An Assessment Tool for Sea Trout Fisheries Based on Life Table Approaches Client: Natural Resources Wales APEM Ref: P*0001193 January 2016 Dr Nigel Milner Reportv4. Final Summary A spreadsheet model was developed to calculate various metrics of sea trout stocks in order to support decisions on Net Limitation Orders and other forms of catch regulation. The principal feature is the use of life table approaches which allow the calculation of future life time egg production (FLE) of individual fish, age and size classes. FLE is an index of the long term reproductive value of fish and varies systematically with age through the interaction of future expectation of life (survival) and increased size and fecundity (growth). The loss of FLE resulting from fisheries catch therefore offers a measure of the impacts that fisheries exert on stock fitness. Other metrics include catch (number of fish), catch weight and annual egg deposition. While these have their advantages, they are less informative than FLE about the population dynamics outcomes of fishing. An additional supporting metric, termed gain, is introduced which reflects the component of FLE due to survival beyond the current spawning year. Key outputs are tabulated and plotted on a summary sheet. The models can be used to simulate the effect of changing regulations by changing for example size limits in the rod fishery. Models are set up for the Tywi and Teifi and example outputs are discussed to compare the metrics, illustrate sources of errors and uncertainties, sensitivity to parameters and the interpretation of results. Full life table models are also briefly outlined for both rivers, but were not explored for the purposes of this project. A number of demographic factors contribute to the uncertainty in the outputs, the most important being the accuracy of the age-weight key used to translate size distributions into age and growth, the form of the post-smolt survivorship curve, sex ratios and the timing of first maturation. In addition, fisheries-based factors, reporting and exploitation rates exert important influence on the results. While the models serve their immediate comparative purpose and improve significantly on previous methods, these uncertainties remain a constraint on the longer term application of population dynamics to sea trout assessment and management. This report refers to models and calculations in Excel Spreadsheets: FLE.TYWI.v1, FLE.TYWI.v4, FLE.TYWI.v4. The original catch data (provided by the NRW) are found in spreadsheets: ST.Tywi.nets, ST.Tywi.rods, ST.Teifi.nets, ST.Teifi.rods. Reportv4. Final INTRODUCTION AND AIM In order to implement NLO reviews and for more general purposes NRW needs to have a method for evaluating the status of sea trout stocks, the relative impact of competing fisheries and how stocks might respond to alternative catch regulations. Based on previous discussions over methods developed during the Celtic Sea Trout Project (CSTP), NRW considered that the use of life table (LT) approaches that derive future life time eggs (FLE) and various other life table outputs for stocks (here taken as synonymous with a river “population”) was a potential way forward. Such metrics link more closely with population dynamics and the actual state of populations than routine catch statistics. The LT modelling approach produces outputs ranging from simple, comparatively robust figures to complex population modelling and calculation of life history variables that are subject to large assumptions, scientific uncertainties and difficulties of practical interpretation. These boundaries need to be recognised and the more involved outputs avoided, at this stage, for the immediate practical assessment purposes. Outputs from the modelling need to be clearly understandable by stakeholders and be readily interpreted, particularly for NLO applications. The aim of this project was to revise and enhance Excel spreadsheet life table models developed in the Celtic Sea Trout Project (CSTP, 2016) and to write guidelines for their use in order for NRW to use them as tools for estimation of FLE and basic LT parameters for NLO rivers. BACKGROUND RATIONALE The abundance of a fish population is determined by its population dynamics, which are tuned by evolutionary adaptations to maximise its reproductive capacity. The long term fitness of a fish is the ability to transmit genes to future generations, and the average across all the individuals of a population determines the population’s overall fitness. Fitness is considered to give stability and resilience to populations and is thus a valuable attribute to conserve (Marschall et al., 1998; Fleming et al., 2014). In some fish species including brown trout anadromy occurs, by which part of the population migrates to sea to grow and mature. This complicates the picture because the marine and freshwater components have very different life history traits of growth, survival and maturation, reflecting their environments and selected to maximise overall fitness. In essence there are two “populations”, although they freely intermingle and breed; thus presenting the problem of which traits to use in modelling. Ideally both would be modelled simultaneously; but parameterising the freshwater stage is particularly difficult Fortunately, it has been shown that in nominal “sea trout” rivers the anadromous form, which is larger and has a higher proportion of females, dominates egg production (CSTP, 2016) and thus also the reproductive capacity. Thus, for practical purposes of this application it is considered acceptable to consider only the traits of the anadromous adults (the sea trout). The essence of the approach is that it recognises and evaluates the long term reproductive value of each fish. The probability of a fish contributing to future generations through egg deposition (Future Lifetime Eggs, FLE) is a balance between decreasing numbers through mortality versus increasing size and fecundity through growth (Stearns, 1990; Solomon, 2006). Moreover, maturation and breeding is energetically demanding and also increases mortality Thus survival and growth rates, coupled with the onset of breeding (determined by age at first maturation) and subsequent breeding schedules drive life history features and population dynamics. The life table approach derives these so-called vital rates from stock assessment data and applies them to age-structured populations. FLE is a more realistic way of describing population impacts of fishing, because it conveys better the true long term impact on the population of fish killed by the fisheries. Complementary metrics are the Reportv4. Final simple catch numbers, catch weight and the annual number of eggs deposited each year, for the population as a whole or for its component age classes. These metrics have contrasts between them and the models will illustrate these, for two rivers the Tywi and Teifi. OUTLINE OF LIFE TABLES Life tables are a convenient, logical way to display the dynamics of reproduction in a population, which are controlled by two main processes: 1) The number of individuals in a population decreases with age due to mortality (or its complement, survival) 2) The average size of individuals increases with growth. In addition, the following principles apply: 3) The number of eggs per individual female increases with size (roughly proportionally to the cube of length). 4) The proportion of mature females (i.e. those producing eggs) increases up to the time of full maturity. 5) The number of eggs per age class is therefore a function the number of females remaining alive, their size and maturity. 6) Sex ratio modifies the proportional abundance of females Life tables give several types of outputs relating to the reproductive potential of the population and the population growth rate which, if the parameterisation of the models is correct, index a population’s fitness and potential growth rate. These include net reproductive rate (R0), the population rate of increase (ʎ), intrinsic growth rate (r) and generation time (G). These terms are explained in Appendix I, but are not used in the core outputs of this report. However, they can be used to project future population size and composition using matrix projection models. Such outputs require high quality data and although provisional indicative values are offered here, derivation of adequately robust estimates requires more detailed analysis and better data. The primary life table result, for the comparison of competing fisheries or the evaluation of their individual impacts on reproduction, is the expected future life time eggs (FLE) that an individual at a given age produces. This can be estimated without too much difficulty and demands on other variables. If FLE per fish is known then the effect of removing that individual by fishing mortality can be estimated and summed for all fish over the fishery catch. Fortunately, the calculation of FLE does not require the parameterisation of the full life cycle population dynamics, in particular the egg to smolt survival, which is problematic because there are so few estimates of survival in this freshwater phase. METHODS Spreadsheets were set up for the Tywi and the Teifi. The use of life tables requires an age (or weight) specific description of the population for each river in terms of abundance (Nx) at age x (yrs) and size at age. In sea trout populations, in which egg deposition is dominated by anadromous tout, this can be conveniently derived from the adult run estimates reconstructed from rod and net catches. A number of assumptions are required which are outlined below. The adult pre-fishery run was reconstructed by assembling weight frequency data (in 1lb classes) separately for each annual net and rod catch set (for years 2010-2015), incorporating various Reportv4. Final adjustments for reporting rate (r) and exploitation rate (U) to give an estimate of the population size structure prior to the net fishery. The size structure is then converted to an age structure using an age/weight key from the River Dee, which, because of its long history of scale reading, offers the best data available for Welsh rivers.