j.jaridenv.2015.10.005

Onatibia, G. R., Aguiar, M. R., Cipriotti, P. A., \& Troiano, F. (2010).
Individual plant and population biomass of dominant shrubs in Patagonian
grazed fields. Ecologia Austral, 20, 269--279.

Paruelo, J. M., Beltran, A., Jobbagy, E., Sala, O. E., \& Golluscio, R.
A. (1998). The climate of Patagonia: general patterns and controls on
biotic processes. Ecologia Austral, 8, 85--101.

Peng, Y., Chen, H. Y. H., \& Yang, Y. (2020). Global pattern and drivers
of nitrogen saturation threshold of grassland productivity. Functional
Ecology, 34(9), 1979--1990. https://doi.org/10.1111/1365-2435.13622

Pereyra, D. A., Bucci, S. J., Arias, N. S., Ciano, N., Cristiano, P. M.,
Goldstein, G., \& Scholz, F. G. (2017). Grazing increases
evapotranspiration without the cost of lowering soil water storages in
arid ecosystems. Ecohydrology, 10(6), 1--12.
https://doi.org/10.1002/eco.1850

Pinheiro, J. C., \& Bates, D. M. (2000). Mixed\_effects models in S and
S-PLUS. Springer.

Pinheiro, J. C., Bates, D., \& Team, R. C. (2023). nlme: Linear and
Nonlinear Mixed Effects Models. R package version 3.1-162.
https://cran.r-project.org/package=nlme

Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J.,
Broquet, G., Canadell, J. G., Chevallier, F., Liu, Y. Y., Running, S.
W., Sitch, S., \& van der Werf, G. R. (2014). Contribution of semi-arid
ecosystems to interannual variability of the global carbon cycle.
Nature, 509(7502), 600--603. https://doi.org/10.1038/nature13376

Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., \& Sorooshian, S.
(1994). A modified soil adjusted vegetation index. Remote Sensing of
Environment, 48(2), 119--126.
https://doi.org/10.1016/0034-4257(94)90134-1

R Core Team (2023). R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
https://www.R-project.org/.

Reichmann, L. G., Sala, O. E., \& Peters, D. P. C. (2013). Water
controls on nitrogen transformations and stocks in an arid ecosystem.
Ecosphere, 4(1), 1--17.
https://doi.org/http://dx.doi.org/10.1890/ES12-00263.1

Ren, H., Xu, Z., Isbell, F., Huang, J., Han, X., Wan, S., Chen, S.,
Wang, R., Zeng, D. H., Jiang, Y., \& Fang, Y. (2017). Exacerbated
nitrogen limitation ends transient stimulation of grassland productivity
by increased precipitation. Ecological Monographs, 87(3), 457--469.
https://doi.org/10.1002/ecm.1262

Robertson, T. R., Bell, C. W., Zak, J. C., \& Tissue, D. T. (2009).
Precipitation timing and magnitude differentially affect aboveground
annual net primary productivity in three perennial species in a
Chihuahuan Desert grassland. New Phytologist, 181(1), 230--242.
https://doi.org/10.1111/j.1469-8137.2008.02643.x

Rudgers, J. A., Chung, Y. A., Maurer, G. E., Moore, D. I., Muldavin, E.
H., Litvak, M. E., \& Collins, S. L. (2018). Climate sensitivity
functions and net primary production: A framework for incorporating
climate mean and variability. Ecology, 99(3), 576--582.
https://doi.org/10.1002/ecy.2136

Sala, O. E., \& Austin, A. T. (2000). Methods of Estimating Aboveground
Net Primary Productivity. In O. E. Sala, R. B. Jackson, H. A. Mooney, \&
R. H. Howarth (Eds.), Methods in Ecosystem Science. (Springer, pp.
31--43).

Sala, O. E., Gherardi, L. A., Reichmann, L., Jobbagy, E., \& Peters, D.
(2012). Legacies of precipitation fluctuations on primary production:
Theory and data synthesis. Philosophical Transactions of the Royal
Society B: Biological Sciences, 367, 3135--3144.
https://doi.org/10.1098/rstb.2011.0347

Saurral, R. I., Camilloni, I. A., \& Barros, V. R. (2017). Low-frequency
variability and trends in centennial precipitation stations in southern
South America. International Journal of Climatology, 37(4), 1774--1793.
https://doi.org/10.1002/joc.4810

Song, J., Wan, S., Piao, S., Knapp, A. K., Classen, A. T., Vicca, S.,
Ciais, P., Hovenden, M. J., Leuzinger, S., Beier, C., Kardol, P., Xia,
J., Liu, Q., Ru, J., Zhou, Z., Luo, Y., Guo, D., Adam Langley, J.,
Zscheischler, J., \ldots{} Zheng, M. (2019). A meta-analysis of 1,119
manipulative experiments on terrestrial carbon-cycling responses to
global change. Nature Ecology and Evolution, 3(9), 1309--1320.
https://doi.org/10.1038/s41559-019-0958-3

Soriano, A., Sala, O. E., \& Perelman, S. B. (1994). Patch structure and
dynamics in a patagonian arid steppe. Vegetatio, 111, 127--135.
https://doi.org/10.1007/BF00040332

Stevens, C. J., Lind, E. M., Hautier, Y., Harpole, W. S., Borer, E. T.,
Hobbie, S., Seabloom, E. W., Ladwig, L., Bakker, J. D., Chu, C.,
Collins, S., Davies, K. F., Firn, J., Hillebrand, H., La Pierre, K. J.,
MacDougall, A., Melbourne, B., McCulley, R. L., Morgan, J., \ldots{}
Wragg, P. D. (2015). Anthropogenic nitrogen deposition predicts local
grassland primary production worldwide. Ecology, 96(6), 1459--1465.
https://doi.org/10.1890/14-1902.1

Swindon, J. G., Burke, I. C., \& Lauenroth, W. K. (2019). Seasonal
Patterns of Root Production with Water and Nitrogen Additions Across
Three Dryland Ecosystems. Ecosystems.
https://doi.org/10.1007/s10021-019-00364-y

Tabatabai, M. A. (1996). Soil Organic Matter Testing: An Overview. In F.
R. Magdoff, M. A. Tabatabai, \& E. A. Hanlon Jr. (Eds.), Soil Organic
Matter: Analysis and Interpretation (Issue 46, pp. 1--10). SSSA Spec.
Pub. 46. American Society of Agronomy.
https://doi.org/10.2136/sssaspecpub46.c1

Tang, Z., Deng, L., An, H., Yan, W., \& Shangguan, Z. (2017). The effect
of nitrogen addition on community structure and productivity in
grasslands: A meta-analysis. Ecological Engineering, 99, 31--38.
https://doi.org/10.1016/j.ecoleng.2016.11.039

Tian, D., Niu, S., Pan, Q., Ren, T., Chen, S., Bai, Y., \& Han, X.
(2016). Nonlinear responses of ecosystem carbon fluxes and water-use
efficiency to nitrogen addition in Inner Mongolia grassland. Functional
Ecology, 30(3), 490--499. https://doi.org/10.1111/1365-2435.12513

Walkley, A., \& Black, I. A. (1934). An examination of the Degtjareff
method for determining soil organic matter, as a proposed modification
of the chromic acid titration method. Soil Science, 37, 29--38.

Wallace, Z. P., Lovett, G. M., Hart, J. E., \& Machona, B. (2007).
Effects of nitrogen saturation on tree growth and death in a mixed-oak
forest. Forest Ecology and Management, 243(2--3), 210--218.
https://doi.org/10.1016/j.foreco.2007.02.015

Wang, Z., Zhang, Q., Xin, X., Ding, Y., Hou, X., Sarula, Li, X., Chen,
H., Yin, Y., Hu, J., \& Liu, Z. (2014). Response of the annual biomass
production of a typical steppe plant community to precipitation
fluctuations. Rangeland Journal, 36(6), 527--534.
https://doi.org/10.1071/RJ14065

Weltzin, J. F., Loik, M. E., Schwinning, S., Williams, D. G., Fay, P.
A., Haddad, B. M., Harte, J., Huxman, T. E., Knapp, A. K., Lin, G.,
Pockman, W. T., Shaw, M. R., Small, E. E., Smith, M. D., Smith, S. D.,
Tissue, D. T., \& Zak, J. C. (2003). Assessing the Response of
Terrestrial Ecosystems to Potential Changes in Precipitation.
BioScience, 53(10), 941--952.
https://doi.org/10.1641/0006-3568(2003)053{[}0941:ATROTE{]}2.0.CO;2

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis.
Springer-Verlag.

Wilcox, K. R., Shi, Z., Gherardi, L. A., Lemoine, N. P., Koerner, S. E.,
Hoover, D. L., Bork, E., Byrne, K. M., Cahill, J., Collins, S. L.,
Evans, S., Gilgen, A. K., Holub, P., Jiang, L., Knapp, A. K., LeCain,
D., Liang, J., Garcia-Palacios, P., Penuelas, J., \ldots{} Luo, Y.
(2017). Asymmetric responses of primary productivity to precipitation
extremes: A synthesis of grassland precipitation manipulation
experiments. Global Change Biology, 23(10), 4376--4385.
https://doi.org/10.1111/gcb.13706

Wood, S. N. (2004). Stable and efficient multiple smoothing parameter
estimation for generalized additive models. Journal of the American
Statistical Association, 99(467), 673--686.
https://doi.org/10.1198/016214504000000980

Wu, D., Ciais, P., Viovy, N., Knapp, A. K., Wilcox, K., Bahn, M., Smith,
M. D., Vicca, S., Fatichi, S., Zscheischler, J., He, Y., Li, X., Ito,
A., Arneth, A., Harper, A., Ukkola, A., Paschalis, A., Poulter, B.,
Peng, C., \ldots{} Piao, S. (2018). Asymmetric responses of primary
productivity to altered precipitation simulated by ecosystem models
across three long-term grassland sites. Biogeosciences, 15(11),
3421--3437. https://doi.org/10.5194/bg-15-3421-2018

Xia, J., \& Wan, S. (2008). Global response patterns of terrestrial
plant species to nitrogen addition. New Phytologist, 179(2), 428--439.
https://doi.org/10.1111/j.1469-8137.2008.02488.x

Yahdjian, L, Gherardi, L., \& Sala, O. E. (2014). Grasses have larger
response than shrubs to increased nitrogen availability: A fertilization
experiment in the Patagonian steppe. Journal of Arid Environments, 102,
17--20. https://doi.org/10.1016/j.jaridenv.2013.11.002

Yahdjian, L, \& Sala, O. E. (2006). Vegetation structure constrains
primary production response to water availability in the patagonian
steppe. Ecology, 87(4), 952--962.
https://doi.org/https://doi.org/10.1890/0012-9658(2006)87{[}952:VSCPPR{]}2.0.CO;2

Yahdjian, Laura, \& Sala, O. E. (2010). Size of precipitation pulses
controls nitrogen transformation and losses in an arid Patagonian
ecosystem. Ecosystems, 13(4), 575--585.
https://doi.org/10.1007/s10021-010-9341-6

Yue, K., Jarvie, S., Senior, A. M., Van Meerbeek, K., Peng, Y., Ni, X.,
Wu, F., \& Svenning, J. C. (2020). Changes in plant diversity and its
relationship with productivity in response to nitrogen addition, warming
and increased rainfall. Oikos, 129(7), 939--952.
https://doi.org/10.1111/oik.07006

Zhang, L., Wang, J., Zhao, R., Guo, Y., \& Hao, L. (2022). Aboveground
net primary productivity and soil respiration display different
responses to precipitation changes in desert grassland. Journal of Plant
Ecology, 15(1), 57--70. https://doi.org/10.1093/jpe/rtab067

Zhang, T., Yu, G., Chen, Z., Hu, Z., Jiao, C., Yang, M., Fu, Z., Zhang,
W., Han, L., Fan, M., Zhang, R., Sun, Z., Gao, Y., \& Li, W. (2020).
Patterns and controls of vegetation productivity and precipitation-use
efficiency across Eurasian grasslands. Science of the Total Environment,
741, 140204.
https://doi.org/https://doi.org/10.1016/j.scitotenv.2020.140204

Zhang, X., Hasi, M., Li, A., Tan, Y., Daryanto, S., Wang, L., Zhang, X.,
Chen, S., \& Huang, J. (2021). Nitrogen addition amplified water effects
on species composition shift and productivity increase. Journal of Plant
Ecology, 14(5), 816--828. https://doi.org/10.1093/jpe/rtab034

Zhang, Y., Lu, X., Isbell, F., Stevens, C., Han, X., He, N., Zhang, G.,
Yu, Q., Huang, J., \& Han, X. (2014). Rapid plant species loss at high
rates and at low frequency of N addition in temperate steppe. Global
Change Biology, 20(11), 3520--3529. https://doi.org/10.1111/gcb.12611

Table 1. Family, life form, leaf phenology, plant height and maximum
rooting depth for species of Patagonian steppe included in this study\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Species} & \textbf{Family} & \textbf{Life form} & \textbf{Leaf
phenology} & \textbf{Plant height (cm)} & \textbf{Maximum rooting depth
(cm)}\tabularnewline
\emph{Azorella prolifera} & Apiaceae & Shrub & Deciduous & 40.8 \selectlanguage{ngerman}±1.8 &
\textless{}100\tabularnewline
\emph{Adesmia volckmannii} & Fabaceae & Shrub & Deciduous & 76.2 \selectlanguage{ngerman}±4.5 &
\textless{}200\tabularnewline
\emph{Pappostipa humilis} & Poaceae & Grass & Evergreen & 33.2 \selectlanguage{ngerman}±1.6 &
\textless{}50\tabularnewline
\emph{Pappostipa speciosa} & Poaceae & Grass & Evergreen & 33.2 \selectlanguage{ngerman}±1.6 &
\textless{}50\tabularnewline
\emph{Poa ligularis} & Poaceae & Grass & Evergreen & 29.7 \selectlanguage{ngerman}±1.5 &
\textless{}50\tabularnewline
\emph{Senecio filaginoides} & Asteraceae & Shrub & Evergreen & 36.1 \selectlanguage{ngerman}±1.4
& \textless{}100\tabularnewline
\bottomrule
\end{longtable}

+ Values of plant height are means \selectlanguage{ngerman}± standard error (n = 15).

Table 2. Total nitrogen (\%), inorganic nitrogen (ppm), C content (\%),
C/N ratio, available phosphorus (ppm) and pH in soil upper layer (up 5
cm depth) for control (C), nutrient addition (+N), water addition (+W)
and co-addition of nutrient and water (+NW) treatments. Each value
represents the mean and CI (n =3). Different letters following the
values indicate significant differences between treatments\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& Treatment & Treatment & Treatment & Treatment & Results of
ANOVA\tabularnewline
\midrule
\endhead
& C & +N & +W & +NW &\tabularnewline
\textbf{Total N} & 0.041 (0.008) & 0.044 (0.013) & 0.041 (0.014) & 0.050
(0.014) & df=3, F = 0.56, \emph{p}=0.65\tabularnewline
\textbf{Inorganic N} & 0.64 (0.33) a & 11.28 (3.43) c & 0.48 (0.26) a &
3.91 (1.44) b & df=3, F =19.4, \emph{p}=0.0005\tabularnewline
\textbf{C} & 0.392 (0.08) & 0.477 (0.137) & 0.427 (0.137) & 0.489
(0.137) & df=3, F=0.80, \emph{p}=0.52\tabularnewline
\textbf{C/N ratio} & 10.62 (3.33) & 10.06 (5.42) & 13.13 (5.43) & 9.62
(5.42) & df=3, F=0.39, \emph{p}=0.76\tabularnewline
\textbf{Available P} & 14.78 (4.21) a & 54.74 (15.25) b & 15.63 (4.44) a
& 59.37 (16.52) b & df=3, F=16.6,
\emph{p}\textless{}0.0001\tabularnewline
\textbf{pH} & 7.15 (0.20) a & 6.40 (0.20) b & 7.10 (0.20) a & 6.48
(0.20) b & df=3, F=20.2, \emph{p}=0.0004\tabularnewline
\bottomrule
\end{longtable}

Table 3. Results of ANOVAs (df, F-value and \emph{p} -value) on the
effects of Treatment and/or Year on shrub and grass cover, ANPP of
shrubs, grasses and total (shrubs + grasses), and NDVI (daily values).
Results of a GLM (df, \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} and \emph{p} ) on the effects
of Treatment and Year on dead shrub individuals.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
& d.f. & F-value or \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} & \emph{p}-value & & d.f. &
F-value & \emph{p}-value\tabularnewline
\midrule
\endhead
\textbf{Shrub cover} & & & & \textbf{Shrub ANPP} & & &\tabularnewline
Treatment & 3 & 2.6 & 0.09 & Treatment & 3 & 3.96 & 0.01\tabularnewline
\textbf{Dead shrub individuals} & & & & Year & 3 & 6.75 &
0.0004\tabularnewline
Treatment & 3 & 22.5 & \textless{}0.0001 & Treatment * Year & 9 & 0.8 &
0.62\tabularnewline
Year & 3 & 32.74 & \textless{}0.0001 & \textbf{Grass ANPP} & &
&\tabularnewline
Treatment * Year & 9 & 14.27 & 0.11 & Treatment & 3 & 22.44 &
\textless{}0.0001\tabularnewline
\textbf{Grass cover} & & & & Year & 6 & 3.40 & 0.004\tabularnewline
Treatment & 3 & 2.43 & 0.10 & Treatment * Year & 18 & 3.44 &
\textless{}0.0001\tabularnewline
\emph{P. humilis} \textbf{cover} & & & & \textbf{Total ANPP} & &
&\tabularnewline
Treatment & 3 & 42.71 & \textless{}0.0001 & Treatment & 3 & 20.81 &
\textless{}0.0001\tabularnewline
\emph{P. speciosa} \textbf{cover} & & & & Year & 3 & 8.15 &
0.0001\tabularnewline
Treatment & 3 & 3.29 & 0.048 & Treatment * Year & 9 & 0.62 &
0.78\tabularnewline
\emph{P. ligularis} \textbf{cover} & & & & \textbf{NDVI} & &
&\tabularnewline
Treatment & 3 & 24.58 & \textless{}0.0001 & Treatment & 2 & 549.05 &
\textless{}0.0001\tabularnewline
\bottomrule
\end{longtable}

Table 4. Results of GAMs on the effects of Treatment and time on MSAVI2
and NDVI for the period July 2019 to July 2020\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& \textbf{MSAVI2} & \textbf{MSAVI2} & \textbf{NDVI} &
\textbf{NDVI}\tabularnewline
\midrule
\endhead
\textbf{Parameter} & & & &\tabularnewline
Family & Gaussian & Gaussian & Gaussian & Gaussian\tabularnewline
Link function & Identity & Identity & Identity & Identity\tabularnewline
Adjusted R\textsuperscript{2} & 0.52 & 0.52 & 0.973 &
0.973\tabularnewline
Deviance explained (\%) & 53.1 & 53.1 & 97.4 & 97.4\tabularnewline
& \textbf{d.f.} & \emph{p}\textbf{-value} & \textbf{d.f.} &
\emph{p}\textbf{-value}\tabularnewline
\textbf{Covariates} & & & &\tabularnewline
Treatment & -2.7 & \textless{}0.0001 & -8.07 &
\textless{}0.0001\tabularnewline
s(time) * Treatment & -27.6 & 0.001 & -17.82 &
\textless{}0.0001\tabularnewline
\bottomrule
\end{longtable}

Table 5. Results of multiple linear regressions (df, F-value and\emph{p}
-value) on the effects of Treatment and Precipitation (or Precipitation
+ Irrigation in +W and +NW treatments) on ANPP of shrubs, grasses and
total (shrubs + grasses)\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& d.f. & F-value & \emph{p}-value\tabularnewline
\midrule
\endhead
\textbf{Shrub ANPP} & & &\tabularnewline
Treatment & 3 & 17.86 & 0.0001\tabularnewline
Precipitation & 1 & 266.88 & \textless{}0.0001\tabularnewline
Treatment * Precipitation & 3 & 0.006 & 0.99\tabularnewline
\textbf{Grass ANPP} & & &\tabularnewline
Treatment & 3 & 68.97 & \textless{}0.0001\tabularnewline
Precipitation & 1 & 27.02 & 0.0001\tabularnewline
Treatment * Precipitation & 3 & 4.24 & 0.025\tabularnewline
\textbf{Total ANPP} & & &\tabularnewline
Treatment & 3 & 234.75 & \textless{}0.0001\tabularnewline
Precipitation & 1 & 106.07 & \textless{}0.0001\tabularnewline
Treatment * Precipitation & 3 & 0 & 1\tabularnewline
\bottomrule
\end{longtable}

Legends of figures

Figure 1. A) Daily precipitation (mm) and air temperature (\selectlanguage{ngerman}°C) from 2015
to 2020. Soil volumetric water content at B) 10 cm depth, C) 40 cm
depth, D) 100 cm depth and E) 200 cm depth for control (C), nutrient
addition (+N), water addition (+W) and co-addition of nutrient and water
(+NW) treatments, from 2015 to 2019. Values within panel A indicate the
annual precipitation.

Figure 2. (A) Percentage of shrub coverage in 2019 and (B) Percentage of
dead shrub individuals from 2016 to 2019, for control (C), nutrient
addition (+N), water addition (+W) and co-addition of nutrient and water
(+NW) treatments. Bars represent mean +CI (n=5). Different lowercase
letters indicate significant differences between treatments for each
year. Different uppercase letters indicate significant differences
between years. ns = not significant

Figure 3. (A) Percentage of total grass coverage and by species:
(B)\emph{P. humilis} , (C) \emph{P. speciosa} and (D) \emph{P.
ligularis} , for control (C), nutrient addition (+N), water addition
(+W) and co-addition of nutrient and water (+NW) treatments. Bars
represent mean +CI (n=5). Different letters indicate significant
differences between treatments. ns = not significant

Figure 4. ANPP (g m\textsuperscript{-2} y\textsuperscript{-1}) of (A)
shrubs, (B) grasses and (C) shrubs + grasses, for control (C), nutrient
addition (+N), water addition (+W) and co-addition of nutrient and water
(+NW) treatments. Bars represent mean +CI (n= 5). Different lowercase
letters indicate significant differences between treatments for each
year. Different uppercase letters indicate significant differences
between years

Figure 5. (A) MSAVI2 vegetation index obtained from Sentinel-2 images
for control (C), nutrient addition (+N), water addition (+W) and
co-addition of nutrient and water (+NW) treatments from July 2019 to
July 2020. Each point corresponds to the index of a pixel within each
plot. The lines correspond to the GAM fitting and the ribbons to the
95\% confidence limits. Different letters indicate significant
differences between treatments. (B) MSAVI2 indices for four dates, each
representative of a season (in order: winter, spring, summer, and fall).
Squares represent plots, and each 10x10m pixel is colored according to
MSAVI2 value

Figure 6. (A) Daily NDVI for control (C), water addition (+W) and
co-addition of nutrient and water (+NW) treatments from July 2019 to
July 2020. The lines correspond to the GAM fitting and the ribbons to
the 95\% confidence limits. (B) Boxplots of NDVI for each treatment.
Different letters indicate significant differences between treatments.
Note: data corresponding to +N treatment are missing due to technical
problems with the downward-looking hemispherical sensor

Figure 7. ANPP (g m\textsuperscript{-2} y\textsuperscript{-1}) in
relation to water input (ambient + experimental precipitation, mm) for
(A) shrubs, (B) grasses and (C) shrubs + grasses, for control (C),
nutrient addition (+N), water addition (+W) and co-addition of nutrient
and water (+NW) treatments. The points and error bars are the mean and
CI of the statistical analysis shown in figure 5. Different lowercase
letters indicate significant differences between treatments\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-1/Fig-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-2/Fig-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-3/Fig-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-4/Fig-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-5/Fig-5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-6/Fig-6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-7/Fig-7}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

