# Chapter 11 - Modeling spatiotemporal distribution of fish species richness and abundance

Olivier Le Pape, Rita P. Vasconcelos, Susana França, Sandrine Vaz, Henrique Cabral, William Cheung

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Olivier Le Pape, Rita P. Vasconcelos, Susana França, Sandrine Vaz, Henrique Cabral, et al., Chapter 11 - Modeling spatiotemporal distribution of fish species richness and abundance. Elsevier. Ecology of Marine Fish, Elsevier, pp.223-238, 2025, 10.1016/B978-0-323-99036-3.00005-2. hal-05070606

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# 11. Modelling spatio-temporal distribution of fish species richness and abundance

O. Le Pape*<sup>a</sup>, R.P. Vasconcelos<sup>b</sup>, S. França<sup>c</sup>, S. Vaz<sup>c</sup>, H. Cabral<sup>d</sup>, W. Cheunge<sup>e</sup>

## Abstract

This chapter proposes a general overview of data and methods allowing for spatio-temporal representation of marine fish species distribution and abundance. The most widespread approach to model and map spatiotemporal distribution of fish species are described. The value of these approaches' outputs to improve knowledge about dynamics of marine ichthyofauna, to estimate the consequences of human disturbances, and to simulate prospective scenarios accounting for global change and management measures, is presented. Last, the interest of this quantitative information to prioritize protection areas and improve fisheries management systems is evidenced.

**Keywords:** modelling, fish distribution, species richness, mapping, predictive models

<footnote><sup>a</sup> DECOD (Ecosystem Dynamics and Sustainability), Institut Agro,F-35042 Rennes, France</footnote>

<footnote><sup>b</sup> IPMA – Instituto Português do Mar e da Atmosfera - Av. Dr. Alfredo Magalhães Ramalho 6, 1495-165 Algés, Portugal.</footnote>

<footnote>MARE - Marine and Environmental Sciences Centre & FCUL - Faculdade de Ciências da Universidade de Lisboa. Campo Grande, 1749-016 Lisboa, Portugal.</footnote>

<footnote><sup>c</sup> IFREMER, UMR MARBEC, Laboratoire Halieutique Méditerranée, Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex - France.</footnote>

<footnote><sup>d</sup> INRAE, UR EABX, Centre Nouvelle-Aquitaine Bordeaux, 50 Avenue de Verdun, 33612 Cestas, France</footnote>

<footnote><sup>e</sup>Changing Ocean Research Unit, Fisheries Centre, The University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada.</footnote>

<footnote>*corresponding-author</footnote>
**11.1. Introduction**

The analysis of fish abundance and species richness has always been a central issue in marine
ecology and is used to investigate the role of the different factors that may affect
ichthyofauna and biodiversity. However marine life, and especially ichthyofauna, is
unevenly distributed and structured in space and time (Mello and Rose, 2005). Fish species
distribution, hence diversity of ichthyofauna, results from the combined action of several
drivers (Planque et al. 2011), such as environmental conditions and food availability, but also
factors internal to the considered species or community (density-dependent processes and
interactions between species). A main challenge still consists in modelling the spatio-
temporal distribution of ichthyofauna and provide reliable quantitative information to (i)
improve knowledge on distribution of fish abundance and species richness (Vasconcelos et
al. 2013); (ii) estimate drivers of this distribution and consequences of environmental and
anthropogenic pressures (Courrat et al. 2009); (iii) predict future scenarios in response to
various changes and stressors (Cheung et al. 2008); (iv) help decision making in conservation
schemes and sustainable spatial management of marine systems (Le Pape et al. 2014).

This chapter first proposes a general overview of data and methods allowing for spatio-
temporal representation of marine fish species (i) distribution and abundance (Habitat
Suitability Models, HSM, term generally used to focus on habitat drivers, Vasconcelos et al.,
2013, or Species Distribution Models, SDM, more used when representing spatial
distribution is the main goal, Dos Santos Rodrigues et al. 2023) and (ii) species richness of fish
communities. The most widespread approach to model and map spatiotemporal distribution
of fish species are described. The value of these approaches' outputs to improve knowledge
about present dynamics of marine ichthyofauna, to estimate the consequences of human
disturbances, but also to simulate prospective scenarios accounting for global change and
management measures, is presented. The use of this quantitative information to prioritize
protection areas and improve fisheries management systems is finally discussed.

**11.2. Data for spatio-temporal representation of fish species distribution, density and richness**

Different types of data can be used and implemented when modelling distribution of fish
species and species richness of fish communities; they can be categorized in two main types:
fish species presence-absence data and/or density data (Rubec et al. 1999):

- Presence-absence data can be used for modelling fish species distribution or fish
community species richness. This is especially valuable in data-poor contexts or even in data-
rich contexts where there is the need/interest in combining data from different sampling
designs; for instance when combining data sets with different sampling methods, difficult to
standardize. Another important type of data is that of species presence-only, i.e. where there
is no data on species absence but only data on their presence. This requires the use of suitable modelling approaches. This is for example the case of data from citizen science sightings and natural history collections (Gomes et al. 2018).

- If the reliability of presence data could be ensured from various source of information, including fisheries catches and citizen science, data on density are generally based on scientific surveys, less influenced by the sampling bias present in other types of data (even if the use of large amounts of spatialized fisheries catches using Vessel monitoring Systems rapidly increases in the recent years, Alglave et al., 2022). Density data standardized in what concerns space, time and sampling method allow for modelling distribution and density of fish species as well as species richness of fish community. Density or capture per a given unit area, time, distance or effort is commonly used as a measure of abundance. But even this type of data does not invalidate the need for standardization (e.g., standardized fisheries catch data; Forrestal et al et al. 2019, or rarefaction techniques for species richness; Shimadzu 2018).

The availability of large sets of reliable spatialized data, for several periods, to account for variability of fish distribution (Sluka et al. 2001; Le Pape et al. 2003; Loots et al. 2010), is the main limit to further modelling and mapping approaches (Guisan and Zimmermann, 2000). Sampling design needs to consider potential sources of variation in distribution and density of fish species and in species richness of fish communities, so that the data used is applicable for the study objective, namely in terms of coverage and resolution. Temporal variation in marine fish generally refers to inter-annual, intra-annual, seasonal and diel variation, whereas spatial variation generally refers to latitudinal gradients and habitat types, among others. Variation can arise from spatial effects (e.g. patchiness; Levin 1996; species area-relationships) and from drivers that affect individual movement and habitat connectivity and suitability for a given species (e.g. habitat suitability as well as biotic interactions; Vasconcelos et al. 2015, Sluka et al. 2001). Gathered information seldom cover the entirety of a given ecosystem nor is it available regularly over time and seasons. Too rare and/or carried out on restricted time periods and spatial extent, data result in an incomplete understanding of the ecosystem functioning, biased estimations and flawed conclusions

One major source of spatio-temporal variation in species distribution and abundance to be highlighted comes from the fact that for numerous marine organisms, habitat requirements change over the course of their development (Harden Jones, 1968), resulting in distinct distributions along the life cycle (Grüss et al. 2019). Such ontogenic habitat shifts structure the spatial distribution of organisms throughout their life cycles (van de Wolfshaar et al. 2011; Vasconcelos et al. 2014). Consequently, the functionality of essential fish habitats, such as spawning (Loots et al. 2010), nursery (Le Pape et al. 2003; van de Wolfshaar et al. 2011) or feeding grounds but also migration routes along ontogenic migrations, is essential to sustain the renewal of marine fish populations (Iles and Beverton, 2000). To obtain an accurate
description and analysis of distribution, density and habitat requirement of fish species with
such ontogenetic shifts it is necessary to use data with sufficient coverage and resolution in
terms of space, habitat types and time. Moreover, and consequently, to attain an accurate
description and analysis of fish community biodiversity metrics (e.g. species richness), it is
also necessary to consider that the data coverage and resolution needs to reflect the different
habitat requirements and shifts of species in the community (Pickens et al. 2021).

**11.3. Methods, models and related mapping approaches**

**11.3.1. Modelling fish species richness and abundance**

Modelling fish species distribution and abundance

Although a habitat is often defined as a place where an organism may live, several
definitions of “habitat” exist. Habitat may be described by a set of abiotic (Kearney, 2006) and
biotic features. However, marine ichthyofauna distributions result from the combined action
of several constraints and the set of conditions required for individual survival and
reproduction constitute the “ecological niche” within which a species may indefinitely
sustain itself (Hutchinson, 1957, dos Santos Rodrigues et al. 2023). The geographical
projection of this fundamental niche corresponds to the potential habitat of the considered
species (Chase and Leibold, 2003). Planque et al. (2007) distinguished three types of habitats
for given functions: potential, realized and effective. The potential habitat represents the
suitable area, i.e. the ecological niche (Warren 2012). The realized habitat corresponds to the
portion of the potential habitat that is effectively occupied at a given time. The effective
habitat corresponds to that portion of realized habitat that will be proved to contribute the
most to the survival of the species by allowing the completion of its life cycle (Dahlgren et al.
2006).

Fish HSM are based on hypotheses on how environmental or other factors control
ichthyofauna distribution (Pickens et al. 2021). Regarding available knowledge and data,
HSM are often based only on hypotheses on how external environmental factors control
species distribution and/or life stages (Guizan and Zimmermann, 2000, dos Santos Rodrigues
et al. 2023), although their capability to explain the observed distribution may sometimes be
limited when they are used alone (Thorson, 2022). A wide array of modelling approach has
been developed to describe these controls and predict habitat suitability (e.g. reviews in
Guizan and Zimmermann, 2000; Le Pape et al. 2014; Chouinard et al. 2014) and many
software have been developed in that aim (e.g. *Domain*; Carpenter et al. 1993; *Maxent*; Phillips
et al. 2006; *SDM*; Naimi and Araujo, 2016; *hdsm*; Vieilledent et al. 2014; *biomod2*; Thuillier et
al. 2016; *dismo*; Hijmans et al. 2011). This wide pool of methods and tools opens the
possibility to explore how ichthyofauna uses habitat, and how their distribution relates to the
marine ecosystems.

When only occurrence data are available, the modelling approach covers only the
identification of determining conditions for fish and/or life stages to survive (Galparsoro et
al. 2009). These HSM are called ecological niches (Sillero, 2011, dos Santos Rodrigues et al.
2023) and are based on canonical response variables, allowing the identification of suitable
habitats (Rotenberry et al. 2006). Logistic regressions (Store and Jokimaki, 2003; Norcross et al. 1999) and their generalized form (Generalized Linear or Generalized Additive models) are widely used to describe presence/absence data. An alternative method, when studying species communities as a whole, consists of using multivariate factorial analysis, such as linear discriminant function analysis (Norcross et al. 1997) or redundancy analysis (Whaley et al. 2007) to describe fish assemblage structure or functionalities. However, these methods are mainly used as exploratory data analyses in order to outline the main patterns and less often as modelling tools. Another widely used method, the Ecological-Niche Factor Analysis (ENFA), computes suitability functions by comparing species distribution within the eco-geographical variable space with that of the whole set of cells (Hirzel et al. 2001). ENFA does not require absence data (Hirzel, et al. 2006; Galparsoro et al. 2009). Data on presence only allow for the use of data compiled in the framework of participative sciences (Edgar and Stuart Smith, 2009; Johnston et al. 2015; Marques Bezerra et al. 2021; Björvik et al. 2021). Other recent advances developed methods to fit presence distribution on presence-only data (e.g. MaxEnt software; VanDerWal et al. 2009; Merckx et al. 2011; McDonald et al. 2013; Merow et al. 2013; but also boosted regression trees, etc ; review and estimation of predictive performances in Valavi et al., 2022). When fitting these HSM based on presence/absence only, selection of the threshold which transforms the predicted probability surface into a presence-absence classification can have dramatic effects on model accuracy and outputs (Liu et al. 2005; Freeman and Moisen, 2008; França and Cabral, 2019) and require to be analysed.

Modelling of fish species density inherently requires fish density data. A wide array of HSM describes and predicts habitat suitability from density data (Guisan and Zimmermann, 2000, Guisan and Thuiller, 2005, Austin, 2007, Guisan, et al. 2006) (Table 11.1):

<table><caption>**Table 11.1: Main methods used to develop HSM from density data.**</caption><thead><tr><td>Family of method</td><td>Models</td><td>References</td></tr></thead><tbody><tr><td>Simple means calculations</td><td>Comparison of mean densities in different habitats, described in classes</td><td>Rogers, 1992; Brown et al. 2000; Vinagre et al. 2006</td></tr><tr><td>Linear models</td><td>Multiple regression, and GLMs</td><td>Riou et al. 2001; Whaley et al. 2007, Lauria et al. 2011</td></tr><tr><td>Non linear regressions</td><td>Polynomial regression and locally weighted approaches (i.e. General Additive models, GAM)</td><td>Rubec et al. 1999; Stoner et al. 2001; Kupschus, 2003; Francis et al. 2005; Loots et al. 2007, Vasconcelos et al. 2010; Gruss et al., 2014</td></tr><tr><td>Zero-inflated models</td><td>Delta models, quasi-poisson process or Tweedie distribution</td><td>Stefansson, 1996; Le Pape et al. 2003; Barry and</td></tr></tbody></table>
<table><tr><td></td><td></td><td>Welsh, 2002; Calama et al. 2011; Trimoreau et al. 2013;</td></tr><tr><td>Ordination and classification techniques</td><td>Categorical analysis, regression trees, random forest, neural networks</td><td>Norcross et al. 1999; Fodrie and Mendoza, 2006; Froeschke and Froeschke, 2011; Guisan and Zimmermann, 2000; Miller et al. 2014; Sundblad et al. 2014</td></tr><tr><td>Non-parametric methods</td><td>Quantile regressions</td><td>Norcross et al. 1997; Eastwood et al. 2001; Planque and Buffaz. 2008, Vaz et al. 2008</td></tr><tr><td>Methods specific to citizen science</td><td>Model for non-null abundance only</td><td>Johnston et al. 2015; Berkstrom et al. 2019; Funk et al. 2020</td></tr></table>

The modelling approaches is generally constrained by data availability, with implications for the type of model / analysis applicable and for the robustness and information provided by the outcome of the model / analysis.

Fitting linear or additive models to fish data usually runs up against the problem of large amounts of null values in survey datasets. As zero-inflated data do not fit with any of the parametric family of distributions, required conditions are frequently not met. To solve this problem, densities could be described with zero-inflated models. For instance, delta models treat presence/absence and positive density values in two different sub-models; a binomial distribution for presence and another for positive abundance values are fitted separately then coupled in order to estimate habitat suitability. Delta models work under the assumption that processes governing presence/absence (often large scale abiotic forcing) are independent from those governing abundance levels (small scale processes including species interactions (Nicolas et al. 2007), seasonal effect, population demography and dynamics). As abundance is represented by two sources of data with different meanings, i.e. the level of non-zero catch rates and the probability of catching species (or the life stage), a model that integrates both kinds of data (Ye et al. 2001) provides complementary information on habitat suitability (Le Pape et al. 2004; Trimoreau et al. 2013). Alternative methods, e.g. compound Poisson process or Tweedie distribution accounting for covariation between presence and positive densities have also been implemented. Other methodologies deal with the filtering of noisy absences and results in lower number of zero values to optimize the resulting species distribution model (Lauzeral et al. 2012).

An alternative approach is the use of ordination techniques or non-parametric correlation coefficients. If simple ordination/classification technique face issues relative to their large sensitivity to data (Norcross et al. 1999), random forest are now widely used in that aim (Sundblad et al. 2014; Waldock et al. 2022). Moreover, regression quantiles allows linear
model parameters to be estimated near the upper bounds of the distribution. This technique is adapted to estimate the effect of factors limiting species abundance. The predictions resulting from an upper quantile tend to describe potential distribution and highlight every possible area exhibiting suitable conditions to the studied species, including those areas that are not necessarily used. As previous models provide a spatial representation of the average realized habitat, this approach, in the same way as environmental envelopes (Guisan and Zimmermann, 2000; Cheung et al. 2009), allows the potential habitat to be considered (Sillero, 2010). As realized habitat maps represent simulated distributions, derived from field observations, potential habitat maps aim to predict distributions from potential living constraints and represent minimum requirement. Other advances allowed the development of methods to fit models based on categorical data (Mieszkowska et al. 2013), when density is only available from a qualitative description.

The aforementioned methods have been widely and routinely implemented aiming at species conservation and biodiversity management. They vary considerably in both performances and predictions of species distributions (Grenouillett et al. 2011, França and Cabral, 2015), namely in terms of the way they model the shape, nature and complexity of species responses, select predictor variables, weight variable contributions or allow for interactions (Guisan and Zimmermann, 2000; Elith et al. 2006). The best performing models are not always the same for different species and assemblages, with their ecological characteristics influencing model's accuracy (Segurado and Araújo, 2004; Hernandez et al. 2006; Barbet-Massin et al. 2009; França and Cabral, 2016). For instance, the effects of the species geographical attributes such as prevalence, latitudinal range, spatial autocorrelation or rarity have been shown to affect model's performance (Grenouillett et al. 2011). In a nut shell, "the best generic model" does not exist, the best modelling option depends on the case study.

## Modelling fish species richness and biodiversity

The use of fish diversity indices has proven to be problematic for model development because of the lack of spatial additivity of these indices and a non-linear relationship between each individual species distribution and diversity indices (Granger et al. 2015). However, as for presence and abundance for a given species, species richness can be modelled using HSM, accounting for natural variability of fish assemblages related to habitat structure, and temporal fluctuations (Nicolas et al. 2010a; Pinca et al. 2011; Guillemot et al. 2014). To examine the response of metrics describing fish diversity to the main factors driving their spatio-temporal variations, multi-factor linear models are currently used (Courrat et al. 2009; Guillemot et al. 2014). Fish species richness can also be derived from the compilation of multiple species-specific models, by stacking SDMs outputs and summing a posteriori species presence predictions (Cheung et al. 2008; Hijuelos et al. 2017). Functional diversity can be described with similar models, after (i) pooling species in ecotrophic guilds (Nilolas et al. 2010b; Delpech et al. 2010), (ii) describing their length structure (i.e. mean length; density of large fish and density of fish of large species; Cheung et al. 2013; Andersen et al. 2016) or (iii) using their position in the trophic chain (Cheung et al. 2008). Integrating functional diversity provides a complementary representation of global biodiversity patterns
in terms of ecological functions than that provided by species counts (Stuart Smith et al. 2013).

Both fish species richness and functional diversity, as important biodiversity components, should be assessed, monitored and compared across different ecological contexts (Grenié et al. 2020). Thus, modelling groups of species or aggregated species in guilds has been increasingly considered for investigation as it provides significant benefits over individual models through the combination of data from multiple species. Species assembly in a fish community results from the interplay of three main mechanism: dispersal limitation, environmental filtering and biotic interactions (Henriques et al. 2017). The range of possible modelling approaches and strategies to model species communities has been expanding, along with a variety of analytical techniques and software for implementing those (D'Amen et al. 2015). These approaches may be differentially implemented: (a) assemble first, predict later: when species distributions are first combined and then modelled; (b) predict first, assemble later: as more biodiversity databases include species occurrences instead of local assemblage composition, this approach first model occurrences, independently for each species, through SDM and then combine or stack their predictions (Ferrier and Guisan, 2006). Nonetheless, usual SDM do not account for biotic interactions (Wisz et al. 2013) and, thus, cannot account for the biotic niche filter in community assembly (Guisan and Rahbek, 2011). Recently, joint SDM have been developed to incorporate species co-occurrence data into HSM, linking SDM and community ecology (Zurell et al. 2020). Joint SDM simultaneously estimate the species-environment relationship of multiple species and the residual correlation between those species that could be indicative of biotic interactions (Zurell et al. 2020). Although Joint SDM have mainly been used to improve understanding and prediction of freshwater fish communities (Wagner et al. 2020; Inoue et al. 2017), it has recently been implemented also to evaluate multispecies fisheries surveys (Zhang et al. 2020).

*Evaluating habitat suitability models and improving reliability of their outputs*

HSM involve evaluating the accuracy of model options, its adjustment, predictive capacity and uncertainty (Robinson et al. 2017). Numerous reviews on HSM (Guzizan and Zimmermann 2000; Segurado and Araújo, 2004; Vaughan and Ormerod, 2005; Maggini et al. 2006; Moisen et al. 2006; Elith et al. 2006) provide guidelines to choose a method with regards to both study objectives and statistical criteria. Only two main warnings to have in mind are summed up here:

- Model selection involves implicit consideration of the respective contributions of the explanatory factors tested against species response. Factors that do not contribute significantly to this response have to be withdrawn from the model and model selection needed to consider both its level of goodness of fit and his level of parcimony (for instance by using AIC to compare model performances). Moreover, data complexity do not enhance accuracy of prediction in distribution and even significant factors have not to be systematically accounted for (Garcia-Callejas and Araújo, 2016). In ecological modelling then when developing HSM, the most useful proverb is probably that “intermediate complexity is optimal” (Fulton et al. 2003). It is also especially important to recognize that the noisy and redundant nature of data gathered in the natural environment largely complicates the
construction and the statistical and ecological relevance of the models. Frequent problems
occur when covariates and factors used as environmental descriptors are auto-correlated
inducing over-estimated explanatory variable significance (Power et al. 2000 ; Maravelias,
2001) but methods (e.g., preliminary selection, using factorial analysis for pooling) on or
synthesis in a covariates ) are available to deal with (Graham, 2003). Last, testing a factor in a
model is not only a statistical question but also and above all an ecological issue; including a
factor in a HSM should be based on an ecological assumption and not based on blind
stepwise approach on wide set of potential available environmental descriptors.

- The nature of spatial distribution and the importance of processes linked to dispersal and spatial covariance, related to the scale and spatial resolution in the dataset (De Knegt et al. 2010; Gottschalk et al. 2011; Nunez-Riboni et al. 2021) also require attention (Ashcroft et al. 2010; Planque et al. 2011a; Blowes and Connoly (2012). Solutions exist to estimate autocorrelation level (Lichstein et al. 2002; Planque et Buffaz, 2008; Calama et al. 2011; Ashcroft et al. 2012), including density dependent process (Thorson 2022), and account for and/or filter it in models (Tiefelsdorf and Griffith 2007; Bivand and Piras, 2015).

Once HSM are fitted, an extremely important part of the procedure is evaluating their
robustness and their capacity to predict habitat suitability (Vasconcelos et al. 2013; Garcia-
Calejas and Araujo, 2016). To that aim, the initial dataset may be, as a preliminary, split
randomly into calibration and validation (i.e. out of the bag data) subsets. The first is used to
calibrate the model whereas the second is used to evaluate the quality of model predictions
(Guisan and Zimmermann, 2000). Models must be tested on both of these datasets, using the
model to predict response variables, based on predictor variables (environmental
descriptors) to measure their fit accuracy and robustness (Power, 1993; Vasconcelos et al.
2013). For presence models, testing predictive capacity could be done with the Area Under
the Curve (AUC) (Elith et al. 2006; Townsend Peterson et al. 2008; Merow et al. 2013; Hattab
et al. 2013). For models based on density (and species richness), the predictive capacity could
be tested according to correlation of the relation density observed versus density predicted
by the model (non parametric rank correlation to test for discrimination power; Waldock et
al. 2022; parametric correlation to estimate accuracy; Pineiro et al. 2008). This validation
procedure is especially important: a lack of assessment could lead to spurious conclusions
from non-robust HSM with poor and/or biased predictive power (Planque et al. 2011b;
Vasconcelos et al. 2013).

Uncertainties can arise from observation processes, and especially data paucity, small-scale
variability and temporal variability, from model formulation (Planque et al. 2011b) and lack
of parcimony (i.e., use of too many environmental descriptors). Minimizing uncertainties
requires a balance between model parsimony, generality, reality and precision (Guizan and
Zimmermann, 2000; Rotenberry et al. 2006). Residual variability is frequently high in fish
HSM (Le Pape et al. 2007; Trimoreau et al. 2013) and problems linked to unexplained
variability and uncertainty in model estimates are especially important (Vasconcelos et al.
2013). In general, not enough attention is paid to most sources of uncertainty and projections
are currently less reliable than generally thought (Planque et al. 2011b). Uncertainty in
parameter estimates could be assessed from statistical models, and related confidence
intervals could be provided (Fletcher, 2008; Calama et al. 2011). Nevertheless, other sources
of uncertainty are rarely accounted for (Planque et al. 2011b). The use of Bayesian methods in HSM provides the advantage of incorporating information within a probabilistic framework (Stewart-Koster et al. 2013). Bayesian methods enable experts' knowledge on sources and levels of uncertainty to be considered and to construct credibility intervals within statistical models to provide inferences, together with an appraisal of estimate and prediction uncertainty (Thomas et al. 2004).

Although most studies conducting HSM on fish species, use single models, relying on it may produce incorrect predictions of future species diversity and distributions, as a "single-best" model is more sensitive to the assumptions and particular characteristics of the modelling method (Algar et al. 2009). Thus, the outcomes and ecological predictions derived from alternative single models could be so variety as to affect the decision making (Guo et al. 2015). In view of this variability between predictions, the emerging recommendation is to simultaneously apply several methods (ensemble modelling, Araújo et al. 2005, Araújo and New, 2007) within a consensus modelling framework (Thuiller, 2004). Ensemble models combine projections from different model types, each limited by its own assumptions and algorithms, to overcome inter-model variability (Thuiller et al. 2009). Such framework allows to reduce predictive uncertainty of using single models by combining their predictions using a measure of the central tendency (e.g. median or mean) which has been shown to be useful in assessing the predictive uncertainty of HSM (Buisson et al. 2010), and to significantly improve their predictive accuracy (Araújo et al. 2005, Marmion et al. 2009). The application of the ensemble modelling approach to fish species has been progressively increasing and generally has been out-performing individual models when predicting fish species distributions in different aquatic environments (Grenouillet et al. 2011; Lauzeral et al. 2015; França and Cabral, 2019) or when used for fisheries management purposes (Bouska et al. 2015; Jardim et al. 2021).

### 11.3.2. Mapping

A wide range of methods have been used to represent the spatial distribution of marine ichthyofauna, from spatio-temporal representation by mapping after interpolation of HSM described previously, linking relevant and widely known descriptors of the marine environment to the distribution of ichthyofauna.

*Maps from simple representation and interpolation of georeferenced data*

This approach consists in spatio-temporal representation from simple mapping of available data to undertaking spatial data interpolation (namely through kriging methods). These approaches are reliable only when data are available on the whole or at least a large part of the distribution of the studied components of life.

Data are frequently scarce with regard to both spatial distribution and variability of the ichthyofauna. Indeed, marine surveys remain prohibitively expensive and time-consuming, and provide only a snapshot view. Especially, mapping highly mobile ichthyofauna distribution is particularly challenging because of sampling limitations.

*HSM and maps*
To face data limitation and to integrate the structuring forces of species distribution, the alternative solution consists in developing maps based on HSM linking relevant descriptors of the marine environment to the spatial distribution of ichthyofauna.

As these relevant habitat descriptors are more widely available than ecological data, (i.e. maps of these descriptors are available, at least for abiotic features), geo-referenced predictions from HSM may be imported into Geographic Information Systems for the quantitative identification and characterization of key sites for the marine life. This widely used method called habitat mapping requires two main successive steps: (i) fitting and evaluating HSM that relate species or stage specific distribution to environmental factors, (ii) using habitat suitability indices from model outputs to create predicted distribution maps within a Geographic Information System (GIS) (Fig. 11.1).

**11.4. Model and map applications and management issues**

HSM and their predicted distribution maps can be used to understand drivers of fish species richness and distribution but also to predict (hindcast; Rochette et al. 2010; or forecast; Cheung et al. 2009) spatio-temporal trends in ichthyofaunal abundance and diversity, using scenarios describing temporal trendsupscale in spatial distribution of marine environment descriptors. They can also be combined in multiscale habitat suitability maps for synthesis, upscaled to community level and management purposes.

**11.4.1. Estimating habitat suitability and spatial patterns**

*Drivers of fish species richness and abundance*

HSM allow for the quantitative identification of the different environmental conditions that are required for ichthyofauna to complete their life cycles and enable to measure their respective importance for population renewal and conservation. According to Beck et al. (2001), a combination of environmental factors characterizes essential fish habitat for particular fish species if it contributes disproportionately to the total number of fish on a per-unit-area basis relative to other habitats (i.e. if an estimated habitat suitability is locally much higher). Even if a habitat covers only a small area, it should be considered important if it produces more fish per unit of area. It is important to conserve and prevent the destruction of these habitats that contribute disproportionately to the population, and habitat suitability maps allow us to focus on their location. HSM could thus be used to investigate which factors are the most structuring for a given population distribution at a particularly important stage of its life cycle (e.g. on spawning areas; Lelièvre et al., 2014 or on nursery grounds; Trimoreau et al. 2013). Many studies were undertaken over large spatial scales (100s km²) and these generally correlated abundances of fish to abiotic variables. Small spatial scale (m²) studies were comparatively sparse and commonly focused on biotic variables (Nicolas et al. 2007; Le Pape et al. 2007; Johnson et al. 2012).

In a multispecies perspective, HSM have been used to investigate the interaction between potentially competing species (Cormon et al. 2014) and the structure of fish biodiversity (Cheung et al. 2008; Guillemot et al. 2014). For instance fish species models have been widely used to investigate drivers of fish diversity in estuaries, from local habitat to regional features (i.e., estuarine system size, its entrance width and the continental shelf width;
Nicolas et al. 2010ab; Pasquaud et al. 2015; Vasconcelos et al. 2015). Large-scale patterns in the structure of estuarine fish assemblage are primarily a result of individual species' responses to environmental gradients, whereas smaller-scale patterns appear to be the result of habitat associations that are most likely driven by foraging, competition, and/or predator avoidance (Martino and Able, 2003).

### *Consequences of anthropogenic disturbances*

Impacts of global change on species and ecosystems functions may be challenging to measure, namely when accounting for multiple stressors. This is even more crucial when focusing on biodiversity hotspots or essential fish habitats such as estuaries and coasts for instance. Although they are among the most valuable and threatened ecosystems on earth, studies examining broad temporal and spatial scales are often focused in freshwater or terrestrial ecosystems. However, the consequences of pressures (e.g. essential fish habitat degradation; Courrat et al. 2009; Pasquaud et al. 2013; Toft et al. 2018; invasive or proliferating species; Le Pape et al. 2004; David et al. 2015; Paumier et al. 2018, or climate change; Cheung et al. 2008) on living resources, may be simulated from HSM, in coastal and estuarine areas. Vasconcelos et al. (2017) analysed the susceptibility of biodiversity in estuaries to stressors from multiple ecosystems, at a global extent. They tested the association between more vulnerable and less resilient fish assemblages with environmental features, high levels of human pressures and/or low levels of protection. Results emphasize that conservation is weakly related with the global distribution of sensitive fish species, aggravated by their association with highly pressured locations.

On other systems, models were used to explore the detection of fishing impacts. In that respect, this was investigated on coral reef fish assemblages by testing changes in different fish metrics, including functional guild, size structure, trophic level and species richness. In that aim the characterisation of spatial and natural variability prior to testing the response to fishing impact was essential, and required rigorous preliminary explorations. By comparing sites with contrasted fishing pressure in exploited coral reef fish assemblages, fishing was shown to induce a decrease in diversity, biomass and size structure (Guillemot et al. 2014).

In the specific case of structurally complex habitats, which are often considered hotspots for biodiversity, models were used to test if restored oyster reefs contributed to increase the abundance and diversity of fish in Australia (Gilby et al. 2019). Fish assemblages at these restored habitats presented higher species richness (1.4 times) and more individuals of commercially important species.

### **11.4.2. Estimating and assessing ecological status from models of fish species richness and abundance**

Borja et al. (2006) adapted the "Driver, Pressure State, Impact, Response" (DPSIR) approach to establish a framework for the protection of different kinds of waters in the context of the European Water Framework directive. By linking metrics of ecological status, based on fish assemblages, to proxies of human disturbance, DPSIR allows to develop indicators for monitoring networks for the assessment of both water quality and ecological function of estuarine systems (Coates et al. 2007). Model describing fish species richness and abundance
have been widely used in that aim in coastal and estuarine ecosystems (e.g. European water
framework directive; Delpech et al. 2010; Cabral et al. 2012; Perez-Dominguez et al. 2012;
tropical areas of mangroves; Ley, 2005); but also in open waters (e.g. European marine
strategy framework directive; Henriques et al. 2008; 2013; USA Magnuson-Stevens
conservation and management act; Rubec et al. 1999). These models appeared particularly
relevant, showing significant relationships with various anthropogenic pressure. They could
help managers to take decisions in order to maintain or reach the good status (Delpech et al.
2010).

In a fishery management perspective, modelling approaches could help manager decision
making by detecting the effects of fishing on fish species richness and functional diversity,
but also length and trophic structure. Models outputs help defining precautionary strategies
for the monitoring and management (Guillemot et al. 2014). Analysing temporal trends of
these indicators allows for assessing fishing impacts and provides consecutive advices for
precautionary fisheries management.

11.4.3. Mapping habitats, species distribution and species richness, towards spatial management

Direct use of habitat maps

From maps describing fish or life stage presence, potential habitats can be identified
spatially. Maps provide powerful tools for identifying suitable habitat in data poor
environment, when density or even absence data are lacking. Such methods are especially
suitable for species over large spatial extents (Rotenberry et al. 2006), when the models are
based on presence data from various and non-standardized sources. Predicted maps based
on HSM could also be combined in order to identify hotspots of realized or potential
biodiversity. In a worldwide approach, Cheung et al. (2009) used a bioclimate envelope
model to represent distributional ranges of more than one thousand exploited marine fish
species.

When maps are based on fish densities, several approaches allow the identification of
essential fish habitats. Habitat mapping allowed to improve the knowledge on spatial
distribution of ichthyofauna for a wild panel of species (e.g. fishing resources at management
unit scale, Martin et al. 2009) at different life stages (e.g. spawners; Loots et al. 2010; Loots et
al. 2011; juveniles; Rochette et al. 2010; sexual segregation; Martin et al., 2012), and to develop
animal atlases. However, these quantitative maps also allow for further investigations. In
addition to Beck et al. (2001) definition of essential fish habitat, based on per unit of area,
Dahlgren et al. (2006) developed a framework for evaluating fish habitats based on their
overall contribution to populations, and introduced the concept of Effective Fish Habitat;
they referred to habitats that make a great overall contribution, regardless of area coverage.
When habitats are represented at the population level, the estimated number of individuals
per habitat type (i.e. the product of the surface area by the corresponding density value, i.e.
effective habitat; Dahlgren et al. 2006) allow to determine the contribution of the different
habitats to the population as a percentage of the total number of individuals, allowing to
prioritize the interest of some restricted habitats contributing to a large amount of
individuals for population supply (Rochette et al. 2010; Trimoreau et al. 2013; French et al. 2018). Habitat mapping also enables the investigation of spatial interactions between increasingly competing species in the face of distribution shifts (Cormon et al. 2014). Predicted maps could also further be combined in order to identify hotspots of biodiversity, by overlaying distribution maps of all considered life components (Cheung et al. 2009, Lelièvre et al. 2014).

## From mapping to projection

When habitat maps are represented at a spatial scale relevant for the ecosystem management level (Le Pape et al. 2014), they allow accurate estimates to be made of the potential effects of pressure (e.g., see scenarios for the common plaice under climate change in the eastern Channel, Fig. 11.2). For instance, Rochette et al. (2010) used HSM to develop quantitative maps of common sole nursery habitats in the Eastern Channel. Next, they used historical maps of the highly degraded Seine estuary to predict habitat suitability in this estuary from 1850. This backward predictive approach showed that habitat loss in the Seine estuary has led to a more than 40% decrease in its nursery capacity. In the same aim, by modelling the relationship between fish and biotic, abiotic and anthropogenic factors, HSM approaches allow assessing the fish sensitivity to these factors, ranking these drivers of fish abundance and richness and, for instance, investigating the impact of specific habitat degradation: e.g., in estuarine habitat, Teichert et al. (2016, 2018) highlighted that contamination and habitat loss are among the main factors impacting estuarine fish and provided insights in terms of conservation and restoration of these ecosystems.

Embedding SDM at the community level is required to make effective forecasts of global change impacts on diversity and ecosystem functioning (França et al. 2012). These approaches also allow to predict (for forecasts and what-if scenarios) future situation of marine resources with regard to changes in environment drivers (e.g. worldwide estimates of the consequences of climate change on fishing resources (Cheung et al. 2009), size structure of fish community (Cheung et al. 2013) and related catch potential (Cheung et al. 2010) or to identify potentially important climate refugia (Morato et al., 2020).

## Management applications, tools and spatial planning

Marine management and conservation requires extensive knowledge of marine habitats distribution and classification (Tempera, 2015). Mapping ichthyofauna provide very powerful biological inputs for the identification of priority areas for both conservation purposes (Brooks et al. 2006) and ecosystem-based marine spatial management related to fisheries (Cogan et al. 2009; Barbier et al. 2011). Quantitative habitat suitability maps may serve as input in specific conservation planning software based on the systematic conservation approach. For instance, protecting restricted but highly productive areas where juvenile fitness is enhanced (Pelletier and Magal, 1996; van de Wolfshaar et al. 2011; Archambault et al. 2016; Champagnat et al. 2021), establishing spawning area closures to preserve fecund individuals and improve fish reproductive capacity (Grüss et al. 2019), or temporarily closing migration routes should increase recruitment success and population
renewal (Mesnildrey et al. 2013). In that aim, the measure of projective uncertainty of HSM is
of particular importance because fish spatial distribution has to be known with a sufficient
level of confidence in order to be useful for further management purposes (Planque et al.
2011b).

Habitat suitability maps for many species and life stages may be stacked and a suitability index, possibly granting different weights to different species, may be computed for each cell of the map (Brown et al. 2000; Store and Jokimäki, 2003; Hattab et al. 2013). Compiled fish habitat maps may thus help decision makers to select relevant protection areas which objectives are to sustain fishing resources and fisheries. Combining these maps with spatial information on cumulative human impacts lead to identify priority areas where marine biodiversity is most and least impacted by human activities (Malcom et al. 2012) and to inform users on a range of key biodiversity elements such as the vulnerability of habitats to deliver different fish and fisheries-related ecosystem services (Cabral et al. 2012).

The relative lack of marine protected area (MPA) devoted to sustainable fishing resources
and fisheries partially reflects the historical focus on fish stock management as the sole
management tool of the marine realm. The failure of the existing fishery management regime
to produce on its own positive effect lead to develop a new marine strategy based on wider
environmental objectives. To reach international and European objectives of marine
environment conservation, it is necessary to set MPA networks in national waters. In
complement to expert advice, systematic conservation planning (Margules and Pressey, 2000)
can be use and is based on an explicit and scientifically defendable process (Cogan et al.
2009).

Several software, based on optimization algorithms, have been developped to design MPA
network, that propose reasonably efficient solutions by selecting combination of locations
offering both a good spatial continuity and ecological representation as well as minimising
socio-economic impacts (e.g., *Marxan*, *Zonation or prioritizr*, Ball and Possigham 2000,
Moilanen et al. 2009, Delavenne et al. 2012). The use of such spatial planning tools require to
integrate several sources of spatial layers including HSM outputs (Delavenne et al. 2012). At
large regional or oceanic scale, they may be used to identify vulnerable marine ecosystems
that should be included in international conservation networks and protected from the ever
expending human impacts in the deep seas (Combes et al. 2021). In spatially congested areas,
such as the European continental shelf, the marine environment is under high and multiple
pressures. Appropriate spatial management to both conserve habitats and avoid conflict for
space requires the inclusion of a large range of socio-economic or anthropologic factors in
complement to predicted outputs from SDM to propose appropriate zoning and human use
levels (Watts et al. 2009; Janßen et al. 2018). The result of such spatial planning exercise can
further be coupled to trophic models to assess the long term impact of the proposed MPA
network and management regime on the ecosystem trophic structure (Metcalfe et al. 2015).
They can also be coupled to fishery dynamics models to assess the middle term impact of
such spatial management on stock and fleet sustainability (Reecht et al. 2015).

**11.5. Main conclusions and research needs**

A large panel of data and methods is available and increasingly easy to implement to describe quantitatively the spatio-temporal distribution of marine ichthyofaunal and their essential habitats. Innovative modelling techniques and habitat mapping technologies are developing rapidly, providing new insights at the larger spatial scales. However, there is a clear need for an increase in resolution additional to the integration of biotic variables (Le Pape et al. 2007; Johnson et al. 2013).

More than a spatio-temporal representation, these approaches allow for analysing the consequences of disturbances and changes, using simulations based on realistic scenarios designed to assess the influence of the evolution of environmental descriptors, with regards to global change or management measures.

These models and maps provide useful tools for managers faced with the multiple demands of resource users when considering strategies for conservation, resource management and spatial planning. Models and maps can be used to help decision making in bio-regionalization, conservation schemes and spatial management of marine systems, to prioritize protection areas (Gaines et al. 2010). In a more structured approach they can be transfered into systematic conservation planning to allow the identification of planning unit from portfolios that achieve conservation targets (Janßen et al. 2018).

**Acknowledgements**

This work had the support of the Fundação para a Ciência e Tecnologia (FCT) through the Strategic Project Grant UIDB/04292/2020 to MARE and project LIFELINE (PTDC/BIA-ECO/29261/2017). RPV was funded by IPMA via the EU Data Collection Framework (namely for Portuguese fisheries data – Programa Nacional de Amostragem Biológica).

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List of figures

Fig. 11.1: Procedure for building fish habitat suitability maps derived from models.

Fig. 11.2. Predicting plaice distribution in the Eastern Channel under a changing environment.

Average spatial patterns of temperature and salinity were used among the predictors of a Eastern Channel Plaice habitat model then were altered to simulate plaice distribution under various climate scenarios. “No change”: average actual distribution, “warmer”: higher temperature, “more rain”: higher precipitation and river flows resulting in lower salinity, “more wind”: increased oceanic influence resulting in more spatially homogeneous salinities.