Comparing Migration Successes Between Groups in Acoustic Telemetry Studies

Overview

Teaching: 0 min
Exercises: 0 min

Questions

Objectives

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:

1. Run a migration analysis in actel
2. Determine the effects of different variables on survival
3. Explore diel patterns and migration speeds between groups
4. Produce publication-grade plots of our results

Expected prior knowledge:

1. Basic R coding
2. 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