dataesr/scientific-paragraphs-categorization huggingface dataset:
The white dots are the CF4 data in a slice of ±2 h -1 100 Mpc on the SGZ axis. In the nearby Universe close to the observer, located at the center of the box, all datasets reconstruct the same large-scale structures, with a robustness in the peculiar velocity field of about 50 km s -1. The Sloan great wall appears at SGY 250 h -1 100 Mpc as a major feature of our local Universe. modulus and the luminosity distance is valid up to a constant. This constant is the zero point of the law that indicates the distance and which must be calibrated. This calibration is not independent of the value of H 0 and its value could influence the reconstruction performed. The conditional probability of the observed redshift knowing the parameters is given by the following: P(z|u, d, σ NL , r) = N u. r, σ 2 cz (1 + z) -2 + σ 2 NL , v r pec , (6) where v r pec is the radial peculiar velocity.
This prior for the over-density field allowed us to derive the prior for the peculiar velocity field in Fourier space using linear perturbation theory. This realization of the velocity field was then constrained by the observational data using the Wiener filter constrained realizations as described, for example, in L15, page 3 of 6  Doumler et al. (2013). This work provides a brief overview on how to reconstruct the 3D peculiar velocity field. The reconstruction codes we have used are continuously improved by our team and thoroughly tested. The one used for this article already shows limitations: namely in the time needed to compute reconstructions of large datasets such as CF4. Thus we are also testing newer reconstruction methods that will allow us to analyze the upcoming datasets of WALLABY, DESI, and 4HS peculiar velocity surveys, which will be an order of magnitude larger than CF4.
Since CF4 is made up of several surveys, an additional step is needed to prevent Malmquist biases and zero-point calibration errors. This prior is an arbitrarily chosen function that models the expected behavior of the distance distribution. We followed Lavaux (  2016 ) by fitting a function with three parameters, a, b, and c, to the distribution of redshifts. Figure  A. 1 shows that the catalog contains two classes of supernovae and also two classes of galaxy distances: a class of data with high accuracy (labeled type 0) with errors for distance moduli being smaller than 0. 5 mag (in blue) and a medium accuracy class of data (labeled type 1) with errors for distance moduli being larger or equal to 0. 5 mag (in green). In this article all computations have been carried out with the ΛCDM cosmological model with initial values of Ω m = 0.
3, H 0 = 74. 6 km s -1 Mpc -1 (the nominal value for the CF4 catalog given in  Tully et al. 2022) , and a limitation of k max = 0. 1 for the smallest phases reconstructed in Fourier space. In order to reach a stable convergence on the parameters, the computation requires approximately 3000 HMC steps for both the supernovae subsample and the full CF4 dataset. However, we have gone well beyond the convergence and computed about 10 000 HMC steps in order to subsequently derive mean solutions that are less correlated and more statistically robust. The resulting over-density δ and gravitational velocity fields were averaged (after removing the burning steps of the HMC). The resulting velocity fields are presented in this article for resolutions of 64 3 up to z = 0. 08 for the CF4 dataset and its supernovae subsample.
This yields about 8 h -1 Mpc per voxel. A voxel is the cubic volume in a 3D density grid. Its size depends on the resolution of the grid. The nonlinear component of the velocity field, which is a free parameter of the HMC exploration, has a mean value of σ Figure  2  shows the reconstructed 3D density δ m and velocity u m gravitational fields for the three datasets. It is important that the same large-scale structures be found within the first 100 h -1 100 Mpc around the observer with all datasets. Those are the first published 3D reconstructions of CF4. The largest structure known in the local Universe, the Sloan great wall, is clearly seen at SGY of 250 h -1 100 Mpc and extends over more than 400 h -1 100 Mpc along the SGX coordinates. One of the improvements compared to previous CosmicFlows reconstructions is that now we also deliver the standard deviation of the peculiar velocity field over the HMC forward modeling process.
The error bars for the 3D reconstruction were computed from the standard deviations on the 10 000 HMC realizations. At large radii, where the prior model of an homogeneous universe dominates over the observational data, the bulk flow vanishes. Both the supernovae and the full CF4 sample concord and detect the large nearby attractors located within   (272 ± 105) km s -1 , which is in complete accordance with our previous measurement of (239±38) km s -1 using the CF2 dataset  (Hoffman et al. 2015). Using 1008 supernovae, we find a bulk flow of 227 ± 131 km s -1 at 100 h -1 100 Mpc (see Table  1 ), which is in agreement with  Turnbull et al. (2012)  using 245 supernovae peculiar velocities who measured a bulk flow of 249 ± 76 km s -1 out to 110 h -1 100 Mpc. Further out from the observer, the impact of the Sloan great wall is clearly seen in Fig.
Conclusions This analysis of the CF4 catalog provides an estimated growth rate of structures of f σ 8 = 0. 36(±0. 05) obs , while the similar previous measurement with CF3 was f σ 8 = 0. 43(±0. 03) obs (±0. 11) cos. var. At the largest distance of the obser-vational data, we obtain a bulk flow measurement of 230 ± 136 km s -1 at a distance of 300 h -1 100 Mpc, which is pointing in the direction of the Sloan great wall. Another conclusion is that, although the current supernovae sample is very small, it is dense enough for an acceptable local f σ 8 measurement. However, the supernovae dataset only extends out to 100 h -1 100 Mpc and is too sparse to fully recover the velocity field and, as a consequence, it cannot be used as a probe of the bulk flow yet. From the tight variation around ΛCDM values of the free parameters explored by the convergence of the HMC forward modeling and in light of the values obtained for two cosmology-related parameters -namely the growth rate and bulk flow -we conclude that this analysis of the local Universe using peculiar velocities is compatible with a ΛCDM cosmology.
Acknowledgement We acknowledge Laurent Maron (Université Paul Sabatier, Toulouse) for discussions at the early stage of this project. We acknowledge Maciej Korzynski (ETHZ) for a help with preparation of the manuscript. We acknowledge Elizaveta A. Suturina (University of Bath) and Aleksander Jaworski (Stockholm University) for the advices concerning quantum chemistry calculations in lanthanide complexes. We thank Martin Bröring (Institut für Anorganische und Analytische Chemie) at TU Braunschweig for providing access to the SQUID magnetometer. 21-CE29-0010-01. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N, project number 2021-5-122.
Whilst fungi are a large fraction of primary biological aerosol particles (PBAPs) and their impact on global climate has been widely recognised, few studies have empirically assessed fungal vertical profiles and diversity relating those with rainfall. Here, we show the results of fungal PBAPs before and after a rainfall event during a fieldwork campaign using a hot-air balloon over a mixed land-use context at the Brazilian Atlantic Forest biodiversity hotspot. Four flights of c. 1 hour each were performed in the early morning from 8th until 11th of March 2022, and data were collected at three sampling heights (0, 150 and 300 m). Rainfall estimation using IMERG data indicated the precipitation event was of 15-20 mm and ERA5/ECMWF data highlighted that most of the airborne samples were taken above the boundary layer height.
To characterise fungal PBAPs vertical profiles, we performed four consecutive flights (between 8th and 11th of March 2022) with a hot-air balloon. Each flight lasted c. 1 hour. Flights were performed in early mornings (06:00 -08:00 local time) since this was the only period in which the balloon could be safely flown. Early morning corresponds to the period of the atmospheric boundary layer growth and this phenomenon can influence the vertical distribution of PBAPs. This is a limitation of the study that was taken into account and appropriately discussed in the following sections. Flights reached the maximum height of c. 800 m from ground level, and since we were not able to collect replicate-samples above 300 m, we show data for 0-300 m only. There was a rain event on March 10th just before our third flight, so the analysis is divided into prior to and after the rainfall, with two flights in each period.
Rainfall estimation using IMERG Half-hourly precipitation data from the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM)  (IMERG, Huffmann et al. , 2019)  were used to estimate local precipitation during the fieldwork campaign. IMERG combines data from passive microwave sensors comprising the GPM constellation to estimate the surface precipitation with a 0. 1º spatial resolution. We analysed the precipitation time series for the campaign period for the closest grid point to the farm location in Arceburgo (21 • 23 ′ 36. 50 ′′ S, 46 • 55 ′ 15. 87 ′′ W). The IMERG rainfall spatial pattern in the 24 h preceding the measurements was also evaluated. The 3-hourly IMERG spatial precipitation pattern in the 24 h preceding the measurement on the 10th of March at 10:00 UTC show an estimated precipitation of 15-20 mm just before the third flight (Fig.
ERA5/ECMWF meteorological data Meteorological data were obtained from the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecast (ECMWF). Specifically, height of the boundary layer (in meters above ground) and the instantaneous surface heat flux (in W m -2 ) were extracted for the period of the samplings from the "ERA5 hourly data on single levels from 1959 to present" dataset  (Hersbach et al. , 2018). The reanalysis covers the entire globe with a horizontal resolution of 0. 25 • x 0. 25 • and an hourly time resolution. For the present work, the closest pixel to the experimental farm was chosen (-46. 93 • longitude, -21. 40 • latitude). Given that the ECMWF convention specifies that negative fluxes are upward, the instantaneous heat flux was multiplied by -1 to have it in the standard micrometeorological convention for which upward fluxes are positive and negative fluxes are downward.
Conclusions Taking altogether, the results presented in our study demonstrate the differential effects exerted by rainfall and altitude from ground level on fungal PBAPs, showing a use for the fungal gradient at different sampling height to compare with a process induced by rain. In general, an increase in height and rain occurrence lead to reductions in the concentration of fungi present in the atmosphere in the study area within the Brazilian Atlantic Forest biome. The higher prevalence of Cladosporium sp. at all sampling heights demonstrates the ubiquity of this fungal species, which could be considered as a proxy for other fungal PBAPs in future studies aiming to investigate cloud and precipitation formation by such microorganisms. This has further implications for research on climate regulation, such as considering the land-use context in where sampling is taken, as well as collating other types of data (e.
Acknowledgements The authors thank the support received from local landowners of Arceburgo city and the COOXUP É-MG association, particularly  Mr. Éder Ribeiro dos Santos. We also thank  Prof. Dr. Maria F. Andrade  for lending fieldwork equipment,  Mr. Jairo Fogaça  and his hot-air balloon team who gave exceptional help in the field, and  Mrs. Solange Lima  for helping with fungal taxonomy and identification.
Catch composition The assessment of fishing operations was done on a subsample of the fishers interviewed in each area. In order to obtain the most comprehensive dataset possible, and considering that different fishers may have different fishing habits, we monitored fishing operations from all fishers willing to take part in the assessment (ranging from 5 for North Brindisi coast to 12 for South Corsica). In a few cases, the relatively small size of the SSF communities and prolonged adverse meteorological conditions, especially in the winter season, contributed to a reduced number of fishing operations assessed compared to the majority of the areas. Fishing operation assessment was restricted to set nets only (i. e. trammel nets, gillnets and combined trammel-gillnets). There were various reasons for this choice: in the sector of the Mediterranean Sea considered, set nets are the most commonly used fishing gear  (Grati et al.
S1  Supplementary Material). Each picture was associated with a unique identifier of the fishing operation (e. g. a small paper tag with a unique reference code) for the subsequent image analysis (Fig. S1 ). For all the fishing operations monitored, the type of set net used and the net length were also recorded, thus enabling the calculation of catch and revenue per unit of effort  (CPUE and RPUE, respectively). An operator using the image-analysis free software ImageJ  (Abràmoff et al. , 2004)  then processed the images. From each picture, we extracted information on species composition and frequency of occurrence in the catches. Individual specimens were identified to the lowest possible taxonomic level (usually species).
Ex-vessel price and RPUE estimation We built an ex-vessel price (i. e. the price that fishers receive directly for their catch, Tai et a 2017) database for each species appearing in the catches. To do so, during summer 2018, in ea area a group of fishers was asked about the yearly average price per kilogram they charge to s their fish. These values were successively used to estimate the revenue per unit of effort (RPU measured as euros/1000 m of net) for each fishing operation and for each species within it, combining prices with the biomass of each species in the catch: 𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 = 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑟𝑟𝑟𝑟𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜 ℎ𝑜𝑜𝑜𝑜 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡 𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 𝑙𝑙𝑙𝑙 𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ℎ 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑜𝑜𝑜𝑜 𝑖𝑖𝑖𝑖𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡 𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏 where 𝑜𝑜𝑜𝑜𝑒𝑒𝑒𝑒𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑒𝑒𝑒𝑒 𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 ℎ𝑏𝑏𝑏𝑏 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡 𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 = ∑ 𝑖𝑖𝑖𝑖 𝑘𝑘𝑘𝑘=1 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 * 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒.
Data analyses The main characteristics of the fishing fleets were investigated through descriptive statistics. Species richness (measured as total number of species in the catch) and the frequency occurrence (i. e. the number of fishing operations in which a species occurred at least once divid by the total number of fishing operations monitored) were used to characterize the composition catches in each area. The species occurrence over the monitoring period was assessed by pooli fishing operations by season and by month. A specific assessment concerned the presence species considered at risk of extinction in the Mediterranean Sea with the aim of providi preliminary insights on the potential impacts set nets may have on these species. For this, followi the criteria defined in  Lloret et al.
Ex-vessel price and RPUE estimation We built an ex-vessel price (i. e. the price that fishers receive directly for their catch,  Tai et al. , 2017)  database for each species appearing in the catches. To do so, during summer 2018, in each area a group of fishers was asked about the yearly average price per kilogram they charge to sell their fish. These values were successively used to estimate the revenue per unit of effort (RPUE, measured as euros/1000 m of net) for each fishing operation and for each species within it, by combining prices with the biomass of each species in the catch: 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑒𝑒𝑒𝑒 𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 ℎ𝑏𝑏𝑏𝑏 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡 𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 = ∑ 𝑖𝑖𝑖𝑖 𝑘𝑘𝑘𝑘=1 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏) 𝑘𝑘𝑘𝑘 with n the total number of species (k) occurring in the fishing operation.
Ex-vessel price and RPUE estimation We built an ex-vessel price (i. e. the price that fishers receive directly for their catch, Tai e 2017) database for each species appearing in the catches. To do so, during summer 2018, in area a group of fishers was asked about the yearly average price per kilogram they charge to their fish. These values were successively used to estimate the revenue per unit of effort (RP measured as euros/1000 m of net) for each fishing operation and for each species within i combining prices with the biomass of each species in the catch: 𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 = 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑟𝑟𝑟𝑟𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜 ℎ𝑜𝑜𝑜𝑜 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡 𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 𝑙𝑙𝑙𝑙 𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ℎ 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑜𝑜𝑜𝑜 𝑖𝑖𝑖𝑖𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡 𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏 where 𝑜𝑜𝑜𝑜𝑒𝑒𝑒𝑒𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑒𝑒𝑒𝑒 𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 ℎ𝑏𝑏𝑏𝑏 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡 𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 = ∑ 𝑖𝑖𝑖𝑖 𝑘𝑘𝑘𝑘=1 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 * 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒.
Data analyses The main characteristics of the fishing fleets were investigated through descriptive statistics. Species richness (measured as total number of species in the catch) and the frequenc occurrence (i. e. the number of fishing operations in which a species occurred at least once div by the total number of fishing operations monitored) were used to characterize the compositio catches in each area. The species occurrence over the monitoring period was assessed by po fishing operations by season and by month. A specific assessment concerned the presenc species considered at risk of extinction in the Mediterranean Sea with the aim of prov preliminary insights on the potential impacts set nets may have on these species. For this, follo the criteria defined in  Lloret et al. (2019) , we identified the species in the catches tha considered as threatened, therefore falling within 'Critically Endangered' (CR), 'Endangered' or 'Vulnerable' (VU) categories (as listed in the IUCN Red List), or vulnerable to fishing (us vulnerability index based on life history traits and ecological characteristics of a marine where 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑒𝑒𝑒𝑒 𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 ℎ𝑏𝑏𝑏𝑏 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡 𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 = ∑ 𝑖𝑖𝑖𝑖 𝑘𝑘𝑘𝑘=1 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏) 𝑘𝑘𝑘𝑘 with n the total number of species (k) occurring in the fishing opera.
Ex-vessel price and RPUE estimation We built an ex-vessel price (i. e. the price that fishers receive directly for their 2017) database for each species appearing in the catches. To do so, during summ area a group of fishers was asked about the yearly average price per kilogram t their fish. These values were successively used to estimate the revenue per unit measured as euros/1000 m of net) for each fishing operation and for each spe combining prices with the biomass of each species in the catch: 𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 = 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑟𝑟𝑟𝑟𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜 ℎ𝑜𝑜𝑜𝑜 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡 𝑜𝑜𝑜𝑜 𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 𝑙𝑙𝑙𝑙 𝑜𝑜𝑜𝑜𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ℎ 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑜𝑜𝑜𝑜 𝑖𝑖𝑖𝑖𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡 𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏 where 𝑜𝑜𝑜𝑜𝑒𝑒𝑒𝑒𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑟𝑒𝑒𝑒𝑒 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑒𝑒𝑒𝑒 𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 ℎ𝑏𝑏𝑏𝑏 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡 𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 = ∑ 𝑖𝑖𝑖𝑖 𝑘𝑘𝑘𝑘=1 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 * 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒.
Data analyses The main characteristics of the fishing fleets were investigated through descriptive Species richness (measured as total number of species in the catch) and occurrence (i. e. the number of fishing operations in which a species occurred at by the total number of fishing operations monitored) were used to characterize t catches in each area. The species occurrence over the monitoring period was as fishing operations by season and by month. A specific assessment concerned species considered at risk of extinction in the Mediterranean Sea with the preliminary insights on the potential impacts set nets may have on these species. F the criteria defined in  Lloret et al. (2019) , we identified the species in the considered as threatened, therefore falling within 'Critically Endangered' (CR), ' or 'Vulnerable' (VU) categories (as listed in the IUCN Red List), or vulnerable t vulnerability index based on life history traits and ecological characteristics 8 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡 ℎ𝑒𝑒𝑒𝑒 𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏 ℎ𝑏𝑏𝑏𝑏 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑏𝑏𝑏𝑏𝑜𝑜𝑜𝑜𝑒𝑒𝑒𝑒𝑜𝑜𝑜𝑜𝑏𝑏𝑏𝑏𝑡𝑡𝑡𝑡 𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑖𝑖𝑖𝑖 = ∑ 𝑖𝑖𝑖𝑖 𝑘𝑘𝑘𝑘=1 (𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏) 𝑘𝑘𝑘𝑘 with n the total number of species (k) occurring in the fishing operation.
Ex-vessel price and RPUE estimation We built an ex-vessel price (i. e. the price that fishers receive directly for their catch,  Tai et al. , 2017)  database for each species appearing in the catches. To do so, during summer 2018, in each area a group of fishers was asked about the yearly average price per kilogram they charge to sell their fish. These values were successively used to estimate the revenue per unit of effort (RPUE, measured as euros/1000 m of net) for each fishing operation and for each species within it, by combining prices with the biomass of each species in the catch: We therefore refer to 'revenue' as the landed value of the catch (i. e. the total amount of money a fisher gains by selling the catch without considering any fixed cost or expense incurred by fishers) in accordance with  Sala et al.
Data analyses The main characteristics of the fishing fleets were investigated through descriptive statistics. Species richness (measured as total number of species in the catch) and the frequency of occurrence (i. e. the number of fishing operations in which a species occurred at least once divided by the total number of fishing operations monitored) were used to characterize the composition of catches in each area. The species occurrence over the monitoring period was assessed by pooling fishing operations by season and by month. A specific assessment concerned the presence of species considered at risk of extinction in the Mediterranean Sea with the aim of providing preliminary insights on the potential impacts set nets may have on these species. For this, following the criteria defined in  Lloret et al.
(2019) , we identified the species in the catches that are considered as threatened, therefore falling within 'Critically Endangered' (CR), 'Endangered' (EN) or 'Vulnerable' (VU) categories (as listed in the IUCN Red List), or vulnerable to fishing (using a vulnerability index based on life history traits and ecological characteristics of a marine fish,  Cheung et al. , 2007). Moreover, we checked for the presence of non-indigenous species for the Mediterranean Sea according to the Marine Mediterranean Invasive Alien Species (MA-MIAS) database (UNEP & RAC/SPA, 2019). Differences in size distribution between areas were graphically inspected through boxplots for the most relevant species in terms of contribution to total biomass caught (see Results). For the species with minimum conservation reference size (MCRS, EU 2008), we estimated the fraction of specimens below this size.
Frequency is probably driven by the natural fluctuation of abundance of these species over the year, but fishers are able to foresee this variability and target areas and periods to optimize their catches  (Forcada et al. , 2010; Maynou et al. , 2011; Quetglas et al. , 2016). Overall, in our assessment two species appeared in gillnets only, while all other species caught by gillnets were also observed in trammel-nets. It is important to note, however, that gillnets and combined nets may be considerably underrepresented, due to the extreme imbalance in the use of these gears in our work compared to trammel nets. On the other hand, our results also highlighted relevant differences in catch composition between areas, with some areas associated with twice the number of species as others. It is important to underline here that almost all the areas were monitored throughout the year, and only in the case of Strunjan were catch data gathered over a shorter time window (only during autumn).