Modelling the skipjack tuna dynamics in the Indian Ocean with APECOSM-E – Part 2: Parameter estimation and sensitivity analysis (original) (raw)
Related papers
Progress in Oceanography, 2008
A Spatial Ecosystem and Population Dynamic Model (SEAPODYM) is used in a data assimilation study aiming to estimate model parameters that describe dynamics of Pacific skipjack tuna population on ocean-based scale. The model based on advection-diffusion-reaction equations explicitly predicts spatial dynamics of large pelagic predators, while taking into account data on several mid-trophic level components, oceanic primary productivity and physical environment. In order to improve its quantitative ability, the model was parameterized through assimilation with commercial fisheries data, and optimization was carried out using maximum likelihood estimation approach. To address the optimization task we implemented an adjoint technique to obtain an exact, analytical evaluation of the likelihood gradient. We conducted a series of computer experiments in order to (i) determine model sensitivity with respect to variable parameters and, hence, investigate their observability; (ii) estimate observable parameters and their errors; and (iii) justify the reliability of the computed solution. Parameters describing recruitment, movement, habitat preferences, natural and fishing mortality of skipjack population were analysed and estimated. Results of the study suggest that SEAPODYM with achieved parameterization scheme can help to investigate the impact of fishing under various management scenarios, and also conduct forecasts of a given species stock and spatial dynamics in a context of environmental and climate changes.
1999
This paper presents a spatial multigear population dynamics model forced by the environment for Atlantic Ocean yellowfin tuna. The model simulates the population's distribution as a function of environmental variables and observed fishing effort. It is age structured to account for age-dependent population processes and catchability. It is based on an advection-diffusion-reaction equation in which the advective term is proportional to the gradient of a habitat suitability index derived from temperature, salinity and tuna forage data. Functional relationships between movement parameters, catchability and environmental variables are based on non linear relationships estimated with generalized additive models (GAM) to characterize, on the one hand, yellowfin environmental preferences and, on the other hand, their catchability to different gears. Analytically formalized, GAM's relationships characterizing environmental preferences enable the habitat index to be calculated at each point in time and space. Also formulated analytically, the relationships characterizing catchability to different gears enable the calculation of predicted catches, which are compared to observed catches to estimate the model parameters. In this paper, the problem of local overfishing of adult tuna in the Gulf of Guinea is addressed through different simulations and discussed.
Fisheries Oceanography, 1998
We are developing a spatial, multigear, multispecies population dynamics simulation model for tropical tunas in the Paci®c Ocean. The model is age-structured to account for growth and gear selectivity. It includes a tuna movement model based on a diffusion± advection equation in which the advective term is proportional to the gradient of a habitat index. The monthly geographical distribution of recruitment is de®ned by assuming that spawning occurs in areas where sea surface temperature is above 25°C. During the ®rst 3 months of their life, simulated tunas are transported by oceanic currents, after which movement is conditioned by gradients in the habitat index. Independent estimates of natural mortality rates and population size from large-scale tagging experiments carried out by the Secretariat of the Paci®c Community are used in the simulations. The habitat index consists of components due to forage density and sea surface temperature, both of which are suspected to play major roles in determining tuna distribution. Because direct observations of forage are not available on a basin scale, we developed a submodel to simulate the surface tuna forage production . At present, only skipjack (Katsuwonus pelamis; a surface tuna species caught by purse seine and by pole-and-line) is considered, at a 1°-square resolution and on a monthly climatological time series. Despite the simplicity of the model and the limitations of the data used, the simulation model is able to predict a distribution of skipjack catch rates, of the different eets involved in the ®shery, that is fairly consistent with observations.
This paper presents a spatial multigear population dynamics model forced by the environment for Atlantic Ocean yellowfin tuna. The model simulates the population's distribution as a function of environmental variables and observed fishing effort. It is age structured to account for age-dependent population processes and catchability. It is based on an advection-diffusion-reaction equation in which the advective term is proportional to the gradient of a habitat suitability index derived from temperature, salinity and tuna forage data. Functional relationships between movement parameters, catchability and environmental variables are based on non linear relationships estimated with generalized additive models (GAM) to characterize, on the one hand, yellowfin environmental preferences and, on the other hand, their catchability to different gears. Analytically formalized, GAM's relationships characterizing environmental preferences enable the habitat index to be calculated at each point in time and space. Also formulated analytically, the relationships characterizing catchability to different gears enable the calculation of predicted catches, which are compared to observed catches to estimate the model parameters. In this paper, the problem of local overfishing of adult tuna in the Gulf of Guinea is addressed through different simulations and discussed.
Progress in Oceanography, 2008
An enhanced version of the spatial ecosystem and population dynamics model SEAPODYM is presented to describe spatial dynamics of tuna and tuna-like species in the Pacific Ocean at monthly resolution over 1°g rid-boxes. The simulations are driven by a bio-physical environment predicted from a coupled ocean physical-biogeochemical model. This new version of SEAPODYM includes expanded definitions of habitat indices, movements, and natural mortality based on empirical evidences. A thermal habitat of tuna species is derived from an individual heat budget model. The feeding habitat is computed according to the accessibility of tuna predator cohorts to different vertically migrating and non-migrating micronekton (mid-trophic) functional groups. The spawning habitat is based on temperature and the coincidence of spawning fish with presence or absence of predators and food for larvae. The successful larval recruitment is linked to spawning stock biomass. Larvae drift with currents, while immature and adult tuna can move of their own volition, in addition to being advected by currents. A food requirement index is computed to adjust locally the natural mortality of cohorts based on food demand and accessibility to available forage components. Together these mechanisms induce bottom-up and top-down effects, and intra-(i.e. between cohorts) and inter-species interactions. The model is now fully operational for running multi-species, multi-fisheries simulations, and the structure of the model allows a validation from multiple data sources. An application with two tuna species showing different biological characteristics, skipjack (Katsuwonus pelamis) and bigeye (Thunnus obesus), is presented to illustrate the capacity of the model to capture many important features of spatial dynamics of these two different tuna species in the Pacific Ocean. The actual validation is presented in a companion paper describing the approach to have a rigorous mathematical parameter optimization . Parameter estimation for basin-scale ecosystem-linked population models of large pelagic predators: application to skipjack tuna. Progress in Oceanography]. Once this evaluation and parameterization is complete, it may be possible to use the model for management of tuna stocks in the context of climate and ecosystem variability, and to investigate potential changes due to anthropogenic activities including global warming and fisheries pressures and management scenarios.
Aquatic Living Resources, 2013
The benefits of implementing no-take Marine Protected Areas (MPAs) for the conservation of highly migratory species are not easy to assess. They depend on several factors, such as the fish mobility, fisher behaviour and the area covered by the MPA with respect to the distribution area of the species to protect. In this study, we explore the simultaneous effects of MPAs and fishing scenarios on skipjack tuna population dynamics, using the spatially-explicit APECOSM-E model. The model represents the size-structured population dynamics of skipjack tuna in the Indian Ocean and their dependence on climatic variability and exploitation by fisheries. Numerical experiments were run from the beginning of industrial fisheries in the early 1980s to the year 2030, considering different scenarios for the future development of fisheries. These scenarios combined different trends in fishing effort and technological development, either assuming a continuous increase following historical trends or a stabilization of these factors at present values. The simulations were designed to explore the effects of two MPAs of different size and location: the recently established Chagos MPA, and a hypothetical MPA covering a large part of the Western Indian Ocean, where most of the skipjack catches are presently made. We modelled the redistribution of fishing effort around the MPAs assuming that the fishers had partial knowledge of the spatial distribution of the skipjack population. The effects of the two MPAs on the population dynamics, catch and fishing mortality are shown. Our results revealed a very minor effect of the Chagos MPA on the skipjack tuna population, while the Western Indian Ocean MPA had an important impact on the fishing mortality and succeeded in stabilizing the spawning population. The simulations also showed that the effect of an MPA depends on the evolution of fisheries and it is therefore important to explore different fishery scenarios to assess the future benefits of an MPA.
An operating model for the Indian Ocean skipjack tuna fishery
2015
A simulation model of the Indian Ocean skipjack tuna fishery was developed for the evaluation of alternative fisheries management procedures. The model partitions the population by region, age, and size and the fishery by region and gear (purse seine, pole-and-line, gill net, others). Prior probability distributions and sensitivity ranges are defined for model parameters for use in conditioning and robustness testing. Performance statistics are defined based and linked to broader management objectives. Three contrasting classes of management procedure (MP) are provided as examples: BRule (a generic harvest control rule based on an estimate of stock status), FRange (a MP which adjusts effort when fishing mortality is outside a target range) and IRate (a MP which recommends a total allowable catch using a CPUE-based biomass index).
Progress in Oceanography
The distribution of marine species is often modeled using Eulerian approaches, in which changes to population density or abundance are calculated at fixed locations in space. Conversely, Lagrangian, or individual-based, models simulate the movement of individual particles moving in continuous space, with broader-scale patterns such as distribution being an emergent property of many, potentially adaptive, individuals. These models offer advantages in examining dynamics across spatiotemporal scales and making comparisons with observations from individual-scale data. Here, we introduce and describe such a model, the Individual-based Kinesis, Advection and Movement of Ocean ANimAls model (Ikamoana), which we use to replicate the movement processes of an existing Eulerian model for marine predators (the Spatial Ecosystem and Population Dynamics Model, SEAPODYM). Ikamoana simulates the movement of either individual or groups of animals by physical ocean currents, habitat-dependent stochastic movements (kinesis), and taxis movements representing active searching behaviours. Applying our model to Pacific skipjack tuna (Katsuwonus pelamis), we show that it accurately replicates the evolution of density distribution simulated by SEAPODYM with low time-mean error and a spatial correlation of density that exceeds 0.96 at all times. We demonstrate how the Lagrangian approach permits easy tracking of individuals' trajectories for examining connectivity between different regions, and show how the model can provide independent estimates of transfer rates between commonly used assessment regions. In particular, we find that retention rates in most assessment regions are considerably smaller (up to a factor of 2) than those estimated by this population of skipjack's primary assessment model. Moreover, these rates are sensitive to ocean state (e.g. El Nino vs La Nina) and so assuming fixed transfer rates between regions may lead to spurious stock estimates. A novel feature of the Lagrangian approach is that individual schools can be tracked through time, and we demonstrate that movement between two assessment regions at broad temporal scales includes extended transits through other regions at finer-scales. Finally, we discuss the utility of this modeling framework for the management of marine reserves, designing effective monitoring programmes, and exploring hypotheses regarding the behaviour of hard-to-observe oceanic animals.
Spatial surplus production modeling of Atlantic tunas and billfish
Ecological Applications, 2011
We simulation test a Spatially Explicit Multispecies Integrated PROduction model (SEMIPRO) that provides a basis with which to undertake multi-species, multi-area stock assessment and carry out the simulation testing of a management strategies, single species assessment methods and procedures for catch per unit of effort standardisation (CPUE). Using this model we demonstrate that regional abundance indices can be used to conduct a simple spatial stock assessment. If there is sufficient contrast between CPUE data and biomass, tagging data are not required to estimate movement using a simple gravity model that includes a parameter controlling the degree of stock mixing among areas. Tagging data can also be included to help inform movement parameters of more complex movement models.