Final Report Dataset

[Yutong-Shao]

Link to the dataset: https://fishbase.mnhn.fr/topic/List.php?group=4

Website that explains what it is: https://www.fishbase.se/manual/English/fishbasethe_diet_table.htm

Summary of the structure of the database: The DIET table comprises 15 columns and includes 41,260 entries, covering information for over 1,400 species, with data on more than 3,000 records. Its primary focus is on detailing the dietary composition of various fish species in specific locations and their corresponding trophic levels. Additionally, it includes some information such as the sampled fish's life stage.

Why this dataset is both biological and interesting: The dataset includes measurements of fish trophic levels, which reflect the roles of different fish species within the ecosystem. Studying the trophic structure of fish can help us explore which fish species occupy different levels of the food chain and understand the interactions between fish species, which is crucial for maintaining ecological balance and biodiversity.

Aims that could be addressed using this dataset: Based on the analysis of trophic levels data, combined with other datasets such as quantity data, geographic distribution, and habitat information, it can be used to investigate the factors influencing trophic levels and identify common characteristics among fish species within the same trophic level.

[DS4B-ANU]

Hi @Yutong-Shao, fishbase is already on the list of approved datasets, so this post is not necessary. You only need to do this if you are proposing to use a dataset that isn't on the approved list.

However, your last part (Aims) suggests that you will be using other datasets on quantity, geography, and habitat. If you want to use datasets not on the list you will need to post here with a full list of all the datasets you intend to use, so that we can assess whether your questions/aims and dataset(s) are appropriate.

As a more general comment, I'd like to see your questions/aims spelled out in enough detail that it's clear what you intend to do. Your post here is very general, and lacks any detail on what questions you are interested in addressing. It also lacks any references to the primary literature, which is what is required to make progress here.

[AdriaWu]

Research Aim: Analyzing the Impact of Air Pollution on Respiratory Diseases Across Different Countries

Link to the Dataset WHO Global Health Observatory

Background and Literature

1. Air Pollution and Health: Air pollution has been consistently linked to adverse health outcomes, particularly affecting respiratory health (Landrigan et al., 2018).
2. Global Disparities: There are disparities in air pollution levels and health outcomes across different countries and regions, which might be influenced by various socio-economic and environmental factors (Lelieveld et al., 2015).
3. Air Pollution and Respiratory Health: Numerous studies have established that air pollution, particularly particulate matter (PM2.5 and PM10), is a significant risk factor for respiratory diseases, including chronic obstructive pulmonary disease (COPD) and asthma (Guarnieri and Balmes, 2014).
4. COVID-19 Pandemic: The COVID-19 pandemic has had multifaceted impacts on air pollution and respiratory health, making it a relevant and timely factor to explore in this context (Venter et al., 2020).

Dataset: WHO Global Health Observatory Data: Provides data on air pollution levels (e.g., PM2.5 concentrations) and respiratory health outcomes (e.g., prevalence of COPD, asthma) across different countries and years.

The Structure of the Database and What it Contains:

Structure: The WHO Global Health Observatory provides a comprehensive database that is structured to include various health-related indicators across different countries and time periods.

Contents: It encompasses data on a wide array of health metrics, including but not limited to:

• Air pollution levels (e.g., PM2.5 concentrations)
• Prevalence and incidence of various diseases (e.g., respiratory diseases, infectious diseases)
• Health system indicators (e.g., healthcare access, healthcare expenditure)
• Socio-economic indicators related to health (e.g., GDP, urbanization)
• Global health targets and frameworks (e.g., Sustainable Development Goals)

Summary of Specific Questions/Aims

1. Temporal Analysis:

• Investigate how air pollution levels and respiratory disease prevalence have evolved over time in different countries.
• Identify periods or events that significantly influenced these trends.

2. Relationship Exploration:

• Determine the correlation or potential causation between air pollution levels (e.g., PM2.5 concentrations) and respiratory disease prevalence (e.g., asthma, COPD) across countries.
• Explore if certain pollutants have a stronger association with specific respiratory diseases.

3. Socio-Economic Modulation:

• Examine how socio-economic factors, such as GDP, healthcare infrastructure, and urbanization, influence the relationship between air pollution and respiratory health.
• Identify which socio-economic factors act as risk or protective elements in this relationship.

4. Disparity Analysis:

• Compare the dynamics of air pollution and respiratory health between high-income and low-income countries.
• Identify factors contributing to disparities in air pollution exposure and respiratory health outcomes.

5. Impact of Global Events:

• Analyze the influence of significant global events, such as the COVID-19 pandemic, on air pollution levels and respiratory health.
• Determine if such events exacerbate or mitigate the effects of air pollution on respiratory diseases.

6. Policy and Intervention Evaluation:

• Assess the effectiveness of air quality policies and interventions in reducing pollution levels and improving respiratory health.
• Provide recommendations for policy enhancements based on data-driven insights.

[Yutong-Shao]

Hi @Yutong-Shao, fishbase is already on the list of approved datasets, so this post is not necessary. You only need to do this if you are proposing to use a dataset that isn't on the approved list.

However, your last part (Aims) suggests that you will be using other datasets on quantity, geography, and habitat. If you want to use datasets not on the list you will need to post here with a full list of all the datasets you intend to use, so that we can assess whether your questions/aims and dataset(s) are appropriate.

As a more general comment, I'd like to see your questions/aims spelled out in enough detail that it's clear what you intend to do. Your post here is very general, and lacks any detail on what questions you are interested in addressing. It also lacks any references to the primary literature, which is what is required to make progress here.

Thank you so much for your explanation and comments.

All the datasets I have used are sourced from FishBase. And here is some additional information regarding the aim part:

Question 1: Which measurable indicators in fish, such as length, weight, and fat content, are associated with trophic levels?

Justification: Among all species, trophic position was found to have a positive correlation with maximum length (Romanuk et al., 2011). However, there is a lack of research indicating whether other measurable indicators in fish, such as body weight and fat content, are correlated with trophic level.

• Romanuk, T.N., Hayward, A. and Hutchings, J.A. (2011), Trophic level scales positively with body size in fishes. Global Ecology and Biogeography, 20: 231-240. https://doi.org/10.1111/j.1466-8238.2010.00579.x

Question 2: How is the range of trophic levels distributed among different fish species when considering their feeding habits? Can we further investigate the interrelationships among different fish species?

Justification: Napazakov et al. (2015) proposed that the position of fish in the predator group is influenced by its primary competitive strength in terms of ecological niche, including the range of ecological regions it inhabits and its food sources. Fishes with different feeding habits exhibit distinct trophic levels, and the diet_items table records the dietary preferences of different fish species, providing an opportunity for visualizations and exploring interrelationships among fish species.

• Napazakov, V.V. Trophic status and trophic interactions of common predatory fish species of the West Kamchatka shelf. J. Ichthyol. 55, 86–96 (2015). https://doi.org/10.1134/S0032945215010130

Question 3: Is there variation in the distribution of trophic levels among different climatic regions and ecosystem types? Can factors such as salinity be considered in a multifactor analysis?

Justification: Changes in trophic architecture are coincident with natural systems affected by evolving exploitation, prey availability, and climate (Durante et al., 2022). The influence of environmental factors on fish trophic structure is complicated and may encompass the combined effects of temperature, habitat type, and salinity, among others. Hence, exploring whether multiple environmental factors collectively influence the trophic levels of fish is worthwhile.

• Durante, L., Wing, S., Ingram, T. et al. Changes in trophic structure of an exploited fish community at the centennial scale are linked to fisheries and climate forces. Sci Rep 12, 4309 (2022). https://doi.org/10.1038/s41598-022-08391-x

Question 4: Building upon the analyses above, from the perspective of ecosystem energy flow, could you provide some recommendations for the conservation of fish species?

[DS4B-ANU]

@Yutong-Shao and @AdriaWu, if your database is already on the approved list, you do not need to post here. Just start filling in your reports!