Seaweed functional ecology models: a comprehensive review of theory and applications

Abstract

Seaweeds are important primary producers in marine ecosystems and often play keystone and significant structural roles in their communities. After almost a century of research, seaweed functional ecology has emerged as a key approach in seaweed biology with applications in environmental monitoring and rocky shore communities. Several models and functional groups have been proposed to compare and understand seaweed morphology, physiology, and ecology, but there is no consensus on preferred approaches, adequate model usage, and terminology. To address this problem and to provide a comprehensive model synopsis, we conducted a literature review on seaweed functional ecology research from 1934 to 2023. We selected the works that proposed general models for the functional ecology of seaweeds and the papers that tested them. We evaluated each model’s strengths, limitations, and scope of application. We also identified the common issues in testing seaweed functional models and suggested a glossary to promote unified and clear terminology. We present hypotheses and classification tables, application flowcharts, and visual aids for each model. We show that most functional models remain untested but are nonetheless often used. After reviewing close to 90 years of research on seaweed functional ecology, we conclude the review by providing new perspectives and highlighting the open questions of the field.

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Vol.:(0123456789)
Journal of Applied Phycology
https://doi.org/10.1007/s10811-024-03293-z
REVIEW
Seaweed functional ecology models: acomprehensive review
oftheory andapplications
JoãoP.G.Machado1,2· ViníciusP.Oliveira1
Received: 5 February 2024 / Revised: 29 May 2024 / Accepted: 1 June 2024
© The Author(s), under exclusive licence to Springer Nature B.V. 2024
Abstract
Seaweeds are important primary producers in marine ecosystems and often play keystone and significant structural roles in
their communities. After almost a century of research, seaweed functional ecology has emerged as a key approach in seaweed
biology with applications in environmental monitoring and rocky shore communities. Several models and functional groups
have been proposed to compare and understand seaweed morphology, physiology, and ecology, but there is no consensus
on preferred approaches, adequate model usage, and terminology. To address this problem and to provide a comprehensive
model synopsis, we conducted a literature review on seaweed functional ecology research from 1934 to 2023. We selected
the works that proposed general models for the functional ecology of seaweeds and the papers that tested them. We evalu-
ated each model’s strengths, limitations, and scope of application. We also identified the common issues in testing seaweed
functional models and suggested a glossary to promote unified and clear terminology. We present hypotheses and classifica-
tion tables, application flowcharts, and visual aids for each model. We show that most functional models remain untested
but are nonetheless often used. After reviewing close to 90 years of research on seaweed functional ecology, we conclude
the review by providing new perspectives and highlighting the open questions of the field.
Keywords Algae· Trait-based ecology· Ecophysiology· Functional group· Literature review· Macroalgae
Introduction
The sorting of vegetal diversity into trait-based groups traces
its origins back to Ancient Greece, from the work of Aris-
totle’s pupil, Theophrastus' “Περὶ φυτῶν ἱστορία” (1916).
Later, in the Roman period, Pliny the Elder in his “Naturalis
Historia” discussed plants and seaweeds with emphasis on
their useful or harmful traits to humans (Pliny1956). This
early exploration of vegetal form and ecology was stalled by
a long dormancy. Modern interest in trait-based approaches
to vegetal diversity reemerged in the 20th Century marked
by the works of Raunkiaer (1934). His work involved cat-
egorizing terrestrial plants into distinct life forms based on
meristem (bud) protection, life history, morphology, habit,
and other traits. He aimed to describe and compare plant
assemblages from different regions and floras. Raunkiaer
(1934) also noted that this was his career’s chief scientific
objective and a seminal effort toward advancing plant biol-
ogy. Raunkiaer’s work was notably silent on microalgae and
seaweeds, but this gap was later addressed in later works by
other authors.
Across the 20th century, two distinct trends emerged in
trait-based approaches to seaweed biology. In the first half of
the 20th century, a descriptive trend was prevalent, wherein
different proposals sorted and classified seaweeds into trait-
based groups. These grouping proposals were interchange-
ably called morphological types, life forms (Chapman and
Chapman 1976), growth forms (Yñiguez etal. 2015), or bio-
logical types (Feldmann 1966). In this initial phase, interest
did not lie in predicting attributes of one or more species
based on their categorization into de facto “functional”
groups but rather in promoting the comparison of flora from
different locations through these groups (Raunkiaer 1934),
given that sole taxonomic comparison can be unproductive
(Dıaz and Cabido 2001).
Emerging after the mid-20th century and continuing up
to now, a second trend shifted the aim of trait-based group
* João P. G. Machado
machadojpg@ufrj.br
1 Institute ofBiology, Federal University ofRio de Janeiro
(UFRJ), RiodeJaneiro, Brazil
2 Institute ofBiology, State University ofRio de Janeiro
(UERJ), RiodeJaneiro, Brazil

Journal of Applied Phycology
proposals from floristic comparisons towards species and
community trait analyses. The main focus was now on the
interplay between species trait patterns and community pro-
cesses, at last termed “functional” (Littler and Littler 1980;
Violle etal. 2007).
Norton etal. (1982) highlighted the necessity for in-
depth research into the correlation between seaweed form
and ecological function, noting that such an investigation
was significantly overdue. While this remark is now over
four decades old, it remains up to date. Controversies such
as over trait choice, grouping, and the utility of mean trait
values remain up for debate across the seaweed literature
(Cappelatti etal. 2019; Mauffrey etal. 2020; Padilla and
Allen 2000; Fong etal. 2023; Griffin etal. 2023; Ryznar
etal. 2023). The ongoing debate over trait selection and
categorization in seaweed research underscores the lack of
a cohesive framework, in contrast with the advances seen in
terrestrial ecosystems by plant functional ecology (Cornelis-
sen etal. 2003; Grime 2006; Bernhardt-Römermann etal.
2008; Caruso etal. 2020; Tilman 2020; Laughlin 2023). In
plant functional ecology research, a trait-based approach has
long enabled significant insights into ecosystem dynamics,
such as the development of predictive models and analyses
for ecosystem sensitivity to global change pivoted on plant
functional traits (Heilmeier 2019; Ahrens etal. 2020; Schle-
uning etal. 2020). These advances have been instrumental
in enhancing our understanding of ecological processes and
informing conservation efforts. While recent progress has
been made in synthesizing comprehensive algae trait data-
bases (Vranken etal. 2023), seaweed functional ecology has
yet to fully capitalize on this potential.
As vegetal trait-based ecology turns ninety, we provide a
qualitative analysis of the literature on seaweed functional
group models from Raunkiaer’s work to the present. We
present and discuss each model’s theory and applications
whilst highlighting open questions and future directions for
research. We also offer a unified terminology proposal aimed
at improving cross-field dialog and integration. Our review
aims to be a timely synthesis of theory and applications to
start bridging the gap between plant and seaweed functional
ecology to meet the challenges and fully seize the opportuni-
ties of the functional model approach.
Survey method
We conducted comprehensive searches using keywords such
as “seaweed” OR “macroalgae” AND (“functional-form”
OR “morpho-functional group” OR “morphological group”
OR “functional group” OR “ecomorphology”) from 1934
to 2023. We manually selected the studies that (a) proposed
and/or (b) tested seaweed functional models. We searched
in Google Scholar, Web of Science, Semantic Scholar, and
Connected Papers databases and manually assessed each
result’s adequacy to our criteria (a and/or b). Feldmann
(1966) was only available in print found by physical library
search. Previous reviews on seaweed ecology were also
assessed (Norton etal. 1982; Padilla and Allen 2000). All
search results were screened by title, abstract, and sections
for adequacy to our criteria. All adequate search results were
then read and discussed in the context of other works where
relevant. We also evaluated and discussed each model based
on the results from other works that tested their hypotheses.
Each model category, its practical applications, and relevant
literature are discussed in detail below.
Results anddiscussion
Following the systematic survey and selection process,
a total of 102 studies were compiled (Supplementary
TableS1). These studies span nearly a century and encom-
pass the development within the domain of functional and
ecological morphology of macroalgae. The models from
the selected studies were systematically categorized in
(Table1). Additionally, a flowchart is provided to elucidate
the practical application of each model (Fig.1).
A. Life form model
The life form model aims to group seaweed taxa into
trait-based groups, following Raunkiaer's work (1934) on
terrestrial plants with adaptations. Raunkiaer’s purpose was
chiefly descriptive, classifying plants based on meristem
protection, life history, and morphology for floristic com-
parison. He succeeded in providing an alternative to tax-
onomy to compare local floras and in identifying common
adaptative syndromes among plants. However, Raunkiaer's
work only addressed embryophytes, excluding a possible
treatment of seaweeds within his “Hydrophyte” group. His
original model would not be useful to apply to seaweeds
since its main classificatory trait is meristem protection. All
seaweeds would fall into the categories of either Phanero-
phytes (unprotected meristems far from the substrate, e.g.,
seaweeds with apical growth) or Chamaephytes (unprotected
meristems close to the substrate, e.g., seaweeds with interca-
lary growth) if classified using Raunkiaer's criteria.
Later works addressed seaweeds specifically using a
similar approach, such as Feldmann (1966) and Chapman
and Chapman (1976). Due to the lack of buds similar to
terrestrial plants in macroalgae, Feldmann (1966) and
Garbary (1976) proposed new criteria and groups more
attune with the particulars of seaweed biology, such as
type of attachment, if any, to the substrate, and seasonal-
ity. Garbary (1976) was the last work following a life form
model scheme. This first trend of trait-based approaches

Journal of Applied Phycology
Table 1 Table of seaweed functional models
Models Author(s) Summary Comprehensive tests Objective Practical applications
A. Life form models Feldmann (1966); Chapman
and Chapman (1976);
Garbary (1976)
The life form model,
originally developed for ter-
restrial plants, categorizes
organisms chiefly based
on traits such as meristem
protection, morphology, and
thallus longevity. The model
was eventually replaced by
the functional group model
approach.
None Floristic comparison Less time-consuming fieldwork
and floristic assessment
B. Functional models
B.1. Classic models
B.1.1. Dayton model Dayton (1975) This model classifies sea-
weeds into three functional
groups based on their
vertical space usage and
successional stage.
None Assemblage structure and
dynamics analysis linked to
vertical space use
Less time-consuming fieldwork,
environmental monitoring,
coastal management, and
conservation
B.1.2. Littler and Littler
model
Littler and Littler (1980; and
further developed and tested
in subsequent articles by the
same authors)
This model classifies sea-
weeds into six functional
groups based on thallus
external morphology and
texture linked to functional
traits and community
processes.
Subsequent works by M.
Littler and D. Littler, Fong
and Fong (2014), and
Mauffrey etal. (2020).
Species ecophysiology and
assemblage structure
Less time-consuming fieldwork,
environmental monitoring,
coastal management and con-
servation, seaweed farming
B.1.3. Steneck and Dethier
model
Steneck and Dethier (1994) This model classifies sea-
weeds into seven func-
tional groups based on
thallus internal anatomy
and external morphology
linked to functional traits
and community processes.
It is the most cited seaweed
functional model.
Phillips etal. (1997), Balata
etal. (2011), Fong and
Fong (2014), and Mauffrey
etal. (2020).
Assemblage structure, pro-
ductivity, and disturbances
Less time-consuming fieldwork,
environmental monitoring,
coastal management, and
conservation
B.2. New models
B.2.1. Balata etal. model Balata etal. (2011) This model directly chal-
lenged the Steneck and
Dethier model by proposing
35 new functional groups
for seaweeds based on both
form and taxonomy.
None Anthropogenic stresses and
disturbances
Less time-consuming fieldwork,
environmental monitoring,
coastal management, and
conservation

Journal of Applied Phycology
to seaweed biology thus died out and was replaced by the
functional group model approach.
Applications The life form model allowed less time-con-
suming fieldwork and large-scale non-taxonomic floris-
tic assessment. The life form model was chiefly directed
towards understanding general and quantitative patterns in
vegetal biology, with aims akin to a modern macroecological
and functional biogeographical approach (Raunkiaer 1934;
Clapham 1935; Violle etal. 2014). The use of life forms
instead of taxonomy enabled a pioneering quantitative and
functional biogeography well captured by Raunkiaer’s 1934
book titled “The life forms of plants and statistical plant
geography”.
B. Functional group models
This trend was started in seaweed biology by Dayton
(1975) on par with the developments in plant strategies
and plant functional ecology (Horn 1971; Grime 1977,
2006; Grace 1991; Steneck and Dethier 1995; Tilman
2020). The functional group approach is based on group-
ing species and community traits to explain and predict
assembly rules and ecosystemic processes (Littler and
Littler 1980; Cornelissen etal. 2003; Griffin etal. 2023;
Luza etal. 2023). Its goal is to be "applicable wherever the
abundances of dominant algae are known, unconstrained
by phylogenetic group, habitat, or geological era" (Littler
and Littler 1984a). All functional group models are based
on grouping seaweed species based on functional traits,
broadly meaning "morphological, physiological, and phe-
nological traits that affect fitness" (Violle etal. 2007; Fong
etal. 2023). The decades-long persistence of these models
led them to become mainstream and be presented in com-
mon textbooks, such as "Seaweed Ecology and Physiol-
ogy" (Hurd etal., pp. 11-15, 2014).
B.1. Classic models
B.1.1 Dayton model
Dayton (1975) presents a functional model that clas-
sifies macroalgae mainly based on vertical space use and
successional stage. The author categorized algae into three
distinct groups: canopy species, which grow above others
and seem to dominate light resources, as shown by algal
blooms occurring after their removal; sub-canopy obli-
gate species, which cannot survive when canopy species
are removed; and fugitive species, quick to occupy new
spaces (Fig.2). Although Dayton's model fell into disuse
and remains untested, it served as a theoretical landmark
from which Littler and Littler (1980) and subsequent mod-
els were developed.
Table 1 (continued)
Models Author(s) Summary Comprehensive tests Objective Practical applications
B.2.2. Arenas etal. model Arenas etal. (2006) This model is based on
seaweed vertical space
usage, proposing the groups
of canopy, subcanopy, turf,
and encrusting. Arenas etal.
(2006) findings contradicted
their initial model’s predic-
tions.
None Assemblage structure and
invasibility
Less time-consuming fieldwork,
environmental monitoring,
coastal management, and
conservation
B.2.3. Mauffrey etal. model Mauffrey etal. (2020) This model proposes the
delimitation of functional
groups quantitatively and
emerging from functional
trait measurement to have
site-specific functional
groups.
None Trait-based and site-specific
emergent group analysis
Less time-consuming fieldwork,
environmental monitoring,
coastal management, and
conservation
In the column designated for “comprehensive tests,” emphasis was placed solely on those studies that have conducted experimental assessments of multiple or the entirety of the assumptions
and/or predictions inherent to a functional model. Tests focusing on just one or a few of a model’s assumptions and/or predictions are discussed within the respective sections of each model

Journal of Applied Phycology
New directions forfuture research ontheDayton
model
Integrating succession dynamics and trait‑space While the
Dayton model was a pioneer in directly tackling succession
dynamics and seaweed functional groups, many questions
remain on trait-space use in different successional stages
by seaweed assemblies. This offers a unique venue for
associating more potentially useful ecological information
to the Dayton model framework, which is especially rel-
evant for anthropogenic disturbance assessment and effective
rocky shore conservation efforts.
Application As the Dayton model has only three functional
groups easily identifiable by the naked eye, this model makes
for easy fieldwork compared to other available models, only
requiring passing knowledge of the local flora and overall
species dynamics. The Dayton model could be valuable for
environmental monitoring and coastal management when
a broader stroke disturbance and succession analysis of a
rocky shore would suffice, but it still requires hypothesis and
assumption testing first.
B.1.2 Littler and Littler model
The Littler and Littler model is based on a series of arti-
cles that classify seaweeds into functional groups accord-
ing to their ecological, physiological, and morphological
traits (Table2) (Fig.3). This model proposes eight hypoth-
eses that relate seaweed thallus form to various ecological
aspects, such as productivity, predation, wave shear, and
resistance. Three of these hypotheses form the core of the
Fig. 1 Synopsis flowchart of the application of all functional models of marine macroalgae as proposed by their authors, excluding critical con-
siderations from this review
Fig. 2 Illustration of Dayton model’s classification of seaweeds by
vertical space use and successional traits
Table 2 Summary of the Littler and Littler model and its predictions per functional group
Functional group Morphology Ecology
Filamentous Thin, thread-like thalli Opportunistic, high productivity, low resistance, low caloric value, high predation
Foliose Flat, leaf-like thalli Opportunistic, high productivity, low resistance, low caloric value, high predation
Crustose Thin, encrusting thalli Climax, low productivity, high resistance, high caloric value, low predation
Coarsely branched Thick, rigid thalli with branches Climax, low productivity, high resistance, high caloric value, low predation
Articulated calcareous Jointed, calcified thalli Climax, low productivity, high resistance, high caloric value, low predation
Thick and leathery Thick, flexible thalli Climax, low productivity, high resistance, high caloric value, low predation
Delicately branched Thin, flexible thalli with branches Intermediate, moderate productivity, moderate resistance, moderate caloric value,
moderate predation

Journal of Applied Phycology
model: Productivity Hypothesis, Succession Hypothesis, and
Predation Hypothesis (Table3). The model was tested and
refined in subsequent articles by the same authors and others
(see Table1 and TableS1).
Later work by Littler and Littler after 1980 mainly
focused on testing the other two core hypotheses of the
model: the Productivity Hypothesis and the Predation
Hypothesis (see e.g., Littler and Arnold 1982; Littler etal.
1983a, b, 1991, 2009; Littler and Littler 1984a, b; 2007;
Littler and Kauker 1984; Dudgeon etal. 1995). Their
model was also applied to specific case studies with vary-
ing degrees of success (Littler and Kauker 1984; Hanisak
etal. 1988). Later on, M. and S. Littler developed an off-
shoot of its general functional model specifically for phase
shifts in coral reefs, deemed the Relative Dominance Model
(RDM) (Littler and Littler 1984b, 1985; Littler etal. 2009).
Additionally, heteromorphic life histories are expressly
addressed by the Littler and Littler model through the
Shifting-strategy Hypothesis (Littler and Littler 1980, 1983;
Littler and Kauker 1984).
The Littler and Littler model remains paradigmatic in sea-
weed ecology and physiology (Hurd etal., pp. 11-15, 2014).
However, further research would be valuable in addressing
lingering shortfalls and uncertainties.
New directions forfuture research ontheLittler
andLittler model
Unknown worldwide applicability The Littler and Lit-
tler model was built on research primarily conducted in
Fig. 3 Illustration of Littler and Littler model’s classification of seaweeds by morpho-anatomical criteria
Table 3 Summary of the Littler and Littler general functional hypotheses
Hypothesis Description
Productivity Hypothesis Opportunistic species have higher net productivity than climax species, and intermediate seral species have
intermediate values
Maintenance Costs Hypothesis Climax species have lower net photosynthesis per unit of total thallus weight than opportunistic species, due
to higher allocation of resources for environmental resistance, interference competition, and anti-predator
defenses
Caloric Hypothesis Climax species have lower caloric values than opportunistic species, due to the higher allocation of materials to
structure at the expense of lipids and protoplasm
Predation Hypothesis Climax species are more resistant to grazing by generalist herbivores than opportunistic species
Structural Hypothesis Selection in constant habitats should increase the allocation of materials to non-pigmented support structures at
the expense of photosynthetic tissue
Wave-shearing Hypothesis Late successional species should show greater resistance to wave shear forces than opportunistic species
Resistance Hypothesis Late successional species should have more resistant thalli than opportunistic species, in terms of physical
disturbances
Shifting-strategy Hypothesis Some macroalgae should shift from an opportunistic strategy to a characteristic of late-successional forms dur-
ing maturation
Successional Hypothesis Thallus morphological complexity should increase towards climax states

Journal of Applied Phycology
the Caribbean and California. This offers an opportunity
for new research to more comprehensively investigate the
worldwide applicability of this model.
Need for group reassessment? In the model’s seminal
paper, Littler and Littler (1980) observed that there was
no significant difference between most functional groups,
except for the extremes of the distribution (Ulva and Coral-
lina). This lack of difference was argued in that paper to be
due to the morphological continuum, from simple to com-
plex forms, with many intermediate species; hence, without
detectable significant difference. Fong and Fong (2014),
Mauffrey etal. (2020), and Ryznar etal. (2021) found no
significant support for the Littler and Littler model’s group
trait predictions, leading to doubts on group consistency
worth exploring in future research.
Applications Littler and Littler model has six functional
groups identifiable by gross morphology and texture alone,
this makes it the easiest model by which to correctly clas-
sify specimens in fieldwork by naked eye and handling, as
no taxonomic identification, nor prior knowledge of suc-
cessional dynamics, anatomy or other traits is necessarily
required. This would be especially useful if the taxonomic
composition and natural history of the site is poorly known
but functional analysis is nonetheless of interest for moni-
toring. As no anatomical analysis is required for classi-
fication, it makes this model more accessible for coastal
monitoring by communities without access to stereo and
compound microscopes for taxonomic identification or
anatomical trait determination (Rickard 2008; Mudge
2018), as required by the Balata etal. and Mauffrey etal.
models and by the Steneck and Dethier model respectively.
Hanisak etal. (1990) suggested that the Littler and Littler
model could also inform seaweed farming applications,
especially due to the correlations found between its func-
tional groups and productivity (Littler and Arnold 1982;
Littler and Littler 1984b). However, the results found by
Fong and Fong (2014) when comparing within-group
and between-group productivity differences showed that
within-group differences and species identity were more
relevant to productivity values than between-group dif-
ferences, therefore violating the assumption of functional
equivalence of algae belonging to the same Littler and Lit-
tler model’s functional group.
B.1.3. Steneck and Dethier model
Steneck and Dethier (1994) proposed a functional group
scheme for macroalgae based on their morphology and
ecology (Table4). They classified algal diversity into
seven functional groups (Fig.4). The Steneck and Dethier
model aims to explain and predict:
1. Community assemblages, given environmental factors of
potential productivity (e.g., light, nutrients, and all other
factors causing biomass gain) and disturbance potential
(desiccation, wave action, herbivory, and all other fac-
tors causing biomass loss), and vice versa.
2. Characteristics (“intrinsic properties”) of each func-
tional group, such as mass-specific productivity, canopy
height, and thallus longevity.
Steneck and Dethier (1994) sampled three locations in
Maine, two in Washington, and three in St. Croix, at dif-
ferent depths. They used different methods to sample and
analyze the communities, focusing on their functional com-
position. While Steneck and Dethier (1994) do not provide
specific titles and concise formulations for their hypothesis,
we have elaborated summarized tenets of their hypotheses
in Table5.
Their model was based on the same three fundamental
hypotheses as the Littler and Littler model (Productivity
Hypothesis, Succession Hypothesis, and Predation Hypoth-
esis). However, Steneck and Dethier (1994) excluded its
applicability to species groups, instead aiming to character-
ize and compare the structuring of entire macroalgal com-
munities based on functional attributes and their relation
to environmental disturbance, productivity, and their con-
sequences on total biomass.
The Steneck and Dethier model focuses on community-
level analyses through functional groups (Steneck and
Dethier 1994). Phillips etal. (1997), Balata etal. (2011),
Fong and Fong (2014), and Mauffrey etal. (2020) directly
tested this model. Padilla and Allen (2000) considered the
Phillips etal. (1997) test of the Steneck and Dethier model
inconclusive and unsupported. However, a more detailed
conceptual analysis reveals that the concept used for dis-
turbance potential was different from that established in the
Steneck and Dethier model. Phillips etal. (1997) measure-
ment of disturbance potential was also different from that
proposed by Steneck and Dethier (1994), since while Phil-
lips etal. (1997) measured only wave exposure as a proxy
for all biomass loss, Steneck and Dethier (1994) measured
only herbivory. This could be a possible reason for the lack
of support they found. Fong and Fong (2014), and Mauf-
frey etal. (2020) also found no support for the Steneck and
Dethier model’s predictions. Balata etal. (2011) and Mauf-
frey etal. (2020) tests of the Steneck and Dethier model
will be discussed in detail with their model in the Balata
etal. model and Mauffrey etal. model sections respectively.
The Steneck and Dethier model also makes predictions
regarding community disturbance and productivity levels
that were not validated (Phillips etal. 1997; Balata etal.
2011; Fong and Fong 2014). Steneck and Dethier model’s
groups also failed to explain species functional trait varia-
tion (Mauffrey etal. 2020).

Journal of Applied Phycology
Table 4 Summary of the Steneck and Dethier model and its functional groups
Functional
group
Morphology Competitive,
Stress-tolerant,
or Ruderal
strategy
Successional
stage
Relative net
primary pro-
ductivity
Resistance
to distur-
bance
Caloric value Predation
susceptibil-
ity
Thallus lon-
gevity
Microalgae Microscopic,
unicellular,
or colonial
ruderal pioneer high low low high very ephemeral
Filamentous
algae
Thin, thread-
like thalli,
single-cell
layer, uni-
directional
growth
ruderal pioneer high low low high ephemeral
Foliose algae Flat, leaf-like
thalli, single-
cell layer,
bidirectional
growth
ruderal intermediate high low low high short
Corticated
foliose algae
Flat, leaf-like
thalli, with a
little cortica-
tion
competitive intermediate moderate moderate moderate moderate intermediate
Corticated
macrophytes
Branched thalli
with a thin
cortex layer
and thicker
medulla
competitive intermediate moderate moderate moderate moderate long
Leathery
macrophytes
Thick, rigid
thalli, with
a well-
differentiated
cortex and
medulla
competitive climax low high high low very long
Articulated
calcareous
algae
Jointed, calci-
fied thalli
stress-tolerant climax low high high low intermediate
Crustose algae Encrusting
thalli
stress-tolerant pioneer low high high low longest
Fig. 4 Illustration of Steneck and Dethier model’s classification of seaweeds by morpho-anatomical criteria

Journal of Applied Phycology
The Steneck and Dethier model is arguably the most suc-
cessful model to date on account of its number of citations.
However, it has some challenges and unknowns that should
be addressed in future research.
New directions forfuture research ontheSteneck
andDethier model
Reassessing accuracy and precision No statistical tests were
made to validate the Steneck and Dethier model’s hypotheses
in its seminal paper. For example, in their “Fig.5” (Steneck
and Dethier 1994; Padilla and Allen 2000), the crustose func-
tional group showed a higher correlation with high produc-
tivity and high disturbance than the other functional groups,
contrary to the model’s predictions. As discussed before, the
Steneck and Dethier model has faced mixed responses from
tests. Besides the need for more comprehensive testing of its
assumptions and hypotheses, a meta-analysis would after-
wards be able to address the mixed test results.
More ways to assess total disturbance The results that laid
the foundations of this model only experimentally assessed
herbivory as a factor causing biomass loss. Other physical
factors such as water flow can affect the morphology and
ecology of macroalgae. For example, water flow can influ-
ence the growth, shape, and attachment of macroalgae, as
well as their nutrient uptake, photosynthesis, and dispersal
(Kaandorp and Kübler 2001; Stewart and Carpenter 2003).
Integrating other factors that impart on total disturbance
would offer complementary insights into disturbance poten-
tial measurement.
Applications The Steneck and Dethier model was aimed
especially at seaweed assemblage analysis. This highlights
its application to describe and model seaweed assemblage
structure, for which it was been validated (Veiga etal. 2013).
However, the correct classification of specimens into func-
tional groups is dependent upon microscopic examination
of anatomical traits, for example, to differentiate corticated
foliose from foliose algae. The requirement of stereo and
compound microscopy can be prohibitive for some coastal
communities involved in local monitoring (Rickard 2008;
Mudge 2018). Destructive sampling is also necessary for
correct specimen classification, this might render some
applications not suitable for this model usage such as suc-
cession analyses of a given area.
B.2. New models
New models have emerged to explain the morphology and
function of macroalgae, often building on the classic works
of Littler and Littler or Steneck and Dethier. The effort in this
field, after the traditional models were proposed, has been to
test their hypotheses and expand on the established paradigms,
as seen in Balata etal. (2011), or challenging them, develop-
ing new theoretical frameworks, models, and hypotheses, as
in Mauffrey etal. (2020).
B.2.1. Balata etal. model
Balata etal. (2011) developed a new model to analyze spe-
cies composition and abundance under different stress levels.
35 new functional groups based on morpho-anatomical traits
and taxonomy were proposed. They sampled ten locations on
the Tuscan coast and archipelago that differed in their proxim-
ity to urban centers. This was taken as a proxy for the distur-
bance potential, sensu Steneck and Dethier (1994). By com-
paring the results from the Steneck and Dethier model groups
against theirs, statical analyses indicated that the Balata etal.
model was significantly more accurate and precise in portray-
ing changes in community structure in a disturbance potential
gradient. Due to these results, Balata etal. (2011) suggested
the use of their model as the preferable ecological indicator
of disturbance.
The Balata etal. model has not been tested yet, as none of
the articles that cited the article by Balata and colleagues on
Google Scholar attempted to replicate or validate their model.
The work of Balata etal. (2011) represented a resurgence of
tests and propositions of new functional models in macroalgae
after Steneck and Dethier (1994).
New directions forfuture research ontheBalata
etal. model
Concepts of disturbance The Steneck and Dethier model
test by Balata etal. (2011) was undertaken with a radically
Table 5 Summary of the Steneck and Dethier model and its hypotheses
Hypothesis Description
Complexity Hypothesis The dominant seaweed functional group anatomical and morphological complexity in a rocky shore community will
correlate positively with competitive ability and negatively with disturbance frequency and intensity
Dominance Hypothesis The dominant seaweed functional group in a rocky shore community will be the one best suited to the environmental
disturbance potential and the productivity potential
Similarity Hypothesis There is greater average similarity within a group in terms of mass-specific productivity, canopy height, and thallus
longevity than average similarity between groups

Journal of Applied Phycology
different disturbance definition than the one given by Steneck
and Dethier (1994, 1995), as it included various stressors,
such as heavy sedimentation, and organic and chemical
pollution (Balata etal. 2011). Moreover, the productivity
potential, another component of the Steneck and Dethier
model, was also not evaluated. This raises some doubts over
the comprehensiveness of the Balata etal. (2011) test of the
Steneck and Dethier model. Studies comparing the effect of
using the Balata etal. model and the Steneck and Dethier
model would offer a new venue for future research.
Problems with subjective trait determination Relative terms
such as “smaller size” and “larger size,” make it difficult to
apply uniformly because their qualifier is relative to the size
distribution that happens to be sampled. This may introduce
ambiguity and inconsistency in the functional group clas-
sification and the comparison with the Steneck and Dethier
model, for example. Exploring more precise and objective
criteria for the seaweed traits could improve model data reli-
ability and reproducibility.
Taxonomic trouble Taxonomic affinity, even at the phylum
level, can be problematic when depending on simple visual
inspection in situ, without the possibility of destructive
sampling followed by taxonomic identification. This occurs
especially due to high phenotypic plasticity and morpho-
logical convergence in macroalgae (Fong and Fong 2014;
Ryznar etal. 2021, 2023). As incorporating phylogenetic
data in functional groups is still being debated in functional
ecology at large (E‐Vojtkó etal. 2023; Fintelman-Oliveira
etal. 2023; Fong etal. 2023; Luza etal. 2023), the question
of whether taxonomy could help or hinder our understand-
ing of community functioning offers an important venue for
future research.
Applications The Balata etal. model was developed to assess
the effect of anthropogenic stressors on seaweed assem-
blages. It stresses that the disappearance of some functional
groups when analyzing disturbance gradients could serve as
an ecological indicator. For these indicators to be determined,
site-specific analyses must take place comparing the presence
and absence of functional groups across the assemblages in
disturbance gradient. As this model requires both taxonomic
identification and anatomical trait examination for correct
specimen grouping, it might not be accessible for communi-
ties involved in coastal monitoring but lacking microscopes
and seaweed identification expertise. Destructive sampling is
also necessary for correct specimen classification.
B.2.2 Arenas etal. model
Arenas etal. (2006) proposed a classification model
for macroalgae based on the use of vertical space as a
criterion, similar to the Dayton model. They aimed to
determine susceptibility to invasion, contrasting func-
tional diversity and taxonomic diversity in macroalgae
communities. Arenas etal. model proposed four functional
groups: encrusting, turf, sub-canopy, and canopy (Fig.5).
They hypothesized that communities with more functional
groups present (functional richness) would be less suscep-
tible to invasion by new species than communities with
less functional groups present. Contrary to their hypoth-
esis, they found that invasibility was not explained by
functional richness, but by which species composed such
groups and local resource availability. Derived models
such as canopy-turf or canopy, subcanopy, and turf have
also been proposed, as noted by Mauffrey etal. (2020).
Besides the test by Arenas etal. (2006), a similar ver-
sion to this model was tested by (Mauffrey etal. 2020)
showing little support for within-group species trait simi-
larity as assumed by the Arenas etal. model and its vari-
ations, as discussed below.
New directions forfuture research ontheArenas
etal. model
Functional groups and biotic resistance In essence, the ques-
tion raised by Arenas etal. (2006) harks back to Elton’s idea
Fig. 5 Illustration of Arenas
etal. model classification by
vertical space

Journal of Applied Phycology
of communities being more susceptible to invasion if the
invading species was capable of occupying a vacant niche:
Elton’s biotic resistance hypothesis (Levine and D’Antonio
1999; Lowry etal. 2013; Elton 2020). While the Arenas
etal. model failed to predict the invasibility of the com-
munities experimentally assessed in the model’s seminal
study (Arenas etal. 2006), further assessment is needed to
evaluate if functional group absence would also not affect
community invasibility in other sites. Additionally, it would
also be important to assess whether trait-space occupation
would differ in seaweed assemblages under invasion, similar
to what is being assessed in fish (Shuai etal. 2018; Campbell
and Mandrak 2021; Renault etal. 2022) and plant (Sodhi
etal. 2019; Liao etal. 2021) functional ecologies.
Applications The Arenas etal. model was developed to
assess the invasibility of seaweed assemblages. Its functional
groups are easy to correctly identify by visual inspection
and non-destructive sampling by workers with some knowl-
edge of seaweed species and basic community dynamics on
the site. However, many uncertainties still lay ahead of this
model’s applied potential.
B.2.3 Mauffrey etal. model
Mauffrey etal. (2020) analyzed 12 traits related to com-
petitive dominance and resource economics in 95 algae spe-
cies in the United Kingdom, aiming to: (1) Group species to
compose emergent functional groups based on the similarity
of measured trait values; (2) Test the explanatory power of
these emergent groups against Littler and Littler models,
Steneck and Dethier models, Canopy-turf, as well as the
Canopy, subcanopy, turf model. Mauffrey etal. (2020) con-
ducted a PCA to obtain emergent functional groups, thus
constituting them post-hoc, i.e., not by previously assumed
trait importance, as in other functional models. Mauffrey
etal. (2020) found that: the least explanatory model was
Canopy-turf, accounting for about 20% of the variation;
classical models were able to explain a third of the attribute
variation; their emergent groups could explain 69%. Due to
the higher explanatory capacity of emergent groups from
post-hoc analyses, Mauffrey etal. (2020) advocate for fur-
ther studies in other regions to determine other emergent
groups applicable to each locality.
A similar approach to the Mauffrey etal. model was
developed by Fong etal. (2023) albeit with a focus on cat-
egorical traits rather than on continuous ones as did Mauf-
frey etal. (2020). The Mauffrey etal. model, while with
paradigm-changing potential, awaits further assessment
by tests that would replicate exactly its proposed trait and
emergent group analysis methodology as well as assess its
applicability in more regions.
New directions forfuture research ontheMauffrey
etal. model
Model scope and testing While seaweed functional mod-
els have been developed over specific traits and aims,
the extension to which models can be applied and tested
beyond their scope remains up for debate and can posi-
tively delimit a model’s reach and best usage. Among the
12 traits measured by Mauffrey etal. (2020), few align
with the traits predicted and measured in the works of
Littler and Littler or in the Steneck and Dethier model.
Indeed, Steneck and Dethier (1994) have expressly stated
that their model is not intended to evaluate species-level
traits, nor does the Littler and Littler model directly bear
on attributes measured by Mauffrey etal. (2020). This
could potentially be a confounding factor affecting the low
predictive accuracy reported when comparing the classic
models to Mauffrey etal.’s. Work replicating the traits
chosen by the classic models but then conducting post-
hoc emergent group analyses could offer a complementary
venue to assess the emergent group approach to functional
groups.
Finding the Golden Mean for functional trait
choice Groups in the Mauffrey etal. model are to be
constructed post-hoc, after analyzing each species and
its attributes, their greater explanatory power concern-
ing the initial sample is expected, as it occurs circularly.
The nine emergent functional groups from Mauffrey etal.
(2020) could be explained by groupings by phylum with
88% sd 11.8% accuracy, upon our examination. The fact
that phyletic grouping explains more trait variance than
all other proposed functional groups tested by Mauffrey
etal. (2020) indicates that perhaps either an excess of
traits is being evaluated or the proposed groupings are
not productive regarding the study’s aim (see e.g., Fong
and Fong 2014).
As traits measured and included in a functional ecol-
ogy study increase and are used to assess trait similari-
ties between species, trait variance will be increasingly
explained by phylogeny, if two conditions are met: (1) if
species phylogenetic proximity is linearly proportional to
the amount shared traits, which is a tendency (Gerhold
etal. 2015); (2) as any entity similarities (e.g., species-
species similarities) decrease as non-identical differentia-
tion criteria are added, which is a property of entity match-
ing (Köpcke and Rahm 2010; Wang etal. 2011). Future
research on seaweed functional ecology should strive for
a golden mean in trait number choice: not too few as to be
uninformative, nor too many as to cease being functional
and become descriptive autecology.

Journal of Applied Phycology
Categorical, continuous, and mean trait values The use
of categorical, quantitative, and mean trait values in sea-
weed ecology has spurred a recent debate on functional
trait choice (Cappelatti etal. 2019; Mauffrey etal. 2020;
Fong etal. 2023; Griffin etal. 2023; Ryznar etal. 2023).
Studies comparing categorical, continuous, and mean trait
values approaches would be valuable especially to target
specific applied concerns, such as providing a baseline for
prospecting novel seaweed species to be cultured and pro-
viding differentially powered analyses for monitoring and
conservation applications.
Applications The Mauffrey etal. model enables the devel-
opment of site-specific functional groups that may be more
adequate for the analysis of local species traits and their
associated community dynamics. It is the most labor-
intensive model to date, as it requires the assessment of
multiple traits to develop emergent functional groups. The
broad span of analyses required by this model might make
it unsuitable for non-specialist usage. However, after the
site-specific emergent groups are established, this model
could be more accurate for trait analyses than the other
models, as shown by the results of Mauffrey etal. (2020).
Conclusion
The practical question: What’s the best model to use? The
best choice of functional models for basic and applied con-
cerns relies fundamentally upon the adequate selection of a
model whose scope, objective, and functional traits meas-
ured that can effectively address the question of interest.
While every seaweed functional model has different uncer-
tainties, strengths, and limitations, few models had their
hypotheses tested. Having in mind the aforementioned gen-
eral lack of studies, we have provided a practical flowchart
for the models that had their usage tested for a particular
aim and shown overall support (Fig.6).
Major questions yetunaddressed inseaweed
functional ecology
Individual traits As presented earlier, seaweed functional
models were built upon species or group-level traits. Plant
functional ecology has embraced the analysis of individ-
ual-level traits now for some time. Phycology lags behind
in its investigation of individual traits, but it has not been
so without a reason. Individuality in seaweeds is a highly
contentious topic, with different seaweed taxa exhibiting
different degrees of consensual individuality definitions
(Herron etal. 2013; Demes and Pruitt 2019). As seaweed
functional ecology is also very field-focused, how to dif-
ferentiate individuals in mixed turf algae clumps? Are thalli
generated by mitotic spores (monospores or mitospores) the
same individual? What about parthenosporophytes? What
about chimaeras? The uncertain boundaries of individuality
in seaweeds and their functional effects constitute a major
open question to be explored in future research.
Ontogenetic changes Seaweeds can change remarkably
during their development (Kilar etal. 1989; Stewart 2006;
Steinhagen etal. 2022), yet an overall static approach has
prevailed in previous functional ecology research (with few
exceptions, e.g., Ryznar etal. 2023). Understanding how
functional traits change over the course of normal develop-
ment would allow the development of comprehensive mod-
els, with significant potential in seaweed agriculture. This
would also bring seaweed functional ecology on par with
recent developments in plant functional ecology, such as the
interplay between function and ontogenesis (Barton 2024).
Complex life histories While the issue of heteromorphic life
histories has been addressed by the Littler and Littler model
as discussed above, seaweed’s complex life histories can
involve more than morphology changes (Herron etal. 2013;
Liu etal. 2017; Demes and Pruitt 2019; Borlongan etal.
2020; Krueger-Hadfield 2020; Heesch etal. 2021; Krueger-
Hadfield etal. 2024). Trait variation in heteromorphic stages
Fig. 6 Application of seaweed
functional models for which
they were experimentally tested
and showed overall support

Journal of Applied Phycology
could offer additional venues to be explored and incorpo-
rated into future functional models.
Plasticity Seaweeds are notoriously plastic (Charrier etal.
2012; Flukes etal. 2015; Yñiguez etal. 2015; Coleman and
Martone 2020). No two thalli are the same and changes from
color to form to tolerance are induced through development
by genome-environment interactions. Integrating knowledge
of environment-induced responses in seaweeds would be key
to modeling climate change-induced effects on seaweed
functional traits.
Glossary andterminology unification
A major obstacle in the seaweed functional ecology litera-
ture is the lack of a unified terminology with clear defini-
tions. Littler and Littler (1980) first introduced the term
“functional forms” to arrange seaweeds into trait-based
groups. Later, Littler etal. (1983a, b) used “morpho-func-
tional groups” and “functional morphology groups” inter-
changeably with “functional forms”. While reviewing land
plant functional ecology, Gitay and Noble (1997) suggested
that functional groups and functional types should be treated
as synonymous terms. The morphology-based functional
groups from Littler etal. (1983a, b) or Steneck and Deth-
ier (1994) could also be referred to as ecomorphs, as they
would fit the definition of “ecomorph” (sensu Bock 1994).
However, to unify the terminology and improve crosstalk
between fields we recommend using only “functional group”
when referring to functional trait-based groups, as has been
an increasingly popular trend in seaweed biology (Mauffrey
etal. 2020; Fong etal. 2023; Ryznar etal. 2023) bringing
it on par with functional ecology research on other systems
(Davison etal. 2020; Graham etal. 2020; Newbold etal.
2020; Mihalitsis and Bellwood 2021; Wang etal. 2021).
"Functional model” and “functional scheme” should be
treated as synonyms as has been done in recent work (Fong
and Fong 2014; Mauffrey etal. 2020). “Functional model”,
however, should be the preferred term to bring seaweed
functional ecology on par with what has been used in func-
tional ecology in general (Kearney etal. 2021).
The way forward
There is still much to be done to understand the relationships
between form and function in seaweeds. As discussed above,
foundational assumptions and hypotheses about seaweed
biology still need further testing and evaluation. As MacAr-
thur (1984) stated: "The future principles of the ecology of
coexistence will (…) for organisms of type A in environment
of structure B, such and such relationship will hold”. If this
vision comes to fruition, then with a better understanding of
the fundamental relationships between the form, function,
and ecology of seaweeds, we will finally be able to reveal the
full potential of functional models: explaining and predicting
the what, where and how of seaweed functional traits and
environmental interaction dynamics.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s10811- 024- 03293-z.
Authors' contributions João P. G. Machado and Vinícius P. Oliveira
contributed equally to the study.
Funding No funding was received to assist with the preparation of
this manuscript.
Availability of data and material All data is publicly available on
https:// www. webof scien ce. com/ wos/ woscc/ summa ry/ ce2a7 a1e- 8e0a-
4f1a- 9735- 73a75 4e137 85- 6da45 8c8/ relev ance/1. Articles selected for
the review are listed in Supplementary Table (S1).
Declarations
Competing interests The authors have no competing interests to
declare that are relevant to the content of this article.
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