This module introduces students to the principles and applications of environmental modelling. It covers fundamental modelling concepts, including data handling, analysis, processing, basic model development and evaluation. The teaching approach emphasises theoretical knowledge, data analysis and practical computer-based lab work using GIS and popular programming languages and environments for modelling (eg. R, Python). Real-world datasets relevant to different types of ecosystems will be utilised to address challenges and assess environmental processes. Through a blend of theory and practical exercises, students will develop essential modelling skills to help manage ecosystems, which is crucial for environmental management relating to sustainability and climate change mitigation.

1. Critically describe and evaluate fundamental environmental modelling concepts and their significance in ecosystem management, sustainable land use and/or climate change mitigation.
2. Apply and justify appropriate data handling, analysis, and processing techniques for environmental modelling, including the selection, integration and evaluation of relevant environmental datasets.
3. Compare and critically evaluate the application of empirical, statistical, process-based, and biogeochemical modelling approaches across different spatial and temporal scales, considering assumptions and limitations.
4. Develop and assess environmental models and their outputs in conjunction with geospatial approaches and real-world datasets, including calibration, validation, sensitivity analysis and interpretation of uncertainty.
5. Critically appraise and communicate the practical application of environmental modelling for addressing environmental challenges such as sustainability, climate change mitigation, ecosystem protection, and/or land-use management, acknowledging limitations and implications for decision-making.

This module introduces Machine Learning (ML)-powered geospatial analyses to enhance natural resource and ecosystem service management, with a focus on practical Geographic Information System (GIS) applications. Learners will gain theoretical knowledge and applied computer skills to utilise ML-driven geospatial approaches with a focus on the environmental sector. Topics include GIS and basic Remote Sensing (RS) concepts for classification applications, geospatial analyses, mapping complex geospatial data, and classifications with integrated application of ML-powered geospatial analysis toolkits/plugins, including relevant basic modelling approaches.

1. Critically evaluate essential GIS concepts and their role in managing, analysing and visualising spatial data for environmental management applications.
2. Appraise fundamental Remote Sensing (RS) concepts for classification tasks alongside basic modelling approaches to interpret and process geospatial data in environmental contexts.
3. Apply and justify the use of geospatial analysis and mapping techniques using industry-standard GIS software to generate spatial insights and produce professional-quality maps that support environmental decision-making.
4. Select and implement appropriate geospatial analytical toolkits and extensions within GIS environments, including traditional statistical and machine learning approaches for environmental assessment and spatial modelling.
5. Develop and integrate technical, analytical and interpretative skills to critically evaluate, communicate and justify the results of geospatial analyses and classifications for informed environmental management decisions.

This module introduces students to the concepts of SMART sensing and the Internet of Things (IoT) in environmental monitoring and management. It focuses on integrating advanced sensing technologies with IoT solutions to effectively monitor and manage environmental resources, providing a data-supported framework for decision-making and sustainability. The course emphasises theoretical knowledge, principles of measurement, monitoring needs and sensor selection, data analysis and practical-based work for data gathering and processing. Additionally, the module incorporates modelling elements and system design principles to enhance the learners' understanding and application of smart sensing technologies. Students will develop foundational skills to analyse, interpret and critically evaluate data from smart sensors, and to design and assess IoT-enabled environmental monitoring systems that support informed and sustainable decision-making.

1. Compare and critically discuss the basic principles of IoT architecture and SMART sensing and their significance for environmental management.
2. Contrast different types of environmental sensors and justify their applications in monitoring contexts.
3. Assess and evaluate how IoT devices collect, process and transmit environmental data for analysis, including architectural considerations (e.g. edge and cloud computing).
4. Appraise and apply modelling and data processing techniques (e.g. time-series analysis, anomaly detection, or basic predictive approaches) to interpret environmental sensor data and support informed decision-making.
5. Design and critically evaluate an IoT-based environmental monitoring system, considering sensor selection, deployment strategy, data workflows, uncertainty, scalability and relevant ethical/governance considerations.

This module will introduce students to the principles underpinning molecular ecology and practical components of biomolecular methodologies which apply to environmental sciences. Basic and advanced PCR-based methods will be applied in the laboratory using samples collected from both aquatic and terrestrial matrices. Focus will also be placed on the use of real-time PCR protocols for the detection, identification and quantification of specific microorganisms and the application of metabarcoding protocols for community structure assessments. Case studies will be reviewed through interactive workshops, familiarising the learners with state-of-the-art approaches used for applied molecular ecology and biosurveillance for community diversity analyses and their implications for conservation initiatives.

1. Assess and evaluate the main principles underpinning advanced DNA-based analyses in the context of their suitability for specific molecular ecology and biosurveillance applications.
2. Perform field sample acquisition of various biological matrices and subsequent downstream purification processes for nucleic acids based molecular analyses.
3. Appraise and contrast the typology of various nucleic acids based strategies applied in a range of environmentally relevant aquatic and terrestrial case studies.
4. Integrate and interpret environment-derived molecular data generated via nucleic acids focused laboratory experiments.

This module will equip the students with the morphotaxonomic skills needed to identify key biological species whose presence is used as a proxy to determine the ecological quality status of specific natural habitats. Field surveys will be undertaken to collect a variety of biological specimens (e.g. protists, flora, fauna) found in aquatic and terrestrial ecosystems. Samples will be examined in the laboratory through the use of specialist taxonomic keys and microscopy analysis for morphometric measurements. Complementary DNA-based protocols will be used to reinforce the identification diagnoses (e.g. based on mitochondrial gene fragment sequencing). Case studies will be reviewed through interactive workshops, offering the students relevant practical insights on the conduct of community diversity assessments for applied ecological management, conservation and biosurveillance initiatives.

1. Integrate ecological field sampling design and strategies aimed at surveying biodiversity in a range of aquatic and terrestrial environments.
2. Catalogue, identify and evaluate at genus/species level key aquatic and terrestrial bioindicators of ecosystem health and quality in the context of relevant environmental policies and biosurveillance.
3. Appraise, select and apply genetic marker tools for the confirmation of biological identification relating to taxonomic groups of high interest for environmental quality and health assessments.
4. Classify and assess the functional and diversity structures of biological communities in pristine and pollution-impacted habitats in both aquatic and terrestrial environments.

This module requires the learner to develop research competencies aligned with cognisant topics in environmental science, integrating elements of research methodologies and articulating core thematics into the development of a specific research proposal. The module will equip students with a range of theoretical insights and practical skills necessary for good research practice. This activity encourages innovation and exploratory learning, laying the foundations for the subsequent module Research Project. This unit of learning exposes students to the application of research methodologies with the view to developing independent self-learning, critical thinking and analytical skills. The formal requirements of a dissertation will be outlined in order to prepare the student for the process of writing a MSc research thesis.

1. Appraise the steps involved in a research process and select and implement relevant research methodologies.
2. Communicate effectively research outcomes using a variety of methods (written and oral).
3. Evaluate critically the scientific literature related to environmental science thematics.
4. Develop a research proposal and study design to address a research question contextualised to an environmental science theme.

This module introduces the students to the risks posed by alien invasive species (IAS) in relation to environmental quality and health aspects. A number of both aquatic and terrestrial key case studies relevant to the Irish, European and international contexts will be reviewed and discussed within the framework of emerging regulatory initiatives developed to better monitor and sustainably manage biological invasions in ecosystems at risk. Field sampling considerations and the incorporation of emerging eDNA-based technologies into IAS-targeting monitoring and research initiatives will be integrated in the case studies reviewed within interactive workshops, thereby equipping the learner with state-of-the-art know-how to deal with bioinvasion scenarios.

1. Evaluate key policy drivers relevant at national and international levels to the monitoring and management of bioinvasions in both aquatic and terrestrial environments.
2. Interrogate biodiversity databases and extract, categorise and process data to retrieve and analyse biogeographic distribution patterns of specific IAS of interest.
3. Recognise, analyse and interpret trends in the biogeographical distributions of IAS of concern in aquatic and terrestrial ecological niches of high ecosystem service value.
4. Critically contrast and appraise recent bioinvasion monitoring and management strategies introduced in aquatic and terrestrial systems both at national and international levels.

This module introduces students to core data analysis principles and practices tailored to environmental science applications. It provides foundational skills in data processing, statistical analysis and interpretation using popular programming languages and statistical software (eg. R, Python, SPSS), supporting the analytical needs applicable to evidence-based practice and impactful environmental research. Students will explore research methodologies, statistical tools and critical appraisal techniques, preparing them to design and implement robust research projects. Through a blend of theoretical and practical learning, students will learn to manage, visualise and analyse environmental datasets so as to address contemporary challenges in environmental sciences.

1. Critically evaluate research methodologies relevant to environmental sciences, demonstrating the ability to describe key concepts in data analysis (data types, sampling, statistical distributions…).
2. Interpret statistical outputs, effectively communicating findings and their implications for environmental science practice through graphs, tables and reports.
3. Develop theoretical and applied knowledge of statistical principles and their application to environmental science data and contexts.
4. Integrate data processing, transformation, analysis and visualisation skills using basic programming environments (eg. R and Python) to support data analysis workflows in environmental contexts.

Learners will undertake an approved research project under the direction of an internal academic supervisor (with, where applicable, scope for collaboration with some of the various external stakeholders of the programme) using knowledge skills and competencies acquired at earlier stages of the Master's Programme. Students will further develop the knowledge and competencies associated with successfully developing and implementing a research plan. Learners will execute a research proposal using appropriate methodologies, analyse data, evaluate findings, infer conclusions, demonstrating self-direction and originality of thought. As such, the students will further augment their capacity to adequately manage research planning, timelines, delivery and communication. The learner will submit a thesis in the form of a scientific paper aligned with the format of an original research article drafted for submission to an internationally peer-reviewed journal. Critical thinking skills will be further honed through the analysis of research data and the presentation of research findings in the format of thesis, conference-style poster, presentation and viva voce.

1. Construct an independent research project within a structured supervision framework.
2. Evaluate critically sourced academic literature to draw evidence-based inferences.
3. Interpret collected data qualitatively and quantitatively using appropriate statistical methods.
4. Defend coherently inferred conclusions and recommendations from research findings.
5. Develop the communication skills to present, explain and justify aspects of the research project via workshop discussion, presentation, poster and viva voce formats.
6. Integrate professional practice competencies including project management, activity risk assessments, time management and scientific writing style.
7. Compose a thesis underpinned by environmental science thematics meeting adequate standards of technical and theoretical expertise.