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5 Projects, page 1 of 1

  • Canada
  • UK Research and Innovation
  • UKRI|NERC
  • 2008

  • Funder: UKRI Project Code: NE/F001673/1
    Funder Contribution: 15,818 GBP
    Partners: CAF, Lund University, SDSU, University of Leicester, University of Sheffield

    Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

  • Funder: UKRI Project Code: NE/F001681/1
    Funder Contribution: 184,956 GBP
    Partners: University of Reading, SDSU, Lund University, CAF, University of Sheffield

    Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

  • Funder: UKRI Project Code: NE/G000360/1
    Funder Contribution: 53,759 GBP
    Partners: Queen's University Canada, WLU, Swansea University

    Understanding the exchange of energy and gases between the earth's surface and the lower atmosphere is essential for answering many questions related to, e.g., the global carbon budget, ecosystem functioning, air pollution mitigation, greenhouse gas emissions, weather forecasting, and projections of climate change. However, uncertainties in carbon dioxide (CO2) and water vapour (H2O) budgets limit our ability to reproduce and project these exchange processes. Exchange processes are usually analysed based on micrometeorological measurements from tall flux towers, thought to be representative of large area averages. A limitation of this approach is that the actual source areas of these fluxes are not always known and that the impact of land-surface heterogeneity (at small or large scale) on the fluxes is not yet completely understood. The micrometeorological measurements of the major carbon flux networks around the world, such as Ameriflux, Canadian Carbon Program, CarboEurope (in which the UK plays a prominent role) and Oz-Net, are essential to validate global estimates of CO2 sources and sinks, to develop and validate land surface models and to understand the sensitivity of CO2 fluxes under changing climate conditions. Unfortunately, flux tower measurements currently suffer from substantial uncertainty, which is primarily due to the indeterminate relationship of fluxes and their source areas; at present our current understanding can explain 60-80% of the variance of the fluxes. The overall goal of this project is to incorporate information on topography and structure of vegetation (tree height, canopy depth, and foliage density) in footprint estimates and thereby substantially reducing the potential errors in the calculation of the CO2 and H2O budgets. The selected forested sites consist of the very few long-term flux stations within the boreal forest biomes and represent the three dominant species of the boreal forest (jack pine, black spruce, aspen). The combination of these three forest stands will provide data that is sufficiently representative to allow for upscaling to the boreal forest biome scale. The boreal forest constitutes the world's second largest forested biome (after the tropical forest) and plays an important role in regulating the climate of the northern hemisphere and in the global carbon cycle. The footprint model developed by the PI and widely used by the international community will be applied on long-term data sets to estimate the size and location of the area containing the sources or sinks (footprint) of CO2 and H2O fluxes measured at the three sites. The footprints will account for, and depend on, atmospheric conditions, such as wind speed and boundary layer stability, and surface characteristics, e.g. roughness. This footprint model is one of very few models that are valid over a huge range of stratifications and receptor heights. The major improvement of the footprint model will incorporate three-dimensional information on the structure of the forest, derived from airborne scanning LiDAR measurements, leading to exceptionally detailed high temporal resolution source information. Unlike data from passive sensors, the unique LiDAR data set provides information from within the tree canopy. The results will be used to analyse impacts of structure of vegetation and small changes in elevation on the net CO2 and H2O fluxes. The new understanding will assist future studies of upscaling from flux towers to the spatially heterogeneous boreal forest landscape and will reduce the uncertainty in the modelling of carbon budgets at local, regional and continental scale. It will lead to a greater understanding of local structural effects on carbon sources and sinks and thus the dynamics of carbon cycling and to major improvements of the description of these exchange processes in land surface models. Hence, the new insights will help reducing uncertainty in projections of climate change.

  • Funder: UKRI Project Code: NE/F001584/1
    Funder Contribution: 165,257 GBP
    Partners: KCL, Lund University, SDSU, CAF, University of Sheffield

    Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

  • Funder: UKRI Project Code: NE/E015212/1
    Funder Contribution: 389,323 GBP
    Partners: University of Guelph, University of St Andrews

    Iceland represents a natural laboratory for studying the colonization of freshwater habitats by fish since rivers and lakes all date from the end of the last Ice-Age less than 10,000 years ago. The North Atlantic provided a refuge for species such as arctic charr (Salvelinus alpinus) which invaded freshwater once the ice retreated. New habitats and the lack of competing species led to the appearance of different forms of Artic charr, called morphs. In particular, 27 discrete populations of dwarf charr have been identified with specialised feeding morphology that enables them to exploit the small larval fissures on the bottom of streams and lakes. Our Icelandic and Canadian partners have collected an enormous amount of data on each of the dwarf populations including, habitat characteristics (temperature and bottom type), diet, maximum body size, size and age at sexual maturity and cranial morphology. Other studies in progress on rapidly evolving DNA sequences we will enable us to determine the relationships between each population and estimate which ones arose independently allowing us to study the repeatability of evolution for populations living in similar habitats. Studies involving such diverse organisms as worms, flies and vertebrates suggest that poor nutrition alone is sufficient to produce dwarfism via effects on the signaling pathways controlled by the hormone Insulin-like growth factor-I (IGF-I): indicating a universal and conserved biological mechanism. Intriguingly, in the zebrafish, which is often used for studies of development, so-called 'knock-outs' of an IGF-binding-protein gene also caused alterations to the shape of the head which are reminiscent of those found in dwarf charr. We will therefore experimentally test the hypothesis that interactions between the environment and the IGF-hormone system during development can produce the specialised jaw and cranial morphology characteristic of the dwarf phenotype. Since early development in fish is entirely dependent on genetic messages passed through the egg yolk we will conduct experiments to determine whether it is the environment of the mother, the embryo or both that are important for producing fish with dwarf characteristics. Thingvallavatn, the largest and oldest lake in Iceland, contains four Arctic charr morphs, including a dwarf form, which are specialised to exploit different habitats. Laboratory breeding experiments have shown that the large differences in body size, morphology and life history such as the size at sexual maturity are heritable. This suggests that intense competition between morphs and reproductive isolation has resulted in natural selection and specialization for characters helping each morph to survive in their chosen environment. Previously we showed that dwarfism in the Thingvallavatn charr has resulted in a reduction in the number of muscle fibres in the trunk, which is thought to lower costs of maintenance relative to the ancestral charr. By studying a large number of Arctic charr populations (15 dwarfs and 5 generalists) we will test the generality of the hypothesis that the relative importance of developmental plasticity versus selection for setting muscle fibre number is related to the age and stability of the habitat and is different depending on whether there is competition with other morphs. The research is important because it addresses the fundamental question of how natural selection and plasticity operate to produce different forms of the same species at the level of physiological systems. The evolution of different morphs of the same species is relatively common and is found, for example, in sticklebacks and African cichclids. The practical application of this research is in understanding how the biodiversity of fish populations arises and how it may be conserved for future generations.

Advanced search in
Projects
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Searching FieldsTerms
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includes
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The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
5 Projects, page 1 of 1
  • Funder: UKRI Project Code: NE/F001673/1
    Funder Contribution: 15,818 GBP
    Partners: CAF, Lund University, SDSU, University of Leicester, University of Sheffield

    Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

  • Funder: UKRI Project Code: NE/F001681/1
    Funder Contribution: 184,956 GBP
    Partners: University of Reading, SDSU, Lund University, CAF, University of Sheffield

    Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

  • Funder: UKRI Project Code: NE/G000360/1
    Funder Contribution: 53,759 GBP
    Partners: Queen's University Canada, WLU, Swansea University

    Understanding the exchange of energy and gases between the earth's surface and the lower atmosphere is essential for answering many questions related to, e.g., the global carbon budget, ecosystem functioning, air pollution mitigation, greenhouse gas emissions, weather forecasting, and projections of climate change. However, uncertainties in carbon dioxide (CO2) and water vapour (H2O) budgets limit our ability to reproduce and project these exchange processes. Exchange processes are usually analysed based on micrometeorological measurements from tall flux towers, thought to be representative of large area averages. A limitation of this approach is that the actual source areas of these fluxes are not always known and that the impact of land-surface heterogeneity (at small or large scale) on the fluxes is not yet completely understood. The micrometeorological measurements of the major carbon flux networks around the world, such as Ameriflux, Canadian Carbon Program, CarboEurope (in which the UK plays a prominent role) and Oz-Net, are essential to validate global estimates of CO2 sources and sinks, to develop and validate land surface models and to understand the sensitivity of CO2 fluxes under changing climate conditions. Unfortunately, flux tower measurements currently suffer from substantial uncertainty, which is primarily due to the indeterminate relationship of fluxes and their source areas; at present our current understanding can explain 60-80% of the variance of the fluxes. The overall goal of this project is to incorporate information on topography and structure of vegetation (tree height, canopy depth, and foliage density) in footprint estimates and thereby substantially reducing the potential errors in the calculation of the CO2 and H2O budgets. The selected forested sites consist of the very few long-term flux stations within the boreal forest biomes and represent the three dominant species of the boreal forest (jack pine, black spruce, aspen). The combination of these three forest stands will provide data that is sufficiently representative to allow for upscaling to the boreal forest biome scale. The boreal forest constitutes the world's second largest forested biome (after the tropical forest) and plays an important role in regulating the climate of the northern hemisphere and in the global carbon cycle. The footprint model developed by the PI and widely used by the international community will be applied on long-term data sets to estimate the size and location of the area containing the sources or sinks (footprint) of CO2 and H2O fluxes measured at the three sites. The footprints will account for, and depend on, atmospheric conditions, such as wind speed and boundary layer stability, and surface characteristics, e.g. roughness. This footprint model is one of very few models that are valid over a huge range of stratifications and receptor heights. The major improvement of the footprint model will incorporate three-dimensional information on the structure of the forest, derived from airborne scanning LiDAR measurements, leading to exceptionally detailed high temporal resolution source information. Unlike data from passive sensors, the unique LiDAR data set provides information from within the tree canopy. The results will be used to analyse impacts of structure of vegetation and small changes in elevation on the net CO2 and H2O fluxes. The new understanding will assist future studies of upscaling from flux towers to the spatially heterogeneous boreal forest landscape and will reduce the uncertainty in the modelling of carbon budgets at local, regional and continental scale. It will lead to a greater understanding of local structural effects on carbon sources and sinks and thus the dynamics of carbon cycling and to major improvements of the description of these exchange processes in land surface models. Hence, the new insights will help reducing uncertainty in projections of climate change.

  • Funder: UKRI Project Code: NE/F001584/1
    Funder Contribution: 165,257 GBP
    Partners: KCL, Lund University, SDSU, CAF, University of Sheffield

    Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

  • Funder: UKRI Project Code: NE/E015212/1
    Funder Contribution: 389,323 GBP
    Partners: University of Guelph, University of St Andrews

    Iceland represents a natural laboratory for studying the colonization of freshwater habitats by fish since rivers and lakes all date from the end of the last Ice-Age less than 10,000 years ago. The North Atlantic provided a refuge for species such as arctic charr (Salvelinus alpinus) which invaded freshwater once the ice retreated. New habitats and the lack of competing species led to the appearance of different forms of Artic charr, called morphs. In particular, 27 discrete populations of dwarf charr have been identified with specialised feeding morphology that enables them to exploit the small larval fissures on the bottom of streams and lakes. Our Icelandic and Canadian partners have collected an enormous amount of data on each of the dwarf populations including, habitat characteristics (temperature and bottom type), diet, maximum body size, size and age at sexual maturity and cranial morphology. Other studies in progress on rapidly evolving DNA sequences we will enable us to determine the relationships between each population and estimate which ones arose independently allowing us to study the repeatability of evolution for populations living in similar habitats. Studies involving such diverse organisms as worms, flies and vertebrates suggest that poor nutrition alone is sufficient to produce dwarfism via effects on the signaling pathways controlled by the hormone Insulin-like growth factor-I (IGF-I): indicating a universal and conserved biological mechanism. Intriguingly, in the zebrafish, which is often used for studies of development, so-called 'knock-outs' of an IGF-binding-protein gene also caused alterations to the shape of the head which are reminiscent of those found in dwarf charr. We will therefore experimentally test the hypothesis that interactions between the environment and the IGF-hormone system during development can produce the specialised jaw and cranial morphology characteristic of the dwarf phenotype. Since early development in fish is entirely dependent on genetic messages passed through the egg yolk we will conduct experiments to determine whether it is the environment of the mother, the embryo or both that are important for producing fish with dwarf characteristics. Thingvallavatn, the largest and oldest lake in Iceland, contains four Arctic charr morphs, including a dwarf form, which are specialised to exploit different habitats. Laboratory breeding experiments have shown that the large differences in body size, morphology and life history such as the size at sexual maturity are heritable. This suggests that intense competition between morphs and reproductive isolation has resulted in natural selection and specialization for characters helping each morph to survive in their chosen environment. Previously we showed that dwarfism in the Thingvallavatn charr has resulted in a reduction in the number of muscle fibres in the trunk, which is thought to lower costs of maintenance relative to the ancestral charr. By studying a large number of Arctic charr populations (15 dwarfs and 5 generalists) we will test the generality of the hypothesis that the relative importance of developmental plasticity versus selection for setting muscle fibre number is related to the age and stability of the habitat and is different depending on whether there is competition with other morphs. The research is important because it addresses the fundamental question of how natural selection and plasticity operate to produce different forms of the same species at the level of physiological systems. The evolution of different morphs of the same species is relatively common and is found, for example, in sticklebacks and African cichclids. The practical application of this research is in understanding how the biodiversity of fish populations arises and how it may be conserved for future generations.