http://orcid.org/0000-0002-1682-0041
Figures
Abstract
Fish stocks interact through predation and competition for resources, yet stocks are typically managed independently on a stock-by-stock basis. The need to take account of multi-species interactions is widely acknowledged. However, examples of the application of multi-species models to support management decisions are limited as they are often seen as too complex and lacking transparency. Thus there is a need for simple and transparent methods to address stock interactions in a way that supports managers. Here we introduce LeMaRns, a new R-package of a general length-structured fish community model, LeMans, that characterises fishing using fleets that can have different gears and species catch preferences. We describe the model, package implementation, and give three examples of use: determination of multi-species reference points; modelling of mixed-fishery interactions; and examination of the response of community indicators to dynamical changes in fleet effort within a mixed-fishery. LeMaRns offers a diverse array of options for parameterisation. This, along with the speed, comprehensive documentation, and open source nature of the package makes LeMans newly accessible, transparent, and easy to use, which we hope will lead to increased uptake by the fisheries management community.
Citation: Spence MA, Bannister HJ, Ball JE, Dolder PJ, Griffiths CA, Thorpe RB (2020) LeMaRns: A Length-based Multi-species analysis by numerical simulation in R. PLoS ONE 15(2): e0227767. https://doi.org/10.1371/journal.pone.0227767
Editor: Athanassios C. Tsikliras, Aristotle University of Thessaloniki, GREECE
Received: July 17, 2019; Accepted: December 28, 2019; Published: February 3, 2020
Copyright: © 2020 Spence et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files. he R-package is available on CRAN (https://cran.r-project.org/web/packages/LeMaRns/index.html).
Funding: MAS, HJB, JEB, PJD, CAG and RBT were all funded by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773713 (PANDORA). No the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
In fisheries management, fish stocks are typically managed on a stock-by-stock basis, with most assessment models having a single-species focus that does not explicitly model interactions between stocks. Single-species models are useful for assessing the current state of a stock and making short-term forecasts as you only need information regarding the stock in question. However, these models assume that inter-stock interactions (predation and competition for resources) are either fixed or vary only in a simple way with time, and so on longer timescales their use becomes increasingly problematic. Multi-species models that explicitly represent some or all of these interactions are generally more suited to making predictions as the timescale of interest increases [1]. As a result, a large number of multi-species models have been developed. The models take various approaches: statistical models, such as the Stochastic Multi-Species model (SMS) [2], are similar to age-structured single-species assessment models; biomass dynamic models, such as surplus-production models [3], describe the dynamics of bulk biomass; while mechanistic models, such as Ecopath [4], attempt to describe the processes that lead to the emergent system based on ecological theory. For a discussion regarding the relative merits of single and multi-species approaches see [1].
Size-based multi-species models are a class of mechanistic models [5] that, in contrast to single-species models, explicitly take account of foodweb interactions. In marine ecosystems size is often a key trait [6]. By representing many processes, including fishing, natural mortality, and predation as a function of length, it is possible to reproduce many aspects of the community dynamics (such as the tendency of diet to change with increasing predator size; [7]) with a relatively small number of parameters and a modest requirement for data in model set up. This makes the framework particularly suitable for use in data-limited fisheries. However, size-based multi-species models lack uptake by both managers and fisheries scientists due to the perceived complexity and lack of transparency of the models [8].
Here we introduce the new LeMaRns R-package, within which we implement a general length-structured fish community model, LeMans (Length-based Multi-species analysis by numerical simulation) [9, 10], that can be adapted to any marine ecosystem with only a modest amount of data. The speed, increased functionality, comprehensive documentation, diverse array of parameterisation, and open source nature of the package makes LeMans newly accessible to model developers and general users alike. We hope this will lead to increased user uptake and novel model development by the fisheries community.
In this paper we give a brief overview of the LeMaRns model, we describe how the package works and we provide three applications of LeMaRns. The code for these applications can be found in S1 File. We conclude with a discussion regarding future work.
Model overview
The LeMans model framework was originally developed by [9] to represent the Georges Bank fish community, but was subsequently adapted for use in the North Sea by [10–14].
LeMans is well suited for use whenever there is a need for multi-species or mixed-fisheries analysis but where there is insufficient data to support the use of more complicated models, such Atlantis [15]. The model has been used to assess the impact of mixed-fisheries [12, 13], evaluate the effect of harvest control rules [14], and as part of a multi-model ensemble along with other multi-species models [16]. LeMans models fish length because a) it is generally easier to measure than weight in the field [17], and b) fisheries selectivity is normally characterised in terms of length [18] and is thus more straightforward to relate to the parameterisation of mixed-fisheries.
The model has no spatial dependency and describes the dynamics of multiple species in nl discrete length classes through time. A year in the model is subdivided into a number of equal time steps of length δt. Spatial information can be included in the model implicitly via the predator-prey interaction matrix (e.g. [19]).
Let Nj,i,t−1 be the number of individuals of the ith species in the jth length class after t−1 time steps. During each time step three processes occur: recruitment, mortality, and growth. The number of individuals after the recruitment phase of the tth time step is
where Ri,t is the number of recruits of the ith species at time t. Ri,t depends on the spawning stock biomass of the ith species as well as the time step of the model. In [9] and [10] recruitment occurred in the first time step of a new year, using the a Ricker recruitment curve [20] and a hockey-stick recruitment curve [21] respectively (see S2 File pages 5-6 for more details).
The number of individuals after the mortality phase of the time step is
where M1j,i is the background mortality, M2j,i,t is the predation mortality, and Fj,i,t is the fishing mortality. The background mortality, M1j,i, is size and species dependent (see S2 File page 7 for more details). Predation mortality is size- and species-dependent. The size preference of a predator is described using a preference function based upon a log-normal distribution, whilst species preference is described using a predator-prey interaction matrix indicating who eats whom [10, 11] (see S2 File pages 4, 7-8 and 13 for more details).
Fishing mortality is constructed from the joint effect of a number of fishing gears exploiting different species with different size and targeting preferences. More specifically, the fishing mortality of the ith species in the jth length class at time step t is
(1)
where ek,t is the effort of the kth gear, for k = 1…H (the total number of gears in the fishery) and qj,i,k is the catchability. The units for F are yr−1 and the units of qj,i,k are F per unit effort.
The number of individuals after the growth phase, and the end of the time step, is
where ϕj,i is the proportion of individuals of species i that leave length class j due to growth over the time step according to the von-Bertalanffy growth equation [22] (see S2 File pages 3-4 for more details). Further details of the model can be found in S2 File (pages 2-14) and in [9].
Using LeMaRns
LeMaRns is available on CRAN (https://cran.r-project.org/web/packages/LeMaRns/index.html) and GitHub (https://github.com/CefasRepRes/LeMaRns).
Data requirements
Biological data.
The minimum amount of information required to set up a model using LeMaRns includes: species-specific maximum length (Linf), length at 50% maturity (Lmat), length-weight conversion parameters (e.g. W_a and W_b), the growth parameter from the specialised von Bertalanffy growth function (k) [23], and the recruitment parameters (recruit_params; see S2 File, page 5 for further details). The length-weight and life history parameters are often available from survey data, online databases (e.g. Fishbase [24]), or through ‘life history invariants’ [25, 26]. The recruitment parameters are typically harder to determine and can be thought of as ‘tuning’ parameters [6]. The fitting of these parameters is done outside of the LeMaRns. An illustrative example, based on the methods in [27], can be found in S3 File.
In addition to the required parameters described above, users may specify species-specific values for M1, background mortality (i.e. mortality not from fishing or predation), and the rate of change from immaturity to maturity, although default values are given for these. Users may also input a predator-prey interaction matrix, tau, which describes the diet information and spatial overlap of predators and prey. tau defaults to one for all predator-prey combinations, although we recommend that this is replaced with an ecosystem-specific matrix based on available diet information, spatial overlap, and/or expert judgement.
In LeMaRns there are five built-in recruitment functions: hockey-stick [21] (the default option), Ricker [20], Beverton-Holt [23], linear, or constant, as well as three background mortality functions: std_RNM (the default option), constant, and linear (see S2 File, pages 5 and 7).
The predator-prey mass ratio, the width of the predator-prey size preference, and the theoretical growth efficiency of a fish of length zero are all species-independent parameters in the current version of LeMaRns.
Fishing.
LeMaRns allows mixed-fisheries analyses to be conducted through the definition of fishing gears. In the LeMans model, the fishing mortality is calculated using Eq 1. This means that the catchability, qj,i,k, for the ith species in the jth length class with the kth gear must be defined for all species, length classes, and gears. The catchability is fixed in time but effort, ek,t, can be dynamic. In LeMaRns there are three built-in functions that can be used to create catchability curves: logistic, log_gaussian, and knife-edge (see S2 File, page 8). In addition, there is an option that allows users to input their own catchabilities.
Test dataset.
In LeMaRns, we provide a dataset, NS_par, for 21 species in the North Sea based on [10]. The dataset contains Linf, Lmat, W_a, W_b, k, and the recruitment parameters (a and b) for each species. We also include NS_other to represent other food. The recruitment parameters, a and b, and NS_other were calibrated to the North Sea (see S3 File for details). The predator-prey matrix (NS_tau) contains information regarding the diet of the 21 species and is based on [10]. In addition, we provide information regarding a number of fishing fleets (NS_mixed_fish), with catchability parameters (NS_eta and NS_L50) that are based on [12].
Setting up the model
In LeMaRns a model can be set up using the LeMansParam() function. This function returns an object of class LeMans_param, which contains all of the information required to run the LeMans model. Below is an example of how to use the provided data to set up the model:
NS_params <- LeMansParam(NS_par, tau = NS_tau,
eta = NS_eta, L50 = NS_L50, other = NS_other)
The LeMansParam() function takes the parameters described in the previous section, as well as optional inputs including: nl, the number of length classes (nsc, the default is 32); the boundaries of the length classes (bounds, the default depends on max(Linf)), and δt, the time step of the model in years (phi_min, the default is 0.1).
All default values, with the exception of tau, are the same as those used in [10].
Running the model
In LeMaRns a model can be run using the run_LeMans() function:
run_LeMans(NS_params)
By default, run_LeMans() uses the get_N0() function to initialise the population and is run for for 10 years with no fishing. However, users can specify their own initial population with the input N0, a feature that also allows users to extend model runs when required (see S2 File for examples).
Although gear catchability is calculated in LeMansParam(), fishing effort is an input to run_LeMans(), thus allowing effort to be dynamic. years is also an input to run_LeMans() and is used to define the number of years that the model should be run for. Below we run the model for 50 years with a constant effort of 0.25, which equates to an F of 0.25 on the length-class with highest selection, for each gear:
no_of_gears <- dim(NS_params@Qs) [3]
effort_mat <- matrix(0.25, 50, no_of_gears)
model_run <- run_LeMans(NS_params, years = 50, effort = effort_mat)
Model outputs
run_LeMans() returns an object of class LeMans_output, which contains a time series of the number of individuals in each length class for each species and time step (N), the weight caught in each length class for each species and time step (Catch), the predation mortality in each length class for each species and time step (M2), and the number of recruits of each species and time step (R).
The LeMaRns package also includes a number of built-in functions that enable users to explore the outputs of a model run in more detail. These functions can be used to calculate and plot community and species-specific total biomass and Spawning Stock Biomass (SSB), as well as several ecosystem indicators including the Large Fish Indicator (LFI), Mean Maximum Length (MML), Typical Length (TyL), and Length Quantiles (LQ). Functions also exist to calculate Catch Per Unit Effort (CPUE) and Catch Per Gear (CPG). See S2 File, page 18 for definitions of these outputs.
An example is shown in Fig 1, which is created using plot_SSB(NS_params, model_run).
Case studies
In this section we provide three applications of the LeMaRns package. The first example focuses on finding long-term multi-species fishing targets; the second examines the effect of different fishing scenarios on long-term stock status in a mixed-fishery; and the third example explores the effect of dynamic fishing effort in a mixed-fishery on ecosystem indicators. In the applications we assume that each species is a single stock. The code for generating these examples can be found in S1 File.
Nash equilibrium
Fish stocks are often managed by considering the fishing mortality that maximises the long-term yield, i.e. the Maximum Sustainable Yield (MSY) [28]. We can define fi(Fi, F−i) as the ith stock’s long-term yield, where Fi is the fishing mortality of the ith stock and F−i are the fishing mortalities of the other stocks. Many stocks are managed on a stock-by-stock basis using single-species models. This means that
, and then
is commonly well defined. However, stocks often interact with one another and the fishing mortality of the jth stock affects the catch of the ith stock, i.e.
We therefore need to define a multi-species MSY. One possibility is the Nash equilibrium [13], which is defined as the point at which we are unable to increase fi(Fi, F−i) by changing Fi only, ∀i. Formally, FNash,i is a Nash equilibrium when
Using LeMaRns and starting from the FMSY values given in [12], we can find FNash,i for i = 1, …, 21. We can also find FMSY,i values to compare to our FNash,i; this is not trivial as we need to define fishing mortalities for all of the species and FMSY,i will be sensitive to these. Arbitrarily, we can set the fishing mortality for the other species to the values given in [12]. Fig 2 provides a comparison between FMSY,i and FNash,i; they appear to be similar for the species with lower FMSY,i and FNash,i, but differ more for larger values.
The solid line is the 1-1 line.
In this study we arbitrarily chose to hold the fishing mortality of the other stocks at the FMSY values given in [12]. However, if we had chosen to hold them at FNash,−i, then FMSY,i = FNash,i, ∀i, as FNash,i is a solution of FMSY,i. This highlights the sensitivity of FMSY,i to the fishing mortality on the other stocks.
Mixed-fishery
Here we explore the mixed-fishery example described in [12], which involves four idealised fishing fleets, i.e. a single species is caught by only one gear type. We investigated the risk of stock collapse under different fishing scenarios. A stock is deemed to have collapsed if its SSB falls below 10% of its unfished SSB [12, 29].
The dataset NS_mixed_fish contains information on which fleet catches which species. In this example, the selectivity of each species follows the logistic curve with catchability parameters eta and L50.
In a scenario, the effort of each of the four fishing fleets, Industrial, Otter, Beam, and Pelagic, was one of five levels, c(0,0.5,1,1.5,2), which was held constant for 50 years. We ran all possible combinations of these levels resulting in 625 different scenarios.
Fig 3 depicts the number of stocks at risk under varying levels of fishing effort for each fleet; the number of stocks at risk of collapse is mostly sensitive to the effort of the Otter and Beam fleets.
Dynamic fishing
Here we add another fleet (Recreational) to the idealised fleets in the previous example. This fleet is set up to catch cod, haddock, herring, horse mackerel, mackerel, plaice, saithe, and whiting, with all fish exceeding the minimum landing size [30] being retained. Any fish that are discarded are assumed to have survived (following a knife-edge selectivity function). Having run the LeMans model for 50 years with no fishing, the model was run for a further 20 years with dynamical fishing effort (see Fig 4 and S2 File, page 39 for the time series of fishing effort).
The recreational fishing fleet is not shown but increases linearly from 0.1 in the first year to 0.15 in the final year.
Fig 5 depicts the MML, TyL, the LFI (40cm threshold), and the 0.1, 0.5, and 0.9 LQs in the last 20 years of the model run. The different fishing fleets seem to have a different effect on the dynamics of the indicators. MML and the LFI seem to correlate with the fishing effort of the Otter fleet, whilst TyL is additionally affected by the Pelagic fleet. The dynamics of the LQs suggest that larger fish are affected by the Otter fleet and medium sized fish by the Pelagic fleet. The smaller fish do not have a large reaction to fishing, but seem to have inter-annual variation due to spawning.
This plot was created using the plot_indicators() function.
Conclusions and further work
LeMaRns provides a convenient and user friendly way to run the LeMans model, with comprehensive documentation. The package contains the required functions to explore different fishing scenarios in a mixed-fisheries multi-species model and allows for the customisation and tailoring of inputs to model specific environments, conditions, and scenarios. This, along with the low data requirements, makes LeMaRns a transparent, easy to use, and broadly applicable fisheries assessment tool that encourages model development and experimentation. Further, we hope this will lead to an increased uptake of LeMans by the fisheries management community.
Several developments are planned for future releases, including food-dependent growth and stochastic recruitment. The package is currently being used to explore the effects of harvest control rules in the North Sea and to explore seasonal effects using a similar method to [31].
Supporting information
S1 File. R script.
The R script to run the case studies and generate the figures in the paper.
https://doi.org/10.1371/journal.pone.0227767.s001
(R)
S2 File. R package vignette.
Contains the description of the model and further explanation of the package.
https://doi.org/10.1371/journal.pone.0227767.s002
(PDF)
S3 File. An example of calibrating the model.
Contains an example of calibrating the model with robust uncertainty quantification using a Bayesian framework.
https://doi.org/10.1371/journal.pone.0227767.s003
(PDF)
Acknowledgments
This work was funded by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773713 (PANDORA). We would also like to thank Gary Saggers, Finlay Scott, Sean M. Lucey and Athanassios C. Tsikiras for invaluable comments on an earlier version of the paper.
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