Abundant feasts: Favoring the invasion of an American fish species in Europe

Abundant feasts: Favoring the invasion of an American fish species in Europe | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Abundant feasts: Favoring the invasion of an American fish species in Europe Gala Gonzalez Gonzalez, Cesar Vilas, Francisco Baldo, Carlos Fernandez-Delgado, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3882111/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The ready availability of abundant food sources can be a key factor in the success of biological invasions. This study provides information about feeding habits, dietary niche, and seasonal and ontogenetic diet changes of the American invasive weakfish Cynoscion regalis in the Gulf of Cádiz, where its population is increasing exponentially since 2011 when its presence was reported in the area. By content analysis of 340 stomachs, we assessed the diet composition, prey diversity and abundance of juveniles and adults present in the Guadalquivir River Estuary. Fish and crustaceans accounted for more than 90% of their diet. Mysids are the main food intake for juveniles and piscivory becomes more important as C. regalis grows in size. Stomachs were significantly fuller during the summer and autumn months, coinciding with the higher abundance of small pelagic fish during that time in the estuary, especially the anchovy Engraulis encrasicolus , the main prey consumed through all months of the year, that showed a consumption peak in September and October. Adults also show significant monthly variations in the diet composition (P < 0.01) respect to Total Length. Juveniles show a specialist behaviour feeding almost exclusively on Mesopodopsis slabberi , while adults show a mixed feeding strategy. This research constitutes a comprehensive study of weakfish diet along the year in the non-native area, including for the first time, juvenile’s stages. Feeding habits Gulf of Cádiz invasive species stomach content analysis weakfish Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Abundant food resources can play a significant role in facilitating the success of invasive species in new environments (Byers 2000 ). When an exotic species enters a new ecosystem, having access to a readily available and plentiful food source favours reproduction and out competition with native species for these resources, being able to become invasive if they have traits that allow them to exploit those resources. The Gulf of Cadiz, located along the southwestern coast of Europe, is a highly productive marine ecosystem (Torres et al. 2013 ). Its productivity is closely linked to the long-term productivity of the Guadalquivir Estuary, which is one of the most significant contributors of nutrients and juvenile rearing to the Gulf's ecological richness (González-Ortegón et al. 2015 ; De Carvalho-Souza et al. 2019 ). This coupled ecosystem is a clear proof of productivity which plays a vital role in sustaining marine life, supporting local economies (Carvalho-Souza et al. 2019 ). The ready availability of abundant food sources can be a key factor in the success of biological invasions, in a vulnerable area to the introductions of non-native species due to the high volume of shipping, rising seawater temperatures and extensive anthropogenic changes (González-Ortegón et al. 2010 , 2020 ). In recent years several non-native species have been identified in Europe, including the American weakfish Cynoscion regalis (Bloch and Schneider, 1801) (Béarez et al. 2016 ; Bañón et al. 2017 ), a sciaenid fish native to the east coast of North America. The presence of C. regalis was first documented outside of its native range in the Schelde estuary in Belgiumin 2009 (Agentschap voor Natuur en Bos 2011 ) and later in the Gulf of Cádiz in 2011 (Béarez et al. 2016 ; Bañón et al. 2017 ). Since then, it has been reported in various locations along the Iberian Peninsula such as the Guadiana River, the Sado Estuary, the Tagus Estuary and the central-west coast of Portugal (Béarez et al. 2016 ; Morais and Teodósio 2016 ; Bañón et al. 2017 ; Gomes et al. 2017 ), and in the north of the Iberian Peninsula in Galician waters (Bañón et al. 2018 ). In Europe, and specifically in Iberian waters of the Gulf of Cádiz, the weakfish has become one of the most successful introduced fish species (Oliva-Paterna et al. 2020 ), with a well-established population (Bañón et al. 2017 ), and an exponential rise of landings since 2015. Currently, catches have increased from 1.3 tonnes in 2016 to a total of 75 tonnes in 2023 (as registered in the regional fish captures database (IDAPES 2023 )) especially at Chipiona and Sanlúcar de Barrameda fishing harbors (Cádiz) where most of the landings occur (Fig. 1 ). Paradoxically, East coast of North America native populations(Lowerre-Barbieri et al. 1996 ; McEachran and Fechhelm 2010 ; Willis et al. 2015 ) have been declining since the 1990s(ASMFC 2016 , 2019 ) to the extent of being classified as Endangered (EN) and currently included in the Red List of the International Union for Conservation of Nature (Barbieri and Barbieri 2020 ; IUCN 2023 ). The American weakfish is a demersal fish species that inhabits shallow coastal waters, but highly dependent on estuaries for food, shelter spawning and breeding (Mercer 1989 ; Boutin 2008 ; Turnure et al. 2014 ; Boutin and Targett 2019 ;). In the Gulf of Cádiz, the Guadalquivir River Estuary is the main feeding and nursery area for the early life stages of many marine species with commercial interest (Fernández-Delgado et al. 2007 ; González-Ortegón et al. 2012 ). Juveniles and larvae of the weakfish were detected for the first time in August 2019 in the Guadalquivir Estuary by the Guadalquivir Long-Term Ecological Research (GUADALQUIVIR-LTER 2023 ) monitoring program (1997–2023). This first record of a juvenile weakfish in the Iberian Peninsula were individuals ranging from 38 to 64 mm total length (TL). Since then, it is not unusual to find them as part of the fauna sampled in that area, especially in summer and autumn. Although the introduction of a new non-native species into an ecosystem does not necessarily implies a negative impact - only about 10% of marine exotic (non-native) species are pointed out as invasive (Anton et al. 2019 ) - there is indeed, a widespread fear among scientists and society in general that these invasions and following new species establishment will cause dramatic consequences, alter invaded ecosystems and declines in native populations (Coll et al. 2007 ; Costello et al. 2010 ; Bianchi et al. 2012 ;). Food webs can be restructured, leading to changes in the ecosystem function, productivity and degradation of ecosystem goods and services (Clavel et al. 2011 ; Gallardo et al. 2016 ). Feeding ecology studies are particularly relevant in this field, as determining the diet and feeding habits is crucial for predicting how non-native species will affect the food webs of receiving systems (Brandner et al. 2013 ; Garvey and Whiles 2017 ). If a recent organism has access to a significant amount of food resources in its new environment, then it is more likely to experience greater success in terms of survival, reproduction, and long-term persistence in that ecosystem. This work aims to explore whether the success of the introduction of the American weakfish may be related to the high abundance of prey species, such as the abundant small pelagic fish in the Gulf of Cádiz. In its native area, C. regalis shows a generalist feeding strategy, with occasional opportunistic behaviour on a wide variety of prey (Merriner 1975 ; Willis et al. 2015 ). It shows ontogenetic dietary shifts (Nemerson and Able 2004; Deary 2015 ; Willis et al. 2015 ), with mysid shrimps dominating the diet of juvenile weakfish (Thomas 1971 ; Chao and Musick 1977 ; Boutin and Targett 2019 ), transitioning to piscivory as they grow older, but also feeding on a lesser extent on crustaceans, polychaetes, molluscs, shrimp, squid and other estuarine preys (Merriner 1975 ; Nemerson 2001 ; Litvin and Weinstein 2004 ; Able and Fahay 2010 ; Willis et al. 2015 ). Weakfish has been well studied in its native area as an important commercial and recreational fishing resource, but little is known about how it behaves in the newly invaded areas. A first study examining the diet of weakfish along the Portuguese coast, (Cerveira et al. 2021 ), although limited in time and the number of individuals analysed, confirmed that weakfish also shows a generalist feeding strategy, being likely one of the many reasons for its invasiveness. Our study aims to provide a comprehensive overview of the feeding habits, dietary niche, and seasonal and ontogenetic dietary changes. This information is essential to comprehend the success of the invasive weakfish in the Gulf of Cádiz, its ecological role in this new ecosystem for the species and provide relevant information for future management measures of this species. Methods Study area The present study was carried out in the Gulf of Cádiz and the Guadalquivir River Estuary (Fig. 2 ), currently the main area of establishment and invasion of weakfish in Europe (Bañón et al. 2018 ).The Gulf of Cádiz (Atlantic Ocean) is strongly influenced by the Guadalquivir Estuary outflow and a high biological production and fisheries (De Carvalho-Souza et al. 2019 ). This estuary is recognized as a key nursery area for many marine species (Baldo and Drake 2002 ; González-Ortegón et al. 2015 ; De Carvalho-Souza et al. 2019 ), with the weakfish being added to this list of marine species in the last four years. Field methods Adult fish individuals were collected monthly from local fishermen captures from March 2021 to April 2022. A total of 340 adult weakfish (TL > 18 cm) were sampled from the fishing fleet landings operating in the Gulf of Cádiz, mainly within the coastal waters surrounding the Guadalquivir River mouth. Fish were obtained from the fish markets of Chipiona and Sanlúcar de Barrameda (Cádiz) (Fig. 2 ). Main fishing gears used were trammel nets anchored at low depths along the coast and bottom trawls, which usually operate at greater depths. Fish from different boats and consecutive days in a week were pooled in order to get the closest possible to 60 individuals per month. Once captured, fish were transferred in ice to the laboratory within the next 24h after the capture. Juveniles were collected monthly by GUADALQUIVIR-LTER team from a traditional river fishing boat with a 1 mm mesh net at a standstill in 2 different locations: Bonanza and Tarfía (as described inGonzález-Ortegón et al. ( 2015 )) (Fig. 2 ). Historical stored samples were reviewed to identify juvenile C. regalis specimens that might have been mixed up with other species or misidentified. A total of 26 specimens were found in August and September 2019, October 2020 and June 2022. Fish were preserved in 4% formaldehyde and remained in these conditions until the moment of dissection in the laboratory. From now on we will refer to these 2 different sets of samples as adults and juveniles. Stomach content analysis All specimens were sexed, measured (Total Length, TL, and Standard Length, SL, ± 1 mm) and weighed (Total Weight, TW, and Gutted Weight, GW, ± 1 g). Guts were extracted, weighed and stored for different analysis. Stomachs contents (excluding the intestinal tract) were examined by binocular microscopes and preys identified using different manuals and books (Zariquiey Alvarez 1968; Drake and Arias 1990 ; Ballesteros and Llobet 2015 ) to the lowest taxonomic level possible. Hard structures such as otoliths or scales were used to identify the prey groups (Categories) and, in some cases, species were identified by the otoliths shape using an otoliths atlas (Tuset et al. 2008 ). All prey were counted, measured and weighed if allowed by the level of digestion. Preys were classified into 9 main categories: bivalves, cephalopods, crustacean, echinoids, fish, gastropods, polychaetes, scyphozoans and vegetal. In some cases, prey items could only be identified at the group level due to high degree of digestion and labeled as 'unknown' as the species level. These items were initially included in the analysis under broader categories but excluded when conducting species-specific analysis. For each prey, a percentage of relative presence (%RP) in each stomach was assigned during the visual assessment of the stomach contents. Each stomach was assigned a total percentage of 100% and portions of this percentage were assigned to the different preys according to their estimated contribution to the stomach volume (Hynes 1950 ; Amundsen and Sánchez-Hernández). Percentage of the total number of prey items (%N) was also used to express the dietary composition, which is the percentage that a particular prey species or group represents of the total number of prey items found (Hynes 1950 ; Amundsen and Sánchez-Hernández 2019 ). Frequency of occurrence (%F) of each prey item was calculated as the percentage of stomachs containing food in which a given prey item occurred (Hynes 1950 ; Hyslop 1980 ; Amundsen and Sánchez-Hernández 2019 ). When expressing all these indices by category, we considered prey items that were identified up to category level, while when expressing them by prey species, we only consider preys items that were identified up to species level, thus excluding unidentified fish or crustaceans. Stomach fullness index was used according to Garrido et al. ( 2008 ) and AFSC ( 2015 ) ranging from 1 to 9 (where: 1: empty, 2: traces-25%, 3: 25%, 4: 25–50%, 5: 50%, 6: 50–75%, 7: 75%, 8: 75–100%, 9: 100% distended). Costello ( 1990 ) diagram modified by Amundsen was used to assess the feeding behavior of weakfish, by plotting the percentage of occurrence against the prey-specific abundance of each prey taxon (Costello 1990 ; Amundsen et al. 1996 ), where: Percentage of occurrence: \({F}_{i}=\left(\frac{{N}_{i}}{N}\right)\) , where N i is the number of predators with prey i in their stomach and N is the total number of predators with stomach contents. Prey-specific abundance in terms of relative presence (%RP): \({P}_{i}=\left(\frac{\sum {S}_{i}}{\sum {S}_{ti}}\right)x100\) , where P i is the prey-specific abundance of prey i , S i is the stomach content comprised of prey I , and S ti is the total stomach content in only those predators with prey i in their stomachs. Data analysis Diet of weakfish was analyzed using a multivariate approach with the statistical software PRIMER 7 version 7.0.21 (Clarke and Gorley 2006 ) with PERMANOVA + (Anderson et al. 2008 ). Preys were grouped by category or species and a multivariate data analysis was carried out by non-metric multidimensional scaling (MDS) ordination with the Bray-Curtis similarity measurement for diet composition (Clarke and Gorley 2006 ). Pairwise Bray–Curtis similarity coefficients were calculated for the %RP and %N of prey items, which provide a rough measure of dietary overlap of intra-specific differences (see (González-Ortegón et al. 2010 ). Main prey categories and the percent contribution of each prey item responsible for similarity and dissimilarity in each considered group were identified using SIMPER (Clarke and Warwick 2001 ). One-way ANOSIM permutation test was used to assess if diet composition differences between adults and juveniles were statistically significant. In addition, PERMANOVA analysis were carried out to evaluate adults’ differences in stomach contents (using weakfish total length) and to detect significant differences on the interactions between months/seasons, sex and total length. The biodiversity indexes species richness (S) and Shannon-Wiener (H´) were calculated. Results Adults population The adult weakfish sampled consisted of 340 individuals, with 53.53% females, 43.24% males, and only 3.24% undetermined. Individual sizes ranged from 19.8 to 54 cm (TL) and 78.82 to 1412.64 g (TW), with 60% of the population falling between 25 and 35 cm (TL). Only one individual smaller than 20 cm (TL) was sampled, while two were larger than 50 cm (TL). Larger individuals over 40 cm were mostly females (78%). Of the 340 stomachs analyzed, 228 contained food (67.1%) while 112 remained empty (32.9%). Relative index of stomach fullness showed that half (53.51%) of the stomachs containing food belonged to the category 2 while only 10.09% fell into category 9. The average number of prey items consumed by weakfish was 2.88 prey items per individual, with a higher number of prey items per stomach during the summer months compared to the rest of the year. Fish and crustaceans were the two main groups consumed by C. regalis both in terms of number (52.05% and 42.01% respectively) and relative presence (73.80% and 19.95% respectively), indicating that these two groups represented more than 90% of the diet. Frequency of occurrence was 79.59% for fish and 34.69% for crustaceans. The relative presence of fish in the diet was always above 59%, regardless of the season. Other prey groups found in the stomachs were bivalves, polychaetes, cephalopods, echinoderms, gastropods and scyphozoans. The European anchovy ( Engraulis encrasicolus ) and Processa sp. were the most abundant prey in each group, representing 75.51% and 47.25% of the total relative presence of preys in each group, respectively (Table 1 ). Table 1 . Groups and identified prey species found in the stomach content from adults and juveniles of C. regalis . Results of different indexes (Numerical percentage (N%), relative presence percentage (%RP) and frequency of occurrence percentage (F%) are shown for each prey group and identified prey species by season. For juveniles, no season differentiation is made. Prey species vary according to the seasons, but only 3 species are always present: Processa sp., E. encrasicolus and Aphia minuta . In all seasons, the consumption of crustaceans and fish was always the main food intake, showing in spring a bigger variability of preys consumed (Table 1 ). This is also shown by the biodiversity indices (S and H´), where there is a greater diversity of preys in spring (1.30 and 0.18 respectively) as they all present higher values compared to summer (1.24 and 0.11), autumn (1.19 and 0.09) and winter (1.19 and 0.11) (S and H´ respectively). E. encrasicolus is the main prey found in the stomachs, both in terms of %RP and %N (Table 1 ). It shows a temporal variability, being consumed in lower amounts in the winter months and towards early spring, progressively increasing towards summer and early Autumn, reaching the highest values in September and October (Fig. 3 ). The Amundsen et al. ( 1996 ) approach to evaluate dietary composition shows that the adult weakfish population mainly exhibits a specialist feeding strategy while in few cases predators exhibit a more generalist behavior (preys located in the lower half of the diagram). The preferred prey chosen by each individual differs in some cases. The diagram reveals that the prey that we find located closer to the top right corner, is the most dominant prey, E. encrasicolus . Preys located closer to the 0 on the x-axis indicate preys consumed by rare predators and are not common preys chosen by the population but by individual predators instead (individual specialization). In this case, the population exhibits a broad niche width and a high inter-phenotype component, indicating that various predators will specialize in different prey species (Fig. 4 ). PERMANOVA test to assess differences in the diet composition of the weakfish along the size range (TL) revealed that there were significant differences ( p < 0.05) with size, between months and sex. Having an effect on the diet composition in both cases, using the %RP and N values and grouping the preys at group level or species level (Table 2 ). When examining the size relationship between weakfish predator and the number anchovies (the most abundant prey) in the stomach contents, a positive but weak relationship was found (r = 0.24). Table 2 PERMANOVA results to test differences in the diet composition along the size range (TL) of C. regalis . Results are shown by grouping the preys by category (group level) and by identified species and using 2 different indexes: relative presence percentage (%RP) and numerical values (N). Symbols: MS = mean squares of factors; p(MC) = Monte Carlo p-value. Significant terms p(MC) < 0.05 and p(MC) < 0.01 are highlighted in bold, p(MC) < 0.01 values are also identified by an asterisk. Preys identified at group level %RP N Factor df MS Pseudo-F p(MC) MS Pseudo-F p(MC) Total Lenght 1 16976.0 12.279 0.0003* 16759.0 10.297 0.0001* Month 11 5165.4 37.363 0.0001* 5873.7 36.091 0.0001* Sex 1 1438.5 10.405 0.3225 2396.1 14.723 0.2055 Month x Sex 10 2589.3 18.729 0.0208 3422.5 2.103 0.0012 Res 194 1382.5 1627.5 Total 217 Preys identified at species level %RP N Factor df MS Pseudo-F p(MC) MS Pseudo-F p(MC) Total Lenght 1 6619.5 19.822 0.0459 6668.8 20.084 0.0401 Month 11 9062.0 27.136 0.0001* 9671.8 29.128 0.0001* Sex 1 2060.0 0.617 0.8071 1710.4 0.515 0.8995 Month x Sex 9 3933.5 11.779 0.1497 4444.0 13.384 0.0329 Res 90 3339.4 3320.4 Total 112 Stomach fullness of weakfish showed significant differences along the year on the different months. PERMANOVA test to assess differences in the fullness state along the size range (TL), sex and months revealed that size, sex and mainly months had significant effect (Table 3 ). The individuals caught between June and November (summer and autumn) had fuller stomachs than those captured between December and May (spring and winter) (Fig. 5 ). Table 3 . PERMANOVA and Pairwise test to evaluate the influence of sex and the time of the year (months) on the stomach fullness state on C. regalis . Data on the top-right triangle represent p-value, triangle in the bottom-left contains T-value data. Symbols: MS=mean squares of factors; p(MC) = Monte Carlo p-value. Significant terms p(MC) < 0.05 and p(MC) < 0.01 are highlighted in bold, p(MC) < 0.01 values are also identified by an asterisk. Juveniles A total of 26 juvenile weakfish from the Guadalquivir River Estuary were sampled, ranging from 2.4 to 8.4 cm in TL and 0.082 and 6.6 g in TW. Of the 26 stomachs analyzed only 1 of them was found empty. Among the 25 stomachs containing food, 48% were completely full (n = 12), while only 12% (n = 3) were filled up to 25% of the stomach capacity, the rest (n = 10) showed different degrees of fullness between 25–100%. The average number of preys consumed by juvenile weakfish was 9.92 prey items per individual. The diet of juveniles was based on crustaceans and early stages of fish, showing a clear preference for crustaceans which made 94.33% of the preys consumed. More specifically the mysid Mesopodopsis slabberi constituted 99.57% of the preys (n = 230), with the exception of 1 individual of the shrimp Palaemon longirostris found. Relative presence showed that crustaceans constituted 77.37% of the diet, polychaetes 11.77%, and fish 10.86% (Table 1 ). Juveniles show a very clear specialist behavior with M. slabberi as the dominant prey species (Fig. 4 ). There were significant ontogenetic differences in the composition of the weakfish diet between adults and juveniles (r = 0.32, p = 0.001) when they cohabited during the months of autumn and summer. After performing a SIMPER analysis, we found that the average similarity in the adult diet was 23.86%, of which E. encrasicolus contributed a 92.16% to it. For juveniles the similarities were greater (57.58%), with M. slabberi being the main contributor of this similarity (96.51%). Among the groups, adults and juveniles, the dissimilarity is 94.78%, with both E. encrasicolus and M. slabberi as the main preys contributing to these differences. Discussion Biological invasions can lead to unpredictable effects in the recipient ecosystem, especially in the marine environment where ecosystems are highly connected across broad spatial scales (Giakoumi et al. 2019 ). One way to understand the ecological impacts and effects of these species is to investigate their diet composition. Based on our results, it is plausible that C. regalis invasive success is closely related to high prey abundance and similarity of physiological characteristics and life cycles between native and invaded areas. Currently, there is a lack of information and studies in the invaded area. A preliminary study on C. regalis was carried out in the Guadiana estuary in June 2017, evaluating the diet of a limited number of individuals of homogeneous size (33 individuals between 27.2 and 60.0 cm TL) collected over a period of one month (Cerveira et al. 2021 ). Their results showed that C. regalis has a generalist feeding strategy, mainly based on a diversity of benthic crustacean and fish. In this study, it has a diet composed mainly of crustaceans and bony fishes, especially the anchovy E. encrasicolus , which was the dominant prey through all the year. In adult weakfish, fish appeared as prey in more than 76% of the stomachs, a value very similar to the one obtained in previous studies (63%) in its native area (Merriner 1975 ; Willis et al. 2015 ). In terms of volume, weakfish diet in Gulf of Cádiz is mainly based on fish and crustaceans, similar to diet described at its native area. There, the main food sources are bony fishes such as anchovies and herrings and crustaceans such as penaeids and mysid shrimps (Merriner 1975 ; Nemerson and Able 2004; Willis et al. 2015 ). Although cannibalism has been detected before (Cerveira et al. 2021 ; Merriner 1975 ) and this behaviour seems to be common in other Sciaenidae species (such as Argyrosomus regius) , we did not detect any sign of cannibalism in our study. However, due to the high degree of degradation of some of the stomach contents analysed, some fish could not be identified. Therefore, we cannot exclude the possibility that some weakfish were among the preys. Among adult main preys, E. encrasicolus was the dominant prey, followed by Sardina pilchardus and A. minuta , and Processa sp. as the main crustacean constituting the weakfish diet. Probably, the availability of abundant food sources in the Gulf of Cadiz, such as anchovies, is causing this American fish species to successfully invade southwestern Europe. Monthly differences were found in stomach fullness, with stomachs being fuller in the summer and autumn months than in spring and winter. This could be due to the increase in food availability during the warm months as well as by a possible more active predation behaviour. The fact that the average number of prey items consumed is higher in summer than in other seasons is due to the higher intake of small crustaceans. The number of small crustaceans they need to eat to satisfy their dietary needs is much higher than when feeding on, for example, anchovies. Estuarine species were common preys in the diet of weakfish in the Gulf of Cádiz in all seasons. The composition of the estuarine fauna in the Guadalquivir Estuary is mainly dominated by the anchovy E. encrasicolus and A. minuta in summer and winter, respectively (González-Ortegón et al. 2015 ), which explains the higher consumption of E. encrasicolus by weakfish during the months of August, September and October. The fact that the samples were collected close to the mouth of the Guadalquivir Estuary, may probably explain the presence of the species Processa sp., E. encrasicolus and A. minuta in their stomach contents in all seasons. Our results confirm that weakfish is using the Guadalquivir River Estuary ecosystem as its nursery area. There are no previous studies of juvenile diet in the invaded areas, so this is the first assessment of juvenile diet outside of its native range. Juveniles weakfish collected inside the Guadalquivir Estuary behaved as specialists with a clear food preference for the mysid shrimp M. slabbery . Similar to the most abundant species of estuarine macrofauna and fish juveniles present in the Guadalquivir Estuary (Baldo and Drake 2002 ; Vilas et al. 2008 ; González-Ortegón et al. 2010 ). In addition, other three species were found in the juvenile stomachs in very small proportion: E. encrasicolus , Palaemon longirostris and the polychaeta Glycera sp. In the Guadalquivir estuary, E. encrasicolus and the white shrimp P. longirostris are the most dominant nektonic macrofauna species (González-Ortegón et al. 2006 , 2010 , 2015 ), so there is a high probability that these were more casual encounters rather than active predation by weakfish, as it can also be deducted from the Amundsen (1996) plot, in which these are part of rare preys identified. Results of juveniles from the native area (Boutin and Targett 2019 ) were extracted from a study conducted along summer and autumn so data are comparable to our study. That is, fish and polychaeta were also part of the diet in the native area in smaller proportions than mysids (Boutin and Targett 2019 ). In another study, Merriner ( 1975 ) also stated that the principal food items of age 0 weakfish were shrimp and anchovy. If we compare our results to previous studies conducted in the native area, we can conclude that juveniles are also maintaining the same feeding behaviour, feeding on the same functional groups and on similar proportions. The feeding strategy was characterized using the modified Costello ( 1990 ) diagram, showing that adult weakfish in the Gulf of Cádiz exhibit a mixed feeding strategy. This is consistent with the feeding strategy shown by weakfish in its native area (Willis et al. 2015 ). Outside of its native range, in Portuguese waters, the same feeding strategy has been observed (Cerveira et al. 2021 ). This strategy is not entirely effective in the Gulf of Cádiz compared to the native area, as the proportion of anchovies in adults or mysids in juveniles (which are the most abundant prey in the Gulf of Cádiz and the Guadalquivir Estuary) was very significant and there is a low diversity of prey. In addition, juveniles showed a very narrow diet, mainly preying on M. slabberi , with a much more specialized behavior in contrast to adults. This suggest that there is an ontogenetic shift in the diet, transitioning from crustaceans to fish as they grow, with the relative proportion of fish increasing together with the size. Adults consume mainly fish while juveniles prey mainly on crustaceans, present in 88% of the stomachs vs. 23% fish. Similar dietary shifts from crustaceans to fish are typical for other sciaenidae species(Griffiths 2011 ; Spitz et al. 2013 ; Hubans et al. 2017 ) and have been previously shown for the weakfish in its native area, where it feeds mainly on crustaceans, more specifically on the mysid shrimp Neomysis americana , transitioning to fish as it increases in size (Nemerson 2001 ; Litvin and Weinstein 2004 ). The fact that we found such clear differences in diet between the two groups: juveniles and adults and that we did not see a transition in the results as in other studies (Nemerson 2001 ; Litvin and Weinstein 2004 ; Boutin and Targett 2019 ), can be due to the lack of intermediate sizes of the sampled individuals. We did not have access to individuals in the size range between 8.4–19.8 cm which may explain the lack of sizes at which they go through the transition from predating almost exclusively on crustaceans to a diet based mainly on fish and crustaceans. Mysids consumption appears to be a key factor in driving the spatial dynamics of nursery productivity for juvenile weakfish (Boutin and Targett 2019 ). In any case, the fact that the weakfish is growing at the expense of the most abundant prey might be related to its invasive success in this area. Predation on relatively abundant preys throughout the year, including those that are part of the diet of similar native species (Baldo and Drake 2002 ; Torres et al. 2013 ), could explain the invasion success of C. regalis . In recent years there has been concern about whether this new species will or has already caused damages on the populations of important commercially species in the area, and after evaluating its diet it would be logical to assume that the species most directly affected is E. encrasicolus , as it is the main prey of the weakfish across the year. As an invasive species, besides predation, weakfish can also compete for food with other local species that feed on the same preys such as A. regius , Dicentrarchus labrax or Dicentrarchus punctatus. Based on data from official landings in the Gulf of Cádiz, populations of A. regius have been declining since 2015, which coincides with the year in which weakfish began to appear in greater numbers and was included in official landings. The counterparts species A. regius shows a generalist feeding strategy and a similar ontegenic diet shift: while small sizes (5–15 cm) prey mainly on small decapods ( Crangon crangon ) and mysids, bigger decapods as Processa sp . and fishes ( E. encrasicholus y A. minuta ) become main preys for A. regius > 15 cm (unpublished data: Alconchel 2006). Hence, based on high diet similarity, a strong resource competition between C. regalis and A. regius might be occurring in the area. In any case, further studies are needed to determine whether this depletion is related to the invasion of weakfish or whether it is a mere coincidence and the reason for this decrease is due to any other factor or population dynamics. Realistically, eradication is unlikely for established invasive populations, and the aim of management is generally to reduce their populations to levels that have lower impacts that are considered acceptable (Usseglio et al. 2017 ). Although weakfish is already being exploited as a food resource it is not yet very known among the consumers or valued enough as other similar species (Nuñez et al. 2012 ). One of the only possible means to control the exponential growth of the population would be to increase the fishing pressure on this species and hope for a self-regulation and balanced integration into the ecosystem. It can also be a valuable resource for other multiple species that are struggling to survive in an over-exploited marine area and might find their resources limited. Since weakfish are the dominant prey of bottlenose dolphins Tursiops truncates in their native area (Gannon and Waples 2004 ), and bottlenose dolphins are common in the Gulf of Cádiz, they could play an important role in the ecosystem as top-down controllers (Coll et al. 2007 ; Giménez et al. 2017 ), controlling weakfish population in this area. Conclusion Weakfish is a well-established species in the Gulf of Cádiz, and the current population is growing exponentially, with catches around 60 times higher than in 2016. It feeds mainly on fish and crustaceans, being E. encrasicolus its main food resource in this area. It shows monthly variations in the diet, probably linked to the availability of resources in the Estuary. During juvenile stages, crustaceans are the main food resource, and as they grow in size, diets shift to fish as the main prey consumed. Understanding how the addition of this species to the ecosystem will affect it, is very important in order to make informed decisions. Probably, the availability of abundant food sources in the Gulf of Cádiz is causing American fish species to invade and establish themselves in European waters. This would explain the success that many other species have in their establishment and invasive characteristics. Declarations Contributions GG: Conceptualization, data collection, data curation, formal analysis, investigation, methodology, validation and writing original draft. CV: resources and supervision. FB: supervision. JAC: funding acquisition and resources. EGO: Conceptualization, formal analysis, methodology, funding acquisition, resources and supervision. All authors reviewed, edited and approved the final manuscript. Acknowledgements This work was carried out within the framework of the EcoInvadiz project (P18-RT-5010) funded by the regional government of Andalusia. The authors have declared that no competing interests exist. The authors would like to thank the fishermen from Chipiona and Sanlúcar de Barrameda for collaborating and providing the fish samples throughout the study. References Able KW, Fahay MP (2010) Ecology of Estuarine Fishes: Temperate Waters of the Western North Atlantic. Johns Hopkins University Press, Baltimore, MD AFSC (2015) Resource Ecology and Ecosystem Modeling Stomach Content Analysis Procedures Manual | NOAA Fisheries. Seattle Agentschap voor Natuur en Bos (2011) DNA Ontmaskert de Verdachte. Infoblad Voor de Openbare Visserij in Vlaanderen. Jaargang 28:32 Amundsen PA, Sánchez-Hernández J (2019) Feeding Studies Take Guts – Critical Review and Recommendations of Methods for Stomach Contents Analysis in Fish. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3882111","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269141724,"identity":"d8f0b724-7cdc-410d-86df-537cc811d853","order_by":0,"name":"Gala Gonzalez Gonzalez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYFAC5gYkRgUDAxthLYwNSIwzJGthbCPCWfLuBxs/MPyyyZNvP9j48Oc8O3s+9gOMD3/g0WJ4JrFZgrEvrZixJ7HZQHJbcmIbTwKzMQ8+LQ2JDRKMPYcTmxkS2yQMtx1IYJNgYJPG5zDD/ofNP0Ba2vgftv9InHPAHqiF/Sc+h8lLAA1n+HE4sQfIYDjYcIARyGVjwOcwA4mHbRaJDWmJMyQeNks2HAP5JbFZGp8W+f7kwzc+/LFJnN+ffPDjjxo7e/n2w0AGPlsOAIlE1OiAxxQOW8DSf/CqGQWjYBSMgpEOAOD/TstyHtUkAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0007-2576","institution":"ICMAN: Instituto de Ciencias Marinas de Andalucia","correspondingAuthor":true,"prefix":"","firstName":"Gala","middleName":"Gonzalez","lastName":"Gonzalez","suffix":""},{"id":269141725,"identity":"06af6c50-b15e-41ba-9953-a2028326e469","order_by":1,"name":"Cesar Vilas","email":"","orcid":"","institution":"IFAPA: Instituto Andaluz de Investigacion y Formacion Agraria Pesquera Alimentaria y de la Produccion Ecologica","correspondingAuthor":false,"prefix":"","firstName":"Cesar","middleName":"","lastName":"Vilas","suffix":""},{"id":269141726,"identity":"653f6b64-b99e-4f67-a280-d1f4b0cabf55","order_by":2,"name":"Francisco Baldo","email":"","orcid":"","institution":"IEO CO Cádiz: Instituto Espanol de Oceanografia Centro Oceanografico de Cadiz","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Baldo","suffix":""},{"id":269141727,"identity":"5dadf67f-bf86-48b3-af1e-3b93030a3eea","order_by":3,"name":"Carlos Fernandez-Delgado","email":"","orcid":"","institution":"Universidad de Córdoba Facultad de Ciencias: Universidad de Cordoba Facultad de Ciencias","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Fernandez-Delgado","suffix":""},{"id":269141728,"identity":"74b38f0d-0f52-48b3-b2dd-a85120ba406d","order_by":4,"name":"Jose A Cuesta","email":"","orcid":"","institution":"ICMAN: Instituto de Ciencias Marinas de Andalucia","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"A","lastName":"Cuesta","suffix":""},{"id":269141729,"identity":"a8da3d21-9a3d-4571-9f6c-336a3844fbfe","order_by":5,"name":"Enrique Gonzalez-Ortegon","email":"","orcid":"","institution":"ICMAN: Instituto de Ciencias Marinas de Andalucia","correspondingAuthor":false,"prefix":"","firstName":"Enrique","middleName":"","lastName":"Gonzalez-Ortegon","suffix":""}],"badges":[],"createdAt":"2024-01-20 17:12:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3882111/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3882111/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50380095,"identity":"a1bb072f-992f-45c6-a493-dcd4d0780a62","added_by":"auto","created_at":"2024-01-30 16:38:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107429,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of landings of \u003cem\u003eCynoscion regalis\u003c/em\u003e in the Gulf of Cadiz from 2016 to 2023. Landings are accounted (in tonnes) for the different fishing harbors of the Gulf of Cadiz in Spanish waters (data up to November 2023).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3882111/v1/9c1d8bfea531d4ebc1fba0fb.png"},{"id":50380097,"identity":"dbc8bdd3-c320-46d6-a784-5835c33b65b6","added_by":"auto","created_at":"2024-01-30 16:38:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2885974,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area. Gulf of Cádiz and Guadalquivir Estuary in the Southwest Iberian Peninsula. Red stars: juveniles sampling locations, Bonanza and Tarfía. Yellow dots: Fishing fleet harbor localities where weakfish individuals have been collected from landings. Map data 2022 © Google.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3882111/v1/10f5568e6abdee3164758197.png"},{"id":50380094,"identity":"9abbfc4f-79d0-406b-9cc2-fdb510a7ee87","added_by":"auto","created_at":"2024-01-30 16:38:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51604,"visible":true,"origin":"","legend":"\u003cp\u003eBox-plot distribution showing monthly variation of \u003cem\u003eE. encrasicolus \u003c/em\u003econsumed by\u003cem\u003e C. regalis\u003c/em\u003e. Total number (N) of \u003cem\u003eE. encrasicolus\u003c/em\u003e as prey items consumed by \u003cem\u003eC. regalis\u003c/em\u003e along the year from March 2021 to April 2022.The upper and lower edge of the box indicate the 3\u003csup\u003erd\u003c/sup\u003e quartile and 1\u003csup\u003est\u003c/sup\u003e quartile, respectively. The whiskers indicate the range of plus 1.5 times the interquartile range. The points outside the ends of the whiskers are outliers.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3882111/v1/c39ace9339cfaca6bafbe21e.png"},{"id":50380468,"identity":"4a95f782-a35a-45a0-bdd1-eeab7e2a5c7e","added_by":"auto","created_at":"2024-01-30 16:46:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":151426,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of dietary composition following Amundsen et al., (1996) approach. Results for feeding strategy, prey importance and contribution to niche width (BPC = between phenotype component, WPC = within phenotype component) of weakfish using the modified Costello (1990) diagram (Amundsen \u003cem\u003eet al\u003c/em\u003e., 1996). Results for adults are shown in blue circles, while juvenile are represented by red crosses.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3882111/v1/c3c067b35c142acf88875f7f.png"},{"id":50380093,"identity":"82c8b382-08c0-4b1f-87e0-e22ca659839e","added_by":"auto","created_at":"2024-01-30 16:38:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":43803,"visible":true,"origin":"","legend":"\u003cp\u003eStomach fullness index. Box-plot showing stomach fullness index (state) for \u003cem\u003eCynoscion regalis\u003c/em\u003e individuals collected in the Gulf of Cádiz between March 2021 and April 2022, being: 1: empty, 2: traces-25%, 3: 25%, 4: 25-50%, 5: 50%, 6: 50-75%, 7: 75%, 8: 75-100%, 9: 100%. The upper and lower edge of the box indicate the 3\u003csup\u003erd\u003c/sup\u003e quartile and 1\u003csup\u003est\u003c/sup\u003e quartile, respectively. Median is shown by the band dividing the box. The whiskers indicate the range of plus/minus 1.5 times the interquartile range. The points outside the ends of the whiskers are outliers.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3882111/v1/ed6a4a3400ba4dc71605ec0a.png"},{"id":53745459,"identity":"bed89718-b2e3-41a6-84d1-805149df9236","added_by":"auto","created_at":"2024-03-29 17:40:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2716961,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3882111/v1/777e249d-ebf6-4054-b28d-c6e0479696d9.pdf"}],"financialInterests":"","formattedTitle":"Abundant feasts: Favoring the invasion of an American fish species in Europe","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAbundant food resources can play a significant role in facilitating the success of invasive species in new environments (Byers \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). When an exotic species enters a new ecosystem, having access to a readily available and plentiful food source favours reproduction and out competition with native species for these resources, being able to become invasive if they have traits that allow them to exploit those resources.\u003c/p\u003e \u003cp\u003eThe Gulf of Cadiz, located along the southwestern coast of Europe, is a highly productive marine ecosystem (Torres et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Its productivity is closely linked to the long-term productivity of the Guadalquivir Estuary, which is one of the most significant contributors of nutrients and juvenile rearing to the Gulf's ecological richness (Gonz\u0026aacute;lez-Orteg\u0026oacute;n et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; De Carvalho-Souza et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This coupled ecosystem is a clear proof of productivity which plays a vital role in sustaining marine life, supporting local economies (Carvalho-Souza et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The ready availability of abundant food sources can be a key factor in the success of biological invasions, in a vulnerable area to the introductions of non-native species due to the high volume of shipping, rising seawater temperatures and extensive anthropogenic changes (Gonz\u0026aacute;lez-Orteg\u0026oacute;n et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years several non-native species have been identified in Europe, including the American weakfish \u003cem\u003eCynoscion regalis\u003c/em\u003e (Bloch and Schneider, 1801) (B\u0026eacute;arez et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ba\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), a sciaenid fish native to the east coast of North America. The presence of \u003cem\u003eC. regalis\u003c/em\u003e was first documented outside of its native range in the Schelde estuary in Belgiumin 2009 (Agentschap voor Natuur en Bos \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and later in the Gulf of C\u0026aacute;diz in 2011 (B\u0026eacute;arez et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ba\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Since then, it has been reported in various locations along the Iberian Peninsula such as the Guadiana River, the Sado Estuary, the Tagus Estuary and the central-west coast of Portugal (B\u0026eacute;arez et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Morais and Teod\u0026oacute;sio \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ba\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gomes et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and in the north of the Iberian Peninsula in Galician waters (Ba\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In Europe, and specifically in Iberian waters of the Gulf of C\u0026aacute;diz, the weakfish has become one of the most successful introduced fish species (Oliva-Paterna et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with a well-established population (Ba\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and an exponential rise of landings since 2015. Currently, catches have increased from 1.3 tonnes in 2016 to a total of 75 tonnes in 2023 (as registered in the regional fish captures database (IDAPES \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)) especially at Chipiona and Sanl\u0026uacute;car de Barrameda fishing harbors (C\u0026aacute;diz) where most of the landings occur (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Paradoxically, East coast of North America native populations(Lowerre-Barbieri et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; McEachran and Fechhelm \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Willis et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) have been declining since the 1990s(ASMFC \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to the extent of being classified as Endangered (EN) and currently included in the Red List of the International Union for Conservation of Nature (Barbieri and Barbieri \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; IUCN \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe American weakfish is a demersal fish species that inhabits shallow coastal waters, but highly dependent on estuaries for food, shelter spawning and breeding (Mercer \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Boutin \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Turnure et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Boutin and Targett \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e;). In the Gulf of C\u0026aacute;diz, the Guadalquivir River Estuary is the main feeding and nursery area for the early life stages of many marine species with commercial interest (Fern\u0026aacute;ndez-Delgado et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Gonz\u0026aacute;lez-Orteg\u0026oacute;n et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Juveniles and larvae of the weakfish were detected for the first time in August 2019 in the Guadalquivir Estuary by the Guadalquivir Long-Term Ecological Research (GUADALQUIVIR-LTER \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) monitoring program (1997\u0026ndash;2023). This first record of a juvenile weakfish in the Iberian Peninsula were individuals ranging from 38 to 64 mm total length (TL). Since then, it is not unusual to find them as part of the fauna sampled in that area, especially in summer and autumn.\u003c/p\u003e \u003cp\u003eAlthough the introduction of a new non-native species into an ecosystem does not necessarily implies a negative impact - only about 10% of marine exotic (non-native) species are pointed out as invasive (Anton et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) - there is indeed, a widespread fear among scientists and society in general that these invasions and following new species establishment will cause dramatic consequences, alter invaded ecosystems and declines in native populations (Coll et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Costello et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bianchi et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e;). Food webs can be restructured, leading to changes in the ecosystem function, productivity and degradation of ecosystem goods and services (Clavel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gallardo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Feeding ecology studies are particularly relevant in this field, as determining the diet and feeding habits is crucial for predicting how non-native species will affect the food webs of receiving systems (Brandner et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Garvey and Whiles \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIf a recent organism has access to a significant amount of food resources in its new environment, then it is more likely to experience greater success in terms of survival, reproduction, and long-term persistence in that ecosystem. This work aims to explore whether the success of the introduction of the American weakfish may be related to the high abundance of prey species, such as the abundant small pelagic fish in the Gulf of C\u0026aacute;diz.\u003c/p\u003e \u003cp\u003eIn its native area, \u003cem\u003eC. regalis\u003c/em\u003e shows a generalist feeding strategy, with occasional opportunistic behaviour on a wide variety of prey (Merriner \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Willis et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It shows ontogenetic dietary shifts (Nemerson and Able 2004; Deary \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Willis et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with mysid shrimps dominating the diet of juvenile weakfish (Thomas \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Chao and Musick \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Boutin and Targett \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), transitioning to piscivory as they grow older, but also feeding on a lesser extent on crustaceans, polychaetes, molluscs, shrimp, squid and other estuarine preys (Merriner \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Nemerson \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Litvin and Weinstein \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Able and Fahay \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Willis et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWeakfish has been well studied in its native area as an important commercial and recreational fishing resource, but little is known about how it behaves in the newly invaded areas. A first study examining the diet of weakfish along the Portuguese coast, (Cerveira et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), although limited in time and the number of individuals analysed, confirmed that weakfish also shows a generalist feeding strategy, being likely one of the many reasons for its invasiveness.\u003c/p\u003e \u003cp\u003eOur study aims to provide a comprehensive overview of the feeding habits, dietary niche, and seasonal and ontogenetic dietary changes. This information is essential to comprehend the success of the invasive weakfish in the Gulf of C\u0026aacute;diz, its ecological role in this new ecosystem for the species and provide relevant information for future management measures of this species.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe present study was carried out in the Gulf of C\u0026aacute;diz and the Guadalquivir River Estuary (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), currently the main area of establishment and invasion of weakfish in Europe (Ba\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).The Gulf of C\u0026aacute;diz (Atlantic Ocean) is strongly influenced by the Guadalquivir Estuary outflow and a high biological production and fisheries (De Carvalho-Souza et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This estuary is recognized as a key nursery area for many marine species (Baldo and Drake \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gonz\u0026aacute;lez-Orteg\u0026oacute;n et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; De Carvalho-Souza et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with the weakfish being added to this list of marine species in the last four years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eField methods\u003c/h2\u003e \u003cp\u003eAdult fish individuals were collected monthly from local fishermen captures from March 2021 to April 2022. A total of 340 adult weakfish (TL\u0026thinsp;\u0026gt;\u0026thinsp;18 cm) were sampled from the fishing fleet landings operating in the Gulf of C\u0026aacute;diz, mainly within the coastal waters surrounding the Guadalquivir River mouth. Fish were obtained from the fish markets of Chipiona and Sanl\u0026uacute;car de Barrameda (C\u0026aacute;diz) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Main fishing gears used were trammel nets anchored at low depths along the coast and bottom trawls, which usually operate at greater depths. Fish from different boats and consecutive days in a week were pooled in order to get the closest possible to 60 individuals per month. Once captured, fish were transferred in ice to the laboratory within the next 24h after the capture.\u003c/p\u003e \u003cp\u003eJuveniles were collected monthly by GUADALQUIVIR-LTER team from a traditional river fishing boat with a 1 mm mesh net at a standstill in 2 different locations: Bonanza and Tarf\u0026iacute;a (as described inGonz\u0026aacute;lez-Orteg\u0026oacute;n et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Historical stored samples were reviewed to identify juvenile \u003cem\u003eC. regalis\u003c/em\u003e specimens that might have been mixed up with other species or misidentified. A total of 26 specimens were found in August and September 2019, October 2020 and June 2022. Fish were preserved in 4% formaldehyde and remained in these conditions until the moment of dissection in the laboratory. From now on we will refer to these 2 different sets of samples as adults and juveniles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStomach content analysis\u003c/h2\u003e \u003cp\u003eAll specimens were sexed, measured (Total Length, TL, and Standard Length, SL, \u0026plusmn;\u0026thinsp;1 mm) and weighed (Total Weight, TW, and Gutted Weight, GW, \u0026plusmn;\u0026thinsp;1 g). Guts were extracted, weighed and stored for different analysis. Stomachs contents (excluding the intestinal tract) were examined by binocular microscopes and preys identified using different manuals and books (Zariquiey Alvarez 1968; Drake and Arias \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Ballesteros and Llobet \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) to the lowest taxonomic level possible. Hard structures such as otoliths or scales were used to identify the prey groups (Categories) and, in some cases, species were identified by the otoliths shape using an otoliths atlas (Tuset et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). All prey were counted, measured and weighed if allowed by the level of digestion. Preys were classified into 9 main categories: bivalves, cephalopods, crustacean, echinoids, fish, gastropods, polychaetes, scyphozoans and vegetal. In some cases, prey items could only be identified at the group level due to high degree of digestion and labeled as 'unknown' as the species level. These items were initially included in the analysis under broader categories but excluded when conducting species-specific analysis.\u003c/p\u003e \u003cp\u003eFor each prey, a percentage of relative presence (%RP) in each stomach was assigned during the visual assessment of the stomach contents. Each stomach was assigned a total percentage of 100% and portions of this percentage were assigned to the different preys according to their estimated contribution to the stomach volume (Hynes \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1950\u003c/span\u003e; Amundsen and S\u0026aacute;nchez-Hern\u0026aacute;ndez). Percentage of the total number of prey items (%N) was also used to express the dietary composition, which is the percentage that a particular prey species or group represents of the total number of prey items found (Hynes \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1950\u003c/span\u003e; Amundsen and S\u0026aacute;nchez-Hern\u0026aacute;ndez \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Frequency of occurrence (%F) of each prey item was calculated as the percentage of stomachs containing food in which a given prey item occurred (Hynes \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1950\u003c/span\u003e; Hyslop \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Amundsen and S\u0026aacute;nchez-Hern\u0026aacute;ndez \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). When expressing all these indices by category, we considered prey items that were identified up to category level, while when expressing them by prey species, we only consider preys items that were identified up to species level, thus excluding unidentified fish or crustaceans.\u003c/p\u003e \u003cp\u003eStomach fullness index was used according to Garrido et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and AFSC (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) ranging from 1 to 9 (where: 1: empty, 2: traces-25%, 3: 25%, 4: 25\u0026ndash;50%, 5: 50%, 6: 50\u0026ndash;75%, 7: 75%, 8: 75\u0026ndash;100%, 9: 100% distended).\u003c/p\u003e \u003cp\u003eCostello (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) diagram modified by Amundsen was used to assess the feeding behavior of weakfish, by plotting the percentage of occurrence against the prey-specific abundance of each prey taxon (Costello \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Amundsen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), where:\u003c/p\u003e \u003cp\u003ePercentage of occurrence: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({F}_{i}=\\left(\\frac{{N}_{i}}{N}\\right)\\)\u003c/span\u003e\u003c/span\u003e, where N\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the number of predators with prey \u003cem\u003ei\u003c/em\u003e in their stomach and N is the total number of predators with stomach contents.\u003c/p\u003e \u003cp\u003ePrey-specific abundance in terms of relative presence (%RP): \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{i}=\\left(\\frac{\\sum {S}_{i}}{\\sum {S}_{ti}}\\right)x100\\)\u003c/span\u003e\u003c/span\u003e, where P\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the prey-specific abundance of prey \u003cem\u003ei\u003c/em\u003e, S\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the stomach content comprised of prey \u003cem\u003eI\u003c/em\u003e, and S\u003csub\u003e\u003cem\u003eti\u003c/em\u003e\u003c/sub\u003e is the total stomach content in only those predators with prey \u003cem\u003ei\u003c/em\u003e in their stomachs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eDiet of weakfish was analyzed using a multivariate approach with the statistical software PRIMER 7 version 7.0.21 (Clarke and Gorley \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) with PERMANOVA + (Anderson et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Preys were grouped by category or species and a multivariate data analysis was carried out by non-metric multidimensional scaling (MDS) ordination with the Bray-Curtis similarity measurement for diet composition (Clarke and Gorley \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Pairwise Bray\u0026ndash;Curtis similarity coefficients were calculated for the %RP and %N of prey items, which provide a rough measure of dietary overlap of intra-specific differences (see (Gonz\u0026aacute;lez-Orteg\u0026oacute;n et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Main prey categories and the percent contribution of each prey item responsible for similarity and dissimilarity in each considered group were identified using SIMPER (Clarke and Warwick \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). One-way ANOSIM permutation test was used to assess if diet composition differences between adults and juveniles were statistically significant. In addition, PERMANOVA analysis were carried out to evaluate adults\u0026rsquo; differences in stomach contents (using weakfish total length) and to detect significant differences on the interactions between months/seasons, sex and total length. The biodiversity indexes species richness (S) and Shannon-Wiener (H\u0026acute;) were calculated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eAdults population\u003c/h2\u003e\n\u003cp\u003eThe adult weakfish sampled consisted of 340 individuals, with 53.53% females, 43.24% males, and only 3.24% undetermined. Individual sizes ranged from 19.8 to 54 cm (TL) and 78.82 to 1412.64 g (TW), with 60% of the population falling between 25 and 35 cm (TL). Only one individual smaller than 20 cm (TL) was sampled, while two were larger than 50 cm (TL). Larger individuals over 40 cm were mostly females (78%). Of the 340 stomachs analyzed, 228 contained food (67.1%) while 112 remained empty (32.9%). Relative index of stomach fullness showed that half (53.51%) of the stomachs containing food belonged to the category 2 while only 10.09% fell into category 9. The average number of prey items consumed by weakfish was 2.88 prey items per individual, with a higher number of prey items per stomach during the summer months compared to the rest of the year.\u003c/p\u003e\n\u003cp\u003eFish and crustaceans were the two main groups consumed by \u003cem\u003eC. regalis\u003c/em\u003e both in terms of number (52.05% and 42.01% respectively) and relative presence (73.80% and 19.95% respectively), indicating that these two groups represented more than 90% of the diet. Frequency of occurrence was 79.59% for fish and 34.69% for crustaceans.\u003c/p\u003e\n\u003cp\u003eThe relative presence of fish in the diet was always above 59%, regardless of the season. Other prey groups found in the stomachs were bivalves, polychaetes, cephalopods, echinoderms, gastropods and scyphozoans. The European anchovy (\u003cem\u003eEngraulis encrasicolus\u003c/em\u003e) and \u003cem\u003eProcessa\u003c/em\u003e sp. were the most abundant prey in each group, representing 75.51% and 47.25% of the total relative presence of preys in each group, respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Groups and identified prey species found in the stomach content from adults and juveniles of \u003cem\u003eC. regalis\u003c/em\u003e. Results of different indexes (Numerical percentage (N%), relative presence percentage (%RP) and frequency of occurrence percentage (F%) are shown for each prey group and identified prey species by season. For juveniles, no season differentiation is made.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003ePrey species vary according to the seasons, but only 3 species are always present: \u003cem\u003eProcessa\u003c/em\u003e sp., \u003cem\u003eE. encrasicolus\u003c/em\u003e and \u003cem\u003eAphia minuta\u003c/em\u003e. In all seasons, the consumption of crustaceans and fish was always the main food intake, showing in spring a bigger variability of preys consumed (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This is also shown by the biodiversity indices (S and H\u0026acute;), where there is a greater diversity of preys in spring (1.30 and 0.18 respectively) as they all present higher values compared to summer (1.24 and 0.11), autumn (1.19 and 0.09) and winter (1.19 and 0.11) (S and H\u0026acute; respectively).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eE. encrasicolus\u003c/em\u003e is the main prey found in the stomachs, both in terms of %RP and %N (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). It shows a temporal variability, being consumed in lower amounts in the winter months and towards early spring, progressively increasing towards summer and early Autumn, reaching the highest values in September and October (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe Amundsen et al. (\u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e) approach to evaluate dietary composition shows that the adult weakfish population mainly exhibits a specialist feeding strategy while in few cases predators exhibit a more generalist behavior (preys located in the lower half of the diagram). The preferred prey chosen by each individual differs in some cases. The diagram reveals that the prey that we find located closer to the top right corner, is the most dominant prey, \u003cem\u003eE. encrasicolus\u003c/em\u003e. Preys located closer to the 0 on the x-axis indicate preys consumed by rare predators and are not common preys chosen by the population but by individual predators instead (individual specialization). In this case, the population exhibits a broad niche width and a high inter-phenotype component, indicating that various predators will specialize in different prey species (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003ePERMANOVA test to assess differences in the diet composition of the weakfish along the size range (TL) revealed that there were significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with size, between months and sex. Having an effect on the diet composition in both cases, using the %RP and N values and grouping the preys at group level or species level (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). When examining the size relationship between weakfish predator and the number anchovies (the most abundant prey) in the stomach contents, a positive but weak relationship was found (r\u0026thinsp;=\u0026thinsp;0.24).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePERMANOVA results to test differences in the diet composition along the size range (TL) of \u003cem\u003eC. regalis\u003c/em\u003e. Results are shown by grouping the preys by category (group level) and by identified species and using 2 different indexes: relative presence percentage (%RP) and numerical values (N). Symbols: MS\u0026thinsp;=\u0026thinsp;mean squares of factors; p(MC)\u0026thinsp;=\u0026thinsp;Monte Carlo p-value. Significant terms p(MC)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and p(MC)\u0026thinsp;\u0026lt;\u0026thinsp;0.01 are highlighted in bold, p(MC)\u0026thinsp;\u0026lt;\u0026thinsp;0.01 values are also identified by an asterisk.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003ePreys identified at group level\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e%RP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFactor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edf\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePseudo-F\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep(MC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePseudo-F\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep(MC)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal Lenght\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16976.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0003*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16759.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMonth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5165.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.363\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5873.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1438.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2396.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.723\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2055\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMonth x Sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2589.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.729\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3422.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1382.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1627.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e217\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003ePreys identified at species level\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e%RP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFactor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edf\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePseudo-F\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep(MC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePseudo-F\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep(MC)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal Lenght\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6619.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.822\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0459\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6668.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.084\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0401\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMonth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9062.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9671.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2060.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.617\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1710.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.515\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8995\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMonth x Sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3933.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.779\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4444.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.384\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0329\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3339.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3320.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eStomach fullness of weakfish showed significant differences along the year on the different months. PERMANOVA test to assess differences in the fullness state along the size range (TL), sex and months revealed that size, sex and mainly months had significant effect (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The individuals caught between June and November (summer and autumn) had fuller stomachs than those captured between December and May (spring and winter) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. PERMANOVA and Pairwise test to evaluate the influence of sex and the time of the year (months) on the stomach fullness state on \u003cem\u003eC. regalis\u003c/em\u003e. Data on the top-right triangle represent p-value, triangle in the bottom-left contains T-value data. Symbols: MS=mean squares of factors; p(MC) = Monte Carlo p-value. Significant terms p(MC) \u0026lt; 0.05 and p(MC) \u0026lt; 0.01 are highlighted in bold, p(MC) \u0026lt; 0.01 values are also identified by an asterisk.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eJuveniles\u003c/h2\u003e\n\u003cp\u003eA total of 26 juvenile weakfish from the Guadalquivir River Estuary were sampled, ranging from 2.4 to 8.4 cm in TL and 0.082 and 6.6 g in TW. Of the 26 stomachs analyzed only 1 of them was found empty. Among the 25 stomachs containing food, 48% were completely full (n\u0026thinsp;=\u0026thinsp;12), while only 12% (n\u0026thinsp;=\u0026thinsp;3) were filled up to 25% of the stomach capacity, the rest (n\u0026thinsp;=\u0026thinsp;10) showed different degrees of fullness between 25\u0026ndash;100%. The average number of preys consumed by juvenile weakfish was 9.92 prey items per individual.\u003c/p\u003e\n\u003cp\u003eThe diet of juveniles was based on crustaceans and early stages of fish, showing a clear preference for crustaceans which made 94.33% of the preys consumed. More specifically the mysid \u003cem\u003eMesopodopsis slabberi\u003c/em\u003e constituted 99.57% of the preys (n\u0026thinsp;=\u0026thinsp;230), with the exception of 1 individual of the shrimp \u003cem\u003ePalaemon longirostris\u003c/em\u003e found. Relative presence showed that crustaceans constituted 77.37% of the diet, polychaetes 11.77%, and fish 10.86% (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eJuveniles show a very clear specialist behavior with \u003cem\u003eM. slabberi\u003c/em\u003e as the dominant prey species (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThere were significant ontogenetic differences in the composition of the weakfish diet between adults and juveniles (r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;0.001) when they cohabited during the months of autumn and summer.\u003c/p\u003e\n\u003cp\u003eAfter performing a SIMPER analysis, we found that the average similarity in the adult diet was 23.86%, of which \u003cem\u003eE. encrasicolus\u003c/em\u003e contributed a 92.16% to it. For juveniles the similarities were greater (57.58%), with \u003cem\u003eM. slabberi\u003c/em\u003e being the main contributor of this similarity (96.51%). Among the groups, adults and juveniles, the dissimilarity is 94.78%, with both \u003cem\u003eE. encrasicolus\u003c/em\u003e and \u003cem\u003eM. slabberi\u003c/em\u003e as the main preys contributing to these differences.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBiological invasions can lead to unpredictable effects in the recipient ecosystem, especially in the marine environment where ecosystems are highly connected across broad spatial scales (Giakoumi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). One way to understand the ecological impacts and effects of these species is to investigate their diet composition. Based on our results, it is plausible that \u003cem\u003eC. regalis\u003c/em\u003e invasive success is closely related to high prey abundance and similarity of physiological characteristics and life cycles between native and invaded areas.\u003c/p\u003e \u003cp\u003eCurrently, there is a lack of information and studies in the invaded area. A preliminary study on \u003cem\u003eC. regalis\u003c/em\u003e was carried out in the Guadiana estuary in June 2017, evaluating the diet of a limited number of individuals of homogeneous size (33 individuals between 27.2 and 60.0 cm TL) collected over a period of one month (Cerveira et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their results showed that \u003cem\u003eC. regalis\u003c/em\u003e has a generalist feeding strategy, mainly based on a diversity of benthic crustacean and fish. In this study, it has a diet composed mainly of crustaceans and bony fishes, especially the anchovy \u003cem\u003eE. encrasicolus\u003c/em\u003e, which was the dominant prey through all the year. In adult weakfish, fish appeared as prey in more than 76% of the stomachs, a value very similar to the one obtained in previous studies (63%) in its native area (Merriner \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Willis et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In terms of volume, weakfish diet in Gulf of C\u0026aacute;diz is mainly based on fish and crustaceans, similar to diet described at its native area. There, the main food sources are bony fishes such as anchovies and herrings and crustaceans such as penaeids and mysid shrimps (Merriner \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Nemerson and Able 2004; Willis et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although cannibalism has been detected before (Cerveira et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Merriner \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) and this behaviour seems to be common in other Sciaenidae species (such as \u003cem\u003eArgyrosomus regius)\u003c/em\u003e, we did not detect any sign of cannibalism in our study. However, due to the high degree of degradation of some of the stomach contents analysed, some fish could not be identified. Therefore, we cannot exclude the possibility that some weakfish were among the preys.\u003c/p\u003e \u003cp\u003eAmong adult main preys, \u003cem\u003eE. encrasicolus\u003c/em\u003e was the dominant prey, followed by \u003cem\u003eSardina pilchardus\u003c/em\u003e and \u003cem\u003eA. minuta\u003c/em\u003e, and \u003cem\u003eProcessa\u003c/em\u003e sp. as the main crustacean constituting the weakfish diet. Probably, the availability of abundant food sources in the Gulf of Cadiz, such as anchovies, is causing this American fish species to successfully invade southwestern Europe.\u003c/p\u003e \u003cp\u003eMonthly differences were found in stomach fullness, with stomachs being fuller in the summer and autumn months than in spring and winter. This could be due to the increase in food availability during the warm months as well as by a possible more active predation behaviour. The fact that the average number of prey items consumed is higher in summer than in other seasons is due to the higher intake of small crustaceans. The number of small crustaceans they need to eat to satisfy their dietary needs is much higher than when feeding on, for example, anchovies.\u003c/p\u003e \u003cp\u003eEstuarine species were common preys in the diet of weakfish in the Gulf of C\u0026aacute;diz in all seasons. The composition of the estuarine fauna in the Guadalquivir Estuary is mainly dominated by the anchovy \u003cem\u003eE. encrasicolus\u003c/em\u003e and \u003cem\u003eA. minuta\u003c/em\u003e in summer and winter, respectively (Gonz\u0026aacute;lez-Orteg\u0026oacute;n et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which explains the higher consumption of \u003cem\u003eE. encrasicolus\u003c/em\u003e by weakfish during the months of August, September and October. The fact that the samples were collected close to the mouth of the Guadalquivir Estuary, may probably explain the presence of the species \u003cem\u003eProcessa\u003c/em\u003e sp., \u003cem\u003eE. encrasicolus\u003c/em\u003e and \u003cem\u003eA. minuta\u003c/em\u003e in their stomach contents in all seasons.\u003c/p\u003e \u003cp\u003eOur results confirm that weakfish is using the Guadalquivir River Estuary ecosystem as its nursery area. There are no previous studies of juvenile diet in the invaded areas, so this is the first assessment of juvenile diet outside of its native range. Juveniles weakfish collected inside the Guadalquivir Estuary behaved as specialists with a clear food preference for the mysid shrimp \u003cem\u003eM. slabbery\u003c/em\u003e. Similar to the most abundant species of estuarine macrofauna and fish juveniles present in the Guadalquivir Estuary (Baldo and Drake \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Vilas et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gonz\u0026aacute;lez-Orteg\u0026oacute;n et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition, other three species were found in the juvenile stomachs in very small proportion: \u003cem\u003eE. encrasicolus\u003c/em\u003e, \u003cem\u003ePalaemon longirostris\u003c/em\u003e and the polychaeta \u003cem\u003eGlycera\u003c/em\u003e sp. In the Guadalquivir estuary, \u003cem\u003eE. encrasicolus\u003c/em\u003e and the white shrimp \u003cem\u003eP. longirostris\u003c/em\u003e are the most dominant nektonic macrofauna species (Gonz\u0026aacute;lez-Orteg\u0026oacute;n et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), so there is a high probability that these were more casual encounters rather than active predation by weakfish, as it can also be deducted from the Amundsen (1996) plot, in which these are part of rare preys identified.\u003c/p\u003e \u003cp\u003eResults of juveniles from the native area (Boutin and Targett \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) were extracted from a study conducted along summer and autumn so data are comparable to our study. That is, fish and polychaeta were also part of the diet in the native area in smaller proportions than mysids (Boutin and Targett \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In another study, Merriner (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) also stated that the principal food items of age 0 weakfish were shrimp and anchovy. If we compare our results to previous studies conducted in the native area, we can conclude that juveniles are also maintaining the same feeding behaviour, feeding on the same functional groups and on similar proportions.\u003c/p\u003e \u003cp\u003eThe feeding strategy was characterized using the modified Costello (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) diagram, showing that adult weakfish in the Gulf of C\u0026aacute;diz exhibit a mixed feeding strategy. This is consistent with the feeding strategy shown by weakfish in its native area (Willis et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Outside of its native range, in Portuguese waters, the same feeding strategy has been observed (Cerveira et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This strategy is not entirely effective in the Gulf of C\u0026aacute;diz compared to the native area, as the proportion of anchovies in adults or mysids in juveniles (which are the most abundant prey in the Gulf of C\u0026aacute;diz and the Guadalquivir Estuary) was very significant and there is a low diversity of prey. In addition, juveniles showed a very narrow diet, mainly preying on \u003cem\u003eM. slabberi\u003c/em\u003e, with a much more specialized behavior in contrast to adults. This suggest that there is an ontogenetic shift in the diet, transitioning from crustaceans to fish as they grow, with the relative proportion of fish increasing together with the size. Adults consume mainly fish while juveniles prey mainly on crustaceans, present in 88% of the stomachs \u003cem\u003evs.\u003c/em\u003e 23% fish. Similar dietary shifts from crustaceans to fish are typical for other sciaenidae species(Griffiths \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Spitz et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hubans et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and have been previously shown for the weakfish in its native area, where it feeds mainly on crustaceans, more specifically on the mysid shrimp \u003cem\u003eNeomysis americana\u003c/em\u003e, transitioning to fish as it increases in size (Nemerson \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Litvin and Weinstein \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The fact that we found such clear differences in diet between the two groups: juveniles and adults and that we did not see a transition in the results as in other studies (Nemerson \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Litvin and Weinstein \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Boutin and Targett \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), can be due to the lack of intermediate sizes of the sampled individuals. We did not have access to individuals in the size range between 8.4\u0026ndash;19.8 cm which may explain the lack of sizes at which they go through the transition from predating almost exclusively on crustaceans to a diet based mainly on fish and crustaceans. Mysids consumption appears to be a key factor in driving the spatial dynamics of nursery productivity for juvenile weakfish (Boutin and Targett \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In any case, the fact that the weakfish is growing at the expense of the most abundant prey might be related to its invasive success in this area. Predation on relatively abundant preys throughout the year, including those that are part of the diet of similar native species (Baldo and Drake \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Torres et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), could explain the invasion success of \u003cem\u003eC. regalis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn recent years there has been concern about whether this new species will or has already caused damages on the populations of important commercially species in the area, and after evaluating its diet it would be logical to assume that the species most directly affected is \u003cem\u003eE. encrasicolus\u003c/em\u003e, as it is the main prey of the weakfish across the year. As an invasive species, besides predation, weakfish can also compete for food with other local species that feed on the same preys such as \u003cem\u003eA. regius\u003c/em\u003e, \u003cem\u003eDicentrarchus labrax\u003c/em\u003e or \u003cem\u003eDicentrarchus punctatus.\u003c/em\u003e Based on data from official landings in the Gulf of C\u0026aacute;diz, populations of \u003cem\u003eA. regius\u003c/em\u003e have been declining since 2015, which coincides with the year in which weakfish began to appear in greater numbers and was included in official landings.\u003c/p\u003e \u003cp\u003eThe counterparts species \u003cem\u003eA. regius\u003c/em\u003e shows a generalist feeding strategy and a similar ontegenic diet shift: while small sizes (5\u0026ndash;15 cm) prey mainly on small decapods (\u003cem\u003eCrangon crangon\u003c/em\u003e) and mysids, bigger decapods as \u003cem\u003eProcessa sp\u003c/em\u003e. and fishes (\u003cem\u003eE. encrasicholus\u003c/em\u003e y \u003cem\u003eA. minuta\u003c/em\u003e) become main preys for \u003cem\u003eA. regius\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;15 cm (unpublished data: Alconchel 2006). Hence, based on high diet similarity, a strong resource competition between \u003cem\u003eC. regalis\u003c/em\u003e and \u003cem\u003eA. regius\u003c/em\u003e might be occurring in the area. In any case, further studies are needed to determine whether this depletion is related to the invasion of weakfish or whether it is a mere coincidence and the reason for this decrease is due to any other factor or population dynamics.\u003c/p\u003e \u003cp\u003eRealistically, eradication is unlikely for established invasive populations, and the aim of management is generally to reduce their populations to levels that have lower impacts that are considered acceptable (Usseglio et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although weakfish is already being exploited as a food resource it is not yet very known among the consumers or valued enough as other similar species (Nu\u0026ntilde;ez et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). One of the only possible means to control the exponential growth of the population would be to increase the fishing pressure on this species and hope for a self-regulation and balanced integration into the ecosystem. It can also be a valuable resource for other multiple species that are struggling to survive in an over-exploited marine area and might find their resources limited. Since weakfish are the dominant prey of bottlenose dolphins \u003cem\u003eTursiops truncates\u003c/em\u003e in their native area (Gannon and Waples \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and bottlenose dolphins are common in the Gulf of C\u0026aacute;diz, they could play an important role in the ecosystem as top-down controllers (Coll et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Gim\u0026eacute;nez et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), controlling weakfish population in this area.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWeakfish is a well-established species in the Gulf of C\u0026aacute;diz, and the current population is growing exponentially, with catches around 60 times higher than in 2016. It feeds mainly on fish and crustaceans, being \u003cem\u003eE. encrasicolus\u003c/em\u003e its main food resource in this area. It shows monthly variations in the diet, probably linked to the availability of resources in the Estuary. During juvenile stages, crustaceans are the main food resource, and as they grow in size, diets shift to fish as the main prey consumed. Understanding how the addition of this species to the ecosystem will affect it, is very important in order to make informed decisions. Probably, the availability of abundant food sources in the Gulf of C\u0026aacute;diz is causing American fish species to invade and establish themselves in European waters. This would explain the success that many other species have in their establishment and invasive characteristics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eContributions\u003c/h2\u003e \u003cp\u003eGG: Conceptualization, data collection, data curation, formal analysis, investigation, methodology, validation and writing original draft. CV: resources and supervision. FB: supervision. JAC: funding acquisition and resources. EGO: Conceptualization, formal analysis, methodology, funding acquisition, resources and supervision. All authors reviewed, edited and approved the final manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003e This work was carried out within the framework of the EcoInvadiz project (P18-RT-5010) funded by the regional government of Andalusia. The authors have declared that no competing interests exist. The authors would like to thank the fishermen from Chipiona and Sanl\u0026uacute;car de Barrameda for collaborating and providing the fish samples throughout the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAble KW, Fahay MP (2010) Ecology of Estuarine Fishes: Temperate Waters of the Western North Atlantic. 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Investigaci\u0026oacute;n pesquera\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Feeding habits, Gulf of Cádiz, invasive species, stomach content analysis, weakfish","lastPublishedDoi":"10.21203/rs.3.rs-3882111/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3882111/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe ready availability of abundant food sources can be a key factor in the success of biological invasions. This study provides information about feeding habits, dietary niche, and seasonal and ontogenetic diet changes of the American invasive weakfish \u003cem\u003eCynoscion regalis\u003c/em\u003e in the Gulf of C\u0026aacute;diz, where its population is increasing exponentially since 2011 when its presence was reported in the area. By content analysis of 340 stomachs, we assessed the diet composition, prey diversity and abundance of juveniles and adults present in the Guadalquivir River Estuary. Fish and crustaceans accounted for more than 90% of their diet. Mysids are the main food intake for juveniles and piscivory becomes more important as \u003cem\u003eC. regalis\u003c/em\u003e grows in size. Stomachs were significantly fuller during the summer and autumn months, coinciding with the higher abundance of small pelagic fish during that time in the estuary, especially the anchovy \u003cem\u003eEngraulis encrasicolus\u003c/em\u003e, the main prey consumed through all months of the year, that showed a consumption peak in September and October. Adults also show significant monthly variations in the diet composition (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) respect to Total Length. Juveniles show a specialist behaviour feeding almost exclusively on \u003cem\u003eMesopodopsis slabberi\u003c/em\u003e, while adults show a mixed feeding strategy. This research constitutes a comprehensive study of weakfish diet along the year in the non-native area, including for the first time, juvenile\u0026rsquo;s stages.\u003c/p\u003e","manuscriptTitle":"Abundant feasts: Favoring the invasion of an American fish species in Europe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 16:38:43","doi":"10.21203/rs.3.rs-3882111/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b35b7af-9bcc-46e6-b7c9-8db93fd5000a","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-29T17:32:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 16:38:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3882111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3882111","identity":"rs-3882111","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}