Lyne Morissette

Jellyfish in ecosystems, online databases, and ecosystem models

There are indications that pelagic cnidarians and ctenophores (‘jellyfish’) have increased in abundance throughout the world, or that outbreaks are more frequent, although much uncertainty surrounds the issue, due to the scarcity of reliable baseline data. Numerous hypotheses have been proposed for the individual increases or outbreaks that are better documented, but direct experimental or manipulative studies at the ecosystem scale cannot be used for testing them. Thus, ecological modeling provides the best alternative to understand the role of jellyfish in large fisheries-based ecosystems; indeed, it is an approach consistent with new ecosystem-based fisheries management practices. Here, we provide an overview of online databases available to ecosystem modelers and discuss general aspects and shortcomings of the coverage of jellyfish in these databases. We then provide a summary of how jellyfish have been treated and parameterized by existing ecosystem models (specifically focusing on ‘Ecopath with Ecosim’ as a standard modeling toolset). Despite overall weaknesses in the parameterization of jellyfish in these models, interesting patterns emerge that suggest some systems, especially smaller and more structured ones, may be particularly vulnerable to long-term jellyfish biomass increase. Since jellyfish also feed on the eggs and larvae of commercially important food fish, outbreaks of jellyfish may ultimately imply a reduction in the fish biomass available to fisheries. On the other hand, jellyfish, which have been traditionally fished for human consumption in East and Southeast Asia, are now seen as a potential resource in other parts of the world, where pilot fisheries have emerged. It is also argued here that reduced predation on the benthic and pelagic stages of jellyfish, both a result of fishing, may be a strong contributing factor as well. For marine biologists specializing on jellyfish, this means that their research might become more applied. This implies that they would benefit from adopting some concepts and methods from fisheries biology and ecosystem modeling, and thus from using (and contributing to) online databases, such as SeaLifeBase and FishBase, developed to support such research. This would remedy the situation, documented here, wherein jellyfish are either infrequently included in food web models, typically constructed using the Ecopath with Ecosim software, or included as a single functional group with the characteristic of an ‘average’ jellyfish. Thus, jellyfish specialists could readily improve the jellyfish-related components of such models, and we show how they could do this. Also, it is suggested that when such improvement is performed, the resulting models can lead to non-intuitive inferences and hence interesting hypotheses on the roles of jellyfish in ecosystems. This is illustrated here through (a) an investigation of whether jellyfish are keystone species and (b) the identification of conditions under which (simulated) jellyfish outbreaks may occur.

Global Patterns in Ecological Indicators of Marine Food Webs: A Modelling Approach

Background Ecological attributes estimated from food web models have the potential to be indicators of good environmental status given their capabilities to describe redundancy, food web changes, and sensitivity to fishing. They can be used as a baseline to show how they might be modified in the future with human impacts such as climate change, acidification, eutrophication, or overfishing. Methodology In this study ecological network analysis indicators of 105 marine food web models were tested for variation with traits such as ecosystem type, latitude, ocean basin, depth, size, time period, and exploitation state, whilst also considering structural properties of the models such as number of linkages, number of living functional groups or total number of functional groups as covariate factors. Principal findings Eight indicators were robust to model construction: relative ascendency; relative overhead; redundancy; total systems throughput (TST); primary production/TST; consumption/TST; export/TST; and total biomass of the community. Large-scale differences were seen in the ecosystems of the Atlantic and Pacific Oceans, with the Western Atlantic being more complex with an increased ability to mitigate impacts, while the Eastern Atlantic showed lower internal complexity. In addition, the Eastern Pacific was less organised than the Eastern Atlantic although both of these systems had increased primary production as eastern boundary current systems. Differences by ecosystem type highlighted coral reefs as having the largest energy flow and total biomass per unit of surface, while lagoons, estuaries, and bays had lower transfer efficiencies and higher recycling. These differences prevailed over time, although some traits changed with fishing intensity. Keystone groups were mainly higher trophic level species with mostly top-down effects, while structural/dominant groups were mainly lower trophic level groups (benthic primary producers such as seagrass and macroalgae, and invertebrates). Keystone groups were prevalent in estuarine or small/shallow systems, and in systems with reduced fishing pressure. Changes to the abundance of key functional groups might have significant implications for the functioning of ecosystems and should be avoided through management. Conclusion/significance Our results provide additional understanding of patterns of structural and functional indicators in different ecosystems. Ecosystem traits such as type, size, depth, and location need to be accounted for when setting reference levels as these affect absolute values of ecological indicators. Therefore, establishing absolute reference values for ecosystem indicators may not be suitable to the ecosystem-based, precautionary approach. Reference levels for ecosystem indicators should be developed for individual ecosystems or ecosystems with the same typologies (similar location, ecosystem type, etc.) and not benchmarked against all other ecosystems.

Addressing Uncertainty in Marine Ecosystems Modelling

Ecosystem modelling has become a very important way to study marine ecosystems processes. A valuable tool for model development is the use of the software package Ecopath with Ecosim, which enables the construction of foodwebs and their simulation over time and space according to different scenarios. An important part of the process of ecosystem modelling is to compare results from the model with those from observations, followed by an analysis of the remaining sources of error. However, few of the currently developed Ecopath models have gone so far as to examine the uncertainty in analyses. Thus, it would be useful to address this problem, to clearly define the type of uncertainty that may be encountered in ecosystem modelling, and the means by which it may handled. Sensitivity analyses represent one solution by which one might address uncertainty in Ecopath with Ecosim. This approach functions by examining the sensitive elements as revealed in model results with differing scenarios of model-building and construction. In addition, other tools can also be used to perform uncertainty analysis routines. Examples are the Pedigree, Ecoranger and Autobalance tools, all of which are included the Ecopath software package. Furthermore, it is possible to combine these approaches with other modelling techniques in order to get an even stronger analysis of uncertainty. The purpose of this paper is to examine the strengths and weaknesses of these different approaches in addressing uncertainty.