1.8%). From our database, more lampreys were caught from mobile demersal gear across north-western European waters. The greater presence of lampreys by scientific data will therefore probably have been because of the larger number of scientific hauls within the southern North Sea. Information on lamprey stocks across Europe is sparse (Beaulaton et al. 2008, Thiel et al. 2009, Mateus et al. 2012, ICES 2015, Silva et al. 2017).

## 4.2. Frequency of occurrence

Although our database contains trawls from across the European distribution of both species (Maitland 2003), neither of the 2 species was found in all ICES ecoregions. *Lampetra fluviatilis* presence was closer to the coast than *P. marinus*, which was more dispersed; this finding is in line with existing literature (e.g. Maitland 2003, Thiel et al. 2009, Potter et al. 2015). *L. fluviatilis* was recorded in only 2 ICES ecoregions (the Greater North Sea and the Bay of Biscay and Iberian coast), whereas *P. marinus* was also found to occur in the Celtic Sea and the Mediterranean. The higher presence of both species in the Greater North Sea and the Bay of Biscay may have been a result of the greater sampling effort within these regions.

The presence of *L. fluviatilis* was particularly high along the coast of Germany and Holland within the south-eastern North Sea. Despite this being a heavily fished sea (Berg et al. 1996) few published papers have referred to *L. fluviatilis* presence within this area (e.g. Admiraal et al. 1993, Thiel & Salewski 2003, Pavlov et al. 2017). Conversely, few *L. fluviatilis* were observed off the coast of the Humber estuary, even though significant captures and a commercial fishery exist further upstream within the River Derwent and the River Ouse (Jang & Lucas 2005). The majority of surveys within the North Sea came from DATRAS bottom trawl surveys and NS-IBTSs, which are undertaken between August and February. According to Jang & Lucas (2005), the main pre-spawning upstream migration period for *L. fluviatilis* in the River Derwent is between November and February. This slight mismatch in dates between surveys and lamprey migration may therefore explain the few presences observed in this area of the UK.

No lampreys were observed along the Iberian coast where populations are known to exist (Quintella et al. 2003, Mateus et al. 2012, Silva et al. 2017). This may be because the majority of hauls were under-

taken within the Greater North Sea. Lança et al. (2014) found that *P. marinus* migrate to deeper oceanic regions off the Iberian west coast. The majority of hauls analysed within this study were closer to the coast, with few samples off the Iberian coast, which may explain the lack of either lamprey species observed within this region. Furthermore, the surveys undertaken within this region were from Portuguese IBTSs, which take place between September and November, before seaward and upstream *P. marinus* migration takes place (Silva et al. 2013a, Moser et al. 2015, Hansen et al. 2016).

## 4.3. Size

The majority of *P. marinus* caught were less than 40 cm, with a higher presence of small *P. marinus* between autumn to spring. These observations indicate that the *P. marinus* were most likely post-metamorphic sexually immature individuals migrating into marine waters (Quintella et al. 2003, Silva et al. 2013b, Hansen et al. 2016). Most *P. marinus* were small individuals, most likely because the majority of surveys took place in waters closer to the coast, where small individuals would be present in greater numbers, prior to any dispersal of the host fish.

Although no statistical seasonal latitude variations in small *P. marinus* were observed, the wide range in size of *P. marinus* less than 40 cm may partially be due to latitudinal variations of post-metamorphic migration to marine waters. More northerly populations are known to migrate in autumn to winter and more southerly populations migrate in winter to spring (Silva et al. 2013a, Hansen et al. 2016). The wide range in size of small *P. marinus* may also indicate that juveniles stay close to the coast for these initial seasons before migrating further offshore. Alternatively, some individuals may choose to migrate into marine waters later in the year and at a larger size (King & O'Gorman 2018).

A bimodal length tendency was observed for *P. marinus*, with the majority of small individuals (<40 cm) present from autumn to spring, when metamorphosed seaward migration is considered to take place (Silva et al. 2013a, Hansen et al. 2016). The few large (>60 cm) individuals were found further from the coast and caught over the course of the year. These results corroborate with existing literature (e.g. Halliday 1991, Silva et al. 2013a) that *P. marinus* occupy marine waters for over a year, given that large and small individuals were observed in autumn and winter.
Although fewer hauls were undertaken during summer months, proportionally far fewer lampreys were observed, albeit the ratio of gear types remained the same. Mortality of adult lampreys at sea (e.g. from potential lack of hosts) is poorly understood (Maitland et al. 2015, Hansen et al. 2016), but bycatch fishing mortality at sea appears to be low (Stratoudakis et al. 2016) as opposed to targeted fishing mortality within estuaries (Beaulaton et al. 2008). Fewer individuals were identified in summer, potentially as a result of offshore migration to deeper waters further from the coast (Lança et al. 2014). Larger *P. marinus* may parasitise larger species (e.g. cetaceans and larger elasmobranchs) (Halliday 1991), which are rarely caught in fisheries-dependent and fisheries-independent surveys. Cetaceans and elasmobranchs also often migrate long distances. Lamprey wounds on marine mammals are nonetheless rare (Renaud & Cochran 2019, W. Dabin unpubl. data), and lamprey host size selectivity tendencies are not clear (e.g. Swink 1991).

The majority of *L. fluviatilis* were observed in autumn, with a mean length of 30 cm. This corresponds with adults returning to freshwater to spawn (Maitland 2003, Dawson et al. 2015), given that adult size ranges from 20 to 50 cm (Maitland 1980). A bimodal length frequency for *L. fluviatilis* was not evident. Since the majority of *L. fluviatilis* were caught in autumn within 50 km from the coast and appeared to be adults, our results support the hypothesis that *L. fluviatilis* occupy marine waters for considerably less time (less than a year) than *P. marinus* (Docker & Potter 2019, Renaud & Cochran 2019). Furthermore, few individuals were observed over the course of the summer and *L. fluviatilis* is not known to migrate far from the coast (Maitland 2003, Thiel et al. 2009, Potter et al. 2015).

Increased coastal sampling from February to May, when *P. marinus* and *L. fluviatilis* return to rivers, may help improve the understanding of growth at sea. Tagging studies may be better adapted to understand lamprey migration timing and mortality at sea (Silva et al. 2013a). However, the probability of recapture has been observed to be low (e.g. Silva et al. 2013a), potentially because both lamprey species discussed here adopt the suitable river strategy rather than homing to natal streams (Moser et al. 2015). Furthermore, microacoustic tags have only recently been developed (Mueller et al. 2019), and minimum lamprey size for acoustic implants is 14 cm (Mueller et al. 2019).

Conservation measures have been set up across the globe to try and restore lamprey populations (e.g.

rebuilding programs, improved river connectivity, removal of barriers and weirs, spatial areas of conservation) (Renaud 1997, Thiel et al. 2009, Maitland et al. 2015). As for many diadromous fish, most of those conservation measures concern the freshwater phase of the life cycle. Setting efficient conservation measure at sea is difficult because of our lack of understanding of the ecology and phenology of lampreys at sea. From our database, there were too few presence observations to identify a critical spatio-temporal window to be protected. Fishing mortality from bycatch does, however, seem to be low. Although lamprey sighting from hauls was low, greater awareness is needed in both fisheries-dependent and fisheries-independent surveys to collate more data on non-target species such as lampreys. On-deck examinations of landed fish for signs of lamprey scarring could provide substantial additional information (King 1980, King & O'Gorman 2018). This might be feasible for scientific surveys but the logistics would be difficult with large samples on board commercial fishing vessels. Nonetheless, such information could provide additional information and help unravel important aspects of lamprey life history during their marine life history phase. This could help improve our understanding of threats to lamprey survival and implement more targeted conservation measures, such as temporal spatial closures within estuaries when adults migrate back to freshwater.

**Acknowledgements:** We thank the funders of the project Management of Diadromous Fish in their Environment, OFB-INRAE-Agrocampus Ouest-UPPA. We are extremely grateful to all those who were involved in collecting and compiling the fisheries-dependent and fisheries-independent surveys. We are also grateful to IFREMER and the French marine fisheries and aquaculture administration (DPMA) for access to their data. Finally, we thank the reviewers for their valuable contribution.

## LITERATURE CITED

<figure><img src="image_129.png" /></figure>

* Aarts G, Fieberg J, Matthiopoulos J (2012) Comparative interpretation of count, presence-absence and point methods for species distribution models. Methods Ecol Evol 3:177–187

<figure><img src="image_130.png" /></figure>

* Admiraal W, van der Velde G, Smit H, Cazemier WG (1993) The rivers Rhine and Meuse in The Netherlands: present state and signs of ecological recovery. Hydrobiologia 265:97–128

<figure><img src="image_131.png" /></figure>

* Beamish FWH (1980) Biology of the North American anadromous sea lamprey, *Petromyzon marinus*. Can J Fish Aquat Sci 37:1924–1943

<figure><img src="image_132.png" /></figure>

* Beaulaton L, Taverny C, Castelnau G (2008) Fishing, abundance and life history traits of the anadromous sea lamprey (*Petromyzon marinus*) in Europe. Fish Res 92: 90–101
Although fewer hauls were undertaken during summer months, proportionally far fewer lampreys were observed, albeit the ratio of gear types remained the same. Mortality of adult lampreys at sea (e.g. from potential lack of hosts) is poorly understood (Maitland et al. 2015, Hansen et al. 2016), but bycatch fishing mortality at sea appears to be low (Stratoudakis et al. 2016) as opposed to targeted fishing mortality within estuaries (Beaulaton et al. 2008). Fewer individuals were identified in summer, potentially as a result of offshore migration to deeper waters further from the coast (Lança et al. 2014). Larger *P. marinus* may parasitise larger species (e.g. cetaceans and larger elasmobranchs) (Halliday 1991), which are rarely caught in fisheries-dependent and fisheries-independent surveys. Cetaceans and elasmobranchs also often migrate long distances. Lamprey wounds on marine mammals are nonetheless rare (Renaud & Cochran 2019, W. Dabin unpubl. data), and lamprey host size selectivity tendencies are not clear (e.g. Swink 1991).

The majority of *L. fluviatilis* were observed in autumn, with a mean length of 30 cm. This corresponds with adults returning to freshwater to spawn (Maitland 2003, Dawson et al. 2015), given that adult size ranges from 20 to 50 cm (Maitland 1980). A bimodal length frequency for *L. fluviatilis* was not evident. Since the majority of *L. fluviatilis* were caught in autumn within 50 km from the coast and appeared to be adults, our results support the hypothesis that *L. fluviatilis* occupy marine waters for considerably less time (less than a year) than *P. marinus* (Docker & Potter 2019, Renaud & Cochran 2019). Furthermore, few individuals were observed over the course of the summer and *L. fluviatilis* is not known to migrate far from the coast (Maitland 2003, Thiel et al. 2009, Potter et al. 2015).

Increased coastal sampling from February to May, when *P. marinus* and *L. fluviatilis* return to rivers, may help improve the understanding of growth at sea. Tagging studies may be better adapted to understand lamprey migration timing and mortality at sea (Silva et al. 2013a). However, the probability of recapture has been observed to be low (e.g. Silva et al. 2013a), potentially because both lamprey species discussed here adopt the suitable river strategy rather than homing to natal streams (Moser et al. 2015). Furthermore, microacoustic tags have only recently been developed (Mueller et al. 2019), and minimum lamprey size for acoustic implants is 14 cm (Mueller et al. 2019).

Conservation measures have been set up across the globe to try and restore lamprey populations (e.g.

rebuilding programs, improved river connectivity, removal of barriers and weirs, spatial areas of conservation) (Renaud 1997, Thiel et al. 2009, Maitland et al. 2015). As for many diadromous fish, most of those conservation measures concern the freshwater phase of the life cycle. Setting efficient conservation measure at sea is difficult because of our lack of understanding of the ecology and phenology of lampreys at sea. From our database, there were too few presence observations to identify a critical spatio-temporal window to be protected. Fishing mortality from bycatch does, however, seem to be low. Although lamprey sighting from hauls was low, greater awareness is needed in both fisheries-dependent and fisheries-independent surveys to collate more data on non-target species such as lampreys. On-deck examinations of landed fish for signs of lamprey scarring could provide substantial additional information (King 1980, King & O'Gorman 2018). This might be feasible for scientific surveys but the logistics would be difficult with large samples on board commercial fishing vessels. Nonetheless, such information could provide additional information and help unravel important aspects of lamprey life history during their marine life history phase. This could help improve our understanding of threats to lamprey survival and implement more targeted conservation measures, such as temporal spatial closures within estuaries when adults migrate back to freshwater.

**Acknowledgements:** We thank the funders of the project Management of Diadromous Fish in their Environment, OFB-INRAE-Agrocampus Ouest-UPPA. We are extremely grateful to all those who were involved in collecting and compiling the fisheries-dependent and fisheries-independent surveys. We are also grateful to IFREMER and the French marine fisheries and aquaculture administration (DPMA) for access to their data. Finally, we thank the reviewers for their valuable contribution.

## LITERATURE CITED

<figure><img src="image_126.png" /></figure>

* Aarts G, Fieberg J, Matthiopoulos J (2012) Comparative interpretation of count, presence-absence and point methods for species distribution models. Methods Ecol Evol 3:177-187

<figure><img src="image_127.png" /></figure>

* Admiraal W, van der Velde G, Smit H, Cazemier WG (1993) The rivers Rhine and Meuse in The Netherlands: present state and signs of ecological recovery. Hydrobiologia 265:97-128

<figure><img src="image_128.png" /></figure>

* Beamish FWH (1980) Biology of the North American anadromous sea lamprey, *Petromyzon marinus*. Can J Fish Aquat Sci 37:1924-1943

<figure><img src="image_129.png" /></figure>

* Beaulaton L, Taverny C, Castelnau G (2008) Fishing, abundance and life history traits of the anadromous sea lamprey (*Petromyzon marinus*) in Europe. Fish Res 92: 90-101
Author copy

<figure><img src="image_133.png" /></figure>

Berg S, Krog C, Muus B, Nielsen J and others (1996) Red List of lampreys and marine fishes of the Wadden Sea. Helgol Meeresunters 50:101–105

<figure><img src="image_134.png" /></figure>

Bird DJ, Potter IC, Hardisty MW, Baker BI (1994) Morphology, body size and behaviour of recently-metamorphosed sea lampreys, *Petromyzon marinus*, from the lower River Severn, and their relevance to the onset of parasitic feeding. J Fish Biol 44:67–74

<figure><img src="image_135.png" /></figure>

Brewer MJ, Butler A, Cooksley SL (2016) The relative performance of AIC, AIC<sub>C</sub> and BIC in the presence of unobserved heterogeneity. Methods Ecol Evol 7:679–692

<figure><img src="image_136.png" /></figure>

Cornou AS, Quinio-Scavinner M, Delaunay D, Dimeet J and others (2015) Observations à bord des navires de pêche professionnelle. Bilan de l'échantillonnage 2014. Ifremer, Nantes

Dawson HA, Quintella BR, Almeida PR, Treble AJ, Jolley JC (2015) The ecology of larval and metamorphosing lampreys. In: Docker MF (ed) Lampreys: biology, conservation, and control, Vol 1. Springer, Dordrecht, p 75–137

<figure><img src="image_137.png" /></figure>

Doadrio I (ed) (2001) Atlas y libro rojo de los peces continentes de España. Dirección General de Conservación de la Naturaleza, Museo Nacional de Ciencias Naturales, Madrid. https://www.miteco.gob.es/es/biodiversidad/temas/inventarios-nacionales/atlas_libro_rojo_peces_tcm30-98786.pdf (accessed 18 Sep 2020)

Docker MF, Potter IC (2019) Life history evolution in lampreys: alternative migratory and feeding types. In: Docker MF (ed) Lampreys: biology, conservation, and control, Vol 2. Springer, Dordrecht, p 287–409

<figure><img src="image_138.png" /></figure>

Fauconnet L, Trenkel VM, Morandeau G, Caill-Milly N, Rochet MJ (2015) Characterizing catches taken by different gears as a step towards evaluating fishing pressure on fish communities. Fish Res 164:238–248

<figure><img src="image_139.png" /></figure>

Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49

<figure><img src="image_140.png" /></figure>

Freyhof J, Brooks E (2011) European Red List of freshwater fishes. Publications Office of the European Union, Luxembourg

<figure><img src="image_141.png" /></figure>

Halliday RG (1991) Marine distribution of the sea lamprey (*Petromyzon marinus*) in the northwest Atlantic. Can J Fish Aquat Sci 48:832–842

<figure><img src="image_142.png" /></figure>

Hansen MJ, Madenjian CP, Slade JW, Steeves TB, Almeida PR, Quintella BR (2016) Population ecology of the sea lamprey (*Petromyzon marinus*) as an invasive species in the Laurentian Great Lakes and an imperiled species in Europe. Rev Fish Biol Fish 26:509–535

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ICES (2015) Report of the workshop on lampreys and shads (WKLS), 27–29 November 2014, Lisbon, Portugal. ICESCM 2014/SSGEF:13. https://www.ices.dk/sites/pub/Publication%20Reports/Expert%20Group%20Report/SSGEPD/2015/01%20WKLS%20-%20Report%20of%20the%20Workshop%20on%20Lampreys%20and%20Shads.pdf (accessed 9 Apr 2020)

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Jang MH, Lucas MC (2005) Reproductive ecology of the river lamprey. J Fish Biol 66:499–512

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Keil P, Belmaker J, Wilson AM, Unitt P, Jetz W (2013) Downscaling of species distribution models: a hierarchical approach. Methods Ecol Evol 4:82–94

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King EL Jr (1980) Classification of sea lamprey (*Petromyzon marinus*) attack marks on Great Lakes lake trout (*Salvelinus namaycush*). Can J Fish Aquat Sci 37:1989–200

King JJ, O’Gorman N (2018) Initial observations on feeding juvenile sea lamprey (*Petromyzon marinus* L.) in Irish lakes. Biol Environ Proc R Ir Acad B 118:113–120

<figure><img src="image_147.png" /></figure>

Lança MJ, Machado M, Ferreira R, Alves-Pereira I, Ruivo Quintella BR, Raposo de Almeida PR (2013) Feeding strategy assessment through fatty acid profiles in muscles of adult sea lampreys from the western Iberian coast. Sci Mar 77:281–291

<figure><img src="image_148.png" /></figure>

Lança MJ, Machado M, Mateus CS, Lourenço M, Ferreira AF, Quintella BR, Almeida PR (2014) Investigating population structure of sea lamprey (*Petromyzon marinus*, L.) in western Iberian Peninsula using morphological characters and heart fatty acid signature analyses. PLOS ONE 9:e108110

<figure><img src="image_149.png" /></figure>

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<figure><img src="image_150.png" /></figure>

Lassalle G, Rochard E (2009) Impact of twenty-first century climate change on diadromous fish spread over Europe, North Africa and the Middle East. Glob Change Biol 15: 1072–1089

<figure><img src="image_151.png" /></figure>

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<figure><img src="image_152.png" /></figure>

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Maitland PS, Renaud CB, Quintella BR, Close DA, Docker MF (2015) Conservation of native lampreys. In: Docker MF (ed) Lampreys: biology, conservation, and control, Vol 1. Springer, Dordrecht, p 375–418

<figure><img src="image_153.png" /></figure>

Mateus CS, Rodríguez-Muñoz R, Quintella BR, Alves MJ, Almeida PR (2012) Lampreys of the Iberian Peninsula: distribution, population status and conservation. Endang Species Res 16:183–198

<figure><img src="image_154.png" /></figure>

Merg ML, Dézerald O, Kreutzenberger K, Demski S, Reyjol Y, Usseglio-Polatera P, Belliard J (2020) Modeling diadromous fish loss from historical data: identification of anthropogenic drivers and testing of mitigation scenarios. PLOS ONE 15:e0236575

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Potter I, Gill HS, Renaud CB, Haoucher D (2015) The taxonomy, phylogeny, and distribution of lampreys. In: Docker MF (ed) Lampreys: biology, conservation, and control, Vol 1. Springer, Dordrecht, p 35–73

<figure><img src="image_158.png" /></figure>

Quintella BR, Andrade NO, Almeida PR (2003) Distribution, larval stage duration and growth of the sea lamprey ammocoetes, *Petromyzon marinus* L., in a highly modified river basin. Ecol Freshwat Fish 12:286–293
Author copy

<figure><img src="image_130.png" /></figure>

Berg S, Krog C, Muus B, Nielsen J and others (1996) Red List of lampreys and marine fishes of the Wadden Sea. Helgol Meeresunters 50:101–105

<figure><img src="image_131.png" /></figure>

Bird DJ, Potter IC, Hardisty MW, Baker BI (1994) Morphology, body size and behaviour of recently-metamorphosed sea lampreys, *Petromyzon marinus*, from the lower River Severn, and their relevance to the onset of parasitic feeding. J Fish Biol 44:67–74

<figure><img src="image_132.png" /></figure>

Brewer MJ, Butler A, Cooksley SL (2016) The relative performance of AIC, AIC<sub>C</sub> and BIC in the presence of unobserved heterogeneity. Methods Ecol Evol 7:679–692

<figure><img src="image_133.png" /></figure>

Cornou AS, Quinio-Scavinner M, Delaunay D, Dimeet J and others (2015) Observations à bord des navires de pêche professionnelle. Bilan de l'échantillonnage 2014. Ifremer, Nantes

Dawson HA, Quintella BR, Almeida PR, Treble AJ, Jolley JC (2015) The ecology of larval and metamorphosing lampreys. In: Docker MF (ed) Lampreys: biology, conservation, and control, Vol 1. Springer, Dordrecht, p 75–137

<figure><img src="image_134.png" /></figure>

Doadrio I (ed) (2001) Atlas y libro rojo de los peces continentes de España. Dirección General de Conservación de la Naturaleza, Museo Nacional de Ciencias Naturales, Madrid. https://www.miteco.gob.es/es/biodiversidad/temas/inventarios-nacionales/atlas_libro_rojo_peces_tcm30-98786.pdf (accessed 18 Sep 2020)

Docker MF, Potter IC (2019) Life history evolution in lampreys: alternative migratory and feeding types. In: Docker MF (ed) Lampreys: biology, conservation, and control, Vol 2. Springer, Dordrecht, p 287–409

<figure><img src="image_135.png" /></figure>

Fauconnet L, Trenkel VM, Morandeau G, Caill-Milly N, Rochet MJ (2015) Characterizing catches taken by different gears as a step towards evaluating fishing pressure on fish communities. Fish Res 164:238–248

<figure><img src="image_136.png" /></figure>

Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49

<figure><img src="image_137.png" /></figure>

Freyhof J, Brooks E (2011) European Red List of freshwater fishes. Publications Office of the European Union, Luxembourg

<figure><img src="image_138.png" /></figure>

Halliday RG (1991) Marine distribution of the sea lamprey (*Petromyzon marinus*) in the northwest Atlantic. Can J Fish Aquat Sci 48:832–842

<figure><img src="image_139.png" /></figure>

Hansen MJ, Madenjian CP, Slade JW, Steeves TB, Almeida PR, Quintella BR (2016) Population ecology of the sea lamprey (*Petromyzon marinus*) as an invasive species in the Laurentian Great Lakes and an imperiled species in Europe. Rev Fish Biol Fish 26:509–535

<figure><img src="image_140.png" /></figure>

ICES (2015) Report of the workshop on lampreys and shads (WKLS), 27–29 November 2014, Lisbon, Portugal. ICESCM 2014/SSGEF.13. https://www.ices.dk/sites/pub/Publication%20Reports/Expert%20Group%20Report/SSGEPD/2015/01%20WKLS%20-%20Report%20of%20the%20Workshop%20on%20Lampreys%20and%20Shads.pdf (accessed 9 Apr 2020)

<figure><img src="image_141.png" /></figure>

Jang MH, Lucas MC (2005) Reproductive ecology of the river lamprey. J Fish Biol 66:499–512

<figure><img src="image_142.png" /></figure>

Keil P, Belmaker J, Wilson AM, Unitt P, Jetz W (2013) Downscaling of species distribution models: a hierarchical approach. Methods Ecol Evol 4:82–94

<figure><img src="image_143.png" /></figure>

Kelly FL, King JJ (2001) A review of the ecology and distribution of three lamprey species, *L. fluviatilis*, *L. planeri* and *P. marinus*. Biol Environ Proc R Ir Acad B 101:165–185
King EL Jr (1980) Classification of sea lamprey (*Petromyzon marinus*) attack marks on Great Lakes lake trout (*Salvelinus namaycush*). Can J Fish Aquat Sci 37:1989–200

King JJ, O’Gorman N (2018) Initial observations on feeding juvenile sea lamprey (*Petromyzon marinus* L.) in Irish lakes. Biol Environ Proc R Ir Acad B 118:113–120

<figure><img src="image_144.png" /></figure>

Lança MJ, Machado M, Ferreira R, Alves-Pereira I, Ruivo Quintella BR, Raposo de Almeida PR (2013) Feeding strategy assessment through fatty acid profiles in muscles of adult sea lampreys from the western Iberian coast. Sci Mar 77:281–291

<figure><img src="image_145.png" /></figure>

Lança MJ, Machado M, Mateus CS, Lourenço M, Ferreira AF, Quintella BR, Almeida PR (2014) Investigating population structure of sea lamprey (*Petromyzon marinus*, L.) in western Iberian Peninsula using morphological characters and heart fatty acid signature analyses. PLOS ONE 9:e108110

<figure><img src="image_146.png" /></figure>

Lasne E, Sabatié MR, Jeannot N, Cucherousset J (2015) The effects of dam removal on river colonization by sea lamprey *Petromyzon marinus*. River Res Appl 31:904–911

<figure><img src="image_147.png" /></figure>

Lassalle G, Rochard E (2009) Impact of twenty-first century climate change on diadromous fish spread over Europe, North Africa and the Middle East. Glob Change Biol 15: 1072–1089

<figure><img src="image_148.png" /></figure>

Maitland PS (1980) Review of the ecology of lampreys in northern Europe. Can J Fish Aquat Sci 37:1944–1952

<figure><img src="image_149.png" /></figure>

Maitland PS (2003) Ecology of the river, brook and sea lamprey. Conserving Natura 2000 Rivers, Ecology Series No. 5. English Nature, Peterborough. http://publications.naturalengland.org.uk/publication/75042 (accessed 26 Nov 2020)

Maitland PS, Renaud CB, Quintella BR, Close DA, Docker MF (2015) Conservation of native lampreys. In: Docker MF (ed) Lampreys: biology, conservation, and control, Vol 1. Springer, Dordrecht, p 375–418

<figure><img src="image_150.png" /></figure>

Mateus CS, Rodríguez-Muñoz R, Quintella BR, Alves MJ, Almeida PR (2012) Lampreys of the Iberian Peninsula: distribution, population status and conservation. Endang Species Res 16:183–198

<figure><img src="image_151.png" /></figure>

Merg ML, Dézerald O, Kreutzenberger K, Demski S, Reyjol Y, Usseglio-Polatera P, Belliard J (2020) Modeling diadromous fish loss from historical data: identification of anthropogenic drivers and testing of mitigation scenarios. PLOS ONE 15:e0236575

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<figure><img src="image_155.png" /></figure>

Quintella BR, Andrade NO, Almeida PR (2003) Distribution, larval stage duration and growth of the sea lamprey ammocoetes, *Petromyzon marinus* L., in a highly modified river basin. Ecol Freshwat Fish 12:286–293
<figure><img src="image_159.png" /></figure>

Regimbart A, Guitton J, Le Pape O (2018) Zones fonctionnelles pour les ressources halieutiques dans les eaux sous souveraineté française. Deuxième partie: inventaire. Rapport d'étude. Les publications du Pôle halieutique Agrocampus Ouest no. 46, Rennes. https://hal.archives-ouvertes.fr/hal-02293032/ (accessed 9 Jan 2019)

<figure><img src="image_160.png" /></figure>

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<figure><img src="image_161.png" /></figure>

Silva S, Servia MJ, Vieira-Lanero R, Barca S, Cobo F (2013a) Life cycle of the sea lamprey *Petromyzon marinus*: duration of and growth in the marine life stage. Aquat Biol 18: 59-62

<figure><img src="image_162.png" /></figure>

Silva S, Servia MJ, Vieira-Lanero R, Cobo F (2013b) Downstream migration and hematophagous feeding of newly metamorphosed sea lampreys (*Petromyzon marinus* Linnaeus, 1758). Hydrobiologia 700:277-286

<figure><img src="image_163.png" /></figure>

Silva S, Araújo MJ, Bao M, Mucientes G, Cobo F (2014) The haematophagous feeding stage of anadromous populations of sea lamprey *Petromyzon marinus*: low host selectivity and wide range of habitats. Hydrobiologia 734: 187-199

<figure><img src="image_164.png" /></figure>

Silva S, Vieira-Lanero R, Barca S, Cobo F (2017) Densities and biomass of larval sea lamprey populations (*Petromyzon marinus* Linnaeus, 1758) in north-western Spain and

data comparisons with other European regions. Mar Freshw Res 68:116-122

<figure><img src="image_165.png" /></figure>

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<figure><img src="image_166.png" /></figure>

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<figure><img src="image_167.png" /></figure>

Taverny C, Lassalle G, Ortusi I, Roqueplo C, Lepage M, Lambert P (2012) From shallow to deep waters: habitats used by larval lampreys (genus *Petromyzon* and *Lam-petra*) over a western European basin. Ecol Freshwat Fish 21:87-99

<figure><img src="image_168.png" /></figure>

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<figure><img src="image_169.png" /></figure>

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<figure><img src="image_170.png" /></figure>

UICN Comité français, MNHN, SFI, AFB (2019) La Liste rouge des espèces menacées en France—chapitre poissons d'eau douce de France métropolitaine. Comité français de l'UICN, Paris. https://inpn.mnhn.fr/docs/LR_FCE/liste_rouge_poissons_eau_douce_metropole_2019_fascicule.pdf (accessed 1 Jul 2020)

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Author copy

<figure><img src="image_156.png" /></figure>

Regimbart A, Guitton J, Le Pape O (2018) Zones fonctionnelles pour les ressources halieutiques dans les eaux sous souveraineté française. Deuxième partie: inventaire. Rapport d'étude. Les publications du Pôle halieutique Agrocampus Ouest no. 46, Rennes. https://hal.archives-ouvertes.fr/hal-02293032/ (accessed 9 Jan 2019)

<figure><img src="image_157.png" /></figure>

Renaud CB (1997) Conservation status of Northern Hemisphere lampreys (Petromyzontidae). J Appl Ichthyol 13: 143-148

Renaud CB, Cochran PA (2019) Post-metamorphic feeding in lampreys. In: Docker MF (ed) Lampreys: biology, conservation, and control, Vol 2. Springer, Dordrecht, p 247-285

<figure><img src="image_158.png" /></figure>

Silva S, Servia MJ, Vieira-Lanero R, Barca S, Cobo F (2013a) Life cycle of the sea lamprey *Petromyzon marinus*: duration of and growth in the marine life stage. Aquat Biol 18: 59-62

<figure><img src="image_159.png" /></figure>

Silva S, Servia MJ, Vieira-Lanero R, Cobo F (2013b) Downstream migration and hematophagous feeding of newly metamorphosed sea lampreys (*Petromyzon marinus* Linnaeus, 1758). Hydrobiologia 700:277-286

<figure><img src="image_160.png" /></figure>

Silva S, Araújo MJ, Bao M, Mucientes G, Cobo F (2014) The haematophagous feeding stage of anadromous populations of sea lamprey *Petromyzon marinus*: low host selectivity and wide range of habitats. Hydrobiologia 734: 187-199

<figure><img src="image_161.png" /></figure>

Silva S, Vieira-Lanero R, Barca S, Cobo F (2017) Densities and biomass of larval sea lamprey populations (*Petromyzon marinus* Linnaeus, 1758) in north-western Spain and

data comparisons with other European regions. Mar Freshw Res 68:116-122

<figure><img src="image_162.png" /></figure>

Stratoudakis Y, Mateus CS, Quintella BR, Antunes C, Raposo de Almeida P (2016) Exploited anadromous fish in Portugal: suggested direction for conservation and management. Mar Policy 73:92-99

<figure><img src="image_163.png" /></figure>

Swink WD (1991) Host-size selection by parasitic sea lampreys. Trans Am Fish Soc 120:637-643

<figure><img src="image_164.png" /></figure>

Taverny C, Lassalle G, Ortusi I, Roqueplo C, Lepage M, Lambert P (2012) From shallow to deep waters: habitats used by larval lampreys (genus *Petromyzon* and *Lam-petra*) over a western European basin. Ecol Freshwat Fish 21:87-99

<figure><img src="image_165.png" /></figure>

Thiel R, Salewski V (2003) Verteilung und Wanderung von Neunaugen im Elbeästuar (Deutschland). Limnologica 33:214-226

<figure><img src="image_166.png" /></figure>

Thiel R, Winkler HM, Riel P, Neumann R and others (2009) Endangered anadromous lampreys in the southern Baltic Sea: spatial distribution, long-term trend, population status. Endang Species Res 8:233-247

<figure><img src="image_167.png" /></figure>

UICN Comité français, MNHN, SFI, AFB (2019) La Liste rouge des espèces menacées en France—chapitre poissons d'eau douce de France métropolitaine. Comité français de l'UICN, Paris. https://inpn.mnhn.fr/docs/LR_FCE/liste_rouge_poissons_eau_douce_metropole_2019_fascicule.pdf (accessed 1 Jul 2020)

Zuur AF, Leno EN, Walker NJ, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer-Verlag, New York, NY

Editorial responsibility: Brendan Godley,
University of Exeter, Cornwall Campus, UK

Reviewed by: 3 anonymous referees

Submitted: May 25, 2020

Accepted: January 11, 2021

Proofs received from author(s): April 13, 2021
Data Article

Fisheries-dependent and -Independent data
used to model the distribution of diadromous
fish at-sea

<figure><img src="image_171.png" /></figure>

Sophie A.M. Elliott<sup>a,e,g,*</sup>, Noémie Deleys<sup>a,b</sup>, Laurent Beaulaton<sup>a,f</sup>, Etienne Rivot<sup>a,e</sup>, Elodie Réveillac<sup>d</sup>, Anthony Acou<sup>a,c</sup>

ARTICLE INFO

Article history:

Received 13 December 2022

Revised 26 February 2023

Accepted 24 March 2023

Available online 31 March 2023

Dataset link: Diadromous fish at sea
database (Reference data)

Keywords:

Commercial fisheries data

Scientific survey data

Presence-absence data

Fishing gear

Bycatch

Anadromous and catadromous fish

Distribution modelling

A B S T R A C T

A database of 168 904 hauls covering the period from 1965 to 2019, from 46 surveys containing both fisheries-dependent (fishing vessels) and -independent data (scientific surveys) were collated from across the eastern Atlantic (Greater North Sea, Celtic Sea, Bay of Biscay and Iberian coast) and Metropolitan French Mediterranean waters. Data on diadromous fish (the European sturgeon (*Acipenser stu-rio*), allis shad (*Alosa alosa*), twait shad (*Alosa fallax*), Mediter-ranean twaite shad (*Alosa agone*), European eel (*Anguilla an-guilla*), thinlip mullet (*Chelon ramada*), river lamprey (*Lampe-tra fluviatilis*), sea lamprey (*Petromyzon marinus*), smelt (*Os-merus eperlanus*), European flounder (*Platichthys flesus*), At-lantic salmon (*Salmo salar*) and the sea trout (*Salmo trutta*)) presence-absence was extracted and cleaned. The gear type and gear category which caught these species, their spatial location, and the date of capture (year and month), were also

<footnote>ª Management of Diadromous Fish in their Environment OFB, INRAE, Institut Agro, UPPA, 35042 Rennes, France</footnote>

<footnote>ᵇ French Research Institute for the Exploration of the Sea (IFREMER) Vigies, 44311, Nantes, France</footnote>

<footnote>ᶜ Centre d'expertise et de données sur le patrimoine naturel, Patrinat OFB-MNHN-CNRS-IRD, Station marine du Museum National d'Histoire Naturelle, 35800 Dinard, France</footnote>

<footnote>ᵈ Littoral, Environnement et Sociétés (LIENSs), UMR 7266, La Rochelle Université-CNRS, 17000 La Rochelle, France</footnote>

<footnote>ᶜ DECOD (Ecosystem Dynamics and Sustainability), Institut Agro, Ifremer, INRAE, Rennes, France</footnote>

<footnote n="1">¹ Service Conservation et Gestion Durable des Espèces Exploitées, DRAS, OFB, U3E, 35042 Rennes, France</footnote>

<footnote n="8">⁸ Game & Wildlife Conservation Trust, Salmon & Trout Research Centre, East Stoke, Wareham BH20 6BB, UK</footnote>

<footnote>DOI of original article: 10.1016/j.pocean.2022.102924</footnote>

<footnote>* Corresponding author.
E-mail address: SElliott@gwct.org.uk (S.A.M. Elliott).
Social media: 👤 @Sophie_AElliott (S.A.M. Elliott)</footnote>

<footnote>https://doi.org/10.1016/j.dib.2023.109107</footnote>

<footnote>2352-3409/© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/)</footnote>
<figure><img src="image_168.png" /></figure>

ELSEVIER

Contents lists available at ScienceDirect

Data in Brief

journal homepage: www.elsevier.com/locate/dib

<figure><img src="image_169.png" /></figure>

Data Article

# Fisheries-dependent and -Independent data used to model the distribution of diadromous fish at-sea

<figure><img src="image_170.png" /></figure>

Sophie A.M. Elliott<sup>a,e,g,*</sup>, Noémie Deleys<sup>a,b</sup>, Laurent Beaulaton<sup>a,f</sup>, Etienne Rivot<sup>a,e</sup>, Elodie Réveillac<sup>d</sup>, Anthony Acou<sup>a,c</sup>

## ARTICLE INFO

**Article history:**
Received 13 December 2022
Revised 26 February 2023
Accepted 24 March 2023
Available online 31 March 2023

Dataset link: *Diadromous fish at sea database* (Reference data)

**Keywords:**
Commercial fisheries data
Scientific survey data
Presence-absence data
Fishing gear
Bycatch
Anadromous and catadromous fish
Distribution modelling

## ABSTRACT

A database of 168 904 hauls covering the period from 1965 to 2019, from 46 surveys containing both fisheries-dependent (fishing vessels) and -independent data (scientific surveys) were collated from across the eastern Atlantic (Greater North Sea, Celtic Sea, Bay of Biscay and Iberian coast) and Metropolitan French Mediterranean waters. Data on diadromous fish (the European sturgeon (*Acipenser stu-rio*), allis shad (*Alosa alosa*), twait shad (*Alosa fallax*), Mediter-ranean twaite shad (*Alosa agone*), European eel (*Anguilla an-guilla*), thinlip mullet (*Chelon ramada*), river lamprey (*Lampe-tra fluviatilis*), sea lamprey (*Petromyzon marinus*), smelt (*Os-merus eperlanus*), European flounder (*Platichthys flesus*), At-lantic salmon (*Salmo salar*) and the sea trout (*Salmo trutta*)) presence-absence was extracted and cleaned. The gear type and gear category which caught these species, their spatial location, and the date of capture (year and month), were also

DOI of original article: 10.1016/j.pocean.2022.102924

* Corresponding author.
E-mail address: SElliott@gwct.org.uk (S.A.M. Elliott).
Social media: @Sophie_AElliott (S.A.M. Elliott)

https://doi.org/10.1016/j.dib.2023.109107

2352-3409/© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

<footnote><sup>a</sup> Management of Diadromous Fish in their Environment OFB, INRAE, Institut Agro, UPPA, 35042 Rennes, France</footnote>

<footnote><sup>b</sup> French Research Institute for the Exploration of the Sea (IFREMER) Vigies, 44311, Nantes, France</footnote>

<footnote><sup>c</sup> Centre d'expertise et de données sur le patrimoine naturel, Patrinat OFB-MNHN-CNRS-IRD, Station marine du Museum National d'Histoire Naturelle, 35800 Dinard, France</footnote>

<footnote><sup>d</sup> Littoral, Environnement et Sociétés (LIENSs), UMR 7266, La Rochelle Université-CNRS, 17000 La Rochelle, France</footnote>

<footnote><sup>e</sup> DECOD (Ecosystem Dynamics and Sustainability), Institut Agro, Ifremer, INRAE, Rennes, France</footnote>

<footnote><sup>f</sup> Service Conservation et Gestion Durable des Espèces Exploitées, DRAS, OFB, U3E, 35042 Rennes, France</footnote>

<footnote><sup>g</sup> Game & Wildlife Conservation Trust, Salmon & Trout Research Centre, East Stoke, Wareham BH20 6BB, UK</footnote>
cleaned and standardised. Very little is known about diadromous fish at-sea and modelling data-poor and poorly detectable species such as diadromous fish is challenging for species conservation. Furthermore, databases which contain both scientific surveys and fisheries-dependent data on data-poor species at the temporal and geographical scale of this database are uncommon. This data could therefore be used to improve knowledge of diadromous fish spatial and temporal trends, and modelling techniques for data-poor species.

© 2023 The Author(s). Published by Elsevier Inc.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

## Specifications Table

<table><tr><td>Subject</td><td>Biological science: Biodiversity<br>Environmental science: Ecological modelling, Nature and Landscape Conservation</td></tr><tr><td>Specific subject area</td><td>A database of fisheries and scientific survey data was collated to analyse the distribution of diadromous fish at-sea and their bycatch within western European waters.</td></tr><tr><td>Type of data</td><td>CSV file<br>Table</td></tr><tr><td>How the data were acquired</td><td>Data were acquired by contacting data holders (International Council for the Exploration of the Sea (ICES) Database of Trawl Surveys (DATRAS) the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) and the French Ministry of Fisheries and Aquaculture (DGAMPA) for catch data on diadromous fish at-sea. To clean the data, R and QGIS were used. As a result of the content of fishing activity data, fisheries-dependent data have been provided on an ICES sub-rectangle scale as requested by the DGAMPA.</td></tr><tr><td>Data format</td><td>Raw<br>Analysed</td></tr><tr><td>Description of data collection</td><td>This database was collated from fisheries-dependent and -independent data. It contains the spatiotemporal locations of the haul and gear type of each haul and the presence-absence of each species. Data were cleaned using descriptive statistics and existing literature on the species to see whether the species migration (distance from the coast over the course of the year and their length) matched that of existing literature.</td></tr><tr><td>Data source location</td><td>North-eastern Atlantic waters<br>Metropolitan French Mediterranean waters<br>For raw data sources see Table S1</td></tr><tr><td>Data accessibility</td><td>Repository name: SEANOE<br>Direct URL to data: https://doi.org/10.17882/91719</td></tr><tr><td>Related research article</td><td>S.A.M. Elliott, A. Acou, L. Beaulaton, J. Guitton, E. Réveillac, E. Rivot, 2023. Modelling the distribution of rare and data-poor diadromous fish at sea for protected areas management. Progress in Oceanography. 210: 102924. https://doi.org/10.1016/j.pocean.2022.102924</td></tr></table>

## Value of the Data

* Much information exists on diadromous fish during their freshwater stages, yet precise information on their at-sea habitat occupancy is still missing. This dataset provides valuable temporal and spatial information on diadromous fish during their at-sea life-history stages.

* Scientific survey and onboard fisheries observer data have often been analysed separately. Collating this data and standardising them into a single database represents unprecedented progress.
cleaned and standardised. Very little is known about diadromous fish at-sea and modelling data-poor and poorly detectable species such as diadromous fish is challenging for species conservation. Furthermore, databases which contain both scientific surveys and fisheries-dependent data on data-poor species at the temporal and geographical scale of this database are uncommon. This data could therefore be used to improve knowledge of diadromous fish spatial and temporal trends, and modelling techniques for data-poor species.

© 2023 The Author(s). Published by Elsevier Inc.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

## Specifications Table

<table><tr><td>Subject</td><td>Biological science: Biodiversity</td></tr><tr><td>Specific subject area</td><td>Environmental science: Ecological modelling, Nature and Landscape Conservation<br/>A database of fisheries and scientific survey data was collated to analyse the distribution of diadromous fish at-sea and their bycatch within western European waters.</td></tr><tr><td>Type of data</td><td>CSV file<br/>Table</td></tr><tr><td>How the data were acquired</td><td>Data were acquired by contacting data holders (International Council for the Exploration of the Sea (ICES) Database of Trawl Surveys (DATRAS) the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) and the French Ministry of Fisheries and Aquaculture (DGAMPA) for catch data on diadromous fish at-sea. To clean the data, R and QGIS were used. As a result of the content of fishing activity data, fisheries-dependent data have been provided on an ICES sub-rectangle scale as requested by the DGAMPA.</td></tr><tr><td>Data format</td><td>Raw<br/>Analysed</td></tr><tr><td>Description of data collection</td><td>This database was collated from fisheries-dependent and -independent data. It contains the spatiotemporal locations of the haul and gear type of each haul and the presence-absence of each species. Data were cleaned using descriptive statistics and existing literature on the species to see whether the species migration (distance from the coast over the course of the year and their length) matched that of existing literature.</td></tr><tr><td>Data source location</td><td>North-eastern Atlantic waters<br/>Metropolitan French Mediterranean waters<br/>For raw data sources see Table S1</td></tr><tr><td>Data accessibility</td><td>Repository name: SEANOE<br/>Direct URL to data: https://doi.org/10.17882/91719</td></tr><tr><td>Related research article</td><td>S.A.M. Elliott, A. Acou, L. Beaulaton, J. Guittot, E. Réveillac, E. Rivot, 2023.<br/>Modelling the distribution of rare and data-poor diadromous fish at sea for protected areas management. Progress in Oceanography. 210: 102924.<br/>https://doi.org/10.1016/j.pcean.2022.102924</td></tr></table>

## Value of the Data

* Much information exists on diadromous fish during their freshwater stages, yet precise information on their at-sea habitat occupancy is still missing. This dataset provides valuable temporal and spatial information on diadromous fish during their at-sea life-history stages.

* Scientific survey and onboard fisheries observer data have often been analysed separately. Collating this data and standardising them into a single database represents unprecedented progress.
* By combining these different sources of data into a single database enables the possibility of improved inferences on species distribution using integrated modeling approaches.

* These data could be further used to understand seasonal and long-term changes in the distribution of diadromous fish at-sea.

* The data could be used to understand spatial and seasonal variation in fishing gear.

* The data can be used by fisheries ecologists, conservation managers, and statisticians interested in analysing complex datasets containing rare species.

# 1. Objective

We collated and cleaned this database containing fisheries-dependent and -independent data to improve knowledge on diadromous fish at-sea which inhabit eastern Atlantic waters (Greater North Sea, Celtic Sea, Bay of Biscay, and Iberian coast) and Metropolitan French waters within the Mediterranean. These are a group of species for which very little is known about their at-sea spatiotemporal occupancy and marine migrations.

# 2. Data Description

The data contains long-term (1965-2019) fisheries-dependent (from fishing vessels) and -independent (scientific surveys) data on the distribution of twelve diadromous fish at-sea (Acipenser sturio, Alosa alosa, Alosa fallax, Alosa agone, Anguilla anguilla, Chelon ramada, Lampe-tra fluviatilis, Osmerus eperlanus, Petromyzon marinus, Platichthys flesus, Salmo salar, Salmo trutta) at a large spatial scale (Greater North Sea, Celtic Sea, Bay of Biscay and the Iberian Shelf, French Metropolitan Mediterranean waters).

Fisheries-dependent data came from the French fisheries onboard observer program (ObsMer), held by the French Research Institute for Exploitation of the Sea (IFREMER), and available on request and approval from the French Ministry of Fisheries and Aquaculture (DGAMPA) [1]; Table S1). The ObsMer program was set up in 2003, under European regulation 1639/2001. The aim of the observer program is to assess species caught during commercial fisheries hauls, including bycatch and discards. Table S2 provides a list of the different gear types fisheries observers have collected onboard observer data from. Such data can thus provide valuable information on diadromous fish at-sea and their bycatch since they are rarely targeted by fisheries. The observer data contains spatiotemporal information on diadromous fish caught from 2003 to 2019 and the gear types and categories (Table S2) they were caught by. Absence data are also included in the dataset. As a result of missing information on fishing vessel characteristics, it was not possible to calculate catch per unit effort from the information provided. Due to the sensitive nature of this data, precise latitude and longitudinal information were removed, including metiers used (gear and target species) and information on whether the species were targeted, landed, or discarded. It should be noted that only ~2-4% of French fishing vessels have on-board observers and the observers semi-randomly sample fishing vessels and hauls when on-board [1].

Fisheries-independent data were downloaded from the International Council for the Exploration of the Seas (ICES) Database of Trawl Surveys (DATRAS; Table S1). DATRAS data are collected for Scientific Bottom Trawl Surveys (SBTS) in the Baltic, the Greater North Sea, the Celtic Sea, the English Channel, and the Bay of Biscay and the Iberian coast (https://www.ices.dk/data/data-portals/Pages/DATRAS.aspx). DATRAS SBTSs were initially designed to quantify commercial fish stock status (e.g., herring, cod, haddock, mackerel, etc.). These surveys, however, collect information on all catch and can therefore provide valuable data on other non-target species. 48 separate metropolitan French scientific surveys programs containing information on diadromous fish at-sea were also collated by contacting each of the staff members leading on the different IFREMER surveys (https://campagnes.flotteoceanographique.fr/; [2]; Table S1). Details of fish captured by the different surveys within [2], helped indicate survey information with potentially relevant diadromous fish data. Information from [2] also provides details of the purpose of each of the French scientific surveys. Data from all the scientific sur-
* By combining these different sources of data into a single database enables the possibility of improved inferences on species distribution using integrated modeling approaches.

* These data could be further used to understand seasonal and long-term changes in the distribution of diadromous fish at-sea.

* The data could be used to understand spatial and seasonal variation in fishing gear.

* The data can be used by fisheries ecologists, conservation managers, and statisticians interested in analysing complex datasets containing rare species.

# 1. Objective

We collated and cleaned this database containing fisheries-dependent and -independent data to improve knowledge on diadromous fish at-sea which inhabit eastern Atlantic waters (Greater North Sea, Celtic Sea, Bay of Biscay, and Iberian coast) and Metropolitan French waters within the Mediterranean. These are a group of species for which very little is known about their at-sea spatiotemporal occupancy and marine migrations.

# 2. Data Description

The data contains long-term (1965-2019) fisheries-dependent (from fishing vessels) and -independent (scientific surveys) data on the distribution of twelve diadromous fish at-sea (Acipenser sturio, Alosa alosa, Alosa fallax, Alosa agone, Anguilla anguilla, Chelon ramada, Lampe-tra fluviatilis, Osmerus eperlanus, Petromyzon marinus, Platichthys flesus, Salmo salar, Salmo trutta) at a large spatial scale (Greater North Sea, Celtic Sea, Bay of Biscay and the Iberian Shelf, French Metropolitan Mediterranean waters).

Fisheries-dependent data came from the French fisheries onboard observer program (ObsMer), held by the French Research Institute for Exploitation of the Sea (IFREMER), and available on request and approval from the French Ministry of Fisheries and Aquaculture (DGAMPA) [1]; Table S1). The ObsMer program was set up in 2003, under European regulation 1639/2001. The aim of the observer program is to assess species caught during commercial fisheries hauls, including bycatch and discards. Table S2 provides a list of the different gear types fisheries observers have collected onboard observer data from. Such data can thus provide valuable information on diadromous fish at-sea and their bycatch since they are rarely targeted by fisheries. The observer data contains spatiotemporal information on diadromous fish caught from 2003 to 2019 and the gear types and categories (Table S2) they were caught by. Absence data are also included in the dataset. As a result of missing information on fishing vessel characteristics, it was not possible to calculate catch per unit effort from the information provided. Due to the sensitive nature of this data, precise latitude and longitudinal information were removed, including metiers used (gear and target species) and information on whether the species were targeted, landed, or discarded. It should be noted that only ~2-4% of French fishing vessels have on-board observers and the observers semi-randomly sample fishing vessels and hauls when on-board [1].

Fisheries-independent data were downloaded from the International Council for the Exploration of the Seas (ICES) Database of Trawl Surveys (DATRAS; Table S1). DATRAS data are collected for Scientific Bottom Trawl Surveys (SBTS) in the Baltic, the Greater North Sea, the Celtic Sea, the English Channel, and the Bay of Biscay and the Iberian coast (https://www.ices.dk/data/data-portals/Pages/DATRAS.aspx). DATRAS SBTSs were initially designed to quantify commercial fish stock status (e.g., herring, cod, haddock, mackerel, etc.). These surveys, however, collect information on all catch and can therefore provide valuable data on other non-target species. 48 separate metropolitan French scientific surveys programs containing information on diadromous fish at-sea were also collated by contacting each of the staff members leading on the different IFREMER surveys (https://campagnes.flotteoceanographique.fr/; [2]; Table S1). Details of fish captured by the different surveys within [2], helped indicate survey information with potentially relevant diadromous fish data. Information from [2] also provides details of the purpose of each of the French scientific surveys. Data from all the scientific sur-
veys were collated from when they began until 2019 (Table S1). It should be noted that al-
though some SBTS data back to the 60s, standardisation of these surveys only took place from
1977 and changes to the survey design and specific gear used has taken place over the years
(http://datras.ices.dk/Home/Descriptions.aspx).

For both fisheries-dependent and -independent data, presence and absence, gear type (Table S2), the spatial locations, year and month of capture were combined into a single database and formatted for comparability. Given the little information that currently exists on diadromous fish, this collated and cleaned dataset provides valuable information on these species at-sea life history traits and information on their bycatch risk. Having this data in a single database facilitates the analysis of species distribution using integrated modelling methods [3]. Furthermore, this is an interesting dataset for data scientists interested in finding new ways to model data-poor species.

3. Experimental Design, Materials and Methods

Following access to the fisheries-dependent and -independent data, all data files were re-
formatted and merged into a single comprehensive CSV file. This included joining station data
(spatial, temporal and gear type information), with biological information (number of fish caught
and their length) through a unique identification code, and standardising variables (i.e., species
lengths, numbers captured, and gear types which were displayed in various ways) to the same
units and categories. Data cleaning undertaken included removing points on land, convert-
ing gear types into Fisheries Agricultural Organisation (FAO) subcategory gear types (second
tier) and creating gear categories, based upon the water column the vessels fish and whether
they are static or mobile gears (Table S2; [5]). Existing FAO first tier gear categories group
all trawls into the same category [5]. We found this unhelpful given most fish occupy dif-
ferent water layers and the impact of the different gear types can be very different depend-
ing on water-layer the haul is undertaken. Static gear types were not, however, separated into
different water layers because they can capture much fewer species relative to trawls gear
types.

For fisheries-independent data mean haul latitude and longitude was selected from the start and end of location of the haul. Erroneous negative distances (between the start and end of the haul) and overly long hauls (>100 000m in 30min) were removed from the fisheries-independent data. To ensure only fully marine data was kept for the entire dataset, all hauls below the mean low water tide were kept by clipping the data with shapefiles on freshwater and transitional waterbodies. ICES statistical divisions, squares and sub-divisions were also added to the dataset to aid with spatial data organisation and simplified visualisation. The numbers of fish caught per haul were converted to presence-absence information for distribution modelling purposes. It should also be noted, that due to the different abilities of gear types to capture diadromous fish, presence- absence information is more reliable than the numbers of individuals caught. Diadromous fish length information was standardised to the same unit, including correcting for erroneous length unit (mixing of millimetres and centimetres).

Diadromous fish biological data was cleaned by descriptive analyses prior to statistical analysis [4,6] to see whether their stage specific migration (distance from coast over the course of the year and their length) matched that of existing literature (e.g., [4]). Where it was certain that an identified species did not occur within the area found or was much larger than previously found, it was changed to its paired species or removed. Data that were modified included:

1) *Alosa alosa* and *Alosa fallax* which do not occur within the Mediterranean Sea. They were therefore converted to the *Alosa agone* (40 A. *alosa* presences and 76 A. *fallax* presences, 4% of the data) [6-8].

2) *A. fallax* maximum size is thought to be 50 cm [9,10]. Individuals larger than 55 cm were therefore converted to the larger *A. alosa* (74 presences, 4%) [6].
veys were collated from when they began until 2019 (Table S1). It should be noted that al-
though some SBTS data back to the 60s, standardisation of these surveys only took place from
1977 and changes to the survey design and specific gear used has taken place over the years
(http://datras.ices.dk/Home/Descriptions.aspx).

For both fisheries-dependent and -independent data, presence and absence, gear type (Table S2), the spatial locations, year and month of capture were combined into a single database and formatted for comparability. Given the little information that currently exists on diadromous fish, this collated and cleaned dataset provides valuable information on these species at-sea life history traits and information on their bycatch risk. Having this data in a single database facilitates the analysis of species distribution using integrated modelling methods [3]. Furthermore, this is an interesting dataset for data scientists interested in finding new ways to model data-poor species.

3. Experimental Design, Materials and Methods

Following access to the fisheries-dependent and -independent data, all data files were re-
formatted and merged into a single comprehensive CSV file. This included joining station data
(spatial, temporal and gear type information), with biological information (number of fish caught
and their length) through a unique identification code, and standardising variables (i.e., species
lengths, numbers captured, and gear types which were displayed in various ways) to the same
units and categories. Data cleaning undertaken included removing points on land, convert-
ing gear types into Fisheries Agricultural Organisation (FAO) subcategory gear types (second
tier) and creating gear categories, based upon the water column the vessels fish and whether
they are static or mobile gears (Table S2: [5]). Existing FAO first tier gear categories group
all trawls into the same category [5]. We found this unhelpful given most fish occupy dif-
ferent water layers and the impact of the different gear types can be very different depend-
ing on water-layer the haul is undertaken. Static gear types were not, however, separated into
different water layers because they can capture much fewer species relative to trawls gear
types.

For fisheries-independent data mean haul latitude and longitude was selected from the start and end of location of the haul. Erroneous negative distances (between the start and end of the haul) and overly long hauls (>100 000m in 30min) were removed from the fisheries-independent data. To ensure only fully marine data was kept for the entire dataset, all hauls below the mean low water tide were kept by clipping the data with shapefiles on freshwater and transitional waterbodies. ICES statistical divisions, squares and sub-divisions were also added to the dataset to aid with spatial data organisation and simplified visualisation. The numbers of fish caught per haul were converted to presence-absence information for distribution modelling purposes. It should also be noted, that due to the different abilities of gear types to capture diadromous fish, presence- absence information is more reliable than the numbers of individuals caught. Diadromous fish length information was standardised to the same unit, including correcting for erroneous length unit (mixing of millimetres and centimetres).

Diadromous fish biological data was cleaned by descriptive analyses prior to statistical analysis [4,6] to see whether their stage specific migration (distance from coast over the course of the year and their length) matched that of existing literature (e.g., [4]). Where it was certain that an identified species did not occur within the area found or was much larger than previously found, it was changed to its paired species or removed. Data that were modified included:

1) *Alosa alosa* and *Alosa fallax* which do not occur within the Mediterranean Sea. They were therefore converted to the *Alosa agone* (40 A. *alosa* presences and 76 A. *fallax* presences, 4% of the data) [6-8].

2) *A. fallax* maximum size is thought to be 50 cm [9,10]. Individuals larger than 55 cm were therefore converted to the larger *A. alosa* (74 presences, 4%) [6].
3) *Osmerus eperlanus* maximum size is 30 cm [11–13]. Individuals greater than 30 cm occur-
ring far from the coast were removed (15 individuals) [6].

This collated and cleaned dataset containing 168 414 hauls from both fisheries-dependent and -independent data on twelve diadromous fish presence and absence is the largest in terms of spatio-temporal information that exists to date on diadromous fish. Although miss-identification may still be present within the dataset, extensive cleaning has taken place to try and improve accuracy of this data for modelling purposes [6].

Ethics Statements

The manuscript adheres to ethics in publishing standards. All data holders provided their written consent to have their database published.

CRediT Author Statement

**Sophie Elliott:** Data Curation, Formal analysis, Investigation, Methodology, Writing; **Noemie Deleys:** Data collation and curation; **Etienne Rivot:** Funding acquisition, Project administration, Scientific supervision; **Anthony Acou:** Funding acquisition, Project administration, Scientific supervision; **Elodie Reveillac:** Reviewing; **Laurent Beaulaton:** Funding acquisition, Project administration, Scientific supervision.

Declaration of Competing Interest

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

Data Availability

Diadromous fish at sea database (Reference data) (Seanoe)

Acknowledgments

We thank the funders of the project Management of Diadromous Fish in their Environment, OFB, INRAE, Institut Agro, UPPA. We are extremely grateful to all those who were involved in collecting and compiling the fisheries-dependent and -independent data and their funders. We are also grateful to IFREMER and the French marine fisheries and aquaculture administration (DGAMPA) for access to their data.

**Supplementary Materials**

Supplementary material associated with this article can be found, in the online version, at
doi:10.1016/j.dib.2023.109107.
3) *Osmerus eperlanus* maximum size is 30 cm [11–13]. Individuals greater than 30 cm occur-
ring far from the coast were removed (15 individuals) [6].

This collated and cleaned dataset containing 168 414 hauls from both fisheries-dependent and -independent data on twelve diadromous fish presence and absence is the largest in terms of spatio-temporal information that exists to date on diadromous fish. Although miss-identification may still be present within the dataset, extensive cleaning has taken place to try and improve accuracy of this data for modelling purposes [6].

Ethics Statements

The manuscript adheres to ethics in publishing standards. All data holders provided their
written consent to have their database published.

CRediT Author Statement

**Sophie Elliott:** Data Curation, Formal analysis, Investigation, Methodology, Writing; **Noemie Deleys:** Data collation and curation; **Etienne Rivot:** Funding acquisition, Project administration, Scientific supervision; **Anthony Acou:** Funding acquisition, Project administration, Scientific supervision; **Elodie Reveillac:** Reviewing; **Laurent Beaulaton:** Funding acquisition, Project administration, Scientific supervision.

Declaration of Competing Interest

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

Data Availability

Diadromous fish at sea database (Reference data) (Seanoe)

Acknowledgments

We thank the funders of the project Management of Diadromous Fish in their Environment, OFB, INRAE, Institut Agro, UPPA. We are extremely grateful to all those who were involved in collecting and compiling the fisheries-dependent and -independent data and their funders. We are also grateful to IFREMER and the French marine fisheries and aquaculture administration (DGAMPA) for access to their data.

Supplementary Materials

Supplementary material associated with this article can be found, in the online version, at
doi:10.1016/j.dib.2023.109107.
References

[1] A. Cornou, M. Quinio-scavinner, D. Delaunay, J. Dimeet, N. Goascoz, B. Dube, L. Fauconnet, M. Rochet, Observations à bord des navires de pêche professionnelle, Bilan de l'échantillonnage 2014 (2015), doi:10.13155/39722.

[2] A. Régimbar, J. Guitton, O.Le Pape, Zones fonctionnelles pour les ressources halieutiques dans les eaux sous souveraineté française, Deuxième partie : Inventaire. (2018) https://hal.science/hal-02293032.

[3] N.J.B. Isaac, M.A. Jarzyna, P. Keil, L.I. Dambly, P.H. Boersch-Supan, E. Browning, S.N. Freeman, N. Golding, G. Guillera-Arroita, P.A. Henrys, S. Jarvis, J. Lahoz-Monfort, J. Pagel, O.L. Pescott, R. Schmucki, E.G. Simmonds, R.B. O'Hara, Data integration for large-scale models of species distributions, Trends Ecol. Evol. 35 (2020) 56-67, doi:10.1016/j.tree.2019.08.006.

[4] S.A.M. Elliott, N. Deleys, E. Rivot, A. Acou, E. Réveillac, L. Beaulaton, Shedding light on the river and sea lamprey within western European marine waters, Endanger. Species Res. 44 (2021) 409-419, doi:10.3354/esr01113.

[5] P. He, F. Chopin, P. Suuronen, R. Ferro, J. Lansley, Classification and illustrated definition of fishing gears, FAO (2021), doi:10.4060/cb4966en.

[6] S.A.M. Elliott, A. Acou, L. Beaulaton, J. Guitton, F. Réveillac, E. Rivot, Modelling the distribution of rare and data-poor diadromous fish at sea for protected area management, Prog. Oceanogr. 210 (2023) 102924, doi:10.1016/j.pocean.2022.102924.

[7] P.G. Bianco, The status of the Twaite Shad, Alosa agone, in Italy and the Western Balkans, Mar. Ecol. 23 (2002) 51-64, doi:10.1111/j.1439-0485.2002.tb00007.x.

[8] J.-L. Baglinière, P. Elie, Les alooses (*Alosa alosa* et *Alosa fallax* spp.) ecobiologie et variabilité des populations, Quae, Institut National de la Recherche Agronomique, Paris. (2000) https://www.quae.com/produit/438/9782759210763/les-aloses-alosa-alosa-et-alosa-fallax-spp.

[9] E.I. Kukuev, A.M. Orlov, New Subspecies of Twaite Shad *Alosa fallax balticus* (Clupeidae), Inl. Water Biol. 11 (2018) 407-416, doi:10.1134/S1995082918040119.

[10] C. Taverny, P. Elie, Répartition spatio-temporelle de la grande alose *Alosa alosa* (Linne, 1766) et de l'Alose feinte *Alosa fallax* (Lacépède, 1803) dans le Golfe de Gascogne, Bull. Français La Pêche La Piscic. (2001) 803-821, doi:10.1051/kmae:2001020.

[11] D. Doherty, T.K. McCarthy, The ecology and conservation of European Smelt (*Osmerus eperlanus* L.) from Waterford Estuary, in Southeastern Ireland, Biol. Environ. Proc. R. Irish Acad. 104 (2004) 125-130, doi:10.3318/BIOE.2004.104.2.125.

[12] M. Power, M. Attrill, Temperature-dependent temporal variation in the size and growth of Thames estuary smelt Osmerus eperlanus, Mar. Ecol. Prog. Ser. 330 (2007) 213-222, doi:10.3354/meps330213.

[13] D.T.G. Quigley, F. Igoe, W. O'Connor, The European Smelt *Osmerus eperlanus* L. in Ireland: general biology, ecology, distribution and Status with conservation recommendations, Biol. Environ. Proc. R. Irish Acad. 104 (2004) 57-66, doi:10.3318/BIOE.2004.104.3.57.
References

[1] A. Cornou, M. Quinio-scavinner, D. Delaunay, J. Dimeet, N. Goascoz, B. Dube, L. Fauconnet, M. Rochet, Observations à bord des navires de pêche professionnelle, Bilan de l'échantillonnage 2014 (2015), doi:10.13155/39722.

[2] A. Régimbar, J. Guitton, O.Le Pape, Zones fonctionnelles pour les ressources halieutiques dans les eaux sous souveraineté française, Deuxième partie : Inventaire. (2018) https://hal.science/hal-02293032.

[3] N.J.B. Isaac, M.A. Jarzyna, P. Keil, L.I. Dambly, P.H. Boersch-Supan, E. Browning, S.N. Freeman, N. Golding, G. Guillera-Arroita, P.A. Henrys, S. Jarvis, J. Lahoz-Monfort, J. Pagel, O.L. Pescott, R. Schmucki, E.G. Simmonds, R.B. O'Hara, Data integration for large-scale models of species distributions, Trends Ecol. Evol. 35 (2020) 56-67, doi:10.1016/j.tree.2019.08.006.

[4] S.A.M. Elliott, N. Deleys, E. Rivot, A. Acou, E. Réveillac, L. Beaulaton, Shedding light on the river and sea lamprey within western European marine waters, Endanger. Species Res. 44 (2021) 409-419, doi:10.3354/esr01113.

[5] P. He, F. Chopin, P. Suuronen, R. Ferro, J. Lansley, Classification and illustrated definition of fishing gears, FAO (2021), doi:10.4060/cb4966en.

[6] S.A.M. Elliott, A. Acou, L. Beaulaton, J. Guitton, E. Réveillac, E. Rivot, Modelling the distribution of rare and data-poor diadromous fish at sea for protected area management, Prog. Oceanogr. 210 (2023) 102924, doi:10.1016/j.pocean.2022.102924.

[7] P.G. Bianco, The status of the Twaite Shad, Alosa agone, in Italy and the Western Balkans, Mar. Ecol. 23 (2002) 51-64, doi:10.1111/j.1439-0485.2002.tb00007.x.

[8] J.-L. Baglinière, P. Elie, Les aloses (*Alosa alosa* et *Alosa fallax* spp.) ecobiologie et variabilité des populations, Quae, Institut National de la Recherche Agronomique, Paris. (2000) https://www.quae.com/produit/438/9782759210763/les-aloses-alosa-alosa-et-alosa-fallax-spp.

[9] E.I. Kukuev, A.M. Orlov, New Subspecies of Twaite Shad *Alosa fallax balticus* (Clupeidae), Inl. Water Biol. 11 (2018) 407-416, doi:10.1134/S1995082918040119.

[10] C. Taverny, P. Elie, Répartition spatio-temporelle de la grande alose *Alosa alosa* (Linne, 1766) et de l'Alose feinte *Alosa fallax* (Lacépède, 1803) dans le Golfe de Gascogne, Bull. Français La Pêche La Piscic. (2001) 803-821, doi:10.1051/kmae:2001020.

[11] D. Doherty, T.K. McCarthy, The ecology and conservation of European Smelt (*Osmerus eperlanus* L.) from Waterford Estuary, in Southeastern Ireland, Biol. Environ. Proc. R. Irish Acad. 104 (2004) 125-130, doi:10.3318/BIOE.2004.104.2.125.

[12] M. Power, M. Attrill, Temperature-dependent temporal variation in the size and growth of Thames estuary smelt *Osmerus eperlanus*, Mar. Ecol. Prog. Ser. 330 (2007) 213-222, doi:10.3354/meps330213.

[13] D.T.G. Quigley, F. Igoe, W. O'Connor, The European Smelt *Osmerus eperlanus* L. in Ireland: general biology, ecology, distribution and Status with conservation recommendations, Biol. Environ. Proc. R. Irish Acad. 104 (2004) 57-66, doi:10.3318/BIOE.2004.104.3.57.
# Modelling the distribution of rare and data-poor diadromous fish at sea for protected area management

Sophie A.M. Elliott<sup>a, d, f, *</sup>, Anthony Acou<sup>a, b</sup>, Laurent Beaulaton<sup>a, e</sup>, Jérôme Guitton<sup>d</sup>, Elodie Réveillac<sup>c</sup>, Etienne Rivot<sup>a, d</sup>

<sup>a</sup> Management of Diadromous Fish in their Environment OFB-INRAE-Institut Agro-UPPA, 35042 Rennes, France
<sup>b</sup> UMS OFB-CNRS-MNHN PatriNat, Station marine du Museum National d'Histoire Naturelle, 35800 Dinard, France
<sup>c</sup> Littoral, Environnement et Société (LIENSs), UMR 7266, La Rochelle Université-CNRS, 17000 La Rochelle, France
<sup>d</sup> DECOD (Ecosystem Dynamics and Sustainability), L'Institut Agro, Ifremer, INRAE, Rennes, France
<sup>e</sup> Service Conservation et Gestion Durable des Espèces Exploitées, OFB, DRAS, 35042 Rennes, France
<sup>f</sup> Game & Wildlife Conservation Trust, Salmon & Trout Research Centre, East Stoke, Wareham BH20 6BB, UK

## ARTICLE INFO

### Keywords:

## 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.

Given the coastal distribution of these species, they are exposed to higher anthropogenic pressures from both terrestrial and marine environments. Risk of bycatch at sea for most species appears to be low. Nonetheless, for threatened individuals, even a small amount of bycatch may impact their populations, especially since misreporting is likely to be high. Differences in catchability between gears highlight potential benefits of limiting access of certain gears within protected areas to reduce bycatch.

## 1. Introduction

Diadromous fish undertake long distance migrations between freshwater and marine ecosystems during their varying life history stages (Lassalle et al., 2008; Legrand et al., 2020; Limburg and Waldman, 2009; McDowall, 2009). They are particularly vulnerable since they are subject to both terrestrial and freshwater pressures (e.g., terrestrial runoff, pollution, habitat destruction, barriers to migration, climate change, fishing, etc.) (Costa et al., 2021; Limburg and Waldman, 2009;

Merg et al., 2020; Verhelst et al., 2021). Furthermore, unlike many marine fish that are geographically widespread, numerous anadromous fish (e.g., shad and salmonids) form river-specific populations that are more susceptible to extinction (Limburg and Waldman, 2009; McDowall, 2009). As a result, most diadromous species native to the northern Atlantic Ocean have declined in abundance by at least 90 % since the end of the late 19th century (Drouineau et al., 2018; Limburg and Waldman, 2009; Waldman and Quinn, 2022).

<footnote>* Corresponding author at: Management of Diadromous Fish in their Environment OFB-INRAE-Institut Agro-UPPA, 35042 Rennes, France.
E-mail address: SElliott@gwct.org.uk (S.A.M. Elliott).</footnote>
<figure><img src="image_171.png" /></figure>

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

Sophie A.M. Elliott<sup>a, d, f, *,</sup> Anthony Acou<sup>a, b</sup>, Laurent Beaulaton<sup>a, e</sup>, Jérôme Guittot<sup>d</sup>, Elodie Réveillac<sup>c</sup>, Etienne Rivot<sup>a, d</sup>

## ARTICLE INFO

### Keywords:

## 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.

Given the coastal distribution of these species, they are exposed to higher anthropogenic pressures from both terrestrial and marine environments. Risk of bycatch at sea for most species appears to be low. Nonetheless, for threatened individuals, even a small amount of bycatch may impact their populations, especially since misreporting is likely to be high. Differences in catchability between gears highlight potential benefits of limiting access of certain gears within protected areas to reduce bycatch.

## 1. Introduction

Diadromous fish undertake long distance migrations between freshwater and marine ecosystems during their varying life history stages (Lassalle et al., 2008; Legrand et al., 2020; Limburg and Waldman, 2009; McDowall, 2009). They are particularly vulnerable since they are subject to both terrestrial and freshwater pressures (e.g., terrestrial runoff, pollution, habitat destruction, barriers to migration, climate change, fishing, etc.) (Costa et al., 2021; Limburg and Waldman, 2009;

Merg et al., 2020; Verhelst et al., 2021). Furthermore, unlike many marine fish that are geographically widespread, numerous anadromous fish (e.g., shad and salmonids) form river-specific populations that are more susceptible to extinction (Limburg and Waldman, 2009; McDowall, 2009). As a result, most diadromous species native to the northern Atlantic Ocean have declined in abundance by at least 90 % since the end of the late 19th century (Drouineau et al., 2018; Limburg and Waldman, 2009; Waldman and Quinn, 2022).

<footnote><sup>*</sup> Management of Diadromous Fish in their Environment OFB-INRAE-Institut Agro-UPPA, 35042 Rennes, France</footnote>

<footnote><sup>b</sup> UMS OFB-CNRS-MNHN PatriNat, Station marine du Museum National d'Histoire Naturelle, 35800 Dinard, France</footnote>

<footnote><sup>c</sup> Littoral, Environnement et Sociétés (LIENSs), UMR 7266, La Rochelle Université-CNRS, 17000 La Rochelle, France</footnote>

<footnote><sup>d</sup> DECOD (Ecosystem Dynamics and Sustainability), L'Institut Agro, Ifremer, INRAE, Rennes, France</footnote>

<footnote><sup>e</sup> Service Conservation et Gestion Durable des Espèces Exploitées, OFB, DRAS, 35042 Rennes, France</footnote>

<footnote><sup>f</sup> Game & Wildlife Conservation Trust, Salmon & Trout Research Centre, East Stoke, Wareham BH20 6BB, UK</footnote>

<footnote>* Corresponding author at: Management of Diadromous Fish in their Environment OFB-INRAE-Institut Agro-UPPA, 35042 Rennes, France.
E-mail address: S.Elliott@gwct.org.uk (S.A.M. Elliott).</footnote>
To try and halt the loss of diadromous fish populations, various International Agreements (e.g., Bern Convention, Convention of Migratory Species) and legislations (e.g., EU Habitat Directive, EU Marine Strategy Framework Directive (MSFD)) have been enacted (Table 1). Measures implemented focus principally on freshwater or estuarine habitats, such as restoration of river continuity as required by the EU Water Framework Directive (WFD, 2000/60/EC) and freshwater Spatial Areas of Conservation under the Habitat Directive (HD, 92/43/EEC). However, most diadromous species remain threatened at a national level (https://www.nationalredlist.org; Table 1), and the improvements have yet to be attained throughout their range (Verhelst et al., 2021; Waldman and Quinn, 2022; Wilson and Veneranta, 2019). The marine life history stages and at-sea distribution of diadromous fish, largely remain a black box (Elliott et al., 2021), causing difficulty in evaluating their conservation status required under the various legislations that protect these species (Table 1; Wilson and Veneranta, 2019). Furthermore, few Marine Protected Areas (MPAs) have been designated to protect them (e.g., https://mpa.ospar.org; https://natura2000.eea.europa.eu), and at-sea bycatch is not well understood, even if it is suspected that it could contribute to significant mortality (Kappel, 2005; Stratoudakis et al., 2016; Verhelst et al., 2021; Wilson and Veneranta, 2019).

Under the EU data collection framework, Member States are required to collect fisheries bycatch data through an onboard observer program (Comou et al., 2015). Fisheries data is a rich set of information which provides year-round catch information and can be a valuable source of information for data-deficient species (e.g., Baum et al., 2003; Bisch et al., 2022; Elliott et al., 2020b). Bycatch data for protected species can be difficult to access, therefore fisheries observer data could help understand the distribution and bycatch mortality of data-poor species and meet EU Habitat Directive and MSFD (92/43/EEC; 2008/56/EC) requirements. Use of fisheries data does, however, require biases from the different gear types, the targeted nature of fishing, and un-balanced sampling to be taken into consideration (e.g. Alglave et al., 2022; Bourdaud et al., 2017).

Species Distribution Models (SDMs) provide a means to 'fill in the gaps' and provide complete coverage maps on which to base conservation and management decisions (Guisan and Thuiller, 2005; Guisan and Zimmermann, 2000; Leathwick et al., 2005). Modelling the distribution of diadromous fish could help improve protection measures for threatened and data-poor (insufficient biological information to determine

the current exploitation status (Berkson and Thorson, 2015; Prince and Hordyk, 2019)) diadromous fish within existing MPAs. SDMs could also be used to meet Descriptor 1 (Biodiversity - habitat extent) requirements under the MSFD (2008/56/EC). However, building diadromous fish at sea distribution models remains challenging because most of these fish are either IUCN red listed at a national level (https://www.nationalredlist.org) or data-poor (Limburg and Waldman, 2009; Merg et al., 2020). Modelling the distribution of data-poor and rare species, can lead to underestimation of their true distribution due to imperfect detection (uncertainty in the presence of a species within a site where the data was recorded) (Guillera-Arroita, 2017; MacKenzie et al., 2002).

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

Given the lack of knowledge on the distribution of most diadromous fish during their marine life history stages, and the need to ensure they are sufficiently protected, our objective was to model the current distri-

<caption>Table 1</caption>

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.

<table><thead><tr><th rowspan="2">Latin name</th><th rowspan="2">Common name</th><th rowspan="2">Type</th><th rowspan="2">Water column zone<fnref n="1" /></th><th colspan="2">EU IUCN<fnref n="3" /></th><th colspan="7">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>AI</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>AI</td><td>✓</td><td>-</td><td>✓</td><td>-</td><td>-</td><td>✓</td><td>✓</td></tr><tr><td>Lampetra fluvialis</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>Demersal<fnref n="2" /></td><td>LC</td><td>-</td><td>✓</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Omerus 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>VU<fnref n="4" /></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>

<footnote n="1">¹ Elliott and Dewally, 1995;</footnote>

<footnote n="2">² Almeida, 1996;</footnote>

<footnote n="3">³ Freyhof and Brooks, 2011;</footnote>

<footnote n="4">⁴ Nieto et al., 2015.</footnote>

<footnote>* previously known as *Lisa ramada*.</footnote>
To try and halt the loss of diadromous fish populations, various International Agreements (e.g., Bern Convention, Convention of Migratory Species) and legislations (e.g., EU Habitat Directive, EU Marine Strategy Framework Directive (MSFD)) have been enacted (Table 1). Measures implemented focus principally on freshwater or estuarine habitats, such as restoration of river continuity as required by the EU Water Framework Directive (WFD, 2000/60/EC) and freshwater Spatial Areas of Conservation under the Habitat Directive (HD, 92/43/EEC). However, most diadromous species remain threatened at a national level (https://www.nationalredlist.org; Table 1), and the improvements have yet to be attained throughout their range (Verhelst et al., 2021; Waldman and Quinn, 2022; Wilson and Veneranta, 2019). The marine life history stages and at-sea distribution of diadromous fish, largely remain a black box (Elliott et al., 2021), causing difficulty in evaluating their conservation status required under the various legislations that protect these species (Table 1; Wilson and Veneranta, 2019). Furthermore, few Marine Protected Areas (MPAs) have been designated to protect them (e.g., https://mpa.ospar.org; https://natura2000.eea.europa.eu), and at-sea bycatch is not well understood, even if it is suspected that it could contribute to significant mortality (Kappel, 2005; Stratoudakis et al., 2016; Verhelst et al., 2021; Wilson and Veneranta, 2019).

Under the EU data collection framework, Member States are required to collect fisheries bycatch data through an onboard observer program (Comou et al., 2015). Fisheries data is a rich set of information which provides year-round catch information and can be a valuable source of information for data-deficient species (e.g., Baum et al., 2003; Bisch et al., 2022; Elliott et al., 2020b). Bycatch data for protected species can be difficult to access, therefore fisheries observer data could help understand the distribution and bycatch mortality of data-poor species and meet EU Habitat Directive and MSFD (92/43/EEC; 2008/56/EC) requirements. Use of fisheries data does, however, require biases from the different gear types, the targeted nature of fishing, and un-balanced sampling to be taken into consideration (e.g. Alglave et al., 2022; Bourdaud et al., 2017).

Species Distribution Models (SDMs) provide a means to 'fill in the gaps' and provide complete coverage maps on which to base conservation and management decisions (Guisan and Thuiller, 2005; Guisan and Zimmermann, 2000; Leathwick et al., 2005). Modelling the distribution of diadromous fish could help improve protection measures for threatened and data-poor (insufficient biological information to determine

the current exploitation status (Berkson and Thorson, 2015; Prince and Hordyk, 2019)) diadromous fish within existing MPAs. SDMs could also be used to meet Descriptor 1 (Biodiversity - habitat extent) requirements under the MSFD (2008/56/EC). However, building diadromous fish at sea distribution models remains challenging because most of these fish are either IUCN red listed at a national level (https://www.nationalredlist.org) or data-poor (Limburg and Waldman, 2009; Merg et al., 2020). Modelling the distribution of data-poor and rare species, can lead to underestimation of their true distribution due to imperfect detection (uncertainty in the presence of a species within a site where the data was recorded) (Guillera-Arroita, 2017; MacKenzie et al., 2002).

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

Given the lack of knowledge on the distribution of most diadromous fish during their marine life history stages, and the need to ensure they are sufficiently protected, our objective was to model the current distri-

<caption>Table 1</caption>

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.

<table><thead><tr><th rowspan="2">Latin name</th><th rowspan="2">Common name</th><th rowspan="2">Type</th><th rowspan="2">Water column zone<fnref n="1" /></th><th rowspan="2">EU IUCN<fnref n="3" /></th><th colspan="8">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>AI</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>AI</td><td>✓</td><td>-</td><td>✓</td><td>-</td><td>-</td><td>✓</td><td>✓</td></tr><tr><td>Lampetra fluvialis</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<fnref n="2" /></td><td>Thinlip mullet</td><td>C</td><td>Demersal<fnref n="2" /></td><td>LC</td><td>-</td><td>✓</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Omerus 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>VU<fnref n="4" /></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>

<footnote n="1">¹ Elliott and Dewally, 1995;</footnote>

<footnote n="2">² Almeida, 1996;</footnote>

<footnote n="3">³ Freyhof and Brooks, 2011;</footnote>

<footnote n="4">⁴ Nieto et al., 2015.</footnote>

<footnote n="1">¹ previously known as *Lisa ramada*.</footnote>
bution (2003–2019) of diadromous fish at sea. In addition, we wanted to quantify the risk of bycatch from different gear types and assess the value of Habitat Directive MPAs for their conservation. Since at sea targeted surveys do not exist for these species, we collated a database of 168 904 hauls from fisheries dependent (French fisheries bycatch observer data) and independent data (scientific survey data undertaken to assess fish populations) in eastern Atlantic and French Mediterranean waters. A Bayesian site-occupancy model, taking into account spatial autocorrelation in the probability of presence, and imperfect detection through gear type, was used for these data-poor species (Dormann et al., 2007; Latimer et al., 2006; MacKenzie et al., 2002; Moriarty et al., 2020). From the distribution models we wanted to answer the following questions: 1) Is the distribution of diadromous fish at sea connected to existing knowledge on their freshwater habitats? 2) Do different gears have different catchability, and does this provide information on the risk of bycatch of diadromous fishes? 3) Could existing Habitat Directive MPAs protect diadromous fish if specific measures are implemented?

## 2. Method

### 2.1. Combining fisheries independent and dependent surveys

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) 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 DATRAS were obtained from the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) (https://campagnes.flotteoceanographique.fr/campaign). Fisheries-dependent data came from French fisheries observer data (ObsMer data; Comou 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 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).

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). 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).

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.

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 to 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.

### 2.2. Environmental predictor variables

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

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 et al., 2013) were kept. Depth and distance from coast, and sea surface temperature and net primary production were collinear. Since most species occupied a wide latitudinal range and seasonal or yearly temperature effects were not modelled, net primary production was used instead of sea surface temperature. To identify the model of best fit a backwards stepwise model selection process was undertaken, ensuring distance from coast and depth were not in the same model. The model with the lowest deviance information criterion, which includes a penalty factor for the number of parameters, was selected (Spiegelhalter et al., 2002).

### 2.3. Statistical analysis

A Bayesian hierarchical site occupancy Gaussian intrinsic Conditional Autoregressive (iCAR) model was built independently for each species. Hierarchical models have the advantages of separating the ecological process (here, the habitat suitability model, that is the spatial distribution of the probability of presence/absence, including spatial autocorrelation) from the observation process (here, the imperfect detection and its variability among gears or other factors) (Dormann et al., 2007; Isaac et al., 2020; Latimer et al., 2006; MacKenzie et al., 2002). The fundamental concept of the model is that the true presence/absence spatial field (including spatial autocorrelation) is modelled on a grid, each grid cell being associated with multiple observation events with imperfect detection, with detectability that may vary depending upon the fishing gear.

The site occupancy model integrates two processes:

i) Latent ecological process (habitat suitability).

The latent field of probability of presence is modelled on a grid as a function of environmental predictors and by explicitly accounting for spatial autocorrelation:

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

with $\theta_j$ the latent probability of presence (the habitat suitability) within grid cell j, modelled in the logit scale as a function of envi-
bution (2003–2019) of diadromous fish at sea. In addition, we wanted to quantify the risk of bycatch from different gear types and assess the value of Habitat Directive MPAs for their conservation. Since at sea targeted surveys do not exist for these species, we collated a database of 168 904 hauls from fisheries dependent (French fisheries bycatch observer data) and independent data (scientific survey data undertaken to assess fish populations) in eastern Atlantic and French Mediterranean waters. A Bayesian site-occupancy model, taking into account spatial autocorrelation in the probability of presence, and imperfect detection through gear type, was used for these data-poor species (Dormann et al., 2007; Latimer et al., 2006; MacKenzie et al., 2002; Moriarty et al., 2020). From the distribution models we wanted to answer the following questions: 1) Is the distribution of diadromous fish at sea connected to existing knowledge on their freshwater habitats? 2) Do different gears have different catchability, and does this provide information on the risk of bycatch of diadromous fishes? 3) Could existing Habitat Directive MPAs protect diadromous fish if specific measures are implemented?

## 2. Method

### 2.1. Combining fisheries independent and dependent surveys

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) 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 DATRAS were obtained from the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) (https://campagnes.flotteoceanographique.fr/campaign). Fisheries-dependent data came from French fisheries observer data (ObsMer data; Comou 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 French Ministry of Fisheries and Aquaculture (Direction des Péches Maritimes et de l'Aquaculture; Refer to Elliott et al., 2021 Table S1; Data in brief Table S1).

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). 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).

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.

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 to 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.

### 2.2. Environmental predictor variables

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

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 et al., 2013) were kept. Depth and distance from coast, and sea surface temperature and net primary production were collinear. Since most species occupied a wide latitudinal range and seasonal or yearly temperature effects were not modelled, net primary production was used instead of sea surface temperature. To identify the model of best fit a backwards stepwise model selection process was undertaken, ensuring distance from coast and depth were not in the same model. The model with the lowest deviance information criterion, which includes a penalty factor for the number of parameters, was selected (Spiegelhalter et al., 2002).

### 2.3. Statistical analysis

A Bayesian hierarchical site occupancy Gaussian intrinsic Conditional Autoregressive (iCAR) model was built independently for each species. Hierarchical models have the advantages of separating the ecological process (here, the habitat suitability model, that is the spatial distribution of the probability of presence/absence, including spatial autocorrelation) from the observation process (here, the imperfect detection and its variability among gears or other factors) (Dormann et al., 2007; Isaac et al., 2020; Latimer et al., 2006; MacKenzie et al., 2002). The fundamental concept of the model is that the true presence/absence spatial field (including spatial autocorrelation) is modelled on a grid, each grid cell being associated with multiple observation events with imperfect detection, with detectability that may vary depending upon the fishing gear.

The site occupancy model integrates two processes:

i) Latent ecological process (habitat suitability).

The latent field of probability of presence is modelled on a grid as a function of environmental predictors and by explicitly accounting for spatial autocorrelation:

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

with $\theta_j$ the latent probability of presence (the habitat suitability) within grid cell j, modelled in the logit scale as a function of envi-
<figure><img src="image_172.png" /><figcaption>**Fig. 1.** Diadromous fish presence (blue dots) and absence (light grey dots) data used to model their distribution (2003–2019). Dark grey lines indicate International Council for the Exploration of the Sea statistical divisions. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

ronmental predictors $X_j$, with $\beta$ a vector of fixed effect describing how much the environmental predictor contribute to the suitability process. The spatial autocorrelation $P_j$ is modelled at the scale of the grid cell as an iCAR random effect (2):
<figure><img src="image_172.png" /><figcaption>Fig. 1. Diadromous fish presence (blue dots) and absence (light grey dots) data used to model their distribution (2003–2019). Dark grey lines indicate International Council for the Exploration of the Sea statistical divisions. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

ronmental predictors $X_j$, with $\beta$ a vector of fixed effect describing how much the environmental predictor contribute to the suitability process. The spatial autocorrelation $P_j$ is modelled at the scale of the grid cell as an iCAR random effect (2):
<caption>**Table 2**</caption>

Environmental predictors used to analyse diadromous fish distribution. For all species except *Alosa agone*, variables were extracted at a 0.1 × 0.1 decimal degree resolution. *A. agone* environmental variables were extracted at a 0.04 decimal degree resolution.

<table><thead><tr><th>Variable</th><th>Unit</th><th>Description / Source</th></tr></thead><tbody><tr><td>Depth</td><td>Meters (m)</td><td>Bathymetry data downloaded from National Aeronautics and Space Administration Earth Observations project (https://neo.sci.gsfc.nasa.gov) with a 0.1 × 0.1 grid cell resolutions 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 (> 2 and ≤ 4 mm), coarse grain (> 4 mm and 64 mm), and rock (> 64 mm).</td></tr><tr><td>Salinity</td><td>Practical Salinity Units (PSU%)</td><td>Modelled sea surface salinity at 0.1 × 0.1 decimal degree resolution downloaded from Copernicus (ERSEM, 2020). Modelled at 0.04 × 0.04 decimal degree resolution from Copernicus using Teruzzi et al., (2021).</td></tr><tr><td>Net primary production</td><td>Milligrams of carbon per meters squared (mg C/m²)</td><td>Modelled net primary production at 0.1 × 0.1 decimal degree resolution downloaded from Copernicus (ERSEM, 2020). Modelled at 0.04 × 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 × 0.1 decimal degree resolution downloaded from Copernicus (ERSEM, 2020). Modelled at 0.04 × 0.04 decimal degree resolution from Copernicus using Teruzzi et al., (2021).</td></tr></tbody></table>

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

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

ii) Observation process (detection):

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

$$ y_{j,i} = \begin{cases} 1, & \text{Bernoulli } (z_{j,i} \delta_{j,i}) \\ 0, & \text{Bernoulli } (1 - z_{j,i}) \end{cases} \quad (2) $$

with

$$ z_{j,i} = \begin{cases} 1, & \text{Bernoulli } (\theta_i) \\ 0, & \text{Bernoulli } (1 - \theta_i) \end{cases} \quad (3) $$

and

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

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

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

## 2.4. Grid size and spatial distribution

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

## 2.5. Model evaluation

The probability of presence was calculated from the mean of the posterior distribution of the latent field $\theta_i$. A threshold of 0.4 was used to bin estimated probability of presence into binary presence/absence values. A threshold of 0.4 was used as a compromise given the rarity of the species and accuracy following examining various thresholds and plotting positive predicted values. The model performance was then evaluated using sensitivity (proportion of correctly classified presences), specificity (proportion of correctly classified absences) and percent correct classification (proportion of presences and absences correctly using the R package 'PresenceAbsence' (Freeman and Moisen, 2008a; Fielding and Bell, 1997; Table 3). Max sensitivity specificity which is often used for rare species distribution models (Freeman and Moisen, 2008b; Jiménez-Valverde and Lobo, 2007), was not used since false presences was very high when applying this metric. Additionally, our SDM objective was to try and minimise false presences and absences within the distribution models. The threshold independent Area under the receiver operating curve was also used to evaluate the model performance. These four metrics were used to better understand the accuracy of the model since, some metrics can be biased with low prevalence data and where imperfect detection can arise (Allouche et al., 2006; Fourcade et al., 2018; Guillera-Arroita, 2017; Leroy et al., 2018).

So that areas with probabilities of presence above the threshold could be easily identified for management purposes, all predictions below 0.4 were mapped with a uniform light blue colour (Fig. 2). Uncertainty maps for each model were also produced to gage a better idea of areas of prediction with higher uncertainty (Loiselle et al., 2003; Spiegelhalter et al., 2002). The uncertainty maps were calculated by the mean coefficient of variation from the posterior distribution of spatial
<table><caption>**Table 2**

Environmental predictors used to analyse diadromous fish distribution. For all species except *Alosa agone*, variables were extracted at a 0.1 × 0.1 decimal degree resolution. *A. agone* environmental variables were extracted at a 0.04 decimal degree resolution.</caption><thead><tr><th>Variable</th><th>Unit</th><th>Description / Source</th></tr></thead><tbody><tr><td>Depth</td><td>Meters (m)</td><td>Bathymetry data downloaded from National Aeronautics and Space Administration Earth Observations project (https://neo.sci.gsfc.nasa.gov) with a 0.1 × 0.1 grid cell resolutions 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 (> 2 and ≤ 4 mm), coarse grain (> 4 mm and 64 mm), and rock (> 64 mm).</td></tr><tr><td>Salinity</td><td>Practical Salinity Units (PSU%)</td><td>Modelled sea surface salinity at 0.1 × 0.1 decimal degree resolution downloaded from Copernicus (ERSEM, 2020). Modelled at 0.04 × 0.04 decimal degree resolution from Copernicus using Teruzzi et al., (2021).</td></tr><tr><td>Net primary production</td><td>Milligrams of carbon per meters squared (mg C/m²)</td><td>Modelled net primary production at 0.1 × 0.1 decimal degree resolution downloaded from Copernicus (ERSEM, 2020). Modelled at 0.04 × 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 × 0.1 decimal degree resolution downloaded from Copernicus (ERSEM, 2020). Modelled at 0.04 × 0.04 decimal degree resolution from Copernicus using Teruzzi et al., (2021).</td></tr></tbody></table>

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

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

ii) Observation process (detection):

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

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

with

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

and

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

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

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

## 2.4. Grid size and spatial distribution

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

## 2.5. Model evaluation

The probability of presence was calculated from the mean of the posterior distribution of the latent field $\theta_j$. A threshold of 0.4 was used to bin estimated probability of presence into binary presence/absence values. A threshold of 0.4 was used as a compromise given the rarity of the species and accuracy following examining various thresholds and plotting positive predicted values. The model performance was then evaluated using sensitivity (proportion of correctly classified presences), specificity (proportion of correctly classified absences) and percent correct classification (proportion of presences and absences correctly using the R package 'PresenceAbsence' (Freeman and Moisen, 2008a; Fielding and Bell, 1997; Table 3). Max sensitivity specificity which is often used for rare species distribution models (Freeman and Moisen, 2008b; Jiménez-Valverde and Lobo, 2007), was not used since false presences was very high when applying this metric. Additionally, our SDM objective was to try and minimise false presences and absences within the distribution models. The threshold independent Area under the receiver operating curve was also used to evaluate the model performance. These four metrics were used to better understand the accuracy of the model since, some metrics can be biased with low prevalence data and where imperfect detection can arise (Allouche et al., 2006; Fourcade et al., 2018; Guillera-Aroita, 2017; Leroy et al., 2018).

So that areas with probabilities of presence above the threshold could be easily identified for management purposes, all predictions below 0.4 were mapped with a uniform light blue colour (Fig. 2). Uncertainty maps for each model were also produced to gage a better idea of areas of prediction with higher uncertainty (Loiselle et al., 2003; Spiegelhalter et al., 2002). The uncertainty maps were calculated by the mean coefficient of variation from the posterior distribution of spatial
<figure><img src="image_173.png" /></figure>

(caption on next page)
<figure><img src="image_173.png" /><figcaption>A. agone</figcaption></figure>

<figure><img src="image_174.png" /><figcaption>L. fluviatilis</figcaption></figure>

<figure><img src="image_175.png" /></figure>

<figure><img src="image_176.png" /><figcaption>A. anguilla</figcaption></figure>

<figure><img src="image_177.png" /></figure>

<figure><img src="image_178.png" /><figcaption>P. marinus</figcaption></figure>

(caption on next page)
<figure><img src="image_174.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. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

parameters in the model of best fit for each species (Lambert et al., 2020; Latimer et al., 2006). Probabilities of prediction and uncertainty above 0.4 were classified into 0.2 units to highlight areas of higher and lower probability of presence more clearly (Fig. 2; Fig. S4). As a result of the low diadromous fish presence, splitting the data into learning/
<figure><img src="image_179.png" /><figcaption>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. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

parameters in the model of best fit for each species (Lambert et al., 2020; Latimer et al., 2006). Probabilities of prediction and uncertainty above 0.4 were classified into 0.2 units to highlight areas of higher and lower probability of presence more clearly (Fig. 2; Fig. S4). As a result of the low diadromous fish presence, splitting the data into learning/
<caption>**Table 3**</caption>

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.

<table><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<br>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<br>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<br>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. fluvialis</td><td>Distance + Salinity</td><td>16<br>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<br>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<br>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<br>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<br>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<br>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<br>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>

validation samples led to insufficient presence in the learning data set. Cross validation was therefore not undertaken.

## 2.6. Diadromous fish river basin presence

The predicted distributions were visually compared to the distribution of diadromous fish within their freshwater habitats using the EuroDiad database (version 4; https://data.inrae.fr/dataset.xhtml?persistentId= https://doi.org/10.15454/IVVAIC; Barber-O'Malley et al., 2022; Lassalle, 2021). From EuroDiad database, migrating diadromous fish population presence within the study areas our model encompassed, were extracted using 'recent' literature citing from 1951 to 2020 (see Barber-O'Malley et al., (2022) and Béguer et al., (2007) for more details on the database) and mapped with the hierarchical SDM results (Fig. 2). The EuroDiad4 database was used as opposed to IUCN red list map distribution since it has been peer reviewed and it is more up to date.

## 2.7. Habitat Directive MPAs of relevance to diadromous fish

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

# 3. Results

## 3.1. Diadromous fish hierarchical SDM

For all species the Area under the receiver operating curve and Percent Correct Classification performed very well (≥75 %; Table 3). Correct presence classification from the confusion matrices was consistently lower (<15 %) than that of correct absence classification (≥64 %), and false absences were lower (≤3%) than false presences (≤22 %) because of presence absence imbalance (Table 3; Table S1). The later led to on average better specificity than sensitivity scores (Table 3).

P. flesus was the most prevalent species modelled (5394 presences), whereas salmonids the least (S. salar = 68 presences and S. trutta 63 presences) (Table 3). SDMs that had lower presences (salmonids and lampreys) did not predict as well (lower sensitivity and higher uncertainty maps) as species with higher presence (shads, C. ramada, P. flesus and O. eperlanus). In addition, species that had a more diverse distribution (e.g., A. anguilla and S. salar) did not predict as well (sensitivity ≤ 0.66 for both species) as those with a more aggregated distribution (O. eperlanus sensitivity = 0.81) (Table 3; Figs. 1 and 2). Uncertainty maps showed that in general, areas of high and low predicted probability of presence had lower uncertainty (Fig. S4).

All species were observed to have a relatively shallow (<300 m depth) coastal distribution (~<300 km from the coast; Figs. 2 and 3). The main predictor variables for diadromous fish habitats were depth or distance from coast and salinity (Fig. 3). Since sufficient C. ramada were observed within eastern Atlantic and Mediterranean waters to model, two salinity peaks are observed corresponding to the different salinity ranges of the two regions (Fig. 3).

## 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
<caption>**Table 3**</caption>

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.

<table><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<br>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<br>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<br>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. fluvialis</td><td>Distance + Salinity</td><td>16<br>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<br>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<br>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<br>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<br>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<br>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<br>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>

validation samples led to insufficient presence in the learning data set. Cross validation was therefore not undertaken.

## 2.6. Diadromous fish river basin presence

The predicted distributions were visually compared to the distribution of diadromous fish within their freshwater habitats using the EuroDiad database (version 4; https://data.inrae.fr/dataset.xhtml?persistentId= https://doi.org/10.15454/IVVAIC; Barber-O'Malley et al., 2022; Lassalle, 2021). From EuroDiad database, migrating diadromous fish population presence within the study areas our model encompassed, were extracted using 'recent' literature citing from 1951 to 2020 (see Barber-O'Malley et al., (2022) and Béguer et al., (2007) for more details on the database) and mapped with the hierarchical SDM results (Fig. 2). The EuroDiad4 database was used as opposed to IUCN red list map distribution since it has been peer reviewed and it is more up to date.

## 2.7. Habitat Directive MPAs of relevance to diadromous fish

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

# 3. Results

## 3.1. Diadromous fish hierarchical SDM

For all species the Area under the receiver operating curve and Percent Correct Classification performed very well (≥75 %; Table 3). Correct presence classification from the confusion matrices was consistently lower (<15 %) than that of correct absence classification (≥64 %), and false absences were lower (≤3%) than false presences (≤22 %) because of presence absence imbalance (Table 3; Table S1). The later led to on average better specificity than sensitivity scores (Table 3).

P. flesus was the most prevalent species modelled (5394 presences), whereas salmonids the least (S. salar = 68 presences and S. trutta 63 presences) (Table 3). SDMs that had lower presences (salmonids and lampreys) did not predict as well (lower sensitivity and higher uncertainty maps) as species with higher presence (shads, C. ramada, P. flesus and O. eperlanus). In addition, species that had a more diverse distribution (e.g., A. anguilla and S. salar) did not predict as well (sensitivity ≤ 0.66 for both species) as those with a more aggregated distribution (O. eperlanus sensitivity = 0.81) (Table 3; Figs. 1 and 2). Uncertainty maps showed that in general, areas of high and low predicted probability of presence had lower uncertainty (Fig. S4).

All species were observed to have a relatively shallow (<300 m depth) coastal distribution (~<300 km from the coast; Figs. 2 and 3). The main predictor variables for diadromous fish habitats were depth or distance from coast and salinity (Fig. 3). Since sufficient C. ramada were observed within eastern Atlantic and Mediterranean waters to model, two salinity peaks are observed corresponding to the different salinity ranges of the two regions (Fig. 3).

## 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
<figure><img src="image_175.png" /></figure>

(caption on next page)
<figure><img src="image_180.png" /></figure>

(caption on next page)
<figure><img src="image_176.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. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

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).

### 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 pres-
<figure><img src="image_181.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. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

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).

### 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 pres-
<figure><img src="image_177.png" /><figcaption>Fig. 4. Matrix of the posterior mean (marginal posterior distribution) for gear A) detectability (inverse logit of γ; 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). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

ences 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).

### 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).

<caption>Table 4</caption>

Landed and discarded diadromous fish from the ObsMer data. Excluding targeted catches. Proportion landed = total landed / landed + discards.

<table><caption>Table 5</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>

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.

<table><thead><tr><th>Species</th><th>Number MPAs of relevance</th><th>Proportion of MPAs of relevance</th></tr></thead><tbody><tr><td>A. alosa</td><td>35</td><td>0.08</td></tr><tr><td>A. fallax</td><td>60</td><td>0.13</td></tr><tr><td>A. agone</td><td>10</td><td>0.27</td></tr><tr><td>A. anguilla</td><td>19</td><td>0.04</td></tr><tr><td>L. fluviatilis</td><td>11</td><td>0.02</td></tr><tr><td>P. marinus</td><td>14</td><td>0.03</td></tr><tr><td>C. ramada</td><td>23</td><td>0.05</td></tr><tr><td>P. flesus</td><td>65</td><td>0.15</td></tr><tr><td>O. eperlanus</td><td>13</td><td>0.03</td></tr><tr><td>S. salar</td><td>17</td><td>0.04</td></tr><tr><td>S. trutta</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
<figure><img src="image_182.png" /><figcaption>Fig. 4. Matrix of the posterior mean (marginal posterior distribution) for gear A detectability (inverse logit of γ; 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). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

ences 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).

### 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).

<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>

<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>A. alosa</td><td>35</td><td>0.08</td></tr><tr><td>A. fallax</td><td>60</td><td>0.13</td></tr><tr><td>A. agone</td><td>10</td><td>0.27</td></tr><tr><td>A. anguilla</td><td>19</td><td>0.04</td></tr><tr><td>L. fluviatilis</td><td>11</td><td>0.02</td></tr><tr><td>P. marinus</td><td>14</td><td>0.03</td></tr><tr><td>C. ramada</td><td>23</td><td>0.05</td></tr><tr><td>P. flesus</td><td>65</td><td>0.15</td></tr><tr><td>O. eperlanus</td><td>13</td><td>0.03</td></tr><tr><td>S. salar</td><td>17</td><td>0.04</td></tr><tr><td>S. trutta</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 (Halpem et al., 2015; Limburg and
Waldman, 2009; Lotze et al., 2006; Worn 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 nu-
merous 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 preci-
sion (Guillera-Arroita, 2017). The spatial random field of the probabil-
ity of presence explicitly integrates spatial autocorrelation in the proba-
bility 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 (Aratójo and New, 2007; Latimer et al., 2006; Loiselle et al., 2003).

For conservation purposes low thresholds are advised for low occur-
ring 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 predic-
tions whilst trying to meet conservation needs and minimise potential
impacts on sea-users any spatial protection measure that might be im-
plemented (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 infor-
mation on diadromous fish presence and abundance during their fresh-
water stage (Barber-O'Malley et al., 2022; Béguer et al., 2007). Using
the Eurodiad database to provide large scale understanding of river
basin presence of diadromous fish, we provide a first attempt at qualita-
tively linking diadromous fish freshwater and marine distributions

within north-eastern Atlantic waters. Although the probabilities of
presence for most species were low, and gaps within the EuroDiad data-
base evident (e.g., gaps in diadromous fish presence from Denmark and
Netherlands), connectivity with their freshwater habitats appears to be
visible, indicating migration pathways.

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

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

Given the coastal habitat occupancy for most of these diadromous
fish and declines observed (Limburg and Waldman, 2009; Merg et al.,
2020), detailed analysis of the connectivity between both habitats is es-
sential (Flitcroft et al., 2019; Lin et al., 2017; McDowall, 2009). Unfor-
tunately, such a model was not possible here, because it would require
more detailed knowledge on their freshwater habitat occupancy (i.e.,
numerous outlets diadromous fish were observed within proximity to,
were not contained within the EuroDiad database) and their river fi-
delity. Furthermore, seasonal and stage specific SDMs, which would
provide more information on their ontogenetic migration movements,
were not possible because of the low prevalence of the data and disper-
sion. Developing future research to model the connectivity between
both habitats is key to improving our understanding of the pressures
faced during their migrations (Flitcroft et al., 2019; Lin et al., 2017;
McDowall, 2009).

4.3. *Gear type detectability*

Even though little bycatch was observed, given a number of the di-
adromous fish studied here are threatened, even a small amount of by-
catch may impact their populations (Dulvy et al., 2003). Furthermore,
misreporting and illegal fishing is likely to remain (Elliott et al., 2020a;
ICES, 2005; Stratoudakis et al., 2020; Worn et al., 2013). Bycatch de-
tectability results from our models could help provide management ad-
vice so that gear types and areas with higher bycatch rate be avoided.
Such advice can provide direct information for the MSFD and the Habi-
tat Directive that requires the identification of gear types which may
threaten protected species (1992/43/EEC; 2008/56/EC).

All species were caught by a wide range of gear types (e.g., benthic,
demersal and pelagic trawls, and seine nets), despite the different gear
categories being deployed at different depths. The species were proba-
bly caught by a range of gear categories because of their diel vertical
the importance of investigating the vulnerability of diadromous fish to
coastal anthropogenic pressures (Halpem 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 nu-
merous 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 preci-
sion (Guillera-Arroita, 2017). The spatial random field of the probabil-
ity of presence explicitly integrates spatial autocorrelation in the proba-
bility 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 occur-
ring 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 predic-
tions whilst trying to meet conservations needs and minimise potential
impacts on sea-users any spatial protection measure that might be im-
plemented (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 infor-
mation on diadromous fish presence and abundance during their fresh-
water stage (Barber-O'Malley et al., 2022; Béguer et al., 2007). Using
the Eurodiad database to provide large scale understanding of river
basin presence of diadromous fish, we provide a first attempt at qualita-
tively linking diadromous fish freshwater and marine distributions

within north-eastern Atlantic waters. Although the probabilities of
presence for most species were low, and gaps within the EuroDiad data-
base evident (e.g., gaps in diadromous fish presence from Denmark and
Netherlands), connectivity with their freshwater habitats appears to be
visible, indicating migration pathways.

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

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

Given the coastal habitat occupancy for most of these diadromous
fish and declines observed (Limburg and Waldman, 2009; Merg et al.,
2020), detailed analysis of the connectivity between both habitats is es-
ential (Flitcroft et al., 2019; Lin et al., 2017; McDowall, 2009). Unfor-
tunately, such a model was not possible here, because it would require
more detailed knowledge on their freshwater habitat occupancy (i.e.,
numerous outlets diadromous fish were observed within proximity to,
were not contained within the EuroDiad database) and their river fi-
delity. Furthermore, seasonal and stage specific SDMs, which would
provide more information on their ontogenetic migration movements,
were not possible because of the low prevalence of the data and disper-
sion. Developing future research to model the connectivity between
both habitats is key to improving our understanding of the pressures
faced during their migrations (Flitcroft et al., 2019; Lin et al., 2017;
McDowall, 2009).

4.3. *Gear type detectability*

Even though little bycatch was observed, given a number of the di-
adromous fish studied here are threatened, even a small amount of by-
catch may impact their populations (Dulvy et al., 2003). Furthermore,
misreporting and illegal fishing is likely to remain (Elliott et al., 2020a;
ICES, 2005; Stratoudakis et al., 2020; Worm et al., 2013). Bycatch de-
tectability results from our models could help provide management ad-
vice so that gear types and areas with higher bycatch rate be avoided.
Such advice can provide direct information for the MSFD and the Habi-
tat Directive that requires the identification of gear types which may
threaten protected species (1992/43/EEC; 2008/56/EC).

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

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 overlaying it with the predicted distributions (Elliott et al., 2018; Quemmerais-Amice et al., 2020). Unfortunately, this information was not available to us. Additionally, since the fisheries observer data we had access to is only a small sample of the existing French fishing activity (Comou et al., 2015), and it does not include other countries fishing effort, results would be biased.

### 4.4. MPA relevance

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

Our results show relatively high core area presence of protected diadromous fish within Habitat Directive MPAs. Most marine Sites of Community Importance have not been designated to protect Habitat Directive listed diadromous fish. Nonetheless, our results highlight the value of these MPAs for diadromous fish protection. Specific management measures limiting threats (e.g., limiting access to gear types with higher probability of capture) to diadromous fish within these areas, would be of benefit to their protection (Domisch et al., 2019; Stratoudakis et al., 2016). Detailed analysis of Sites of Community Importance and other categories of designated MPAs should be undertaken to evaluate their potential to protect diadromous fish.

### 4.5. Conclusion

Understanding the spatial distribution of species and their habitats is essential for effective management and conservation (Halpern et al., 2015; Worm et al., 2009, 2005). Much research has been carried out on the conservation of freshwater diadromous fish (Drouineau et al., 2018; Merg et al., 2020; Verhelst et al., 2021). Very little research has been dedicated to the value of marine and estuary habitats for the conservation of these species and their risk of bycatch (Feunteun, 2002; Flitcroft et al., 2019; Lin et al., 2017). Here we provide an insight into the distribution and bycatch of diadromous fish at sea.

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

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

### United references

Almeida, 1996; Elliott and Dewailly, 1995; Worm et al., 2005; Aprahamian et al., 1998; Freyhof and Brooks, 2011; Lassalle, 2021; Legrand et al., 2020; Nieto et al., 2015.

### Declaration of Competing Interest

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

### Data availability

The data will be made available as part of the data in brief

### Acknowledgements

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

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pocean.2022.102924.

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