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# Modelling the distribution of rare and data-poor diadromous fish at sea for protected area management

Sophie Elliott, Anthony Acou, Laurent Beaulaton, Etienne Rivot, Elodie

Réveillac

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Sophie Elliott, Anthony Acou, Laurent Beaulaton, Etienne Rivot, Elodie Réveillac. Modelling the distribution of rare and data-poor diadromous fish at sea for protected area management. 2025. [hal-04618762](https://hal.science/hal-04618762v1)

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Preprint submitted on 19 Jun 2025

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ABSTRACT

Anthropogenic pressures have resulted in declines in diadromous fish. Many diadromous fish which were commercially important are now threatened and protected. Little is known about their marine life history phases, and no observation-based Species Distribution Model exists for this group of species at sea. Yet, fisheries dependent and independent data could provide new insights into the distribution of diadromous fish at sea.

We collated a database of 168 904 hauls from fisheries observer bycatch data and scientific fisheries surveys, from eastern Atlantic and Mediterranean waters. The distribution of eleven rare and data-poor diadromous fish (shads, lampreys, salmonids, the European eel, the thinlip mullet, smelt and the European flounder) were modelled. A Bayesian site occupancy model, that incorporates imperfect detection to account for repeat detections and non-detections, the non-random nature of fishing gear type and spatial autocorrelation was used. From the model outputs, we explored bycatch risk and the role of MPAs, required under the Marine Strategy Framework Directive and Habitat Directive and assessed.

Diadromous fish were observed within relatively shallow coastal areas. Species specific gear bycatch trends were observed. Core distribution areas corresponded to their known water basin presence, indicating connectivity with their freshwater habitats. Numerous Habitat Directive Marine Protected Areas were found to be of relevance.
50 Given the coastal distribution of these species, they are exposed to higher
51 anthropogenic pressures from both terrestrial and marine environments. Risk of
52 bycatch at sea for most species appears to be low. Nonetheless, for threatened
53 individuals, even a small amount of bycatch may impact their populations,
54 especially since misreporting is likely to be high. Differences in catchability
55 between gears highlight potential benefits of limiting access of certain gears within
56 protected areas to reduce bycatch.

57
58 **Key words:** Species Distribution Model, diadromous fish, rare species, imperfect
detection, Marine Protected Areas, bycatch.

60
61 **1. Introduction**

62 Diadromous fish undertake long distance migrations between freshwater and
63 marine ecosystems during their varying life history stages (Lassalle et al., 2008;

64 Legrand et al., 2020; Limburg and Waldman, 2009; McDowall, 2009). They are
65 particularly vulnerable since they are subject to both terrestrial and freshwater

66 pressures (e.g., terrestrial run-off, pollution, habitat destruction, barriers to
67 migration, climate change, fishing, etc.) (Costa et al., 2021; Limburg and

68 Waldman, 2009; Merg et al., 2020; Verhelst et al., 2021). Furthermore, unlike
69 many marine fish that are geographically widespread, numerous anadromous fish

70 (e.g., shad and salmonids) form river-specific populations that are more
71 susceptible to extinction (Limburg and Waldman, 2009; McDowall, 2009). As a

72 result, most diadromous species native to the northern Atlantic Ocean have
73 declined in abundance by at least 90% since the end of the late 19th century

74 (Drouineau et al., 2018; Limburg and Waldman, 2009; Waldman and Quinn, 2022).

75
76 To try and halt the loss of diadromous fish populations, various International
77 Agreements (e.g., Bern Convention, Convention of Migratory Species) and
78 legislations (e.g., EU Habitat Directive, EU Marine Strategy Framework Directive
79 (MSFD)) have been enacted (Table 1). Measures implemented focus principally on
80 freshwater or estuarine habitats, such as restoration of river continuity as required

81 by the EU Water Framework Directive (WFD, 2000/60/EC) and freshwater Spatial

82 Areas of Conservation under the Habitat Directive (HD, 92/43/EEC). However, most
83 diadromous species remain threatened at a national level

84 (www.nationalredlist.org; Table 1), and the improvements have yet to be attained

85 throughout their range (Verhelst et al., 2021; Waldman and Quinn, 2022; Wilson
86 and Veneranta, 2019). The marine life history stages and at-sea distribution of

87 diadromous fish, largely remain a black box (Elliott et al., 2021), causing difficulty

88 in evaluating their conservation status required under the various legislations that
89 protect these species (Table 1; Wilson and Veneranta, 2019). Furthermore, few

90 Marine Protected Areas (MPAs) have been designated to protect them (e.g.,

91 https://mpa.ospar.org; https://natura2000.eea.europa.eu), and at-sea bycatch is

92 not well understood, even if it is suspected that it could contribute to significant

93 mortality (Kappel, 2005; Stratoudakis et al., 2016; Verhelst et al., 2021; Wilson and
94 Veneranta, 2019).

95
96 Under the EU data collection framework, Member States are required to collect
97 fisheries bycatch data through an onboard observer program (Cornou et al., 2015).

98 Fisheries data is a rich set of information which provides year-round catch
99 information and can be a valuable source of information for data-deficient species

100 (e.g., Baum et al., 2003; Bisch et al., 2022; Elliott et al., 2020b). Bycatch data for
101 protected species can be difficult to access, therefore fisheries observer data could

102 help understand the distribution and bycatch mortality of data-poor species and
103 meet EU Habitat Directive and MSFD (92/43/EEC; 2008/56/EC) requirements. Use

104 of fisheries data does, however, require biases from the different gear types, the

105 targeted nature of fishing, and un-balanced sampling to be taken into consideration
106 (e.g. Alglave et al., 2022; Bourdaud et al., 2017).

107
108 Species Distribution Models (SDMs) provide a means to 'fill in the gaps' and provide
109 complete coverage maps on which to base conservation and management decisions

110 (Guisan and Thuiller, 2005; Guisan and Zimmermann, 2000; Leathwick et al.,
111 2005). Modelling the distribution of diadromous fish could help improve protection
112 measures for threatened and data-poor (insufficient biological information to
113 determine the current exploitation status (Berkson and Thorson, 2015; Prince and
114 Hordyk, 2019)) diadromous fish within existing MPAs. SDMs could also be used to
115 meet Descriptor 1 (Biodiversity - habitat extent) requirements under the MSFD
116 (2008/56/EC). However, building diadromous fish at sea distribution models
117 remains challenging because most of these fish are either IUCN red listed at a
118 national level (www.nationalredlist.org) or data-poor (Limburg and Waldman, 2009;
119 Merg et al., 2020). Modelling the distribution of data-poor and rare species, can
120 lead to underestimation of their true distribution due to imperfect detection
121 (uncertainty in the presence of a species within a site where the data was
122 recorded) (Guillera-Arroita, 2017; MacKenzie et al., 2002).

123
124 To model rare and data-poor species' distribution, specific statistical methods are
125 required to combine several sources of data that may not be initially designed to
126 study the species distribution (Engler et al., 2004; Lomba et al., 2010; Simmonds et
127 al., 2020). Using such data requires correction for detection bias arising from
128 observer error, species rarity and variability in environmental conditions (Guillera-
129 Arroita, 2017; Kellner and Swihart, 2014; MacKenzie et al., 2002) to help determine
130 sites where the probability of presence of species is higher (Belmont et al., 2022).
131 The low detectability of rare species can also result in a high proportion of false
132 absences (Guillera-Arroita, 2017). Spatial and temporal variation in observation
133 effort is another important source of heterogeneity in the observations (Guillera-
134 Arroita, 2017; Kellner and Swihart, 2014; Meyer et al., 2011). Integrated
135 hierarchical statistical model for species distributions have a great potential to
136 combine multiple sources of data to infer a single latent field of abundance or
137 presence/absence, and to enhance rare species distribution models for
138 conservation and management purposes (Engler et al., 2004; Lomba et al., 2010;
139 Simmonds et al., 2020). Another common problem which can arise when modelling
140 species distribution is that data can be autocorrelated (more similar when closer
141 together). Residual spatial autocorrelation can arise from population demographic
142 processes or the influence of unobserved variables. Ignoring spatial autocorrelation
143 can lead to inaccurate parameter estimations (Latimer et al., 2006). Developing
144 models that explicitly account for spatial autocorrelation in the latent field of
145 presence/absence is, therefore, necessary for accurate inferences on the
146 distribution.

147
148 Given the lack of knowledge on the distribution of most diadromous fish during
149 their marine life history stages, and the need to ensure they are sufficiently
150 protected, our objective was to model the current distribution (2003-2019) of
151 diadromous fish at sea. In addition, we wanted to quantify the risk of bycatch from
152 different gear types and assess the value of Habitat Directive MPAs for their
153 conservation. Since at sea targeted surveys do not exist for these species, we
154 collated a database of 168 904 hauls from fisheries dependent (French fisheries
155 bycatch observer data) and independent data (scientific survey data undertaken to
156 assess fish populations) in eastern Atlantic and French Mediterranean waters. A
157 Bayesian site-occupancy model, taking into account spatial autocorrelation in the
158 probability of presence, and imperfect detection through gear type, was used for
159 these data-poor species (Dormann et al., 2007; Latimer et al., 2006; MacKenzie et
160 al., 2002; Moriarty et al., 2020). From the distribution models we wanted to
161 answer the following questions: 1) Is the distribution of diadromous fish at sea

162 connected to existing knowledge on their freshwater habitats? 2) Do different gears

163 have different catchability, and does this provide information on the risk of

164 bycatch of diadromous fishes? 3) Could existing Habitat Directive MPAs protect

165 diadromous fish if specific measures are implemented?
<table><caption>**Table 1.** Diadromous fish observed within north-eastern Atlantic waters and their IUCN conservation status. A = Anadromous, C = Catadromous, - = not listed, CR = Critical, LC = Least Concern, VU = Vulnerable. Demersal species are found near the seabed, pelagic species occur in the open sea, demerso-pelagic species are species that migrate between these two water column zones, host dependent refers to lampreys that are parasitic species during their marine phase. ✓ = protected under the specific convention or Directive, letters specify the specific appendix the species is protected under.</caption><thead><tr><th rowspan="2">Latin name</th><th rowspan="2">Common name</th><th rowspan="2">Type</th><th rowspan="2">Water column zone¹</th><th rowspan="2">EU IUCN³</th><th colspan="9">International conventions and legislation</th></tr><tr><th>CITES</th><th>WFD</th><th>HD</th><th>MSFD</th><th>Bern</th><th>Bonn</th><th>OSPAR</th><th>Barcelona</th></tr></thead><tbody><tr><td>Acipenser sturio</td><td>Atlantic sturgeon</td><td>A</td><td>Demersal</td><td>CR</td><td>Al</td><td>✓</td><td>II, IV</td><td>✓</td><td>III</td><td>I, II</td><td>✓</td><td>✓</td></tr><tr><td>Alosa alosa</td><td>Allis shad</td><td>A</td><td>Pelagic</td><td>LC</td><td>-</td><td>✓</td><td>II, V</td><td>✓</td><td>III</td><td>-</td><td>✓</td><td>-</td></tr><tr><td>Alosa fallax</td><td>Twait shad</td><td>A</td><td>Pelagic</td><td>LC</td><td>-</td><td>✓</td><td>II, V</td><td>✓</td><td>III</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Alosa agone</td><td>Mediterranean twaite shad</td><td>A</td><td>Pelagic</td><td>LC</td><td>-</td><td>✓</td><td>II, V</td><td>✓</td><td>III</td><td>-</td><td>-</td><td>✓</td></tr><tr><td>Anguilla anguilla</td><td>European eel</td><td>C</td><td>Demerso-pelagic</td><td>CR</td><td>All</td><td>✓</td><td>-</td><td>✓</td><td>-</td><td>-</td><td>✓</td><td>✓</td></tr><tr><td>Lampetra fluviatilis</td><td>River lamprey</td><td>A</td><td>Host dependent</td><td>LC</td><td>NA</td><td>✓</td><td>II, V</td><td>✓</td><td>III</td><td>-</td><td>-</td><td>✓</td></tr><tr><td>Petromyzon marinus</td><td>Sea lamprey</td><td>A</td><td>Host dependent</td><td>LC</td><td>-</td><td>✓</td><td>II</td><td>✓</td><td>III</td><td>-</td><td>✓</td><td>✓</td></tr><tr><td>Chelon ramada*</td><td>Thinlip mullet</td><td>C</td><td>Demersal²</td><td>LC</td><td>-</td><td>✓</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Osmerus eperlanus</td><td>Smelt</td><td>A</td><td>Pelagic</td><td>LC</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Platichthys flesus</td><td>European flounder</td><td>C</td><td>Demersal</td><td>LC</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Salmo salar</td><td>Atlantic salmon</td><td>A</td><td>Pelagic</td><td>VU⁴</td><td>-</td><td>✓</td><td>II, V</td><td>✓</td><td>III</td><td>-</td><td>✓</td><td>-</td></tr><tr><td>Salmo trutta</td><td>Sea trout</td><td>A</td><td>Pelagic</td><td>LC</td><td>-</td><td>✓</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
171 ¹Elliott and Dewailly, 1995;²Almeida, 1996; ³Freyhof and Brooks, 2011; ⁴Nieto et al., 2015 *previously known as *Lisa ramada*.
## 2. Method

### 2.1. Combining fisheries independent and dependent surveys

174 Fisheries dependent and independent data from 1965 to 2019 were collated within eastern Atlantic waters (Greater North Sea, Celtic Sea, Bay of Biscay and the Iberian coast) and French Mediterranean waters). Scientific bottom trawl surveys were extracted from the International Council for the Exploration of the Sea (ICES)

178 Database of Trawl Surveys (DATRAS) portal (https://www.ices.dk/data/data-portals/Pages/DATRAS.aspx; Refer to Elliott et al., 2021 Table S1; Data in brief Table S1 - S2) and French Metropolitan scientific surveys not submitted to ICES

180 Table S1 - S2) and French Metropolitan scientific surveys not submitted to ICES
181 DATRAS were obtained from the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) (https://campagnes.flotteoceanographique.fr/campaign).

183 Fisheries-dependent data came from French fisheries observer data (ObsMer data;

184 Cornou et al., 2015) that began in 2003. According to the sampling plan, fisheries observers sample fishing vessels and fishing operations when on board (Fauconnet et al., 2015). ObsMer data is held by IFREMER and available on request from the

187 French Ministry of Fisheries and Aquaculture (Direction des Pêches Maritimes et de l'Aquaculture; Refer to Elliott et al., 2021 Table S1; Data in brief Table S1).

189 From the database, diadromous fish presence and absence, length, gear type, spatial location and year and month of capture were extracted (Fig. 1; Fig. S1).

192 Due to missing data and insufficient information on the length of hauls, size of vessels, mesh size, etc., effort was not possible to calculate. Details of gear types and surveys that were used for the analysis can be found within the supplementary materials of Elliott et al., (2021).

196 Prior to model analysis significant data cleaning was undertaken to standardise the surveys and extract relevant data to perform statistical analysis (e.g., converting gear types to gear categories, converting alike variables to the same units, etc.; see details within the Data in brief; Appendix A). Data from 2003 were used for spatial analysis, since a large proportion (74%) of the data came from the ObsMer dataset which started in 2003. In addition, prior to this data few presences were observed.

204 Since the proportion of zeros to presences was very uneven, which is not ideal for predictive modelling (Fielding and Bell, 1997), all surveys, gear types, target species (the intended catch for a particular fishery - from the ObsMer dataset) and ICES statistical divisions (Fig. 1) without diadromous fish presence, were deleted before modelling (Fig. S2 contains a spatial map of all hauls per gear type). Depths outside the ranges the species were observed were also removed. To avoid targeted fishing bias, all species that were targeted were removed. The latter included 2 European eels, *Anguilla anguilla*, 33 thinlip mullets, *Chelon ramada* and 41 flat fish which may have included European flounders, *Platichthys flesus* presences. All species were modelled other than the European sturgeon, *Acipenser sturio* because of too few presences (11 presences from 2003 - 2019). Note, for *L. fluviatilis*, one individual was caught by Set Longline within the Bay of Biscay. Due to its isolated nature of this capture, this caused problems when modelling (over detection of the line caught individual). Line gear type was therefore removed from the hierarchical modelling process.

220
221 2.2. Environmental predictor variables
222 Six environmental variables were considered as potential predictors of the
223 presence of diadromous fish (seabed depth; distance from coast; sediment type;
224 salinity; net primary production; and sea surface temperature; Table 2; Fig. S3).
225 Depth and distance from the coast are thought to indicate key diadromous fish
226 migratory periods (Taverny et al., 2012; Taverny and Elie, 2001; Trancart et al.,
227 2014). Salinity and temperature are known to have direct physiological effects on
228 diadromous fish, and their changes can indicate migration timing (Arevalo et al.,
229 2020; Trancart et al., 2014). Net primary production is the gross primary
230 production by autotrophs over the rate at which they respire, which is a measure of
231 marine ecosystem functioning (ERSEM, 2020). Sediment type can be considered a
232 proxy for food availability and shelter (Elliott et al., 2016; Trancart et al., 2014).
233 As of result of too few presences and less information on length than the presence
234 of individuals, distribution changes in length at stage, seasonal life history stage
235 migration, and changes in habitat occupancy over the years were not considered.

236
237 To select the model of best fit, variables that were not collinear (Pearson's
correlation coefficient< 0.5 or >-0.5 and a Variance Inflation Factor < 2) (Dormann
238 et al., 2013) were kept. Depth and distance from coast, and sea surface
temperature and net primary production were collinear. Since most species
239 occupied a wide latitudinal range and seasonal or yearly temperature effects were
240 not modelled, net primary production was used instead of sea surface
temperature. To identify the model of best fit a backwards stepwise model
241 selection process was undertaken, ensuring distance from coast and depth were not
242 in the same model. The model with the lowest deviance information criterion,
243 which includes a penalty factor for the number of parameters, was selected
244 (Spiegelhalter et al., 2002).
245
246
247
248

249 Table 2. Environmental predictors used to analyse diadromous fish distribution. For
250 all species except *Alosa agone*, variables were extracted at a 0.1 x 0.1 decimal
251 degree resolution. *A. agone* environmental variables were extracted at a 0.04
252 decimal degree resolution.

<table>
   <thead>
    <tr>
     <td>
      Variable
     </td>
     <td>
      Unit
     </td>
     <td>
      Description / Source
     </td>
    </tr>
   </thead>
   <tbody>
    <tr>
     <td>
      Depth
     </td>
     <td>
      Meters (m)
     </td>
     <td>
      Bathymetry data downloaded from National Aeronautics and Space Administration Earth Observations project (http://neo.sci.gsfc.nasa.gov) with a 0.1 x 0.1 grid cell resolution by using the General Bathymetric Chart of the Oceans grid.
     </td>
    </tr>
    <tr>
     <td>
      Distance to coast
     </td>
     <td>
      Kilometres (km)
     </td>
     <td>
      Calculated from a continent shape file
     </td>
    </tr>
    <tr>
     <td>
      Sediment
     </td>
     <td>
      Categorical
     </td>
     <td>
      Broad-scale seabed habitat types (EUSeaMap) downloaded from EMODnet. Four classes were used according to grain size: mud (≤2mm), sand (&gt; 2 and≤4 mm), coarse grain (&gt; 4 mm and 64 mm), and rock (&gt; 64 mm).
     </td>
    </tr>
    <tr>
     <td>
      Salinity
     </td>
     <td>
      Practical Salinity Units (PSU%)
     </td>
     <td>
      Modelled sea surface salinity at 0.1 x 0.1 decimal degree resolution downloaded from Copernicus (ERSEM, 2020).
     </td>
    </tr>
   </tbody>
  </table>
<table><tr><td>Net primary production</td><td>Milligrams of carbon per meters squared (mg C/m²)</td><td>Modelled at 0.04 x 0.04 decimal degree resolution from Copernicus using Teruzzi et al., (2021).<br/>Modelled net primary production at 0.1 x 0.1 decimal degree resolution downloaded from Copernicus (ERSEM, 2020).<br/>Modelled at 0.04 x 0.04 decimal degree resolution from Copernicus using Teruzzi et al., (2021).</td></tr><tr><td>Sea surface temperature</td><td>degrees centigrade (°C)</td><td>Modelled Sea surface temperature at 0.1 x 0.1 decimal degree resolution downloaded from Copernicus (ERSEM, 2020).<br/>Modelled at 0.04 x 0.04 decimal degree resolution from Copernicus using Teruzzi et al., (2021).</td></tr></table>
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254

255 **Fig. 1.** Diadromous fish presence (blue dots) and absence (light grey dots) data

256 used to model their distribution (2003-2019). Dark grey lines indicate International

257 Council for the Exploration of the Sea statistical divisions.
258
259 **2.3. Statistical analysis**
260 A Bayesian hierarchical site occupancy Gaussian intrinsic Conditional Autoregressive
261 (iCAR) model was built independently for each species. Hierarchical models have
262 the advantages of separating the ecological process (here, the habitat suitability
263 model, that is the spatial distribution of the probability of presence/absence,
264 including spatial autocorrelation) from the observation process (here, the
265 imperfect detection and its variability among gears or other factors) (Dormann et
266 al., 2007; Isaac et al., 2020; Latimer et al., 2006; MacKenzie et al., 2002). The
267 fundamental concept of the model is that the true presence/absence spatial field
268 (including spatial autocorrelation) is modelled on a grid, each grid cell being
269 associated with multiple observation events with imperfect detection, with
270 detectability that may vary depending upon the fishing gear.
271

272 The site occupancy model integrates two processes:

273
274 i) Latent ecological process (habitat suitability)
275 The latent field of probability of presence is modelled on a grid as a function of
276 environmental predictors and by explicitly accounting for spatial autocorrelation:

277 $\operatorname{logit}(\theta_j) = X_j\beta + P_j$ (1)

278 with $\theta_j$ the latent probability of presence (the habitat suitability) within grid cell
279 cell j, modelled in the logit scale as a function of environmental predictors $X_j$, with
280 $\beta$ a vector of fixed effect describing how much the environmental predictor
281 contribute to the suitability process. The spatial autocorrelation $P_j$ is modelled at
282 the scale of the grid cell as an iCAR random effect (2):

283 iCAR: $P_j \sim \text{Normal}(\mu_j, \frac{V_p}{n_j})$ (2)

284 with $\mu_j$ the mean of the random effects calculated over the $n_j$ cells considered in
285 the neighbour of cell $j$ ($n_j$ is the number of the neighbours which is 8 in most cases,
286 and <8 when the cell is on the boundary of the spatial domain), $V_p$ the variance of
287 the spatial random effect. A site is defined here as the area/volume covered during
288 a given sampling/fishing operation. Any site $i$ within the cell $j$ as the same latent
289 probability of presence $\theta_j$.

290
291 ii) Observation process (detection):
292 Conditionally upon the latent probability of presence/absence in each grid cell $j$ as
293 describe above, the multiple detection events $y_{j,i}$ (data 0/1) associated to the
294 same grid cell $j$ are modelled as mutually independent two steps Bernoulli process
295 with detectability $\delta_{j,i}$:

296 $y_{j,i} \sim \text{Bernoulli}(z_{j,i} \delta_{j,i})$ (2)

297 with

298 $z_{j,i} \sim \text{Bernoulli}(\theta_j)$ (3)

299 and

300 $\operatorname{logit}(\delta_{j,i}) = W_{j,i}\gamma$

301 where $\delta_{j,i}$ denotes the probability of detecting the species at site $i$ within cell $j$,
302 modelled as a fixed effect of the gear type associated with observation $i$. $W_{j,i}$ is the
303 raw vector of the design matrix, and $\gamma$ is the vector of the gear effects, so as $W_{j,i}\gamma$
304 is the gear affect associated with observation at site $i$.
305

306 All SDMs were undertaken using the 'hSDM' package (Vieilledent et al., 2014) using
307 the 'mod.hSDM.siteocc.iCAR' function in R version 3.6.3 (R Code Team 2018). The
308 prior distribution for the variance of the spatial random effect followed weakly
309 informative uniform distribution. The effect of the detectability from gear and
310 from the suitability process was modelled as fixed effects drawn from an weakly
311 informative Gaussian prior centred at zero with a fixed standard deviation of 2
312 (Gelman et al., 2008; Northrup and Gerber, 2018). Each model was run with 50 000
313 Gibbs iterations, five Markov chain Monte Carlo simulations and a burn-in phase of
314 50 000 iterations.

315

316 2.4. *Grid size and spatial distribution*

317 A critical issue in our modelling framework is the choice of the grid size. Since the
318 latent field of presence/absence is modelled at the scale of the grid cell (eq. 1),
319 the larger the grid resolution the higher the probability to get at least one
320 presence within a grid cell. A finer grid cell will limit the quantity of available
321 observations falling within each cell, which therefore increases difficulty in model
322 fitting. We selected the finest grid as possible that allow the model to converge.
323 For almost all species modelled within Eastern Atlantic waters, a grid of 0.1x0.1
324 decimal degrees was used. Because of the scarcity of data for Lampreys, the model
325 did not converge with such a resolution and a coarser resolution of 0.2x0.2 decimal
326 degrees was therefore used for these species. For *A. agone*, that only occurs within
327 the Mediterranean, a grid resolution of 0.04 x 0.04 decimal degrees was used
328 because of the smaller area encompassed. This spatial resolution (0.04 x 0.04
329 decimal degrees) was not used for other species since it was not available at the
330 scale required within Eastern Atlantic waters. All environmental variables were
331 then aggregated and modelled to their relevant spatial resolution. There were
332 insufficient presences within the Mediterranean for *A. anguilla*, *P. marinus*, *C.*
333 *ramada*, *P. flesus* predictions (issues with model convergence). Predictions within
334 the Mediterranean for these were therefore excluded (Fig. 2).

335

336 2.5. *Model evaluation*

337 The probability of presence was calculated from the mean of the posterior
338 distribution of the latent field θj. A threshold of 0.4 was used to bin estimated
339 probability of presence into binary presence/absence values. A threshold of 0.4
340 was used as a compromise given the rarity of the species and accuracy following
341 examining various thresholds and plotting positive predicted values. The model
342 performance was then evaluated using sensitivity (proportion of correctly classified
343 presences), specificity (proportion of correctly classified absences) and percent
344 correct classification (proportion of presences and absences correctly using the R
345 package 'PresenceAbsence' (Freeman and Moisen, 2008a; Fielding and Bell, 1997;
346 Table 3). Max sensitivity specificity which is often used for rare species distribution
347 models (Freeman and Moisen, 2008b; Jiménez-Valverde and Lobo, 2007), was not
348 used since false presences was very high when applying this metric. Additionally,
349 our SDM objective was to try and minimise false presences and absences within the
350 distribution models. The threshold independent Area under the receiver operating
351 curve was also used to evaluate the model performance. These four metrics were
352 used to better understand the accuracy of the model since, some metrics can be
353 biased with low prevalence data and where imperfect detection can arise (Allouche
354 et al., 2006; Fourcade et al., 2018; Guillera-Arroita, 2017; Leroy et al., 2018).
355 So that areas with probabilities of presence above the threshold could be easily 356 identified for management purposes, all predictions below 0.4 were mapped with a 357 uniform light blue colour (Fig. 2). Uncertainty maps for each model were also 358 produced to gage a better idea of areas of prediction with higher uncertainty 359 (Loiselle et al., 2003; Spiegelhalter et al., 2002). The uncertainty maps were 360 calculated by the mean coefficient of variation from the posterior distribution of 361 spatial parameters in the model of best fit for each species (Lambert et al., 2020; 362 Latimer et al., 2006). Probabilities of prediction and uncertainty above 0.4 were 363 classified into 0.2 units to highlight areas of higher and lower probability of 364 presence more clearly (Fig. 2; Fig. S4). As a result of the low diadromous fish 365 presence, splitting the data into learning/validation samples led to insufficient 366 presence in the learning data set. Cross validation was therefore not undertaken. 367 368

369 2.6. *Diadromous fish river basin presence*
370 The predicted distributions were visually compared to the distribution of 371 diadromous fish within their freshwater habitats using the EuroDiad database 372 (version 4; https://data.inrae.fr/dataset.xhtml?persistentId=doi:10.15454/IVVAIC; 373 Barber-O'Malley et al., 2022). From EuroDiad database, migrating diadromous fish 374 population presence within the study areas our model encompassed, were 375 extracted using 'recent' literature citing from 1951 to 2020 (see Barber-O'Malley et 376 al., (2022) and Béguer et al., (2007) for more details on the database) and mapped 377 with the hierarchical SDM results (Fig. 2). The EuroDiad4 database was used as 378 opposed to IUCN red list map distribution since it has been peer reviewed and it is 379 more up to date. 380

381 2.7. *Habitat Directive MPAs of relevance to diadromous fish*
382 'Core areas' with a probability of presence >0.4 were selected and intersected with 383 European Union Natura 2000 Habitat Directive Sites of Community Importance 384 (https://www.eea.europa.eu/data-and-maps/data/natura-12). A threshold of > 0.4 385 probability of presence was selected as a result of the low probability of presence 386 of diadromous fish (particularly Habitat Directive protected species), and to ensure 387 consistency with the model evaluation threshold. Since only one Site of Community 388 Importance was submitted to the European Union from the United Kingdom, Special 389 Areas of Conservation were taken into consideration (https://jncc.gov.uk/our- 390 work/special-areas-of-conservation-overview/). For each species, the number of 391 MPAs (Sites of Community Importance and Special Areas of Conservation) and the 392 proportion of MPAs of relevance to diadromous fish (core areas within an MPA) 393 were recorded. The extent of the core area within the MPAs was not calculated as 394 this would be dependent of the model grid used (Table 3). Furthermore, numerous 395 MPAs were smaller than the grid. 396

396
397 3. Results
398 3.1. *Diadromous fish hierarchical SDM*
399 For all species the Area under the receiver operating curve and Percent Correct 400 Classification performed very well (≥75%; Table 3). Correct presence classification 401 from the confusion matrices was consistently lower (<15%) than that of correct 402 absence classification (≥64%), and false absences were lower (≤3%) than false 403 presences (≤22%) because of presence absence imbalance (Table 3; Table S1). The 404 later led to on average better specificity than sensitivity scores (Table 3).
405

406 *P. flesus* was the most prevalent species modelled (5394 presences), whereas

407 salmonids the least (*S. salar* = 68 presences and *S. trutta* 63 presences) (Table 3).

408 SDMs that had lower presences (salmonids and lampreys) did not predict as well

409 (lower sensitivity and higher uncertainty maps) as species with higher presence

410 (shads, *C. ramada*, *P. flesus* and *O. eperlanus*). In addition, species that had a

411 more disperse distribution (e.g., *A. anguilla* and *S. salar*) did not predict as well

412 (sensitivity ≤ 0.66 for both species) as those with a more aggregated distribution

413 (*O. eperlanus* sensitivity = 0.81) (Table 3; Fig. 1 and 2). Uncertainty maps showed

414 that in general, areas of high and low predicted probability of presence had lower

415 uncertainty (Fig. S4).

416

417 All species were observed to have a relatively shallow (<300m depth) coastal

418 distribution (~<300 km from the coast; Fig. 2 and 3). The main predictor variables

419 for diadromous fish habitats were depth or distance from coast and salinity (Fig. 3).

420 Since sufficient *C. ramada* were observed within eastern Atlantic and

421 Mediterranean waters to model, two salinity peaks are observed corresponding to

422 the different salinity ranges of the two regions (Fig. 3).

423
<table><caption>**Table 3.** Hierarchical species distribution model outputs. All models contained gear as part of the observation process. AUC = Area Under the receiver operating Curve score, PCC = Percent Correct Classification, NetPP = Net Primary Production. Max depth is the maximum depth at which the dataset was cut. Note most environmental variables (all except distance and depth) were not available within very coastal areas (e.g., within estuaries and inlets). Numerous presences and absences were therefore lost when extracting the environmental variables for modelling.</caption><thead><tr><th>Species</th><th>Model selected</th><th>Hauls</th><th>Presence</th><th>Absence (%)</th><th>Max depth (m)</th><th>Sensitivity</th><th>Specificity</th><th>AUC</th><th>PCC</th><th>Cell size (decimal degrees)</th></tr></thead><tbody><tr><td>A. alosa</td><td>Depth + Salinity + NetPP + Sediment</td><td>86 292</td><td>802</td><td>99</td><td>300</td><td>0.79</td><td>0.92</td><td>0.95</td><td>0.95</td><td>0.1x0.1</td></tr><tr><td>A. fallax</td><td>Distance + Salinity + NetPP + Sediment</td><td>91 135</td><td>1385</td><td>98</td><td>300</td><td>0.88</td><td>0.81</td><td>0.90</td><td>0.82</td><td>0.1x0.1</td></tr><tr><td>A. agone</td><td>Depth + Salinity + Sediment</td><td>5086</td><td>176</td><td>97</td><td>200</td><td>0.81</td><td>0.74</td><td>0.86</td><td>0.75</td><td>0.04x0.04</td></tr><tr><td>A. anguilla</td><td>Depth + Salinity + NetPP</td><td>54 124</td><td>176</td><td>99</td><td>250</td><td>0.53</td><td>0.99</td><td>0.98</td><td>0.98</td><td>0.1x0.1</td></tr><tr><td>L. fluviatilis</td><td>Distance + Salinity</td><td>16 536</td><td>68</td><td>99</td><td>150</td><td>0.71</td><td>0.95</td><td>0.96</td><td>0.94</td><td>0.2x0.2</td></tr><tr><td>P. marinus</td><td>Distance + Salinity</td><td>28 582</td><td>74</td><td>99</td><td>300</td><td>0.60</td><td>0.98</td><td>0.94</td><td>0.97</td><td>0.2x0.2</td></tr><tr><td>C. ramada</td><td>Distance + Salinity + NetPP + Sediment</td><td>53 083</td><td>925</td><td>98</td><td>300</td><td>0.84</td><td>0.93</td><td>0.96</td><td>0.92</td><td>0.1x0.1</td></tr><tr><td>P. flesus</td><td>Distance + Salinity + NetPP + Sediment</td><td>92 400</td><td>5394</td><td>94</td><td>300</td><td>0.90</td><td>0.93</td><td>0.97</td><td>0.93</td><td>0.1x0.1</td></tr><tr><td>O. eperlanus</td><td>Depth + Salinity + NetPP</td><td>48 877</td><td>1035</td><td>98</td><td>450</td><td>0.81</td><td>0.99</td><td>0.99</td><td>0.99</td><td>0.1x0.1</td></tr><tr><td>S. salar</td><td>Depth + Salinity + NetPP</td><td>36 008</td><td>68</td><td>99</td><td>150</td><td>0.66</td><td>0.98</td><td>0.95</td><td>0.98</td><td>0.1x0.1</td></tr><tr><td>S. trutta</td><td>Depth + Salinity + NetPP</td><td>31 754</td><td>63</td><td>99</td><td>150</td><td>0.71</td><td>0.94</td><td>0.93</td><td>0.93</td><td>0.1x0.1</td></tr></tbody></table>
<figure><img src="image_14.png" /><figcaption>A. agone</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>A. fallax</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>L. fluviatilis</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>A. anguilla</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>P. marinus</figcaption></figure>

<figure><img src="image_19.png" /></figure>
<figure><img src="image_20.png" /><figcaption>P. flesus</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>O. eperlanus</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>S. salar</figcaption></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>
Fig. 2. Diadromous fish hierarchical species distribution model probability of presence. Darker blue shades = SDM probability of presence > 0.4, light blue shade = SDM probability of presence ≤ 0.4. Habitat Directive Marine protected Areas (MPAs) outlined in black. MPAs of relevance to core (probability of presence > 0.4) diadromous fish distribution, highlighted in dark red. Dark red cells are larger than the gridded cells to ensure smaller core areas within MPAs are visible. Darker grey terrestrial areas represent river basins which the diadromous fish migratory populations have been observed (EuroDiad V.4). Note, no data were available within the EuroDiad database from Dutch river basins.
<figure><img src="image_25.png" /></figure>

A. alosa

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

A. fallax

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

A. agone

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

A. anguilla

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

L. fluviatilis

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

P. marinus

<figure><img src="image_42.png" /></figure>
<figure><img src="image_43.png" /><figcaption>Fig. 3. Diadromous fish hierarchical species distribution model environmental predictor effects. NetPP = Net Primary Production, C = Course grain, M = Mud, S = Sand, R = Rock. The solid line represents the smooth function estimate; the shaded region represents the approximate 95% credibility interval. Boxplot blue stars represent means and small black dots the predictions.</figcaption></figure>

443

444

445

446

447

448

449
3.2. *Diadromous fish river basin connectivity*

2717 references contained within the EuroDiad database were used to map the 11

diadromous fish presence within the river basin. Core distributions of diadromous

fish at sea largely matched with the freshwater areas of presence (Fig. 2). The

spatial extent of diadromous fish at sea was often less than that observed within

river basins (e.g., lampreys, *C. ramada*, *O. eperlanus* and *S. trutta*; Fig. 2). It

should be noted that there are gaps in the EuroDiad database (refer to Barber-

O'Malley et al., (2022)). For example, no diadromous fish presence data were

available within Dutch freshwater habitats, and salmon populations are known to

exist within Danish waters (de Groot et al., 2012; Maes et al., 2007; Rikardsen et

al., 2021). Presences of *A. alosa* and *A. fallax* within the Mediterranean from the

EuroDiad database will have been due to historic classification (Bagliniere and Elie,

2000; Keith et al., 2020).

463

3.3. *Gear type detectability*

All species were caught by a range of gear categories ranging from benthic,

demersal and pelagic mobile trawls, seine nets, line gear types (Fig. 4; Table S2).

Some general patterns were, however, evident. Line gear types caught the least

diadromous fish (2% (weighted), 17 presences over ~2887 hauls), whereas demersal

mobile trawl gear types (otter beam trawl and otter twin trawl) caught the most

(67%, 7409 presences over ~82 531 hauls; Fig. 4; Table S2). Shad had a higher

detectability with static and demersal mobile gears. *A. anguilla* were detected by a

wide range of gear types. Lamprey were mainly detected by mobile trawl gears. *P.*

*flesus* and *O. eperlanus* had a higher detectability with demersal and benthic

mobile gear, and salmonid had a higher detectability with static nets and pelagic

gears. Rarer species and species that were caught by a wider range of gear types

had lower detectability than more abundance species caught by fewer gear types

(Fig. 4). On average static nets (e.g., trammel net and set gillnets) and pelagic

mobile gears (e.g., Otter midwater trawls) caught larger diadromous fish than

mobile demersal and benthic gear types (Fig. S1). Although the modelled species

were not target species of the fisheries within the observer database, some species

were landed (Table 4).
<figure><img src="image_44.png" /><figcaption>Fig. 4. Matrix of the posterior mean (marginal posterior distribution) for gear A) detectability (inverse logit of $\gamma$; the probability to detect the presence of fish when they are present) and B) its standard deviation. Lighter blue shades represent higher detectability (A) and darker green shades higher detectability standard deviation (B).</figcaption></figure>

<table><caption>Table 4. Landed and discarded diadromous fish from the ObsMer data. Excluding targeted catches. Proportion landed = total landed / landed + discards.</caption><thead><tr><th>Species</th><th>Landed</th><th>Discard</th><th>Hauls</th><th>Proportion landed</th></tr></thead><tbody><tr><td>A. alosa</td><td>182</td><td>515</td><td>70 378</td><td>0.26</td></tr><tr><td>A. fallax</td><td>152</td><td>608</td><td>64 300</td><td>0.20</td></tr><tr><td>A. agone</td><td>28</td><td>69</td><td>7373</td><td>0.29</td></tr><tr><td>A. anguilla</td><td>10</td><td>8</td><td>28 194</td><td>0.56</td></tr><tr><td>L. fluviatilis</td><td>0</td><td>2</td><td>828</td><td>0.00</td></tr><tr><td>P. marinus</td><td>2</td><td>18</td><td>10 030</td><td>0.10</td></tr><tr><td>C. ramada</td><td>643</td><td>101</td><td>45 699</td><td>0.86</td></tr><tr><td>P. flesus</td><td>856</td><td>648</td><td>66 196</td><td>0.57</td></tr><tr><td>O. eperlanus</td><td>6</td><td>45</td><td>31 311</td><td>0.12</td></tr><tr><td>S. salar</td><td>29</td><td>27</td><td>26 009</td><td>0.52</td></tr><tr><td>S. trutta</td><td>43</td><td>18</td><td>31 666</td><td>0.70</td></tr></tbody></table>

### 3.4. Habitat Directive MPAs relevance

A total of 482 Habitat Directive MPAs have been designated within the area in which the species were modelled (Fig. 2). Despite the small area of core habitats relative to the model prediction area, for most Habitat Directive listed species, numerous MPAs overlap with species core habitats, indicating their potential for the protection of diadromous fish at sea (Table 5; Fig. 2).

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499
<table><caption>**Table 5.** Habitat Directive Marine Protected Areas of relevance to core diadromous fish distribution (probability of presence >0.4). Diadromous fish listed under the Habitat Directive in bold.</caption><thead><tr><th>Species</th><th>Number MPAs of relevance</th><th>Proportion of MPAs of relevance</th></tr></thead><tbody><tr><td><strong>A. alosa</strong></td><td>35</td><td>0.08</td></tr><tr><td><strong>A. fallax</strong></td><td>60</td><td>0.13</td></tr><tr><td><strong>A. agone</strong></td><td>10</td><td>0.27</td></tr><tr><td><strong>A. anguilla</strong></td><td>19</td><td>0.04</td></tr><tr><td><strong>L. fluviatilis</strong></td><td>11</td><td>0.02</td></tr><tr><td><strong>P. marinus</strong></td><td>14</td><td>0.03</td></tr><tr><td><strong>C. ramada</strong></td><td>23</td><td>0.05</td></tr><tr><td><strong>P. flesus</strong></td><td>65</td><td>0.15</td></tr><tr><td><strong>O. eperlanus</strong></td><td>13</td><td>0.03</td></tr><tr><td><strong>S. salar</strong></td><td>17</td><td>0.04</td></tr><tr><td><strong>S. trutta</strong></td><td>28</td><td>0.06</td></tr></tbody></table>

## 4. Discussion

To our knowledge this is the first comprehensive study to model the distribution of diadromous fish at sea across eastern Atlantic and French Mediterranean waters. Using a hierarchical model allowed us to integrate an extensive database that we collated, containing both fisheries dependent and independent data to model the distribution of eleven rare diadromous fish over a wide spatial area. The model also enabled us to evaluate the probability to detect the presence of fish by the different gear types, and thus provide key information to evaluate the risk of bycatch of diadromous fish by commercial fisheries at sea.

Diadromous fish are bound to coastal areas because of their migratory behaviour. They are known to be fragile species because of the physiological and osmoregulatory changes required to undertake migrations between fresh water and marine environments (McDowall, 2009). Yet, coastal ecosystems, particularly within eastern Atlantic and Mediterranean waters, are subject to some of the highest anthropogenic impacts (Halpern et al., 2015; Korpinen et al., 2021; Lotze et al., 2006). Even though more data were collected within coastal areas, and the species are known to migrate from freshwater habitats, our study confirms that the investigated species were found to mainly concentrate in coastal and relatively shallow waters. The strong dependency of diadromous fish species to coastal areas, as evidenced in this study, reinforces the importance of investigating the vulnerability of diadromous fish to coastal anthropogenic pressures (Halpern et al., 2015; Limburg and Waldman, 2009; Lotze et al., 2006; Worm et al., 2006).

### 4.1. Hierarchical Bayesian SDM

On average, the eleven SDMs performed well. All the models had lower sensitivity and positive prediction values scores, because of low presence. Areas of both high probability and very low probability of presence had lower uncertainty, giving a
more accurate picture of confidence in the distributions (e.g., higher certainty that most species are absent within waters further from the coast). In addition to problems with very low occurrence, the areas of higher uncertainty for certain species (e.g., *S. salar* and *S. trutta*, *A. alosa* and *A. fallax*), may be partially because of misidentification between these groups of diadromous fish. The core areas of probability of presence, in combination with the confidence maps provide a better understanding of confidence of these predictions. To improve problems of false presence predictions which might occur with species that are difficult to identify, Bayesian models incorporating false positive errors could be undertaken (e.g. Diana et al., 2021; Guillera-Arroita, 2017; Royle and Link, 2006).

Separating out the spatial random field of presence absence from the observations enabled us to explicitly consider the imperfect detection of the observation process. Since our database contained non-diadromous fish specific survey and catch data which sampled the same grid cell numerous times, the use of the site occupancy model enabled imperfect detection parameters to be estimated. Disregarding false absences would have led to biased inferences and over-stated parameter precision (Guillera-Arroita, 2017). The spatial random field of the probability of presence explicitly integrates spatial autocorrelation in the probability of presence, which limits the bias in inferences due to the non-random spatial repartition of observations.

Since there were few presences for most species, our results were sensitive to the choice of prior on the variance of the spatial random effect. We tested both the inverse-gamma or a uniform distribution (the two prior forms that are available using the hSDM package; Vieilledent et al., 2014) and found that the uniform distribution provided better result. Some alternative prior choice such as the half-Cauchy are advocated in the literature (Gelman, 2006), and future work should explore if other prior choice could provide more accurate inferences. Equally, for increased confidence in predictions of rare species, several different distribution models could be compared or an ensemble model (that uses the means of several models) implemented to stabilise inferences (Araújo and New, 2007; Latimer et al., 2006; Loiselle et al., 2003).

For conservation purposes low thresholds are advised for low occurring species such as diadromous fish (Freeman and Moisen, 2008a;b; Jiménez-Valverde and Lobo, 2007). However, when lowering the threshold an increase in false positives can occur (Freeman and Moisen, 2008b). Our objective for the distribution models was to minimise false presence and absences where possible, to provide more accurate predictions whilst trying to meet conservation needs and minimise potential impacts on sea-users any spatial protection measure that might be implemented (Domisch et al., 2018; Loiselle et al., 2003; Maxwell and Jennings, 2005). Lack of understanding in species distribution can lead to inefficient spatial protection measures (Wauchope et al., 2022) and resource conflict (Probst et al., 2021).

## 4.2. Diadromous fish river basin connectivity

The EuroDiad database is a large database that incorporates information on diadromous fish presence and abundance during their freshwater stage (Barber-O'Malley et al., 2022; Béguer et al., 2007). Using the Eurodiad database to provide
582 large scale understanding of river basin presence of diadromous fish, we provide a
583 first attempt at qualitatively linking diadromous fish freshwater and marine
distributions within north-eastern Atlantic waters. Although the probabilities of
584 presence for most species were low, and gaps within the EuroDiad database
585 evident (e.g., gaps in diadromous fish presence from Denmark and Netherlands),
586 connectivity with their freshwater habitats appears to be visible, indicating
587 migration pathways.
588

589
600 It is thought *A. alosa* no longer exists within the Greater North Sea (Baglinière et
601 al., 2003; Wilson and Veneranta, 2019). Presences observed may have been from
602 misidentified *A. fallax*. The high probability of presence of *A. fallax* along the east
603 coast of the UK may be because of suitable conditions for this species as few
604 spawning rivers have been recorded along the east coast (Aprahamian et al., 1998).
605 For *A. agone* probabilities of presence clearly match that of their watersheds.
606 Predicted *A. anguilla* presences closely match tagged river presences from Righton
607 et al., (2016) despite the gaps found in the EuroDiad database. Predicted presence
608 of *L. fluviatilis* was higher along the north-west coast of Germany and Holland.
609 Presences have been observed here, albeit in low numbers (Admiraal et al., 1993;
Pavlov et al., 2017; Thiel and Salewski, 2003). *P. marinus* predicted presence was
610 slightly more scattered which fits with its wide distribution including into deeper
611 waters (Elliott et al., 2021; Lança et al., 2014). The very low occurrences of *A.*
612 *anguilla*, and the lamprey species is also likely to be due to inadequate sampling
613 methods used to study these species.
614

615
616
617 Probabilities of presence of *C. ramada*, *P. flesus* and *O. eperlanus* matched
618 relatively well their known presence from the EuroDiad database. From the
619 EuroDiad database, there are few *S. salar* river basin presences within the North
620 Sea. However, *S. salar* are known to occur within the North Sea and migrate
621 Northwards to their feeding grounds (Mork et al., 2012; Rikardsen et al., 2021).
622
623 Occurrences observed were most likely 1 sea winter and 2 sea winters individuals
624 returning to their natal rivers given their size ranges. From the rivers that are
625 monitored, *S. trutta* distribution matched well their freshwater river occupancy
626 (ICES, 2020). The few salmonids observed may be as a result of the relatively few
627 pelagic trawls, in addition to misreporting (ICES, 2005).
628

629
630 Given the coastal habitat occupancy for most of these diadromous fish and declines
631 observed (Limburg and Waldman, 2009; Merg et al., 2020), detailed analysis of the
632 connectivity between both habitats is essential (Flitcroft et al., 2019; Lin et al.,
633 2017; McDowall, 2009). Unfortunately, such a model was not possible here,
634 because it would require more detailed knowledge on their freshwater habitat
635 occupancy (i.e., numerous outlets diadromous fish were observed within proximity
636 to, were not contained within the EuroDiad database) and their river fidelity.
637 Furthermore, seasonal and stage specific SDMs, which would provide more
638 information on their ontogenetic migration movements, were not possible because
639 of the low prevalence of the data and dispersion. Developing future research to
640 model the connectivity between both habitats is key to improving our
641 understanding of the pressures faced during their migrations (Flitcroft et al., 2019;
642 Lin et al., 2017; McDowall, 2009).

630
631 **4.3. Gear type detectability**

632 Even though little bycatch was observed, given a number of the diadromous fish
633 studied here are threatened, even a small amount of bycatch may impact their
634 populations (Dulvy et al., 2003). Furthermore, misreporting and illegal fishing is
635 likely to remain (Elliott et al., 2020a; ICES, 2005; Stratoudakis et al., 2020; Worm
636 et al., 2013). Bycatch detectability results from our models could help provide
637 management advice so that gear types and areas with higher bycatch rate be
638 avoided. Such advice can provide direct information for the MSFD and the Habitat
639 Directive that requires the identification of gear types which may threaten
640 protected species (1992/43/EEC; 2008/56/EC).

641
642 All species were caught by a wide range of gear types (e.g., benthic, demersal and
643 pelagic trawls, and seine nets), despite the different gear categories being
644 deployed at different depths. The species were probably caught by a range of gear
645 categories because of their diel vertical migratory behaviour whilst at sea
646 (Kristensen et al., 2018; Lança et al., 2014; Righton et al., 2016). In addition,
647 certain gear types (e.g., trawls and static nets), can catch fish within both the
648 demersal and pelagic water zones (He et al., 2021; Borges et al., 2008). Higher
649 detectability from certain gear types were, however, observed. For example, shad
650 were mainly caught by demersal mobile gear and static gear, salmonids static and
651 pelagic mobile gear types, and *P. flesus* and *O.eperlanus* were largely captured by
652 demersal and benthic mobile gear. These results broadly match the water column
653 habitat occupancy these species are known to occupy and existing literature (ICES,
654 2005; Wilson and Veneranta, 2019). As it is thought that lampreys detach from
655 their host upon capture (Elliott et al., 2021; Halliday, 1991), bycatch is likely to be
656 minimal for these species.

657
658 To improve understanding of bycatch risk on a spatial level, fishing intensity by
gear type should be mapped, ideally using Vessel Monitoring System data and
659 overlaying it with the predicted distributions (Elliott et al., 2018; Quemmerais-
660 Amice et al., 2020). Unfortunately, this information was not available to us.
661
662 Additionally, since the fisheries observer data we had access to is only a small
663 sample of the existing French fishing activity (Cornou et al., 2015), and it does not
664 include other countries fishing effort, results would be biased.

665
666 **4.4. MPA relevance**

667 To improve protection of diadromous fish at sea, numerous mechanisms could be
668 implemented. For example, identifying areas of high bycatch risk with measures to
669 limit bycatch within these areas, improving diadromous fish migratory pathways,
670 and spatial protection measures (Verhelst et al., 2021; Wilson and Veneranta,
671 2019). Few MPAs within European waters protect marine fish and instead focus on
672 protecting seabed features with a few exceptions (e.g., protected seabirds and
673 marine mammals). Therefore the only protection measure that exist for vulnerable
674 marine fish is through the removal of targeted fishing (Dureuil et al., 2018; Probst
675 et al., 2021; Stratoudakis et al., 2016). There is, however, increasing evidence that
676 MPAs may have positive impacts on fish populations if appropriate management
677 measures are implemented (Davies et al., 2021; Moland et al., 2013; Probst et al.,
678 2021; Worm et al., 2006). The value of protected areas for highly migratory species
679 has been questioned, but there is evidence that MPAs may have positive impacts on
680 migratory species (Pendoley et al., 2014; Takashina and Mougi, 2014).

681
682 Our results show relatively high core area presence of protected diadromous fish
683 within Habitat Directive MPAs. Most marine Sites of Community Importance have
684 not been designated to protect Habitat Directive listed diadromous fish.

685 Nonetheless, our results highlight the value of these MPAs for diadromous fish
686 protection. Specific management measures limiting threats (e.g., limiting access to
687 gear types with higher probability of capture) to diadromous fish within these
688 areas, would be of benefit to their protection (Domisch et al., 2019; Stratoudakis
689 et al., 2016). Detailed analysis of Sites of Community Importance and other
690 categories of designated MPAs should be undertaken to evaluate their potential to
691 protect diadromous fish.

692
693 **4.5. Conclusion**

694 Understanding the spatial distribution of species and their habitats is essential for
695 effective management and conservation (Halpern et al., 2015; Worm et al., 2009,
696 2005). Much research has been carried out on the conservation of freshwater
697 diadromous fish (Drouineau et al., 2018; Merg et al., 2020; Verhelst et al., 2021).

698 Very little research has been dedicated to the value of marine and estuary habitats
699 for the conservation of these species and their risk of bycatch (Feunteun, 2002;
700 Flitcroft et al., 2019; Lin et al., 2017). Here we provide an insight into the
701 distribution and bycatch of diadromous fish at sea.

702
703 Despite diadromous fish vulnerability, targeted fishing within estuaries for
704 numerous species remains (Aprahamian and Walker, 2008; Castelnaud, 2000;
705 Feunteun, 2002; Kappel, 2005; Stratoudakis et al., 2020). Given the very coastal
706 distribution of these species, more detailed analysis into such catch data (small
707 fishing vessels targeting diadromous fish) is required. Smaller vessels and artisanal
708 fisheries are still largely overlooked, regardless of their potential threat to
709 diadromous fish (Beaulaton et al., 2008; Castelnaud, 2000; Stratoudakis et al.,
710 2016). Having a better understanding of fishing pressure, combined with the
711 outputs of the distributions and gear capture from these models, could help
712 improve understanding of fishing impacts on diadromous fish.

713
714 Finally, it has been well acknowledged that although a large impetus for the
715 designation of MPAs has been undertaken in the last two decades (Probst et al.,
716 2021; Worm et al., 2006). The implementation of management measures has been
717 slow to put in place (Dureuil et al., 2018; Probst et al., 2021; Stratoudakis et al.,
718 2016). Furthermore, little has been undertaken to protect diadromous fish within
719 marine ecosystems (Dureuil et al., 2018; Probst et al., 2021; Stratoudakis et al.,
720 2016). We highlight the value of Habitat Directive MPAs that have the jurisdiction
721 to protect diadromous fish. Limiting access of fisheries with a higher probability of
722 capture to such areas could provide additional protection for diadromous fish and
723 their habitats.

724
725 Acknowledgements

726 We thank the funders of the project Management of Diadromous Fish in their

727 Environment, OFB-INRAE-Institute Agro-UPPA. We are extremely grateful to all
728 those who were involved in collecting and compiling the fisheries-dependent and
729 fisheries-independent data and their funders. We are also grateful to IFREMER and
730 the French marine fisheries and aquaculture administration (DPMA) for access to
731 their data. Finally, thanks the reviewers for their valuable contribution.

732

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