Potential role of predators on carbon dynamics of marine ecosystems as assessed by a Bayesian belief network.

While the effects of climate change on top predators are well documented, the role of predation on ecosystem level carbon production is poorly developed, despite it being a logical consequence of trophic dynamics. Trophic cascade effects have shown predator mediated changes in primary production, but we predict that predators should lower the overall biomass capacity of any system with top down control. Through a simple Bayesian belief network model of a typical marine foodweb, we show that predator removal, as is common through activities such as fishing and shark finning, results in higher biomasses of lower trophic level fish and zooplankton, resulting in higher net carbon production by the system. In situations common throughout much of the ocean, where activities such as shark finning and over fishing reduce the highest tropic levels, the probability of net carbon production increasing in the model was ~ 60%, and unlike previous studies on simple food chains, trophic cascade effects were not present. While the results are preliminary, and sources of uncertainty in data and models are acknowledged, such results provide even more strength to the argument to protect open sea fish stocks, and particularly large predators such as sharks, cetaceans and game fish

Conceptual Ecological Modelling of Shallow Sublittoral Mud Habitats to Inform Indicator Selection

The purpose of this study is to produce a series of Conceptual Ecological Models (CEMs) that represent the shallow sublittoral mud habitat in the UK. CEMs are diagrammatic representations of the influences and processes that occur within an ecosystem. The models can be used to identify critical aspects of an ecosystem that may be developed for further study, or serve as the basis for the selection of indicators for environmental monitoring purposes. The models produced by this project are ‘control diagrams’, representing the unimpacted state of the environment, free from anthropogenic pressures. It is intended that the models produced by this project will be used to guide indicator selection for the monitoring of this habitat in UK waters. CEMs will eventually be produced for a range of habitat types defined under the UK Marine Biodiversity Monitoring R&D Programme (UKMBMP), which, along with stressor models designed to show the interactions within impacted habitats, would form the basis of a robust method for indicator selection. This project builds on the work to develop CEMs for shallow sublittoral coarse sediment habitats (Alexander. 2014). The project scope included the Marine Strategy Framework Directive (MSFD) predominant habitat type ‘shallow sublittoral mud’. This definition includes those habitats that fall into the EUNIS Level 4 classifications A5.33 Infralittoral Sandy Mud, A5.34 Infralittoral Fine Mud, A5.35 Circalittoral Sandy Mud and A5.36 Circalittoral Fine Mud, along with their constituent Level 5 biotopes which are relevant to UK waters. A species list of characterising fauna to be included within the scope of the models was identified using an iterative process to refine the full list of species found within the relevant Level 5 biotopes. A literature review was conducted using a pragmatic and iterative approach to gather evidence regarding species traits and information that would be used to inform the models and the interactions that occur within the shallow sublittoral mud habitat. All information gathered during the literature review was entered into a data logging pro forma spreadsheet which accompanies this report. Wherever possible, attempts were made to collect information from UK-specific peer-reviewed studies, although other sources were used where necessary. All data gathered was subject to a detailed confidence assessment. Expert judgement by the project team was utilised to provide information for aspects of the models for which references could not be sourced within the project timeframe. A model hierarchy was developed based on groups of fauna with similar species traits which aligned with previous sensitivity studies of ecological groups. One general control model was produced that indicated the high level drivers, inputs, biological assemblages, ecosystem processes and outputs that occur in shallow sublittoral mud habitats. In addition to this, five detailed sub-models were produced, which each focussed on a particular functional group of fauna within the habitat: tube building fauna, burrowing fauna, suspension and deposit feeding infauna, mobile epifauna, scavengers and predators, and echinoderms and sessile epifauna. Each sub-model is accompanied by an associated confidence model that presents confidence in the links between each model component. The models are split into seven levels and take spatial and temporal scale into account through their design, as well as magnitude and direction of influence. The seven levels include regional to global drivers, water column processes, local inputs/processes at the seabed, habitat and biological assemblage, output processes, local ecosystem functions, and regional to global ecosystem functions. The models indicate that whereas the high level drivers which affect each functional group are largely similar, the output processes performed by the biota and the resulting ecosystem functions vary both in number and importance between groups. Confidence within the Conceptual Ecological Modelling of Shallow Sublittoral Mud Habitats models as a whole is generally high, reflecting the level of information gathered during the literature review. Important drivers that influence the ecosystem include factors such as wave exposure, depth, water currents, climate and propagule supply. These factors, in combination with seabed and water column processes, such as primary production, suspended sediments, water chemistry, temperature and recruitment define and influence the food sources consumed by the biological assemblages of the habitat, and the biological assemblages themselves. In addition, the habitat sediment type plays an important factor in shaping the biology of the habitat. Output processes performed by the biological assemblage are variable between functional faunal groups depending on the specific fauna present and the role they perform within the ecosystem. Important processes include secondary production, biodeposition, bioturbation, bioengineering and the supply of propagules; these in turn influence ecosystem functions at the local scale such as nutrient and biogeochemical cycling, supply of food resources, sediment stability, habitat provision and in some cases microbial activity. The export of biodiversity and organic matter, biodiversity enhancement and biotope stability are the resulting ecosystem functions that occur at the regional to global scale. Features within the models that are most useful for monitoring habitat status and change due to natural variation have been identified; as have those which may be useful for monitoring to identify anthropogenic causes of change within the ecosystem. Physical and chemical features of the ecosystem have mostly been identified as potential indicators to monitor natural variation, whilst biological factors have predominantly been identified as most likely to indicate change due to anthropogenic pressures

Mid-Term Review Great Barrier Reef Climate Change Action Plan 2007-2012 and delivery program

GHD was commissioned by the Great Barrier Reef Marine Park Authority (GBRMPA) to conduct a midterm review of the Great Barrier Reef Climate Change Action Plan 2007-12 (the Action Plan) and its delivery program. The objectives of the review were to provide a mid-term evaluation (review) of progress made towards achieving the desired outcomes outlined in the Great Barrier Reef Climate Change Action Plan 2007-2012 (impact) management of the delivery program and projects (implementation) ; record and share the lessons learned so far; provide recommendations to guide adaptive management and continuous improvement of the implementation program; contribute to building evaluation awareness and capacity within the Climate Change Group (CCG) and other GBRMPA staff involved in delivering against the Action Plan. This final report describes the review methodology and findings. It provides summary findings in relation to implementation, impact, alignment with action on climate change at other levels, and contribution to the GBRMPA Corporate Plan 2009-2014 in the main body of the report, and detailed results in the form of a series of results charts in an appendix.See the Summary Report at http://hdl.handle.net/11017/958 and the Climate Change Action Plan at http://hdl.handle.net/11017/19

Modeling Small Scale Impacts of Multi-Purpose Platforms: An Ecosystem Approach

Aquaculture and marine renewable energy are two expanding sectors of the Blue Economy in Europe. Assessing the long-term environmental impacts in terms of eutrophication and noise is a priority for both the EU Water Framework Directive and the Marine Strategy Framework Directive, and cumulative impacts will be important for the Maritime Spatial Planning under the Integrated Maritime Policy. With the constant expansion of aquaculture production, it is expected that farms might be established further offshore in more remote areas, as high-energy conditions offer an opportunity to generate more power locally using Marine Renewable Energy (MRE) devices. A proposed solution is the co-location of MRE devices and aquaculture systems using Multi-Purpose Platforms (MPPs) comprising offshore wind turbines (OWTs) that will provide energy for farm operations as well as potentially shelter the farm. Disentangling the impacts, conflicts and synergies of MPP elements on the surrounding marine ecosystem is challenging. Here we created a high-resolution spatiotemporal Ecospace model of the West of Scotland, in order to assess impacts of a simple MPP configuration on the surrounding ecosystem and how these impacts can cascade through the food web. The model evaluated the following specific ecosystem responses: (i) top-down control pathways due to distribution changes among top-predators (harbor porpoise, gadoids and seabirds) driven by attraction to the farming sites and/or repulsion/killing due to OWT operations; (ii) bottom-up control pathways due to salmon farm activity providing increasing benthic enrichment predicated by a fish farm particle dispersal model, and sediment nutrient fluxes to the water column by early diagenesis of organic matter (recycled production). Weak responses of the food-web were found for top-down changes, whilst the results showed high sensitivity to increasing changes of bottom-up drivers that cascaded through the food-web from primary producers and detritus to pelagic and benthic consumers, respectively. We assessed the sensitivity of the model to each of these impacts and the cumulative effects on the ecosystem, discuss the capabilities and limitations of the Ecospace modeling approach as a potential tool for marine spatial planning and the impact that these results could have for the Blue Economy and the EU’s New Green Deal

Using dynamic Bayesian networks with hidden variables for change inference of the plankton community in the Archipelago Sea

Vuorovaikutukset planktonyhteisön sisällä ovat monimutkaisia ja näiden vuorovaikutusten realistinen mallintaminen on haaste ekosysteemitason mallinnuksessa. Tämän opinnäytetyön tavoitteena oli tutkia soveltuisivatko Bayes-verkot näiden vuorovaikutusten mallintamiseen. Työn toinen tavoite oli tutkia mahdollista muutosta ekosysteemitasolla. Tutkimuksessa käytettiin dynaamisia Bayes-verkkoja piilomuuttujilla ja tarkkailtiin, voisivatko muutokset planktonyhteisöjen rakenteessa heijastua laajempiin muutoksiin akvaattisissa ekosysteemeissä. Mallien tarkkuuden ja suorituskyvyn vertailua varten luotiin kaksi Bayes-ravintoverkkoa, joissa havaintojen väliset kausaaliset linkit eroavat toisistaan. Yksinkertaisempi rakenne, joka perustuu Markovin piilomalliin, suoriutui paremmin ja havaitsi piilomuuttujassa selkeän trendin. Tämä trendi aikasarjassa viittaa siihen, että tarkasteltavien muuttujien väliset suhteet ovat muuttuneet tutkimusjakson aikana. Analyyseissä käytetty planktonaineisto oli kerätty vuosien 1991 ja 2016 välillä Saaristomeren tutkimusasemalta ja mallin tuloksia arvioitiin yhdessä näytteistä kerätyn aineiston kanssa. Kasviplanktonin kokonaisbiomassa näytteissä kasvoi tutkimusajanjakson aikana, ja vastaavasti samaan aikaan eläinplanktonin kokonaisbiomassa näytteissä väheni. Bayes-verkko huomioi muutokset tarkastelluissa muuttujissa ja samaan aikaan maksimoi piilomuuttujan sopivuuden. Havaittu muutos piilomuuttujassa viittaa siis siihen, että jotkin muuttujat, jotka eivät ole havaittavissa, vaikuttavat molempien planktonyhteisöjen rakenteeseen. Havaittu kehityssuunta piilomuuttujassa saattaa viitata Saaristomeren rehevöitymiseen tutkimusajanjakson aikana, mutta tarkempien syiden selvittäminen vaatii lisätutkimuksia. Tämän opinnäytetyön perusteella piilomuuttujilla varustettu dynaaminen Bayes-verkko on lupaava menetelmä planktonyhteisön mallintamiseen.The interactions within plankton communities are complex, and realistic modelling of these interactions create a challenge in large-scale environmental models. The objective of this thesis was to evaluate whether Bayesian networks could be a suitable method in the modelling of these communities. Besides observing the interactions between different groups within phyto- and zooplankton communities, another goal was to focus on the potential change on the ecosystem level. To achieve this, dynamic Bayesian networks with hidden variables were used to observe whether structural changes in plankton communities could reveal larger trends in the aquatic ecosystem. To compare performance and accuracy of the model, two Bayesian food webs with differing causal links between observations were built. Of the two models, the simpler construct utilizing hidden Markov model fared better, and a clear trend was detected in the hidden variable. This trend in the time series signify that the relationships between the observed variables have changed during the study period. The plankton data set was collected from the Archipelago Sea between 1991 and 2016 and the results from the model were further analyzed alongside with this observational plankton data. In the samples the total biomass of phytoplankton grew throughout the study period, whereas at the same time the total biomass of zooplankton declined. As the Bayesian network considers the observable variables while maximizing the fit of the hidden variable, the observed trend in the hidden variable indicate that some unobservable variables are affecting both phyto- and zooplankton communities. This clear trend detected by the hidden variable might be related to a trend of increasing eutrophication in the study area, but to better understand the drivers causing this change further research is needed. Besides detecting underlying trends, the dynamic Bayesian networks are a promising method to study the interactions within plankton communities

How models can support ecosystem-based management of coral reefs

Despite the importance of coral reef ecosystems to the social and economic welfare of coastal communities, the condition of these marine ecosystems have generally degraded over the past decades. With an increased knowledge of coral reef ecosystem processes and a rise in computer power, dynamic models are useful tools in assessing the synergistic effects of local and global stressors on ecosystem functions. We review representative approaches for dynamically modeling coral reef ecosystems and categorize them as minimal, intermediate and complex models. The categorization was based on the leading principle for model development and their level of realism and process detail. This review aims to improve the knowledge of concurrent approaches in coral reef ecosystem modeling and highlights the importance of choosing an appropriate approach based on the type of question(s) to be answered. We contend that minimal and intermediate models are generally valuable tools to assess the response of key states to main stressors and, hence, contribute to understanding ecological surprises. As has been shown in freshwater resources management, insight into these conceptual relations profoundly influences how natural resource managers perceive their systems and how they manage ecosystem recovery. We argue that adaptive resource management requires integrated thinking and decision support, which demands a diversity of modeling approaches. Integration can be achieved through complimentary use of models or through integrated models that systemically combine all relevant aspects in one model. Such whole-of-system models can be useful tools for quantitatively evaluating scenarios. These models allow an assessment of the interactive effects of multiple stressors on various, potentially conflicting, management objectives. All models simplify reality and, as such, have their weaknesses. While minimal models lack multidimensionality, system models are likely difficult to interpret as they require many efforts to decipher the numerous interactions and feedback loops. Given the breadth of questions to be tackled when dealing with coral reefs, the best practice approach uses multiple model types and thus benefits from the strength of different models types

Do bacteria thrive when the ocean acidifies? Results from an off-­shore mesocosm study

Marine bacteria are the main consumers of the freshly produced organic matter. In order to meet their carbon demand, bacteria release hydrolytic extracellular enzymes that break down large polymers into small usable subunits. Accordingly, rates of enzymatic hydrolysis have a high potential to affect bacterial organic matter recycling and carbon turnover in the ocean. Many of these enzymatic processes were shown to be pH sensitive in previous studies. Due to the continuous rise in atmospheric CO2 concentration, seawater pH is presently decreasing at a rate unprecedented during the last 300 million years with so-far unknown consequences for microbial physiology, organic matter cycling and marine biogeochemistry. We studied the effects of elevated seawater pCO2 on a natural plankton community during a large-scale mesocosm study in a Norwegian fjord. Nine 25m-long Kiel Off-Shore Mesocosms for Future Ocean Simulations (KOSMOS) were adjusted to different pCO2 levels ranging from ca. 280 to 3000 µatm by stepwise addition of CO2 saturated seawater. After CO2 addition, samples were taken every second day for 34 days. The first phytoplankton bloom developed around day 5. On day 14, inorganic nutrients were added to the enclosed, nutrient-poor waters to stimulate a second phytoplankton bloom, which occurred around day 20. Our results indicate that marine bacteria benefit directly and indirectly from decreasing seawater pH. During both phytoplankton blooms, more transparent exopolymer particles were formed in the high pCO2 mesocosms. The total and cell-specific activities of the protein-degrading enzyme leucine aminopeptidase were elevated under low pH conditions. The combination of enhanced enzymatic hydrolysis of organic matter and increased availability of gel particles as substrate supported higher bacterial abundance in the high pCO2 treatments. We conclude that ocean acidification has the potential to stimulate the bacterial community and facilitate the microbial recycling of freshly produced organic matter, thus strengthening the role of the microbial loop in the surface ocean

THE POPULATION BIOLOGY AND ECOSYSTEM EFFECTS OF THE SEA NETTLE, CHRYSAORA CHESAPEAKEI

Some of the longest population records of jellyfish are collected from visual shore-based surveys. As surface counting is inexpensive and simple, it is of interest to determine what can be learned from such records as well as the usefulness of the method. A 4-year time series of Chrysaora chesapeakei (formerly quinquecirrha) medusa counts collected using three sampling methods was analyzed. Medusa abundance was modeled by change points and was highly correlated between the sampling methods. The remaining signal was random, and indices indicated that medusae were aggregated.  This study suggests more monitoring from visual shore-based surveys is an effective, low-cost method to increase information on jellyfish.  Data from another long-term visual survey show that C. chesapeakei in the Cheasapeake Bay have declined since the 1960s.  It is hypothesized that their loss results in a trophic cascade and increases in phytoplankton.   However, due to confounding factors, it is not clear that C. chesapeakei drives the changes observed.  A new 0-dimensional mechanistic model was formulated to include jellyfish.  A data assimilation method, Approximate Bayesian Computation, was used to objectively calibrate the model and guide its development.  The model fit to observations was improved by the addition of refractory non-living organic materials.  Additionally, comments and suggestions related to the model development process are provided. Using the model, perturbation experiments were conducted to study the effect of changing modeled C. chesapeakei (CHRY).  Then, sensitivity experiments of the environmental and ecological parameters were conducted to understand the conditions that are important in driving the response.  The change in CHRY had the potential to affect every state variable and throughflow but the response did not always conform to the trophic cascade concept and was highly dependent on the parameters.  The parameters that were most important in varying the response were related to the energetics of the zooplankton and parameters related to alternative pathways of loss or gains of the state variables.  The resulting complexity highlights the far-reaching ecosystem effects of C. chesapeakei as well as the need for new frameworks to understand the response of ecosystems to perturbations

Ecosystem functioning under the influence of bottom-trawling disturbance : an experimental approach and field observations from a continental slope area in the West Iberian Margin

Understanding the effects of bottom-trawling induced changes in benthic community structure, diversity and ecosystem functioning across different benthic-size components is imperative to determine the future sustainability of bottom-trawling fisheries in deep-sea regions. In this study, we combined field sampling observations with a pulse-chase experiment on sediments obtained from two stations of interest along the West Iberian Margin (WIM) distinguished by different trawling pressures. We compared these two stations in terms of meio- and macrofauna (infauna) standing stocks, biodiversity and several ecosystem function proxies. These proxies included: (i) 13C uptake by bacterial communities, (ii) infauna respiration rates, (iii) penetration of 13C in the sediment, and (iv) sediment pore-water nutrient concentrations. The pulse-chase experimental results were complemented with a larger biological dataset partially compiled from previous studies in the area, to investigate structural and functional diversity ecosystem functioning (respiration) patterns across the WIM. Our observations indicated that different regimes of trawling pressure influenced both macrofaunal respiration rates with disturbed sediments predominantly composed of deposit-/detritus-feeding smaller-sized macrofauna species. Moreover, sediment biogeochemical functioning (ammonium profiles) and 13C bacterial uptake showed differences among the two disturbance regimes. On the contrary, the biomass of small-sized biota, including bacteria and meiofauna, did not show marked differences between stations. The general depletion in macrofauna species richness across impacted areas of the study region was also correlated with a reduction in total biomass and respiration, suggesting that the long history of trawling disturbance at the WIM may affect regulatory ecosystem functions. These preliminary findings alert for the impacts of trawling on crucial functions of benthic ecosystems that may be imperceptible to the current tools used in monitoring programs