\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{Turf facilitation in global marine forests}



\author[1]{Allison Barner}%
\author[2]{Arley Muth}%
\affil[1]{Colby College}%
\affil[2]{University of Texas at Austin}%


\vspace{-1em}



  
  \date{May 18, 2024}


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





\selectlanguage{english}
\begin{abstract}
The Stress Gradient Hypothesis (SGH) predicts primary producer
competition and facilitation is related to environmental stress. Despite
being well-documented in terrestrial systems, facilitation is rarely
integrated into our understanding of the dynamics in marine forests.
Critical coastal habitats created by canopy-forming seaweeds have
recently been seen to transition to less biodiverse regimes dominated by
turf algae and populations are predicted to increasingly transition
based on the paradigm that turf algae negatively affect canopy seaweeds.
Meta-analysis was used to estimate the effect of turfs on canopy species
using experimental and observational data. We found that turf species do
compete with marine canopies in subtidal kelp forests, however, these
interactions become increasingly facilitative at shallower depths and
high latitude, supporting the SGH. This work provides new insight into
the dynamics of a highly-studied ecosystem, and emphasizes the need to
re-assess the importance of facilitation when predicting the response of
systems to global change.%
\end{abstract}%



\sloppy


\emph{Introduction}

Competitive interactions among primary producers have been widely shown
to shift to facilitative when conditions become stressful, as predicted
by the Stress Gradient Hypothesis (SGH; Bertness \& Callaway 1994; Bruno
et al. 2003; He et al. 2013). For example, in a study of alpine plant
interactions, identically and globally replicated experiments
demonstrated that interactions shifted from competitive at low
elevations to facilitative at higher, more stressful, elevations
(Callaway et al. 2002). This shift from competitive to facilitative
interactions along gradients of stress has likewise been shown for arid
plants and nurse shrubs (Armas et al. 2011), pitcher plants and spiders
(Lim et al. 2018), and alpine herbivores (Barrio et al. 2013), among
others. Similar to terrestrial elevational shifts from negative to
positive interactions, species interactions among marine and limnal taxa
have also been shown to shift from competitive to facilitative in 1)
drier, more stressful tidal elevations (intertidal marsh plants:
Bertness \& Hacker 1994; barnacles and mussels: Kawai \& Tokeshi 2007),
and in 2) warmer, more stressful latitudes (Bennett et al. 2015; McAfee
et al. 2016). Despite being well-documented in terrestrial and brackish
ecosystems, the SGH has not been tested in marine forests, and instead
competition is thought to be the dominant structuring mechanism (though
see Bennett et al. 2015). In this study, marine forest ecosystems were
used to examine influences of species facilitation, positive
interactions and principles of the SGH in structuring algal communities.

The past decade has seen dramatic declines in marine forest cover (Ling
et al. 2015; Krumhansl et al. 2016; Wernberg et al. 2016; Verg\selectlanguage{ngerman}és et al.
2016). Foundation species provide habitat and energy to associated
organisms (Dayton 1972), ultimately increasing biodiversity. Foundation
species persistence is critical for understanding the dynamics of the
systems they sustain. One of the most well studied, conspicuous groups
of foundation species are canopy-forming brown seaweeds, belonging
primarily to the orders Laminariales (kelp) and Fucales (fucoids). Many
disturbed marine forests are shifting from canopy-dominated to systems
dominated by turf-forming or crustose algae (Filbee-Dexter \& Scheibling
2014; Filbee-Dexter \& Wernberg 2018), an example of alternative stable
states (Dayton \& Tegner 1984). With a suite of abiotic and biotic
stressors contributing to the decline of kelp forests, their recovery is
thought to be prevented, in part, by competition with algal species that
live in the understory (Filbee-Dexter \& Scheibling 2014; Filbee-Dexter
\& Wernberg 2018).

Negative effects of algal turfs on canopies are well supported from
long-term subtidal research programs and experimental studies (Dayton et
al. 1984; Hernández -Carmona et al. 2005; Gorman \& Connell 2009).
Although many canopy-forming species are dominant competitors (Dayton et
al. 1984), abiotic and biotic disturbance can lead to the dominance of
turf algae, if those turf algae compete with the canopy (Fig. 1A). For
example, the co-occurrence of El Niño-driven warm temperatures, nutrient
depletion, and unusually strong storms devastated more than 500 hectares
of kelp forest in southern California (Dayton \& Tegner 1984). In the
wake of massive loss of kelp adults and kelp recruitment failure, a new,
turf-dominated community emerged, comprised of turf species and
subdominant understory kelp. Algal turfs, unaffected by the El Niño
disturbances, inhibited the recruitment of new canopy kelp, slowing the
kelp forest recovery (Dayton et al. 1984). Although the kelp forest
generally recovered after this El Niño, the kelp-dominated community
never returned at its southernmost range edge (Hernández-Carmona et al.
2005). The dominance of these turf species, particularly
herbivore-resistant coralline algae, is often maintained by the presence
of kelp grazers (Vergés et al. 2016), forming the classic coralline
algae/urchin barren alternative stable state (Filbee-Dexter \&
Scheibling 2014). The assumption of competitive exclusion by the turf
now forms the basis of recommended restoration practices
(Hernandez-Carmona et al. 2005) and even predictions for the future of
canopy-forming marine ecosystems (Connell et al. 2013). For example, in
the turf-as-competitors framework (Fig. 1A), as coralline turfs are
expected to decline with ocean acidification, kelp abundance would be
expected to increase under ocean acidification, due to the assumed
release from competition and physiological effects from
increased\emph{p} CO\textsubscript{2} (Harley et al. 2012). Despite the
dominance of the competition framework in the study of marine forests,
many examples exist of canopy facilitation by turfs (Fig. 1B). These
examples of facilitation have yet to be comprehensively incorporated
into our understanding of canopy-turf dynamics or marine forest
alternative stable states.

In this study, we approach competition and facilitation in marine
forests within the framework of the SGH and the well-documented patterns
seen in terrestrial systems. We examine the longstanding hypothesis that
the effect of turf algae on canopy-forming species is competitive, using
a new global dataset of marine turf-canopy interactions. We used
meta-analysis to answer the following questions: a) What is the overall
effect of the turf on canopy-forming seaweeds? b) Does this effect
differ among turf functional groups? c) Does the effect of turfs on the
canopy vary along stress gradients, i.e. depth? d) Does the effect of
turfs on the canopy vary latitudinally?, and d) Does herbivory modify
the effect of turfs on the canopy? We expand the number of studies
previously included in a meta-analysis of kelp-turf interactions
four-fold, and broaden the scope from the effect only on recruitment to
the effect on all life-history stages of canopy forming species (O'Brien
\& Scheibling 2018). We show that the previously well-documented
competitive effects of turf species on canopy species are part of a
continuum of positive to negative interactions that depend predictably
on stress.

\emph{Methods}

\emph{Study Systems}

Marine forests generally are created by the large, three-dimensional
structure of brown seaweeds in the class Phaeophyceae, primarily the
``kelps'' (order Laminariales) and fucoids (order Fucales). Generalized
terms used to describe functional groups within these systems are not
consistent, thus, for the sake of clarity we will use ``canopy-forming
species'' to describe brown seaweeds that create three-dimensional
habitat. This includes all canopy species referred to as floating,
stipitate, and prostrate canopies in Dayton et al. (1984), also termed
surface, understory, and bottom canopies (Schiel \& Foster 2015). Algal
turfs are a diverse functional group that includes species from all
three algal phyla, but refer to shrub-like, filamentous, and branched
species that can be opportunistic and provide very little structure
(Filbee-Dexter \& Wernberg 2018). Alternately, algal crusts are
low-lying, prostrate species that closely adhere to the substrate
(Steneck \& Dethier 1994). These two functional groups can further be
divided by whether or not the species is calcified (Steneck \& Dethier
1994). These delineations result in four functional groups that interact
with canopy-forming species: coralline turf, coralline crust,
non-coralline turf, non-coralline crust. Throughout the manuscript, we
often refer to ``turfs'', given their increasing importance in
transforming coastal environments (Filbee-Dexter \& Wernberg 2018), but
we consider the interactions of each of these four functional groups
that live below the canopy.

\emph{Data Compilation}

We searched the ISI Web of Science database (26 October 2017) and
ProQuest Dissertation \& Theses database (25 November 2017) to find
studies of the effects of turf algae on canopy algal species (see
Appendix S1 in Supporting Information for a list of search terms). These
searches resulted in 1,916 unique manuscripts. Of these results, studies
were included if they met the following inclusion criteria:

\begin{itemize}
\tightlist
\item
  Study type: empirical (no theoretical or modeling papers were
  included)
\item
  Species studied: canopy-forming algae and understory algae (crustose
  or turf-forming)
\item
  Treatment: presence/absence of understory algae or gradient of
  understory abundance
\end{itemize}

\begin{itemize}
\tightlist
\item
  Responses: canopy algae fitness or population growth in the
  presence/absence of understory algae, including: density of recruits,
  population size, mechanical strength, survival, survival
\item
  Comparator: appropriate controls for experimental studies,
  non-understory algal substrates (when comparing kelp fitness when
  associated with understory substratum)
\item
  Mean and variance for both experimental and control treatments were
  reported
\end{itemize}

A list of the total papers assessed, and whether each was included, can
be found in Appendix S1 Table S1. We supplemented these records with
additional studies that met our inclusion criteria, obtained via
personal communication and literature cited sections of papers. This
resulted in included studies from 41 manuscripts (some manuscripts
included multiple studies with unique response variables or treatment
types). For each study, we recorded the treatment and control effect
sizes and variance, the sample sizes, latitude, longitude, study year,
response type (count, percent cover, rate, etc.), experiment type (turf
removal, substrate comparison), plot size (meters\textsuperscript{2}) or
container volume (milliliter), whether or not herbivory was
experimentally tested, the life-history stage of the canopy species,
species identities, and depth (in meters relative to mean lower low
water, ``MLLW''). When the latitude and longitude was not provided for a
study, we used GEOLocate to estimate the location from locality
(\emph{http://www.museum.tulane.edu/geolocate/} ). When a depth range
was reported, the midpoint was recorded. For studies where depth was not
reported, we imputed depth as follows. Using information provided in the
text, if the study was described as low or mid intertidal, we assigned a
depth of +0 and +1.0 m MLLW, respectively (Appendix S1). Generally,
``intertidal'' refers to studies at depths shallower than 0 m MLLW,
while ``subtidal'' are the zones deeper than the lowest low tide (m MLLW
\textless{} 0). For four studies, no depth or zone was reported and
additional information from the literature was sought to assign depth
(Appendix S1). When plot sizes were not standardized among replicates
(only Gunnill 1980), plot area was recorded as the midpoint between the
two sizes.

When papers reported results from studies replicated at multiple sites
or time points, we recorded data from each replicated study in our
database. When an experiment tested the effect of multiple factors, we
included data from only the treatment most similar to ambient or natural
conditions (e.g., a no-nutrient addition treatment, or a canopy-intact
treatment). In two studies, the effect of artificial turfs on the canopy
was measured. These effects did not differ from the effect of ``live''
turfs (Appendix S2) and were thus included. When raw data or means and
variance estimates were not available in text or tables, we extracted
data from figures using `digitize' for R (Poisot 2011; R Core Team
2019).

\emph{Analysis}

We estimated the effect of turf species on canopy-forming species as the
log-transformed response ratio (LNRR; Hedges et al. 1999). LNRR was
chosen for its similarity to the calculation of non-trophic species
interaction strengths from experimental data (Armas et al. 2004). The
direction of effects from all studies can be interpreted as species
interactions. A positive LNRR results from the negative effect that the
understory has on the canopy, such that, for example, the removal of
turfs results in higher canopy abundance in the experimental plot than
in the unmanipulated control. Because several metrics resulted in a
negative data point (e.g., negative growth rates), we standardized data
by study by adding the minimum data value to each data point in the
study to make the minimum data value zero, and then a small amount
(0.001) was added to all zero means and standard deviations. We used a
version of LNRR corrected for small sample size (equations 10 and 11 in
Lajeunesse 2015).

We analyzed the data using mixed effects meta-regression, with random
effects to account for variation within and among studies. Separate
models were run for observational and experimental studies, as the
methods differed significantly among the two study types. The random
effect structure for both experimental and observational data allowed
intercepts to vary among studies. Experimental random effects included
an additional term allowing intercepts to vary among different response
types (density, proportion, canopy length, growth rate), as, unlike in
observational studies, experimental studies often reported the results
of multiple different experiments that measured responses in multiple
ways (Appendix S2).

In addition to the fundamental differences between experimental and
observational methods, experimental interactions were often estimated at
multiple time points. There are many ways to model repeated measures in
meta-analysis (Koricheva et al. 2013). Because we were interested in
whether the effect of the understory changed through time, the number of
days since the start of the experiment was included in each experimental
model. For certain nested models, likelihood ratio tests were used to
determine whether additive or multiplicative model structures should be
used (Appendix S2 Table S3). In all analyses, model coefficients are
weighted by study precision (inverse variance) to give more weight to
more highly replicated studies.

To quantify the overall effect of turfs on the canopy, across all
studies, we used a random effects model with no fixed effects, removing
studies that manipulated herbivory. To quantify the effects of our
explanatory variables (functional group, depth, latitude), we used a
mixed effects model with the same random effect structure as our overall
effects model. To quantify the effects of herbivores, we subsetted the
data to only studies that included an herbivory treatment. We also ran
each experimental model separately for canopies in the order
Laminariales (``kelps'') and canopies in the order Fucales (Appendix
S3). Other orders present in the dataset (Dictyotales and
Desmarestiales) were not independently tested as there were too few
samples (\emph{n} Dictyotales = 2, \emph{n} Desmarestiales = 5).
Pseudo-R\textsuperscript{2} was calculated for each mixed model as the
proportion of total variance explained relative to a random-effects only
model. Models were fit with restricted maximum-likelihood estimation and
the significance of model coefficients and of linear contrasts was
determined using Wald-Type Tests (Viechtbauer 2010). We conducted a
series of analyses (Appendix S2) demonstrating that LNRR was not related
to study year, plot size, or inclusion of artificial understory.
Finally, publication bias was tested for all data (Appendix S2).

\emph{Results}

We identified 41 studies that evaluated the effect of the understory on
marine canopies, yielding 337 interaction estimates, spanning 46
canopy-forming species from four orders and 61 distinct turf seaweed
species (Appendix S2 Figure S1). Studies were drawn from multiple
biogeographic regions on five continents, as early as 1973, co-occurring
with regions in which canopy-turf regime shifts have occurred
(Filbee-Dexter \& Scheibling 2014; Fig. 2). Overall, we found little
evidence of publication bias in our dataset, supporting the results we
describe below (Appendix S2).

\emph{Experimental Studies}

Overall, the experimental removal of an algal understory had a mean
neutral effect on the canopy (random effects model, mean
LNRR\textsubscript{exp} = 1.55 ± 0.94, \emph{p} = 0.099), with
interactions ranging from strongly competitive to strongly facilitative
(Fig. 3). The mean interaction did not change over the course of an
experiment, measured as the number of days since the start of the
experiment (\selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{days} = 0.0012, \emph{p} = 0.052). The
effect of turf species on the canopy varied among turf functional groups
(overall Wald-type test, QM\textsubscript{{[}4{]}, exp} = 17.16, \emph{p
=} 0.0018, pseudo-R\textsuperscript{2} = 0.35, Fig. 3). Specifically,
the overall competitive effect of turfs on the canopy was driven by the
negative effects of coralline turf and non-coralline crust
(\selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{coralline turf} = 3.59, \emph{p} = 0.0009;
\selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{non-coralline turf} = 2.91, \emph{p} = 0.034); all other
turf species had no significant effect on the canopy (all
\emph{p}\textgreater{} 0.05; Appendix S3 Table S1). However, the effect
of turf species differed among canopy types. Among kelps, non-coralline
turf taxa also had a negative effect on the canopy
(\selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{non-coralline turf} = 5.04, \emph{p} = 0.026). Among
Fucales, all turf functional groups had an overall neutral effect on the
canopy (all \emph{p} \textgreater{} 0.10, Appendix S3 Table S1).

However, these overall effects marked geographical and latitudinal
variation in the effect of turfs on canopies. In line with the
widespread paradigm of kelp forest interactions, the effect of turfs was
overall competitive in subtidal systems. Specifically, the effect of
coralline turf on canopy taxa was competitive in the subtidal, and
increasingly facilitative in the intertidal (Fig. 4,
pseudo-R\textsuperscript{2} = 0.16, \selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{depth * coralline
turf} = -0.61, \emph{p} = 0.0003). Though found in a narrower depth
range than turfs, non-coralline crust also had an increasingly positive
effect on the canopy at shallower depth (\selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{depth *
non-coralline crust} = -1.33, \emph{p} = 0.009). The effect of coralline
crust and non-coralline turf on the canopy did not change across depth,
and this pattern held for canopy kelps (Appendix S3 Table S2). Studies
of canopy Fucales, conducted primarily in the intertidal zone,
demonstrated no difference in interactions across depth (all functional
groups, \emph{p} \textsubscript{functional group: depth} \textgreater{}
0.05, pseudo-R\textsuperscript{2} = 0.27; Appendix S3 Table S2).

The effect of turfs on the canopy varied across latitude
(QM\textsubscript{{[}8{]}} = 26.69, \emph{p} = 0.0008,
pseudo-R\textsuperscript{2} = 0.39). In particular, the effect of
non-coralline crust became more competitive at higher latitudes
(\selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{latitude * non-coralline crust} = 0.40, \emph{p} =
0.027). Further, the effect of coralline turf was more facilitative at
higher latitudes, with marginal significance (\selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{latitude *
coralline turf} = -0.35, \emph{p} = 0.058; Appendix S3 Table S3).

Next, we looked only at variation in the interaction across life history
stage of the canopy, and did not include variation across depth and
latitude. The effect of turfs on the canopy differed among canopy life
history stages, depending on the turf functional group and the identity
of the canopy (QM\textsubscript{{[}31{]}} = 45.13, \emph{p} \textless{}
0.0001, pseudo-R\textsuperscript{2} = 0.13; Fig. 3). Turf species had
competitive effects on the canopy, primarily at early life history
stages. Coralline crust and all turfs negatively affected germling
canopies (overall effect, \emph{p} for linear contrasts \textless{}
0.01: \selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{coralline crust} = 4.77, \emph{p} = 0.029;
\selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{coralline turf} = 9.65, \emph{p} = 0.00008;
\selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{non-coralline turf} = 5.43, \emph{p} = 0.014). Further,
coralline crusts negatively affected the later canopy ``recruit'' stage
(overall effect: \selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{coralline crust} = 4.77, \emph{p} =
0.004). All other effects were not modified by life history stage of the
canopy (Appendix S3 Table S4). Finally, considering only studies that
manipulated herbivory (n = 28), the mean effect of turfs on the canopy
did not depend on the presence or absence of herbivores (linear
contrast, \selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{-herbivore - +herbivore} = 2.84 \selectlanguage{ngerman}±
2.10,\emph{p} = 0.18; pseudo-R\textsuperscript{2} = 0.44, Appendix S3
Figure S1).

\emph{Observational studies}

Observational studies of understory effects on canopy algae were mostly
conducted on kelp (\emph{n} Laminariales = 90, \emph{n} Fucales = 1), at
early life history stages (recruits or juveniles). Further, these
studies were exclusively carried out in the intertidal, significantly
shallower than experimental studies and at a higher mean latitude
(both\emph{t} -test, \emph{p} \textsubscript{obs - exp} \textless{}
0.001). Thus, these results are comparable only to a subset of the
experimental results above. Among observational studies, the absence of
an algal understory had a strong negative effect on the canopy,
indicating a positive interaction between the two groups (random effects
model, mean LNRR\textsubscript{obs} = -1.46 ± 0.74, \emph{p} = 0.048).
The effect of turfs on the canopy varied among turf functional groups
(overall Wald-type test, QM\textsubscript{{[}3{]}, obs} = 17.37, \emph{p
\textless{}} 0.0001, pseudo-R\textsuperscript{2} = 0.71). In
observational studies, most turf species had no effect on the canopy,
but unlike experimental studies, coralline turf facilitated the canopy
(\selectlanguage{greek}ϐ\selectlanguage{english}\textsubscript{coralline turf} = -2.74, \emph{p} = 0.0016). This
effect did not change across depth or latitude (Appendix S3 Tables
S5-7).

\emph{Discussion}

Globally, canopy-dominated marine ecosystems are decreasing, shifting to
``urchin barrens'' dominated by crustose algae and herbivores (Ling et
al. 2015), or turf-dominated ecosystems (Filbee-Dexter \& Wernberg
2018). When canopies are lost, marine forests decline in productivity
(Chapman 1981), habitat complexity (Miller et al. 2015), and diversity
(Graham 2004). Given the ecological and economic importance of these
systems, much attention is being paid to factors that might contribute
to their decline or prevent their restoration (Gorman \& Connell 2009).
In particular, crustose and turf-forming algae are thought to be major
barriers to the persistence of marine forests, especially under climate
change (Connell \& Russell 2010), given the assumption that these turf
and crustose taxa compete with and exclude canopy-forming species
(Connell et al. 2013).

We tested the widespread hypothesis that turf species in marine forests
have negative effects on canopies (O'Brien \& Scheibling 2018). We found
that, although turf algae do compete with the canopy, competition occurs
in a limited number of cases and the effect of the turfs on the canopy
was highly variable, often neutral, and even facilitative in many cases
(Table 1). Importantly, we found that in subtidal ecosystems, turf
species do compete with the canopy (primarily kelps; Fig. 4), which
supports the existing conceptualization of competition as a driving
mechanism in subtidal kelp forests (Dayton et al. 1984; Reed \& Foster
1984; Connell \& Russell 2010; Connell et al. 2013). Further,
experimental findings thought to contradict the competition paradigm
(e.g., Barner et al. 2016) can be situated in a continuum of
interactions that is predictable from the SGH (Bertness \& Callaway
1994; Bennett et al. 2015): negative subtidal interactions shift to
positive, facilitative interactions in the intertidal. The mechanisms
underlying the turf-canopy interactions in this study were variable
(Table 1). It is beyond the scope of this study to describe these
mechanisms in detail (recently reviewed in Edwards \& Connell 2012;
O'Brien \& Scheibling 2018), but despite the differences in the
physiological, chemical, and physical processes that generate
turf-canopy interactions, generalities emerged in the strength and sign
of interactions.

\emph{Stress Gradient Hypothesis in marine forests}

Although highly variable, the effect of turfs on the canopy was
consistently facilitative in intertidal environments and at higher
latitudes. Intertidal communities are characterized by extreme abiotic
stress for marine organisms, primarily thermal and desiccation stress
due to immersion. As predicted by the SGH (Bertness \& Callaway 1994),
we found that the effect of the turf species on the canopy shifted from
competitive in the subtidal to facilitative in the intertidal.
Specifically, coralline turfs increasingly facilitated the canopy at
shallower depths in experimental studies (Fig. 4). Among the
observational studies (all intertidal), both coralline and non-coralline
turfs facilitated the canopy (Fig. 3). For example, coralline turfs
facilitated the intertidal kelp, \emph{Postelsia palmaeformis} , due to
the cooler temperatures and higher humidity among turfs than on bare
rock (Hutto 2011). Thus, turfs may be critical for ameliorating
stressful abiotic conditions, therefore critical for canopy-forming
species recruitment and persistence in certain cases (also reported in
Bennett et al. 2015).

We also found a suggested facilitative effect of turf algae at higher
latitudes. Among experimental studies alone, there was a trend of
coralline turf facilitating canopy algae at higher latitudes (Fig. 5,
Appendix S3, Table S3). Although non-coralline crusts had a stronger
competitive effect at higher latitudes, we discuss our reservations in
interpreting the effect of non-coralline crust below
(section\emph{Assumptions and limitations} ). Further, observational
studies were conducted at higher latitudes and demonstrated overall
facilitation by turf species. When combined, these two lines of evidence
support the conclusion that facilitation of the canopy may be stronger
at higher latitudes. Conflicting with our results, multiple marine and
terrestrial studies have demonstrated stronger facilitation at lower,
warmer latitudes (Bertness et al. 1999; Pennings et al. 2003; McAfee et
al. 2016). A reason for this discrepancy may be the complexity of
latitudinal gradients in sea surface temperature. Many of our included
studies were conducted along the Northeast Pacific coastline, where
upwelling results in little change in sea surface temperature across
\textasciitilde{}25\selectlanguage{ngerman}º of latitude (Schoch et al. 2006). Ultimately,
across ecosystems, the functional relationship between temperature and
the strength of facilitation remains unclear. Both water depth and
latitude in this study are proxies for stress, and more work needs to be
done to truly isolate the effect of desiccation stress (or drought
stress for terrestrial plants) and temperature on the prevalence of
facilitation.

\emph{Other factors influencing interactions with marine canopies}

Facilitation is also predicted to be stronger at early life history
stages (Callaway \& Walker 1997). The ``nurse plant syndrome'' has been
widely demonstrated among plants, where adult ``nurse'' plants
facilitate the recruitment and persistence of seedlings (Miriti 2006; He
et al. 2013). We found conflicting evidence for the presence of this
syndrome in turf/crust - canopy interactions. Experimental studies
generally demonstrated competitive effects of multiple understory
species on canopies at early life history stages, while observational
studies supported strong facilitation of canopy recruits by coralline
turf. Overall, few studies in our meta-analysis quantify effects on the
canopy across multiple life history stages (Appendix S3 Fig. S2), which
precludes any strong conclusions here. Given that canopy-forming brown
seaweeds produce microscopic stages as part of their recruitment process
(Schiel and Foster 2006), these stages must be included in studies as
they are fundamental to population recovery and persistence.

We also found little support for the hypothesis that the effect of turfs
on the canopy is influenced by the presence of herbivores. Experiments
that excluded herbivores demonstrated the same interaction as paired
plots that included herbivores (Appendix S3 Figure S1). Herbivory should
only alter the interaction if herbivores were facilitated or excluded as
a function of turf removal. For example, some understory turf algae
prevent the influx of sea urchins at the kelp forest boundary by
limiting their movement, resulting in a positive indirect effect of turf
on kelp (Konar et al. 2014). On the other hand, recruitment facilitation
by turf algae of canopy herbivores can drive the emergence of kelp
forest - urchin barren alternative stable states (Baskett \& Salomon
2010). Marine herbivores generally have strong effects on primary
producers (Poore et al. 2012), and urchin herbivory in particular is a
dominant force structuring global kelp forests (Pearse 2006; Ling et al.
2015). However, few studies have explicitly examined the effect of
herbivory on marine turf-canopy interactions (included in present
study,\emph{n} = 14), with factorial experiments to disentangle these
interactions. Work in the Patagonian steppe has even found that the
interaction among grasses was mediated by the strength of herbivory
(Graff et al. 2007) - a mechanism of the SGH that may be worth exploring
in marine forests. Future studies should examine the interaction of turf
species with canopy herbivores to fully quantify the direct and indirect
effects of competition, facilitation, and herbivory.

\emph{Assumptions and limitations}

In addition to variability, clear patterns emerged between experimental
and observational studies moving from neutral effects to facilitation of
turfs on canopy species. However, it is important to note that
observational studies were conducted in intertidal, shallow subtidal
and/or high latitude environments, all factors that influence
facilitation interactions. Thus, the different results for observational
and experimental studies may be attributable instead to these
confounding differences. Here, the included observational studies
generally used a study design where the number of canopy individuals
were counted on a given substrate (e.g., turf, crust, bare rock), scaled
by the availability of that substrate (n = 68 out of 91 observational
studies). For three pairwise interactions in this study, effects were
estimated both observationally and experimentally, yielding similar
effect size estimates (Appendix S3 Fig. S3). More work is needed to
understand the relationship between experimental and observational
effect sizes in non-trophic interactions, but the similarity here in
response ratios is encouraging.

Another limitation of the present study is an imbalance in sample size
among turf functional groups. Of the four groups, there are fewer
estimates of the effect of non-coralline crustose algae on canopies
(n\textsubscript{non-coralline crust} = 28,
n\textsubscript{non-coralline turf} = 79, n\textsubscript{coralline
crust} = 114, n\textsubscript{coralline turf} = 102). Estimates of the
abundance and importance of these species vary in canopy-forming
systems: in an Alaskan subtidal system, non-coralline crusts dominate
the substrate relative to other species, while classic studies fail to
mention non-coralline crusts as a component of the community (e.g., Reed
\& Foster 1984). The present study includes no estimates of the effect
of non-coralline crusts in deep water (deepest is -5m relative to MLLW)
and our finding that the non-coralline crust effect is more positive at
shallower depths (Fig. 4) should be interpreted with caution.

\emph{Next steps: species interactions, global change, and regime
shifts}

Natural or anthropogenic disturbances may create situations in which
turfs could alternately compete with or facilitate canopy-species
recovery (Table 1). In particular, non-coralline turfs tend to be more
resilient to disturbances such as El Niño (Filbee-Dexter \& Wernberg
2018) and other disturbances (eutrophication, sedimentation, ocean
acidification) actually favor the growth of non-coralline turfs over
other taxa (Connell \& Russell 2010; Muth et al. 2017). If turf or
crustose algae inhibit canopy recruitment, then the marine forest may
never recover after disturbance. However, if facilitation occurs, canopy
species may recover from a disturbance more quickly in environments with
stronger or more frequent positive interactions. Previous global change
predictions for marine forests were based on a negative effect of turfs
on canopy-forming species (Harley et al. 2011). Undeniably, competition
with turfs contributes to the globally-observed regime shift away from
canopy-dominated ecosystems (Petraitis \& Dudgeon 2004; Filbee-Dexter \&
Scheibling 2014; O'Brien \& Scheibling 2018). However, in many
ecosystems, facilitation creates a positive feedback that maintains
contrasting system states (Kéfi et al. 2016). Given the frequency at
which we observe turf and crust facilitation of canopies in this study,
these positive effects may also contribute to alternative stable states
in marine forests. Future work should explicitly test for positive
interactions in these systems, and examine the role of facilitation in
amplifying or dampening the observed global regime shift to turf- and
crust-dominated marine forests.

We found that interactions in this system formed a continuum from
competitive to facilitative, across a predictable stress gradient. We
used latitude as a geographic proxy for temperature, as few studies in
this meta-analysis included environmental data alongside interaction
estimates. In the future, abiotic data, and especially temperature,
should be reported and possibly experimentally manipulated in studies of
marine forest interactions. We found a stress gradient effect when we
combined intertidal and subtidal studies in the same analysis, though
few empirical studies treat these as one system with an ``elevational''
gradient. We encourage more empirical work integrating the subtidal and
intertidal ecosystems, especially given environmental changes that may
drive species to deeper (warming) or shallower (sea level rise) water.
Finally, the interactions included in this study are only a few of the
diverse set of trophic and non-trophic interactions in marine forests.
Herbivory, competition, facilitation, predation, and indirect effects
are all interactions that shape community structure and play a role in
regime shifts (Petraitis \& Dudgeon 2004). Linking the effect of the
canopy on turfs and crusts with the interactions in this study would be
a feasible first step in understanding the emergent impact of all these
interactions (Bennett et al. 2015; O'Brien \& Scheibling 2018). Our
results highlight the importance of testing for facilitative
interactions where competition is thought to dominate, shedding new
light on a well-studied interaction involved in a global regime shift.

\emph{Acknowledgements}

We are grateful to Lauren Ponisio and Tye Kindinger for statistical
advice, and to Kathryn Schoenrock and Ragan Callaway for feedback that
greatly improved this manuscript. AB was supported by a James S.
McDonnell Foundation Postdoctoral Fellowship in Studying Complex
Systems. AM contributed to the development of this publication under
STAR Fellowship Assistance Agreement no. FP917814 awarded by the U.S.
Environmental Protection Agency (EPA). ~It has not been formally
reviewed by EPA. ~The views expressed in this publication are solely
those of Arley F. Muth, and EPA does not endorse any products or
commercial services mentioned in this publication. The authors declare
no conflicts of interest.

\emph{Supporting Information}

Appendix S1 - Data compilation

Appendix S2 - Supplemental analyses

Appendix S3 - Supplemental results

\emph{Reference list}

Armas, C., Rodríguez-Echeverría, S., \& Pugnaire, F. I. (2011). A field
test of the stress-gradient hypothesis along an aridity
gradient.\emph{Journal of Vegetation Science} , \emph{22} (5), 818--827.

Barner, A.K., Hacker, S.D., Menge, B.A. \& Nielsen, K.J. (2016). The
complex net effect of reciprocal interactions and recruitment
facilitation maintains an intertidal kelp community. \emph{J. Ecol.} ,
104, 33--43.

Barrio, I. C., Hik, D. S., Bueno, C. G., \& Cahill, J. F. (2013).
Extending the stress-gradient hypothesis - is competition among animals
less common in harsh environments? Oikos, 122(4), 516--523.

Baskett, M.L. \& Salomon, A.K. (2010). Recruitment facilitation can
drive alternative states on temperate reefs. \emph{Ecology} , 91,
1763--73.

Bennett, S., Wernberg, T., de Bettignies, T., Kendrick, G.A., Anderson,
R.J., Bolton, J.J., \emph{et al.} (2015). Canopy interactions and
physical stress gradients in subtidal communities. \emph{Ecol. Lett.}

Bertness, M.D. \& Callaway, R. (1994). Positive interactions in
communities. \emph{Trends Ecol. Evol.} , 9, 191--193.

Bertness, M.D. \& Hacker, S.D. (1994). Physical stress and positive
associations among marsh plants. \emph{Am. Nat.} , 144, 363--372.

Bertness, M.D., Leonard, G.H., Levine, J.M. \& Bruno, J.F. (1999).
Climate-driven interactions among rocky intertidal organisms caught
between a rock and a hot place. \emph{Oecologia} , 120, 446.

Bruno, J.F., Stachowicz, J.J. \& Bertness, M.D. (2003). Inclusion of
facilitation into ecological theory. \emph{Trends Ecol. Evol.} , 18,
119--125.

Callaway, R.M., Brooker, R.W., Choler, P., Kikvidze, Z., Pugnaire, F.I.,
Newingham, B., \emph{et al.} (2002). Positive interactions among alpine
plants increase with stress. \emph{Nature} , 417, 844--848.

Callaway, R.M. \& Walker, L.R. (1997). Competition and facilitation: a
synthetic approach to interactions in plant communities. \emph{Ecology}
, 78, 1958--1965.

Chapman, A.R.O. (1981). Stability of sea urchin dominated barren grounds
following destructive grazing of kelp in St. Margaret's Bay, Eastern
Canada. \emph{Mar. Biol.} , 62, 307--311.

Connell, S.D., Kroeker, K.J., Fabricius, K.E., Kline, D.I. \& Russell,
B.D. (2013). The other ocean acidification problem:
CO\textsubscript{2}as a resource among competitors for ecosystem
dominance. \emph{Philos. Trans. R. Soc. Lond. B. Biol. Sci.} , 368,
20120442.

Connell, S.D. \& Russell, B.D. (2010). The direct effects of increasing
CO\textsubscript{2} and temperature on non-calcifying organisms:
increasing the potential for phase shifts in kelp forests. \emph{Proc.
R. Soc. B Biol. Sci.} , 277.

Datyon, P.K. 1972. Toward an understanding of community resilience and
the potential effects of enrichments to the benthos at McMurdo Sound
Antarctica. \emph{In} : Proceedings of the Colloquium on Conservation
Problems in Antartica (Parker, B.C., ed.), pp.81-96, Allen Press

Dayton, P.K., Currie, V., Gerrodette, T., Keller, B.D., Rosenthal, R. \&
Ven Tresca, D. (1984). Patch dynamics and stability of some California
kelp communities. \emph{Ecol. Monogr.} , 54, 254--289.

Dayton, P.K. \& Tegner, M.J. (1984). Catastrophic storms, El Niño, and
patch stability in a Southern California kelp community. \emph{Science}
, 224, 283--285.

Edwards, M.S. \& Connell, S.D. (2012). Competition, a major factor
structuring seaweed communities. In: \emph{Seaweed Biology: Novel
Insights into Ecophysiology, Ecology and Utilization} , Ecological
Studies (eds. Wiencke, C. \& Bischof, K.). Springer Berlin Heidelberg,
Berlin, Heidelberg, pp. 135--156.

Filbee-Dexter, K. \& Scheibling, R.E. (2014). Sea urchin barrens as
alternative stable states of collapsed kelp ecosystems. \emph{Mar. Ecol.
Prog. Ser.} , 495, 1--25.

Filbee-Dexter, K. \& Wernberg, T. (2018). Rise of turfs: a new
battlefront for globally declining kelp forests. \emph{BioScience} , 68,
64--76.

Gorman, D. \& Connell, S.D. (2009). Recovering subtidal forests in
human-dominated landscapes. \emph{J. Appl. Ecol.} , 46, 1258--1265.

Graff, P., Aguiar, M.R. \& Chaneton, E.J. (2007). Shifts in positive and
negative plant interactions along a grazing intensity
gradient.\emph{Ecology} , 88, 188--199.

Graham, M.H. (2004). Effects of local deforestation on the diversity and
structure of Southern California giant kelp forest food
webs.\emph{Ecosystems} , 7, 341--357.

Gunnill, F.C. (1980). Demography of the intertidal brown
alga\emph{Pelvetia fastigiata} in Southern California, USA. \emph{Mar.
Biol.} , 59, 169--179.

Harley, C.D.G., Anderson, K.M., Demes, K.W., Jorve, J.P., Kordas, R.L.,
Coyle, T.A., \emph{et al.} (2012). Effects of climate change on global
seaweed communities. \emph{J. Phycol.} , 48, 1064--1078.

Harris, L.G., Ebeling, A.W., Laur, D.R. \& Rowley, R.J. (1984).
Community recovery after storm damage: a case of facilitation in primary
succession. \emph{Science} , 224, 1336--1338.

He, Q., Bertness, M.D. \& Altieri, A.H. (2013). Global shifts towards
positive species interactions with increasing environmental
stress.\emph{Ecol. Lett.} , 16, 695--706.

Hedges, L.V., Gurevitch, J. \& Curtis, P.S. (1999). The meta-analysis of
response ratios in experimental ecology. \emph{Ecology} , 80,
1150--1156.

Hernández-Carmona, G., García, O., Robledo, D. \& Foster, M. (2005).
Restoration techniques for \emph{Macrocystis pyrifera} (Phaeophyceae)
populations at the southern limit of their distribution in
México.\emph{Bot. Mar.} , 43, 273--284.

Hutto, S.V. (2011). Differential recruitment of \emph{Postelsia
palmaeformis} across substrate types and the potential facilitative
effects of turfing algae. M.Sc. California State University, Monterey
Bay, Moss Landing Marine Labs.

Kawai, T., \& Tokeshi, M. (2007). Testing the facilitation-competition
paradigm under the stress-gradient hypothesis: Decoupling multiple
stress factors. \emph{Proceedings of the Royal Society B: Biological
Sciences} , \emph{274} (1624), 2503--2508.

Kéfi, S., Holmgren, M. \& Scheffer, M. (2016). When can positive
interactions cause alternative stable states in ecosystems? \emph{Funct.
Ecol.} , 30, 88--97.

Konar, B., Edwards, M.S. \& Estes, J.A. (2014). Biological interactions
maintain the boundaries between kelp forests and urchin barrens in the
Aleutian Archipelago. \emph{Hydrobiologia} , 724, 91--107.

Koricheva, J., M. D. Jennions, J. Lau. 2013. Temporal trends in effect
sizes: causes, detection, and implications. \emph{In} Handbook of
Meta-analysis in Ecology and Evolution. Editors: J. Koricheva, J.
Gurevitch, K. Mengersen. Princeton University Press. Princeton, NJ.

Krumhansl, K.A., Okamoto, D.K., Rassweiler, A., Novak, M., Bolton, J.J.,
Cavanaugh, K.C., \emph{et al.} (2016). Global patterns of kelp forest
change over the past half-century. \emph{Proc. Natl. Acad. Sci.} , 113,
201606102.

Lajeunesse, M. (2015). Bias and correction for the log response ratio in
ecological meta\selectlanguage{english}-analysis. \emph{Ecology} , 96, 2056--2063.

Lim, R.J.Y., Lam, W.N. \& Tan, H.T.W. (2018). Novel pitcher
plant--spider mutualism is dependent upon environmental resource
abundance. \emph{Oecologia} , 188, 791--800.

McAfee, D., Cole, V.J. \& Bishop, M.J. (2016). Latitudinal gradients in
ecosystem engineering by oysters vary across habitats. \emph{Ecology} ,
97, 929--939.

McCoy, S.J. \& Kamenos, N.A. (2014). Coralline algae (Rhodophyta) in a
changing world: integrating ecological, physiological, and geochemical
responses to global change. \emph{J. Phycol.} , 51, 6--24.

Miller, R.J., Page, H.M. \& Reed, D.C. (2015). Trophic versus structural
effects of a marine foundation species, giant kelp (\emph{Macrocystis
pyrifera} ). \emph{Oecologia} , 179, 1199--1209.

Miriti, M.N. (2006). Ontogenetic shift from facilitation to competition
in a desert shrub. \emph{J. Ecol.} , 94, 973--979.

Muth, A. F., Henriquez-Tejo, E. A., \& Buschmann, A. H. (2017).
Influence of sedimentation in the absence of macrograzers on recruitment
of an annual population of Macrocystis pyrifera in Metri Bay,
Chile.\emph{Austral Ecology} , \emph{42} (7), 783--789.

O'Brien, J.M. \& Scheibling, R.E. (2018). Turf wars: competition between
foundation and turf-forming species on temperate and tropical reefs and
its role in regime shifts. \emph{Mar. Ecol. Prog. Ser.} , 590, 1--17.

Okamoto, D.K., Stekoll, M.S. \& Eckert, G.L. (2013). Coexistence despite
recruitment inhibition of kelps by subtidal algal crusts. \emph{Mar.
Ecol. Prog. Ser.} , 493, 103--112.

Pearse, J.S. (2006). Ecological role of purple sea
urchins.\emph{Science} , 314, 940--941.

Pennings, S.C., Selig, E.R., Houser, L.T. \& Bertness, M.D. (2003).
Geographic variation in positive and negative interactions among salt
marsh plants. \emph{Ecology} , 84, 1527--1538.

Petraitis, P.S. \& Dudgeon, S.R. (2004). Detection of alternative stable
states in marine communities. \emph{J. Exp. Mar. Biol. Ecol.} , 300,
343--371.

Poisot, T. (2011). The `digitize' package: extracting numerical data
from scatterplots. \emph{The R Journal} , 3, 25-26.

Poore, A.G.B., Campbell, A.H., Coleman, R.A., Edgar, G.J., Jormalainen,
V., Reynolds, P.L., \emph{et al.} (2012). Global patterns in the impact
of marine herbivores on benthic primary producers. \emph{Ecol. Lett.} ,
15, 912--22.

R Core Team. (2019). R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria. URL:
https://www.R-project.org. Version 3.6.0.

Reed, D., Washburn, L., Rassweiler, A., Miller, R., Bell, T. \& Harrer,
S. (2016). Extreme warming challenges sentinel status of kelp forests as
indicators of climate change. \emph{Nat. Commun.} , 7.

Reed, D.C. \& Foster, M.S. (1984). The effects of canopy shadings on
algal recruitment and growth in a giant kelp forest. \emph{Ecology} ,
65, 937.

Schiel, D. R., \& Foster, M. S. (2006). The Population Biology of Large
Brown Seaweeds: Ecological Consequences of Multiphase Life Histories in
Dynamic Coastal Environments. \emph{Annual Review of Ecology, Evolution,
and Systematics} , \emph{37} (1), 343--372.

Schiel, D. R., \& Foster, M. S. (2015). The biology and ecology of giant
kelp forests. Berkeley, CA: University of California Press.

Schoch, G.C., Menge, B.A., Allison, G., Kavanaugh, M., Thompson, S.A. \&
Wood, S.A. (2006). Fifteen degrees of separation: Latitudinal gradients
of rocky intertidal biota along the California Current. \emph{Limnol.
Oceanogr.} , 51, 2564--2585.

Smale, D.A., Wernberg, T., Oliver, E.C.J., Thomsen, M., Harvey, B.P.,
Straub, S.C., \emph{et al.} (2019). Marine heatwaves threaten global
biodiversity and the provision of ecosystem services. \emph{Nat. Clim.
Change} , 9, 306--312.

Steneck, R.S. \& Dethier, M.N. (1994). A functional group approach to
the structure of algal-dominated communities. \emph{Oikos} , 64,
476--498.

Verges, A., Doropoulos, C., Malcolm, H.A., Skye, M., Garcia-Piza, M.,
Marzinelli, E.M., \emph{et al.} (2016). Long-term empirical evidence of
ocean warming leading to tropicalization of fish communities, increased
herbivory, and loss of kelp. \emph{Proc. Natl. Acad. Sci.} , 201610725.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the
metafor.\emph{Journal of Statistical Software} , \emph{36} (3), 1--48.

Wernberg, T., Bennett, S., Babcock, R.C., Bettignies, T. de, Cure, K.,
Depczynski, M., \emph{et al.} (2016). Climate-driven regime shift of a
temperate marine ecosystem. \emph{Science} , 353, 169--172.

\emph{Figure legends}

Figure 1. Two simplified examples of the effect of turf species on
canopy-forming macroalgae. A) In this example, the canopy kelp is
negatively affected by two species that live in the understory below.
This competitive effect can be measured experimentally, through the
removal of the turf and crust understory. If the abundance (or biomass,
etc.) of the canopy increases in the absence of the turf and crust
species, the interaction is estimated as competitive. B) However, if
these species facilitate the canopy kelp, then the removal of turfs and
algal crusts should have a negative effect on the canopy.

Figure 2. Locations of the studies included in the meta-analysis, by
study type. On the right is the distribution of studies in the Northeast
Pacific Ocean, where a high density of studies have been conducted.

Figure 3. The overall effect of different turf functional groups on the
canopy (kelps and Fucales combined). Each point in grey represents an
individual measured interaction, with means and 95\% confidence
intervals in black. If log response ratio (LNRR) is negative, then the
effect of turf species on the canopy is facilitative. If LNRR is
positive, then the effect is competitive.

Figure 4. The experimental effect of different turf functional groups on
the canopy, by depth. Each point is a raw data point, scaled in size by
the study sample size, with 95\% confidence intervals shaded around each
line. Note that the slope and intercept of the coralline crust and
non-coralline turf lines do not statistically differ from zero.

Figure 5. Across latitude (top), mixed evidence for increasingly
facilitative interactions at higher latitudes. Below, patterns across
life history stage (raw data in grey, means and 95\% confidence
intervals for the mean in black). For life history stage, observations
were collected primarily on the recruit canopy stage (see Results:
Observational studies). Plot across latitude includes only coralline
turfs, plot across life history includes all canopy and turf taxa.

\emph{Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5.

\emph{Tables}

Table 1. Examples of the different interactions between the algal
understory functional groups and canopy-forming species.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Effect of turf or crust on canopy} & \textbf{Functional group} &
\textbf{Example mechanism} & \textbf{Example citation}\tabularnewline
Competition & Turfs & Space limitation prevents canopy recruitment &
Dayton et al. 1984\tabularnewline
Competition & Turfs & Abiotic disturbance (sedimentation,
eutrophication) favors the growth of opportunistic turfs, which
outcompete canopy-forming species & Reviewed in Filbee-Dexter \&
Wernberg 2018\tabularnewline
Competition & Coralline crusts & Epithelial sloughing of coralline
surface cells prevents recruitment and establishment of canopy-forming
species & Reviewed in McCoy and Kamenos 2013\tabularnewline
Facilitation & Coralline turfs & Coralline turfs ameliorate desiccation
stress in canopy algae & Hutto 2011\tabularnewline
Facilitation & Turfs & Understory algae provide refuge from herbivory
for canopy recruits & Harris et al. 1984\tabularnewline
\bottomrule
\end{longtable}

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

