Integrating tagging and fisheries data into a spatial population dynamics model to improve its predictive skills (original) (raw)
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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.
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.
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.
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
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.
Investigations into population structure have been at the forefront of fisheries research for decades, yet it is generally ignored in stock assessmentmodels. As the complex nature ofmarine population structure has been uncovered, models have attempted to accurately portray it through the development of spatially explicit assessments that allow for movement between subpopulations. Although current tag-integrated movement models are highly complex, many arose from the relatively simple models of Beverton and Holt (1957). This article traces the historical development of these models and compares their features. Originally estimation of movement utilized only tag-recapture models, but now tag-integrated assessment models incorporate several sources of fishery, survey, and tag-recapture information to inform movement estimates. As spatial management measures become more widely used, it is increasingly important that assessment models include the spatial complexities of population structure and patterns of fishery removals, in order for more reliable monitoring of population rebuilding to take place. A generalized metapopulation model is proposed for use in fisheries stock assessment, which allows for adult movement among spatially discrete sub-populations. The input requirements for the model include region-specific catch-at-age, a tag-recapture dataset, and auxiliary information, such as a fishery-independent abundance index.
Aquatic Living Resources, 2002
An advection diffusion reaction model was used to estimate movement and tag attrition parameters from skipjack tuna tagging data off the Maldives. Two sets of data were available from the experiments carried out during two distinct periods: 1990-1991 and 1993-1995. The results of the analysis were compared with the previous analyses and discussed in relation to management of skipjack fisheries in the Maldives and in the Indian Ocean. The movements were found to be highly variable in space and time, and few consistent patterns were observed between the two data sets. Similarly, significantly different estimates of fishing and natural mortality rates were observed from the two data sets. These differences were found, in part, to be due to the uneven distribution of tag releases in both space and time. Estimates of movement and attrition rates show that emigration from the Maldivian fishery to the rest of the Indian Ocean's was small. The exploitation rate was found to be substantial, contributing about 30-40% of the total attrition in the fishery area. Such levels of localized exploitation may be maintained by steady immigration from outside of the Maldives, but more extensive tagging is required to be certain. The impact of tuna fisheries elsewhere in the Indian Ocean on the domestic Maldivian fishery cannot be determined until a comprehensive large-scale tagging program, including all the fisheries in the Indian Ocean, is completed.
2005
This paper describes the model FASST. FASST is a fully structured age, size, space and time stock assessment model. It is expressed as a deterministic system of partial differential equations continuous in age, size and time. Space is represented using large discrete zones interconnected by exchange rates. All the parameterizations used to represent recruitment, growth, mortality and movements are chosen to be size-dependent and age independent. Consequently, for simplicity purposes, the age dimension can be simplified. The model is integrated numerically to simulate the stock dynamics. A complex process -and observationerror framework is used to estimate the model parameters, and hence the past stock status, in a Bayesian context. The model uses simultaneously most fishery data currently available for tuna stock assessment such as catch by fleet in number and weight, length-frequency samples, otolith analysis, tag-recapture data. It provides a new way to integrate that information into a consistent stock assessment framework.
A state-space model to derive bluefin tuna movement and habitat from archival tags
Oikos, 2005
Archival tagging provides a unique way to study the spatial dynamics and habitat of pelagic fish. This technique generates lagrangian data of a particular type in marine ecology: although highly informative about processes at different scales (e.g. horizontal movements versus diving behaviour), such data are impaired by location errors and the lack of combination with actual environmental variability. The present paper introduces a framework for modelling bluefin tuna movement in relation to its habitat, using records of light, depth and temperature from archival tags. Based on data assimilation concepts and methods, we show how an explicit formulation of the observation process and the statistics of external variables (e.g. ambient temperature) can improve precision in geolocation. The proposed method is tested on synthetic data: significant reduction (40 to 50%) in the initial root-mean square error is achieved under different noise scenarios. Assimilating sea surface temperature also allows to perform on-line estimation of a range of observation biases. The performance of the model greatly benefits from the adequate formalisation of different variability sources, and allows potentially to reveal interactions between the fish and its habitat. Using this probabilistic approach, we, however, show that some patterns of interest (e.g. foraging in surface fronts) can hardly be retrieved in a context of large observational and environmental noise.