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2024 OTN Symposium Early Career Researcher Workshops
This is a stub site meant to support the Early Career Researcher workshops at the 2024 OTN Symposium. There are four workshops, each of which requires a different set of code and data. You can find instructions to download the code on the setup page.
Additionally, each workshop has its own episode page for archival purposes that contains all the lesson material currently available to OTN, as well as the submitted abstract for he workshop.
Key Points
Comparing Migration Successes Between Groups in Acoustic Telemetry Studies
Overview
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This page archives the lesson materials for “Comparing Migration Successes Between Groups in Acoustic Telemetry Studies”, given by Hugo Flávio at the 2024 OTN Symposium ECR Workshop.
Abstract
As technology becomes more intertwined with how we produce knowledge in a given field, the field must modernize its approach to analysis and synthesis of that data. In animal tracking, these fine-scale, non-repeatable observations can be vital to informing policy and action. Subject matter experts in the field of biology and ecology are being pressed into service as database designers, statisticians, and software developers as the volume and velocity of real-world data increases. This pattern calls for a coordinated and coordinating response. The novel efforts of a community of technically inclined movement ecologists can be supported, organized, and optimized for wide adoption using a set of software development best practices and an open-source, FAIR philosophy. Here, we lay down the foundations for a new, unified framework that will harness the combined power of the researchers producing and maintaining tools, and at the same time enhance and broaden the data exploration and analysis capabilities of movement ecologists across the globe.
Acoustic telemetry is often used to track coordinated migrations. As animals move through several gates of receivers, researchers can then determine migration success, migration speed, diel preferences, among others. Often, these studies include comparisons between different study groups (e.g. different species, different sources, different release sites, etc). In this workshop, we will explore how to analyze these data, and then how to model our variables to obtain the results we would like to publish.
Learning objectives:
- Run a migration analysis in actel
- Determine the effects of different variables on survival
- Explore diel patterns and migration speeds between groups
- Produce publication-grade plots of our results
Expected prior knowledge:
- Basic R coding
- Basic statistics
Code The code and data are available at this repository. Instructions for accessing the GitHub repository are on the setup page.
Key Points
Estimating Detection Efficiency of Acoustic Receivers Using glatos
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This page archives the lesson materials for “Estimating Detection Efficiency of Acoustic Receivers Using glatos”, given by Ben Hlina at the 2024 OTN Symposium ECR Workshop.
Abstract
Currently there are multiple methods used to estimate detection efficiency of acoustic receivers. Typically, these methods use transmitter drift tests or stationary deployed transmitters/receivers at predetermined distances (e.g., 100, 250 m, ect.) from a representative receiver to estimate short-term detection efficiency. There are, however, limited protocols and knowledge about deploying and understanding detection efficiency of acoustic receivers over longer durations such as a multi-year study. Therefore, we developed a standard operating procedure and a function in the package glatos to assist in understanding changes in detection efficiency over the course of a two-year fish movement study. We observed daily and seasonal changes in detection efficiency that might not be incorporated into existing and future acoustic telemetry studies. We recommend users to follow the developed operating procedures to account for changes in the detection efficiency of acoustic receivers over their study period. The goal of this workshop is to: 1) walk users through what detection efficiency is, what variables influence it, and how to deploy and design an effective detection efficiency methodology to incorporate into your study; 2) how to use a new functions in glatos and other packages to preliminary estimate detection efficiency; and 3) demonstrate how to estimate daily detection efficiency over the course of study period at a given distance. Attendees will leave the workshop having a better understanding of how acoustic receivers operate, how to use a new function in glatos, and how to design and deploy their own detection efficiency study within their overall study.
The estimating detection efficiency of acoustics receivers using glatos workshop will entail users learning how acoustic transmitters and receivers work, what influences the ability of an acoustic receiver to hear a detection, and how to design a preliminary and long-term detection efficiency study. Users will be guided through a vignette that has been recently added to the package glatos on how to effectively calculate initial detection efficiency at given locations (e.g., 100, 250, 500, and 750 m away from a receiver) over a short time period (e.g., 24 hr). This vignette will also guide users through using a new function in glatos that allows the user to estimate the distance that can be expected for a given detection efficiency (e.g., 50%) to occur based on preliminary data. Lastly, users will walk through how to redeploy sentinel transmitters to further estimate detection efficiency over the course of their study and will be shown an example of what they could potentially see for a given study system.
Code
The code and data are available at this repository. Instructions for accessing the GitHub repository are on the setup page.
Presentation Slides The Powerpoint Presentation (.pptx file) for Ben Hlina’s workshop can be downloaded from this link.
Key Points
Using GAMs for Analysing Movement Data
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This page archives the lesson materials for “Using GAMs for Analysing Movement Data”, given by Eric Pedersen at the 2024 OTN Symposium ECR Workshop.
Abstract
The first hour of the workshop will focus on what a GAM is, and the core concepts needed to interpret GAMs (basis functions, penalties, restricted maximum likelihood), with a focus on movement-relevant examples. The second hour of the workshop will focus on specific issues with applying GAMs to high-frequency movement data (handling big data sets, dealing with strong spatiotemporal autocorrelation in observations), as well as using GAMs to model special types of data.
The workshop will use the R package mgcv for fitting and estimating GAMs, and the gratia package for visualizing and interpreting GAM outputs. Attendees are expected to have at least some working experience with Generalized Linear models, and some familiarity with coding in R.
The workshop will include both lecture and live coding, with data and code provided to attendees to follow along. Please bring a laptop with R and Rstudio (or other IDE) installed, running at least version 4.1 of R. Attendees should also install the gratia package prior to the workshop.
Code
The code and data are available in this ZIP archive. You can access them by downloading and extracting the archive, then opening the .Rproj file in RStudio.
Key Points
Machine Learning with Telemetry Data
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This page archives the lesson materials for “Machine Learning with Telemetry Data”, given by Jake Brownscombe at the 2024 OTN Symposium ECR Workshop.
Abstract
Machine learning approaches are highly useful for characterizing the underlying structure of complex telemetry datasets and developing accurate predictive models of ecological systems. This workshop will cover a range of basic to more advanced machine learning techniques with acoustic telemetry data, with the aim of providing useful knowledge and analytical code for those with varied experience levels in this field. This is a re-occurring workshop that has been presented previously at OTN and GLATOS meetings, with new developments and contributions with each iteration.
This workshop covers applications of machine learning techniques to acoustic telemetry data, from the basics to some more advanced techniques. Students and researchers of all levels may find value here, in exploring the underlying structure of their telemetry data and developing accurate predictive models of ecological systems.
Code
The code and data are available at this repository. Instructions for accessing the GitHub repository are on the setup page.
Key Points