Robustness of early warning signals of regime shifts in time-delayed ecological models

Various ecological and other complex dynamical systems may exhibit abrupt regime shifts or critical transitions, wherein they reorganize from one stable state to another over relatively short time scales. Because of potential losses to ecosystem services, forecasting such unexpected shifts would be valuable. Using mathematical models of regime shifts, ecologists have proposed various early warning signals of imminent shifts. However, their generality and applicability to real ecosystems remain unclear because these mathematical models are considered too simplistic. Here, we investigate the robustness of recently proposed early warning signals of regime shifts in two well-studied ecological models, but with the inclusion of time-delayed processes. We find that the average variance may either increase or decrease prior to a regime shift and, thus, may not be a robust leading indicator in time-delayed ecological systems. In contrast, changing average skewness, increasing autocorrelation at short time lags, and reddening power spectra of time series of the ecological state variable all show trends consistent with those of models with no time delays. Our results provide insights into the robustness of early warning signals of regime shifts in a broader class of ecological systems.

Ecological models and GIS maps of macroalgae, as bioindicator for ecological status of Hormozgan rocky shores

In this study a total of 75 species were identified, from which 17 species, 9 genes and 6 families; belonged to Green Algae, 18 species, 7 genes and 4 families; belonged to Brown Algae, and 40 species, 18 genes and 11 families; belonged to Red Algae. From total times spent for sampling, it was determined that at lengeh harbor with 6 species, had the lowest diversity of green algae. The species diversity of brown algae at Michael location with 10 species each; had the highest, and Tahooneh location with 5 species; had the lowest species diversity. Species diversity of red algae at Michael location with 28 species; had the highest, and Sayeh Khosh location with 13 species; had the lowest diversity. From all locations where sampling took place, the highest species diversity regarding Time and Space for all three groups of algae; were associated to Late February (20th. Feb. ), and late March(20th. March). Coverage data of macroalgae and Ecological Evaluation Index indicate a high level of eutrophication for the Saieh khosh, and Bostaneh, They are classified as zones with a bad and poor ecological status. It has been proved that concentrations of biogenic elements and phytoplankton blooming are higher in these zones. The best values of the estimated metrics at Tahooneh and Michaeil could be explained with the good ecological conditions in that zone and the absence of pollution sources close to that transect . The values of abundance of macroalgae and Ecological Evaluation Index indicate a moderate ecological conditions for the Koohin, Lengeh and Chirooieh.

Ecological aspects of biosecurity surveillance design for the detection of multiple invasive animal species

Complex surveillance problems are common in biosecurity, such as prioritizing detection among multiple invasive species, specifying risk over a heterogeneous landscape, combining multiple sources of surveillance data, designing for specified power to detect, resource management, and collateral effects on the environment. Moreover, when designing for multiple target species, inherent biological differences among species result in different ecological models underpinning the individual surveillance systems for each. Species are likely to have different habitat requirements, different introduction mechanisms and locations, require different methods of detection, have different levels of detectability, and vary in rates of movement and spread. Often there is a further challenge of a lack of knowledge, literature, or data, for any number of the above problems. Even so, governments and industry need to proceed with surveillance programs which aim to detect incursions in order to meet environmental, social and political requirements. We present an approach taken to meet these challenges in one comprehensive and statistically powerful surveillance design for non-indigenous terrestrial vertebrates on Barrow Island, a high conservation nature reserve off the Western Australian coast. Here, the possibility of incursions is increased due to construction and expanding industry on the island. The design, which includes mammals, amphibians and reptiles, provides a complete surveillance program for most potential terrestrial vertebrate invaders. Individual surveillance systems were developed for various potential invaders, and then integrated into an overall surveillance system which meets the above challenges using a statistical model and expert elicitation. We discuss the ecological basis for the design, the flexibility of the surveillance scheme, how it meets the above challenges, design limitations, and how it can be updated as data are collected as a basis for adaptive management.

Applying the ecological model of behavior change to a physical activity trial in retirement communities : description of the study protocol

Objectives To describe the intervention protocol for the first multilevel ecological intervention for physical activity in retirement communities that addresses individual, interpersonal and community influences on behavior change. Design A cluster randomized controlled trial design was employed with two study arms: a physical activity intervention and an attention control successful aging condition. Setting Sixteen continuing care retirement communities in San Diego County. Participants Three hundred twenty older adults, aged 65 years and older, are being recruited to participate in the trial. In addition, peer leaders are being recruited to lead some study activities, especially to sustain the intervention after study activities ceased. Intervention Participants in the physical activity trial receive individual, interpersonal and community intervention components. The individual level components include pedometers, goal setting and individual phone counseling. The interpersonal level components include group education sessions and peer-led activities. The community level components include resource audits and enumeration, tailored walking maps, and community improvement projects. The successful aging group receives individual and group attention about successful aging topics. Measurements The main outcome is light to moderate physical activity, measured objectively by accelerometry. Other objective outcomes included physical functioning, blood pressure, physical fitness, and cognitive functioning. Self report measures include depressive symptoms and health related quality of life. Results The intervention is being delivered successfully in the communities and compliance rates are high. Conclusion Ecological Models call for interventions that address multiple levels of the model. Previous studies have not included components at each level and retirement communities provide a model environment to demonstrate how to implement such an intervention.

Learning style and digital activity: An ecological study

In order to understand student engagement in higher education through the use of digital technologies, it is necessary to appreciate the broader use of differing technologies. Forty-eight first-year university students completed an online survey that queried patterns of digital activity across home, school and community contexts and that included rating scale items that measured learning style (i.e., active-reflective, sensing-intuitive, visual-verbal, sequential-global). Results suggest that students vary widely in digital activities and that such variation is related to differences in learning style. For example, active learners were more likely than reflective learners to engage in digital activities in the community and users of some specific application, as opposed to non-users, were more likely to be verbal than visual learners. Implications for instructional applications of digital technology in higher education are presented.

Report of the special session of "Chesapeake Bay Ecological Forecasting: Moving Ecosystem Modeling from Research to Operation" of the 2010 Chesapeake Modeling Symposium

Moving ecosystem modeling from research to applications and operations has direct management relevance and will be integral to achieving the water quality and living resource goals of the 2010 Chesapeake Bay Executive Order. Yet despite decades of ecosystem modeling efforts of linking climate to water quality, plankton and fish, ecological models are rarely taken to the operational phase. In an effort to promote operational ecosystem modeling and ecological forecasting in Chesapeake Bay, a meeting was convened on this topic at the 2010 Chesapeake Modeling Symposium (May, 10-11). These presentations show that tremendous progress has been made over the last five years toward the development of operational ecological forecasting models, and that efforts in Chesapeake Bay are leading the way nationally. Ecological forecasts predict the impacts of chemical, biological, and physical changes on ecosystems, ecosystem components, and people. They have great potential to educate and inform not only ecosystem management, but also the outlook and opinion of the general public, for whom we manage coastal ecosystems. In the context of the Chesapeake Bay Executive Order, ecological forecasting can be used to identify favorable restoration sites, predict which sites and species will be viable under various climate scenarios, and predict the impact of a restoration project on water quality.

The importance of high frequency data in ecological modelling.

The purpose of this note is to discuss the role of high frequency data in ecological modelling and to identify some of the data requirements for the further development of ecological models for operational oceanography. There is a pressing requirement for the establishment of data acquisition systems for key ecological variables with a high spatial and temporal coverage. Such a system will facilitate the development of operational models. It is envisaged that both in-situ and remotely sensed measurements will need to combined to achieve this aim.

Conceptual Ecological Modelling of Sublittoral Rock Habitats to Inform Indicator Selection

The purpose of this study is to produce a series of Conceptual Ecological Models (CEMs) that represent sublittoral rock habitats in the UK. CEMs are diagrammatic representations of the influences and processes that occur within an ecosystem. They can be used to identify critical aspects of an ecosystem that may be studied further, 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 may 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, are 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 et al 2014). The project scope included those habitats defined as ‘sublittoral rock’. This definition includes those habitats that fall into the EUNIS Level 3 classifications A3.1 Atlantic and Mediterranean high energy infralittoral rock, A3.2 Atlantic and Mediterranean moderate energy infralittoral rock, A3.3 Atlantic and Mediterranean low energy infralittoral rock, A4.1 Atlantic and Mediterranean high energy circalittoral rock, A4.2 Atlantic and Mediterranean moderate energy circalittoral rock, and A4.3 Atlantic and Mediterranean low energy circalittoral rock as well as the constituent Level 4 and 5 biotopes that 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 characterise the interactions that occur within the sublittoral rock habitat. All information gathered during the literature review was entered into a data logging pro-forma spreadsheet that 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 multivariate analysis approach was adopted to assess ecologically similar groups (based on ecological and life history traits) of fauna from the identified species to form the basis of the models. A model hierarchy was developed based on these ecological groups. One general control model was produced that indicated the high-level drivers, inputs, biological assemblages, ecosystem processes and outputs that occur in sublittoral rock habitats. In addition to this, seven detailed sub-models were produced, which each focussed on a particular ecological group of fauna within the habitat: ‘macroalgae’, ‘temporarily or permanently attached active filter feeders’, ‘temporarily or permanently attached passive filter feeders’, ‘bivalves, brachiopods and other encrusting filter feeders’, ‘tube building fauna’, ‘scavengers and predatory fauna’, and ‘non-predatory mobile fauna’. 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 whilst the high level drivers that affect each ecological 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 models as a whole is generally high, reflecting the level of information gathered during the literature review. Physical drivers which influence the ecosystem were found to be of high importance for the sublittoral rock habitat, with factors such as wave exposure, water depth and water currents noted to be crucial in defining the biological assemblages. Other important factors such as recruitment/propagule supply, and those which affect primary production, such as suspended sediments, light attenuation and water chemistry and temperature, were also noted to be key and act to influence the food sources consumed by the biological assemblages of the habitat, and the biological assemblages themselves. Output processes performed by the biological assemblages are variable between ecological groups depending on the specific flora and fauna present and the role they perform within the ecosystem. Of particular importance are the outputs performed by the macroalgae group, which are diverse in nature and exert influence over other ecological groups in the habitat. Important output processes from the habitat as a whole include primary and secondary production, bioengineering, biodeposition (in mixed sediment habitats) 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 (in mixed sediment habitats), habitat provision and population and algae control. 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 that may be useful for monitoring to identify anthropogenic causes of change within the ecosystem. Biological, physical and chemical features of the ecosystem have been identified as potential indicators to monitor natural variation, whereas biological factors and those physical /chemical factors most likely to affect primary production have predominantly been identified as most likely to indicate change due to anthropogenic pressures.

Assessing the sensitivity of subtidal sedimentary habitats to pressures associated with marine activities. Phase 1 Report: Rationale and proposed ecological groupings for Level 5 biotopes against which sensitivity assessments would be best undertaken.

The purpose of this study is to produce a series of Conceptual Ecological Models (CEMs) that represent sublittoral rock habitats in the UK. CEMs are diagrammatic representations of the influences and processes that occur within an ecosystem. They can be used to identify critical aspects of an ecosystem that may be studied further, 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 may 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, are 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 et al 2014). The project scope included those habitats defined as ‘sublittoral rock’. This definition includes those habitats that fall into the EUNIS Level 3 classifications A3.1 Atlantic and Mediterranean high energy infralittoral rock, A3.2 Atlantic and Mediterranean moderate energy infralittoral rock, A3.3 Atlantic and Mediterranean low energy infralittoral rock, A4.1 Atlantic and Mediterranean high energy circalittoral rock, A4.2 Atlantic and Mediterranean moderate energy circalittoral rock, and A4.3 Atlantic and Mediterranean low energy circalittoral rock as well as the constituent Level 4 and 5 biotopes that 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 characterise the interactions that occur within the sublittoral rock habitat. All information gathered during the literature review was entered into a data logging pro-forma spreadsheet that 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 multivariate analysis approach was adopted to assess ecologically similar groups (based on ecological and life history traits) of fauna from the identified species to form the basis of the models. A model hierarchy was developed based on these ecological groups. One general control model was produced that indicated the high-level drivers, inputs, biological assemblages, ecosystem processes and outputs that occur in sublittoral rock habitats. In addition to this, seven detailed sub-models were produced, which each focussed on a particular ecological group of fauna within the habitat: ‘macroalgae’, ‘temporarily or permanently attached active filter feeders’, ‘temporarily or permanently attached passive filter feeders’, ‘bivalves, brachiopods and other encrusting filter feeders’, ‘tube building fauna’, ‘scavengers and predatory fauna’, and ‘non-predatory mobile fauna’. 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 whilst the high level drivers that affect each ecological 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 models as a whole is generally high, reflecting the level of information gathered during the literature review. Physical drivers which influence the ecosystem were found to be of high importance for the sublittoral rock habitat, with factors such as wave exposure, water depth and water currents noted to be crucial in defining the biological assemblages. Other important factors such as recruitment/propagule supply, and those which affect primary production, such as suspended sediments, light attenuation and water chemistry and temperature, were also noted to be key and act to influence the food sources consumed by the biological assemblages of the habitat, and the biological assemblages themselves. Output processes performed by the biological assemblages are variable between ecological groups depending on the specific flora and fauna present and the role they perform within the ecosystem. Of particular importance are the outputs performed by the macroalgae group, which are diverse in nature and exert influence over other ecological groups in the habitat. Important output processes from the habitat as a whole include primary and secondary production, bioengineering, biodeposition (in mixed sediment habitats) 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 (in mixed sediment habitats), habitat provision and population and algae control. 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 that may be useful for monitoring to identify anthropogenic causes of change within the ecosystem. Biological, physical and chemical features of the ecosystem have been identified as potential indicators to monitor natural variation, whereas biological factors and those physical /chemical factors most likely to affect primary production have predominantly been identified as most likely to indicate change due to anthropogenic pressures.

Testing a social ecological model for relations between political violence and child adjustment in Northern Ireland

Relations between political violence and child adjustment are matters of international concern. Past research demonstrates the significance of community, family, and child psychological processes in child adjustment, supporting study of interrelations between multiple social ecological factors and child adjustment in contexts of political violence. Testing a social ecological model, 300 mothers and their children (M = 12.28 years, SD = 1.77) from Catholic and Protestant working class neighborhoods in Belfast, Northern Ireland, completed measures or community discord, family relations, and children's regulatory processes (i.e., emotional security) and outcomes. Historical political violence in neighborhoods based on objective records (i.e., politically motivated deaths) were related to family members' reports of current sectarian antisocial behavior and nonsectarian antisocial behavior. Interparental conflict and parental monitoring and children's emotional security about both the community and family contributed to explanatory pathways for relations between sectarian antisocial behavior in communities and children's adjustment problems. The discussion evaluates support for social ecological models for relations between political violence and child adjustment and its implications for understanding relations in other parts of the world.

Ecological modeling and pest population management: a possible and necessary connection in a changing world

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ecological-economic modeling for biodiversity management: Potential, pitfalls, and prospects

Ecologists and economists both use models to help develop strategies for biodiversity management. The practical use of disciplinary models, however, can be limited because ecological models tend not to address the socioeconomic dimension of biodiversity management, whereas economic models tend to neglect the ecological dimension. Given these shortcomings of disciplinary models, there is a necessity to combine ecological and economic knowledge into ecological-economic models. It is insufficient if scientists work separately in their own disciplines and combine their knowledge only when it comes to formulating management recommendations. Such an approach does not capture feedback loops between the ecological and the socioeconomic systems. Furthermore, each discipline poses the management problem in its own way and comes up with its own most appropriate solution. These disciplinary solutions, however are likely to be so different that a combined solution considering aspects of both disciplines cannot be found. Preconditions for a successful model-based integration of ecology and economics include (1) an in-depth knowledge of the two disciplines, (2) the adequate identification and framing of the problem to be investigated, and (3) a common understanding between economists and ecologists of modeling and scale. To further advance ecological-economic modeling the development of common benchmarks, quality controls, and refereeing standards for ecological-economic models is desirable.

Chebanov law and Vakar formula in mathematical models of complex systems

Ecological models written in a mathematical language L(M) or model language, with a given style or methodology can be considered as a text. It is possible to apply statistical linguistic laws and the experimental results demonstrate that the behaviour of a mathematical model is the same of any literary text of any natural language. A text has the following characteristics: (a) the variables, its transformed functions and parameters are the lexic units or LUN of ecological models; (b) the syllables are constituted by a LUN, or a chain of them, separated by operating or ordering LUNs; (c) the flow equations are words; and (d) the distribution of words (LUM and CLUN) according to their lengths is based on a Poisson distribution, the Chebanov's law. It is founded on Vakar's formula, that is calculated likewise the linguistic entropy for L(M). We will apply these ideas over practical examples using MARIOLA model. In this paper it will be studied the problem of the lengths of the simple lexic units composed lexic units and words of text models, expressing these lengths in number of the primitive symbols, and syllables. The use of these linguistic laws renders it possible to indicate the degree of information given by an ecological model.

Farm it right : the sustainable farming simulation

Farm It Right is an innovative creative work that simulates sustainable farming techniques using ecological models prepared by academics at Bradford University (School of Life Sciences). This interactive work simulates the farming conditions and options of our ancestors and demonstrates the direct impact their actions had on their environment and on the ’future of their cultures’ (Schmidt 2008). Specifically, the simulation allows users to explore and experiment with the complex relationships between environmental factors and human decision making within the harsh conditions of an early (9th century) Nordic farm. The simulation interface displays both statistical and graphical feedback in response to the users selections regarding animal reproduction rates, shelter provisions, food supplies etc. as well as demonstrating resulting impacts to soil erosion, water supply, animal population sizes etc.---------- 'Farm It Right' is now used at Bradford University (School of Life Sciences) as a dynamic e-Learning resource for incorporating environmental archaeology with sustainable development education, improving the engagement with complex data and the appreciation of human impacts on the environment and the future of their cultures. 'Farm It Right' is also demonstrated as an exemplar case study for interaction design students at Queensland University of Technology.