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Geospatial dataSampling locations were established, flagged, and recorded in June 2016, using a Trimble Geo7X global navigation satellite system (GNSS) receiver using the Trimble® VRS Now real-time kinematic (RTK) correction. Location accuracies were verified to within ±2 cm. Points were imported into a geodatabase using Esri ArcMap (Advanced license, Version 10.5) and projected using the Universal Transverse Mercator (UTM), Zone 17 North projection, with the 1983 North American datum (NAD83). Field investigators navigated to the flagged locations by visually locating them in the field or by using recreational grade GNSS receivers with the locations stored as waypoints.Plant tissue sampling and preparationMiscanthus x giganteus grows in clumps of bamboo-like canes. A single cane was cut at soil level from each of the five sample collection points in each circular plot, individually labelled, and brought to the lab for processing (Fig. 2). Each stem was measured from the cut at the base to the last leaf node, and the length was recorded. Green, fully expanded leaves were cut from each stem and leaves and stems from each plant were placed in separate paper bags and dried at 60 °C. The dry leaf and stem tissues were ground to pass a 1 mm screen (Wiley Mill Model 4, Thomas Scientific, Swedesboro, New Jersey, USA). Subsamples of the ground material were analyzed for total carbon (C) and nitrogen (N), acid-digested for the analysis of total macro- and micronutrients, and water-extracted for spectroscopic analysis and the characterization of the water extractable organic matter (WEOM) (Fig. 2).Fig. 2Images of field samples, and diagram of plant tissue processing. Center panel – flow chart outlining the procedures for plant tissue processing, the kinds of analyses performed, and the type of data generated. Upper left inset panel – ground level picture of Miscanthus x giganteus circular plots. Upper right inset panel – some plant samples on the day of collection.Full size imageTotal carbon and nitrogenDried and ground leaf and stem material (~4–6 mg) was analyzed for total C and N content by combustion (Vario EL III, Elementar Americas Inc., Mt. Laurel, New Jersey, USA). The instrument was calibrated using an aspartic acid standard (36.08% C ± 0.52% and 10.53% N ± 0.18%). Validation by inclusion of two aspartic acid samples as checks in each autosampler carousel (80 wells) resulted in a net positive bias of 1.44 and 1.68% for C and N, respectively. The mean C and N concentrations and standard deviations for the sample set are presented in Table 1.Table 1 Giant miscanthus composition including leaf (L) and stem (S) dry weight, length, and carbon (C) and nitrogen (N) concentrations (n = 165). Values are reported as means ± standard deviations.Full size tableMacro- and micronutrientsPlant tissue samples were analyzed for a suite of macro- and micronutrients including aluminum (Al), arsenic (As), boron (B), calcium (Ca), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), molybdenum (Mo), sodium (Na), nickel (Ni), phosphorus (P), lead (Pb), sulfur (S), selenium (Se), silicon (Si), titanium (Ti), vanadium (V), and zinc (Zn) using Inductively Coupled Plasma with Optical Emission Spectroscopy (ICP-OES). Samples (0.5 g) were digested using 10 mL of trace metal grade nitric acid (HNO3) in a microwave digestion system (Mars 6, CEM, Matthews, North Carolina, USA). During the digestion procedure (CEM Mars 6 Plant Material Method), the oven temperature was increased from room temperature to 200 °C in 15 minutes and held at 200 °C for 10 minutes. The pressure limit of the digestion vessels was set to 800 psi although it was not monitored during individual runs. Sample digestates were transferred quantitatively to centrifuge tubes, diluted to 50 mL with 2% HNO3 (prepared with lab grade deionized water), and centrifuged at 2500 rpm for 10 min (Sorvall ST8 centrifuge, Thermo Fisher Scientific, San Jose, California, USA). The digestates were decanted into clean centrifuge tubes and analyzed using an iCAP 7400 ICP-OES Duo equipped with a Charge Injection Device detector (Thermo Fisher Scientific, San Jose, California, USA). An aliquot of digested sample was aspirated from the centrifuge tube using a CETAC ASX-520 autosampler (Teledyne CETAC Technologies, Omaha, Nebraska, USA) and passed through a concentric tube nebulizer. The resulting aerosol was then swept through the plasma using argon as the carrier gas with a flow rate of 0.5 L/min and a nebulizer gas flow rate of 0.7 L/min. Macro- and micronutrients were quantified by monitoring the emission wavelengths (Em λ) reported in Table 2.Table 2 Macro- and micronutrients measured, and emission wavelengths (Em λ) used to quantify them in the miscanthus leaves (L) and stems (S), the total number and percentage detected (n = 150 for leaves and 162 for stems), the mean detected concentration ± standard deviation, and the mean method detection limit (MDL) ± standard deviation.Full size tableCharacterization of the water extractable organic matter (WEOM)The WEOM of the giant miscanthus leaves and stems was isolated by extracting the plant material with deionized water at room temperature6. The water extractions were performed by mixing ~0.2 g of dry, ground leaves and stems with 100 mL of deionized water in 125 mL pre-washed brown Nalgene bottles. All brown Nalgene bottles used for these extractions were pre-washed by soaking them for 24 hours in a 10% hydrochloric acid solution followed by 24 hours in a 10% sodium hydroxide solution, and a thorough rinse with deionized water. The bottles containing the extraction solution were shaken on an orbital shaker at 180 rpm for 24 hours. The extract was vacuum filtered using 0.45 µm glass fibre filters (GF/F, Whatman) into pre-washed 60 mL brown Nalgene bottles. The filtered water extracts containing the WEOM were stored in the dark in a refrigerator (4 °C) until analysis by UV-Visible and fluorescence spectroscopy. Samples were visually inspected just prior to analysis to ensure no colloids or precipitates had formed during storage. Samples that had become visually cloudy were re-filtered.On the day of analysis, the water extracts were removed from the refrigerator and allowed to warm up to room temperature. Chemical characteristics of the WEOM were assessed through the analysis of optical properties on an Aqualog spectrofluorometer (Horiba Scientific, New Jersey, USA) equipped with a 150 W continuous output Xenon arc lamp. Excitation-emission matrix (EEM) scans were acquired in a 1 cm quartz cuvette with excitation wavelengths (Ex λ) scanned using a double-grating monochrometer from 240 to 621 nm at 3 nm intervals. Emission wavelengths (Em λ) were scanned from 246 to 693 nm at 2 nm intervals and emission spectra were collected using a Charge Coupled Device (CCD) detector. All fluorescence spectra were acquired in sample over reference ratio mode to account for potential fluctuations and wavelength dependency of the excitation lamp output. Samples were corrected for the inner filter effect7 and each sample EEM underwent spectral subtraction with a deionized water blank to remove the effects due to Raman scattering. Rayleigh masking was applied to remove the signal intensities for both the first and second order Rayleigh lines. Instrument bias related to wavelength-dependent efficiencies of the specific instrument’s optical components (gratings, mirrors, etc.) was automatically corrected by the Aqualog software after each spectral acquisition. The fluorescence intensities were normalized to the area under the water Raman peak collected on each day of analysis and are expressed in Raman-normalized intensity units (RU). All sample EEM processing was performed with the Aqualog software (version 4.0.0.86).The optical data obtained from the EEM scans were used to calculate several indices representative of WEOM chemical composition (Table 3) including the absorbance at 254 nm (Abs254), the ratio of the absorbance at 254 to 365 nm (Abs254:365), the ratio of the absorbance at 280 to 465 nm (Abs280:465), the spectral slope ratio (SR), the fluorescence index (FI), the humification index (HIX), the biological index (BIX), and the freshness index (β:α). The SR was calculated as the ratio of two spectral slope regions of the absorbance spectra (275–295 and 350–400 nm)8. The FI was calculated as the ratio of the emission intensities at Em λ 470 and 520 nm, at an Ex λ of 370 nm9. The HIX was calculated by dividing the emission intensity in the 435–480 nm region by the sum of emission intensities in the 300–345 and 435–480 nm regions, at an Ex λ of 255 nm10. The BIX was calculated as the ratio of emission intensities at 380 and 430 nm, at an Ex λ of 310 nm11. The freshness index β:α was calculated as the emission intensity at 380 nm divided by the maximum emission intensity between 420 and 432 nm, at an Ex λ of 310 nm12. To further characterize the giant miscanthus WEOM, the fluorescence intensity at specific excitation-emission pairs was also identified. The fluorescence peaks identified here have previously been reported for surface water samples and water extracts13 and include peak A (Ex λ 260, Em λ 450), peak C (Ex λ 340, Em λ 440), peak M (Ex λ 300, Em λ 390), peak B (Ex λ 275, Em λ 310), and peak T (Ex λ 275, Em λ 340). A brief description of these optical indices is provided in Table 3.Table 3 Description of the optical indices calculated from the excitation-emission matrix (EEM) fluorescence scans and used to analyze the WEOM composition of giant miscanthus leaves and stems.Full size table More

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Fluorescent markingDispersal of SLF adults was tracked using a fluorescent marking system (FMS), which has been demonstrated to be applicable for multiple insect species including SLF nymphs21,22,24. To mark the SLF, either red, yellow, or blue fluorescent paint (#1166R, #1166Y, #1166B, BioQuip Products, USA) was diluted with distilled water (1:4). The mixture was then gently sprayed three times (ca. 20 mg each time) on each SLF individual using a mist sprayer from a distance of 30–50 cm (SI 2). Throughout the field survey, a handheld ultraviolet (UV) laser (PX 600 mW, class IIIB purple laser, 405 nm, Big Lasers, USA) was used to detect fluorescent-marked SLF individuals25.Effect of fluorescent marking on SLFPrior to field survey, the potential effects of fluorescent marking on the survivorship and flight behavior of SLF adults (sex ratio 1:1) were evaluated. SLF adults were collected using sweeping nets (BioQuip Products, USA) from Gyeonggi-do, South Korea (37°47′85.95″ N, 127°11′64.58″E) in September 2020. Two hours after fluorescent marking of SLF, both fluorescent-marked and unmarked SLF were subjected to survivorship and flight behavior assessment.Survivorship of insects was measured on two A. altissima trees (ca. 2 m in height) located in Gachon University, South Korea (37°45′38.50″N, 127°13′37.75″E). Two fluorescent-marked and two unmarked insects were placed in a cylindrical mesh cage [25 × 30 cm (radius × height)] enclosing a tree branch; a total of 20 groups were tested (n = 40). Then, survivorship of SLF was determined once every two days until no individuals were alive. Survivorship was compared between fluorescent-marked and unmarked SLF using Kaplan-Meir survivorship analysis (JMP 12, SAS Institute Inc., USA).The effects of fluorescent marking on flight behavior were evaluated in an open space (986 m2) in Gachon University, South Korea (37°45′08.37″N, 127°12′79.69″E) at 26 ± 1 °C and a relative humidity of 30 ± 5%. To induce flight of SLF adults, a wooden square rod [3 × 3 × 100 cm (width × length × height)] was established upright at the center of the arena. The SLF adult was placed individually 10 cm away from the top on the wooden square rod. To minimize any unnecessary stimuli from experimenter, SLF flight was induced by following the same sequence: once the insect climbed up the rod and oriented itself staying still to a random direction, then an experimenter carefully positioned at the back of the insect and gently pecked the forewings using tweezers to initiate its flight33,34. Pecking was intended to mimic predatory behavior of birds. Once the insect jumped away, an operator followed the individual until it landed on the ground (n = 30). The experiment was conducted for 2 h between 13:00–15:00 and marked and unmarked SLF were randomly tested during the evaluation. The number of pecks to initiate the flight, flight duration, and flight distance of SLF were compared using t-test (JMP 12, SAS Institute Inc., USA).Field study sitesDispersal patterns of SLF adults in A. altissima patches and their oviposition patterns were investigated in multiple A. altissima patches located along two streams in Gyeonggi-do, South Korea: Tan stream in Seongnam-si (37°48′01.80″N, 127°11′56.03″E) and Gyeongan stream in Gwangju-si (37°41′54.21″N, 127°27′12.37″E). Both Tan and Gyeongan streams run along suburban residential areas in their respective cities, with pedestrian lanes built along the streams. We selected seven A. altissima patches as study patches when more than 10 SLF adults were found per patch (Fig. 3). In the study patch, all SLF individuals or ca. up to 30 adults were florescent-marked. In addition, when the number of SLF adults was less than 10 from an A. altissima patch, those patches were designated as neighboring patches (Fig. 3). Dispersal and oviposition of SLF adults were monitored from both study and neighboring patches during the study.In Tan stream, four study patches (patches A–D) and one neighboring patch, which were distributed over ca. 1760 m, were selected (Fig. 3a). Areas around the patches were generally covered with grass and shrubs, and the areas were occasionally managed by local administration. Deciduous trees were regularly planted along the pedestrian lanes. There were a total of four, four, 61, and 47 A. altissima trees in patches A to D, respectively (Table 2). Compared with Tan stream, A. altissima patches were located closely to each other in Gyeongan stream: three study patches (patches E–G) and three neighboring patches were spread over only ca. 90 m (Fig. 3b). Vegetation surrounding A. altissima patches consisted of grasses and small shrubs as well as deciduous trees planted along the border of residential area nearby. There were a total of 69, nine, and 53 A. altissima trees in patches E to G, respectively (Table 2). Unlike Tan stream, 45% of A. altissima trees had trunks having cut off by local administration in Gyeongan stream (Table 2; Fig. 5).Dispersal pattern of SLF on A. altissima
Three fluorescent paint colors were used to mark SLF individuals in the study patches (Fig. 3; SI 2). Insects that took off during marking were captured and excluded from the experiment. Among the selected study patches, SLF adults were generally distributed throughout each patch, while SLF adults were observed only from one out of 61 A. altissima trees in patch C. As a result, in Tan stream, 15 (color of paint used to fluorescent-marking; red), 31 (yellow), 11 (blue), and 32 (red) adults were marked from patches A to D, respectively, whereas in Gyeongan stream, 30 (red), 30 (blue), and 33 (yellow) adults were marked from patches E to G, respectively. Starting from September 14th, 2020 in Tan stream and September 18th in Gyeongan stream, fluorescent-marked SLF adults on A. altissima trees in both study and neighboring patches were counted with a UV laser twice a week (Fig. 3). Survey continued until no individuals were observed from the study patches.Oviposition pattern of SLF on A. altissima
Oviposition pattern of SLF was surveyed on all A. altissima trees in the study patches in December in both streams (Table 2). For the survey, SLF egg masses were categorized into three types as follows: egg mass with waxy layer, egg mass without waxy layer, and scattered eggs (SI 3). Eggs that were not covered with waxy layer and did not form aggregates were categorized as scattered (SI 3). In the field, A. altissima trees were visually inspected to identify SLF egg mass, and the number of egg masses and their distances from the ground were recorded. In addition, the number of eggs per egg mass was recorded for egg masses located  5 generally indicates collinearity35,36. VIF between height and DRC was 1.56, and therefore the two variables were included together in the GLMM model.Policy statementExperiments involving Ailanthus altissima were conducted in compliance with relevant institutional, national, and international guidelines and legislation. More

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We took as a starting point the hake – sole – Norway lobster Bay of Biscay ISIS-Fish database used for COSELMAR project16,20 (see http://isis-fish.org/download.html section “Bay of Biscay scenario dataset”, Database V0 in Fig. 1). This database was built using 2010 data, and was not calibrated, as it was designed for a geo-foresight study. Since our aim was to describe the system over a decade and simulate realistic dynamics close to available observations to assess management measures, we needed to update the parametrisation and calibrate the database. We took 2010–2012 as the calibration period, and 2013–2020 as the simulation period (grey arrow Fig. 1). The database has a monthly temporal resolution (constrained by the ISIS-Fish framework) and the spatial scale was set to match ICES statistical rectangles (0.5° latitude by 1° longitude rectangles, defined by the International Council for the Exploration of the Sea (ICES) https://www.ices.dk/data/maps/Pages/ICES-statistical-rectangles.aspx), consistent with available knowledge and data.In this section, we firstly describe all the data sources used to update and calibrate the database. Then, for each main component of an ISIS-Fish database – i.e. populations, exploitation and management – we describe this paper’s database parameters and assumptions. We finally describe the calibration procedure (inspired by previous work21,22), of which some results are shown in the Technical Validation section. We summarized this workflow in Fig. 1.Data sourcesData sources, estimates, and literature (including grey literature) were needed to update and calibrate the model. They are marked in Fig. 1 with salmon (data sources and estimates) and mustard (literature) blocks:

SACROIS23: French landings and effort logbook declarations for 2010 were made available at the log-event*commercial category*ICES statistical rectangle*population scale. It was used to design exploitation features of the database, as well as populations spatial structure.

LANGOLF survey: 2006–2010 LANGOLF surveys observations for 2006–2010 were made available for Norway lobster. They were used to work on Norway lobster abundance per length class and sex.

Intercatch: catch observations for 2010–2020 in the Bay of Biscay for hake, at the quarter-métier group scale, and catch observations per class for sole on 2010–2012, and 2010 Norway lobster catch observations per sex and length class24, used to describe the inter-annual effort dynamics, to calibrate and validate the model.

Estimates of hake abundance per size class in 2010, and hake quarterly estimates of recruitment on 2010–2012 from a northern hake spatial stock assessment model21, used to inform hake biology assumptions (named Other 1 in Fig. 1).

ICES WGBIE24 2010 estimates of abundance per class (sole and Norway lobster), to inform their abundance at the initial time step; 2010–2012 yearly fishing mortality estimates per age class (sole) to calibrate the database (named Other 2 in Fig. 1).

Other population, exploitation and management assumptions were informed with scientific literature25 and grey literature26,27 (Literature block in Fig. 1).

Management assumptions were informed with legal texts2,4,28,29,30,31,32,33,34 and reported quota values in working group reports24.

About populationsThis section describes for each species the assumptions and parameters values, except for accessibility, which has been calibrated, as described in section Calibration procedure. For all assumptions and values, more details are provided in Supplementary Information’s section 2.2.HakeThe stock size structure was defined with 1 cm size bins for [1;40[cm individuals, 2 cm for [40;100[cm individuals, and 10 cm for [100;130+] cm individuals35. Areas of presence were defined based on 2010 SACROIS French landings data per commercial category and statistical rectangle23, leading to the definition of a presence, a recruitment, an interim recruitment and a spawning area25 (see Supplementary Information’s section 2.2 and Figure S1). These areas allow for the description of intra-Bay of Biscay migrations related to spawning and recruitment processes: mature individuals aggregate at the beginning of the year on the shelf break to spawn, and then disperse on the shelf36,37,38,39,40 (at the beginning of April and July in the model). Also, from age 1 (around 20 cm), individuals in recruitment zone spread in interim recruitment zone, to model a diffusion towards areas neighbouring the nursery area, at the beginning of each time step (see Supplementary Information’s section 2.2 and Table S11). Maturity-at-size and weight-at-length relationships were the same functions as used by ICES working group35,41. Natural mortality was fixed at 0.5, basing on preliminar runs, instead of the commonly used 0.442. Recruitment values were defined prior to the simulation for 2010–2020 using available estimates on the 2010–2015 time series21,27. Deterministic estimates from these sources were allocated to the recruitment area in the Bay of Biscay and the beginning of each month in January-September on the whole time series, of which values are provided in the Supplementary Information’s section 2.2 and Table S3. Growth is modelled through monthly growth increments5,25. However, given the different widths of size bins in the implemented size structure, a correction was provided to values in the transition matrix to eliminate artifacts when growing to a size bin wider than the size bin of origin, as detailed in Supplementary Information’s section 2.2. Abundance at the initial step in each zone was estimated from Bay of Biscay abundance estimates for 201021. Mature individuals over 20 cm were allocated to the spawning area, all individuals strictly shorter than 20 cm were allocated to the recruitment area (as they were assumed to be less than 1 year old), and remaining individuals were allocated to the interim recruitment area. None were allocated to the presence area, in which individuals will go later in the time series, after disaggregating from the spawning area25 (Table S13).SoleThe stock is age structured, with 7 classes going from ages 2 to 7+43 (Table S2). No seasonal variations were implemented. Only a single presence zone was defined (see Supplementary Information’s section 2.2 and Figure S1), as in preliminary runs defining more presence areas for sole did not yield more knowledge in this study. We implemented ICES working group values for natural mortality, weight-at-age (Table S1) and maturity-at-age43. Recruitment occurs at the beginning of each year, individuals being recruited at age 2 (ages 0–1 were not modelled; Table S4). We implemented ICES working group estimates27 for abundance at initial time step (Table S14).Norway lobsterThe stock has a sex-size structure, with 1 mixed recruitment class at 0 cm; 33 length classes for males at 2 carapace length mm intervals, from [10;12[to [72;74[carapace length mm; 23 length classes for females at 2 carapace length mm intervals, from [10;12[to [52;54[carapace length mm. A single presence area was defined: the Great Mudbank21 (see Supplementary Information’s section 2.2 and Figure S1). Several seasonal processes occur for this stock, impacting recruitment, accessibility and growth: 1/ January, begins with the annual recruitment. Females are inside their burrows, less accessible; 2/ February-March females are inside their burrows, less accessible; 3/ April: Spring moulting, females are more accessible; 4–5/ May-August females are more accessible; 6/ September, females are inside their burrows, less accessible; 7/ October: Autumn moulting only for immature females and all males, females are inside their burrows, less accessible; 8/ November-December, females are inside their burrows, less accessible44. We implemented ICES working group values for natural mortality, weight-at-class and maturity-at-class45,46,47. Growth occurs twice a year, when moulting in April and October, and is modelled with growth increments. Recruitment occurs at the beginning of each year, modelled with a Beverton-Holt relationship26, and was assumed to have the same spatial distribution as spawning stock biomass. Abundance at initial step was derived from LANGOLF survey observations and ICES WGBIE estimates25,26 (Table S16).About exploitationThe fishing exploitation structure (fleets, strategies, métiers and gears) were derived following a classification method on SACROIS 2010 landings and effort data13,23 from French fleets, and taken from a TECTAC project (https://cordis.europa.eu/project/id/Q5RS-2002-01291) database for Spanish trawlers. More details on their definition are provided in Supplementary Information’s section 2.3, Tables S5–S9 and S20–S21 and Figure S3. Spanish longliners and gillnetters fleets exploitation was described based on catch (observations from Intercatch48) rather than effort.Hake selectivity and discarding functions (one for each gear) were taken from estimates of a spatial hake stock assessment model21. Parameters values and formulæ are provided in Supplementary Information’s section 2.3 and Tables S6-S7. On top of this, inter-annual fleet dynamics factors were included in equation (21) of ISIS-Fish documentation8 in order to account for observed catch temporal variations. These factors are therefore multiplicative parameters of the target factor of each species for each métier. They are computed using observed catch27 and differ according to the period and targeted species:

over 2010–2016, it is a ratio of observed catch in weight per year over catch observations for 2010: for hake, one per métier *season*year (left(frac{ObservedCatc{h}_{metier,season,year}}{ObservedCatc{h}_{metier,season,2010}}right)), for sole, one per métier *year (left(frac{ObservedCatc{h}_{metier,year}}{ObservedCatc{h}_{metier,2010}}right)), and for Norway lobster, one per year (identical for each métier catching Norway lobster) (left(frac{ObservedCatc{h}_{year}}{ObservedCatc{h}_{2010}}right));

over 2017–2020: at the time of writing these assumptions, more recent data was not available, and ratios were deduced from trends on 2014–2016. A linear model was fitted on ratios deduced earlier on 2014–2016. If a significant trend was identified (hake: whitefish trawlers quarters 2 and 4, longliners and gillnetters seasons 2–3; sole and Norway lobster: all métiers), the slope was used to deduce 2017–2020 ratios (the slope was halved for hake whitefish trawlers and sole and Norway lobster values to avoid unrealistic high values of effort). Otherwise, 2016 ratios were used.

All values are provided in Supplementary Information’s section A.2 Tables S22–S24, and the final values of target factors are derived from the Calibration procedure.About managementWe implemented a set of management rules close to what is currently implemented in the Bay of Biscay.All stocks are managed by TALs (Total Allowable Landings) until 2015 and then by TACs (Total Allowable Catch), except for Norway lobster, managed by TALs on the whole time series, not being under the landings obligation. To favour a better parametrisation, allowing for more reliable dynamics on the following years of the time series, no TALs were implemented during the calibration period (2010–2012; Fig. 1). These regulations were implemented from 2013 using historically TALs and TACs values24.Landings of the three stocks are also constrained by a Minimum Conservation Reference Size regulation that was implemented for all stocks using values currently enforced in the studied fishery28. Likewise, from 2016, the Landings Obligation was implemented, with de minimis exemptions for hake and sole, depending on the year and the gear used to fish them2,31,32,33,34. See Supplementary Information’s sections 2.4 and A.3, Figure S2 and Table S10 for further details on these restrictions.In response to the above management rules, a fishers’ behaviour algorithm has been developed to describe fishermen adaptation. Some métiers may be forbidden, depending on some conditions – the catch quota has been reached, the landings obligation is enforced – but also some values – the proportion of discarded catch, and also catch on previous years. Therefore fishermen change métiers within their strategy métiers set through a re-allocation of fishing effort to the latter set. This re-allocation aims to avoid quota overshooting. Further details about this algorithm are provided in the Supplementary Information’s sections 2.4 and A.3 and Figure S2.Calibration procedureThe model has been calibrated using two parameters (population accessibility and fishing target factor) involved in the catchability process (equation (21) in ISIS-Fish documentation8). The objective of the calibration is to reproduce the dynamics of catch over 2010–2012 at the species*métiers group scale, for each year or quarter depending on available data’s granularity. Calibration is sequentially performed: accessibility parameters for each population were estimated first followed by the target factors. The estimation of each parameter set (parameter type * population) combination was separated, and values were estimated jointly within each parameter set. To account for the specificity of each population model dynamics (global age-based for sole, spatial and size-based for hake, spatial, sex and size-based for Norway lobster), an objective function is defined for each population to calibrate their accessibility. More details on objective functions and procedures are provided in Supplementary Information’s section 2.5, as well as estimated values in Tables S17–S19.Hake accessibilityThe calibration for hake accessibility is based on a procedure developed for a former version of the database25. One parameter was estimated per quarter, all values being equal across length classes. The model outputs were fitted to hake catch observations in weight in the Bay of Biscay in 2010–2012 per length class.Sole accessibilityOne parameter was estimated per age class. The model outputs were fitted to WGBIE fishing mortality per age class for sole27 in 2010–2012.Norway lobster accessibilityOne parameter was calibrated per sex and length class. The model outputs were fitted to catch in numbers per length class and sex in 2010 per quarter provided by WGBIE.About target factorsTarget factors drive how the effort is distributed between populations, métiers and season*year combinations. They were split in 3 components: a fixed component derived from the SACROIS effort dataset analysis (Tables S25–S27), another fixed component driving inter-annual variations of fishing effort (Tables S22–S24), derived from catch observations, and finally an estimated component (Tables S28–S30), allowing to tune the model’s dynamics to observed catch. This section focuses on the estimation of the latter.Hake target factors20 parameters were defined, for each combination of the 5 groups of métiers (longliners, gillnetters, whitefish trawler (coastal), whitefish trawler (not coastal), Norway lobster trawler, see definition Table S8) and 4 quarters. We fitted the model’s outputs to the same data and with the same objective function as for hake’s accessibilities estimation.Sole target factors1 estimated component per group of métiers (gillnetters, Norway lobster trawlers and whitefish trawlers) and quarter. We fitted the model’s outputs to sole catch in weight on 2010–2012 for each métier and quarter.Norway lobster target factors1 estimated component per group of métiers (Norway lobster trawlers and whitefish trawlers). We fitted the model’s outputs to monthly Norway lobster landings data per length and sex class for 2010.Base simulationThe base simulation ran from January 2010 to December 2020 inclusive, with a monthly time step, using the database and parameters values described in this document. Several outputs of interest may be explored after a run: catch (discards and landings), as done in several figures in this paper, but also biomass (total biomass or mature biomass), fishing mortality values, or effort, all at a fine spatio-temporal scale. More