scholarly journals Teaching basic numeracy, predictive models and socioeconomics to marine ecologists through Bayesian belief networks

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 312 ◽  
Author(s):  
Richard Stafford ◽  
Rachel Williams
Keyword(s):  
Predictive Models ◽  
Species Interactions ◽  
Rocky Shore ◽  
Marine Ecosystem ◽  
Predictive Modelling ◽  
Bayesian Belief Networks ◽  
Fishing Effort ◽  
Management Approach ◽  
Afternoon Session ◽  
Belief Network

Teaching numeric disciplines to higher education students in many life sciences disciplines is highly challenging. In this study, we test whether an approach linking field observations with predictive models can be useful in allowing students to understand basic numeracy and probability, as well as developing skills in modelling, understanding species interactions and even community/ecosystem-service interactions.  We presented a field-based lecture in a morning session (on rocky shore ecology), followed by an afternoon session parameterising a belief network using a simple, user-friendly interface. The study was conducted with students during their second week of a foundation degree, hence having little prior knowledge of these systems or models. All students could create realistic predictive models of competition, predation and grazing, although most initially failed to account for trophic cascade effects in parameterising their models of the rocky shore they had previously seen. The belief network was then modified to account for a marine ecosystem management approach, where fishing effort and economic benefit of fishing were linked to population abundance of different species, and management goals were included. Students had little difficultly in applying conceptual links between species and ecosystem services in the same manner as between species. Students evaluated their understanding of a range of variables from rocky shore knowledge to marine management as increasing over the session, but the role of the predictive modelling task was indicated as a major source of learning, even for topics we thought may be better learned in the field. The study adds evidence to the theories that students benefit from exposure to numeric topics, even very early in their degree programmes, but students grasp concepts better when applied to real world situations which they have experience of, or perceive as important.

scholarly journals Predicting ecological regime shift under climate change: New modelling techniques and potential of molecular-based approaches

2013 ◽  
Vol 59 (3) ◽  
pp. 403-417 ◽  
Author(s):  
Richard Stafford ◽  
V. Anne Smith ◽  
Dirk Husmeier ◽  
Thomas Grima ◽  
Barbara-ann Guinn
Keyword(s):  
Climate Change ◽  
Bayesian Network ◽  
Species Interactions ◽  
Rocky Shore ◽  
Regime Shift ◽  
Predictive Modelling ◽  
Kelp Forests ◽  
Stable Regime ◽  
Ecological Regime ◽  
Stable Community

Abstract Ecological regime shift is the rapid transition from one stable community structure to another, often ecologically inferior, stable community. Such regime shifts are especially common in shallow marine communities, such as the transition of kelp forests to algal turfs that harbour far lower biodiversity. Stable regimes in communities are a result of balanced interactions between species, and predicting new regimes therefore requires an evaluation of new species interactions, as well as the resilience of the ‘stable’ position. While computational optimisation techniques can predict new potential regimes, predicting the most likely community state of the various options produced is currently educated guess work. In this study we integrate a stable regime optimisation approach with a Bayesian network used to infer prior knowledge of the likely stress of climate change (or, in practice, any other disturbance) on each component species of a representative rocky shore community model. Combining the results, by calculating the product of the match between resilient computational predictions and the posterior probabilities of the Bayesian network, gives a refined set of model predictors, and demonstrates the use of the process in determining community changes, as might occur through processes such as climate change. To inform Bayesian priors, we conduct a review of molecular approaches applied to the analysis of the transcriptome of rocky shore organisms, and show how such an approach could be linked to meas-ureable stress variables in the field. Hence species-specific microarrays could be designed as biomarkers of in situ stress, and used to inform predictive modelling approaches such as those described here.

scholarly journals Sustainable exploitation of temperate fish stocks

2009 ◽  
Vol 6 (1) ◽  
pp. 124-127 ◽  
Author(s):  
Henrik Sparholt ◽  
Robin M. Cook
Keyword(s):  
Species Interactions ◽  
Fishery Management ◽  
Regional Scale ◽  
Single Species ◽  
Fishing Effort ◽  
Theoretical Concept ◽  
Maximum Sustainable Yield ◽  
Production Model ◽  
The Status ◽  
Long Time

The theory of maximum sustainable yield (MSY) underpins many fishery management regimes and is applied principally as a single species concept. Using a simple dynamic biomass production model we show that MSY can be identified from a long time series of multi-stock data at a regional scale in the presence of species interactions and environmental change. It suggests that MSY is robust and calculable in a multispecies environment, offering a realistic reference point for fishery management. Furthermore, the demonstration of the existence of MSY shows that it is more than a purely theoretical concept. There has been an improvement in the status of stocks in the Northeast Atlantic, but our analysis suggests further reductions in fishing effort would improve long-term yields.

Altering species interactions outweighs the effects of experimental warming in structuring a rocky shore community

2017 ◽  
Vol 496 ◽  
pp. 22-28 ◽  
Author(s):  
Justin A. Lathlean ◽  
Russell A. McWilliam ◽  
Jonathan Pankhurst ◽  
Todd E. Minchinton
Keyword(s):  
Species Interactions ◽  
Rocky Shore ◽  
Experimental Warming

Bayesian Belief Networks for Data Cleaning

2011 ◽  
pp. 204-219
Author(s):  
Enrico Fagiuoli ◽  
Sara Omerino ◽  
Fabio Stella
Keyword(s):  
Data Quality ◽  
Missing Values ◽  
Data Cleaning ◽  
Joint Probability ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Computational Techniques ◽  
Belief Network ◽  
General Statistical ◽  
Joint Probability Distributions

The importance of data cleaning and data quality is becoming increasingly clear as evidenced by the surge in software, tools, consulting companies and seminars addressing data quality issues. In this contribution the authors present and describe how Bayesian computational techniques can be exploited for data cleaning purposes to the extent of reducing the time to clean and understand the data. The proposed approach relies on the computational device named Bayesian belief network, which is a general statistical model that allows the efficient description and treatment of joint probability distributions. This work describes the conceptual framework that maps the Bayesian belief network computational device to some of the most difficult tasks in data cleaning, namely imputing missing values, completing truncated datasets and outliers detection. The proposed framework is described and supported by a set of numerical experiments performed by exploiting the Bayesian belief network programming suite named HUGIN.

THE INNOVATION OF VULNERABLE FISHERIES USING ECOSYSTEM-BASED FISHERY MANAGEMENT APPROACH: A TEST CASE IN KARIMUNJAWA ECOSYSTEM, CENTRAL JAVA, INDONESIA

2016 ◽  
Vol 78 (4-2) ◽  
Author(s):  
Mayanggita Kirana ◽  
Indah Susilowati ◽  
Kuperan Viswanathan
Keyword(s):  
Fisheries Management ◽  
Fishery Management ◽  
Meta Analysis ◽  
Marine Ecosystem ◽  
Primary Data ◽  
Management Approach ◽  
New Paradigm ◽  
Initial Finding ◽  
Central Java ◽  
The Government

The sustainability of marine ecosystem has become a major concern the government; however, the implementation of sustainability-based fisheries management has not been fully carried out and well controlled. Therefore, having a concept of ecosystem-based fisheries management (EBFM) is essential in protecting it preserved. The aim of this study was to analyze the implementation of EBFM in Karimunjawa ecosystem, Central Java, Indonesia. The analysis of this study was based on the primary data collected from fishermen and stakeholders using in-depth interviews, and the secondary data gathered from stakeholders of Karimunjawa documentation. Meta-analysis with triangulation was invoked in this study. The result showed that the vulnerability of marine ecosystem, particularly fisheries’ resource in the pilot project is in progress. The conventional approach has not yet succeeded in managing fisheries’ resource in terms of sustainability attributes. Moreover, the EBFM has not yet proven to be a suitable approach for some reasons; although, this concept is very promising in encouraging a new paradigm for sustainable management in Indonesia with a protocol concept. This initial finding needs to be furthered in order to explore other aspects of development. 

Building an ecosystems-type fisheries management approach for the Campeche Bank, subarea in the Gulf of Mexico Large Marine Ecosystem

2017 ◽  
Vol 22 ◽  
pp. 143-149 ◽  
Author(s):  
Francisco Arreguín-Sánchez ◽  
Pablo del Monte Luna ◽  
Manuel Jesús Zetina-Rejón ◽  
Arturo Tripp-Valdez ◽  
Mirtha O. Albañez-Lucero ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Fisheries Management ◽  
Marine Ecosystem ◽  
Management Approach ◽  
Large Marine Ecosystem

scholarly journals Functional Genomics Differentiate Inherent and Environmentally Influenced Traits in Dinoflagellate and Diatom Communities

2020 ◽  
Vol 8 (4) ◽  
pp. 567 ◽  
Author(s):  
Stephanie Elferink ◽  
Uwe John ◽  
Stefan Neuhaus ◽  
Sylke Wohlrab
Keyword(s):  
Signal Transduction ◽  
Functional Traits ◽  
Species Interactions ◽  
Marine Ecosystem ◽  
Biotic Interactions ◽  
Sulfur Cycle ◽  
Functional Responses ◽  
Physiological Importance ◽  
Metabolic Interactions ◽  
Lysine Degradation

Dinoflagellates and diatoms are among the most prominent microeukaryotic plankton groups, and they have evolved different functional traits reflecting their roles within ecosystems. However, links between their metabolic processes and functional traits within different environmental contexts warrant further study. The functional biodiversity of dinoflagellates and diatoms was accessed with metatranscriptomics using Pfam protein domains as proxies for functional processes. Despite the overall geographic similarity of functional responses, abiotic (i.e., temperature and salinity; ~800 Pfam domains) and biotic (i.e., taxonomic group; ~1500 Pfam domains) factors influencing particular functional responses were identified. Salinity and temperature were identified as the main drivers of community composition. Higher temperatures were associated with an increase of Pfam domains involved in energy metabolism and a decrease of processes associated with translation and the sulfur cycle. Salinity changes were correlated with the biosynthesis of secondary metabolites (e.g., terpenoids and polyketides) and signal transduction processes, indicating an overall strong effect on the biota. The abundance of dinoflagellates was positively correlated with nitrogen metabolism, vesicular transport and signal transduction, highlighting their link to biotic interactions (more so than diatoms) and suggesting the central role of species interactions in the evolution of dinoflagellates. Diatoms were associated with metabolites (e.g., isoprenoids and carotenoids), as well as lysine degradation, which highlights their ecological role as important primary producers and indicates the physiological importance of these metabolic pathways for diatoms in their natural environment. These approaches and gathered information will support ecological questions concerning the marine ecosystem state and metabolic interactions in the marine environment.

scholarly journals Modelling Reef Fish Population Responses to Fisheries Restrictions in Marine Protected Areas in the Coral Triangle

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Divya Varkey ◽  
Cameron H. Ainsworth ◽  
Tony J. Pitcher
Keyword(s):  
Reef Fish ◽  
Marine Protected Area ◽  
Marine Ecosystem ◽  
Ecosystem Model ◽  
Fish Population ◽  
Fishing Effort ◽  
Coral Triangle ◽  
Trade Offs ◽  
Coral Reef Ecosystems ◽  
Marine Ecosystem Models

Marine ecosystem models are used to investigate marine protected area (MPA) benefits for coral reef ecosystems located in Raja Ampat, in the heart of the Coral Triangle. Field data from an integrated and diverse research project is used to develop a spatial ecosystem model using Ecopath, Ecosim, and Ecospace modelling software. The ecological and fisheries responses of a reef ecosystem to different levels of fishing effort restrictions inside MPAs are explored. The trade-offs of allowing some fisheries to operate inside the MPAs versus designating the MPAs as no-take zones are highlighted. The results show that rapid rebuilding of reef fish populations, especially the large charismatic species, requires no-take areas. Distinct trade-offs in spillover benefits are observed between partially fished and no-take MPAs.

scholarly journals Benthos distribution modelling and its relevance for marine ecosystem management

2014 ◽  
Vol 72 (2) ◽  
pp. 297-315 ◽  
Author(s):  
Henning Reiss ◽  
Silvana Birchenough ◽  
Angel Borja ◽  
Lene Buhl-Mortensen ◽  
Johan Craeymeersch ◽  
...  
Keyword(s):  
Ecosystem Management ◽  
Spatial Scales ◽  
Marine Ecosystem ◽  
Distribution Patterns ◽  
Bayesian Belief Networks ◽  
Early Warning Systems ◽  
Biological Traits ◽  
Benthic Species ◽  
Distribution Modelling ◽  
Spatial Coverage

Abstract Marine benthic ecosystems are difficult to monitor and assess, which is in contrast to modern ecosystem-based management requiring detailed information at all important ecological and anthropogenic impact levels. Ecosystem management needs to ensure a sustainable exploitation of marine resources as well as the protection of sensitive habitats, taking account of potential multiple-use conflicts and impacts over large spatial scales. The urgent need for large-scale spatial data on benthic species and communities resulted in an increasing application of distribution modelling (DM). The use of DM techniques enables to employ full spatial coverage data of environmental variables to predict benthic spatial distribution patterns. Especially, statistical DMs have opened new possibilities for ecosystem management applications, since they are straightforward and the outputs are easy to interpret and communicate. Mechanistic modelling techniques, targeting the fundamental niche of species, and Bayesian belief networks are the most promising to further improve DM performance in the marine realm. There are many actual and potential management applications of DMs in the marine benthic environment, these are (i) early warning systems for species invasion and pest control, (ii) to assess distribution probabilities of species to be protected, (iii) uses in monitoring design and spatial management frameworks (e.g. MPA designations), and (iv) establishing long-term ecosystem management measures (accounting for future climate-driven changes in the ecosystem). It is important to acknowledge also the limitations associated with DM applications in a marine management context as well as considering new areas for future DM developments. The knowledge of explanatory variables, for example, setting the basis for DM, will continue to be further developed: this includes both the abiotic (natural and anthropogenic) and the more pressing biotic (e.g. species interactions) aspects of the ecosystem. While the response variables on the other hand are often focused on species presence and some work undertaken on species abundances, it is equally important to consider, e.g. biological traits or benthic ecosystem functions in DM applications. Tools such as DMs are suitable to forecast the possible effects of climate change on benthic species distribution patterns and hence could help to steer present-day ecosystem management.

Evaluating the influence of predator–prey interactions on stock assessment and management reference points for a large lake ecosystem

2016 ◽  
Vol 73 (9) ◽  
pp. 1372-1388 ◽  
Author(s):  
Hiroyuki Kurota ◽  
Murdoch K. McAllister ◽  
Eric A. Parkinson ◽  
N.T. Johnston
Keyword(s):  
Species Interactions ◽  
Single Species ◽  
Reference Points ◽  
Management Policy ◽  
Management Approach ◽  
Lake Ecosystem ◽  
Large Lake ◽  
Ecosystem Models ◽  
Predator Prey ◽  
Results Management

Ecosystem models are thought to offer advantages over single-species models in terms of management policy analysis. This hypothesis has proven difficult to test because of underlying system complexities, coupled with short time series and minimal contrast in environmental conditions or management policies. This paper presents a Bayesian statistical catch-at-age model to compare ecosystem models and test hypotheses about the management of a recreational fishery based on a predator–prey system using a relatively simple and data-rich ecosystem in a large lake, Kootenay Lake, British Columbia, where kokanee (Oncorhynchus nerka) are the prey and piscivorous rainbow trout (Oncorhynchus mykiss) are the predator. A model that explicitly incorporates the predator–prey interaction explained long-term data of field and fishery surveys much better than single-species models without any interactions. Minimally realistic multispecies models that treated predation identically but differed in their representation of the effects of prey abundance on predator mortality produced quite different results. Management reference points, for example, differed considerably between the models. Our study thus emphasizes that the choice of a management approach for this type of fishery will depend strongly on the model form and should take into consideration results from empirically based models that include species interactions.

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