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\begin{document}

\title{Global change re-structures alpine plant communities under 15 years of
warming, nitrogen, and snow addition: disentangling density independent
and dependent effects}



\author[1]{Courtney Collins}%
\author[2]{Sarah Elmendorf}%
\author[3]{Jane Smith}%
\author[4]{Lauren Shoemaker}%
\author[4]{Megan Szojka}%
\author[5]{Margaret Swift}%
\author[2]{Katharine Suding}%
\affil[1]{The University of British Columbia}%
\affil[2]{University of Colorado Boulder}%
\affil[3]{University of Colorado}%
\affil[4]{University of Wyoming}%
\affil[5]{Duke University Nicholas School of the Environment}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Global change is altering patterns of community assembly, with net
outcomes dependent on species' responses to the environment, both
directly and mediated through biotic interactions. Here, we assess
alpine plant community responses in a 15-year factorial nitrogen
addition, warming and snow manipulation experiment. We used a dynamic
competition model to estimate the density-dependent and independent
processes underlying changes in species-group abundances over time.
Density-dependent shifts in competitive interactions drove long-term
changes in abundance of species-groups under global change.
Density-independent processes were important when counteracting
environmental drivers limited the growth response of the dominant
species. Furthermore, competitive interactions shifted with
environmental change, primarily with nitrogen, and drove non-linear
abundance responses across environmental gradients. Our results
highlight that global change can either reshuffle species hierarchies or
further favor already dominant species; predicting which outcome will
occur requires incorporating both density-dependent and independent
mechanisms and how they interact across multiple global change factors.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Global change is altering plant community dynamics, yet impacts are
often difficult to predict and can vary across multiple, interacting
drivers (Valladares \emph{et al.} 2015). Understanding the net outcomes
of global change on local plant community structure is challenging
because it requires integrating both direct effects of changing
environmental conditions on individual species as well as shifts in the
magnitude and types of biotic interactions (G\selectlanguage{ngerman}ötzenberger \emph{et
al.}2012; Kraft \& Ackerly 2014; Vandvik \emph{et al.} 2020). Global
change can cause complete restructuring of plant communities via species
turnover and/or reshuffling of competitive hierarchies (Brown \emph{et
al.} 1997; Smith \emph{et al.} 2009; Dovrat \emph{et al.} 2020).
Alternatively, global change may further favor already dominant species
within a community, reducing species diversity via competitive exclusion
or decreased evenness (Sheil 2016; Regina \emph{et al.} 2018). These
dynamics can take years to play out, especially in long-lived and
slow-growing systems, as short term responses may not fully encompass
both environmental effects and shifts in biotic interactions
(Komatsu\emph{et al.} 2019). To meet these challenges, approaches that
assess both density-independent and density-dependent mechanisms over
long time periods are essential.

Adding to this complexity, both the type (e.g. climate change, nutrient
pollution, land use change) and the number of drivers can have
differential effects on plant communities (Komatsu \emph{et al.} 2019).
Warming temperatures and altered precipitation regimes, can shift
species hierarchies through changes in competitive interactions under
novel climate conditions (Hoover \emph{et al.} 2014; Valladares \emph{et
al.} 2015). This has been shown to reshuffle species dominance in field
studies (Evans \emph{et al.} 2011; Cavin \emph{et al.} 2013;
Mariotte\emph{et al.} 2013), particularly in response to drought, given
the well-established trade-off between dominance and stress tolerance
(Gilman \emph{et al.} 2010). On the other hand, nutrient pollution, such
as atmospheric nitrogen deposition, is likely to reduce niche
differentiation by homogenizing habitats and may lead to competitive
exclusion by dominant species (McKinney \& Lockwood 1999; Smart \emph{et
al.} 2006). Reduced species richness and increased production of one or
a few species under nitrogen deposition is common, particularly in
grassland ecosystems (Zavaleta \emph{et al.} 2003; Borge \emph{et
al.}2004). In most natural systems, these different global change
drivers occur simultaneously, and thus their net outcomes on community
structure are often unclear.

While global change is altering plant community dynamics worldwide,
alpine tundra ecosystems are particularly vulnerable, as elevation
dependent warming often amplifies the rate of temperature increase in
high versus low elevation systems (Pepin \emph{et al.} 2015).
Additionally, shifts in winter precipitation and snow pack and
atmospheric nutrient pollution from nearby urban and agricultural areas
also pose a serious threat to the stability and diversity of alpine
plant communities often finely adapted to local gradients of soil
moisture and nutrients (Roth \emph{et al.} 2013; Gobiet \emph{et
al.}2014; Little \emph{et al.} 2016). However, while there is high
confidence that alpine regions will continue to warm at a rate faster
than the global average (IPCC 2018), predictions for changes in snow and
nutrient pollution are much more uncertain, and vary considerably by
region, latitude, and land use history (Hock \emph{et al.} 2019). Thus,
correctly attributing changes in alpine tundra plant communities to
warming temperatures, versus co-occurring changes in snow and nutrient
dynamics, is an ongoing challenge. What's more, how these interacting
global change drivers influence both density-independent and
density-dependent processes is an important knowledge gap in our
understanding of rapidly shifting tundra plant communities.

Recent emphasis has been placed on understanding how dominant species
within a community respond to global change, given their high
abundances, and disproportionate influence on ecosystem functions
(Winfree \emph{et al.} 2015; Wohlgemuth \emph{et al.} 2016;
Hillebrand\emph{et al.} 2018; Avolio \emph{et al.} 2019). Determining
the mechanisms that allow species to dominate under novel environmental
conditions can serve as proxies for whole community and ecosystem
responses to global change (Avolio \emph{et al.} 2019). In fact, the
idea that ``super-dominants,'' or overabundant populations of native
species, may have similar impacts as non-native invasive species on
community and ecosystem function has begun to gain traction
(Regina\emph{et al.} 2018; Zhao \emph{et al.} 2021). Conversely,
deciphering pathways by which dominant and subordinate species become
more evenly distributed is critical for predicting long term maintenance
of biodiversity and the preservation of rare species (Csergo \emph{et
al.}2013; Felton \& Smith 2017). Broadly, viewing changes in plant
community structure from an abundance-based rather than species or trait
lens, has shown to be a powerful way to make general predictions across
systems (Suding \emph{et al.} 2005).

Here, we present a 15-year fully factorial warming, snow manipulation,
and nitrogen (N) addition experiment with corresponding shifts in alpine
plant community composition at Niwot Ridge, Colorado, USA. We estimate
the influence of multiple global change drivers on the
density-independent growth responses and density-dependent interactions
of groups of dominant, subdominant, moderate and rare plant species over
time using gjamTime, a dynamic, biophysical competition model
(Clark\emph{et al.} 2020). We use these model estimates to inform
changes in relative abundance of each species group observed in
experimental field plots. Furthermore, we estimate the net effects of
density-independent and dependent factors on steady-state (ie.
equilibrium) abundances of each species group across both ambient and
experimentally manipulated environmental gradients. We asked: 1) What
global change scenarios lead to further favoring dominant species versus
reordering species hierarchies? 2) How do density-independent and
dependent mechanisms influence the net outcomes of changes in plant
community structure over time? 3) How do density-dependent shifts
influence community stability under global change?

\textbf{Materials and methods}

\textbf{Study design}

The study site is a moist meadow habitat within the alpine tundra at
Niwot Ridge Long Term Ecological Research (LTER) site in the Front Range
of the Rocky Mountains, Colorado, USA (40°03\selectlanguage{english}' N, 105deg35\selectlanguage{english}' W). The
experimental design of this study is described in detail in
Smith\emph{et al.} (2012) and Farrer \emph{et al.} (2014). Briefly,
experimental plots (1m\textsuperscript{2}) were established in 2006 with
a fully factorial deployment of warming (using passive, open-top
plexiglass chambers), nitrogen (N) addition (using slow release
fertilizer), and snow manipulation treatments (using snow fences) (See
supplementary methods).

Our study focuses on warming, as this is the most consistent global
change driver in alpine tundra ecosystems (Hock \emph{et al.} 2019).
While we assess community responses to all three global change drivers:
warming, snow addition and N addition, we restrict our analyses to only
the treatments that include experimental warming either alone or in
combination with snow and N addition (n=40 plots). Thus, we compare the
following four experimental treatments and contrast them to full control
(ambient conditions): 1) warming only (W), 2) N addition and warming
(NW), 3) snow addition and warming (SW) and 4) snow, N addition and
warming (SNW). In addition to warming, altered snowpack is another
crucial impact of global change in alpine ecosystems (Gobiet \emph{et
al.} 2014). While many regions, particularly at lower elevations and
latitudes, are experiencing a decrease in snowpack with climate change,
winter precipitation in the alpine at Niwot ridge has increased since
the 1950s due to shifts in upper-air circulation patterns across the
Continental Divide (Kittel \emph{et al.} 2015). Finally, while
atmospheric N emissions have decreased in many parts of the world, they
are still increasing in highly developing areas and impacts on sensitive
alpine ecosystems can be long-lasting with limited recovery of plant
communities on decadal time scales (Bowman \emph{et al.} 2018).

\textbf{Plant community surveys}

Plant community composition was measured in each plot annually from
2006-2020 during the peak of the growing season with pre-treatment data
collected in 2006. A point-intercept method was used to estimate species
presence at 100 points per plot in the field and these raw species
counts were used in subsequent modeling with a censoring term of the
total number of vegetative hits (i.e. excluding rock, litter,
non-vascular species) in a plot in a given year (mean=90). Thirty-three
unique plant species were present in control plots across all years,
however we only included species (n=20) with at least total 20
observations in control plots across all years.

For our modeling approach (see below), we summed the cover data of these
20 species into four species groups based on natural breaks in their
relative abundance in control plots over time. First, the `dominant'
species, \emph{Deschampsia cespitosa} (grass) had an average of 42 +- 1.2
(SE) plot hits (range: 20-67) in ambient conditions (control plots)
forming a standalone group. Three `subdominant' species were combined
into one group: \emph{Geum rossii} (forb)\emph{, Artemisia
scopulorum}(forb), and \emph{Carex scopulorum} (sedge) which had an
average of 10 +- 0.8 plot hits (range: 14-44) in ambient conditions. Four
species were combined into one `moderate' group: \emph{Gentiana algida}
(forb),\emph{Trifolium parryi} (legume), \emph{Bistorta bistortoides}
(forb), and \emph{Caltha leptosepala} (forb) which had an average of 3 +-
0.3 plot hits (range: 3-29) in ambient conditions. Finally, we placed
the remaining 12 species into one `rare' group which had an average of
0.4 +- 0.2 plot hits (range: 0-10) in ambient conditions (Fig S1). Raw
cover of all species over time in all treatment and control plots are
shown in Fig S2.

We calculated changes in relative cover (plot hits) of each species
group (Dominant, Subdominant, Moderate, and Rare) with respect to the
pretreatment (2006) data for each year over the 15 year period within
each experimental treatment using the \emph{abundance\_change} function
in the package CODYN in R (Hallett \emph{et al.} 2016). We then modeled
these values using a linear mixed model with a fixed 3-way interaction
and a global intercept (0+ time (years since 2006) x species group x
treatment) and a random intercept of (calendar) year to determine
whether each group increased, decreased, or did not change in relative
plot cover over the time period within a given treatment. Models were
run using the \emph{lmer} function in package lme4 in R (Bates \emph{et
al.} 2014; R Core Team 2020).

\textbf{Generalized joint attribute modeling}

\emph{Environmental predictors}

We used a generalized joint attribute model for dynamic data (below) to
assess how density-dependent and independent factors contribute to the
observed changes in relative abundance of species groups over time and
their steady-state predicted abundances across multiple global change
drivers (Clark \emph{et al.} 2020). We jointly estimated the influence
of snow depth, nitrogen deposition and temperature on the
density-independent growth rates of dominant, subdominant, moderate and
rare species groups both in experimentally manipulated and control plots
over time. We incorporated continuous annual environmental data as model
predictors, following the approach of Farrer et al. (2014), as
environmental data were not available at the plot level for the entirety
of the study (See supplementary methods).

\emph{Model specifications}

Joint responses of species groups to environmental predictors,
interactions among species groups, and the combination of these
processes were estimated using the gjamTime model as described in
Clark\emph{et al} (2020) via the package gjam (Clark \emph{et al.} 2017)
with the gjamTime supplemental functions
https://github.com/jimclarkatduke/gjam/blob/master/gjamTimeFunctions.R?raw=True.

Relative abundances of species groups were modeled as raw counts
(`hits') of the \emph{a priori} dominant, subdominant, moderate and rare
species groups in each plot-year combination censored by the total
number of vegetative hits within the same plot-year with a maximum of
100 hits per plot (see Plant community surveys). This censoring value
reflects the observation effort term as described in Clark et al (2020)
and we used the `DA' (discrete abundance) data type specification for
count data. Censored response data are then stored as a latent vector
(w\textsubscript{s}) with a joint multivariate normal distribution with
a mean of \selectlanguage{greek}μ\selectlanguage{english}\textsubscript{s}, which is a length \emph{s} mean vector,
and an error \selectlanguage{greek}Σ, \selectlanguage{english}which is an \emph{s} x \emph{s} covariance matrix. In
other words:

\(w_{s}\sim MVN(u\)\textsubscript{s,} \selectlanguage{greek}Σ\selectlanguage{english}) (eq. 1).

Changes in population density of each species group over time is modeled
using a Lotka-Volterra (LV) model specification from which the gjamTime
model is derived:

\(\frac{dw_{s}}{\text{dt}}\) \textsubscript{=}\({(w}_{s}\ x\ X)\rho_{s}+\ {(w}_{s}\text{\ x\ }w_{s^{\prime}})\alpha_{s}+\ \varepsilon_{s}\)(eq. 2).

The first term defines the density-independent growth rate of a species
group (\selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{s.}) multiplied by the density of species
group\emph{s} and the environmental impact (w\textsubscript{s} \selectlanguage{ngerman}× X). The
second term defines the species-group's density-dependent growth rate
\selectlanguage{greek}α\selectlanguage{english}\textsubscript{s,} which is modified by the density of two interacting
species-groups s and \emph{s} ' (w\textsubscript{s} \selectlanguage{ngerman}×
w\textsubscript{s'}). Finally, the last term encompasses residual
species group error (\selectlanguage{greek}ε\selectlanguage{english}\textsubscript{s}) (eq. 2).

Because this is a community of functionally similar herbaceous plant
species competing for limited resources during a short growing season,
we set model priors for \selectlanguage{greek}α \selectlanguage{english}parameters to allow for negative (-1, 0)
species group interactions (i.e. competition) only. For \selectlanguage{greek}ρ \selectlanguage{english}intercepts, we
set wide model priors from (-1, 1) to allow for species groups to
increase or decrease by a maximum of 100\% of their cover in a given
time step (1 year). We set priors on \selectlanguage{greek}ρ \selectlanguage{english}coefficients as (-0.5, 0.5) to
allow a 50\% change (positive or negative) in \selectlanguage{greek}ρ \selectlanguage{english}in response to a given
environmental driver at each time step.

Equation 2 is then reorganized as the discrete-time version of the LV
model (eq 2.1) for model fitting:

\(\Delta w_{\text{st}}=P^{\prime}v_{\text{st}}+A\ u_{\text{st}}\ +\Sigma^{1/2}\varepsilon_{\text{st}}\)(eq. 2.1)

where \(\Delta w_{\text{st}}\) is the growth increment for population
abundances of \emph{s} species group, P and A are sparse matrices which
reorganize \(\rho\) and \selectlanguage{greek}α \selectlanguage{english}coefficients respectively to
optimize posterior simulation and allow for direct sampling (see Clark
et al 2020 SI Appendix S2.9, 2.10), \(v_{\text{st}}\) is a length-V
vector where V is a block matrix of all possible combinations of species
abundances\(w_{\text{st}}\) and \emph{q} environmental variables
(\(w_{\text{st}}\text{x\ }X_{\text{qt}}\)), \(u_{\text{st}}\) is a length-U vector where U is
a block matrix of all possible combinations of species group
interactions (\(w_{\text{st}}\text{\ x\ }w_{s^{\prime}t}),\ \selectlanguage{greek}\text{\ ε}\selectlanguage{english}_{\text{st}}\) is a random vector and
\(\Sigma^{1/2}\) is a square root matrix for the\emph{s} x \emph{s}
process error covariance.

Finally, steady-state abundance distributions, i.e. probabilistic
predicted equilibrium abundances of species groups, were estimated by
numerical integration of the modeled parameter estimates of
environmental effects on growth rates and interactions among species
groups allowing for interactive and non-linear responses to emerge
across environmental gradients (i.e. Environment x Species interactions-
ESIs, Clark et al 2020). For each model output, we simulated 100
equilibrium abundance values for each species group at 10\^{}3 discrete
steps (10 steps for each covariate x covariate combination) across
observed gradients of snow depth, N deposition and temperature,
calculating a mean and standard deviation of the \(w_{s}^{*}\)
estimates for each set of 100 simulations (See supplementary methods).

We chose to run models at the species group level (dominant,
subdominant, moderate and rare) rather than at the individual species
level to improve model predictions and to address broad questions about
shifts in community structure under global change. This species-group
approach captures rare species' joint contribution to community dynamics
in a biologically meaningful way, and allows for conceptual comparisons
across multiple global change scenarios that would be too complex using
the entire species set (n=20). The species-grouping model also better
predicts the data (Fig S1), in particular for species with moderate and
rare coverage, thus increasing parameter estimate confidence.

\emph{Model outputs}

Models were run in a (state-space) hierarchical Bayesian framework, with
model fitting by Markov chain Monte Carlo for 10000 iterations with a
burn-in period of 2000 using the function `gjam' in the package gjam
(Clark \emph{et al.} 2017). Model convergence was confirmed by visual
assessment of the mixing of chains as well as model-fit diagnostic plots
generated in the \emph{gjamPlot} function of the gjam package (Fig S6
a-e). We ran models separately for each of the four treatment types (W,
NW, SW, SNW), as well as the control (CTL), to compare the influence of
global change drivers on growth rates and biotic interactions over time.
Because the current gjamTime model does not test the influence of
environmental covariates on density-dependent interactions, we ran a
separate gjamTime model for each treatment type and then compared
estimated species interaction matrices between models of each global
change treatment versus control plots.

The effects of environmental drivers on density-independent growth rates
(\(\rho\)\textsubscript{s}) were assessed via the mean and
95\% Bayesian credible intervals of parameter estimates. For
density-dependent interactions of species groups (\selectlanguage{greek}α\selectlanguage{english}\textsubscript{s}),
we calculated the difference in the mean estimates (\selectlanguage{greek}α\selectlanguage{english}\selectlanguage{greek}\textsubscript{µ}\selectlanguage{english})
between control plots and each global change treatment type for all
species group pairs (i.e. \selectlanguage{greek}Δα\selectlanguage{english}s). We then summed all changes in
interspecific competition on a given species group and combined the
interspecific and intraspecific \selectlanguage{greek}Δα\selectlanguage{english}s to estimate the net change in
competition on each species group within each treatment type. We discuss
predicted steady-state distributions when one or more species groups
showed non-linear patterns in equilibrium abundances over a given
environmental gradient (see Clark et al. 2020).

Finally, to assess community stability, we used eigenvalue analysis from
modeled interaction matrices; communities are considered stable if all
real eigenvalues are negative (Allesina \& Tang 2012). We also compared
the rightmost (highest) real eigenvalues to compare stability across
communities whereby lower (more negative) rightmost real eigenvalues
denoted higher stability (Carpentier \emph{et al.} 2021).

\textbf{Results}

\textbf{Net outcomes: changes in cover}

Fifteen years of experimental manipulations resulted in distinct changes
in community structure based on the patterns of relative cover change in
species groups. Most often, global change treatments led to an increase
of the already dominant species within the community (i.e. `Dominant
increase'), while in one scenario global change lead to the reshuffling
of species hierarchies (i.e. `Competitive reshuffling'). In contrast, in
ambient conditions (CTL), net changes strongly overlapped across species
groups and were highly variable between years, indicating little
directional change over time. We describe each outcome below.

\emph{Dominant increase}

In all the global change treatments except for warming only (W), the
dominant species, \emph{Deschampsia cespitosa} increased its relative
cover compared to control plots, while subdominant and moderate species
decreased their relative cover and rare species did not change. While
most of the global change scenarios shared this pattern, the increase in
dominance at the expense of subdominant and moderate species was the
strongest in SNW plots, followed by the SW plots and then NW plots (Fig
1, Table 1).

\emph{Competitive reshuffling}

The nature of community dynamics differed in the W global change
treatment. Instead of increased dominance and a decline in evenness,
species groups reshuffled in abundance whereby the dominant species
declined in cover over time, while subdominant and rare species
increased, and the cover of moderate species did not change (Fig 1,
Table 1). Furthermore, this was the \emph{only} treatment where we
observed a change in the relative abundance of the rare species group
over time.

\emph{Ambient}

We can contrast these two broad types of shifts in community structure
under global change manipulations to the patterns observed under ambient
conditions (CTL), which showed little directional change over time. The
cover of dominant and subdominant species did not change, while the
abundance of moderate species decreased and rare species slightly
increased (marginally significant) (Fig 1, Table 1). Given that
different global change treatments resulted in distinct shifts in
community structure, we then ask whether density-independent responses
to the environment or density-dependent species interactions best
explain these shifts.

\textbf{Density independent processes}

All three environmental drivers (snow, N, and temperature) influenced
the density-independent growth rates of species groups with varying
consistency and magnitude across treatments (Fig 2, Table S1). Drivers
often had counteracting effects, reducing the benefits of enhanced
resource availability (water, N, and temperature) on density-independent
growth rates. In addition, rare species had very few responses to
environmental drivers (Fig S7), consistent with the limited change in
cover of this group over time.

\emph{Dominant increase}

In plots where dominance increased, environmental effects on
density-independent growth rates were not always consistent with changes
in species group abundance over time. For example, in the SNW plots,
only the dominant species had a positive effect of added N, which is
consistent with its increase over time (Fig 2, Table S1). However, in
SNW plots, subdominant and moderate species groups also strongly
decline, suggesting that other density-dependent mechanisms are at play.

In SW plots, where we observed the second highest increase in dominance,
temperature and snow depth had counteracting (but weak) effects
on\emph{Deschampsia} growth rates. In contrast, snow addition and
ambient N deposition increased subdominant growth rates while warming
decreased subdominant and (to a lesser extent) moderate species growth
rates (Fig 2, Table S1). For moderate species, the negative effects of
warming in SW plots were consistent with their decline over time.
However, counteracting (neutral) environmental effects on dominant and
subdominant species do not explain their strong directional shifts in
abundance.

In NW plots, where we observed the weakest increase in the dominant
species, \emph{Deschampsia} growth rates were positively influenced by N
addition and strongly negatively influenced by warming (Fig 2, Table
S1), suggesting that counteracting influences of nitrogen and warming
muted the dominant increase over time. In addition, warming had a
positive effect on the growth rates of moderate species, dampening the
weaker negative effect of N addition, which may have reduced their
magnitude of decline in these plots over time compared to other
treatment types. However, subdominant species had no clear effects of
the environment despite their decline in abundance.

\emph{Competitive reshuffling}

In the W plots, where we observed species reshuffling, warming had no
effects on density-independent growth rates. Instead, ambient snow depth
had a positive effect on the dominant species growth rate and ambient N
had a positive effect on subdominant and moderate species and a negative
effect on the dominant species growth rates (Fig 2, Table S1). This
supports the observed pattern of community reshuffling in that
subdominant and moderate species growth rates increased while
contrasting positive and negative effects cancelled out any benefit of
the environment for the dominant species. Rare species had no effect of
environmental drivers in W plots despite their increase over time (Fig
S7).

\emph{Ambient}

In CTL plots where we observed weak or no directional changes over time,
ambient snow depth had a negative effect on moderate species growth
rates (Fig 2, Table S1), which may have influenced the decline in this
species group over time. Ambient snow depth also had a positive effect
on the dominant species growth rate (Fig 2, Table S1), yet no change was
observed in the dominant species over time.

\textbf{Density dependent processes}

Incorporating density-dependent shifts in competitive interactions more
fully explained the directional changes in species group cover over time
(Fig 3, Table S2, S3). Broadly, global change treatments shifted
intraspecific and interspecific competition in ways that reduced net
competition for the dominant species but increased net competition for
subdominant and to a lesser extent, moderate and rare species (Fig 3,
S8). The major exception to this pattern was in the W plots, where we
observed an increase in net competition for the dominant species. In
addition, density-dependent species interactions shifted along
environmental gradients, primarily N, leading to non-linear abundance
distributions for subdominant species.

\emph{Dominant increase}

Models revealed several changes in species interactions consistent with
observed declines in evenness. First, competitive effects on the
dominant species declined in all plots where \emph{Deschampsia}increased
over time (SNW, SW, and NW) (Fig 3, Table S3). In SNW and NW plots, this
was driven primarily by reduced interspecific competition, while in SW
plots this was driven primarily by reduced intraspecific competition of
the dominant species on itself (Fig 3, Table S3). The dominant species
increased its intraspecific competition in plots with added N (SNW, NW),
consistent with the positive effect of N addition on its
density-independent growth rates (Fig 2). Furthermore, net competitive
effects increased in SNW and SW plots for subdominant and (to a lesser
extent) moderate species, primarily driven by increased interspecific
competition with each other, reflecting higher-order interactions that
benefit the dominant species, and contribute to their decline in these
treatments over time.

In the NW treatment, competitive effects declined for \emph{all} species
groups (except rare) (Fig 3, Table S3). Reduced competition, in
combination with density-independent patterns observed, help explain the
lower magnitude of moderate and subdominant species declines in NW
compared to SW and SNW treatments. However, this pattern was more
pronounced for moderate than subdominant species, likely due to a strong
reduction in the competitive effect of the dominant on moderate species
(Fig S8c). In addition, predictive steady-state distributions revealed a
non-linear (left-skewed) distribution of subdominant species across the
observed N gradient in NW plots, suggesting that subdominant species
only benefits from competitive release at low N levels, after which the
dominant takes over (Fig 4).

\emph{Competitive reshuffling}

In W plots, where we observed competitive reshuffling, competitive
effects strongly increased for the dominant species, mostly driven by
the subdominant species group (Fig 3, S8, Table S3). This enhanced
competitive pressure on the dominant species was not present in any
other global change treatment (Fig 3, Table S3). Competitive effects
also increased on the subdominant species, driven by enhanced
competition from the moderate species group (Fig 3, S8, , Table S3).
Predictive steady-state distributions show that this was likely driven
by competition for ambient N, as both moderate and subdominant species
respond positively to ambient N in W plots (Fig 2). Thus at higher
levels of ambient N, the subdominant species are outcompeted by the
moderate species (Fig 4). Finally, net competition weakened slightly in
W plots for rare species (Fig 3, Table S3), via reduced competition from
the dominant species (Fig S8), consistent with the increase of rare
species in these plots over time.

\emph{Ambient}

Intraspecific competition was high for dominant, subdominant, and
moderate species groups in CTL plots, indicating negative frequency
dependence under ambient conditions (Table S2, Fig S8). There was also a
relatively high competitive effect of moderate species on dominant
species, while competition between dominant and subdominant and moderate
and subdominant species was relatively low, indicating niche
partitioning between these groups (Table S2, Fig S8). Rare species had
neutral intraspecific and interspecific competition in as is expected
due to low abundances (Table S2, Fig S8).

\textbf{Community stability}

Three out of four global change treatments (SNW, NW, W) and CTL
communities were stable based on eigenvalue analysis, while SW
communities were unstable (Table 2). Out of the stable communities, NW
and W communities were more stable, while SNW communities were less
stable than CTL, suggesting treatments with lower magnitude shifts in
species group abundance are more stable over time.

The SW treatment was the only unstable community, likely due to a lack
of self-limitation (intraspecific competition) of the dominant species
(Table S2). We can further see this in that the maximum equilibrium
abundance of the dominant species is lower in SW versus other global
change plots (Fig S9). Thus, the strong rate of increase over the last
15 years suggests that the dominant species has likely overshot its
carrying capacity in SW plots and will ultimately to decline again to
regain community stability.

\textbf{Discussion}

Global change is influencing plant community structure by leading to
shifts in species dominance and competitive hierarchies. Determining the
co-occurring density-independent and dependent mechanisms underlying
these changes is critical to accurately predict net outcomes for
community structure and biodiversity maintenance over long time scales.
While this concept is not new to community ecology, few studies (to our
knowledge) have fully parameterized the density-dependent and
independent components of species changes over long time scales under
multiple scenarios of global change.

Our work highlights the importance of density-dependent mechanisms,
including shifts in intra- and interspecific competitive strengths, in
driving long-term changes in the abundance of species groups under
global change. We show that these competitive interactions can shift
with the environment, in our system primarily with N, and drive
non-linear species responses across environmental gradients further
influencing community structure. Furthermore, despite significant shifts
in community structure under global change, community stability can
persist, or even increase, given that the dominant species maintains
negative density-dependence.

Overall, our results provide a clearer understanding of how global
change can lead to either community reshuffling and varying degrees of
diversity decline through reduced evenness of species groups, and that
only considering density-independent responses to the environment fails
to or only partially explains these outcomes. It is well supported that
that direct (i.e. density-independent) species responses to the
environment insufficiently predict how communities will be restructured
under global change (Suttle \emph{et al.} 2007; Liancourt \emph{et
al.}2013; Alexander \emph{et al.} 2015). Yet many species distribution
modeling (SDM) approaches continue to utilize only climatic or
environmental constraints when predicting future species distributions
(Davis \emph{et al.} 1998; Swab \emph{et al.} 2015; Roe \emph{et
al.}2021). Our work suggests that while this approach may inform changes
for some species groups under certain global change scenarios, such as
dominant species responses to N, it is limited in understanding
community-level responses to multiple global change drivers, which is
critically needed for the maintenance of biodiversity (CITE).

Finally, our approach of utilizing species groups based on dominance
rather than estimating species specific patterns proved highly useful
for predicting changes in community structure over time. Initial
abundance rather than the functional mechanisms of a species were shown
to be a strong predictor of species losses under atmospheric N
deposition across ecosystem types (Suding \emph{et al.} 2005). In
addition, a recent study of plant responses to climate change in the
Arctic tundra suggest that commonness itself may be a strong predictor
of response types, as rates of change in taxa over time were related to
the baseline commonness of species early in the experiment (Post\emph{et
al.} 2021). Rarity and dominance within a community are only informative
at local to regional scales; these designations are based on the species
pool in which the species exists, as well as the mechanisms driving its
local dominance vs. rarity, making global scaling difficult. In
addition, what formally defines dominant vs rare species in a
spatiotemporal setting is still somewhat elusive, and considering both
the relative abundance and frequency of a species as well as its
ecological impacts on the community is critical (Avolio \emph{et
al.}2019). Despite these caveats, understanding how and why patterns of
dominance will shift with global change is of critical importance for
predicting novel community assemblages and corresponding changes in
community diversity and stability.

\textbf{Acknowledgements}

We thank the following people for assistance with plot design, set up
and data collection in the field: Isabel Ashton, Emily Farrer, Charlotte
Riggs, Marko Spasojevic, Matt Khosh, Warren Sconniers, Kali Blevins,
Beth McGarry, Bridget Hass, David Gonzalez, Krista Beckley, Eva
Franzova, Sagar Yeraballi, Liana Nichols, Robert Weller, Adam Formica,
Keishla Rodriguez, Shiyang Fu, Trang Pham, Robert Weller, Julia Reich,
Liana Nichols, Jehane Semaha, Luke Tillmann, Pierre Mariotte, Joan
Dudney, Riva Madan, Conor Bernard, Jared Huxley, Sam Sartwell, Caitlin
White, Cliff Bueno de Mesquita, Kelsey Elwood, Josh Addison, AJ Lodge,
Austin Willbern, Kenya Gates, and Annika Rose-Person. This study was
funded by the Niwot Ridge LTER. LGS and MCS were supported by NSF grant
\#2033292.

\textbf{Data DOIs}

Plant community
~https://doi.org/10.6073/pasta/c771ce4b4eb9527b85bceb67c59b3bab~

Snow depth
~https://doi.org/10.6073/pasta/8186d641539c37787495804b817e55ed~

Air temperature
https://doi.org/10.6073/pasta/846acb40f7e8a0491ee789428f4e4bea~

Nitrogen deposition https://nadp.slh.wisc.edu/sites/ntn-CO02/

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\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/454378/articles/552003-global-change-re-structures-alpine-plant-communities-under-15-years-of-warming-nitrogen-and-snow-addition-disentangling-density-independent-and-dependent-effects}

\textbf{Fig. 1} \textbf{Net Changes in Cover} . Changes in cover of
species groups at the plot level with respect to pre-treatment values
(2006) for each year (2007-2020). Points show the change in cover (i.e.
`hits') versus 2006 for a given plot by each species group within each
year (n=6 plots per treatment x 4 groups x 14 years). Lines reflect
modeled estimates of cover change by treatment type, species group, and
duration of treatment (number of years since 2006) with a random effect
of calendar year with 95\% confidence intervals plotted around line
estimates (Table 1).

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/454378/articles/552003-global-change-re-structures-alpine-plant-communities-under-15-years-of-warming-nitrogen-and-snow-addition-disentangling-density-independent-and-dependent-effects}

\textbf{Fig. 2 Density Independent mechanisms.} Posterior parameter
estimates for responses of density-independent growth rates
(\selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{s}) to standardized environmental covariates
(Temp-average summer air temperature (\selectlanguage{ngerman}°C), Snow depth-mean April snow
depth (cm), N dep-average summer nitrogen deposition
(g/m\textsuperscript{2}/yr). Points show mean estimates and error bars
show 95\% Bayesian credible intervals. We set wide priors on \selectlanguage{greek}ρ
\selectlanguage{english}coefficients (-0.5, 0.5) to allow a 50\% change (increase or decrease)
in \selectlanguage{greek}ρ\selectlanguage{english}\textsubscript{s} in response to a given environmental covariate at
each time step and estimates here reflect posterior sampling across all
time steps. Rare species showed weak DI responses to environmental
variables (Fig S7). Estimates are standardized by treatment as models
were run separately for each treatment, thus the magnitude of parameter
estimates and credible intervals should be compared between species
groups \emph{within} a treatment but not across treatments (colors).

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/454378/articles/552003-global-change-re-structures-alpine-plant-communities-under-15-years-of-warming-nitrogen-and-snow-addition-disentangling-density-independent-and-dependent-effects}

\textbf{Fig. 3 Density Dependent mechanisms.} Changes in the mean
competitive interactions (\selectlanguage{greek}Δα\selectlanguage{english}s) of each species group in each global
change treatment versus control. Intraspecific (Intra) shows the mean
change in competition of a species group on itself (i.e.
self-limitation). Interspecific (Inter) shows the sum of the mean
changes of all other species groups on that group. Net is the
combination of intra and interspecific changes within each treatment and
species group. Values to the left, right of the dotted zero line signify
that that competition on a species group became stronger, weaker in
global change vs control conditions respectively. Raw pairwise \selectlanguage{greek}α \selectlanguage{english}and
\selectlanguage{greek}Δα\selectlanguage{english}values shown in Tables S2, S3, and Fig S8 (a-d).

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/454378/articles/552003-global-change-re-structures-alpine-plant-communities-under-15-years-of-warming-nitrogen-and-snow-addition-disentangling-density-independent-and-dependent-effects}

\textbf{Fig. 4} \textbf{Competitive interactions drive non-linear
responses to ambient and added N.} Estimated steady-state abundance
distributions (relative plot proportions) for each species group across
the observed gradients of ambient (left) and added (right) N deposition
(zero centered +/- two standard deviations) in NW and W global change
plots (See Fig S9 for all treatments). Points show simulated equilibrium
abundances (100 at each value of x). Dotted lines show best model fit
from a general additive model in the \emph{geom\_smooth} function in
ggplot2 (Wickham 2009)..

\textbf{Table 1. Changes in species group cover over time}

Estimated lmer modeled slopes of changes in cover (`plot hits') of
species groups with respect to pre-treatment (2006) cover per year over
the 15 year period. Mixed models predicted the change in plot cover per
year with a fixed interaction term of species group x treatment type x
treatment duration (years since 2006) with a random intercept of
calendar year and a global intercept of zero to determine if changes
were positive or negative over time. P values were estimated using the
package lmerTest in R (Kuznetsova \emph{et al.} 2017)with 0.109 SE and
937.7 df for all species groups and treatments.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Species group} & \textbf{Treatment} & \textbf{Estimate} &
\textbf{t value} & \textbf{P value}\tabularnewline
\midrule
\endhead
Dominant & Snow + N + Warming & 2.287 & 20.953 & 0.000\tabularnewline
Subdominant & Snow + N + Warming & -1.667 & -15.270 &
0.000\tabularnewline
Moderate & Snow + N + Warming & -0.665 & -6.096 & 0.000\tabularnewline
Rare & Snow + N + Warming & 0.000 & 0.002 & 0.998\tabularnewline
Dominant & Snow + Warming & 1.732 & 15.866 & 0.000\tabularnewline
Subdominant & Snow + Warming & -1.371 & -12.563 & 0.000\tabularnewline
Moderate & Snow + Warming & -0.523 & -4.790 & 0.000\tabularnewline
Rare & Snow + Warming & 0.148 & 1.358 & 0.175\tabularnewline
Dominant & N + Warming & 0.872 & 7.985 & 0.000\tabularnewline
Subdominant & N + Warming & -1.223 & -11.201 & 0.000\tabularnewline
Moderate & N + Warming & -0.251 & -2.296 & 0.022\tabularnewline
Rare & N + Warming & 0.145 & 1.328 & 0.185\tabularnewline
Dominant & + Warming & -0.751 & -6.876 & 0.000\tabularnewline
Subdominant & + Warming & 0.234 & 2.142 & 0.032\tabularnewline
Moderate & + Warming & -0.062 & -0.572 & 0.567\tabularnewline
Rare & + Warming & 0.330 & 3.020 & 0.003\tabularnewline
Dominant & Control & -0.145 & -1.327 & 0.185\tabularnewline
Subdominant & Control & 0.017 & 0.155 & 0.877\tabularnewline
Moderate & Control & -0.287 & -2.627 & 0.009\tabularnewline
Rare & Control & 0.202 & 1.850 & 0.065\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2. Community stability eigenvalues}

Real and imaginary coordinates of community eigenvalues from modeled
interaction (\selectlanguage{greek}α\selectlanguage{english}) matrices from gjamTime (Clark et al. 2020) for each
treatment (plot type). The number of eigenvalues from each matrix is
equal to the number of members in a community, here 4 species groups
(dominant, subdominant, moderate, and rare). Communities are considered
stable if all real eigenvalues within that community are negative
(Allesina \& Tang 2012). The rightmost real eigenvalue shows the highest
(least negative/ most positive) real eigenvalue within each treatment
(i.e. community) whereby lower (more negative) rightmost real
eigenvalues denote higher stability (Carpentier \emph{et al.} 2021).\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Treatment} & \textbf{Real} & \textbf{Imaginary} &
\textbf{Rightmost real} & \textbf{Stability} & \textbf{Stability rank
(1-4)}\tabularnewline
\midrule
\endhead
Snow + N + Warming & -2.118 & 0.000 & -0.008 & Stable & 1\tabularnewline
Snow + N + Warming & -0.495 & 0.000 & -0.008 & Stable & 1\tabularnewline
Snow + N + Warming & -0.082 & 0.000 & -0.008 & Stable & 1\tabularnewline
Snow + N + Warming & -0.008 & 0.000 & -0.008 & Stable & 1\tabularnewline
Snow + Warming & -2.150 & 0.000 & 0.049 & Unstable & 0\tabularnewline
Snow + Warming & -0.358 & 0.000 & 0.049 & Unstable & 0\tabularnewline
Snow + Warming & -0.099 & 0.000 & 0.049 & Unstable & 0\tabularnewline
Snow + Warming & 0.049 & 0.000 & 0.049 & Unstable & 0\tabularnewline
N + Warming & -1.994 & 0.000 & -0.150 & Stable & 4\tabularnewline
N + Warming & -0.561 & 0.000 & -0.150 & Stable & 4\tabularnewline
N + Warming & -0.150 & 0.148 & -0.150 & Stable & 4\tabularnewline
N + Warming & -0.150 & -0.148 & -0.150 & Stable & 4\tabularnewline
+ Warming & -2.223 & 0.000 & -0.110 & Stable & 3\tabularnewline
+ Warming & -0.285 & 0.011 & -0.110 & Stable & 3\tabularnewline
+ Warming & -0.285 & -0.011 & -0.110 & Stable & 3\tabularnewline
+ Warming & -0.110 & 0.000 & -0.110 & Stable & 3\tabularnewline
Control & -2.085 & 0.000 & -0.072 & Stable & 2\tabularnewline
Control & -0.575 & 0.000 & -0.072 & Stable & 2\tabularnewline
Control & -0.168 & 0.000 & -0.072 & Stable & 2\tabularnewline
Control & -0.072 & 0.000 & -0.072 & Stable & 2\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig1V2/Fig1V2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig2V2/Fig2V2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3V2/Fig3V2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig4/Fig4}

\end{center}
\end{figure}

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

