Daniel Duplisea

Relating Species and Community Dynamics in an Heavily Exploited Marine Fish Community

We examined the dynamics of fish species and how they relate to species assemblage coherence in the heavily exploited Georges Bank fish community. Coherence is defined as reduced temporal variability of total assemblage biomass. We assumed that a higher degree of compensation hence coherence occurs within competitively coupled species assemblages; therefore, fisheries may directly alter the dynamics of certain targeted species sizes but assemblage structure will be relatively more stable owing to compensatory interactions. Species-sizes were grouped, based on negative covariance coupling in biomass time series from survey data. Assemblages representing benthic feeders were clearly identified by this method; furthermore, the most heavily exploited species-sizes were decoupled from other species-sizes suggesting that fisheries have diminished their potential to compensate or to be compensated for by competitive interactions. Biomass of species-sizes within known trophic guilds strongly compensated other guild-member biomass fluctuations if the diet of guild members was more specialized. This is an indication that more competitive conditions (more specialization) foster greater compensatory responses between competitors biomass fluctuations.

Homing and group cohesion in Atlantic cod Gadus morhua revealed by tagging experiments

Homing behaviour and group cohesion in Atlantic cod Gadus morhua from the northern Gulf of St Lawrence were studied based on tagging-recapture data from two periods, the 1980s and a recent period from 1996 to 2008. Two or more tags from a single tagging experiment were frequently recovered together in subsequent years. The null hypothesis was tested that the frequency of matching tag recoveries occurred by chance only through random mixing of tagged G. morhua before their recapture by the commercial fishery. The alternative hypothesis was that non-random, positive association (group cohesion) existed among tagged individuals that persisted through time and during migrations. Results show that the G. morhua population exhibits a homing behaviour, with temporal stability across seasons and years: 50% of recaptured fish in the recent period were caught <34 km from their mark site, even 3 years after release. In the 1980s, G. morhua were located at <10 km from their release site 1 year after tagging during summer and at <16 km during spring and autumn combined. Despite the increasing distance between the mark and recapture sites over time, the difference was not significant. In addition, occurrences of two or more tagged fish from the same release event that were caught together indicated a non-random association among individual fish for periods of one to several years and through migrations over several hundred kilometres. Hence G. morhua showed group cohesion in addition to site fidelity. These two interacting behaviours may be fundamental for the rebuilding and conservation of depleted fish stocks.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

We would like to thank Johan Van Der Molen from CEFAS for providing the ERSEM model outputs, the ICES DATRAS database for the North Sea IBTS data and Historical Catch Statistics, ICES North Sea Integrated Assessment Working Group (WGINOSE) and the organisations which provide data for the ICES assessment process, in particular SAHFOS who have provided the North Sea plankton data, Chiara Franco for general advice and the Natural Environment Research Council, UK (NE/ J01642X/1)who has provided the funding of this research. We gratefully acknowledge support from the European Commission (OCEAN-CERTAIN, FP7-ENV-2013-6.1-1; no: 603773) for David Maxwell and support from CEFAS for Andrew Kenny and David Maxwell

Testing for Compensation in a Multi-species Community

A bstractThe dynamics of a community are said to be compensatory if aggregate biomass is less variable over time than the biomass of the individual components of the system. In broad terms, the presence of compensation reflects interactions between components that tend to stabilize the overall community. A common quantitative measure used to detect compensation is the ratio of the temporal variance of total biomass to the sum of the biomass variances of the components, with a ratio less than 1 indicative of compensation. The purpose of this note is to describe a test for compensation when the variance ratio is estimated from biomass time series data. The test involves a bootstrap procedure that accounts for serial correlation in biomass. Failure to account for positive serial correlation can lead to spurious detection of compensation. The test is illustrated using biomass data for fish stocks on Georges Bank.

Bioinformatics tools in predictive ecology: applications to fisheries

There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse.

A Spatio-temporal Bayesian Network Approach for Revealing Functional Ecological Networks in Fisheries

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. Machine learning techniques can allow such complex, spatially varying interactions to be recovered from collected field data. In this study, we apply structure learning techniques to identify functional relationships between trophic groups of species that vary across space and time. Specifically, Bayesian networks are created on a window of data for each of the 20 geographically different and temporally varied sub-regions within an oceanic area. In addition, we explored the spatial and temporal variation of pre-defined functions (like predation, competition) that are generalisable by experts’ knowledge. We were able to discover meaningful ecological networks that were more precisely spatially-specific rather than temporally, as previously suggested for this region. To validate the discovered networks, we predict the biomass of the trophic groups by using dynamic Bayesian networks, and correcting for spatial autocorrelation by including a spatial node in our models.

Forecasting the Major Influences of Predation and Environment on Cod Recovery in the Northern Gulf of St. Lawrence

The northern Gulf of St. Lawrence (NGSL) stock of Atlantic cod (Gadus morhua), historically the second largest cod population in the Western Atlantic, has known a severe collapse during the early 1990 s and is currently considered as endangered by the Committee on the Status of Endangered Wildlife in Canada. As for many fish populations over the world which are currently being heavily exploited or overfished, urgent management actions in the form of recovery plans are needed for restoring this stock to sustainable levels. Stochastic projections based on a statistical population model incorporating predation were conducted over a period of 30 years (2010–2040) to assess the expected outcomes of alternative fishing strategies on the stock recovery under different scenarios of harp seal (Pagophilus groenlandicus) abundance and environmental conditions. This sensitivity study shows that water temperature is key in the rebuilding of the NGSL cod stock. Model projections suggest that maintaining the current management practice under cooler water temperatures is likely to maintain the species in an endangered status. Under current or warmer conditions in the Gulf of St. Lawrence, partial recovery might only be achieved by significant reductions in both fishing and predation pressure. In the medium-term, a management strategy that reduces catch could be favoured over a complete moratorium so as to minimize socio-economic impacts on the industry.

Extinction Debt and Colonizer Credit on a Habitat Perturbed Fishing Bank.

Temporal changes in occupancy of the Georges Bank (NE USA) fish and invertebrate community were examined and interpreted in the context of systems ecological theory of extinction debt (EDT). EDT posits that in a closed system with a mix of competitor and colonizer species and experiencing habitat fragmentation and loss, the competitor species will show a gradual decline in fitness (occupancy) eventually leading to their extinction (extirpation) over multiple generations. A corollary of this is a colonizer credit, where colonizer species occupancy may increase with fragmentation because the disturbance gives that life history a transient relative competitive advantage. We found that competitor species occupancy decreased in time concomitant with an increase in occupancy of colonizer species and this may be related to habitat fragmentation or loss owing to industrialized bottom trawl fishing. Mean species richness increased over time which suggests less specialization (decreased dominance) of the assemblage that may result from habitat homogenization. These analyses also showed that when abundance of species was decreased by fishing but eventually returned to previous levels, on average it had a lower occupancy than earlier in the series which could increase their vulnerability to depletion by fishing. Changing occupancy and diversity patterns of the community over time is consistent with EDT which can be exacerbated by direct impacts of fishery removals as well as climate change impacts on the fish community assemblage.

Exploration of Trends in Interspecific Abundance-Occupancy Relationships Using Empirically Derived Simulated Communities

The interspecific abundance-occupancy relationship (AOR) is a widely used tool that describes patterns of habitat utilization and, when evaluated over time, may be used to identify large-scale changes in community structure. Our primary goal for this research was to validate the utility of AORs as temporal indicators of community state. We used long-term survey data in four regions of the northwest Atlantic coastal shelf (NWACS) to estimate the diversity of spatial behaviors in each community, which we modeled with negative binomial (NB) distributions. NB parameters were used to generate time series data for simulated communities, from which AORs were then estimated and evaluated for temporal trends. We found that AORs from simulated communities were similar in year-to-year variation to empirical relationships. In order to further understand the role of spatial diversity in the generation of AOR trends, we did additional simulations where NB parameters were manually manipulated. In one instance, we ran simulations while holding species’ parameters constant over time. This treatment effectively removed trends, suggesting that temporal change in community relationships was the result of genuine variation in intraspecific spatial use. In another set of simulations, we conducted a case study to evaluate the impact of a select group of schooling and spatially aggregating species on an especially rapid shift in AORs in the Gulf of Maine from 1973 to 1983. Removals of these species reduced the magnitudes of most trends, demonstrating their importance to observed community changes. This research directly links variation in AORs to distribution and density-related processes and provides a potentially powerful framework to identify community-level change and to test ecological and mechanistic hypotheses.

Incorporating Regime Metrics into Latent Variable Dynamic Models to Detect Early-Warning Signals of Functional Changes in Fisheries Ecology

In this study, dynamic Bayesian networks have been applied to predict future biomass of geographically different but functionally equivalent fish species. A latent variable is incorporated to model functional collapse, where the underlying food web structure dramatically changes irrevocably (known as a regime shift). We examined if the use of a hidden variable can reflect changes in the trophic dynamics of the system and also whether the inclusion of recognised statistical metrics would improve predictive accuracy of the dynamic models. The hidden variable appears to reflect some of the metrics’ characteristics in terms of identifying regime shifts that are known to have occurred. It also appears to capture changes in the variance of different species biomass. Including metrics in the models had an impact on predictive accuracy but only in some cases. Finally, we explore whether exploiting expert knowledge in the form of diet matrices based upon stomach surveys is a better approach to learning model structure than using biomass data alone when predicting food web dynamics. A non-parametric bootstrap in combination with a greedy search algorithm was applied to estimate the confidence of features of networks learned from the data, allowing us to identify pairwise relations of high confidence between species.