# Beyond taxonomy: A framework for biological trait analysis to assess the functional structure of benthic foraminiferal communities

Tamar Guy-Haim, Vincent M.P. Bouchet

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Tamar Guy-Haim, Vincent M.P. Bouchet. Beyond taxonomy: A framework for biological trait analysis to assess the functional structure of benthic foraminiferal communities. Marine Pollution Bulletin, 2025, 213, pp.117699. 10.1016/j.marpolbul.2025.117699. hal-05152768

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# Beyond taxonomy: A framework for biological trait analysis to assess the functional structure of benthic foraminiferal communities

Tamar Guy-Haim<sup>a,b</sup>, Vincent M.P. Bouchet<sup>c,*</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

### Keywords:

Foraminifera

Meiobenthos

Morphogroup approach

Trait-based approach

Ecosystem functioning

Functional diversity

Biomonitoring

Environmental change

## ABSTRACT

Benthic foraminifera, as key components of marine and transitional ecosystems, provide a valuable opportunity to investigate the effects of environmental changes on marine biodiversity. While traditional taxonomic approaches have been instrumental in assessing benthic foraminifera assemblages, a trait-based approach offers a more holistic perspective on their ecological roles. This study introduces a framework for biological trait analysis (BTA) of benthic foraminifera, emphasizing the importance of morphological, physiological, and ecological traits. We examine the potential of morphogroup analysis as a precursor to BTA, acknowledging its limitations, and propose a list of functional traits relevant to benthic foraminifera. These include both response and effect traits, such as wall texture, test size, chamber arrangement, chamber disposition, pore density, pore size, life mode, feeding mode, bioturbation mode, respiration mode, association and indicative value. Additionally, we address the challenges and opportunities associated with quantifying and analyzing foraminiferal functional traits, and outline key statistical methods for exploring trait-environment relationships. By implementing BTA, we can evaluate the functional diversity and ecological roles of foraminiferal communities, identify the drivers of community structure, predict responses to environmental changes, and assess the impacts of anthropogenic disturbances on marine ecosystems.

## 1. Introduction

The composition and structure of coastal benthic communities are largely constrained by environmental conditions (Rosenberg et al., 2002; Gröger and Rumohr, 2006), in particular by changes related to human activities and climate (Gray et al., 1990; Hyams-Kazphan et al., 2009). The question of the driving forces of benthic communities is often addressed by measuring abundances, diversity and biomass (Warwick and Clarke, 1994; Kröncke et al., 1998; Burone et al., 2006). However, these relevant but rather limited metrics do not directly inform about the consequences of environmental changes on the benthic ecosystem functioning. Functional traits—morphological, physiological, trophic, and phenological characteristics that impacts organism's fitness by affecting its growth, reproduction, and survival—provide new insights into the link between species composition and ecosystem functioning, helping to assess the effects of environmental changes on this relationship (Bremner et al., 2003; Violle et al., 2007). Biological trait analysis (BTA) is an analytical framework that was developed for describing

ecological functioning of marine benthic communities (Bremner et al., 2006). It considers species traits in relation to the functional aspects of the ecosystem, combining structural data of a community (abundance or biomass) with the information on functional traits of every single species (Marchini et al., 2008; Vesal et al., 2021; Magni et al., 2023). Every species can play an important role in the ecosystems; hence, any change in species composition can significantly modify the benthic ecosystem functioning (Solan et al., 2004). Therefore, many studies have applied BTA on benthic species to assess the health of marine benthic habitats (Marchini et al., 2008; Vesal et al., 2021; Justino et al., 2023; Magni et al., 2023), and to examine the effects of chemical pollution (Oug et al., 2012), eutrophication (Mitwally and Fleeger, 2016), aquaculture (Mascorda-Cabre et al., 2023), and bioinvasions (Evans et al., 2020). The majority of the marine benthic fauna trait-based studies have focused on macro-invertebrates, whereas only 2 % have utilized meio-fauna (de Juan et al., 2022), all of which involved nematodes. While previous studies on benthic foraminiferal communities have assessed environmental changes through species-specific ecological preferences,

<footnote><sup>a</sup> National Institute of Oceanography, Israel Oceanographic and Limnological Research, Tel-Shikmona, P.O.B. 8030, Haifa 31080, Israel</footnote>

<footnote><sup>b</sup> Department of Life Sciences, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel</footnote>

<footnote><sup>c</sup> Univ. Lille, CNRS, Univ. Littoral Côte d'Opale, IRD, UMR 8187, LOG, Laboratoire d'Océanologie et de Géosciences, Station Marine de Wimereux, F 59000 Lille, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: vincent.bouchet@univ-lille.fr (V.M.P. Bouchet).</footnote>
most have relied on taxonomic approaches, including species richness, alpha diversity (the diversity of species within a particular area or ecosystem) and abundance. Developing a benthic foraminiferal trait-based approach may help capturing key changes in environmental conditions that taxonomic-based studies do not record (Hansen et al., 2023).

Benthic foraminifera is one of the most abundant meiobenthic groups, representing up to 50 % of the benthic eukaryotic biomass in coastal sediments (Moodley et al., 2000). They also contribute significantly to the carbon, nitrogen and phosphorus biogeochemical cycles in coastal ecosystems (Jauffrais et al., 2019; Choquel et al., 2021; Glock et al., 2025). They occupy a major trophic position through bacterivory, herbivory or carnivory (Nomaki et al., 2008; Chronopoulou et al., 2019). They also actively participate in the benthic ecosystem functioning via sediment reworking (Bouchet and Seuront, 2020; Deldicq et al., 2021b; Deldicq et al., 2023), bioirrigation and structuration of bacterial communities (Langlet et al., 2023). In addition, foraminifera can accumulate large quantities of nitrate in their cell and reduce it through denitrification under low-oxygen conditions (Risgaard-Petersen et al., 2006; Piña-Ochoa et al., 2010); their contribution to the nitrogen cycle being, for instance, up to 70 % in the North Sea (Piña-Ochoa et al., 2010) or ranging from 50 % to 100 % in Swedish fjords (Choquel et al., 2021). Benthic foraminiferal communities also reflect well environmental conditions (Hayward et al., 1996; Alve and Murray, 1999; Scott et al., 2001). They can respond to natural changes, e.g. salinity (de Rijk, 1995), organic matter (Mojtahid et al., 2009), productivity (Licari et al., 2003), and seasonal variations (Boltovskoy and Lena, 1969; Burone et al., 2007), and are also largely constrained by pollution, e.g. organic enrichment due to fish farms (Dubois et al., 2021), industries (Schafer et al., 1991), and thermal pollution (Arieli et al., 2011). The response of foraminifera to environmental changes is often explored by measuring abundances (Mojtahid et al., 2006), diversity (Francescangeli et al., 2020) and by targeting indicator species (Alve, 2003). Fundamental research on the ecology of benthic foraminifera has led to the development of biotic indices based on diversity (Alve et al., 2009; Bouchet et al., 2012; Bouchet et al., 2018) and species sensitivity/tolerance to pollution (Barras et al., 2014; Alve et al., 2016; Dimiza et al., 2016; Jorissen et al., 2018; Bouchet et al., 2021) for assessing the ecological quality of transitional and marine habitats. All these approaches address well the effects of environmental changes on alpha diversity (e.g., O'Brien et al., 2021), but any variations in functional diversity are yet to be considered. Although a trait-based framework for planktonic foraminifera has been recently developed (Grigoratou et al., 2019), there are only a handful of studies on biological traits in benthic foraminifera (Cleary and Renema, 2007; Hansen et al., 2023), and a global framework for trait-based analysis of benthic foraminifera has yet to be established.

In the present study, we argue that biological trait approaches for benthic foraminifera will improve how we understand and predict not only foraminiferal species ecology but also community responses to environmental drivers and effects on ecosystem processes. This work aims at providing a general biological trait framework and methodology for benthic foraminifera, and point to future research directions. It requires the identification and description of relevant biological traits which could be used in this context. To do so, this work benefits from the few studies exploring traits in benthic foraminifera, mostly based on morphological features (e.g., Corliss and Chen, 1988; Nagy, 1992; Langer, 1993; Murray et al., 2011).

## 2. Morphogroup approach as precursor to biological trait analysis

Several studies on living foraminifera suggested a significant relationship between microhabitat preference, i.e. life position and test morphology (Bandy, 1960; Chamney, 1976; Corliss, 1985), and between habitat environmental features and morphology (Severin, 1983). Indeed, epifaunal species found in the first centimeter of the sediment

column are typically plano-convex or biconvex shapes and trochospiral coiling with pores only on the spiral side or absent (Corliss, 1985). Conversely, infaunal species are planispiral with a rounded periphery and covered by pores (Corliss, 1991). The greater pore density is meant to be adapted to lower oxygen concentrations within the sedimentary column; flattened forms are also more abundant in anoxic sediment, their high surface area:volume ratio allowing them to exhibit more pores to enhance gas exchange to survive in such an adverse environmental setting (Bernhard, 1986; Moodley and Hess, 1992; Glock, 2023). These findings led to the classification of benthic foraminifera to morphological groups or morphogroups or morphotypes (Severin, 1983; Jones and Charnock, 1985; Bernhard, 1986; Langer, 1993).

The seminal work from Jones and Charnock (1985) introduced the morphogroup approach on modern agglutinated assemblages. Earlier, Severin (1983) used test morphology or growth patterns of test of calcareous and agglutinated benthic foraminifera to characterize different habitats. His classification was based solely on external test morphology and did not consider any taxonomical criteria. Specifically, six morphological groups were defined, i.e. straight-cylindrical, plano-convex, elongate-flattened, biconvex-keeled, tapered and rounded-planispiral (Severin, 1983). The Jones and Charnock's framework consists of four morphogroups (test form) and 8 subgroups (number of chambers, Table 1). It was based on the relationship between feeding habit (suspension, deposit, herbivore, detritivore, scavenger and omnivore), life position (attached, epifaunal and infaunal) and test morphology. This scheme covered environments ranging from marsh to deep-sea. Morphogroup A consists of elongated tubular forms, epifaunal suspension feeders. Morphogroup B includes four subgroups (B1-B4) of surface-dwelling herbivores, detritivores and omnivores with diverse test shapes. Morphogroup C comprises infaunal herbivores and detritivores with elongate tests, subdivided to C1 (uniserial, biserial and triserial) and C2 (elongated/milioline). Morphogroup D consists of plano-convex, low trochospiral trochamminids, which are epiphytal herbivores.

Another morphotype categorization is the scheme of Langer (1993) for epiphytic foraminifera considering hyaline, porcelaneous and agglutinated forms. This approach relies on the different modes of attachment of epiphytic foraminifera either (i) motile species with crawling motion-behavior using their pseudopods to attach to the surface or (ii) permanently attached species by mean of the secretion of adhesive materials (see details in Langer, 1993). These features allowed Langer (1993) to define four epiphytic morphotypes (Table 2).

The morphogroups approach has been criticized and questioned (van der Zwaan et al., 1999). Noticeably, although some evidences point toward a link between test shape and life habit, few studies stressed that the correlation may be weak (Jorissen et al., 1992; Moodley et al., 1997). Furthermore, the infaunal living mode would require smooth, slender tests according to the morphogroup concept. However, several planispiral species, such as Melonis affinis (van der Zwaan et al., 1999) or trochospiral forms like Ammonia sp. (Bouchet et al., 2009), are deep dwelling species, which seems contradictory when considering their test morphology (van der Zwaan et al., 1999). In fact, this relation may be complex, or altered throughout the lifetime in response to environmental change, suggesting that determining life habit based on test shape may not always be accurate (Jorissen, 1999). Furthermore, the fact that biological traits (i.e. test morphology, pore size and patterns) are actually functional traits was also questioned, van der Zwaan et al. (1999) arguing that there was no consistent proofs. Some recent works actually suggest the opposite. Burrowing behavior in intertidal foraminifera is linked to test shape and it further constrains their surface sediment reworking rates (Deldicq et al., 2021b). Planispiral species, i.e. Haynesina germanica and Elphidium williamsoni burrowed by successively orienting both their umbilical and apertural sides perpendicular to the sediment surface (Fig. 1). Conversely, the trochospiral Ammonia tepida buried itself in the sediment with a corkscrew movement with its umbilical side oriented toward the direction of the displacement (Fig. 1;
<caption>**Table 1**</caption>

The morphogroup framework for agglutinated foraminifera modified from (Jones and Charnock, 1985) and (Murray et al., 2011). *Trochamminids: B3 globular or biconvex forms, B4 permanently attached scaly, watch-glass shaped, concavo-convex forms, D trochospiral or plano-convex forms.

<table><caption>**Table 2**</caption><thead><tr><th>Morpho group</th><th>Subgroup</th><th>Chambers</th><th>Test form</th><th>Life position</th><th>Feeding habit</th><th>Examples</th></tr></thead><tbody><tr><td>A</td><td></td><td>Unilocular/ multilocular</td><td>Tubular or branching</td><td>Epifaunal</td><td>Suspension-feeders</td><td>Komokiaceans, Astrorhizids</td></tr><tr><td rowspan="4">B</td><td>B1</td><td>Unilocular</td><td>Globular</td><td rowspan="4">Surface-dwelling</td><td rowspan="4">Herbivores, detritivores, omnivores</td><td rowspan="4">Saccamminids<br/>Ammodiscids?Rzehakinids</td></tr><tr><td>B2</td><td>Unilocular/? multilocular</td><td>Coiled, flattened</td></tr><tr><td>B3</td><td>Multilocular</td><td>Planispiral/Trochospiral</td></tr><tr><td>B4</td><td>Unilocular/ multilocular</td><td>Irregular, conical, concavo-convex</td></tr><tr><td rowspan="2">C</td><td>C1</td><td>Multilocular</td><td>Elongate, mixed growth</td><td rowspan="2">Infaunal</td><td rowspan="2">Herbivores, detritivores</td><td>Most Lituolids<br/>Trochamminids*</td></tr><tr><td>C2</td><td>Multilocular</td><td>Elongate, quinqueloculine</td><td>Attached forms<br/>Trochamminids*</td></tr><tr><td>D</td><td></td><td>Multilocular</td><td>Trochospiral/Plano-convex</td><td>Epiphytic</td><td>Herbivores</td><td>Some Rzehakinids <i>Miliammina</i><br/>Trochamminids*</td></tr></tbody></table>

The Epiphytic foraminiferal morphotype scheme for hyaline, porcelaneous and agglutinated forms.

<table><thead><tr><th>Morphotypes</th><th>Features</th></tr></thead><tbody><tr><td>A</td><td>e.g. <i>Planorbulina, Miniacina, Cyclocibicides</i><br/>Sessile, attachment surfaces flat, concave, or folder-over, attached to the substrate by glycosaminoglycans, average life-span 1 year</td></tr><tr><td>B</td><td>e.g. <i>Rosalina</i><br/>Temporary motile, aperturale faces wide, often papillae, motion by "swimming" on pseudopodial network, average life-span 2-5 months</td></tr><tr><td>C</td><td>e.g. <i>Elphidium</i><br/>Motile, extrusion of pseudopods from canal-system and multiple apertural openings, "suspending" the shell in the mesh-work filamentous algal blades, average life-span 3-4 months</td></tr><tr><td>D</td><td>e.g. <i>Quinqueloculina</i><br/>Permanently motile, apertural faces narrowing to bottle-neck apertures, motion by striding on the apertural face in upright position, short life-spans</td></tr></tbody></table>

(Modified from Langer, 1993)

Langer et al., 1989, Deldicq et al., 2021b). Prolate ellipsoid-shaped species such as *Quinqueloculina seminulum* and *Miliammina fusca* burrowed with their aperture oriented toward the sediment (Fig. 1). Furthermore, test ornamentation is involved in feeding behavior in kleptoplastidic foraminiferal species, facilitating diatom frustule breaking (Bernhard and Bowser, 1999; Austin et al., 2005). This feature is also species-specific with *H. germanica* fracturing the large benthic

diatom frustules around the foraminiferal apertural and umbilical ornamentation, while *E. williamsoni* uses a different strategy, breaking the large pennate diatom frustules with the fossette "teeth" on the dorsal side of the foraminiferal test (Jesus et al., 2022). Recent works highlighted the direct correlation between environmental conditions and the surface area of pores and pore densities. In deep sea epifaunal foraminifera, there is a strong, negative logarithmic correlation between bottom-water oxygen concentrations and pore surface area (Rathburn et al., 2018). In addition, oxygen concentrations can influence pore density and pore size (Moodley and Hess, 1992). Specifically, pore density is correlated to oxygen and nitrate bottom water concentrations (Glock et al., 2011) suggesting that in nitrate-depleted habitats, benthic foraminifera may optimize their nitrate uptake by increasing pore density. Morphological and pore traits i.e. pore density, porosity in *Bolivina spissa* and *Bolivina seminuda* suggested that the response is species-specific with the latter being more adapted to survive in dysoxic environments (Glock et al., 2011). These evidences on morphology, behavior and pore features suggest that some foraminiferal biological traits can be seen as functional traits, defined as any trait that impacts fitness indirectly via its effect on growth, reproduction and survival (Violle et al., 2007).

Notwithstanding the potential aforementioned limits, the morphogroup approach (Jones and Charnock, 1985) proved to be efficient to link foraminiferal community distribution patterns to environmental conditions (see review in Murray et al., 2011). Specifically, meaningful distribution patterns were obtained for morphogroups from marsh to deep sea off north-western Europe confirming a relationship between living foraminiferal morphology and environment of life (Murray et al.,

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Burrowing behavior trait of intertidal foraminifera according to test morphology: (A) planispiral, (B) trochospiral and (C) prolate ellipsoid-shaped (from Deldicq et al., 2021b).</figcaption></figure>
2011). In the western Bay of Bengal (Indian Ocean), in a freshwater influx dominated area, the different morphogroups significantly correlated with environmental parameters (Saalim et al., 2019). In detail, morphogroups A, B1 and B3 (Table 1) are positively correlated with water depth and negatively correlated with temperature, whereas morphogroups B2 and C1 are positively with organic carbon and negatively correlated with dissolved oxygen. Morphogroup C2 is positively correlated with dissolved oxygen, temperature, coarse fraction and organic carbon, and negatively correlated with depth. These findings allowed the authors to define the environmental preferences of each morphogroup in the Bay of Bengal using living foraminifera (Saalim et al., 2019). The Jones and Charnock's framework has been substantially applied to fossil sediments, for instance to Jurassic North Sea deltas (Nagy, 1992). Middle Jurassic benthic foraminiferal morphogroups (agglutinated and calcareous) provided information on sedimentation rate, oxygenation, position of the redox boundary in anoxic environments (Tyszka, 1994). Using the Langer's epiphytic foraminiferal scheme (Table 2) in seagrass meadows in the Mediterranean sea, it was evidenced that foraminiferal species (e.g., *Planorbulina mediterranensis*) from morphotype A dominated in sediments colonized by *Posidonia oceanica* (Mateu-Vicens et al., 2010). Conversely, morphotypes B (e.g., *Lobatula lobatula* and *Rosalina bradyi*) and D (hauerinids and textulariids) were characteristic of sediments associated with *Caulerpa* spp.. Dominance by one of these morphotypes is related to the interaction in terms of competition between *P. oceanica* and *Caulerpa* spp. (Mateu-Vicens et al., 2010). Epiphytic morphotypes, in combination with the FORAM Index (Hallock et al., 2003), an index evaluating the ecological quality of coral reef habitats, helped to assess the health of benthic ecosystems around Mallorca Island in the Mediterranean Sea (Mateu-Vicens et al., 2014).

### 3. Toward a biological trait framework

The term “trait” is used in different contexts in the literature, it is meant to describe specific features of an individual. The use of “trait”, “functional trait”, “morphological trait”, “physiological trait”, “behavioral trait” is rather confusing, and the definition of “trait” and “functional trait” remained ambiguous until Violle et al. (2007) proposed a unified framework for trait’s definitions. Noticeably, “trait” was defined as “any morphological, physiological or phenological feature measurable at the individual level, from the cell to the whole organism level, without reference to the environment or any other level of organization” (Violle et al., 2007). In turn, a “functional trait” is “any trait that impacts fitness indirectly via its effect on growth, reproduction and survival”

(Violle et al., 2007). In this framework, we did not distinguish between traits and functional traits as the links between traits and their functions are not well documented in the literature on benthic foraminifera with exception of, for instance, pore features (Glock et al., 2011) or test morphology (Jesus et al., 2022). In fact, the definition of functional trait would be mostly based on correlative approach, i.e. collection of community data and environmental parameters, rather than on mechanistic approach, i.e. testing individual traits.

Existing approaches primarily focus on species morphology, whereas the trait-based approach incorporates a broader set of functional traits, including morphological, physiological, trophic, and phenological traits (Fig. 2). These traits can be linked to ecological functions regardless of species identity, providing a more flexible and generalizable framework for studying community dynamics and ecosystem functioning. Moreover, trait-based analysis enables large-scale community comparisons and the detection of environmental changes by using standardized traits. A standardized trait list is particularly required for benthic foraminifera, as previous studies used heterogeneous methods for trait assignment. The morphogroup/morphotype approach uses solely test morphology of agglutinated or agglutinated/calcareous species. The two *stricto sensu* BTA studies implemented different trait lists: form/shape, symbiont type, chamber arrangement and skeletal structure in a study from the Spermonde Archipelago (Indonesia) (Cleary and Renema, 2007); pore patterns, living strategy/modes, substrate preferences, test material, oxygen tolerance and food preferences of fossil foraminifera in Arctic settings (Hansen et al., 2023). A holistic trait scheme should also consider interactions with the environment, distinguishing whether a trait’s expression is driven by environmental factors (“response trait”) and whether it influences environmental conditions, community or ecosystem properties (“effect trait”, Violle et al., 2007).

We provide a list of response and effect traits for conducting BTA with benthic foraminifera in Table 3. For instance, benthic foraminifera adapt their wall structure to environmental conditions (response), which allowed the development of diversified life strategies (Murray, 2006). The mineralogical test composition of agglutinated foraminifera is primarily driven by sediment grain size (Armynot du Châtelet et al., 2013). Carbonate availability, salinity and temperature can also have an effect on thinning or thickening of hyaline and porcelaneous tests (Boltovskoy et al., 1991). Furthermore, porcelaneous species dominates on sandy sediment in coastal and deltaic settings out of the influence of riverine input (Barbieri et al., 2019). Conversely, prodelta areas subject to significant deposition of riverine material are dominated by hyaline taxa such as *Ammonia parkinsoniana*, *A. tepida* and *Nonionella turgida* (Goineau et al., 2015). Furthermore, as carbonate-producing organisms,

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Examples of biological traits of benthic foraminifera to be used in trait-based analyses.</figcaption></figure>
<caption>**Table 3**</caption>

Traits and modalities proposed for Biological Trait Analysis with benthic foraminifera. Functions and processes were adapted to foraminifera from Beauchard et al. (2017). In the last two columns, "+" indicates when the trait expresses fitness ("Response") and/or the impact of this expression on the habitat abiotic and/or biotic features ("Effect").

<table><thead><tr><th>Traits</th><th>Modalities</th><th>Functions and processes</th><th>Response</th><th>Effect</th></tr></thead><tbody><tr><td>Wall texture</td><td>Hyaline<br>Porcelaneous<br>Agglutinated<br>Organic-walled</td><td>Gas exchange with ambient seawater, Protection to environmental pressure, Carbonate flux</td><td>+</td><td>+</td></tr><tr><td>Test size</td><td>&lt;125 µm<br>125-500 µm<br>500-1000 µm<br>&gt;1000 µm</td><td>Sensitivity (large) or tolerance (small) to adverse environmental conditions, metabolic oxygen consumption rate</td><td>+</td><td>+</td></tr><tr><td>Chamber arrangement</td><td>Spiral<br>Serial<br>Milioline<br>Mono-chamber<br>Mixte</td><td>Foraging mode, vertical migration in sediment</td><td>+</td><td>+</td></tr><tr><td>Disposition of chamber</td><td>Planispiral<br>Trochospiral<br>Uniserial<br>Biserial<br>Triserial<br>Biloculine<br>Triloculine<br>Quinqueloculine<br>Mixte</td><td>Foraging mode, vertical migration in sediment</td><td>+</td><td>+</td></tr><tr><td>Pore density</td><td>No pore<br>Some<br>High</td><td>Sensitivity (no pore) or resistance (pore) to anoxic conditions, gas exchange with ambient seawater</td><td>+</td><td>+</td></tr><tr><td>Pore size</td><td>No pore<br>Small<br>Big</td><td>Sensitivity (no pore) or resistance (pore) to anoxic conditions, gas exchange with ambient seawater</td><td>+</td><td>+</td></tr><tr><td>Test malformation</td><td>Chamber abnormalities (aberrant chamber shape)<br>Abnormal coiling<br>Absence</td><td>Sensitivity or resistance to change in environmental conditions, either natural or due to human activities (i.e. pollution)</td><td>+</td><td>+</td></tr><tr><td>Life mode</td><td>Epiphyte<br>Epifaunal<br>Infaunal</td><td>Foraging mode, Role on sediment biogeochemistry</td><td>+</td><td>+</td></tr><tr><td>Feeding mode</td><td>Herbivore<br>Carnivore<br>Detritivore<br>Omnivore</td><td>Food acquisition, growth requirement, control on prey populations, nutrient cycling</td><td>+</td><td>+</td></tr><tr><td>Bioturbation mode</td><td>Epifaunal-biodiffusor<br>Surficial-biodiffusor<br>Gallery-biodiffusor</td><td>Impact on sediment mixing and biogeochemistry, organic matter cycling, niche creation, inter-specific facilitation</td><td>+</td><td>+</td></tr><tr><td>Respiration mode</td><td>Aerobic<br>Anaerobic</td><td>Nutrient cycling (nitrate, phosphate), Biogeochemical impacts</td><td>+</td><td>+</td></tr><tr><td>Symbiosis association</td><td>No symbiont<br>Algal-symbiont<br>Kleptoplast<br>Endobiont</td><td>Trophic level, Biogeochemical impacts</td><td>+</td><td>+</td></tr><tr><td>Organic matter sensitivity ("Indicative value")</td><td>Sensitive<br>Tolerant<br>Indifferent<br>2nd order opportunistic<br>1st order opportunistic</td><td>Sensitivity/tolerance level to total organic carbon in sediment</td><td>+</td><td>+</td></tr></tbody></table>

they contribute 2.5 % of the annual global ocean carbonate production making them a key group in the CaCO₃ budget of the world's oceans (Langer, 2008), hence wall structure can be considered an effect trait as it is involved in benthic ecosystem functioning.

Test size is also a meaningful response trait as it may reflect an evolutionary consequence of a survival adaptation as a response to abiotic parameters. For instance, smaller species are dominant in harsh environmental conditions like in low-oxygenated habitats (Bernhard, 1986; Sen Gupta and Machain-Castillo, 1993). Seasonal variations of food availability can lead to smaller chamber formation (Murray, 1963). The dominance of dwarf foraminifera is also an indicator of natural environmental stress or of pollution, e.g. industrial, agricultural and domestic effluents (Samir and El-Din, 2001), heavy metal and organic matter (Vilela et al., 2011) and Polycyclic Aromatic Hydrocarbons (PAHs) and mercury (Di Leonardo et al., 2007). Test size may also likely

matter in surface sediment reworking rate (effect, Deldicq, 2021).

Morphology, i.e. chamber arrangement and disposition of chamber traits, is extensively discussed in Section 2. To summarize, the morphological composition of foraminiferal community reflects environmental conditions on one hand (response) and may affect sediment reworking on the other hand (effect) (response, Jones and Charnock, 1985 and above-mentioned references in Section 2).

Similarly, pore presence/absence, size and density are discussed in details in Section 2. Pore traits, i.e. pore density and pore size, are constrained by the bottom-water oxygen concentrations and can be viewed as an adaptation to survive these adverse environmental conditions (Moodley and Hess, 1992; Glock et al., 2011). These traits can also be considered effect traits, as pores are involved in gas-exchange, playing a role in bottom-sediment biogeochemistry (effect, Glock, 2023).
Benthic foraminifera can often exhibit test malformations. For instance, chamber abnormalities like aberrant chamber shape, twisted chamber arrangement or additional chamber, and abnormal coiling i.e. a wrong direction of coiling, poor development of the last whorl and development of several whorls with different axes of rotation (see details in Polovodova and Schönfeld, 2008) can indicate a change in environmental conditions, either of natural origin (e.g., Geslin et al., 2000) or due to pollution (e.g., Samir and El-Din, 2001; Le Cadre and Debenay, 2006). Abnormal tests can be seen as a response trait to be used as an indicator of the environmental conditions occurring in a studied environment.

A fairly large amount of studies has focused on life modes of benthic foraminifera. Microhabitat, i.e. epiphyte, epifaunal and infaunal, depending on the environmental conditions (response, Linke and Lutze, 1993, Duijnstee et al., 2003, Bouchet et al., 2007), and benthic foraminifera can adapt their microhabitats by vertical and horizontal movements (Severin and Erskian, 1981; Kitazato, 1988; Wetmore, 1988). The life mode and associated vertical movements also strongly impact fluxes at the sediment-water interface (Langlet et al., 2023) and contribute to the microhabitat creation, i.e. gallery or cavity (Severin et al., 1982; Wetmore, 1988), as an effect trait.

Benthic foraminifera occupy a key trophic position between bacteria/microalgae and higher trophic levels (Chronopoulou et al., 2019). They can feed on microphytobenthos, bacteria and metazoans, i.e. copods, nematodes (Nomaki et al., 2008; Pascal et al., 2008; Dupuy et al., 2010). The in-situ feeding behavior of intertidal benthic foraminifera is complex (Chronopoulou et al., 2019), and they seem to be able to adapt their feeding mode to the availability of the food sources (response). For instance, Ammonia tepida may prefer to consume algae, but is also able to feed on bacteria (Pascal et al., 2008). Feeding mode may further express the effect of foraminiferal species on the trophic food web through e.g. predator-prey relation or microphytobenthos grazing (effect). Although detailed knowledge of the feeding behavior of all foraminiferal species is lacking, which limits the implementation of this trait, this lack of knowledge should not prevent the inclusion of feeding behavior as a trait in the BTA scheme. Instead, it should highlight the need for future research to fill this knowledge gap using stable isotope analysis, metabarcoding, and feeding assays.

Bioturbation mode is species-specific in benthic foraminifera (Deldicq et al., 2020) which implies that species would contribute differently to benthic-ecosystem functioning (effect, Bouchet and Seuront, 2020, Deldicq et al., 2021b). Foraminiferal vertical movements can (i) have major impacts on the stability and erodibility of the surficial sediment layer (Severin et al., 1982; Severin, 1987; Deldicq et al., 2021b), (ii) produce a substantial downward transport of particles (Gross, 2002; Deldicq et al., 2023) and (iii) enhance fluxes of dissolved oxygen and solutes at the sediment-water interface (Hemleben and Kitazato, 1995; Langlet et al., 2023). They are further able to mediate the prokaryotic community, suggesting that they play a major role in the benthic ecosystem functioning (Langlet et al., 2023). The composition of foraminiferal species, hence the diversity of bioturbation mode, and the intensity of bioturbation are driven by environmental parameters (response), for instance temperature (Deldicq et al., 2021a).

Respiration modes cover aerobic and anaerobic pathways (Risgaard-Petersen et al., 2006; Høgslund et al., 2008). Denitrification or intracellular NO₃⁻ storage is a widespread phenomenon in benthic foraminifera (see review in Glock, 2023) allowing them to survive in oxygen depleted environments (response). Other possible anaerobic pathway in benthic foraminifera are fermentation and dephosphorylation of creatine phosphate (Orsi et al., 2020; Gomaa et al., 2021). The respiration activity of foraminifera in sediment is a key component of the benthic oxygen (Heinz and Geslin, 2008), nitrate (Piña-Ochoa et al., 2010) and phosphate (Glock et al., 2020) cycles (effect). For instance, foraminiferal denitrification can contribute to up to 100 % of the nitrogen cycle in Swedish fjords (Choquel et al., 2021).

Benthic foraminifera can exhibit different symbiosis associations, i.e.

microalgae (Hallock, 1999), stolen chloroplast (Lopez, 1979) or endobiont (Bernhard et al., 2006). The presence and abundance of symbiotic foraminiferal species is driven by environmental parameters (response), for instance large symbiont-bearing foraminifera prefers good water quality in coral-reef ecosystems (Hallock et al., 2003). Furthermore, symbiont-bearing and kleptoplastic foraminiferal species may play a role in CO₂ fixation (effect) via the photosynthetic activity of microalgae in the former (Hallock, 1999) and sequestered chloroplasts in the later (Jesus et al., 2022).

The indicative value trait expresses the sensitivity/tolerance/opportunistic level to total organic carbon (response). This response trait can also be considered as an ecological performance trait which corresponds to the “optimum and/or the breadth of the distribution of performance traits along an environmental gradient” (Violle et al., 2007). The ecological performance depends on the expression of multiple traits to environmental conditions like environmental tolerances, habitat or ecological preferences (Violle et al., 2007). The response of benthic foraminifera to total organic carbon (TOC) was recently explored to assign them to ecological groups (EGs) (Alve et al., 2016; Jorissen et al., 2018; Bouchet et al., 2021). Foraminiferal species were assigned to five EGs (Table 3): (i) EGI - “sensitive species” are species sensitive to TOC enrichment, and their relative abundance is highest at the lowest TOC values, dropping to zero as organic carbon concentration increases; (ii) EGII - “indifferent species” which unbiased to organic carbon enrichment, and never dominate the assemblage, occurring in low relative abundance over a broad range of organic carbon concentrations, being absent at very high concentrations; (iii) EGIII - “tolerant species” are stimulated by the excess organic carbon enrichment but they may occur at low TOC, and are absent at very high organic carbon concentrations; (iv) EGIV - “second-order opportunistic species” have a clear positive response to organic carbon enrichment with maximum abundances between the maxima of EGIII and EGV; and (v) EGV - “first-order opportunistic species” exhibit a clear positive response to excess organic carbon enrichment with maximum abundances at the higher level induced by organic load.

# 4. Analysis of trait data

Several methods have been developed and employed for analyzing biological trait data and investigating trait-environment relationships. A summary of the key methods is provided below (see a flow-chart framework in Fig. 3 and corresponding key questions addressed by these methods in Table 4).

## 4.1. Trait-coding

Different techniques are used to translate qualitative or quantitative traits into analyzable formats. Binary coding indicates a presence/absence of a specific trait, while categorical coding allows for multiple discrete trait categories (e.g., sexual/asexual reproductive mode). Fuzzy coding allows species to be associated with multiple traits simultaneously, recognizing that species may exhibit traits that overlap with multiple categories. It does so by assigning a value between 0 and 1 to represent the extent to which a trait is expressed. This approach is particularly useful for traits that are continuous or exhibit partial presence, such as body size, feeding habits, or habitat preferences. Fuzzy coding was widely applied in assessing ecological functioning of benthic marine macro-invertebrates (e.g., Bremner et al., 2006; Paganelli et al., 2012; Beauchard et al., 2017), offering a flexible framework to handle uncertainty and overlap in trait expression.

## 4.2. Community-weighted mean (CWM)

Community Weighted Mean (CWM) data is an effective method for summarizing trait values at the community level. It calculates a single value representing the average trait value of the community by
<figure><img src="image_6.png" /><figcaption>Fig. 3. A step-by-step framework of common methods in biological trait analysis, suggested as a guideline for benthic foraminifera community analysis.</figcaption></figure>

<table><caption>**Table 4**
Key questions addressed by biological trait analysis methods with examples from benthic foraminifera.</caption><thead><tr><td>Method</td><td>Key question answered</td><td>Example using benthic foraminifera</td></tr></thead><tbody><tr><td>CWM</td><td>How do dominant traits vary with environmental change?</td><td>Do symbiont-bearing foraminifera increase in high-light areas?</td></tr><tr><td>FD metrics</td><td>How does functional diversity respond to environmental change?</td><td>Are foraminiferal traits more diverse in well-oxygenated sediments?</td></tr><tr><td>FCA</td><td>What are the main traits structuring communities?</td><td>Do test composition and pore features discriminate between foraminiferal assemblages in pristine and polluted areas?</td></tr><tr><td>RLQ + Fourth corner</td><td>Which traits are most responsive to environmental factors?</td><td>Is wall texture linked to sediment grain size?</td></tr><tr><td>Functional trait modeling</td><td>How will functional traits respond to future climate change?</td><td>Will calcareous foraminifera decline with ocean acidification?</td></tr></tbody></table>

weighting each species' trait value according to its relative abundance. CWM is rooted in the 'mass-ratio hypothesis' (Grime, 1998), according to which the traits of the species with the highest biomass will have the strongest impact on ecosystem functioning. A change in a CWM trait over time or across space generally reflects a shift in community structure, often driven by environmental changes or disturbances (Mouillot et al., 2013). CWM analysis was used to examine how trait values of benthic macroinvertebrates respond to environmental change (e.g., Weigel et al., 2016; Sutton et al., 2021; Polo et al., 2024), and to infer past environmental conditions from biotic assemblages of testate amoeba (Van Bellen et al., 2017).

### 4.3. Functional diversity (FD) metrics

The multidimensional framework representing the diversity of functional traits within a community, with each dimension corresponding to a specific trait, is termed functional space. Functional diversity (FD) is the extent to which species occupy this space, capturing their ecological roles and contributions to ecosystem functioning. To quantify FD, the following metrics were developed: functional richness (FRic), which measures the amount of functional trait space occupied by a community; functional evenness (FEve), which evaluates how evenly traits are distributed across the functional space; functional divergence

(FDiv), which assesses how trait values are spread relative to the center of trait space, reflecting niche differentiation; and functional dispersion (FDis), which measures the maximum functional distance between any two species in the community (Laliberté et al., 2014; Grenié and Gruson, 2023). FD metrics have been shown to outperform other indices in detecting environmental status using benthic ciliate communities (Xu et al., 2018) and in highlighting spatio-temporal changes within a marine protected area (Rincón-Díaz et al., 2018).

### 4.4. Fuzzy correspondence analysis (FCA)

Fuzzy Correspondence Analysis (FCA) is a multivariate method designed for analyzing fuzzy-coded data, particularly useful for discrete variables like biological trait modalities (Chevenet et al., 1994). The samples are ordinated using Euclidean distances, which are derived from the relative frequencies of abundance-weighted biological traits in each sample. FCA provides the variability explained by each axis and the correlation ratios of every trait along the principal axes. Therefore, samples close to one another along the FCA plot coordinates are indicative of functional similarity. This method was used to investigate the functional structure of different microbenthic communities (e.g., Bremner et al., 2006; Paganelli et al., 2012; Festjens et al., 2023).

### 4.5. RLQ analysis

RLQ is a multivariate statistical method that explores the relationships between environmental variables, species traits, and species abundances (Dolédec et al., 1996; Dray et al., 2014). It measures the co-variation among three matrices:

* R: a matrix of environmental variables (environment-sites matrix).

* L: a matrix of species traits (species-trait matrix).

* Q: a matrix of species abundances (species-sites matrix).

The RLQ analysis aims to find the best correspondence between these three spaces: R-L correspondence: How environmental variables influence species traits, R-Q correspondence: How environmental variables influence community composition, and L-Q correspondence: How species traits influence community composition. RLQ analysis integrates these three analyses, maximizing the cross-covariance between environmental and trait ordinations. This generates a co-structure among the three matrices, quantified by RLQ axes. The associations along the RLQ axes represent the optimal compromise between species traits and environmental variables, mediated by species abundances. Variables with the highest positive or most negative scores on the RLQ axes
contribute most to the observed spatial patterns and trait-environment relationships, while variables with scores near zero have minimal influence. RLQ analysis is widely used to identify how environmental factors and stressors affect species traits and how these traits, in turn, influence community composition (e.g., Piló et al., 2016; D'Alessandro et al., 2020; Dias et al., 2023).

## 4.6. Fourth corner analysis

The fourth-corner analysis tests pairwise relationships between traits and environmental variables. This method tests hypotheses about how traits influence the responses of species to environmental conditions and vice versa (Legendre et al., 1997). This analysis involves calculating correlation coefficients or regression-based measures to link environmental variables to traits through weighted species abundances. To ensure robustness, permutation tests are applied to evaluate the statistical significance of these relationships while controlling for the interdependence of data matrices. This analysis is often combined with RLQ analysis as a complementary method, with RLQ revealing patterns and fourth-corner analysis confirming statistically significant relationships (e.g., Piló et al., 2016; D'Alessandro et al., 2020).

## 4.7. Functional trait modeling

As an alternative approach to the multivariate analyses, Generalized Linear Models (GLMs) provide a framework to model the relationship between traits and the environment. Generalized Additive Models (GAMs) extend GLMs to non-linear relationships, offering greater flexibility in ecological data analysis where complex, non-linear environmental responses are common. These models are often used to identify trait-environment linkages by including functional traits as response variables or covariates, allowing to infer which traits drive species' distribution patterns or community composition under varying environmental conditions. For instance, GAMs can model how traits like body size or feeding type vary along temperature or nutrient gradients, while GLMs might be used to predict the likelihood of specific trait combinations under given environmental pressures. Both approaches can also integrate species abundance data, using weighted averages of traits (e.g., Community Weighted Means, CWMs) to examine how community trait distributions shift across landscapes. Functional trait modeling with GAMs/GLMs is particularly useful in detecting ecological thresholds, forecasting ecosystem responses to environmental change, and informing conservation strategies based on trait-based vulnerability assessments (Miller et al., 2019; Mérillet et al., 2021).

Applying a trait-based approach offers several advantages over traditional methods that rely on species-specific ecological preferences to evaluate ecosystem state or biodiversity. By comparing functional traits across species, BTA can provide insights into the underlying mechanisms driving community structure and biodiversity patterns, allowing for more predictive and transferable conclusions across different environments and systems (Díaz et al., 2013; Mouillot et al., 2013). Furthermore, trait-based assessments can overcome the challenges posed by temporal and spatial variability in species distributions, making it particularly useful for monitoring environmental changes and guiding conservation and management strategies.

Although BTA offers significant advantages over the taxonomic approach and is increasingly utilized in the biomonitoring of benthic communities, it is important to acknowledge its limitations and challenges (de Juan et al., 2022). One limitation is the absence of standardized criteria for trait selection and the frequent reliance on expert judgment. While this reliance provides flexibility in the BTA approach, it also introduces uncertainties and potential biases. Another significant limitation is the insufficient consideration of habitat-specific and life-history variability in trait expression. To address these issues, it is recommended to justify trait selection using mechanistic evidence and to clearly document the origin of the trait data, specifying whether it is

derived from expert knowledge, literature, or empirical studies. Furthermore, incorporating a sensitivity analysis (to account for uncertainty in input data) and conducting a confidence assessment of the data sources are essential steps to enhance the robustness of BTA applications (de Bello et al., 2021; de Juan et al., 2022).

# 5. Conclusions and future directions

The application of biological trait analysis in the marine environment is relatively recent (Bremner et al., 2003, 2006), with a growing body of literature primarily focusing on benthic macrofauna and, to a lesser extent, nematodes (de Juan et al., 2022; Justino et al., 2023). In contrast, this approach remains rare in benthic foraminifera, though promising results have been reported (Cleary and Renema, 2007) (Hansen et al., 2023). The trait framework proposed here is an important step toward implementing globally-standardized BTA in foraminiferal studies. Most of the traits listed in Table 3 are relatively easy to measure, but the precise nature of their functions remains uncertain (van der Zwaan et al., 1999). More mechanistic studies will help upgrading these biological characteristics to functional traits. Further knowledge gaps hinder the implementation of all the traits presented in Table 3. For instance, our knowledge of foraminiferal trophic ecology, and results obtained experimentally do not always match with what is observed in-situ (Chronopoulou et al., 2019). More studies combining carbon and nitrogen isotopes and metabarcoding analyses of the cytoplasm are needed to identify and quantify the food sources (Nomaki et al., 2008; Chronopoulou et al., 2019). Similarly, research on bioturbation by foraminifera is still in its early infancy. Although recent studies have made important progress in our knowledge of this phenomenon (Deldicq et al., 2020; Deldicq et al., 2021b; Deldicq et al., 2023), it was mostly on sediment mixing considering only a handful of species; for instance their bio-irrigation potential is a fairly untapped question (Langlet et al., 2023). Additionally, minimizing redundancy among traits is essential to avoid biased results (Beauchard et al., 2017). For example, traits like chamber arrangement and the disposition of chamber traits may overlap, and future tests of this framework should aim at determining which of these traits is the most relevant to use.

Future research must prioritize making biological trait data available, for example, by including it as supplementary material in publications. References must be also clearly indicated to strengthen trait inventories. In fact, mentioning the exact reference for every single trait for every single species will help the progress of this new field of research in the foraminiferal realm. For the moment, information on foraminiferal traits is spread in multiple publications; and only the seminal book of Murray (2006) gather substantial and numerous details. The lack of readily available trait data poses a major obstacle to apply BTA across biogeographic datasets. Trait assignment also require a considerable level of expertise, as does the design and analyses of trait data. Intense collaborations and dedicated workshops would facilitate the standardized development of BTA for benthic foraminifera, enable the sharing of expertise, and promote tangible advancements. For instance, the FOBIMO (FOraminiferal Bio-MOnitoring) international working group could serve as a good collaborative platform for that purpose.

Pioneer studies have highlighted the value of incorporating benthic foraminifera biological traits to complement species assemblage analysis, especially in addressing questions on species distribution patterns, communities and ecosystems responses to global changes. A unified trait framework is crucial for identifying and understanding global distribution patterns of benthic foraminifera.

## CRediT authorship contribution statement

**Tamar Guy-Haim:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. **Vincent M.P. Bouchet:** Writing – review & editing,
Writing – original draft, Visualization, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors would like to thank Anastasia Iakovleva for editing the
graphical abstract. Vincent M.P. Bouchet thank Pr Elisabeth Alve for
earlier inspiring discussions on foraminiferal traits. Vincent M.P. Bou-
chet contribution was financially supported by the Pierre-Hubert Curien
Maïmonide program (grant No. 50061RK), led by the French-Israel High
Council for Scientific and Technological Cooperation and implemented
by the French Ministry for Europe and Foreign Affairs, the French
Ministry of Higher Education, Research and Innovation and the Ministry
of Innovation, Science and Technology of the state of Israel and by the
Foram-INDIC project aiming at providing biomonitoring methods based
on benthic foraminifera funded by the Water agencies Agence de l'Éau
Artois-Picardie and Agence de l'Éau Seine-Normandie (Grant Nos.
58183 and 1082222/2019, respectively). The stay of Tamar Guy-Haim
at the Laboratory of Oceanology and Geosciences in Wimereux was
supported by the University of Lille through its "Invited Professor"
program.

Data availability

Data are available in the manuscript

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