# Leveraging Computer Vision for Efficient and Scalable Biodiversity Monitoring in Marine Ecosystems: A Multi-Year Study on 3 Ecologically Important Fishes

Bürgi Kilian, Sun Rémy, Bonnaud Elsa, Bouveyron Charles, Diane Lingrand, Dérijard Benoit, Frédéric Precioso, Sabourault Cécile

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Bürgi Kilian, Sun Rémy, Bonnaud Elsa, Bouveyron Charles, Diane Lingrand, et al. Leveraging Computer Vision for Efficient and Scalable Biodiversity Monitoring in Marine Ecosystems: A Multi-Year Study on 3 Ecologically Important Fishes. 2026. hal-05527169

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# Leveraging Computer Vision for Efficient and Scalable Biodiversity Monitoring in Marine Ecosystems:

## A Multi-Year Study on 3 Ecologically Important Fishes

Kilian Bürgi<sup>a,c</sup>, Rémy Sun<sup>c</sup>, Elsa Bonnaud<sup>d</sup>, Charles Bouveyron<sup>b</sup>, Diane Lingrand<sup>c</sup>, Benoit Dérijard<sup>a</sup>, Frédéric Precioso<sup>c</sup>, and Cécile Sabourault<sup>a,*</sup>

* Corresponding author, Cecile.SABOURAULT@univ-cotedazur.fr

<sup>a</sup>Université Côte d'Azur, CNRS, ECOSEAS, Nice, France

<sup>b</sup>Université Côte d'Azur, Inria, CNRS, Laboratoire J.A.Dieudonné, Maasai team, Nice, France

<sup>c</sup>Université Côte d'Azur, Inria, CNRS, I3S, Maasai team, Nice, France

<sup>d</sup>Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique Evolution, Gif-sur-Yvette, France

{Kilian.BURGI,Remy.SUN,Charles.BOUVEYRON,Diane.LINGRAND}@univ-cotedazur.fr

{Benoit.DERIJARD,Frédéric.PRECIOSO,Cecile.SABOURAULT}@univ-cotedazur.fr

February 28, 2026

## Acknowledgements

This work was only made possible thanks to collaborations with the projects RECIF and FishHealth funded by FEAMPA (Fonds Européen pour les Affaires Maritimes, la Pêche et l'Aquaculture) and the divers involved who provided the diver- and video data from the corresponding field campaigns. The authors are grateful to the OPAL infrastructure
from Université Côte d'Azur for providing resources and support. A heartfelt thank you
to Dr. Laurent Vigliola for his invaluable feedback, which greatly enriched this article and
strengthened its content. This project was funded through the UCAJEDI Investments in
the Future project managed by the National Research Agency (ANR) with the reference
number ANR-15-IDEX-01 and through 3IA@cote d'azur - ANR-19-P3IA-0002.

Author Contribution Statements (CRediT)

K. Bürgi, R. Sun, C. Bouveyron, D. Lingrand, F. Precioso, B. Dérijard and C. Sabourault conceived the ideas; K. Bürgi, R. Sun, C. Bouveyron, D. Lingrand and F. Precioso designed the machine learning methodology; C. Sabourault and B. Dérijard collected the data; K. Bürgi, C. Sabourault, B. Dérijard and E. Bonnaud analysed the ecological data; K. Bürgi and R. Sun led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

Data Availability

Codes, Scripts and numeric datasets (CSV files) are made available on GitHub:
https://github.com/PiSuMp/conservationArea_eval

Conflict of Interest

The authors declare that they have no conflicts of interest.

Word Count

Words in total: **6035**
Abstract

Marine environments face multiple threats, including climate change, overfishing, and shoreline artificialisation leading to a great loss of biodiversity. Therefore, fast and efficient biodiversity assessments are crucial to understand the current and future state of marine ecosystems. Traditionally, underwater visual censuses (UVCs) have served as the primary method for assessing biodiversity, with divers recording species presence, abundance, and other biological indicators along fixed transects. In this study, we collected video transects recorded by camera-equipped divers, resulting in 41h of video footage that enhanced the data collection but also increasing the volume of material requiring tedious manual analysis. This manual analysis of video footage is in a lot of cases financially and logistically unfeasible. Here, we present a comprehensive multi-year computer-vision-based pipeline applied to all recorded video transects to extract ecological information on three ecologically and economically important species (*Epinephelus marginatus*, *Sciaena umbra* and *Diplodus vulgaris*). Using a fully automated computer vision pipeline that enables researchers rapid and less-biased data extraction from large datasets, we confirmed known depth and substrate preferences of the three target species. Additionally, we identified seasonal variations in *D. vulgaris* that may be disrupted by climate change. Our results demonstrate that computer-vision enables rapid and less-biased data extraction from large datasets, facilitating efficient biodiversity monitoring. We anticipate that this study will serve as a foundation to expand computer-vision applications to other marine regions, species, and environmental scenarios. Increased efficiency in data processing will make more frequent and extensive biodiversity assessments possible, strengthening conservation efforts through improved ecological insights.

*Keywords:* Artificial intelligence; Fish monitoring; Machine learning; Marine conservation; Object detection
# 1 Introduction

Marine ecosystems are facing unparalleled pressures from multiple fronts, including habitat destruction, overfishing and climate change (Halpern et al., 2007). Overfishing alone has driven approximately 38% of global fish stocks beyond sustainable limits, threatening the stability of marine food webs (FAO, 2024). Moreover, the increase in marine anthropogenic activities combined with climatic shifts have accelerated biodiversity loss (Hoegh-Guldberg and Bruno, 2010), led to altered species interactions (Doney et al., 2012), and reshaped whole population structures in marine environments, with unknown consequences for these fragile ecosystems (Worm et al., 2006).

While these threats are well recognised, many studies assessing fish populations rely on single surveys or snapshot-like observations, constrained by cost and limited long-term monitoring efforts (Stuble et al., 2021). This limited temporal replication fails to capture the dynamics necessary to understand ecological processes and evaluate the long-term effectiveness of conservation interventions (Lynch et al., 2022). Global fish densities and distributions are changing over time, but a lack of temporally explicit data limits our ability to evaluate the resilience of marine populations to combined human and environmental pressures (Fisher et al., 2010).

Marine Protected Areas (MPAs) are a widely implemented conservation tool designed to mitigate biodiversity loss by restricting extractive and disruptive human activities (Frid et al., 2023). In the Mediterranean Sea, MPAs have been shown to positively affect fish assemblages (Seytre and Francour, 2008; Bodilis et al., 2011). Since our study location involves different levels of protection, we used the umbrella term 'conservation areas' to describe them.

Many assessments focus on overall community-level metrics, overlooking fine-scale, species-specific, and temporal analyses that are critical for targeted conservation strategies, particularly in the face of climate-driven distributional shifts (Waldock et al., 2018).
Certain species, crucial for marine food web regulation and marine habitat connectivity, are more affected by these pressures than others. For instance, large apex predators such as *Epinephelus marginatus* have experienced historical overfishing and remain vulnerable despite protection, while species like *Sciaena umbra*, intermediate predator, faces drastic population declines due to habitat degradation and exploitation (Garcia-Rubies et al., 2013). Even opportunistic species such as *Diplodus vulgaris*, although generally more abundant, exhibit distributional shifts linked to temperature changes and fishing pressure (La Mesa and Vacchi, 1999). These differences underscore the importance of species-specific monitoring to inform effective management interventions within these designated areas (Alvarez-Berastegui et al., 2018).

Tracking such ecological changes over time is particularly challenging (McDonald, 2019). Often, these changes occur unpredictably, ranging in duration from a single season to several decades, and can follow different trajectories (Jackson, 2023). They may lead to various behavioral responses, including migration (Sih et al., 2011), local disappearance (Ulman et al., 2020), or increased mortality (Candolin and Wong, 2012).

Furthermore, traditional biodiversity monitoring approaches such as underwater visual censuses (UVCs) are labour-intensive and limited in their spatial and temporal coverage, limiting to conduct frequent and large-scale assessments. Recent advances in computer vision and artificial intelligence offer promising ways to overcome these limitations by enabling rapid, automated extraction of ecological data from underwater videos while reducing the need of human effort (Bürgi et al., 2024, 2025).

In this study, we investigate how local frequencies of occurrence and densities of fish populations vary over time and across environmental factors within conservation areas along the French Mediterranean coast. Specifically, we:

(i) evaluate whether computer vision models trained on data from one year (2022) can generalise across subsequent years (2023–2025), assessing their robustness to temporal
shifts;

and (ii) examine the spatial and temporal patterns in the occurrence and abundance of three ecologically and economically important Mediterranean fish species, *E. marginatus*, *S. umbra*, and *D. vulgaris*, across different depths, substrates, and locations.

The *key contributions* of this study are:

- Investigation of the most impacting environmental factors on species frequencies and densities in the French Riviera using computer vision and artificial intelligence

- Automated identification of species, that are more impacted by specific environmental factors, providing large data-driven recommendations to optimise protection and conservation efforts within the investigated conservation areas

- Assessment of the feasibility of leveraging data from different seasons and years for inter-seasonal model generalisation assessment

- Investigation of optimal strategies for model training that align with ecological principles and constraints

# 2 Materials and Methods

## 2.1 Data collection and projects

The video data for this study were collected in a standardised manner. Training data for the deep learning model were gathered during both the warm and cold seasons of 2022 across eight protected areas, with sampling conducted inside and outside the designated conservation zones (Fig. S1). The video data used for the temporal ecological evaluation was collected within three conservation areas: Cap Ferrat (established in 2008), Corniche Varoise (1996), and Esterel (2003) (Fig. 1).
<figure><img src="image_2.png" /><figcaption>Figure 1: Image depicting the conservation areas during the data collection campaign along the French Riviera. In red from left to right - E = Esterel, CV = Corniche Varoise and CF = Cap Ferrat.</figcaption></figure>

The ecological data was collected from the warm season of 2023 to the cold season of 2025, covering four sampling seasons (Fig. 2). At each conservation area, transects were conducted across three habitat categories: shallow rocky substrate (0–15 m), deep rocky substrate (15–30 m), and seagrass meadows (<15 m). Each transect corresponded to a single underwater video recording. Videos had a duration of 1-3 minutes and each transect covered 125 m² seafloor surface. The number of transects collected per habitat and per conservation area is summarized in Table S1.
<figure><img src="image_3.png" /></figure>

Figure 2: The timeline of the data collection for the three different locations evaluated in the ecological analysis. October to December refers to warm season (orange) and February to May refers to the cold season (lightblue).

## 2.2 Species of interest

We used the same three species as in a previous study (Bürgi et al. (2025) - *Epinephelus marginatus*, *Sciaena umbra* and *Diplodus vulgaris*). *E. marginatus* is an emblematic and endemic solitary apex predator of the French Mediterranean Sea. Its populations have historically been overfished but have been under protection since 2003, making the monitoring of its population dynamics particularly important for the conservation of top-down ecological processes. As a second species, we selected the schooling intermediate predator *S. umbra*, whose drastic population decline prompted protective measures. While it has documented socio-economic importance because it was historically targeted by small-scale and recreational fisheries, current protections now restrict direct exploitation. Ecologically, it contributes crucially to mid-trophic food-web structure. Lastly, *D. vulgaris*, which is a more abundant, schooling, and opportunistic species frequently found in numerous videos, plays a key ecological role as a structuring species, contributing to community dynamics through its high abundance and predatory regulation of predators of herbivorous species. It also has economic importance, being a common target in coastal fisheries. This species presents a greater challenge for detection and counting due to its abundance and behaviour.
## 2.3 Training and Evaluation

We trained a YOLOv7 (Wang et al., 2022) model in the same manner as in Bürgi et al. (2024). However, due to impracticality with numerous videos, we trained without the image enhancement preprocessing. We achieved an f1 score of 0.69 and an mAP of 0.692 at threshold 0.50. The f1 score summarises how well the model balances correct detections and missed detections, while mAP reflects overall detection accuracy across confidence levels. The 0.50 threshold indicates that detections are counted as correct when the detector was at least 50% sure that the object is classified correctly. For the ecological evaluation, we evaluated the performance in depicting frequency on 72 videos and head counts on 40 videos. These test videos include roughly 50% seagrass meadow and 50% rock habitats. We used N<sub>Heuristic</sub>, an intuitive estimator of the number of individuals derived from the temporal sequence of detections, with the same calibrated values from Bürgi et al. (2025) to get the estimated fish count. These videos were used (i) to find error rates for the frequency and counts per species for the baseline model and (ii) to evaluate the inclusion or exclusion of certain videos into the training procedure and see the impact on the counting capability (Table 1). High Occurrence Videos (HOV) refers to videos with more than five individuals present since the counting of more than five individuals in a video becomes more challenging. While Low Occurrence Videos (LOV) consider videos that include one to five individuals. A category defined as NTS includes Non-Target Species. We also defined videos based on depth, categorising them as Deep or Shallow, to investigate whether the inclusion of depth of the environment affects counting accuracy.
<table><caption>Table 1: The categories with the number of images per category. There was an effort to balance the different categories that are intercomparable. Abbreviations: HOV = High Occurrence Videos, LOV = Low Occurrence Videos, NTS = Non-Target Species</caption><thead><tr><th>Category</th><th>Number of images</th></tr></thead><tbody><tr><td>100% HOV</td><td>921</td></tr><tr><td>75% HOV & 25% LOV</td><td>986</td></tr><tr><td>50% HOV & 50% LOV</td><td>1108</td></tr><tr><td>25% HOV & 75% LOV</td><td>1126</td></tr><tr><td>100% LOV</td><td>1178</td></tr><tr><td>HOV + NTS</td><td>921 + 906</td></tr><tr><td>LOV + NTS</td><td>1178 + 906</td></tr><tr><td>Deep</td><td>2260</td></tr><tr><td>Shallow</td><td>2320</td></tr></tbody></table>

## 2.4 Error Rates

### 2.4.1 Error Rate Frequency

To find the error rate of the frequency, we used 72 (18 videos per season) videos over the seasons to verify a good coverage of the frequency. Using this estimate, we can ensure that a subset of the data is compared to a human effort and the rest of the data is hypothesised to follow the same error rate. The frequency error rate is 2.78% for *D. vulgaris* and *E. marginatus* while *S. umbra* was predicted perfectly. Specific error rates per season are provided in Figure S2.

### 2.4.2 Error Rate Density

We used 40 videos (10 videos per season) over the seasons 2023 to 2025 to test the generalisation and how the model is able to count accurately our target species. We used videos where at least one or more target species where present. This creates an error rate for the density analysis, that allows us to calculate differences identified by the model
over different years and other environmental factors. We used this error rate around the average of non-zero values to have a fair comparison with the metric.

## 2.5 Error metrics & correction for high occurrence species

### 2.5.1 Mean Absolute Error (MAE)

The density error rate relies on a very punishing metric being the mean absolute error (MAE - Eq. 1). The MAE treats all errors equally, regardless of the scale of the actual value. For example, an error of 20 has the same impact on the metric whether the actual count is 1 or 100, which is not ideal for very high and very low counts.

$$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| \qquad (1)$$

Where:

* $n$: Number of total videos

* $y_i$: Actual count in video i

* $\hat{y}_i$: Estimated count in video i

### 2.5.2 Corrected Mean Absolute Relative Error (CMARE)

We investigated a modification of the traditional MAE that depends on the actual fish count. We normalised the absolute error by dividing the difference between the estimated and actual counts by the actual count. This modification stabilises the error for samples with low actual counts, while reducing the error for samples with higher counts. However, this reduction was too aggressive, leading to under-penalisation in medium- and high-count scenarios. To address this, we introduced a correction factor, k, which preserves sensitivity for low-count samples while reducing the penalty for high-count cases. We termed the correction of MAE into the corrected version Corrected Mean Absolute Relative Error (CMARE - Eq. 2).
$$CMARE = \frac{1}{n} \sum_{i=1}^{n} k * \frac{|y_i - \hat{y}_i|}{k + y_i} \quad (2)$$

Where:

* n: Number of total videos

* $y_i$: Actual count in video i

* $\hat{y}_i$: Estimated count in video i

* k: Correction factor

To determine an ideal correction factor k, we consulted expert divers conducting underwater visual census (UVC). Following expert recommendations, an error margin is considered acceptable when the reported error accounts for at least 70% of the true error. To evaluate this, we computed CMARE for values of k ranging from 1 to 500 at a large individual count of $y_i = 100$ to identify the point at which CMARE captured 70% of the actual error, thereby satisfying the required certainty criterion (Fig. S3). With these calculations, we selected a correction factor k of 234 for the rest of the study. The calculations for $y_i = 1$ and $y_i = 100$ can be found in the supplementary material (Eq. S1 and S2), presenting the sensitivity at high but not a low count videos.

A graphical analysis illustrates the impact of this correction factor k on error penalisation using simulated data (Fig. 3). The data simulation uses 7 different noise levels which are linearly related to the true value. They all have similar MAE values per simulation to compare the effect of using CMARE. Lower end noise increases the CMARE metric, higher end noise decreases the CMARE metric. This proves that high error simulations are less penalised than the errors on low error simulations. Furthermore, we can observe different linear correlations with high error simulations ($R^2 > 0.95$) being more correlated than the low error simulations ($R^2 < 0.95$).
<figure><img src="image_4.png" /><figcaption>Figure 3: Graphical representation of CMARE simulated data in different scenarios High- and Low-Count and a balanced count as a reference. Panel A illustrates the effect of the CMARE metric computed across 100 independent simulations, with each point in the scatter plot representing the outcome of one simulation. The CMARE values are shown in relation to the corresponding Mean Absolute Error (MAE) allowing comparison, while Panel B presents a representative example from a single simulation.</figcaption></figure>
## 2.6 Ecological Evaluation

The ecological analysis included all videos from 2023 to 2025, corresponding to 517 video transects (~38 hours of footage). Each video was linked to a transect, and predictions of fish counts. Video prediction required roughly 4 minutes per video (~34 hours total), while human validation averaged ~30 minutes per video, making the automated approach much faster and scalable.

All statistical analyses were conducted in R v4.4.2. We modelled fish occurrence (frequency) and fish density using generalized linear (mixed) models.

### 2.6.1 Fish frequency (occurrence)

Fish occurrence was modelled as a binary response (presence absence) using a binomial error distribution with a logit link function. For the overall community model (all species combined), we fitted a generalized linear model (GLM):

$$ \text{logit}(p_i) = \beta_0 + \beta_1 \text{Species}_i + \beta_2 \text{ConservationArea}_i + \beta_3 \text{Substrate}_i + \beta_4 \text{Season}_i \quad (3) $$

where $p_i$ represents the probability of fish occurrence per transect $i$. All predictors were included as fixed effects.

Species-specific frequency models were fitted using generalised linear mixed models (GLMMs) including sampling period (Year × Season) as a random intercept:

$$ \text{logit}(p_{ij}) = \beta_0 + \beta_1 \text{ConservationArea}_{ij} + \beta_2 \text{Substrate}_{ij} + \beta_3 \text{Season}_{ij} + b_j \quad (4) $$

where $b_j \sim N(0, \sigma^2)$ represents the random effect of sampling period.
### 2.6.2 Fish density

Fish density was analysed using generalised linear mixed models with a negative binomial error distribution (family = nbinom2) to account for overdispersion in count data:

$$ \log(\mu_i) = \beta_0 + \beta_1 \text{ConservationArea}_i + \beta_2 \text{Species}_i + \beta_3 \text{Substrate}_i + \beta_4 \text{Season}_i \quad (5) $$

Zero-inflated negative binomial models were used where appropriate to account for excess zeros, with a constant zero-inflation probability. Species-specific density models were fitted separately, including site as a random intercept when supported by the data:

$$ \log(\mu_{ij}) = \beta_0 + \beta_1 \text{ConservationArea}_{ij} + \beta_2 \text{SubstrateDepth}_{ij} + \beta_3 \text{Season}_{ij} + u_j \quad (6) $$

where $u_j \sim N(0, \tau^2)$ represents the random effect of site.

### 2.6.3 Model validation

Model assumptions were evaluated using simulated residual diagnostics implemented in the DHARMa package. We assessed uniformity (Kolmogorov-Smirnov test), dispersion, and zero-inflation. No significant deviations from model assumptions were detected (Figs. S4-S6, all p-values > 0.05).

Post hoc pairwise comparisons were conducted for all categorical predictors (species, substrate, season, and conservation area) using estimated marginal means (EMMs) calculated with the emmeans package. Pairwise contrasts were tested using Tukey's adjustment for multiple comparisons, with comparisons averaged over the levels of the remaining predictors.
# 3 Results

In this section, we present the results of our analyses, which are divided into two main parts: the ecological insights (Sec. 3.1) derived from the model outputs and the machine learning performance evaluation (Sec. 3.2). We first explore the ecological patterns revealed by the model predictions, including species frequency and density across spatial, temporal, and environmental factors. Secondly, we assess how various data preparation strategies influence model accuracy, focusing on categorisation approaches such as species occurrence rates and habitat types. These results provide both valuable biological information to support conservation and management decisions and technical insights into model robustness.

## 3.1 Ecological Analysis

### 3.1.1 Frequency

The frequency is a strong indicator for ecological health and can be obtained by different methods which do not require counting of animals (i.e. environmental DNA or the Fish Assemblage Sampling Technique (FAST) method (Francour, 2016)). We used it to evaluate the frequency of fishes for each conservation area over different substrates (rocky and seagrass meadows).

**Frequency in different areas** We investigated differences in the frequency of occurrence of three fish species across three conservation areas (Fig. 4 - A). For *D. vulgaris*, there was a significant difference (p-value = .006) between the areas of Esterel and Cap Ferrat while the other areas are not significantly different (p-value = .1 and p-value = .231, respectively). For (*E. marginatus*, there was a significant difference between Esterel and the other two areas (p-value = .003). The species *S. umbra* showed no difference in frequency between the areas (p-value = .237, p-value = 1 and p-value = .061). Even though, the difference between Esterel and Corniche Varoise can be considered a trend with a value being close to statistical significance (p-value = .061). Overall, *D. vulgaris* was observed more frequently than the other two species, with *S. umbra* being the least commonly recorded among them and the community GLM model showed a significant difference from Esterel compared to
the Cap Ferrat (p-value < .001) and Corniche Varoise (p-value = .001) with the species
being more frequent in the Esterel area.

**Frequency on different substrates** For the second experiment, we looked at the frequency over the different substrates of shallow seagrass meadows, shallow rocks and deep rocks (Fig. 4 - B) to assess substrate and depth preference in all areas pooled together. There was a significant difference for all the substrate types (p-value < .05) for *D. vulgaris* and *E. marginatus* with rocky substrate having highest frequencies of this species and both species preferring deeper waters. For *S. umbra* there was no significant difference (p-value = .966) in the shallower substrates. This species prefers deep rocky habitats over shallow seagrass habitats (p-value = .027). There was no significant difference in the deep and shallow rocky substrate (p-value = .061), even though a trend can be observed towards significant difference.

**Frequency over time** The frequency over the seasons can give valuable insights into seasonality of the different species that can indicate temperature regulation, predator avoidance or breeding periods. In Figure 4 - C we observe that *D. vulgaris* has the highest frequency (p-value < .01). There is a significant difference between the cold and warm seasons for this species with warmer waters showing a higher frequency of occurrence (p-value < .001). For *E. marginatus* and *S. umbra* no significant seasonality was observed (p-value<EM = .36 and p-value<SU = .11). *S. umbra*, frequencies were the lowest throughout all seasons with 0 observations in 2025. The frequencies for *E. marginatus* are increasing over the seasonal data collection from 2023 to 2025 and could indicate a slight recovery of this species.
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

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

Figure 4: Frequency results over the environmental variables investigated with standard deviation calculated with a 14% subset of the data per species. The Frequency of Occurrence in A depicts the different areas investigated, B indicates the different substrates and C depicts the seasons investigated.
3.1.2 Fish count and density

We used the same detections as we used for Section 3.1.1 and counted the fish individuals
to find the density. This way we studied different environmental factors such as areas,
substrates, depth and time over seasons. This assessment allows us to characterise a
species' seasonality, substrate and depth preferences, and to identify trends in the relative
effectiveness of the different areas surveyed.

**Density at different areas** In this section we will investigate the difference in fish densities per transect between the different conservation areas (Fig. 5 - A). They differ in size, age and compliance which has a direct impact on the fish species density. For *D. vulgaris* there were no significant differences (p-value > .05) between the areas, the species was numerous in all the conservation areas. Cap Ferrat and Esterel show evidence of a trend towards significance with Cap Ferrat having more lower count transects than Esterel. The same occurs to be the case for *E. marginatus* (p-value > .05). However, values at Esterel tended to be higher than at Cap Ferrat (p-value = .075) and Corniche Varoise (p-value = .059), while Cap Ferrat and Corniche Varoise did not differ (p-value = .83). For *S. umbra*, counts differed between conservation areas. Abundances were significantly higher in Esterel (p-value < .03) compared to other areas, while Corniche Varoise and Cap Ferrat did not differ significantly (p-value = .921). These results suggest that rarer species like *S. umbra* are unevenly distributed across conservation areas, with Esterel hosting the highest abundances.

**Density on different substrates** We next pooled the transect data of all the areas together to investigate substrate preference (Fig. 5 - B). All densities for *D. vulgaris* are highly significant (p-values < .001) with rocky and deeper substrates being preferred. Also the species *E. marginatus* showed significant differences in density distributions over different substrate. Values were substantially lower in shallow seagrass compared to both deep rocky (p-value < .001) and shallow rocky (p = .042) habitats. Additionally, deep rock habitats housed highly significantly more individuals than shallow rock transects (p-value < .001). Overall, the density was highest in deep rocky habitats, intermediate on
shallow rocks, and lowest in shallow seagrass.

**Density over time** Seasonality can provide important insights into species behaviour, and disruptions to these patterns may reflect human impacts such as overfishing. We therefore examined the effect of season in the following section (Fig. 5 - C). Seasonal effects differed among species. For *D. vulgaris*, values were significantly higher in the warm season than in the cold season (p-value < .001), indicating a clear seasonal pattern. In contrast, no significant seasonal differences were detected for *E. marginatus* (p-value = .131) or *S. umbra* (p-value = .192). Overall, only *D. vulgaris* showed a significant increase during the warm season, whereas *E. marginatus* and *S. umbra* did not vary significantly between seasons.
<figure><img src="image_8.png" /><figcaption>Figure 5: The fish density (individuals per transect) in the environmental variables investigated in this study. The fish density in A depicts the different areas investigated, B indicates the different substrates and C depicts the seasons investigated. Error bars are calculated on ~10% of the data and are presented with the median of each species-group.</figcaption></figure>
## 3.2 Machine Learning Analysis

For the overarching machine learning analysis, we looked at the labelling and training strategy by investigating the impact of categorising the data into Low Occurrence Videos (LOV), High Occurrence Videos (HOV) and inclusion and exclusion of Non-Target Species (NTS). We also examined the effect of restricting the dataset to either deep or shallow videos exclusively. We demonstrate different strategies to tackle the labelling and training of a model and how training on only these categories affects the final count in comparison to the baseline model with all the videos included (Table 2). The categories were balanced in terms of images (roughly 1000 image per category) and the validation set was held constant. The test set count was not used to train or validate to avoid leakage. This will allow us to isolate videos and identify which videos have a higher importance for the final count of the different species.

We can see the baseline model including all images, as expected, outperforms the categorised models. The CMARE errors are used as error bars for the ecological density analysis in Sec. 3.1.2. Constant high correlation values above 0.8 give an indication that the model is performing very well on the test videos. Difference from MAE and CMARE are only observable for the common fish *D. vulgaris*, which appears in bigger groups in the videos and therefor is more affected by the correction of higher occurrence videos.

When we compare the HOV, mix LOV-HOV and LOV inclusion we can see that the two schooling species *D. vulgaris* and *S. umbra* are positively affected when we include HOVs. While the solitary species *E. marginatus* is not performing with 100% HOVs but shows lower errors when including LOVs. This gives an indication that the behaviour of the species should dictate the labeling procedure and would allow an efficient training process without unnecessary videos included. The proposed metric, CMARE, demonstrates enhanced sensitivity and provides a more nuanced representation of error trends, particularly for *D. vulgaris* in both the 100% HOV and the 75% HOV & 25% LOV categories. Notably, while the Mean Absolute Error (MAE) remains approximately constant across
these categories (~4.40), CMARE exhibits a marginally improved performance for the CMARE in the 75% HOV & 25% LOV category (~3.62) than 100% HOV (~3.69) category.

In terms of including or excluding NTS, we found that it has a marginal positive effect on *E. marginatus* and a greater positive effect on *D. vulgaris*. For *D. vulgaris* this could come from the fact, that in the NTS category there is another class of *Diplodus* sp. that causes confusion in the detections (Bürgi et al., 2024) and with the inclusion to the training, this confusion is accounted for. The best performing category is the HOV with the inclusion of the NTS.

We compared the categories of deep and shallow images which has an impact on the performance. However, both of these video types, if available, should be included to cover both scenarios of the species in deeper and more shallow waters. The increase in performance can be attributed to the fact that there are more videos from deeper waters in the test videos.

The most important factor for improving the training and the counting capability are the different occurrence categories. Therefore, it is important to include specific videos according to the chosen target species. In our case, we include both videos to cover the schooling *D. vulgaris* and *S. umbra* with HOVs as well as the solitary *E. marginatus* with LOVs and in any case include NTS videos to help with potential confusions.
<table><caption>Table 2: The mean absolute error and the correlation in comparison to the groundtruth counts of the error videos and the effect of adding different categories of videos to the dataset can be seen here. Abbreviations: HOV = High Occurrence Videos, LOV = Low Occurrence Videos, NTS = Non-Target Species</caption><thead><tr><th>Category (no. images)</th><th>Species</th><th>MAE</th><th>CMARE</th><th>Correlation</th></tr></thead><tbody><tr><td>Baseline (6901)</td><td>Epinephelus marginatus</td><td>0.22</td><td>0.21</td><td>0.89</td></tr><tr><td rowspan="2">7% HOV, 39% LOV, 54% NTS</td><td>Diplodus vulgaris</td><td>3.75</td><td>3.08</td><td>0.96</td></tr><tr><td>Sciaena umbra</td><td>0.29</td><td>0.28</td><td>0.96</td></tr><tr><td rowspan="3">10% HOV &amp; 40% LOV &amp; 50% NTS (1028)</td><td>Epinephelus marginatus</td><td>0.55 ± 0.15</td><td>0.55 ± 0.15</td><td>0.6 ± 0.15</td></tr><tr><td>Diplodus vulgaris</td><td>5.8 ± 0.81</td><td>4.68 ± 0.57</td><td>0.94 ± 0.01</td></tr><tr><td>Sciaena umbra</td><td>1.37 ± 0.57</td><td>1.35 ± 0.58</td><td>0.7 ± 0.14</td></tr><tr><td rowspan="3">100% HOV (921)</td><td>Epinephelus marginatus</td><td>0.81 ± 0.23</td><td>0.81 ± 0.23</td><td>0.48 ± 0.15</td></tr><tr><td>Diplodus vulgaris</td><td>4.4 ± 0.27</td><td>3.69 ± 0.2</td><td>0.96 ± 0.01</td></tr><tr><td>Sciaena umbra</td><td>1.54 ± 0.27</td><td>1.53 ± 0.86</td><td>0.57 ± 0.29</td></tr><tr><td rowspan="3">75% HOV &amp; 25% LOV (986)</td><td>Epinephelus marginatus</td><td>0.47 ± 0.18</td><td>0.47 ± 0.18</td><td>0.71 ± 0.13</td></tr><tr><td>Diplodus vulgaris</td><td>4.4 ± 0.34</td><td>3.62 ± 0.26</td><td>0.95 ± 0.01</td></tr><tr><td>Sciaena umbra</td><td>1.05 ± 0.15</td><td>1.01 ± 0.15</td><td>0.53 ± 0.05</td></tr><tr><td rowspan="3">50% HOV &amp; LOV (1108)</td><td>Epinephelus marginatus</td><td>0.43 ± 0.06</td><td>0.43 ± 0.06</td><td>0.64 ± 0.1</td></tr><tr><td>Diplodus vulgaris</td><td>5.14 ± 0.65</td><td>4.12 ± 0.44</td><td>0.93 ± 0.02</td></tr><tr><td>Sciaena umbra</td><td>2.24 ± 0.94</td><td>2.22 ± 0.94</td><td>0.48 ± 0.19</td></tr><tr><td rowspan="3">25% HOV &amp; 75% LOV (1126)</td><td>Epinephelus marginatus</td><td>0.52 ± 0.1</td><td>0.52 ± 0.1</td><td>0.42 ± 0.17</td></tr><tr><td>Diplodus vulgaris</td><td>4.68 ± 0.96</td><td>3.81 ± 0.7</td><td>0.94 ± 0.02</td></tr><tr><td>Sciaena umbra</td><td>1.38 ± 0.27</td><td>1.33 ± 0.28</td><td>0.29 ± 0.09</td></tr><tr><td rowspan="3">100% LOV (1178)</td><td>Epinephelus marginatus</td><td>0.52 ± 0.15</td><td>0.52 ± 0.15</td><td>0.44 ± 0.25</td></tr><tr><td>Diplodus vulgaris</td><td>7.36 ± 1.24</td><td>5.72 ± 0.87</td><td>0.93 ± 0.01</td></tr><tr><td>Sciaena umbra</td><td>1.41 ± 0.37</td><td>1.36 ± 0.36</td><td>0.26 ± 0.08</td></tr><tr><td rowspan="3">50% HOV + 50% NTS (921 + 906)</td><td>Epinephelus marginatus</td><td>0.33 ± 0.09</td><td>0.33 ± 0.09</td><td>0.74 ± 0.1</td></tr><tr><td>Diplodus vulgaris</td><td>3.99 ± 0.33</td><td>3.32 ± 0.22</td><td>0.96 ± 0.01</td></tr><tr><td>Sciaena umbra</td><td>0.7 ± 0.25</td><td>0.68 ± 0.25</td><td>0.82 ± 0.15</td></tr><tr><td rowspan="3">50% LOV + 50% NTS (1178 + 906)</td><td>Epinephelus marginatus</td><td>0.38 ± 0.04</td><td>0.38 ± 0.04</td><td>0.65 ± 0.06</td></tr><tr><td>Diplodus vulgaris</td><td>7.37 ± 0.27</td><td>5.75 ± 0.19</td><td>0.92 ± 0.02</td></tr><tr><td>Sciaena umbra</td><td>1.18 ± 0.16</td><td>1.13 ± 0.15</td><td>0.37 ± 0.05</td></tr><tr><td rowspan="3">100% Deep (2260)</td><td>Epinephelus marginatus</td><td>0.36 ± 0.06</td><td>0.36 ± 0.07</td><td>0.76 ± 0.12</td></tr><tr><td>Diplodus vulgaris</td><td>4.86 ± 0.38</td><td>3.92 ± 0.3</td><td>0.93 ± 0.01</td></tr><tr><td>Sciaena umbra</td><td>0.88 ± 0.12</td><td>0.85 ± 0.12</td><td>0.73 ± 0.08</td></tr><tr><td rowspan="3">100% Shallow (2320)</td><td>Epinephelus marginatus</td><td>0.46 ± 0.05</td><td>0.45 ± 0.06</td><td>0.49 ± 0.14</td></tr><tr><td>Diplodus vulgaris</td><td>6.24 ± 1.13</td><td>4.98 ± 0.81</td><td>0.95 ± 0.01</td></tr><tr><td>Sciaena umbra</td><td>1.12 ± 0.1</td><td>1.07 ± 0.1</td><td>0.46 ± 0.14</td></tr></tbody></table>
# 4 Discussion

**Summary** To our knowledge our study is the first study to utilise artificial intelligence (AI) in an applied manner to fully automate the processing of multi-seasonal underwater video data. This significantly reduces the time required for manual analysis. In comparison, the computer vision model took 8 times less time per video to gather the data without the need for breaks. This automation has simplified the species identification and count in videos, enabling the analysis of future videos that would otherwise be too time-consuming and costly. This advancement further supports the potential of AI methods in ecological research, particularly in marine biodiversity assessments and monitoring.

**Frequency & Density** Analysis of species frequency and density indicates distinct spatial differences in habitat use. In particular, Esterel can be considered as a critical conservation area for *E. marginatus*, a territorial species that exhibits strong site fidelity (Desiderà et al., 2021). This is especially interesting since this species has recently been protected and is under recovery over the past years. In contrast, *D. vulgaris* and *S. umbra* show more variable patterns, showing that additional factors than area influence their distribution. These factors could stem from the environment, human impact or other site characteristics not accounted for in this study. Notably, the overall model reveals that *S. umbra* is significantly rarer than both other target species, with occurrence rates approximately 45-fold lower than *D. vulgaris* and 6-fold lower than *E. marginatus*, emphasizing the urgency of conservation efforts for this species. Interestingly, while *S. umbra* does not occur at significantly more locations within Esterel compared to other sites, the density analysis reveals that when present, populations are substantially more numerous in Esterel. This pattern suggests that Esterel contains specific high-quality refuge habitats or aggregation sites for this species, rather than supporting widespread but sparse distribution.

All species expressed a preference for deep rocky substrates that they use for foraging and predator avoidance (Harmelin-Vivien et al., 1995; Harmelin and Harmelin-Vivien, 1999; Garcia et al., 2022). Importantly, within rocky habitats themselves, all three species
showed significant depth stratification, consistently preferring deeper rocky areas over shallower rocky zones. This fine-scale depth partitioning suggests that depth-related environmental gradients, such as temperature, light availability, or prey distribution (REF), play a critical role in structuring these fish assemblages beyond simple substrate type. The two rarer species were rarely seen over the seagrass substrate. However, the seagrass meadows are known as nursing habitats and are of equal importance to the target species as well as provider of food for the species. A balance in conservation is important (Kalogirou et al., 2010).

The deep affinity of *D. vulgaris* also revealed an effect of seasonality for this species. This is most likely due to temperature regulation and follows a pattern similarly observed by other studies (Aspillaga et al., 2016; Sbragaglia et al., 2019). They retreat to deeper waters during the warmer summer months to be able to cope with the increase in water temperature and will stay below the sea's thermocline. Climate change could have an impact on a species like this, that relies on deeper colder waters to survive and a deepening thermocline beyond the current depths could have significant ecological repercussions (Rutterford et al., 2015). In contrast, *E. marginatus* did not exhibit significant seasonal variation in our study, though previous observations have suggested vertical migrations towards shallower waters during summer months (Koeck et al., 2014). The lack of a detectable seasonal pattern in our data may reflect spatial heterogeneity in thermal refuge availability or indicate that other factors beyond temperature influence this species' depth distribution. There has been reports of this species distribution range into much deeper waters of up to 250m (Harmelin and Harmelin-Vivien, 1999), which are inaccessible and unsafe for human divers, therefore a remote operated vehicle (ROV) or other non-human methods should be employed for data collection, further highlighting the need for computer vision approaches in ecological monitoring.

**Implications** We identified different implications in this study to help conserve these species. First, we look at species-specific protection needs. The common species *D. vulgaris*
exhibits seasonal variations, highlighting a necessity for flexible management strategies such as seasonal fishing bans to mitigate population declines. The emblematic species *E. marginatus* demonstrates site-specific patterns, allowing for targeted conservation measures to protect key habitats and areas. The uncommon species *S. umbra* appears to face unknown threats, likely linked to unsustainable fishing practices or lack of surveillance, warranting further investigation. The identification of high-density aggregation sites within Esterel for *S. umbra* highlights the need for fine-scale spatial protection measures targeting these critical refuges. Increasing the collection of underwater videos of *S. umbra* could improve monitoring efforts and allow for more precise predictions of its population trends, conservation status and refuge locations.

The spatial analysis identified Esterel as an important refuge and should be targeted for increased surveillance and monitoring also during the high touristic summer months. For Corniche Varoise an absence of active regulation highlights that there is a requirement for a shift in management strategies since the frequency as well as the density suggest low occurrence of the rarer species in need of protection while the more common *D. vulgaris* seems to thrive in this area. This could also be due to other factors, for example the distance to the nearest city and accessibility.

**CMARE** One key contribution of this study is the introduction of the Corrected Mean Absolute Relative Error (CMARE) metric. This metric uses a correction factor that allows an uncertainty on high occurrence videos. This better reflects the data that has been collected by divers, which with an increase in number of fish in a school also have an uncertainty. We used this new error metric to introduce dynamic error rates for the density analysis.

With this introduction of the error, we also have beside the quantitative statistical tests, a qualitative indication of uncertainty between the environmental factors such as depth, substrate or time. Since the majority of the videos do not contain our target species
(especially the rarer ones), this error bar around the median allows for quick comparison.
The errors for *D. vulgaris* show the lowest variability making it the best representation of
the new metric since the rarer species are affected by the distribution of the data. In most
cases when there was a significant difference, the distributions do not cross the error bars
neither this makes the resulting ecological implications more robust.

**Categorised training** We used an exhaustive approach to identify the video category most affecting the performance on fish counting. The most influential category is the High Occurrence Videos (HOV) category including non target species (NTS). This could be due to the availability of more angles and more divers labels in HOV as well as a better overall representation for the species. The NTS inclusion affects mostly the HOV category which is important to acknowledge. The species *D. vulgaris* and *S. umbra* are affected most by the inclusion of NTS but mostly only in context of the HOV + NTS category. The solitary *E. marginatus* is positively affected by the inclusion of LOV. This effect can be attributed to the inclusion of similar species that have a close resemblance to this species. Overall the best performing category was HOV + NTS which highlights the importance to include videos that correspond to the ecological niche for *D. vulgaris* and *S. umbra* as they are schooling fish. This connection of ecology and machine learning strengthens the reliability of AI models in detecting and interpreting ecological patterns.

**4.1 Conclusion**

Our study underscores the pivotal role of computer vision and AI tools in ecological
monitoring. We demonstrated the effectiveness of computer vision tools in automating
species identification and counting. The results provided valuable information on habitat
preferences, seasonal patterns and conservation needs that contribute to evidence-based
management strategies. Species-specific and area-based recommendations offer a framework
for targeted conservation efforts. In the future, research should focus on the ecological
implications of environmental changes, ensuring the conservation of seasonally dependent
species and promote adaptive, long-term biodiversity conservation.
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Supplementary Material

<figure><img src="image_9.png" /><figcaption>Figure S1: Areas of the 2022 data collection.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Figure S2: The error rates per season for the frequency per species. Panel A depicts *D. vulgaris*, panel B - *E. marginatus* and panel C - *S. umbra*.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure S3: This graph shows the certainty of the CMARE metric representing the actual error. The higher we chose k, the closer CMARE comes to MAE and with k = ∞, CMARE almost equals MAE. We wanted to show that CMARE equals 70% of MAE at yᵢ = 100.</figcaption></figure>

Equation used to get the uncertainty of CMARE at an error of 1 when there is one individual in the video. Parameters: n = 1, yᵢ = 1, ŷᵢ = 2, k = 234.

$$CMARE = 234 * \frac{|1 - 2|}{234 + 1} = \frac{234}{235} \approx 1 \qquad (S1)$$

Equation used to get the uncertainty of CMARE at an error of 1 when there are 100 individuals in the video. Parameters: n = 1, yᵢ = 100, ŷᵢ = 101, k = 234.

$$CMARE = 234 * \frac{|100 - 101|}{234 + 100} = \frac{234}{334} \approx 0.7 \qquad (S2)$$
<table><caption>Table S1: Number of videos per environmental variable assessed during the ecological analysis for the 533 videos.</caption><thead><tr><th>Areas</th><th>Substrate</th><th>Depths</th><th>Season</th><th>Number of Videos</th><th>Experiment Type</th></tr></thead><tbody><tr><td>Cap Ferrat</td><td>ALL</td><td>ALL</td><td>ALL</td><td>112</td><td>Exp: Areas</td></tr><tr><td>Corniche Varoise</td><td>ALL</td><td>ALL</td><td>ALL</td><td>261</td><td></td></tr><tr><td>Esterel</td><td>ALL</td><td>ALL</td><td>ALL</td><td>160</td><td></td></tr><tr><td>ALL</td><td>Rock</td><td>ALL</td><td>ALL</td><td>354</td><td>Exp: Substrate</td></tr><tr><td>ALL</td><td>Seagrass</td><td>ALL</td><td>ALL</td><td>179</td><td></td></tr><tr><td>Cap Ferrat</td><td>Rock</td><td>ALL</td><td>ALL</td><td>75</td><td></td></tr><tr><td>Cap Ferrat</td><td>Seagrass</td><td>ALL</td><td>ALL</td><td>37</td><td></td></tr><tr><td>Corniche Varoise</td><td>Rock</td><td>ALL</td><td>ALL</td><td>179</td><td></td></tr><tr><td>Corniche Varoise</td><td>Seagrass</td><td>ALL</td><td>ALL</td><td>82</td><td></td></tr><tr><td>Esterel</td><td>Rock</td><td>ALL</td><td>ALL</td><td>100</td><td></td></tr><tr><td>Esterel</td><td>Seagrass</td><td>ALL</td><td>ALL</td><td>60</td><td></td></tr><tr><td>ALL</td><td>Rock</td><td>Shallow</td><td>ALL</td><td>172</td><td>Exp: Rock Depth</td></tr><tr><td>ALL</td><td>Rock</td><td>Deep</td><td>ALL</td><td>182</td><td></td></tr><tr><td>Cap Ferrat</td><td>Rock</td><td>Shallow</td><td>ALL</td><td>37</td><td></td></tr><tr><td>Cap Ferrat</td><td>Rock</td><td>Deep</td><td>ALL</td><td>38</td><td></td></tr><tr><td>Corniche Varoise</td><td>Rock</td><td>Shallow</td><td>ALL</td><td>83</td><td></td></tr><tr><td>Corniche Varoise</td><td>Rock</td><td>Deep</td><td>ALL</td><td>96</td><td></td></tr><tr><td>Esterel</td><td>Rock</td><td>Shallow</td><td>ALL</td><td>52</td><td></td></tr><tr><td>Esterel</td><td>Rock</td><td>Deep</td><td>ALL</td><td>48</td><td></td></tr><tr><td>ALL</td><td>Rock</td><td>Deep</td><td>Cold</td><td>83</td><td>Exp: Deep Rock Seasonal</td></tr><tr><td>ALL</td><td>Rock</td><td>Deep</td><td>Warm</td><td>99</td><td></td></tr><tr><td>ALL</td><td>Rock</td><td>Deep</td><td>Warm 2023</td><td>52</td><td></td></tr><tr><td>ALL</td><td>Rock</td><td>Deep</td><td>Cold 2024</td><td>42</td><td></td></tr><tr><td>ALL</td><td>Rock</td><td>Deep</td><td>Warm 2024</td><td>47</td><td></td></tr><tr><td>ALL</td><td>Rock</td><td>Deep</td><td>Cold 2025</td><td>41</td><td></td></tr><tr><td>Cap Ferrat</td><td>Rock</td><td>Deep</td><td>Warm 2023</td><td>9</td><td></td></tr><tr><td>Cap Ferrat</td><td>Rock</td><td>Deep</td><td>Cold 2024</td><td>9</td><td></td></tr><tr><td>Cap Ferrat</td><td>Rock</td><td>Deep</td><td>Warm 2024</td><td>12</td><td></td></tr><tr><td>Cap Ferrat</td><td>Rock</td><td>Deep</td><td>Cold 2025</td><td>8</td><td></td></tr><tr><td>Corniche Varoise</td><td>Rock</td><td>Deep</td><td>Warm 2023</td><td>25</td><td></td></tr><tr><td>Corniche Varoise</td><td>Rock</td><td>Deep</td><td>Cold 2024</td><td>24</td><td></td></tr><tr><td>Corniche Varoise</td><td>Rock</td><td>Deep</td><td>Warm 2024</td><td>24</td><td></td></tr><tr><td>Corniche Varoise</td><td>Rock</td><td>Deep</td><td>Cold 2025</td><td>23</td><td></td></tr><tr><td>Esterel</td><td>Rock</td><td>Deep</td><td>Warm 2023</td><td>18</td><td></td></tr><tr><td>Esterel</td><td>Rock</td><td>Deep</td><td>Cold 2024</td><td>9</td><td></td></tr><tr><td>Esterel</td><td>Rock</td><td>Deep</td><td>Warm 2024</td><td>11</td><td></td></tr><tr><td>Esterel</td><td>Rock</td><td>Deep</td><td>Cold 2025</td><td>10</td><td></td></tr></tbody></table>
<figure><img src="image_12.png" /><figcaption>Figure S4: QQ Plot residuals and the DHARMa residual vs predicted analysis plots for the overall GLM model for all species combined. A depicts the frequency models, while B depicts the density models.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figure S5: Quantile-quantile plots of simulated DHARMA residuals (top row) and residuals versus rank-transformed fitted values (bottom row) for **frequency** models of (A) *D. vulgaris*, (B) *E. marginatus*, and (C) *S. umbra*. In the QQ plots, observed residuals closely follow the expected 1:1 line, indicating good model fit. Kolmogorov-Smirnov (KS), dispersion, and outlier tests show no significant deviations (n.s.), suggesting no violations of distributional assumptions. Residuals plotted against fitted values show no systematic patterns, further indicating adequate model performance and absence of heteroscedasticity or model misspecification.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Figure S6: Quantile-quantile plots of simulated DHARMa residuals (top row) and residuals versus rank-transformed fitted values (bottom row) for **density** models of (A) *D. vulgaris*, (B) *E. marginatus*, and (C) *S. umbra*. In the QQ plots, observed residuals closely follow the expected 1:1 line, indicating good model fit. Kolmogorov-Smirnov (KS), dispersion, and outlier tests show no significant deviations (n.s.), suggesting no violations of distributional assumptions. Residuals plotted against fitted values show no systematic patterns, further indicating adequate model performance and absence of heteroscedasticity or model misspecification.</figcaption></figure>