‘Tropicalization’ of megafauna community in a South Atlantic warming hot spot: evidences from demersal fisheries off Brazil

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Among them, a process of ‘tropicalization’ of demersal fauna has been proposed, as determined by the increasing influence of the warm Brazil Current, gradually expanding towards higher latitudes. We identified signals of fauna ‘tropicalization’ analysing commercial catches of 29,021 multispecies demersal fishing operations conducted in the Brazilian Meridional Margin between 2000 and 2019. These signals included changes in species catch composition and patterns of biomass gains and losses of species with affinities for warm- and cold-waters, respectively. In addition, annual variability of the Mean Temperature of the Catches increased sharply from 2013 onwards at a rate of 0.41°C yr − 1 , explained by increasing sea bottom temperatures (with 0 and 1-year time-lag) and the transport volumes of the Brazil Current (4-year time-lag). Southwest Atlantic Ocean demersal fisheries global warming Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Oceans have absorbed most of the heat increase of the atmosphere since pre-industrial times (~ 1.0°C) gradually warming, on average, by 0.61°C (IPCC, 2019 ). Direct observations have revealed important geographic and depth variability in such change, as well as in related physical and biogeochemical transformations including: sea level rise, increased frequency of storms, deceleration of thermohaline circulation, expansion of areas with well stratified water columns, decrease of net primary productivity, and deoxygenation (Fu et al., 2016 ; Schmidtko et al., 2017; Trenberth et al., 2018 ; Caesar et al., 2021 ). In addition, nearly 1/3 of CO 2 added to the atmosphere by anthropogenic activities has been absorbed at the ocean surface changing seawater chemistry towards a more acidic state, which decreases carbonate availability for the development of numerous life forms (Doney et al., 2009 ). Exposed to such environmental changes, marine species have shown alterations in abundance, phenology, and spatial distribution ranges (both bathymetric and geographic), modifying community species composition (beta diversity), the structure of trophic chains and the metabolic and consumption rates of their trophic levels (Jennings et al., 2008 , Dulvy et al., 2008 , Cheung et al., 2009 ; Blanchard et al., 2012 ; Poloczanska et al., 2013 ; Poloczanska et al., 2016 ; Hu et al., 2022 ). Across different spatial scales, alterations in species diversity and functions are expected to disturb marine ecosystems functioning and the services they provide to society, including fisheries (Pecl et al., 2017 ). Poleward expanding isotherms have favoured the invasion of tropical/ subtropical species to suitable habitats in higher latitudes and the retraction of the equatorward limits of temperate species distribution (Cheung et al., 2009 ; Poloczanska et al., 2016 ). These processes have increased the diversity of pelagic and benthic fauna in subtropical regions and the replacement of cold-water species by warm-water ones (Fujiwara et al., 2019 ; Chaudhary et al., 2021 ). In such regions, where multispecies fisheries have historically developed, reshuffling the diversity of fish and shellfish assemblages may have altered catch composition, gradually including higher and lower proportions of species with warm- and cold-water affinities, respectively. Cheung et al. ( 2013 ) explored this concept by developing a metric defined as the ‘mean temperature of the catches’ ( MTC ), which involves averaging optimal temperature preferences of all species included in commercial catches during one year, weighed by their annual catch. In their analysis, MTC annual variability between 1970 and 2006 evidenced ocean warming signals in 52 large marine ecosystems and were shown to be related with increasing trends of regional sea surface temperatures. This global process was defined as a ‘tropicalization’ of the catch, also characterized in different regional studies (e.g. Keskin and Pauly, 2014 ; Tsikliras et al., 2015 ; Liang et al., 2018 ; Gianelli et al., 2019 ; Lekanda et al., 2021 ). The southwest South Atlantic Ocean (SWAO) extending from Cabo Frio (Brazil, 22°S) to Tierra del Fuego (Argentina, 55°S), comprises one of the world’s largest ‘marine warming hotspots’, i.e. regions where temperature has increased above global average in recent years (Hobday and Pecl, 2014 ; Popova et al., 2016 ). In this region, satellite-derived sea surface temperatures have shown positive anomalies of 0.5–1.0°C between 1950 and 1999, and of 0.5°C between 2000–2016 (Hobday and Pecl, 2014 , Franco et al., 2020 ). These anomalies have been produced by a poleward displacement of wind patterns over the South Atlantic leading to a southward expansion of the warm waters of the Brazil Current, which created, over the past decades, a warming region along its path (Fig. 1) (Lumpkin and Garzolli, 2011; Artana et al., 2019 ; Franco et al., 2020 ). Ecosystem responses to this ocean warming process in the region have been poorly studied, but Franco et al. ( 2020 ) reviewed existing evidences of more frequent harmful algal blooms, events of shellfish mass mortalities, and modifications in fisheries regimes. Particularly relevant was the study by Gianelli et al. ( 2019 ), who revealed an increasing trend of MTC calculated for demersal catches at the Argentinian – Uruguayan Common Fishing Zone (AUCFZ, ~ 34° − 40°S) between 1973 and 2017. Authors detected a decreasing representation of cold-water species in the catches, a pattern significantly related with a sea surface temperature increase in the period. The Brazilian Meridional Margin (BMM- sensu Alberoni et al., 2019 ) occupies the northern sector of SWAO (~ 20°S − 34°S) (Fig. 1). It is the southernmost region of Brazilian Continental Margin extending from the Vitória-Trindade Seamount Chain (~ 20°S) to the Brazilian EEZ border with Uruguay (~ 34°S). The region is influenced by the Brazil Current (BC), a contour current of the South Atlantic subtropical gyre, that flows southwards along the shelf break and slope carrying Tropical Waters (TW), South Atlantic Central Waters (SACW) and, south of 28°S, deep Antarctic Intermediate Waters (AAIW) (Silveira et al., 2020 ). At approximately 38°S, the BC collides with subantarctic waters carried northwards by the Malvinas Current, deflecting eastwards over the South Atlantic Ocean basin. This oceanographic front, known as the Brazil-Malvinas Confluence (Fig. 1), has gradually displaced poleward over the past decades (0.6–0.9° latitude per decade), as the BC expanded southwards, in association with the ocean temperature increasing trend in the region (Lumpkin and Garzolli, 2011; Popova et al., 2016 ; Artana et al., 2019 ). Along its path on the BMM, the BC also influences shelf waters in different ways. Firstly, the BC flow over the upper slope induce local upwellings of the SACW over the shelf break as a result of (a) the development of anti-cyclonic meanders and eddy shedding, and of (b) changes in along-shore pressure gradients, as determined by shear with the irregular slope bottom topography (Campos et al., 2000 ; Palma and Matano, 2009 ). Between 23°S and 28°S (the subregion known as the ‘South Brazil Bight’ – SBB, Fig. 1), these shelf-break upwellings contribute with NE wind-driven summer subsurface intrusions of the nutrient-rich SACW over the continental shelf promoting a regional increase in biological productivity (Campos et al., 2000 , Piola et al., 2018 ). Secondly, the BC interacts, through lateral mixing of TW, with northward flowing coastal waters derived from the discharge plumes of the La Plata River and the Patos/Mirim Lagoon systems (PPW, Fig. 1) to form the Subtropical Shelf Waters (STSW, Piola et al., 2008 ). At the southern extreme of the BMM, this warm water mass is intersected by a wedge of cold Subantarctic Shelf Waters (SASW), derived from the Patagonian Continental Shelf, forming a sharp thermohaline front (Piola et al., 2008 ). This, so called, Subtropical Shelf Front (STSF, Fig. 1), extends from the inner shelf at 32°S to the shelf break at 36°S, and is regarded as a shoreward continuation of the Brazil–Malvinas Confluence (Piola et al., 2018 ). Along the SWAO, these ocean-shelf interactions suggest that global warming-induced changes in the BC dynamics, and the resulting ocean warming process, may have extended to shelf waters altering species habitats and affecting fauna diversity (Gianelli et al., 2019 ). Descriptions of demersal fauna geographic distribution patterns have long characterized the BMM as a transition zone between subtropical and temperate faunas (e.g. Briggs and Bowen, 2012 ; Spalding et al., 2017 , Pinheiro et al., 2018 ) formed as a consequence of historical processes of diversification in the Western Atlantic (Caires, 2014 ) and the influence of seasonal latitudinal fluctuations of the Brazil-Malvinas Confluence and the STSF (Haimovici, 1997 ). These fronts affect the latitudinal and seasonal distribution of subtropical and warm-temperate species and the extent to which they seasonally overlap in the BMM (Martins and Haimovici, 2016 ). The availability of this heterogeneous fauna has driven the development, since the 1960’s, of large-scale multispecies demersal fisheries mostly sustained by bottom trawl and gillnet operations, which economically thrived from the catch of assorted subtropical (here ‘warm-water’) and warm-temperate (here ‘cold-water’) teleosts, elasmobranchs, crustaceans and cephalopod species (Haimovici et al. 1994 ; Haimovici, 1997 , Valentini and Pezzuto, 2006 , Rossi-Wongstchowski et al. 2007, Perez et al., 2009 ; Martins and Haimovici, 2016 ). Jointly, these species have composed annual catches oscillating around 88,000 t between 1986–2004, which represented over 35% of total catches in the region, on average (Valentini and Pezzuto, 2006 ). Based on the fact that the BMM is inserted within the SWAO marine warming hotspot area (Fig. 1), and the ‘warming’ catch patterns revealed by Gianelli et al., ( 2019 ) at the AUCFZ, we postulate that: (a) changes in the demersal community have taken place in the BMM during the past decades towards a tropicalization scenario, and (b) these changes have produced detectable signals in the composition of demersal catches. We addressed these premises by analysing demersal catch composition data, monitored in the fishing harbours of Santa Catarina state, southern Brazil, between 2000 and 2019. The study explored two distinct analytical approaches; the analysis of annual MTC index variability (Cheung et al., 2013 ; Gianelli et al., 2019 and others), and the analysis of species composition and beta diversity applied to species recorded in the catches of the demersal fisheries (Legendre and Legendre, 2012 ). In a previous analysis, using reconstructed regional catch data, Cheung et al. ( 2013 ) obtained an oscillating pattern of MTC in the region. Conversely, a study on clupeoid fish populations in coastal areas of Rio de Janeiro State (23°S) provided robust evidence of tropical species replacing subtropical ones (Araújo et al., 2018 ). In the present study we reveal signals of tropicalization of the catches during the past decades and identified patterns of abundance gains and losses of species with affinities for warm- and cold-waters, respectively, throughout this process. Results And Discussion Catch composition and thermal preferences We analysed landings of 29,021 fishing trips of double-rig trawlers (56.4%), pair trawlers (6.9%), stern trawlers (5.7%) and gillnet vessels (31.0%) (Supplementary Table 1). The number of fishing trips recorded each year varied between 561 and 2,036, and total catches varied between 11,000 and 53,000 t.yr − 1 during the studied period (2000–2019) (Supplementary Fig. 1). Catches reached maximum levels in 2006–2012, decreasing sharply thereafter reaching low levels in 2019. The whitemouth croaker ( Micropogonias furnieri ) and the argentine croaker ( Umbrina canosai ) were the dominant species in the catches. Jointly, they represented, on average, over 50% of total landed biomass in the period (Supplementary Fig. 1). This biomass included other 78 species: 62 teleosts, 3 elasmobranchs, 8 crustaceans and 5 molluscs. Overall, catch composition maintained a 1.5:1 ratio of species with warm- and cold-water affinities from the beginning of the time series until 2012. After that, warm-water species abundance increased in the catch reaching 80.6% of total landed biomass in 2019 (Fig. 2). Mean Temperature of the Catches Annual MTC oscillated around 21°C (SD = 0.63°C) between 2000 and 2019. Until 2013, MTC time-series exhibited peaks (2005, 2010) and troughs (2008, 2013), but no particular trend was evidenced. After 2013, MTC increased continuously reaching maximum values in 2019 (Fig. 3). The segmented regression model defined one significant discontinuity in 2012 (95% CI: 2010–2015), which delimited an early period (2000–2012) when MTC oscillated with no significant trend (p-value = 0.789), from a late period (2013–2019) when MTC increased sharply at a 0.41°C yr − 1 (p-value < 0.001) (Table 1 ). Similar catch warming trends have been described in Large Marine Ecosystems around the globe (Cheung et al., 2013 ) and in more limited regions including the Aegean and Ionian Seas (Tsikliras et al., 2015 ), the Yellow and East China Seas (Liang et al., 2018 ) and the Bay of Biscay (Cantabrian Sea – NE Atlantic) (Punzón et al., 2021 ). Considering the entire time-series, the MTC increase rate in the BMM was equal to 0.57°C. decade − 1 , exceeding estimates for the world ocean (0.19° C. decade − 1 ) and for non-tropical regions (0.23° C. decade − 1 ) (Cheung et al., 2013 ), as well as for the regions above, except Yellow and China Seas. During the 2013–2019 period, the decadal MTC increasing rate (4.11° C. decade − 1 ) largely exceeded any regional estimate reported, a pattern consistent with the expected ecosystem changes in a region of intense ocean temperature increase (Fig. 1) (Hobday and Pecl, 2014 , Popova et al., 2016 ). Table 1 Analysis of temporal trends in the mean temperature of the catch ( MTC ) of the demersal fisheries in the Brazilian Meridional Margin between 2000 and 2019. Other variables included were: sea bottom temperature ( SBT ), transport volumes of the Brazil Current ( BCt ) and the index of métier diversity ( Dm ). Ranges and slopes of fitted linear models are indicated (MTC.yr − 1 , SBT.yr − 1 , BCt.yr − 1 and Dm. yr − 1 ) for the entire time series (2000–2019) and for two consecutive periods discriminated by the segmented regression analysis. Significant code for reference: (*) p-value < 0.05; (**) p-value < 0.01; (***) p-value < 0.001. Period MTC range (°C) MTC . yr − 1 (°C) p-value R 2 2000–2019 20.07–22.60 0.057 0.016** 0.281 2000–2012 20.36–21.44 0.007 0.789 0.007 2013–2019 20.07–22.60 0.411 < 0.001*** 0.945 Period SBT range (°C) SBT . yr − 1 (°C) p-value R 2 2000–2019 14.97–15.79 0.013 0.058 0.185 2000–2012 14.97–15.45 -0.001 0.931 0.001 2013–2019 14.79–15.79 0.077 0.012* 0.750 Period BCt range (Sv) BCt . yr − 1 (Sv) p-value R 2 2000–2017 -30.10 - -19.06 -0.313 0.0315* 0.273 2000–2012 -26.34 - -19.06 -0.221 0.240 0.123 2013–2017 -30.10 - -22.96 0.728 0.504 0.160 Period Dm range Dm . yr − 1 p-value R 2 2000–2017 0.70–0.84 -0.948 0.825 0.003 2000–2012 0.78–0.83 <-0.001 0.927 0.001 2013–2017 0.70–0.84 0.010 0.371 0.162 SBT values remained relatively stable from 2001 to 2010, but increased continuously from 2013 to 2019 (Fig. 3), as confirmed by the significant positive linear trend (0.077°C yr − 1 , p-value = 0.012). MTC variability was significantly explained by SBT with 0 and 1-year time-lag (p-value = 0.001 and p-value = 0.023, respectively) (Table 2 ). Table 2 Results for linear models fitted between the mean temperature of the catch ( MTC – response variable) and the explanatory variables : sea bottom temperature ( SBT ), annual transport volumes of the Brazil Current ( BCt ) and index of métier diversity ( Dm ) with and without time-lags (years). R 2 values computed for each model are informed. Significant code for reference: (*) p-value < 0.05; (**) p-value < 0.01; (***) p-value < 0.001. Variable Time lag (yr.) Slope SE p-value R 2 SBT 0 2.356 0.583 0.001** 0.476 1 2.119 0.848 0.023* 0.269 2 1.449 1.057 0.189 0.105 3 0.514 1.157 0.663 0.013 4 0.791 1.175 0.512 0.031 BCt 0 -0.007 0.033 0.842 0.002 1 0.026 0.043 0.558 0.022 2 0.062 0.052 0.253 0.081 3 0.114 0.048 0.031* 0.273 4 0.124 0.051 0.029* 0.296 Dm 0 -0.948 4.218 0.825 0.003 1 2.417 4.570 0.604 0.016 2 3.624 4.695 0.451 0.036 3 -2.622 4.895 0.600 0.019 4 -14.113 3.696 0.002** 0.510 These results were also consistent with global patterns (Cheung et al., 2013 ) but particularly relevant were the trends described in the Argentinean-Uruguayan Common Fishing Zone (AUCFZ, 35–40°S, Fig. 1), where a MTC warming trend was described from 1985 to 2017 (Gianelli et al., 2019 ). This trend was explained by sea surface temperature ( SST ) variability which also increased steadily since mid-1990. Authors suggested that an important ‘oceanographic change has occurred in the region, which modulated the MTC index’. Because the MTC series analysed in the BMM is shorter (2000–2019) than the one analysed in the AUCFZ (1973–2017), and considered temperature at the sea bottom rather than at the surface, a direct comparison between regions is not fully informative. However, INALT20 model-derived SBT time-series, available since 1985 (Schwarzkopf et al., 2019 ) (Fig. 3), showed that positive anomalies became frequent from 1994 onwards, as observed in the AUCFZ SST time-series, indicating that such oceanographic change is noticed in both adjacent regions in the SWAO. In the BMM, whereas the MTC time series precluded from assessing the catch composition response to such change, a noticeable steady increase of both MTC and SBT seems to take place from 2012 onwards, suggesting a second, more recent, regional shift. If only this time period is analysed in the AUCFZ time series (Fig. 4 in Gianelli et al., 2019 ) positive anomalies would predominate approximately from 2010 onwards and years of maximum MTC anomalies would occur after 2014, coinciding with those identified in the BMM time series. This suggests that the signal of the second MTC shift is also present in the AUCFZ. Several studies have demonstrated that a SWAO general warming process is associated with the poleward expansion of the BC and the Brazil-Malvinas Confluence (Matano et al. 2010 ; Lumpkin and Garzolli, 2011 and others). Artana et al. ( 2019 ) showed that this feature migrated southward between 1997 and 2006, oscillating widely thereafter (with southernmost positions in 1998, 2004, 2011, 2015 and 2017), and that BC transport volumes tended to increase from 1998 to 2016. These trends are consistent with (a) periods of intensified SBT positive anomalies in the BMM time-series, and (b) the positive effect of SBT and BCt on MTC variability, the latter with a 4-year time-lag (p-value = 0.029) (Table 2 ). Gianelli et al., ( 2019 ) argued that whereas many species in this dynamic transition region may be adapted to environmental oscillations, such a sustained oceanographic change would gradually provoke ‘unprecedented changes in the composition and structure of ecological assemblages’. The caveat here, however, is that fishing data may affect the MTC analyses in different ways, e.g. through market-oriented behaviour of fishing fleets, which tend to establish temporal and spatial strategies in pursuit of profitable concentrations of their main fishing targets (Punzón et al., 2021 ). In the BMM, demersal fishing fleets explore a great variety of resources available in geographical space and different seasons in order to attain economic stability (Rossi-Wongtschowski et al., 2007 ). By doing so, they tend to integrate, in their catch composition, a wide spectrum of megafauna community. However, trawl and gillnet vessels have developed a variety of métiers , i.e. a combination of target species, fishing area, gear, and time of the year (Dias and Perez, 2016 ; Pio et al., 2016 ), whose operational patterns in the BMM could partially modulate MTC variability. For instance, if fishing operations of a particular métier aiming at an abundant cold-water species predominated in relation to operations of other métiers during a year, an environment-independent MTC drop would be observed in such year. We assessed these fishery-dependent effects in two ways. Firstly, an annual index of métier diversity ( Dm , based on Sympson species diversity index) was computed and used to express the effect of ‘dominance’ (low Dm values) vs. ‘evenness’ (high Dm values) of métiers in the catches. Annual Dm did not exhibit any particular trend along the analysed time-series (p-value > 0.3, Table 1 ) and affected negatively TMS with a 4-year time-lag (p-value = 0.002, Table 2 ). This approach was first proposed by Gianelli et al. ( 2019 ), who found similar results in the AUCFZ, i.e. the effect of fishing métiers on MTC time-series were either non-significant or in an opposed direction of that exerted by the ocean temperature. Secondly, we submitted the MTS time-series to a species sensitivity analysis, showing that none of the species absence in the catch changed significantly the accentuated positive trend of MTC between 2013 and 2019 (Table 3 ). Between 2000 and 2012, however, the estimated slope of the linear model increased, becoming significantly positive, when the codling Urophycis mystacea was excluded from the time-series (Table 3 ). This is an abundant slope species (mean thermal preference = 16°C) whose catches remained above average between 2007 and 2013 (Supplementary Fig. 2), mostly through the activity of a double-rig trawl métier which included slope species in the period (DR_1, Supplementary Table 1). In the AUCFZ, the exclusion of the most abundant argentine hake (cold-water affinity) and the whitemouth croaker (warm-water affinity) from the catch time –series changed considerably MTC variability (Gianelli et al., 2019 ). Important catch reductions of the former have been attributed to overfishing, which has also a potential for modulating MTC time-series. This can be the case of several cold- and warm-water species that largely contributed to BMM demersal catches during the studied period, whose exploitation regime were categorised as unsustainable (Haimovici and Cardoso, 2017 ). In this region, for instance, important abundance declines of the cold-water argentine croaker (from 2010 onwards) and the monkfish (from 2000 onwards) in the BMM (Cardoso et al., unpub. results) could modulate MTC leading towards a catch warming scenario. However, at least in the latter species, biomass levels have remained extremely low after 2010, a period when fishing effort was maintained below critical levels, suggesting that factors other than fishing pressure (i.e. ocean warming) could be operating. In any case, as pointed out in other MTC study regions (eg. Tsikliras et al., 2015 ; Liang et al., 2018 ), overfishing effects on MTC seems hardly dissociable from, for instance, poleward retractions of cold-water species and, in fact, may have a synergistic effect. Table 3 Sensitivity analysis of temporal trends estimated for mean temperature of the catch ( MTC ) of the demersal fisheries in the Brazilian Meridional Margin between 2000 and 2019 . The species listed are those that, when excluded from the analysis, changed the slope of the fitted linear model in more than 5% (positive or negative). Thermal affinities are included: warm > 21.11°C; cold < 21.11°C. Significant code for reference: (*) p-value < 0.05; (**) p-value < 0.01; (***) p-value < 0.001. Species Thermal affinity Slope p-value Slope change (%) 2000–2012 0.007 0.789 Urophycis mystacea Cold 0.068 0.012* 870.3 Cynoscion guatucupa Warm 0.013 0.634 80.4 Merluccius hubbsi Cold 0.012 0.638 68.3 Cynoscion jamaicensis Warm 0.011 0.684 54.1 Octopus americanus Warm 0.010 0.690 48.3 Nemadactylus bergi Cold 0.009 0.723 31.0 Paralonchurus brasiliensis Warm 0.008 0.745 20.3 Percophis brasiliensis Cold 0.008 0.749 18.9 Penaeus paulensis Cold 0.008 0.756 15.2 Doryteuthis pleii Warm 0.008 0.764 12.0 Porichthys porosissimus Cold 0.008 0.768 9.0 Carcharias taurus Warm 0.008 0.770 8.2 Polymixia lowei Cold 0.007 0.772 7.0 Cynoscion acoupa Cold 0.007 0.805 -5.6 Zenopsis conchifer Warm 0.006 0.803 -7.7 Doryteuthis sanpaulensis Cold 0.006 0.810 -11.1 Conger orbignianus Cold 0.006 0.813 -12.7 Paralichthys patagonicus Cold 0.006 0.821 -14.8 Genypterus brasiliensis Cold 0.006 0.814 -15.1 Penaeus brasiliensis Warm 0.006 0.829 -19.3 Balistes capriscus Warm 0.005 0.854 -30.0 Xiphopenaeus kroyeri Warm 0.003 0.919 -64.3 Illex argentinus Cold 0.002 0.942 -71.5 Umbrina canosai Cold -0.006 0.855 -181.3 Pleoticus muelleri Cold -0.006 0.805 -186.8 Lophius gastrophysus Cold -0.007 0.823 -195.2 Artemesia longinaris Cold -0.010 0.645 -248.3 Micropogonias furnieri Warm -0.034 0.214 -591.8 2013–2019 0.411 < 0.001*** Cynoscion guatucupa Warm 0.4446 0.0005*** 8.2 Umbrina canosai Cold 0.4320 0.0003*** 5.1 Merluccius hubbsi Cold 0.3656 0.0010** -11.0 Artemesia longinaris Cold 0.3569 0.0002*** -13.2 Urophycis mystacea Cold 0.3410 0.0035** -17.0 Catch composition analysis Changes of species abundances in the catches of the demersal fisheries in the BMM evidence strong contrasts between early (2000–02) and late (2017-19) periods of the time-series. These periods were aggregated into two largely dissimilar year-groups by MRT - PCoA analysis (Fig. 4), which discriminated an initial scenario (Group I), when annual catches were characterized by 15 main species, most of them with cold-water affinity, from a late scenario (Group IV) defined by scores attributed by eight species mostly with warm-water affinity (Fig. 4). Such a contrast was also corroborated by (a) the elevated contributions of these year-groups to the estimated total beta diversity, statistically significant in 2000 (14.2%), 2001 (10.8%), 2002 (9.2%) and 2019 (10.1%) (Supplementary Fig. 4), and (b) the Temporal Beta Diversity indices (TBI) comparing years within Groups I and IV, which resulted in significant losses in species abundance (Fig. 5). Total biomass changes were small between these extreme periods (i.e. total catches were similar in both periods, Fig. 6), but it is important to note that mean biomass gains and losses were dominated by warm- and cold-water species, respectively (Supplementary Table 2). The argentine croaker ( U. canosai ) concentrated 15% of cold-water species biomass losses in the catches, along with the argentine hake ( M. hubbsi ), the argentine stiletto shrimp ( Artemesia longinaris ), the monkfish ( L. gastrophysus ) and the southern king weakfish ( Macrodon atricauda ). The whitemouth croaker ( M. furnieri ) concentrated 25.6% of warm-water species biomass gains in the catches, followed by the grey triggerfish ( B. capriscus ) and the spotted pink shrimp ( P. brasiliensis ). Jointly, these biomass gains and losses contributed to a ‘warming’ of the catches between the two extreme periods, and supported the process of “tropicalization”, as revealed by the MTC analysis. Catch composition analysis also suggested that the important changes in the demersal assemblages, as proposed by Gianelli et al ( 2019 ), may have taken place in the BMM between 2003 and 2012. Unlike in the MTC analysis, however, a more precise shift period was not evident. The progression of years in the 2-D ordination plot (Fig. 4) suggested a temporal modification in catch composition from the initial scenario, when cold-water species were abundant in the catches (Group I), to two intermediate scenarios when these species were gradually less abundant and substituted by other cold-water species (Group II and III, Fig. 4). TBIs calculated between years within Groups I and II produced three significant comparisons, two indicating losses (2000/2003, 2000/2006) and one indicating gains (2000/2007) (Fig. 5). There were important mean biomass gains between these two periods (Fig. 6), concentrated in the warm-water whitemouth croaker (39.0%) and stripped weakfish (10.3%), and the cold-water argentine croaker (14.9%) and codling (11.1%) (Supplementary Table 2). Biomass changes were limited between years within Groups II and III. Cold-water species, chiefly the codling and the argentine croaker, dominated both gains and losses of biomass, respectively (Fig. 6, Supplementary Table 2), but TBI comparisons indicated that these were not significant changes (Fig. 5). The last transition in the catch composition (Group III and IV) was marked by great biomass losses mostly of cold-water species (Fig. 6), including the codling (16.3%), argentine croaker (15.3%), the argentine stiletto shrimp (9.6%), the argentine hake (6.9%), the monkfish (2.4%) and others (Supplementary Table 2). TBI comparisons also indicated species losses, but they were significant only in relation to year 2019 (Fig. 5). Interpreting such changes in demersal catch composition, in light of the warming trend in the SWAO, required prior consideration of the physical processes associated with known patterns of spatial distribution of fish and shellfish populations (Punzón et al. 2021 ). In the BMM, demersal fauna diversity tends to change from typically subtropical in the South Brazil Bight (23°S – 28°S) to a mixed subtropical / warm-temperate towards the ‘central shelf’ subregion (south of 28°S, Fig. 1), which comprises the continental shelf area off southern Brazil, Uruguay and northern Argentina (Piola et al., 2018 ). In this subregion, off southern Brazil, Martins and Haimovici ( 2016 ) described four teleost fish demersal assemblages formed by species with similar temperature affinities, whose latitudinal and bathymetric distribution are associated with seasonal interactions of coastal, subantarctic and subtropical shelf water masses. A ‘cold shelf assemblage’ was shown to expand over mid-shelf bottoms during the austral winter, as driven by the increased influence of subantarctic shelf waters and the northward displacement of STSF. This assemblage contained some abundant cold-water species present in the demersal catches, including the argentine croaker and the argentine hake, which have accounted for important biomass losses (> 20%) in the BMM. In addition, a ‘coastal’ and a ‘warm shelf’ assemblages were shown to expand southwards over the shelf during the austral summer. These assemblages contained fish species with warm-water affinity, including the whitemouth croaker, which alone accounted for 25% of biomass gains in demersal catches. In an ocean warming scenario, induced by the southward displacement of the BC and its influence over the shelf, a southward retraction the ‘cold water shelf’ assemblage and expansion of the ‘coastal’ and ‘warm water shelf’ assemblages would be expected, justifying the observed MTC trends and temporal patterns of species abundance in the BMM demersal catches. Deviations from this general pattern, however, were also characterized partially because targeted species may display different levels of adaptation and respond differently to a warming environment (Hu et al., 2022 ). For instance, the whitemouth croaker contributed significantly to warm-water species biomass gains in the period, but also to biomass losses. The species exhibits a complex stock structure that includes three spatially-delimited stocks: one occupying the SBB (Southeastern Brazil Stock) and two extending over the central shelf off southern Brazil (Southern Brazilian Stock) and at the AUCFZ (common Argentinean – Uruguayan stock) (Haimovici et al., 2016 ). Despite its warm-water affinity, the species exhibits a wide thermal tolerance, making it plausible that these stocks display some level of adaptation to local conditions and respond differently to the ocean warming process they have been exposed to in the SWAO. In addition, the La Plata River and the Patos/Mirim Lagoon systems are important nursery grounds to this species that may also respond to other climate-change-related effects such as fresh water discharge variability in these systems (Gianelli et al., 2019 ). In that sense, the general ‘tropicalization’ scenario characterized in the BMM demersal catches may be affected in different ways by multiple specific population processes operating at smaller spatial scales. Notwithstanding such limitations, historical catch data has proven to be an effective proxy to global climate effects on marine ecosystems regionally, with the advantage of further signalling to future changes in the economic performance of current of fishing regimes. How the demersal fishing industry will adapt to changes in the availability of traditional and non-traditional targets in the BMM? Which métiers will no longer be viable and which ones may emerge to explore expanding stocks of subtropical species? What adaptive measures can be incorporated in fishing management regimes (both national and transnational) to attain ecological and economic objectives in the coming decades? These are critical questions that will guide further applications and improvements of these analytical approaches in the SWAO and other ocean regions. Methods Catch composition data, thermal preferences and climatic data Analysed data included catches reported in the harbours of Santa Catarina state (Itajaí and Navegantes) from 2000 to 2019. These harbours have historically concentrated approximately 25% of total catches reported in the region (BRASIL/MPA, 2012 ) and a significant part of the demersal fishing fleet that operates widely on the BMM, from 21°S to southern border of Brazilian EEZ (34°S), and from the coastal areas down to 500 m depths (Fig. 1). Landed catches were monitored by University of ‘Vale do Itajaí’ (UNIVALI) along a sequence of scientific projects and contracts developed to meet governmental demands for oceanic and deep fisheries development and management, and in support on the licencing processes of the offshore oil and gas exploration activities. Demersal catches monitored have been dominated by sciaenid fish (e.g. the whitemouth croaker M. furnieri , the argentine croaker U. canosai , the stripped weakfish C. guatucupa , the southern king weakfish M. atricauda ) and the shrimps X. kroyeri (Atlantic seabob shrimp), A. longinaris (argentine stiletto shrimp), P. paulensis (São Paulo pink shrimp) and P. brasiliensis (spotted pink shrimp). Demersal fishing expanded to the upper slope from 2001 onwards adding some new fishing resources: the argentine hake M. hubbsi , the codling U. mystacea and the monkfish L. gastrophysus (Perez et al., 2009 ). The analysed database included landing records of individual fishing trips conducted by vessels operating bottom trawls (double-rig, stern trawl and pair trawl) and bottom gillnets. Reported catches were pooled by year, containing records of 133 fish and shellfish categories. These were defined by single species or groups of species (e.g. ‘rays’, ‘Squalus spp.’). Only single species categories were included in the analysis, which resulted in a total of 78 species jointly representing 81.3% of total reported biomass. A temperature preference was assigned to each of the considered species, as obtained from global compilations made available by Cheung et al. ( 2013 ) and in FishBase (Froese and Pauly, 2022). In both compilations, thermal preferences derive from considerations about the species distribution ranges and sea surface temperature maps (e.g. Cheung et al., 2013 supplementary material). An overall mean temperature preference value was calculated for all 78 species combined (21.11°C) and used to assign ‘warm-’ or ‘cold-’ water affinities for species whose thermal preferences were above or below this value, respectively. The ‘Mean Temperature of the Catch - MTC’ was estimated for each year (y) of the time series, as proposed by Cheung et al. ( 2013 ): $${MTC}_{y}=\frac{\sum _{i}^{n}{T}_{i}{C}_{i,y}}{{\sum }_{i}^{n}{C}_{i,y}}$$ where n is the total number of species recorded in one year, T i is the temperature preference of the i -th species and C i,y is the recorded catch of the i -th species in the y -th year. Sea bottom temperature ( SBT ) was considered a predictor of MTC variability in the BMM. SBT derived from estimates provided by the high-resolution ocean general circulation model INALT20 model (Schwarzkopf et al., 2019 ) for the study period (2000–2019), and was calculated by averaging the temperatures over 0.25° x 0.25° grid cells of the BMM and a water column up to 50 m above seafloor. Additionally, MTC was confronted to annual volume transports of the BC ( BCt ) near the Brazil-Malvinas confluence (in Sverdrups, Sv) as estimated between 2000–2017 by Artana et al. ( 2019 ) using high-resolution (1/12°) global Mercator Ocean reanalysis (GLORYS12) from Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/ ). Annual values of both variables were normalized by their mean value over the time series, and expressed as anomalies. In Fig. 1, daily gridded sea surface temperature ( SST ) data were obtained from the National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature (OISST) V2.0 with a horizontal resolution of 1/4° for the period 1982–2020 (Reynolds et al. 2007 ). Marine heatwaves are events when SSTs exceed the 90th percentile relatively to a climatological mean for at least five consecutive days (Hobday et al., 2016 ). Marine heatwave cumulative intensity is the integral of intensity over every event per year. The linear trends in SST and marine heatwave cumulative intensity were computed using the minimum square root method. The Mann and Kendall test was used to determine where the trends were statically significant at the 99th confidence interval. In addition, we tested the effect of fishers’ behaviour over catch composition, which could introduce environment-independent signals in the MTC (eg. driven by market oscillations and other factors). For that purpose, individual fishing trips within the database were firstly classified by ‘ métiers’ (i.e. combination of target species, gear, and time of the year) using K-means clustering algorithm (Steinley and Brusco, 2007 ). Within the years of the time-series, catches of each métier were summed and used to calculate an annual index of métier diversity ( Dm ) using the Simpson diversity index formulation (Gianelli et al., 2019 ): $${Dm}_{y}=1-\sum _{m=1}^{M}{\left(\frac{{C}_{m,y}}{{C}_{y}}\right)}^{2}$$ where m is the fishing métier and M is the total number of métiers defined in the time-series. Temporal trends of MTC , SBT , BCt and Dm were explored by fitting linear models to their variability through time. A segmented regression model was also adjusted to MTC time series in order to detect potential trend shifts through time and their association with SBT , BCt and Dm variability. Estimated MTC trends were tested for the influence of individual species in the catch data (species sensitivity analysis). This procedure intended to verify whether catch variability of the most abundant species could modulate MTC variability, significantly masking the combined effect of the wider group of species present in the catches. In this analysis the linear models fitted to MTC along time were adjusted to scenarios where the species were interactively excluded one-by-one. In each scenario the estimated slope of the regression was compared to the slope obtained with all species included and verified whether the original trend was maintained or significantly changed. The effect of the environmental predictors SBT , BCt and Dm , over MTC variability was tested by fitting linear models that included a time-lag structure of 0–4 years, intended to verify any delayed responses of MTC to SBT , BCt and Dm variability. Catch composition analysis The patterns of change in the abundance of species present in the BMM demersal catches along the 19-year time-series were explored using ordination methods and estimates of beta diversity. Initially a Multiple Regression Tree (MRT) procedure was applied to Hellinger-transformed annual species catches (abundance data) using the mvpart fuction of R package ‘mvpart’ (Therneau and Atkinson, 2014). The size of the tree (i.e. number of splits) was selected after calculating the cross-validation error and deciding between best-fitted and more parsimonious models (Legendre and Legendre, 2012 ). A Principal Coordinate Analysis (PCoA) was applied to the Hellinger distances to ordinate years in the 2-D (Euclidean) space and explore patterns of similarity/ dissimilarity among years and among groups of years as previously defined by the MRT analysis (Legendre and Legendre, 2012 ). The total non-directional beta diversity ( BD total ) was estimated by computing the total sum of squares ( SS total ) of the years vs . species matrix and the total variance by dividing SS total by n -1. BD total was further partitioned into relative contributions of years (here named YCBD ) (Legendre and DeCáceres, 2013). YCBD estimates were tested for significance by 999 random independent permutations of the columns of years vs . species matrix, using the beta.div fuction of R package ‘adespatial’ (Dray et al., 2021 ). This analysis was used to identify year(s) when the catch composition was particularly altered. Temporal changes in catch composition were investigated by computing Temporal Beta Diversity indices ( TBI ), using the TBI function of R package ‘adespatial’ (Dray et al., 2021 ). This procedure involved computing Percentage Difference dissimilarity indices between years (two-by-two) and partitioning these dissimilarities into ‘gains’ (1 > TBI > 0) and ‘losses’ (0 > TBI>-1) (Legendre, 2019 ). The computed difference between gains and losses were tested using a paired t -test. Patterns of gains and losses between time periods (e.g. groups of similar years as defined by the ordination methods) were investigated by analysing biomass catch variability of individual species and thermal preferences. Declarations Acknowledgements Authors acknowledge the support of the EU H2020-BG-2018-2020 project iAtlantic – ‘Integrated Assessment of Atlantic Marine Ecosystems in Space and Time’ (Grant Agreement 818123). We are indebted to Kristin Burmeister (Scottish Association for Marine Science, UK) and Regina Rodrigues Rodrigues (Federal University of Santa Catarina, Brazil) for the provision of the oceanographic data series analysed in this study. J.A.A.P. is supported by the National Council for Scientific and Technologic Development – CNPq, through the National Institute of Science and Technology - INCT Mar-COI (Process 400551/2014-4) and a productivity fellowship (Process 307992/2019-5). Author contributions J.A.A.P. and R.S. contributed equally to the conception if this study and interpretation of results. R.S. also led quantitative data processing and analysis. J.A.A.P. led writing and preparation of the submitted manuscript. Competing Interests The authors declare no competing interests. Data availability The datasets analysed during the current study are available in the PANGEA repository. References Alberoni. A.A.L., Jeck, I.K., Silva, C.G. & Torres, L.C. 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São Paulo. 56 p.(2006) Additional Declarations There is NO Competing Interest. Supplementary Files PerezSantanaMTCBrazilSuppMat.docx Cite Share Download PDF Status: Published Journal Publication published 04 Oct, 2022 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-1569390","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":100285184,"identity":"fea7b3dc-2ac9-4b09-9a2a-a4af3bba420e","order_by":0,"name":"JOSE ANGEL PEREZ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYLCCBxCKEUhLMDCwMzYwE9SSAKGYDcBamEnQwiYB0QlGuAH/7PaLDxIY7siZt7c/q+bdYZHY38zcwFy4B7cWiTtnig0SGJ4Zy5w5kHab94xE4ozDQIfNeIbHmhs5aRIJDIcTZ0gkHLvN2yZhzADSwnMAtw75GznpP4Ba6mfIP2wrBmmRJ6TF4Eb6MaD3DydISDCzMQO1yBkQ0mJ4I4dZIsHgmeEMnjRmyblALYZALYdn4NEidyP94YcPFXfkJdiPP/zwtq2OR+54+8PHBXi0MDDwACPQAE0FXg3A9PGAsJpRMApGwSgY2QAARLNP/4TMRd8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6431-5237","institution":"Universidade do Vale do Itajaí","correspondingAuthor":true,"prefix":"","firstName":"JOSE","middleName":"ANGEL","lastName":"PEREZ","suffix":""},{"id":100285185,"identity":"6caa69e9-6ff0-46c8-947d-27e5fa2b423c","order_by":1,"name":"Rodrigo Sant'Ana","email":"","orcid":"https://orcid.org/0000-0003-2252-6014","institution":"Universidade do Vale do Itajaí","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"Sant'Ana","suffix":""}],"badges":[],"createdAt":"2022-04-18 14:20:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1569390/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1569390/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-022-00553-z","type":"published","date":"2022-10-04T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":20746171,"identity":"c7705b19-09a1-45e0-b084-62842c256e0a","added_by":"auto","created_at":"2022-04-25 21:03:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":651483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Brazilian Meridional Margin (BMM) and study area\u003c/strong\u003e. a) general map including schematic view of main oceanographic features; b) linear trend of sea surface temperature (in ˚C per year) and b) marine heatwave cumulative intensity (in ˚C-day per year) for the period 1982-2020. Solid lines encompass areas where the linear trends are statistically significant at the 99th confidence level (see methods for more details). PPW, Rio de La Plata plume waters; SASW, Subantarctic Shelf Waters; STSW, Subtropical Shelf Waters; STSF, Subtropical Shelf Front; BMC, Brazil-Malvinas Confluence; SBB, South Brazil Bight; AUFZ, Argentinean and Uruguayan Common Fishing Zone\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1569390/v1/0c79a243b456d915cc5bd1db.png"},{"id":20746173,"identity":"40d0fcf6-8ba0-4ab7-86e0-fe7ae7a31af5","added_by":"auto","created_at":"2022-04-25 21:03:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual variability of the proportion of species with cold- and warm-water affinities in the catches of the demersal fisheries in Brazilian Meridional Margin (BMM) monitored between 2000 and 2019 in the harbours of Santa Catarina State,\u003c/strong\u003e \u003cstrong\u003esouthern Brazil.\u003c/strong\u003e Colours represent “warm-” (thermal preferences \u0026gt; 21.11°C) and “cold-” (thermal preferences \u0026lt; 21.11°C) water affinities.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-1569390/v1/a4bfbeb5159d9526258f68f1.png"},{"id":20746170,"identity":"542ba65a-f2d3-42dd-aef8-a1d044df5524","added_by":"auto","created_at":"2022-04-25 21:03:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual variability of the Mean Temperature of the Catch (MTC, white dots) of the demersal fisheries in the Brazilian Meridional Margin (BMM) monitored between 2000 and 2019 in the harbours of Santa Catarina State\u003c/strong\u003e. \u003cstrong\u003esouthern Brazil\u003c/strong\u003e. Superimposed is the sea bottom temperatures (SBT - black dots) in the same period. a) 2000 – 2019 time-series, b) 1958 – 2019 time-series.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-1569390/v1/f76da9c3504ef1b83e12aff8.png"},{"id":20746526,"identity":"8007dc7e-f8b4-4ff4-9bc6-42ea8b58ae4d","added_by":"auto","created_at":"2022-04-25 21:08:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":353409,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the catch composition of the demersal fisheries in the Brazilian Meridional Margin (BMM) monitored between 2000 and 2019 in the harbours of Santa Catarina State, southern Brazil\u003c/strong\u003e. Principal Coordinate Analysis ordination diagram representing a) the spatial distribution of years included in the time-series and b) of the species present in the catch according with scores of the first two extracted axis. Encircled years are groups discriminated by the Multiple Regression Tree (MRT) procedure. Species in red and blue are those with warm- and cold- water affinities, respectively. Blue arrows indicate time progression between year groups.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-1569390/v1/3fa658882986db2a55a397f5.png"},{"id":20746175,"identity":"1bbe9268-7c39-4f7a-890c-26d11da1785e","added_by":"auto","created_at":"2022-04-25 21:03:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":293094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the catch composition of the demersal fisheries in the Brazilian Meridional Margin (BMM) monitored between 2000 and 2019 in the harbours of Santa Catarina State, southern Brazil\u003c/strong\u003e. Heat map of Temporal Beta Diversity Indices (TBI) computed between all possible pairs of years within the time series considered (Biomass gains - TBI\u0026gt;0, Biomass losses – TBI\u0026lt;0). Boxes enclose comparisons between years included in the four groups discriminated by the MRT - PCoA.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-1569390/v1/b033d6a108742c1a4732afa5.png"},{"id":20746174,"identity":"749f6b89-863c-432b-984e-dcc1c58b38c0","added_by":"auto","created_at":"2022-04-25 21:03:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":59014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the catch composition of the demersal fisheries in the Brazilian Meridional Margin (BMM) monitored between 2000 and 2019 in the harbours of Santa Catarina State, southern Brazil.\u003c/strong\u003e Mean biomass gains and losses (in tonnes) between periods 2000-02/ 2003-07 (GII – G I), 2003-07/ 2008-16 (GIII – GII), 2006-16/ 2017-19 (GIV-GIII) and 2000-02/2017-19 (GIV – GI). Bar colours indicate mean gains and losses of species with “warm-water” (thermal preferences \u0026gt; 21.11°C) and “cold-water” (thermal preferences \u0026lt; 21.11°C) affinities. Species that concentrated the majority of mean biomass changes (in %) during each transition are indicated next to the bars. Species name colours also indicate their thermal preference.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-1569390/v1/6f6a4bd790f2094ade261a60.png"},{"id":27355900,"identity":"9cf6b5a0-fb1e-4618-a44f-7a6aec6ce1e4","added_by":"auto","created_at":"2022-10-05 07:07:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1948437,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1569390/v1/dad0434e-5ef8-4b08-b0e6-230c78395624.pdf"},{"id":20746169,"identity":"6913a5e3-2433-4184-8281-51bc4ca3e29d","added_by":"auto","created_at":"2022-04-25 21:03:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":348406,"visible":true,"origin":"","legend":"","description":"","filename":"PerezSantanaMTCBrazilSuppMat.docx","url":"https://assets-eu.researchsquare.com/files/rs-1569390/v1/06b17c7b2fb593ee3ff00e54.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"‘Tropicalization’ of megafauna community in a South Atlantic warming hot spot: evidences from demersal fisheries off Brazil","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOceans have absorbed most of the heat increase of the atmosphere since pre-industrial times (~\u0026thinsp;1.0\u0026deg;C) gradually warming, on average, by 0.61\u0026deg;C (IPCC, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Direct observations have revealed important geographic and depth variability in such change, as well as in related physical and biogeochemical transformations including: sea level rise, increased frequency of storms, deceleration of thermohaline circulation, expansion of areas with well stratified water columns, decrease of net primary productivity, and deoxygenation (Fu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Schmidtko et al., 2017; Trenberth et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Caesar et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, nearly 1/3 of CO\u003csub\u003e2\u003c/sub\u003e added to the atmosphere by anthropogenic activities has been absorbed at the ocean surface changing seawater chemistry towards a more acidic state, which decreases carbonate availability for the development of numerous life forms (Doney et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Exposed to such environmental changes, marine species have shown alterations in abundance, phenology, and spatial distribution ranges (both bathymetric and geographic), modifying community species composition (beta diversity), the structure of trophic chains and the metabolic and consumption rates of their trophic levels (Jennings et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Dulvy et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Cheung et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Blanchard et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Poloczanska et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Poloczanska et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Across different spatial scales, alterations in species diversity and functions are expected to disturb marine ecosystems functioning and the services they provide to society, including fisheries (Pecl et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePoleward expanding isotherms have favoured the invasion of tropical/ subtropical species to suitable habitats in higher latitudes and the retraction of the equatorward limits of temperate species distribution (Cheung et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Poloczanska et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These processes have increased the diversity of pelagic and benthic fauna in subtropical regions and the replacement of cold-water species by warm-water ones (Fujiwara et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chaudhary et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In such regions, where multispecies fisheries have historically developed, reshuffling the diversity of fish and shellfish assemblages may have altered catch composition, gradually including higher and lower proportions of species with warm- and cold-water affinities, respectively. Cheung et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) explored this concept by developing a metric defined as the \u0026lsquo;mean temperature of the catches\u0026rsquo; (\u003cem\u003eMTC\u003c/em\u003e), which involves averaging optimal temperature preferences of all species included in commercial catches during one year, weighed by their annual catch. In their analysis, \u003cem\u003eMTC\u003c/em\u003e annual variability between 1970 and 2006 evidenced ocean warming signals in 52 large marine ecosystems and were shown to be related with increasing trends of regional sea surface temperatures. This global process was defined as a \u0026lsquo;tropicalization\u0026rsquo; of the catch, also characterized in different regional studies (e.g. Keskin and Pauly, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tsikliras et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gianelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lekanda et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe southwest South Atlantic Ocean (SWAO) extending from Cabo Frio (Brazil, 22\u0026deg;S) to Tierra del Fuego (Argentina, 55\u0026deg;S), comprises one of the world\u0026rsquo;s largest \u0026lsquo;marine warming hotspots\u0026rsquo;, i.e. regions where temperature has increased above global average in recent years (Hobday and Pecl, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Popova et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this region, satellite-derived sea surface temperatures have shown positive anomalies of 0.5\u0026ndash;1.0\u0026deg;C between 1950 and 1999, and of 0.5\u0026deg;C between 2000\u0026ndash;2016 (Hobday and Pecl, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Franco et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These anomalies have been produced by a poleward displacement of wind patterns over the South Atlantic leading to a southward expansion of the warm waters of the Brazil Current, which created, over the past decades, a warming region along its path (Fig.\u0026nbsp;1) (Lumpkin and Garzolli, 2011; Artana et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Franco et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ecosystem responses to this ocean warming process in the region have been poorly studied, but Franco et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reviewed existing evidences of more frequent harmful algal blooms, events of shellfish mass mortalities, and modifications in fisheries regimes. Particularly relevant was the study by Gianelli et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who revealed an increasing trend of \u003cem\u003eMTC\u003c/em\u003e calculated for demersal catches at the Argentinian \u0026ndash; Uruguayan Common Fishing Zone (AUCFZ, ~\u0026thinsp;34\u0026deg; \u0026minus;\u0026thinsp;40\u0026deg;S) between 1973 and 2017. Authors detected a decreasing representation of cold-water species in the catches, a pattern significantly related with a sea surface temperature increase in the period.\u003c/p\u003e \u003cp\u003eThe Brazilian Meridional Margin (BMM- \u003cem\u003esensu\u003c/em\u003e Alberoni et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) occupies the northern sector of SWAO (~\u0026thinsp;20\u0026deg;S \u0026minus;\u0026thinsp;34\u0026deg;S) (Fig.\u0026nbsp;1). It is the southernmost region of Brazilian Continental Margin extending from the Vit\u0026oacute;ria-Trindade Seamount Chain (~\u0026thinsp;20\u0026deg;S) to the Brazilian EEZ border with Uruguay (~\u0026thinsp;34\u0026deg;S). The region is influenced by the Brazil Current (BC), a contour current of the South Atlantic subtropical gyre, that flows southwards along the shelf break and slope carrying Tropical Waters (TW), South Atlantic Central Waters (SACW) and, south of 28\u0026deg;S, deep Antarctic Intermediate Waters (AAIW) (Silveira et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At approximately 38\u0026deg;S, the BC collides with subantarctic waters carried northwards by the Malvinas Current, deflecting eastwards over the South Atlantic Ocean basin. This oceanographic front, known as the Brazil-Malvinas Confluence (Fig.\u0026nbsp;1), has gradually displaced poleward over the past decades (0.6\u0026ndash;0.9\u0026deg; latitude per decade), as the BC expanded southwards, in association with the ocean temperature increasing trend in the region (Lumpkin and Garzolli, 2011; Popova et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Artana et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlong its path on the BMM, the BC also influences shelf waters in different ways. Firstly, the BC flow over the upper slope induce local upwellings of the SACW over the shelf break as a result of (a) the development of anti-cyclonic meanders and eddy shedding, and of (b) changes in along-shore pressure gradients, as determined by shear with the irregular slope bottom topography (Campos et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Palma and Matano, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Between 23\u0026deg;S and 28\u0026deg;S (the subregion known as the \u0026lsquo;South Brazil Bight\u0026rsquo; \u0026ndash; SBB, Fig.\u0026nbsp;1), these shelf-break upwellings contribute with NE wind-driven summer subsurface intrusions of the nutrient-rich SACW over the continental shelf promoting a regional increase in biological productivity (Campos et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Piola et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Secondly, the BC interacts, through lateral mixing of TW, with northward flowing coastal waters derived from the discharge plumes of the La Plata River and the Patos/Mirim Lagoon systems (PPW, Fig.\u0026nbsp;1) to form the Subtropical Shelf Waters (STSW, Piola et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). At the southern extreme of the BMM, this warm water mass is intersected by a wedge of cold Subantarctic Shelf Waters (SASW), derived from the Patagonian Continental Shelf, forming a sharp thermohaline front (Piola et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This, so called, Subtropical Shelf Front (STSF, Fig.\u0026nbsp;1), extends from the inner shelf at 32\u0026deg;S to the shelf break at 36\u0026deg;S, and is regarded as a shoreward continuation of the Brazil\u0026ndash;Malvinas Confluence (Piola et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Along the SWAO, these ocean-shelf interactions suggest that global warming-induced changes in the BC dynamics, and the resulting ocean warming process, may have extended to shelf waters altering species habitats and affecting fauna diversity (Gianelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDescriptions of demersal fauna geographic distribution patterns have long characterized the BMM as a transition zone between subtropical and temperate faunas (e.g. Briggs and Bowen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Spalding et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Pinheiro et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) formed as a consequence of historical processes of diversification in the Western Atlantic (Caires, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and the influence of seasonal latitudinal fluctuations of the Brazil-Malvinas Confluence and the STSF (Haimovici, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). These fronts affect the latitudinal and seasonal distribution of subtropical and warm-temperate species and the extent to which they seasonally overlap in the BMM (Martins and Haimovici, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The availability of this heterogeneous fauna has driven the development, since the 1960\u0026rsquo;s, of large-scale multispecies demersal fisheries mostly sustained by bottom trawl and gillnet operations, which economically thrived from the catch of assorted subtropical (here \u0026lsquo;warm-water\u0026rsquo;) and warm-temperate (here \u0026lsquo;cold-water\u0026rsquo;) teleosts, elasmobranchs, crustaceans and cephalopod species (Haimovici et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Haimovici, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, Valentini and Pezzuto, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Rossi-Wongstchowski et al. 2007, Perez et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Martins and Haimovici, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Jointly, these species have composed annual catches oscillating around 88,000 t between 1986\u0026ndash;2004, which represented over 35% of total catches in the region, on average (Valentini and Pezzuto, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the fact that the BMM is inserted within the SWAO marine warming hotspot area (Fig.\u0026nbsp;1), and the \u0026lsquo;warming\u0026rsquo; catch patterns revealed by Gianelli et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) at the AUCFZ, we postulate that: (a) changes in the demersal community have taken place in the BMM during the past decades towards a tropicalization scenario, and (b) these changes have produced detectable signals in the composition of demersal catches. We addressed these premises by analysing demersal catch composition data, monitored in the fishing harbours of Santa Catarina state, southern Brazil, between 2000 and 2019. The study explored two distinct analytical approaches; the analysis of annual \u003cem\u003eMTC\u003c/em\u003e index variability (Cheung et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gianelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e and others), and the analysis of species composition and beta diversity applied to species recorded in the catches of the demersal fisheries (Legendre and Legendre, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In a previous analysis, using reconstructed regional catch data, Cheung et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) obtained an oscillating pattern of \u003cem\u003eMTC\u003c/em\u003e in the region. Conversely, a study on clupeoid fish populations in coastal areas of Rio de Janeiro State (23\u0026deg;S) provided robust evidence of tropical species replacing subtropical ones (Ara\u0026uacute;jo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the present study we reveal signals of tropicalization of the catches during the past decades and identified patterns of abundance gains and losses of species with affinities for warm- and cold-waters, respectively, throughout this process.\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCatch composition and thermal preferences\u003c/h2\u003e \u003cp\u003eWe analysed landings of 29,021 fishing trips of double-rig trawlers (56.4%), pair trawlers (6.9%), stern trawlers (5.7%) and gillnet vessels (31.0%) (Supplementary Table\u0026nbsp;1). The number of fishing trips recorded each year varied between 561 and 2,036, and total catches varied between 11,000 and 53,000 t.yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e during the studied period (2000\u0026ndash;2019) (Supplementary Fig.\u0026nbsp;1). Catches reached maximum levels in 2006\u0026ndash;2012, decreasing sharply thereafter reaching low levels in 2019. The whitemouth croaker (\u003cem\u003eMicropogonias furnieri\u003c/em\u003e) and the argentine croaker (\u003cem\u003eUmbrina canosai\u003c/em\u003e) were the dominant species in the catches. Jointly, they represented, on average, over 50% of total landed biomass in the period (Supplementary Fig.\u0026nbsp;1). This biomass included other 78 species: 62 teleosts, 3 elasmobranchs, 8 crustaceans and 5 molluscs. Overall, catch composition maintained a 1.5:1 ratio of species with warm- and cold-water affinities from the beginning of the time series until 2012. After that, warm-water species abundance increased in the catch reaching 80.6% of total landed biomass in 2019 (Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMean Temperature of the Catches\u003c/h2\u003e \u003cp\u003eAnnual \u003cem\u003eMTC\u003c/em\u003e oscillated around 21\u0026deg;C (SD\u0026thinsp;=\u0026thinsp;0.63\u0026deg;C) between 2000 and 2019. Until 2013, \u003cem\u003eMTC\u003c/em\u003e time-series exhibited peaks (2005, 2010) and troughs (2008, 2013), but no particular trend was evidenced. After 2013, \u003cem\u003eMTC\u003c/em\u003e increased continuously reaching maximum values in 2019 (Fig.\u0026nbsp;3). The segmented regression model defined one significant discontinuity in 2012 (95% CI: 2010\u0026ndash;2015), which delimited an early period (2000\u0026ndash;2012) when \u003cem\u003eMTC\u003c/em\u003e oscillated with no significant trend (p-value\u0026thinsp;=\u0026thinsp;0.789), from a late period (2013\u0026ndash;2019) when \u003cem\u003eMTC\u003c/em\u003e increased sharply at a 0.41\u0026deg;C yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similar catch warming trends have been described in Large Marine Ecosystems around the globe (Cheung et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and in more limited regions including the Aegean and Ionian Seas (Tsikliras et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the Yellow and East China Seas (Liang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the Bay of Biscay (Cantabrian Sea \u0026ndash; NE Atlantic) (Punz\u0026oacute;n et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Considering the entire time-series, the \u003cem\u003eMTC\u003c/em\u003e increase rate in the BMM was equal to 0.57\u0026deg;C. decade\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, exceeding estimates for the world ocean (0.19\u0026deg; C. decade\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and for non-tropical regions (0.23\u0026deg; C. decade\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Cheung et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), as well as for the regions above, except Yellow and China Seas. During the 2013\u0026ndash;2019 period, the decadal \u003cem\u003eMTC\u003c/em\u003e increasing rate (4.11\u0026deg; C. decade\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) largely exceeded any regional estimate reported, a pattern consistent with the expected ecosystem changes in a region of intense ocean temperature increase (Fig.\u0026nbsp;1) (Hobday and Pecl, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Popova et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eAnalysis of temporal trends in the mean temperature of the catch (\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eMTC\u003c/span\u003e\u003cb\u003e) of the demersal fisheries in the Brazilian Meridional Margin between 2000 and 2019.\u003c/b\u003e Other variables included were: sea bottom temperature (\u003cem\u003eSBT\u003c/em\u003e), transport volumes of the Brazil Current (\u003cem\u003eBCt\u003c/em\u003e) and the index of \u003cem\u003em\u0026eacute;tier\u003c/em\u003e diversity (\u003cem\u003eDm\u003c/em\u003e). Ranges and slopes of fitted linear models are indicated (MTC.yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, SBT.yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, BCt.yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and Dm. yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for the entire time series (2000\u0026ndash;2019) and for two consecutive periods discriminated by the segmented regression analysis. Significant code for reference: (*) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; (**) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01; (***) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMTC\u003c/em\u003e range\u003c/p\u003e\u003cp\u003e(\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMTC\u003c/em\u003e. yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.07\u0026ndash;22.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.36\u0026ndash;21.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.07\u0026ndash;22.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSBT\u003c/em\u003e range\u003c/p\u003e\u003cp\u003e(\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSBT\u003c/em\u003e. yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.97\u0026ndash;15.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.97\u0026ndash;15.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.79\u0026ndash;15.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.012*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBCt\u003c/em\u003e range\u003c/p\u003e\u003cp\u003e(Sv)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eBCt\u003c/em\u003e. yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(Sv)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-30.10 - -19.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0315*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-26.34 - -19.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u0026ndash;2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-30.10 - -22.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDm\u003c/em\u003e range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eDm\u003c/em\u003e. yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70\u0026ndash;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.78\u0026ndash;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u0026ndash;2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70\u0026ndash;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eSBT\u003c/em\u003e values remained relatively stable from 2001 to 2010, but increased continuously from 2013 to 2019 (Fig.\u0026nbsp;3), as confirmed by the significant positive linear trend (0.077\u0026deg;C yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, p-value\u0026thinsp;=\u0026thinsp;0.012). \u003cem\u003eMTC\u003c/em\u003e variability was significantly explained by \u003cem\u003eSBT\u003c/em\u003e with 0 and 1-year time-lag (p-value\u0026thinsp;=\u0026thinsp;0.001 and p-value\u0026thinsp;=\u0026thinsp;0.023, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eResults for linear models fitted between the mean temperature of the catch (\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eMTC\u003c/span\u003e \u003cb\u003e\u0026ndash; response variable) and the explanatory variables\u003c/b\u003e: sea bottom temperature (\u003cem\u003eSBT\u003c/em\u003e), annual transport volumes of the Brazil Current (\u003cem\u003eBCt\u003c/em\u003e) and index of \u003cem\u003em\u0026eacute;tier\u003c/em\u003e diversity (\u003cem\u003eDm\u003c/em\u003e) with and without time-lags (years). R\u003csup\u003e2\u003c/sup\u003e values computed for each model are informed. Significant code for reference: (*) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; (**) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01; (***) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime lag (yr.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSBT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBCt\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDm\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-14.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese results were also consistent with global patterns (Cheung et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) but particularly relevant were the trends described in the Argentinean-Uruguayan Common Fishing Zone (AUCFZ, 35\u0026ndash;40\u0026deg;S, Fig.\u0026nbsp;1), where a \u003cem\u003eMTC\u003c/em\u003e warming trend was described from 1985 to 2017 (Gianelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This trend was explained by sea surface temperature (\u003cem\u003eSST\u003c/em\u003e) variability which also increased steadily since mid-1990. Authors suggested that an important \u0026lsquo;oceanographic change has occurred in the region, which modulated the \u003cem\u003eMTC\u003c/em\u003e index\u0026rsquo;. Because the \u003cem\u003eMTC\u003c/em\u003e series analysed in the BMM is shorter (2000\u0026ndash;2019) than the one analysed in the AUCFZ (1973\u0026ndash;2017), and considered temperature at the sea bottom rather than at the surface, a direct comparison between regions is not fully informative. However, INALT20 model-derived \u003cem\u003eSBT\u003c/em\u003e time-series, available since 1985 (Schwarzkopf et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) (Fig.\u0026nbsp;3), showed that positive anomalies became frequent from 1994 onwards, as observed in the AUCFZ \u003cem\u003eSST\u003c/em\u003e time-series, indicating that such oceanographic change is noticed in both adjacent regions in the SWAO. In the BMM, whereas the \u003cem\u003eMTC\u003c/em\u003e time series precluded from assessing the catch composition response to such change, a noticeable steady increase of both \u003cem\u003eMTC\u003c/em\u003e and \u003cem\u003eSBT\u003c/em\u003e seems to take place from 2012 onwards, suggesting a second, more recent, regional shift. If only this time period is analysed in the AUCFZ time series (Fig.\u0026nbsp;4 in Gianelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) positive anomalies would predominate approximately from 2010 onwards and years of maximum \u003cem\u003eMTC\u003c/em\u003e anomalies would occur after 2014, coinciding with those identified in the BMM time series. This suggests that the signal of the second \u003cem\u003eMTC\u003c/em\u003e shift is also present in the AUCFZ. Several studies have demonstrated that a SWAO general warming process is associated with the poleward expansion of the BC and the Brazil-Malvinas Confluence (Matano et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lumpkin and Garzolli, 2011 and others). Artana et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) showed that this feature migrated southward between 1997 and 2006, oscillating widely thereafter (with southernmost positions in 1998, 2004, 2011, 2015 and 2017), and that BC transport volumes tended to increase from 1998 to 2016. These trends are consistent with (a) periods of intensified \u003cem\u003eSBT\u003c/em\u003e positive anomalies in the BMM time-series, and (b) the positive effect of \u003cem\u003eSBT\u003c/em\u003e and \u003cem\u003eBCt\u003c/em\u003e on \u003cem\u003eMTC\u003c/em\u003e variability, the latter with a 4-year time-lag (p-value\u0026thinsp;=\u0026thinsp;0.029) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGianelli et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) argued that whereas many species in this dynamic transition region may be adapted to environmental oscillations, such a sustained oceanographic change would gradually provoke \u0026lsquo;unprecedented changes in the composition and structure of ecological assemblages\u0026rsquo;. The caveat here, however, is that fishing data may affect the \u003cem\u003eMTC\u003c/em\u003e analyses in different ways, e.g. through market-oriented behaviour of fishing fleets, which tend to establish temporal and spatial strategies in pursuit of profitable concentrations of their main fishing targets (Punz\u0026oacute;n et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the BMM, demersal fishing fleets explore a great variety of resources available in geographical space and different seasons in order to attain economic stability (Rossi-Wongtschowski et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). By doing so, they tend to integrate, in their catch composition, a wide spectrum of megafauna community. However, trawl and gillnet vessels have developed a variety of \u003cem\u003em\u0026eacute;tiers\u003c/em\u003e, i.e. a combination of target species, fishing area, gear, and time of the year (Dias and Perez, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pio et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), whose operational patterns in the BMM could partially modulate \u003cem\u003eMTC\u003c/em\u003e variability. For instance, if fishing operations of a particular \u003cem\u003em\u0026eacute;tier\u003c/em\u003e aiming at an abundant cold-water species predominated in relation to operations of other \u003cem\u003em\u0026eacute;tiers\u003c/em\u003e during a year, an environment-independent \u003cem\u003eMTC\u003c/em\u003e drop would be observed in such year. We assessed these fishery-dependent effects in two ways. Firstly, an annual index of \u003cem\u003em\u0026eacute;tier\u003c/em\u003e diversity (\u003cem\u003eDm\u003c/em\u003e, based on Sympson species diversity index) was computed and used to express the effect of \u0026lsquo;dominance\u0026rsquo; (low \u003cem\u003eDm\u003c/em\u003e values) vs. \u0026lsquo;evenness\u0026rsquo; (high \u003cem\u003eDm\u003c/em\u003e values) of \u003cem\u003em\u0026eacute;tiers\u003c/em\u003e in the catches. Annual \u003cem\u003eDm\u003c/em\u003e did not exhibit any particular trend along the analysed time-series (p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.3, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and affected negatively \u003cem\u003eTMS\u003c/em\u003e with a 4-year time-lag (p-value\u0026thinsp;=\u0026thinsp;0.002, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This approach was first proposed by Gianelli et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who found similar results in the AUCFZ, i.e. the effect of fishing \u003cem\u003em\u0026eacute;tiers\u003c/em\u003e on MTC time-series were either non-significant or in an opposed direction of that exerted by the ocean temperature. Secondly, we submitted the MTS time-series to a species sensitivity analysis, showing that none of the species absence in the catch changed significantly the accentuated positive trend of \u003cem\u003eMTC\u003c/em\u003e between 2013 and 2019 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Between 2000 and 2012, however, the estimated slope of the linear model increased, becoming significantly positive, when the codling \u003cem\u003eUrophycis mystacea\u003c/em\u003e was excluded from the time-series (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This is an abundant slope species (mean thermal preference\u0026thinsp;=\u0026thinsp;16\u0026deg;C) whose catches remained above average between 2007 and 2013 (Supplementary Fig.\u0026nbsp;2), mostly through the activity of a double-rig trawl \u003cem\u003em\u0026eacute;tier\u003c/em\u003e which included slope species in the period (DR_1, Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eIn the AUCFZ, the exclusion of the most abundant argentine hake (cold-water affinity) and the whitemouth croaker (warm-water affinity) from the catch time \u0026ndash;series changed considerably \u003cem\u003eMTC\u003c/em\u003e variability (Gianelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Important catch reductions of the former have been attributed to overfishing, which has also a potential for modulating \u003cem\u003eMTC\u003c/em\u003e time-series. This can be the case of several cold- and warm-water species that largely contributed to BMM demersal catches during the studied period, whose exploitation regime were categorised as unsustainable (Haimovici and Cardoso, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this region, for instance, important abundance declines of the cold-water argentine croaker (from 2010 onwards) and the monkfish (from 2000 onwards) in the BMM (Cardoso et al., unpub. results) could modulate \u003cem\u003eMTC\u003c/em\u003e leading towards a catch warming scenario. However, at least in the latter species, biomass levels have remained extremely low after 2010, a period when fishing effort was maintained below critical levels, suggesting that factors other than fishing pressure (i.e. ocean warming) could be operating. In any case, as pointed out in other \u003cem\u003eMTC\u003c/em\u003e study regions (eg. Tsikliras et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), overfishing effects on \u003cem\u003eMTC\u003c/em\u003e seems hardly dissociable from, for instance, poleward retractions of cold-water species and, in fact, may have a synergistic effect.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSensitivity analysis of temporal trends estimated for mean temperature of the catch (\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eMTC\u003c/span\u003e\u003cb\u003e) of the demersal fisheries in the Brazilian Meridional Margin between 2000 and 2019\u003c/b\u003e. The species listed are those that, when excluded from the analysis, changed the slope of the fitted linear model in more than 5% (positive or negative). Thermal affinities are included: warm\u0026thinsp;\u0026gt;\u0026thinsp;21.11\u0026deg;C; cold\u0026thinsp;\u0026lt;\u0026thinsp;21.11\u0026deg;C. Significant code for reference: (*) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; (**) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01; (***) p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThermal affinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSlope change (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUrophycis mystacea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e870.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCynoscion guatucupa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMerluccius hubbsi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCynoscion jamaicensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOctopus americanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNemadactylus bergi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eParalonchurus brasiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePercophis brasiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePenaeus paulensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDoryteuthis pleii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePorichthys porosissimus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCarcharias taurus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePolymixia lowei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCynoscion acoupa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZenopsis conchifer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDoryteuthis sanpaulensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConger orbignianus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eParalichthys patagonicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-14.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGenypterus brasiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-15.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePenaeus brasiliensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-19.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBalistes capriscus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-30.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eXiphopenaeus kroyeri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-64.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIllex argentinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-71.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUmbrina canosai\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-181.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePleoticus muelleri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-186.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLophius gastrophysus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-195.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArtemesia longinaris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-248.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMicropogonias furnieri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-591.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCynoscion guatucupa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUmbrina canosai\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMerluccius hubbsi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0010**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArtemesia longinaris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-13.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUrophycis mystacea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0035**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-17.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCatch composition analysis\u003c/h2\u003e \u003cp\u003eChanges of species abundances in the catches of the demersal fisheries in the BMM evidence strong contrasts between early (2000\u0026ndash;02) and late (2017-19) periods of the time-series. These periods were aggregated into two largely dissimilar year-groups by MRT - PCoA analysis (Fig.\u0026nbsp;4), which discriminated an initial scenario (Group I), when annual catches were characterized by 15 main species, most of them with cold-water affinity, from a late scenario (Group IV) defined by scores attributed by eight species mostly with warm-water affinity (Fig.\u0026nbsp;4). Such a contrast was also corroborated by (a) the elevated contributions of these year-groups to the estimated total beta diversity, statistically significant in 2000 (14.2%), 2001 (10.8%), 2002 (9.2%) and 2019 (10.1%) (Supplementary Fig.\u0026nbsp;4), and (b) the Temporal Beta Diversity indices (TBI) comparing years within Groups I and IV, which resulted in significant losses in species abundance (Fig.\u0026nbsp;5). Total biomass changes were small between these extreme periods (i.e. total catches were similar in both periods, Fig.\u0026nbsp;6), but it is important to note that mean biomass gains and losses were dominated by warm- and cold-water species, respectively (Supplementary Table\u0026nbsp;2). The argentine croaker (\u003cem\u003eU. canosai\u003c/em\u003e) concentrated 15% of cold-water species biomass losses in the catches, along with the argentine hake (\u003cem\u003eM. hubbsi\u003c/em\u003e), the argentine stiletto shrimp (\u003cem\u003eArtemesia longinaris\u003c/em\u003e), the monkfish (\u003cem\u003eL. gastrophysus\u003c/em\u003e) and the southern king weakfish (\u003cem\u003eMacrodon atricauda\u003c/em\u003e). The whitemouth croaker (\u003cem\u003eM. furnieri\u003c/em\u003e) concentrated 25.6% of warm-water species biomass gains in the catches, followed by the grey triggerfish (\u003cem\u003eB. capriscus\u003c/em\u003e) and the spotted pink shrimp (\u003cem\u003eP. brasiliensis\u003c/em\u003e). Jointly, these biomass gains and losses contributed to a \u0026lsquo;warming\u0026rsquo; of the catches between the two extreme periods, and supported the process of \u0026ldquo;tropicalization\u0026rdquo;, as revealed by the \u003cem\u003eMTC\u003c/em\u003e analysis.\u003c/p\u003e \u003cp\u003eCatch composition analysis also suggested that the important changes in the demersal assemblages, as proposed by Gianelli et al (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), may have taken place in the BMM between 2003 and 2012. Unlike in the MTC analysis, however, a more precise shift period was not evident. The progression of years in the 2-D ordination plot (Fig.\u0026nbsp;4) suggested a temporal modification in catch composition from the initial scenario, when cold-water species were abundant in the catches (Group I), to two intermediate scenarios when these species were gradually less abundant and substituted by other cold-water species (Group II and III, Fig.\u0026nbsp;4). TBIs calculated between years within Groups I and II produced three significant comparisons, two indicating losses (2000/2003, 2000/2006) and one indicating gains (2000/2007) (Fig.\u0026nbsp;5). There were important mean biomass gains between these two periods (Fig.\u0026nbsp;6), concentrated in the warm-water whitemouth croaker (39.0%) and stripped weakfish (10.3%), and the cold-water argentine croaker (14.9%) and codling (11.1%) (Supplementary Table\u0026nbsp;2). Biomass changes were limited between years within Groups II and III. Cold-water species, chiefly the codling and the argentine croaker, dominated both gains and losses of biomass, respectively (Fig.\u0026nbsp;6, Supplementary Table\u0026nbsp;2), but TBI comparisons indicated that these were not significant changes (Fig.\u0026nbsp;5). The last transition in the catch composition (Group III and IV) was marked by great biomass losses mostly of cold-water species (Fig.\u0026nbsp;6), including the codling (16.3%), argentine croaker (15.3%), the argentine stiletto shrimp (9.6%), the argentine hake (6.9%), the monkfish (2.4%) and others (Supplementary Table\u0026nbsp;2). TBI comparisons also indicated species losses, but they were significant only in relation to year 2019 (Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eInterpreting such changes in demersal catch composition, in light of the warming trend in the SWAO, required prior consideration of the physical processes associated with known patterns of spatial distribution of fish and shellfish populations (Punz\u0026oacute;n et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the BMM, demersal fauna diversity tends to change from typically subtropical in the South Brazil Bight (23\u0026deg;S \u0026ndash; 28\u0026deg;S) to a mixed subtropical / warm-temperate towards the \u0026lsquo;central shelf\u0026rsquo; subregion (south of 28\u0026deg;S, Fig.\u0026nbsp;1), which comprises the continental shelf area off southern Brazil, Uruguay and northern Argentina (Piola et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this subregion, off southern Brazil, Martins and Haimovici (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) described four teleost fish demersal assemblages formed by species with similar temperature affinities, whose latitudinal and bathymetric distribution are associated with seasonal interactions of coastal, subantarctic and subtropical shelf water masses. A \u0026lsquo;cold shelf assemblage\u0026rsquo; was shown to expand over mid-shelf bottoms during the austral winter, as driven by the increased influence of subantarctic shelf waters and the northward displacement of STSF. This assemblage contained some abundant cold-water species present in the demersal catches, including the argentine croaker and the argentine hake, which have accounted for important biomass losses (\u0026gt;\u0026thinsp;20%) in the BMM. In addition, a \u0026lsquo;coastal\u0026rsquo; and a \u0026lsquo;warm shelf\u0026rsquo; assemblages were shown to expand southwards over the shelf during the austral summer. These assemblages contained fish species with warm-water affinity, including the whitemouth croaker, which alone accounted for 25% of biomass gains in demersal catches. In an ocean warming scenario, induced by the southward displacement of the BC and its influence over the shelf, a southward retraction the \u0026lsquo;cold water shelf\u0026rsquo; assemblage and expansion of the \u0026lsquo;coastal\u0026rsquo; and \u0026lsquo;warm water shelf\u0026rsquo; assemblages would be expected, justifying the observed \u003cem\u003eMTC\u003c/em\u003e trends and temporal patterns of species abundance in the BMM demersal catches.\u003c/p\u003e \u003cp\u003eDeviations from this general pattern, however, were also characterized partially because targeted species may display different levels of adaptation and respond differently to a warming environment (Hu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, the whitemouth croaker contributed significantly to warm-water species biomass gains in the period, but also to biomass losses. The species exhibits a complex stock structure that includes three spatially-delimited stocks: one occupying the SBB (Southeastern Brazil Stock) and two extending over the central shelf off southern Brazil (Southern Brazilian Stock) and at the AUCFZ (common Argentinean \u0026ndash; Uruguayan stock) (Haimovici et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite its warm-water affinity, the species exhibits a wide thermal tolerance, making it plausible that these stocks display some level of adaptation to local conditions and respond differently to the ocean warming process they have been exposed to in the SWAO. In addition, the La Plata River and the Patos/Mirim Lagoon systems are important nursery grounds to this species that may also respond to other climate-change-related effects such as fresh water discharge variability in these systems (Gianelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In that sense, the general \u0026lsquo;tropicalization\u0026rsquo; scenario characterized in the BMM demersal catches may be affected in different ways by multiple specific population processes operating at smaller spatial scales.\u003c/p\u003e \u003cp\u003eNotwithstanding such limitations, historical catch data has proven to be an effective proxy to global climate effects on marine ecosystems regionally, with the advantage of further signalling to future changes in the economic performance of current of fishing regimes. How the demersal fishing industry will adapt to changes in the availability of traditional and non-traditional targets in the BMM? Which \u003cem\u003em\u0026eacute;tiers\u003c/em\u003e will no longer be viable and which ones may emerge to explore expanding stocks of subtropical species? What adaptive measures can be incorporated in fishing management regimes (both national and transnational) to attain ecological and economic objectives in the coming decades? These are critical questions that will guide further applications and improvements of these analytical approaches in the SWAO and other ocean regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCatch composition data, thermal preferences and climatic data\u003c/h2\u003e \u003cp\u003eAnalysed data included catches reported in the harbours of Santa Catarina state (Itaja\u0026iacute; and Navegantes) from 2000 to 2019. These harbours have historically concentrated approximately 25% of total catches reported in the region (BRASIL/MPA, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and a significant part of the demersal fishing fleet that operates widely on the BMM, from 21\u0026deg;S to southern border of Brazilian EEZ (34\u0026deg;S), and from the coastal areas down to 500 m depths (Fig.\u0026nbsp;1). Landed catches were monitored by University of \u0026lsquo;Vale do Itaja\u0026iacute;\u0026rsquo; (UNIVALI) along a sequence of scientific projects and contracts developed to meet governmental demands for oceanic and deep fisheries development and management, and in support on the licencing processes of the offshore oil and gas exploration activities. Demersal catches monitored have been dominated by sciaenid fish (e.g. the whitemouth croaker \u003cem\u003eM. furnieri\u003c/em\u003e, the argentine croaker \u003cem\u003eU. canosai\u003c/em\u003e, the stripped weakfish \u003cem\u003eC. guatucupa\u003c/em\u003e, the southern king weakfish \u003cem\u003eM. atricauda\u003c/em\u003e) and the shrimps \u003cem\u003eX. kroyeri\u003c/em\u003e (Atlantic seabob shrimp), \u003cem\u003eA. longinaris\u003c/em\u003e (argentine stiletto shrimp), \u003cem\u003eP. paulensis\u003c/em\u003e (S\u0026atilde;o Paulo pink shrimp) and \u003cem\u003eP. brasiliensis\u003c/em\u003e (spotted pink shrimp). Demersal fishing expanded to the upper slope from 2001 onwards adding some new fishing resources: the argentine hake \u003cem\u003eM. hubbsi\u003c/em\u003e, the codling \u003cem\u003eU. mystacea\u003c/em\u003e and the monkfish \u003cem\u003eL. gastrophysus\u003c/em\u003e (Perez et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analysed database included landing records of individual fishing trips conducted by vessels operating bottom trawls (double-rig, stern trawl and pair trawl) and bottom gillnets. Reported catches were pooled by year, containing records of 133 fish and shellfish categories. These were defined by single species or groups of species (e.g. \u0026lsquo;rays\u0026rsquo;, \u0026lsquo;Squalus spp.\u0026rsquo;). Only single species categories were included in the analysis, which resulted in a total of 78 species jointly representing 81.3% of total reported biomass. A temperature preference was assigned to each of the considered species, as obtained from global compilations made available by Cheung et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and in FishBase (Froese and Pauly, 2022). In both compilations, thermal preferences derive from considerations about the species distribution ranges and sea surface temperature maps (e.g. Cheung et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e supplementary material). An overall mean temperature preference value was calculated for all 78 species combined (21.11\u0026deg;C) and used to assign \u0026lsquo;warm-\u0026rsquo; or \u0026lsquo;cold-\u0026rsquo; water affinities for species whose thermal preferences were above or below this value, respectively. The \u0026lsquo;Mean Temperature of the Catch - \u003cem\u003eMTC\u0026rsquo;\u003c/em\u003e was estimated for each year (y) of the time series, as proposed by Cheung et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${MTC}_{y}=\\frac{\\sum _{i}^{n}{T}_{i}{C}_{i,y}}{{\\sum }_{i}^{n}{C}_{i,y}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003en\u003c/em\u003e is the total number of species recorded in one year, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the temperature preference of the \u003cem\u003ei\u003c/em\u003e-th species and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,y\u003c/em\u003e\u003c/sub\u003e is the recorded catch of the \u003cem\u003ei\u003c/em\u003e-th species in the \u003cem\u003ey\u003c/em\u003e-th year.\u003c/p\u003e \u003cp\u003eSea bottom temperature (\u003cem\u003eSBT\u003c/em\u003e) was considered a predictor of \u003cem\u003eMTC\u003c/em\u003e variability in the BMM. \u003cem\u003eSBT\u003c/em\u003e derived from estimates provided by the high-resolution ocean general circulation model INALT20 model (Schwarzkopf et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for the study period (2000\u0026ndash;2019), and was calculated by averaging the temperatures over 0.25\u0026deg; x 0.25\u0026deg; grid cells of the BMM and a water column up to 50 m above seafloor. Additionally, \u003cem\u003eMTC\u003c/em\u003e was confronted to annual volume transports of the BC (\u003cem\u003eBCt\u003c/em\u003e) near the Brazil-Malvinas confluence (in Sverdrups, Sv) as estimated between 2000\u0026ndash;2017 by Artana et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) using high-resolution (1/12\u0026deg;) global Mercator Ocean reanalysis (GLORYS12) from Copernicus Marine Environment Monitoring Service (CMEMS, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://marine.copernicus.eu/\u003c/span\u003e\u003cspan address=\"http://marine.copernicus.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Annual values of both variables were normalized by their mean value over the time series, and expressed as anomalies. In Fig.\u0026nbsp;1, daily gridded sea surface temperature (\u003cem\u003eSST\u003c/em\u003e) data were obtained from the National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature (OISST) V2.0 with a horizontal resolution of 1/4\u0026deg; for the period 1982\u0026ndash;2020 (Reynolds et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Marine heatwaves are events when \u003cem\u003eSSTs\u003c/em\u003e exceed the 90th percentile relatively to a climatological mean for at least five consecutive days (Hobday et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Marine heatwave cumulative intensity is the integral of intensity over every event per year. The linear trends in SST and marine heatwave cumulative intensity were computed using the minimum square root method. The Mann and Kendall test was used to determine where the trends were statically significant at the 99th confidence interval.\u003c/p\u003e \u003cp\u003eIn addition, we tested the effect of fishers\u0026rsquo; behaviour over catch composition, which could introduce environment-independent signals in the \u003cem\u003eMTC\u003c/em\u003e (eg. driven by market oscillations and other factors). For that purpose, individual fishing trips within the database were firstly classified by \u0026lsquo;\u003cem\u003em\u0026eacute;tiers\u0026rsquo;\u003c/em\u003e (i.e. combination of target species, gear, and time of the year) using K-means clustering algorithm (Steinley and Brusco, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Within the years of the time-series, catches of each \u003cem\u003em\u0026eacute;tier\u003c/em\u003e were summed and used to calculate an annual index of \u003cem\u003em\u0026eacute;tier\u003c/em\u003e diversity (\u003cem\u003eDm\u003c/em\u003e) using the Simpson diversity index formulation (Gianelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e):\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${Dm}_{y}=1-\\sum _{m=1}^{M}{\\left(\\frac{{C}_{m,y}}{{C}_{y}}\\right)}^{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003em\u003c/em\u003e is the fishing \u003cem\u003em\u0026eacute;tier\u003c/em\u003e and \u003cem\u003eM\u003c/em\u003e is the total number of \u003cem\u003em\u0026eacute;tiers\u003c/em\u003e defined in the time-series.\u003c/p\u003e \u003cp\u003eTemporal trends of \u003cem\u003eMTC\u003c/em\u003e, \u003cem\u003eSBT\u003c/em\u003e, \u003cem\u003eBCt\u003c/em\u003e and \u003cem\u003eDm\u003c/em\u003e were explored by fitting linear models to their variability through time. A segmented regression model was also adjusted to \u003cem\u003eMTC\u003c/em\u003e time series in order to detect potential trend shifts through time and their association with \u003cem\u003eSBT\u003c/em\u003e, \u003cem\u003eBCt\u003c/em\u003e and \u003cem\u003eDm\u003c/em\u003e variability. Estimated \u003cem\u003eMTC\u003c/em\u003e trends were tested for the influence of individual species in the catch data (species sensitivity analysis). This procedure intended to verify whether catch variability of the most abundant species could modulate \u003cem\u003eMTC\u003c/em\u003e variability, significantly masking the combined effect of the wider group of species present in the catches. In this analysis the linear models fitted to \u003cem\u003eMTC\u003c/em\u003e along time were adjusted to scenarios where the species were interactively excluded one-by-one. In each scenario the estimated slope of the regression was compared to the slope obtained with all species included and verified whether the original trend was maintained or significantly changed. The effect of the environmental predictors \u003cem\u003eSBT\u003c/em\u003e, \u003cem\u003eBCt\u003c/em\u003e and \u003cem\u003eDm\u003c/em\u003e, over \u003cem\u003eMTC\u003c/em\u003e variability was tested by fitting linear models that included a time-lag structure of 0\u0026ndash;4 years, intended to verify any delayed responses of \u003cem\u003eMTC\u003c/em\u003e to \u003cem\u003eSBT\u003c/em\u003e, \u003cem\u003eBCt\u003c/em\u003e and \u003cem\u003eDm\u003c/em\u003e variability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCatch composition analysis\u003c/h2\u003e \u003cp\u003eThe patterns of change in the abundance of species present in the BMM demersal catches along the 19-year time-series were explored using ordination methods and estimates of beta diversity. Initially a Multiple Regression Tree (MRT) procedure was applied to Hellinger-transformed annual species catches (abundance data) using the mvpart fuction of R package \u0026lsquo;mvpart\u0026rsquo; (Therneau and Atkinson, 2014). The size of the tree (i.e. number of splits) was selected after calculating the cross-validation error and deciding between best-fitted and more parsimonious models (Legendre and Legendre, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). A Principal Coordinate Analysis (PCoA) was applied to the Hellinger distances to ordinate years in the 2-D (Euclidean) space and explore patterns of similarity/ dissimilarity among years and among groups of years as previously defined by the MRT analysis (Legendre and Legendre, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe total non-directional beta diversity (\u003cem\u003eBD\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e) was estimated by computing the total sum of squares (\u003cem\u003eSS\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e) of the years \u003cem\u003evs\u003c/em\u003e. species matrix and the total variance by dividing \u003cem\u003eSS\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e by \u003cem\u003en\u003c/em\u003e-1. \u003cem\u003eBD\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e was further partitioned into relative contributions of years (here named \u003cem\u003eYCBD\u003c/em\u003e) (Legendre and DeC\u0026aacute;ceres, 2013). \u003cem\u003eYCBD\u003c/em\u003e estimates were tested for significance by 999 random independent permutations of the columns of years \u003cem\u003evs\u003c/em\u003e. species matrix, using the beta.div fuction of R package \u0026lsquo;adespatial\u0026rsquo; (Dray et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This analysis was used to identify year(s) when the catch composition was particularly altered. Temporal changes in catch composition were investigated by computing Temporal Beta Diversity indices (\u003cem\u003eTBI\u003c/em\u003e), using the TBI function of R package \u0026lsquo;adespatial\u0026rsquo; (Dray et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This procedure involved computing Percentage Difference dissimilarity indices between years (two-by-two) and partitioning these dissimilarities into \u0026lsquo;gains\u0026rsquo; (1\u0026thinsp;\u0026gt;\u0026thinsp;TBI\u0026thinsp;\u0026gt;\u0026thinsp;0) and \u0026lsquo;losses\u0026rsquo; (0\u0026thinsp;\u0026gt;\u0026thinsp;TBI\u0026gt;-1) (Legendre, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The computed difference between gains and losses were tested using a paired \u003cem\u003et\u003c/em\u003e-test. Patterns of gains and losses between time periods (e.g. groups of similar years as defined by the ordination methods) were investigated by analysing biomass catch variability of individual species and thermal preferences.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors acknowledge the support of the EU H2020-BG-2018-2020 project iAtlantic – ‘Integrated Assessment of Atlantic Marine Ecosystems in Space and Time’ (Grant Agreement 818123). We are indebted to Kristin Burmeister (Scottish Association for Marine Science, UK) and Regina Rodrigues Rodrigues (Federal University of Santa Catarina, Brazil) for the provision of the oceanographic data series analysed in this study. J.A.A.P. is supported by the National Council for Scientific and Technologic Development – CNPq, through the National Institute of Science and Technology - INCT Mar-COI (Process 400551/2014-4) and a productivity fellowship (Process 307992/2019-5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.A.A.P. and R.S. contributed equally to the conception if this study and interpretation of results. R.S. also led quantitative data processing and analysis. J.A.A.P. led writing and preparation of the submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in the PANGEA repository.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlberoni. A.A.L., Jeck, I.K., Silva, C.G. \u0026amp; Torres, L.C. The new Digital Terrain Model (DTM) of the Brazilian Continental Margin: detailed morphology and revised undersea feature names. 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S\u0026atilde;o Paulo. 56 p.(2006)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Southwest Atlantic Ocean, demersal fisheries, global warming","lastPublishedDoi":"10.21203/rs.3.rs-1569390/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1569390/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Southwest Atlantic Ocean comprises a major \u0026lsquo;marine warming hotspot\u0026rsquo; subject to significant marine ecosystem changes. Among them, a process of \u0026lsquo;tropicalization\u0026rsquo; of demersal fauna has been proposed, as determined by the increasing influence of the warm Brazil Current, gradually expanding towards higher latitudes. We identified signals of fauna \u0026lsquo;tropicalization\u0026rsquo; analysing commercial catches of 29,021 multispecies demersal fishing operations conducted in the Brazilian Meridional Margin between 2000 and 2019. These signals included changes in species catch composition and patterns of biomass gains and losses of species with affinities for warm- and cold-waters, respectively. In addition, annual variability of the Mean Temperature of the Catches increased sharply from 2013 onwards at a rate of 0.41\u0026deg;C yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, explained by increasing sea bottom temperatures (with 0 and 1-year time-lag) and the transport volumes of the Brazil Current (4-year time-lag).\u003c/p\u003e","manuscriptTitle":"‘Tropicalization’ of megafauna community in a South Atlantic warming hot spot: evidences from demersal fisheries off Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-04-25 21:03:29","doi":"10.21203/rs.3.rs-1569390/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3b0eeb81-4fdb-484d-a5b7-e38384eefd89","owner":[],"postedDate":"April 25th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2022-10-05T07:07:40+00:00","versionOfRecord":{"articleIdentity":"rs-1569390","link":"https://doi.org/10.1038/s43247-022-00553-z","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2022-10-04 04:00:00","publishedOnDateReadable":"October 4th, 2022"},"versionCreatedAt":"2022-04-25 21:03:29","video":"","vorDoi":"10.1038/s43247-022-00553-z","vorDoiUrl":"https://doi.org/10.1038/s43247-022-00553-z","workflowStages":[]},"version":"v1","identity":"rs-1569390","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1569390","identity":"rs-1569390","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}