DOI: 10.5061/dryad.qz612jmn5
title: Data and code: Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management

description: Sustainable management of living resources must reconcile biodiversity
 conservation and socioeconomic viability of human activities. In the case
 of fisheries, sustainable management design is made challenging by the
 complex spatiotemporal interactions between fish and fisheries. We develop
 a comprehensive metapopulation framework integrating data on species
 life-history traits, connectivity and habitat distribution to identify
 priority areas for fishing regulation and assess how management impacts
 are spatially distributed. We trial this approach on European hake
 fisheries in the north-western Mediterranean, where we assess area-based
 management scenarios in terms of stock status and fishery productivity to
 prioritize areas for protection. Model simulations show that local fishery
 closures have the potential to enhance both spawning stock biomass and
 landings on a regional scale compared to a status quo scenario, but that
 improving protection is easier than increasing productivity. Moreover, the
 interaction between metapopulation dynamics and the redistribution of
 fishing effort following local closures implies that benefits and
 drawbacks are heterogeneously distributed in space, the former being
 concentrated in the proximity of the protected site. A network analysis
 shows that priority areas for protection are those with the highest
 connectivity (as expressed by network metrics) if the objective is to
 improve the spawning stock, while no significant relationship emerges
 between connectivity and potential for increased landings. Synthesis and
 applications – Our framework provides a tool for 1) assessing area-based
 management measures aimed at improving fisheries outcomes in terms of both
 conservation and socioeconomic viability and 2) describing the spatial
 distribution of costs and benefits, which can help guide effective
 management and gain stakeholder support. Adult dispersal remains the main
 source of uncertainty that needs to be investigated to effectively apply
 our model to fisheries regulation.
description: # Data and code for: Assessing fish-fishery dynamics from a spatially
 explicit metapopulation perspective reveals winners and losers in
 fisheries management
 [https://doi.org/10.5061/dryad.qz612jmn5](https://doi.org/10.5061/dryad.qz612jmn5) This code has been produced in the context of a study aimed to assess the spatiotemporal effects of local fishery closures to support sustainable management of marine resources. We consider the European hake fishery of the Northwestern Mediterranean sea (with a focus on Geographic Subarea 9, Ligurian Sea and northern Tyrrhenian Sea) as a case study.   In detail, the scripts evaluate the performance of closing hake fisheries in a cell of the domain in terms of spawning stock biomass (SSB) and landings (Y). Stock dynamics are simulated over 50 years, and average values of SSB and Y over the last 10 years of simulation are compared with those of a status quo scenario in which fishing effort is kept unchanged. A Monte Carlo approach is used to propagate uncertainty on model inputs (parameters, recruitment from GSA 10 to GSA 9 and fishing effort). For each closure scenario, the model is run 100 times, each time with a parameter set (including the catchability of different fishing gears and the parameter regulating adult dispersal) randomly drawn from the bootstrapped empirical distribution obtained via calibration, and using input time series of recruitment and fishing effort resampled from the original ones via a nearest neighbour bootstrap algorithm. ## Description of the data and file structure This repository contains a folder of data linked to scripts (available at https://zenodo.org/record/8321856) . The scripts are: * *run.m* This script runs a loop to calculate the performances of closing each cell of the domain (calling the script *metapop.m*) and plot the overall performances of different cells, reproducing fig. 3 of the manuscript. * *metapop.m* This script runs the metapopulation model, which calculates (a) the abundances of each age class in each cell at each time step (1 year) and (b) outputs (SSB = the spawning spawning stock biomass; Y = landings) disaggregated by cell. It is called by the script *run.m* These scripts upload MATLAB data from the data.zip folder. The archive data.zip needs to be unzipped before running. The MATLAB files called by *run.m* are: * cell\_areas.mat: area of each cell (in km2) * cell\_ids.mat: ordered vector of cell identifiers * distances.mat: distance matrix (in km) * fisheries\_parameters.mat: reset area, cell identifier, and fishing footprint coefficients (%) for each sub-cell. This file is called also by the script *metapop.m* * M.mat: connectivity matrix (normalised by rows) * nurseries.mat: nurseries (% by cell) * recruitment\_and\_effort.mat: resampled time series of recruitment from GSA 10 (adimensional) and fishing effort in GSA 9 (absolute number) * region\_boundaries.mat: table associating cell ranges (in rows) to regions (row numbers). Region codes: 1 = "Sicily", 2 = "Calabria", 3 = "Campania, 4 = "Lazio", 5 = "Tuscany", 6 = "Liguria") * spawning\_grounds.mat: spawning grounds (% by cell) * sub\_cells.mat: vector linking each sub-cell (for the calculation of fishing footprint) to the index of its corresponding cell * Theta\_boot.mat: parameter set (100 bootstrap resamplings). Each column corresponds to one of the 5 parameters: q\_GNS62.5 (adimensional), q\_GNS82 (adimensional), q\_OTB33 (adimensional), q\_OTB40 (adimensional), sigma\_D (km) ## Sharing/Access information Additional data which may help the user to contextualise this repository are available in the article *Assessing fish-fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management* (Radici et al., accepted 2023) and in the attached supplementary information. ## Code/Software The code is written in MATLAB. Data are stored within a zipped folder, which needs to be unzipped before running the code.