Lecture Set 1: Harnessing Diverse Data for Ecological Insights Across Scales

Task A

Author

AJ Smit

Published

June 28, 2020

Lecture Series Assignment: Harnessing Diverse Data for Ecological Insights Across Scales

We increasingly rely on a rich ‘landscape’ of data sources to untangle the complexities of ecosystems at local, regional, and global scales. This assessment task requires the class to critically examine the wide array of ecological data, their acquisition, integration, and the transformative role of technology in driving ecological research.

Objective: Develop a comprehensive understanding of the diverse data sources available to ecologists, their strengths and limitations, and how to harness them effectively to address research questions across scales.

Approach: You will prepare a set of lectures that will explore the landscape of ecological data, from open data repositories to field campaign datasets, and discuss the methodologies for integrating and analysing these diverse sources. The role of technology in advancing ecological research and the principles of open science will also be highlighted.

Due Date: From 13 June 2025

Lecture Series Structure

Create a lecture set covering the following key areas:

Topic 1: A History of Quantitative Ecology

In this lecture topic, I want you to discuss the origin of what has today become the field of quantitative ecology. You should go back to when someone first studied ecosystems or communities – in other words, the full complement of species within combined multiple populations occupying landscapes – and asked questions about what structures these communities across space and time. See if you can highlight some of the earliest studies that employed techniques involving the analysis of multiple species, and then track the progression of the field over time, highlighting key developments that have taken place. What is seen as state-of-the-art today? What are some of the most important discoveries made about how ecosystems work?

Topic 2: Challenges of Studying Multiple Species

In this lecture topic, I would like you to talk about the various challenges that quantitative ecologists face. These could include challenges around how people go about acquiring the data they need – such as data about species and the environmental conditions across the landscapes occupied by those species – all the way through to the analysis of the data they collect.

Topic 3: Case Studies

Please focus on two important and interesting case studies involving the analysis of quantitative ecological data. Discuss the methodologies employed in conducting these studies, outline the aims, objectives, and findings, and address the implications of these studies. Why are these studies interesting, and why did you decide to include them here? When you research these case studies, ensure they have been published in well-known journals and are supported by public data repositories. We may use these data in our lecture series to test, explain, and demonstrate various quantitative ecological methods, such as ordinations and cluster analyses.

Topic 4: Data Integration Methodologies

Describe the challenges and opportunities associated with integrating disparate data sources. Explain the importance of data standardisation, cleaning, harmonisation, and alignment in time and space.
Discuss various data integration methods, such as data fusion, data assimilation, and meta-analysis. Explain how these methods enhance ecological research by combining information from multiple sources.

Topic 5: Case Studies Across Scales

Choose three specific case studies or research scenarios that exemplify the use of open data and field campaign data to address ecological questions across different spatial and temporal scales.
For each case study:
- Briefly describe the research question and ecological context.
- Identify the specific data sources used (both open and field campaign).
- Explain how the data was integrated and analysed.
- Summarise the key findings and their broader ecological implications.

Topic 6: The Role of Technology

Explore the advancements in data analytics, machine learning, and computational modeling that have revolutionised the way ecologists access, analyse, and integrate data.
Discuss how these technologies facilitate the extraction of patterns, identification of drivers, and forecasting of ecological dynamics.
Provide examples of how technology has enabled ecological research that was previously not feasible (e.g., large-scale species distribution modeling, ecosystem service assessments).

Topic 7: Open Science and FAIR Principles

Explain the principles of open science, with a focus on open data and the FAIR principles (Findable, Accessible, Interoperable, Reusable).
Discuss how open science fosters collaboration, accelerates scientific discovery, and enhances the reproducibility of ecological research.
Critically reflect on the potential of open science to democratize access to ecological data and empower researchers in under-resourced regions or disciplines.

Topic 8: Future Directions

Speculate on the future of data-driven ecological research. How might advancements in technology and the adoption of open science further transform the field?
Identify emerging challenges and opportunities, such as the need for robust data management practices, ethical considerations surrounding data use, and the potential for citizen science to contribute valuable ecological data.

Assessment Criteria

This Task is not formally assessed.

Reuse

CC BY-NC-SA 4.0

Citation

BibTeX citation:

@online{smit,_a._j.2020,
  author = {Smit, A. J., and Smit, AJ},
  title = {Lecture {Set} 1: {Harnessing} {Diverse} {Data} for
    {Ecological} {Insights} {Across} {Scales}},
  date = {2020-06-28},
  url = {http://tangledbank.netlify.app/BCB743/tasks/Task_A1.html},
  langid = {en}
}

For attribution, please cite this work as:

Smit, A. J., Smit A (2020) Lecture Set 1: Harnessing Diverse Data for Ecological Insights Across Scales. http://tangledbank.netlify.app/BCB743/tasks/Task_A1.html.

--- title: "Lecture Set 1: Harnessing Diverse Data for Ecological Insights Across Scales" subtitle: "Task A" author: - "AJ Smit" date: '2020/06/28 (updated: `r Sys.Date()`)' --- ## Lecture Series Assignment: Harnessing Diverse Data for Ecological Insights Across Scales We increasingly rely on a rich 'landscape' of data sources to untangle the complexities of ecosystems at local, regional, and global scales. This assessment task requires the class to critically examine the wide array of ecological data, their acquisition, integration, and the transformative role of technology in driving ecological research. **Objective**: Develop a comprehensive understanding of the diverse data sources available to ecologists, their strengths and limitations, and how to harness them effectively to address research questions across scales. **Approach**: You will prepare a set of lectures that will explore the landscape of ecological data, from open data repositories to field campaign datasets, and discuss the methodologies for integrating and analysing these diverse sources. The role of technology in advancing ecological research and the principles of open science will also be highlighted. **Due Date**: From 13 June 2025 ### Lecture Series Structure Create a lecture set covering the following key areas: #### Topic 1: A History of Quantitative Ecology - In this lecture topic, I want you to discuss the origin of what has today become the field of quantitative ecology. You should go back to when someone first studied ecosystems or communities -- in other words, the full complement of species within combined multiple populations occupying landscapes -- and asked questions about what structures these communities across space and time. See if you can highlight some of the earliest studies that employed techniques involving the analysis of multiple species, and then track the progression of the field over time, highlighting key developments that have taken place. What is seen as state-of-the-art today? What are some of the most important discoveries made about how ecosystems work? #### Topic 2: Challenges of Studying Multiple Species - In this lecture topic, I would like you to talk about the various challenges that quantitative ecologists face. These could include challenges around how people go about acquiring the data they need -- such as data about species and the environmental conditions across the landscapes occupied by those species -- all the way through to the analysis of the data they collect. #### Topic 3: Case Studies - Please focus on two important and interesting case studies involving the analysis of quantitative ecological data. Discuss the methodologies employed in conducting these studies, outline the aims, objectives, and findings, and address the implications of these studies. Why are these studies interesting, and why did you decide to include them here? When you research these case studies, ensure they have been published in well-known journals and are supported by public data repositories. We may use these data in our lecture series to test, explain, and demonstrate various quantitative ecological methods, such as ordinations and cluster analyses. #### Topic 4: Data Integration Methodologies - Describe the challenges and opportunities associated with integrating disparate data sources. Explain the importance of data standardisation, cleaning, harmonisation, and alignment in time and space. - Discuss various data integration methods, such as data fusion, data assimilation, and meta-analysis. Explain how these methods enhance ecological research by combining information from multiple sources. #### Topic 5: Case Studies Across Scales - Choose *three* specific case studies or research scenarios that exemplify the use of open data and field campaign data to address ecological questions across different spatial and temporal scales. - For each case study: - Briefly describe the research question and ecological context. - Identify the specific data sources used (both open and field campaign). - Explain how the data was integrated and analysed. - Summarise the key findings and their broader ecological implications. #### Topic 6: The Role of Technology - Explore the advancements in data analytics, machine learning, and computational modeling that have revolutionised the way ecologists access, analyse, and integrate data. - Discuss how these technologies facilitate the extraction of patterns, identification of drivers, and forecasting of ecological dynamics. - Provide examples of how technology has enabled ecological research that was previously not feasible (e.g., large-scale species distribution modeling, ecosystem service assessments). #### Topic 7: Open Science and FAIR Principles - Explain the principles of open science, with a focus on open data and the FAIR principles (Findable, Accessible, Interoperable, Reusable). - Discuss how open science fosters collaboration, accelerates scientific discovery, and enhances the reproducibility of ecological research. - Critically reflect on the potential of open science to democratize access to ecological data and empower researchers in under-resourced regions or disciplines. #### Topic 8: Future Directions - Speculate on the future of data-driven ecological research. How might advancements in technology and the adoption of open science further transform the field? - Identify emerging challenges and opportunities, such as the need for robust data management practices, ethical considerations surrounding data use, and the potential for citizen science to contribute valuable ecological data. ### Assessment Criteria This Task is not formally assessed.