PhD in Biological Sciences and Applied Biotechnologies

Innovative Methods in Environmental Monitoring

Innovative Methods in Environmental Monitoring

Academic year 2024/2025

Sommario del corso

• Course objectives
• Results of learning outcomes
• Program
• Course delivery
• Learning assessment methods
• Notes
• Teaching tools
• Course card

Course objectives

Recent developments have opened up new possibilities for environmental monitoring, resulting in new discoveries and significant advancements in ecological research. Moreover, technological developments have facilitated a growth in biodiversity monitoring through Citizen Science programmes, in particular due to the availability of apps that can automatically identify species.

The objectives of this course are to provide the students with practical experience of the latest developments in data collection and subsequent data processing within a wider background of Citizen Science monitoring and Big Data. There are two general themes (i) Biodiversity monitoring methods – we will demonstrate the use of Passive Acoustic Monitoring (invertebrates and birds) and camera traps (mammals) to survey animals, including data management and the use of software for species identification. We will also demonstrate the use of freely available apps for mobile phones that can identify species through sounds (birds) and photographs (e.g. trees, butterflies), encouraging the students to critically assess the advantages and disadvantages of the use of such methods for monitoring species populations. (ii) Remote sensing and biodiversity databases – we will present the latest methodological advancements in remote mapping of habitats and landscapes, including the use of drones and how such data can interface with GIS databases. We will review the most recent developments in open access biodiversity monitoring databases, with practical guidance on sourcing and downloading these databases, and their use in scientific research.

Results of learning outcomes

Knowledge and Understanding

• A grounding in the key methodological developments in environmental monitoring
• An understanding of the underlying principles of the use of acoustic monitoring and camera traps to monitor animals
• An understanding of the advantages and disadvantages of the use of apps for plant and animal species identification and monitoring
• An understanding of the importance of remote sensing to monitoring vegetation dynamics, ecosystem health, and climate impacts
• Knowledge of the importance of key online biodiversity databases, and their associated pros and cons

Applying knowledge and understanding

• The basic ability to use diverse monitoring techniques (acoustic monitoring and camera traps) in the field, and to analyse and interpret data arising from their use
• The ability to use common mobile phone apps to identify species automatically based on photos and sounds, and to critically assess the efficacy of such methods
• The basic ability to use remote sensing data as a tool in environmental monitoring
• The ability to locate, download and interpret freely available online biodiversity databases

Communication

• Improved clarity of presentation of innovative research methods in scientific presentations and publications

Program

1. Decoding Nature's Sounds: Exploring Passive Acoustic Methods in Invertebrate Research (Casacci)
Recent advances in technology have transformed wildlife monitoring, creating new possibilities for biodiversity assessment and ecological research. This module provides an in-depth look at passive acoustic monitoring (PAM), with a focus on its use in surveying terrestrial invertebrates. The module will examine the development and expansion of PAM studies across various research topics, animal groups, and methodologies, and synthesizes current applications, emerging trends, key challenges, and future directions.

2. Innovative Methods in Acoustic Bird Monitoring (Caprio)

Innovative acoustic bird monitoring employs advanced techniques and instruments to study avian populations and behaviour. Passive Acoustic Monitoring (PAM) captures bird sounds over extended periods, even in remote areas. Machine learning algorithms, including deep learning, are used for automatic species identification from these recordings, reducing manual analysis time, but they're not always reliable. Real-time monitoring through smart sensors allows immediate data collection and analysis. During this module, we will learn how to deploy recorders and how to use bioacoustic software for sound visualization and pattern recognition in birds. 

3. Camera-Traps in Eco-Ethological Research (Bertolino)
Camera traps have emerged as fundamental tools in eco-ethological research, enabling detailed investigations into the presence and distribution of animal species, particularly mammals. Beyond basic monitoring, these devices allow researchers to study space use, activity patterns, species interactions, behavioural traits, and other ecological and ethological aspects. This seminar will introduce the diverse range of studies that can be conducted using camera traps, highlighting their versatile applications across numerous research fields.

4. Biodiversity at your Fingertips: Using Apps for Biodiversity Monitoring (Alba)
In recent years, mobile applications have transformed the way in which we interact with biodiversity on a daily basis. Apps like Merlin, which uses advanced machine learning to analyze bird sounds via spectrograms, PlantNet or iNaturalist, which apply computer vision techniques for real-time image recognition, offer powerful, accessible methods for species identification. This module will introduce these applications, focusing on their functionalities, data accuracy, and practical applications in environmental surveys. Students will gain insights into how these tools use machine learning and image processing to facilitate biodiversity monitoring and ecological research, exploring both the possibilities and limitations of app-generated data for scientific purposes.

5. Remote Sensing for Vegetation Monitoring (Oddi)
Remote sensing (RS) has become an essential tool for vegetation monitoring due to its ability to provide large-scale, repeatable, and multi-temporal data. This capability allows researchers and conservationists to monitor vegetation dynamics, assess ecosystem health, and study climate impacts effectively. The module will introduce some key aspects of remote sensing, such as the different types of sensors currently available, and the potential applications of this technique in vegetation monitoring, presenting some of the most common vegetation indices.

6. Navigating Species Distribution Data Resources (Bonelli)
At the moment, the distribution data of Italian, European and global fauna and flora are collected in databases that have very different characteristics in terms of scale, definition, possibility of identification errors, geographical precision and accessibility. This module will give an overview of all potential online sources of species distribution data, including details on access and use of the data, and the potential biases that are present for a given dataset.

Course delivery

Teaching in person will consist of traditional lessons interspersed with practical demonstrations of various methods in the field, or the use of software and databases in the lab, followed by class discussions.

Learning assessment methods

Qultitative assessment based on discussions with students.

Notes

The course is divided into 6 modules, each of 2 hours duration. The course will run in late April/early May 2025. The timetable will follow in due course.

The modules (and the module leader) will be as follows:

1. Decoding Nature's Sounds: Exploring Passive Acoustic Methods in Invertebrate Research (Luca Casacci)

2. Innovative Methods in Acoustic Bird Monitoring (Enrico Caprio)

3. Camera-Traps in Eco-Ethological Research (Sandro Bertolino)

4. Biodiversity at your Fingertips: Using Apps for Biodiversity Monitoring (Riccardo Alba)

5. Remote Sensing for Vegetation Monitoring (Ludovica Oddi)

6. Navigating Species Distribution Data Resources (Simona Bonelli)

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