# Community assembly processes and drivers shaping marine fish community structure in the North Sea

Marcel Montanyès, Benjamin Weigel, Martin Lindegren

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## To cite this version:

Marcel Montanyès, Benjamin Weigel, Martin Lindegren. Community assembly processes and drivers shaping marine fish community structure in the North Sea. Ecography, 2023, 2023 (10), pp.e06642. 10.1111/ecog.06642. hal-04220955

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ECOGRAPHY

Research article

# Community assembly processes and drivers shaping marine fish community structure in the North Sea

Marcel Montanyès ID, Ⓔ<fnref n="1" />, Benjamin Weigel ID<fnref n="2" />,<fnref n="3" /> and Martin Lindegren<fnref n="1" /> ID

**Correspondence:** Marcel Montanyès(mamont@aqua.dtu.dk)

## Ecography

2023: e06642

doi: 10.1111/ecog.06642

Subject Editor: Miguel G. Matias

Editor-in-Chief: Miguel Araújo

Accepted 30 May 2023

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To preserve natural ecosystems and their biodiversity, there is a need to anticipate future ecosystem changes through better understanding of underlying drivers and assembly processes determining community composition. Assembly processes can be understood as a set of filters acting at different spatio-temporal scales that jointly define the structure and composition of communities. Here, we explore the underlying assembly processes shaping marine fish species distribution and composition, using the heavily exploited North Sea. Our aims are to study 1) the relative importance of the different assembly processes shaping marine fish communities, 2) the key environmental drivers determining species distributions and composition, and to 3) quantify the role of traits in determining species niches and responses to the environment. Specifically, we fit a joint species distribution model (JSDM) using 31 years of standardized scientific bottom trawl survey data for 67 fish species. We use a set of environmental variables and species' traits representing morphology, life history, reproduction and diet, while also accounting for phylogenetic relationships of species. Environmental variables, primarily related to temperature, explained over one third of the variance in species occurrence, while spatial effects explained half of the variability across species. This shows that environmental filtering and spatially-structured processes are the main drivers shaping the community assembly. Furthermore, among the total variance of individual species occurrences, 12.5% could be explained by traits, which improve the mechanistic understanding on species responses to environmental change. Hence, model predictions from JSDMs accounting for traits, environmental niches and potential interactions among multiple species can provide relevant simulations and forecasts with the potential to inform spatial management and conservation efforts aiming to preserve biodiversity and its associated services vital for human well-being.

Keywords: biodiversity, community assembly rules, joint species distribution models (JSDMs), marine management, traits

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<footnote n="1">¹Centre for Ocean Life, National Institute of Aquatic Resources, Technical University of Denmark, Lyngby, Denmark</footnote>

<footnote n="2">²Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland</footnote>

<footnote n="3">³INRAE, EABX, Cestas, France</footnote>
Introduction

Ecosystems worldwide are exposed to a broad range of natu-
ral and anthropogenic pressures, including overexploitation,
habitat loss and climate change (Millennium 2005, Maclean
and Wilson 2011, Worm and Tittensor 2011, Halpern et al.
2015). In response to such pressures, notably global warm-
ing, many communities have already demonstrated shifts
in species distributions and abundances (Perry et al. 2005,
Last et al. 2011, Pinsky et al. 2013, Poloczanska et al. 2013,
Freeman et al. 2018). Furthermore, the different rates of
range shifts between species suggest a pronounced reorgani-
zation in community composition, as well as the structure
and functioning of ecosystems at large (Poloczanska et al.
2013). In order to take the necessary management and con-
servation actions that seek to preserve natural ecosystems and
their biodiversity, there is a need to anticipate these changes
through a better understanding of the underlying drivers
and community assembly processes that shape these com-
munities (McGill et al. 2006, Maclean and Wilson 2011,
Mouillot et al. 2013, Beukhof et al. 2019c).

The community assembly rules framework is one of the
current standing community ecology theories. The assem-
bly rules or assembly processes can be understood as a set of
filters acting at different spatio-temporal scales that jointly
define the structure and composition of communities. First,
speciation, large-scale migration and dispersal determine the
set of species that will be able to reach a particular region
based on their evolutionary history and dispersal capabilities
(Keddy 1992). From this set of species, the abiotic environ-
ment filters out species that are poorly adapted to the specific
conditions of a given area, while biotic interactions further
modify this subset through competitive or facilitative interac-
tions (Diamond 1975, Zobel 1997, Bruno et al. 2003). The
set of species that has gone through all these filters will com-
prise the species pool of any given community. Moreover,
these assembly processes are directly determined by species
traits, since these filters are excluding species that lack certain
traits (or their combinations) (Zobel 1997). Environmental
constraints filter out species with poorly adapted traits to the
specific local conditions, while biotic interactions will also
be defined by the trait composition of the species (Keddy
1992, Zobel 1997). A well-known example considering the
role of traits in biotic interactions is in the context of inva-
sion ecology, where non-native species typically have great
impacts upon native species with similar traits through com-
petitive exclusion (Mouillot et al. 2013). Besides ecologi-
cal filters, anthropogenic impacts, such as exploitation, can
also have direct effects on communities conditioned on their
traits. Notably, commercially important species have shown
to shift towards smaller lengths at maturity due to high fish-
ing pressure on large-sized individuals (Dulvy et al. 2004,
Genner et al. 2010, Engelhard et al. 2014).

Predictions of species community composition have been
widely addressed by combining (i.e. stacking) outputs from
single species distribution models (SDMs) (Norberg et al.
2019, Grenié et al. 2020). However, this approach primarily

reflects individual species' habitat suitability (Ferrier and Guisan 2006), thus not sufficiently describing the underlying community assembly processes that determine the observed communities (Guisan and Rahbek 2011). Alternatively, joint species distribution models (JSDMs) consider all species simultaneously, thus accounting for the multivariate nature of biological communities (Ovaskainen and Soininen 2011, Jetz et al. 2019). Moreover, recent advances in JSDM frameworks allow for the inclusion of species' traits, which can reveal key trait-environment relationships and improve model performance, especially of rare species (Norberg et al. 2019). Hence, JSDMs allow seeking for community-level patterns in how species respond to the environment and how those responses are influenced by species traits.

Hierarchical modeling of species communities (HMSC;
Ovaskainen et al. 2017, Ovaskainen and Abrego 2020) is
a recent Bayesian JSDMs framework that has been applied
to study a variety of natural communities and with differ-
ent purposes, such as studying biogeographical processes,
phenological changes or towards conservation and manage-
ment (Murillo et al. 2020, Elo et al. 2021, Marjakangas et al.
2021, Odriozola et al. 2021, Weigel et al. 2021). HMSC is
strongly rooted within community ecology theory by link-
ing model setup and outputs to the underlying commu-
nity assembly rules that define community composition
(Ovaskainen and Abrego 2020). The hierarchical structure
of the framework allows to seek for shared patterns across
species in how they respond to the environment. This allows
improving the performance for many species, especially the
rare ones (Ovaskainen and Soininen 2011, Norberg et al.
2019, Poggiato et al. 2021). Moreover, trait information can
be included in HMSC, allowing to relate community-level
responses to the environment to traits. This enables included
traits to influence species niches. Lastly, phylogenetic relat-
edness among species can also be included, allowing to
evaluate phylogenetic constrains of species response traits
(Ovaskainen et al. 2017).

However, HMSC has not been previously applied to study
the drivers and community assembly rules acting on fish com-
munities in large and open marine ecosystems. In this study,
we explore the underlying assembly rules shaping marine fish
species distribution and composition, using the North Sea as
a case study. The North Sea is exposed to a range of human
activities and is therefore considered as one the most heavily
impacted seas worldwide (Halpern et al. 2008, Bowler et al.
2020). As a result of such impacts, the area has previously
demonstrated substantial declines in individual fish popu-
lations (Fernandes and Cook 2013, Clausen et al. 2018,
Lindegren et al. 2018), as well as large-scale changes in taxo-
nomic and functional diversity (Dulvy et al. 2008, Hiddink
and ter Hofstede 2008, Dencker et al. 2017, Beukhof et al.
2019b). The above mentioned impacts, as well as the pro-
nounced spatio-temporal heterogeneity of both environment
and fish community makes the North Sea a suitable case study
for exploring community assembly processes (Cohen et al.
2017). For that, we pursue the following research questions:
1) What is the relative importance of the different assembly processes shaping marine fish communities?

2) What are the key environmental drivers determining species distributions and composition?

3) How large is the role of certain traits in determining species niches and responses to the environment?

# Material and methods

## Community data collection

We collected data from the North Sea International Bottom Trawl Survey (NS-IBTS) from the publicly available scientific monitoring survey DATRAS, hosted by the International Council for the Exploration of the Sea (ICES) (https://datras.ices.dk). The temporal span of the data was restricted to winter and summer surveys (i.e. first and third quarters of the calendar year) for the period between 1986 and 2016, for which there is a good coverage of the entire study area. Spatially, the survey is structured on the basis of the ICES statistical rectangles; these are rectangles of 30' in latitude by 1° in longitude (approximately 56 by 64 km at the center of the study area) that divide the area between 36°N and 85°30'N and 44°W and 68°30'E into grid cells (Fig. 1). The ICES statistical rectangles gridding system is a formal spatial structure used in fisheries assessment and marine management advice within ICES. The DATRAS survey identifies catches at species level whenever possible, which we verified and updated with the World Register of Marine Species (WoRMS Editorial Board 2022) whenever needed. Non-fish taxa were discarded and only organisms identified to species level from the following classes were kept: *Actinopterygii*, *Elasmobranchii*, *Holocephali*, *Myxini* and *Petromyzonti*. The data went through a quality check procedure where we removed invalid hauls and invalid species records, which resulted in presence-absence data for 247 species. Since abundances of pelagic species are generally underestimated, due to their lower catchability in bottom-towed gear (Walker et al. 2017), we used presence-absence data only, allowing us to represent the entire fish community, including both pelagic and demersal species. However, we excluded rare species with less than 10 occurrence records over time (year) and space (ICES rectangles) in the entire data set (135 species), as well as those species where no trait or phylogenetic information was available (45 species), resulting in a total of 67 species stemming from 17,319 unique hauls across 170 ICES rectangles (Fig. 1).

## Environmental data collection

The availability of in situ environmental data was limited to a few CTD records (i.e. 15% of the hauls) of temperature and salinity. Therefore, in order to ensure a complete and consistent coverage of both physical and environmental covariates across all unique sampling events we used model re-analysis products from the NEMO-MEDUSA coupled hydro-geochemical model runs (Yool et al. 2013, Gurvan et al. 2022).

The available covariates included both surface and bottom temperature (°C), salinity (PSU), detritus (mmol N per m³), chlorophyll *a* (mmol N per m³), dissolved inorganic nitrogen (DIN; mmol N per m³) and depth. To reflect seasonality in environmental conditions, previously known to affect fish diversity and dynamics (Dencker et al. 2017, Beukhof et al. 2019c, Maureaud et al. 2019) we calculated temperature and salinity seasonality as the standard deviation in each corresponding sampling point and year. We acknowledge that relying on model-derived data may incur potential sources of uncertainty and errors, at least for variables less well-informed by in situ observations or remote sensing, such as sea bottom temperature (SBT). However, given the high correlation (r = 0.87; p < 0.001) between modelled data and the relatively few available CTD records of SBT measured prior to sampling we consider the risk of introducing errors as marginal. Moreover, seabed substrate composition, reflecting the benthic habitats corresponding to each sampling point was retrieved from EMODnet (European Marine Observation and Data network; www.emodnet-geology.eu; ver. September 2021) with a maximum spatial resolution of 4 km². Finally, to account for potential effects of exploitation, annual fishing effort data (fishing hours) per ICES statistical rectangle for both otter and beam trawlers was extracted (Couce et al. 2020). All the above-mentioned covariates were tested for multi-collinearity and when a correlation > 0.7 or < -0.7 was found, the variable which was least correlated with other covariates was kept for the analysis (Supporting information).

## Species traits and phylogeny

We wanted to capture the diversity of traits in the community and quantify their role in determining species niches and responses to the environment. We therefore talk about community cluster traits, i.e. traits that translate taxonomic biodiversity into trait diversity (Streit and Bellwood 2022). We selected eleven traits in order to represent species' morphology, life history, reproduction and diet (Dencker et al. 2017, Supporting information). Trait values for each species were collected from available trait data bases (Beukhof et al. 2019a), supplemented with information from recent literature Coulon et al. (unpubl.). Morphology of the species was described as body shape, caudal fin shape and maximum length (cm). Life history was characterized by the age of maturity (years) and the von Bertalanffy growth coefficient K (year⁻¹), while reproduction was represented by spawning type, fecundity (number of eggs) and offspring size (egg diameter in mm). Dietary aspects were captured by diet and trophic level, while the position in the water column was included to represent a pelagic or demersal life style. Finally, we calculated phylogenetic relatedness among species using 'fishtree' R package ver. 0.3.4. (www.r-project.org, Chang et al. 2019).

## Model fitting and diagnostics

To examine the underlying community assembly rules acting on the North Sea fish community we used the HMSC
<figure><img src="image_6.png" /><figcaption>Figure 1. Position of all unique hauls of the IBTS surveys performed in the North Sea during the summer and winter seasons between 1986 and 2016. The grid represents the official ICES rectangles included in the study.</figcaption></figure>

framework (Ovaskainen et al. 2017, Tikhonov et al. 2020) through the R package 'Hmsc' ver. 3.0-11 (www.r-project.org, Ovaskainen and Abrego 2020, Tikhonov et al. 2021). Similar to other modelling tools, HMSC quantifies the explained variation within the observed communities through included environmental covariates, as well as through random effects, which can account for e.g. patterns arising from spatio-temporal processes. However, HMSC may include random

factors that can be linked to species-specific co-occurrence patterns within the unexplained (residual) variation. These random effects are shown through either positive or negative pair-wise species associations reflecting ecological processes that take place in addition to species' responses to the environment (e.g. dispersal limitation, biotic interactions, and random level specific covariates not captured by the fixed effects). Nonetheless, such residual associations are
not computed for each pair of species, as the number of
parameters to be estimated would quickly escalate with the
number of species in the community. Instead, latent variables
are used for estimating such parameters, which can be viewed
as model-based ordinations (Ovaskainen and Abrego 2020).

We fitted a presence-absence HMSC model with a probit
link function using individual hauls as sampling unit. As
linear fixed effects, we included SBT, SBT seasonality, sea
bottom salinity (SBS) seasonality, seafloor detritus, surface
chlorophyll a concentration, surface DIN concentrations,
and fishing effort. Since fishing effort was represented by an
annual total, we applied a one-year lag, as the possible effects
of fishing are expected to be appreciable in the following year's
community. Since species usually display optimum ranges in
their environmental niches, especially for temperature, we
also included a quadratic term for SBT in the model. The
environmental covariate and trait matrices are scaled by default
by HMSC so that they have zero mean and unit variance
over the columns. Such scaling is invisible to the user as the
estimated parameters are back-transformed to the original scale
when further processed. Finally, we included year and season
within year (winter and summer) as temporal unstructured
random effects, and the ICES statistical rectangles as spatially
explicit random effect. All three random effects were treated
independently, and the number of latent variables for each
was constrained to a minimum of one and a maximum of five.
The model was fitted assuming the default priors described in
the Supporting Information of Tikhonov et al. (2020). Four
Markov chain Monte Carlo (MCMC) chains were run, each
collecting 250 samples, resulting in 1000 posterior samples.
We applied a thinning of 100, resulting in 37 500 iterations
per chain of which the first 12 500 were discarded as burn in.
MCMC convergence was assessed by examining the potential
scale reduction factors (Gelman and Rubin 1992) of model
parameters. MCMC convergence is considered satisfactory if
the mean values do not exceed 1.1 (Tikhonov et al. 2020).
The model performance was evaluated as explanatory and
predictive power (through a fivefold cross-validation) by
means of the area under the receiver operating characteristic
curve (AUC), Tjur R² and root mean square error (RMSE).
AUC is widely used to test SDMs discriminatory ability
(i.e. the ability to distinguish between a presence and an
absence) by evaluating its sensitivity (true positive rate) and
specificity (true negative rate) (Fielding and Bell 1997). Values
of 0.5 suggest no discrimination, 0.7–0.8 as acceptable,
0.8–0.9 as excellent and > 0.9 as outstanding (Mandrekar
2010). Likewise, Tjur R² provides a measure of the model’s
discriminatory ability (Tjur 2009), while RMSE is a measure
of the accuracy (i.e. how close the measurement are to the true
value). We conducted all statistical analyses in the R software,
ver. 4.2.1 (www.r-project.org).

Assessment of assembly rules, drivers and trait-
environment relationships

In order to study the contribution of the different assembly
processes determining species distributions and community

composition, we examined the variance partitioning of the
modelled species niches, i.e. how much of the explained
niche variance can be attributed to the included covariates
(Ovaskainen et al. 2017). We explored species' niches by
plotting their responses to the environmental covariates,
denoted as *beta* parameters in the model, which had
statistical support of at least 95% posterior probability. We
also examined the joint proportions of variation explained
by different sets of covariates, grouped into temperature
(temperature-related variables) and productivity variables
(chlorophyll *a*, detritus and DIN), also accounting for
the joint fractions of explained variation within these
groups. HMSC models species environmental niches by
incorporating information on how species traits respond
to environmental covariates, denoted as gamma parameters
in the model. This allows quantifying how much of the
variation in species responses to the environment can be
attributed to species traits. Since, traits were found to
explain a substantial proportion of species response to
surface chlorophyll *a*, we explored the trait relationship
with this variable. We used community-weighted means
traits to study such relationships, which can be computed
with the 'Hmsc' package (Ovaskainen and Abrego 2020,
Tikhonov et al. 2021). Since many traits are phylogenetically
conserved (following niche conservationism, i.e. the
tendency of species to retain ancestral traits) (Wiens and
Graham 2005), we included the phylogenetic correlation
parameter *rho* in our model that measures if the residual
variation (after accounting for traits) of species responses
to the environment is independent or phylogenetically
structured. *Rho* ranges between 0 and 1, where 1 would imply
fully phylogenetically structured responses of taxa to the
environmental covariates, i.e. closely related species respond
similarly. Strong phylogenetic signals may therefore point to
response traits that have not been specifically accounted for
in the model. We thus explored the presence and strength of
such phylogenetic signal, looking for signs of missing trait
information that can influence species niches.

In addition, we assessed species co-occurrence patterns
at the level of the spatial random effect included in the
model. These species-specific co-occurrences are based
on the covariance structure of the model residuals, after
having accounted for the fixed environmental effects.
This information highlights pairs of species that co-occur
more (or less) frequently than by random, after accounting
for their environmental niche HMSC represent these
co-occurrences through latent (unobserved) variables,
which can be understood as a model-based ordination
representing the main axes of variation (Warton et al. 2015,
Ovaskainen et al. 2016, Clark et al. 2017). Hence, areas with
different site scores (spatial latent factor values) represent
different species co-occurrence patterns. However, since
our model structure is spatially explicit, the latent factors
represent both spatial variation of unobserved co-variates as
well as species-to-species variation (Ovaskainen and Abrego
2020). Here, we assessed the spatial structure of the first
three latent factors.
## Results

The MCMC convergence was satisfactory indicated by the mean (and SD) of the potential scale reduction factor being < 1.1 for both beta and gamma parameters, reflecting species responses to the environment and the role of traits, respectively (Supporting information). The effective sample size of the MCMC was close to the number of posterior samples, and we can therefore assume samples not being autocorrelated (Supporting information). The explanatory power of the model had a mean AUC of 0.89, Tjur R² of 0.246 and RMSE of 0.199, while the mean predictive power (based on a five-fold cross validation) was of 0.88, 0.24 and 0.2, respectively (Supporting information). Although there are four exceptions (i.e. *Pholis gunnellus* for AUC, and *Phycis blennoides*, *Dicentrarchus labrax* and *Trachinus draco* for Tjur R², being the difference of minimal), the explanatory power always outperformed the predicting power, showing no major signs of model overfitting (Supporting information).

### Variance partitioning and traits

The variance in species' occurrence explained by the model was attributed to both, the included fixed and random effects that explained on average 35 and 65% of the variance, respectively (Fig. 2). The spatial random effect was the variable that explained most variability across species, i.e. 50%. Subsequently, the temperature variables (i.e. SBT and SBT seasonality), explained 25% of the variance. Finally, year and productivity explained 12 and 7% of the variance, respectively. The remaining variables, namely season, sediment SBS seasonality and fishing, each explained ≤ 5% of the variance in species occurrences. Nonetheless, the contribution of each of the variables (both fixed and random) varies across species

<figure><img src="image_7.png" /></figure>

Figure 2. The proportion of total variation explained by each group of variables across species. The black dot denotes the mean variance explained by each covariate across the whole fish community, and the actual value is found below each violinplot. The variables belonging to the fixed and random parts of the model are shown on the left and right part of the model, respectively, and separated by a dashed line.

(Supporting information). The species responses to the environmental variables were partially explained by the set of traits included in the model. The proportion of species responses to the different environmental variables explained by traits varied from a maximum of 61% for the mixed sediment, to a minimum of 11% for fishing effort (Supporting information). From the thirteen environmental variables considered (HMSC treats each sediment type as an individual variable), traits explained more than 20% of the response in seven, and between 10 to 20% in six environmental variables.

### Species niches and trait-environment relationships

There is a great diversity of species-specific responses to the set of environmental variables included in the model with a high level of statistical support (posterior probability > 0.95), including negative, positive, or non-significant relationships (Fig. 3, Supporting information), such as the derived SBT responses of Atlantic cod *Gadus morhua*, sprat *Sprattus sprattus* and European eel *Anguilla anguilla*. We found that among the total variance of individual species occurrences, 12.5% could be explained by traits. From the included traits, age at maturity, growth coefficient K, maximum length, body shape, caudal fin shape, feeding mode and habitat show a response to at least one of the environmental covariates (Fig. 4). For example, the growth coefficient K and age at maturity show a negative response to SBT, while age at maturity and maximum length demonstrate a negative or positive response to SBT seasonality, respectively. We found strong support for phylogenetic niche conservatism, with the phylogenetic correlation parameter rho being 0.82 (95% credible interval: 0.62–0.89). This indicates that a set of phylogenetically-structured traits, beyond the traits already included in the model, likely influence species niches.

Additionally, we explored the trait relationship with surface chlorophyll *a* concentration, as traits explain a substantial proportion of species response towards this covariate (i.e. 37%; Supporting information). We found strong relationships between some community-weighted mean traits and chlorophyll *a* concentration (posterior support > 0.95, Supporting information). A positive response to chlorophyll *a* concertation was found for demersal (habitat), eel-like and flat body shapes, pointed and rounded fin shapes, guarder spawners and offspring size. In contrast, negative responses were found for bathypelagic and pelagic (habitat), fusiform, truncate fin, non-guarder spawners, fecundity and trophic level.

### Species associations

Since the spatial random factor is the variable that explains most variance of the community composition, we further explored the residual species associations at the included spatial scale (ICES statistical rectangles). After accounting for the fixed effects, representing species responses to the environment conditioned on their traits, we found pronounced residual species co-occurrence patterns with strong statistical supported (posterior support >
<figure><img src="image_8.png" /><figcaption>Figure 3. Heatmap of estimated beta coefficients indicating positive (red), negative (blue) or no relationships (blank) of species responses to the set of environmental covariates included (with at least a posterior probability of 0.95). Species are sorted vertically according to their phylogenetic relatedness.</figcaption></figure>

0.95). These reflect both positive and negative pairwise species associations (Fig. 5), potentially reflecting biotic interactions, dispersal limitation and/or patterns arising from environmental covariates that have not been included in the model. The first latent factor showed high values in both northern and southern regions of the North Sea, but lower values in the central area, especially towards the western part (Fig. 6). Low values were also found in the easternmost region, along the German coast. Latent factor two shows a more heterogeneous pattern, with alternating high and low values in the north, while towards the center and southern part there is a smoother transition between larger regions characterized by either low, or high values. Lastly, latent factor three shows a very clear and smooth latitudinal gradient, from high values in the north to low in the south-west, with the exception of three outlying rectangles in the English Channel that have high values.

## Discussion

Understanding the underlying assembly processes shaping communities is crucial to predict and anticipate future shifts in species distributions and community composition due to climate change (McGill et al. 2006, Maclean and Wilson 2011, Mouillot et al. 2013, Beukhof et al. 2019c). While recent developments in JSDMs allow modelling multiple species' responses to the environment (Jetz et al. 2019), their implementation to understand the responses and range shifts of large, mobile and commercially important marine fish species are scarce (Monaco et al. 2021, Zhang et al. 2022). To overcome this knowledge gap, we used hierarchical modelling of species communities (HMSC) to investigate the main drivers and assembly process acting on community composition, as well as the contribution of traits on species responses, using the North Sea fish community as a case study.
<figure><img src="image_9.png" /><figcaption>Figure 4. Heatmap of estimated gamma coefficients indicating positive (red), negative (blue) or no relationships (blank) of traits to the set of environmental covariates included (with at least a posterior probability of 0.95).</figcaption></figure>

The variance explained by the fixed effects of the model reflects species responses to the environment, conditioned on their traits, and thus the resulting patterns of community composition arising from environmental filtering. This key assembly process explained about 35% of the observed species' occurrences. However, this is likely a conservative estimate since we cannot discard the possible contribution of additional, yet unaccounted environmental variables, whose effects might be captured in the random part of the model (Ovaskainen et al. 2017). Nevertheless, our results suggest that temperature-related covariates such as SBT and SBT seasonality are the main environmental drivers acting on species distribution and community composition, which is in accordance with previous studies on biodiversity indicators in the area (Dulvy et al. 2008, Rutterford et al. 2015, Beukhof et al. 2019b, Burrows et al. 2019). Since temperature is the main environmental driver, the expected warming following the increasing pace of climate change will likely lead to shifts in the community as species track their thermal niche (Pinsky et al. 2013, McLean et al. 2021). This lends support to previous findings demonstrating a northward range shift and deepening of the North Sea fish community in response to warming (Perry et al. 2005, Dulvy et al. 2008).

Although the North Sea is a heavily fished area with a long history of commercial exploitation (Ruth Callaway et al. 2007, Bennema and Rijnsdorp 2015, Couce et al. 2020), our study shows that fishing intensity had a minor direct

role in explaining the current overall species' occurrence patterns. While fishing pressure can have direct long-term effects on individual species distributions and demography (Dulvy et al. 2004, Genner et al. 2010, Last et al. 2011, Engelhard et al. 2014), the effects on overall community composition are more likely visible in species abundances or biomasses, since a very high fishing pressure would be needed to observe local extinction of species (Jennings and Kaiser 1998, Last et al. 2011). Moreover, the effects of fishing are more likely to be observed when fishing is introduced in an unfished, or moderately exploited system (Jennings and Kaiser 1998), while the North Sea has been exploited for centuries (Bennema and Rijnsdorp 2015). Consequently, past changes in species abundances and community composition arising from fishing, notably the historical decline and disappearance of many large and slow growing species, including sharks and rays (Last et al. 2011, Bennema and Rijnsdorp 2015) may have acted as a primary filtering process on the present communities, even if we cannot attribute a strong overall effect using observations from recent decades. The effect of fishing on species occurrence is highly unlikely, at least on a time scale as the one used here. Therefore, to further investigate the effects of fishing on species occurrence there is a need for studies analyzing longer temporal series (Beukhof et al. 2019c).

The large variety of individual species responses to the environment illustrates the diversity of environmental niches
<figure><img src="image_10.png" /><figcaption>Figure 5. Residual species association matrix at the random effect level of ICES statistical rectangle. The red and blue colors show species with positive and negative pair-wise associations (with at least a posterior probability of 0.95), respectively.</figcaption></figure>

within the fish community. Notable examples include the opposite responses of the key ecologically and commercially exploited species, cod *G. morhua* and sprat *S. sprattus*, whose reproduction and general population dynamics has been shown to be either negatively, or positively related to increasing temperature (Stige et al. 2006, MacKenzie et al. 2012, Lindegren and Eero 2013, Sguotti et al. 2018). In order to disentangle this complexity and better understand the underlying mechanisms shaping species niches and community composition at large, embracing a trait-based approach is clearly warranted (Funk et al. 2017). In our model, the included traits were able to explain 12.5% of the variation in species niches across the whole community. In particular, we found that age at maturity, growth, maximum length and pelagic fish are responding to temperature. The negative relationships between age at maturity with temperature and temperature seasonality are consistent with trait-environment relationships demonstrated for marine fish across large spatial scales (Beukhof et al. 2019a), while the negative relationship with growth was not supported in these previous studies. Whether due to the smaller spatial extent of our study, or reflecting a region, or ecosystem specific response is unclear and merit further investigation. Nevertheless, our findings highlight the importance of fish life history traits and its reliance on temperature and

its seasonality (Beukhof et al. 2019b, c). Furthermore, we found that traits strongly condition the responses of species to other environmental factors, notably sediment type and chlorophyll *a* concentration, even if the importance of these environmental factors explaining the actual species occurrences is rather low. This highlights the relevance of accounting for traits to better characterize species niches and responses to other factors besides temperature, reflecting their potential associations to particular habitats, or areas with different (primary) productivity. The community weighted means for traits associated to the seafloor (demersal habitat, flat or eel-like shape) were found to have a positive response surface chlorophyll *a* concentration. Also, a positive response was found for rounded fin shape, which tends to be associated to species that have small swimming ranges and live in complex habitats (Giammona 2021). A likely explanation for the above-mentioned positive relationships is the sinking of organic matter, which is higher in more productive areas. This organic matter is a direct energetic input to the benthic habitats, which can thrive better compared to other areas where this energetic input is lower. In contrast, the negative response for pelagic habitat, fusiform body shape, truncate fins and higher trophic levels is likely linked to highly mobile and pelagic species (Webb 1984). Finally, the presence of a strong phylogenetic signal in our model suggests that there
<figure><img src="image_11.png" /><figcaption>Figure 6. Maps of the first three spatial latent factors at the ICES rectangle random level. The latent factors comprise spatially-structured site scores of model residuals, representing unexplained abiotic patterns, dispersal limitation and biotic interactions as species-to-species associations.</figcaption></figure>

is likely a set of traits shared among phylogenetically similar species of the broader taxon covered that have not been included in the model that might shape species responses to the environment. Hence, the inclusion of such traits would bring in relevant information that could improve our understanding of the underlying processes determining species niches and community composition. Therefore, we stress the need for further trait-based studies identifying key response traits of marine fish and their links to environmental conditions across multiple species and taxon.

After having accounted for the fixed effects, and similar to other HMSC works, the random effects in the model

explain a large proportion of the variance (Chiu et al. 2020, Marjakangas et al. 2021, Weigel et al. 2023), especially the spatial random effect (i.e. 50%). This means that there are other spatially-dependent and stochastic processes besides those captured by the environmental drivers that act on species distributions and community composition. The spatial random effect can be explained by the combination of three different, yet not mutually exclusive assembly processes, including: 1) biotic filtering resulting from species interactions; 2) filtering due to small-scale dispersal limitation and 3) filtering caused by environmental variables unaccounted for in the model. Although the environmental variables included
in the model are among the primary covariates affecting species distribution and community composition in the North Sea (Cohen et al. 2017), there may still be other unaccounted covariates that contribute to the patterns captured by the spatial random effect. Yet, it is likely that a significant amount of such patterns is derived from biotic interactions and dispersal limitation of the species. The residual species association matrix (derived from the spatial random effect) showed that even after accounting for species niches, there are significant species co-occurrence patterns demonstrating several pairs, or groups of species that co-occur more (or less) often than what would be expected from their environmental niches alone. While we cannot conclude concrete ecological interactions from such kind of co-occurrences (Blanchet et al. 2020), they may still provide indications for potential biotic interactions through the positive and negative residual associations (Araújo and Rozenfeld 2014, Morales-Castilla et al. 2015, Ovaskainen and Abrego 2020). One potential interaction could be reflected through positive co-occurrence is a facilitation process (Morales-Castilla et al. 2015, D'Amen et al. 2018), where the presence of a species increase the probability of occurrence of another species and vice versa. Another, more common and plausible biotic interaction is a predatory (feeding) interaction, where a given predator is expected to be found in an area relatively close to where their prey can be found. Consequently, a spatial overlap in their occurrences should be expected, at least at a scale of a few km, such as considered in this study. In terms of the documented negative associations, these could reflect competitive exclusion (MacArthur 1984) where two species with an overlapping environmental niche, but competing for a common resource would have negative co-occurrences. Lastly, the pairwise species associations could be reflecting responses to third party species, i.e., other species within the community that have not been explicitly considered in this study (Popovic et al. 2019). This way, a positive association between two of the modelled species could represent a common response to a third unaccounted species. Taken together, the latent factors of our model reflect underlying community assembly processes shaping species associations, whether these are due to biotic interactions, or the other processes listed above. Interestingly, some of the pronounced spatial structures and patterns of these latent variables partly resembles areas of the North Sea previously suggested as being influenced by limiting similarity (Callaway et al. 2002, Dencker et al. 2017). However, more research is needed to better understand species co-occurrences patterns and the underlying assembly processes explaining the pronounced spatial structuring of the latent variables demonstrated in this study.

## Conclusions

Our modelling study aimed to investigate the relative importance of different community assembly processes and identify drivers shaping the fish community in the North Sea. Using an advanced JSDM (i.e. HMSC) we show that environmental

filtering, primarily related to temperature and seasonality, explains a large part of the variance in species distributions and community composition. This supports previous findings that predict important shifts in natural communities with global warming (Perry et al. 2005, Last et al. 2011, Pinsky et al. 2013, Poloczanska et al. 2013, Freeman et al. 2018) and highlights the need to predict such shifts to anticipate and adapt the necessary conservation and management actions. We also found that there are other assembly processes with a strong spatial structure playing a role in shaping these communities, while at the same time significantly improving model performance. Notably, we argue that biotic factors are likely important in this regard but call for further research to better understand the underlying interactions involved. Finally, we stress the importance of accounting for species traits since their inclusion improves the mechanistic understanding on species responses to environmental change. Hence, model predictions from JSDMs accounting for traits, environmental niches and potential interactions among multiple species can provide relevant simulations and forecasts of species, or community-level responses to various climate and management scenarios deemed relevant by a broad range of stakeholders. This has the potential to inform spatial management and conservation efforts, such as the placement of marine protected areas aiming to preserve biodiversity and its associated services vital for human well-being.

**Acknowledgements** – We would like to thank all contributors of the North Sea International Bottom Trawl Survey for their efforts in collecting the fish survey data. We thank two anonymous reviewers for their constructive comments contributing to improve the quality and clarity of the study.

**Funding** – MM acknowledge financial support from the European Union's Horizon 2020 projects "ECOTIP" (ID: 869383). BW acknowledge financial support from the Strategic Research Council of the Academy of Finland (grant no. 312650 to the BlueAdapt Consortium). ML acknowledges financial support from the European Union's Horizon 2020 project "FutureMARES" (ID: 869300) and from the European Union's Horizon Europe research and innovation programme project "B-USEFUL" (ID: 101059823).

## Author contributions

**Marcel Montanyès**: Conceptualization (equal); Data curation-Lead, Formal analysis (lead); Methodology (equal); Validation (lead); Visualization (lead); Writing – original draft (lead); Writing – review and editing (equal). **Benjamin Weigel**: Formal analysis (supporting); Methodology (lead); Writing – review and editing (equal). **Martin Lindegren**: Conceptualization (equal); Formal analysis (supporting); Methodology (supporting); Supervision (equal); Writing – review and editing (equal).

## Transparent peer review

The peer review history for this article is available at https://publons.com/publon/10.1111/ecog.06642.
## Data availability statement

The North Sea International Bottom Trawl Survey data are available from the DATRAS (https://datras.ices.dk/Data_products/Download/Download_Data_public.aspx).

Trait data were collected from available Beukhof et al. 2019a trait data bases, supplemented with information from recent literature Coulon et al. (unpubl.).

Fishing effort data were collected from Couce et al., 2019 available from the Cefas Data Hub https://doi.org/10.14466/CefasDataHub.61 Seabed substrate composition data retrieved from EMODnet (European Marine Observation and Data network; www.emodnet-geology.eu; ver. September 2021).

Environmental data was retrieved from the NEMO-MEDUSA coupled hydro-geochemical model runs data available from Yool et al. 2013, Gurvan et al. 2022 & Laverick 2022.

The compiled data and R code used for the present study are available at https://github.com/marcelxel0/NS_fishCommunity.

## Supporting information

The Supporting information associated with this article is available with the online version.

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