Use of SARIMA models to assess data-poor fisheries: a case study with a sciaenid fishery off Portugal

Research on assessment and monitoring methods has primarily focused on fisheries with long multivariate data sets. Less research exists on methods applicable to data-poor fisheries with univariate data sets with a small sample size. In this study, we examine the capabilities of seasonal autoregressive integrated moving average (SARIMA) models to fit, forecast, and monitor the landings of such data-poor fisheries. We use a European fishery on meagre (Sciaenidae: Argyrosomus regius), where only a short time series of landings was available to model (n=60 months), as our case-study. We show that despite the limited sample size, a SARIMA model could be found that adequately fitted and forecasted the time series of meagre landings (12-month forecasts; mean error: 3.5 tons (t); annual absolute percentage error: 15.4%). We derive model-based prediction intervals and show how they can be used to detect problematic situations in the fishery. Our results indicate that over the course of one year the meagre landings remained within the prediction limits of the model and therefore indicated no need for urgent management intervention. We discuss the information that SARIMA model structure conveys on the meagre lifecycle and fishery, the methodological requirements of SARIMA forecasting of data-poor fisheries landings, and the capabilities SARIMA models present within current efforts to monitor the world’s data-poorest resources.

Pinniped diet composition: a comparison of estimation models

Along the west coast of the United States, the potential impact of increasing pinniped populations on declining salmonid (Oncorhynchus spp.) stocks has become an issue of concern. Fisheries managers need species-specific estimates of consumption by pinnipeds to evaluate their impact on salmonid stocks. To estimate consumption, we developed a model that estimates diet composition by reconstructing prey biomass from fecal samples. We applied the model to data collected from harbor seals (Phoca vitulina) that are present year-round in the lower Columbia River where endangered stocks of salmonids pass as returning adults and as seaward-migrating smolts. Using the same data, we applied the split-sample frequency of occurrence model, which avoids reconstructing biomass by assuming that each fecal sample represents an equal volume of consumption and that within each sample each prey item represents an equal proportion of the volume. The two models for estimating diet composition yielded size-specific differences in consumption estimates that were as large as tenfold for the smallest and largest prey. Conclusions about the impact of harbor seal predation on adult salmonids, some of their largest prey species, remain uncertain without some appropriate rationale or further information (e.g. empirical captive studies) to discriminate between these models.

Testable models of aquatic systems: A NERC special topic for integrating experiments and modelling

A major part of the support for fundamental research on aquatic ecosystems continues to be provided by the Natural Environment Research Council (NERC). Funds are released for ”thematic” studies in a selected special topic or programme. ”Testable Models of Aquatic Ecosystems” was a Special Topic of the NERC, initiated in 1995, the aim of which was to promote ecological modelling by making new links between experimental aquatic biologists and state-of-the-art modellers. The Topic covered both marine and freshwater systems. This paper summarises projects on aspects of the responses of individual organisms to the effects of environmental variability, on the assembly, permanence and resilience of communities, and on aspects of spatial models. The authors conclude that the NERC Special Topic has been highly successful in promoting the development and application of models, most particularly through the interplay between experimental ecologists and formal modellers.

Models in the implementation of the Water Framework Directive - benchmarking as part of the modelling proccess

The Water Framework Directive (WFD; European Commission 2000) is a framework for European environmental legislation that aims at improving water quality by using an integrated approach to implement the necessary societal and technical measures. Assessments to guide, support, monitor and evaluate policies, such as the WFD, require scientific approaches which integrate biophysical and human aspects of ecological systems and their interactions, as outlined by the International Council for Science (2002). These assessments need to be based on sound scientific principles and address the environmental problems in a holistic way. End-users need help to select the most appropriate methods and models. Advice on the selection and use of a wide range of water quality models has been developed within the project Benchmark Models for the Water Framework Directive (BMW). In this article, the authors summarise the role of benchmarking in the modelling process and explain how such an archive of validated models can be used to support the implementation of the WFD.

The predicting power of models for eutrophication

Restoration of water-bodies from eutrophication has proved to be extremely difficult. Mathematical models have been used extensively to provide guidance for management decisions. The aim of this paper is to elucidate important problems of using models for predicting environmental changes. First, the necessity for a proper uncertainty assessment of the model, upon calibration, has not been widely recognized. Predictions must not be a single time trajectory; they should be a band, expressing system uncertainty and natural variability. Availability of this information may alter the decision to be taken. Second, even with well-calibrated models, there is no guarantee they will give correct projections in situations where the model is used to predict the effects of measures designed to bring the system into an entirely different ”operating point”, as is typically the case in eutrophication abatement. The concept of educated speculation is introduced to partially overcome this difficulty. Lake Veluwe is used as a case to illustrate the point. Third, as questions become more detailed, such as ”what about expected algal composition”, there is a greater probability of running into fundamental problems that are associated with predicting the behaviour of complex non-linear systems. Some of these systems show extreme initial condition sensitivity and even, perhaps, chaotic behaviour, and are therefore fundamentally unpredictable.

Age and growth of spiny dogfish (Squalus acanthias) in the Gulf of Alaska: analysis of alternative growth models

Ten growth models were fitted to age and growth data for spiny dogfish (Squalus acanthias) in the Gulf of Alaska. Previous studies of spiny dogfish growth have all fitted the t0 formulation of the von Bertalanffy model without examination of alternative models. Among the alternatives, we present a new two-phase von Bertalanffy growth model formulation with a logistically scaled k parameter and which estimates L0. A total of 1602 dogfish were aged from opportunistic collections with longline, rod and reel, set net, and trawling gear in the eastern and central Gulf of Alaska between 2004 and 2007. Ages were estimated from the median band count of three independent readings of the second dorsal spine plus the estimated number of worn bands for worn spines. Owing to a lack of small dogfish in the samples, lengths at age of small individuals were back-calculated from a subsample of 153 dogfish with unworn spines. The von Bertalanffy, two-parameter von Bertalanffy, two-phase von Bertalanffy, Gompertz, two-parameter Gompertz, and logistic models were fitted to length-at-age data for each sex separately, both with and without back-calculated lengths at age. The two-phase von Bertalanffy growth model produced the statistically best fit for both sexes of Gulf of Alaska spiny dogfish, resulting in L∞ = 87.2 and 102.5 cm and k= 0.106 and 0.058 for males and females, respectively.

In search of climate effects on Atlantic Croaker (Micropogonias undulatus) stock off the U.S. Atlantic coast with Bayesian state-space biomass dynamic models

Atlantic Croaker (Micropogonias undulatus) production dynamics along the U.S. Atlantic coast are regulated by fishing and winter water temperature. Stakeholders for this resource have recommended investigating the effects of climate covariates in assessment models. This study used state-space biomass dynamic models without (model 1) and with (model 2) the minimum winter estuarine temperature (MWET) to examine MWET effects on Atlantic Croaker population dynamics during 1972–2008. In model 2, MWET was introduced into the intrinsic rate of population increase (r). For both models, a prior probability distribution (prior) was constructed for r or a scaling parameter (r0); imputs were the fishery removals, and fall biomass indices developed by using data from the Multispecies Bottom Trawl Survey of the Northeast Fisheries Science Center, National Marine Fisheries Service, and the Coastal Trawl Survey of the Southeast Area Monitoring and Assessment Program. Model sensitivity runs incorporated a uniform (0.01,1.5) prior for r or r0 and bycatch data from the shrimp-trawl fishery. All model variants produced similar results and therefore supported the conclusion of low risk of overfishing for the Atlantic Croaker stock in the 2000s. However, the data statistically supported only model 1 and its configuration that included the shrimp-trawl fishery bycatch. The process errors of these models showed slightly positive and significant correlations with MWET, indicating that warmer winters would enhance Atlantic Croaker biomass production. Inconclusive, somewhat conflicting results indicate that biomass dynamic models should not integrate MWET, pending, perhaps, accumulation of longer time series of the variables controlling the production dynamics of Atlantic Croaker, preferably including winter-induced estimates of Atlantic Croaker kills.