It’s finally time to announce the winners of the WILDLABS Awards 2024! 

From December 1 to January 14, we received a whopping 194 applications for this year’s WILDLABS Awards. The WILDLABS team reviewed each and every application, then sent the top 45 submissions to our partners at Arm to narrow the list down even further. (For more statistics around the awards, click here!)

An external judging panel spent 2 weeks reviewing the 30 final applications, where they ultimately selected 13 awardees. While we initially intended to give out 14 awards, the quality of the 60k applications was so high that we combined two of the 30k grants into a third 60k grant!

Today, we are so pleased to officially announce the winners of this year’s WILDLABS Awards! 

We'll be publishing discussion threads for each project over the next few days, which will give WILDLABS members the opportunity to chat with each team about their work. 

Meet the winners 

10k Awardees 

BumbleBuzz (@JeremyFroidevaux and @DarrylCox )

Using advances in acoustic sensors and AI, the project aims to create a bioacoustics toolbox for efficient bumblebee monitoring, with key objectives including the creation of a sound library, training of AI algorithms for species/behaviour recognition, and field testing for conservation applications

Click HERE to know more about this project and discuss with the Awardees. 

Underwater Passive Acoustic Monitoring (UPAM) for Threatened Andean Water Frogs (@Mauricio_Akmentins, @SoleG, @Martincho, @palomas) 

This project is developing the first Underwater Passive Acoustic Monitoring (UPAM) program to assess the conservation status and long-term monitoring of population trends of an Andean water frogs in the high-altitude arid environment of Central Andes in northwest Argentina.

Click HERE to know more about this project and discuss with the Awardees. 

BoutScout: Monitoring System for Avian Nesting Behavior Studies (@JorgeLizarazo, @jcguerra10, @hefca)

The BoutScout merges a multi-sensor datalogger with AI post-analysis to transform bird nesting studies, enhance data accuracy, and promote conservation awareness. Our project introduces a low-cost, multi-sensor datalogger combined with AI analysis that  enables comprehensive monitoring of various environmental factors impacting nest behavior, such as light, humidity, and movement. 

Click HERE to know more about this project and discuss with the Awardees. 

GreenCrossingAI: Wildlife Conservation using Sustainable ML (@bernie318, @magerk)

We will implement green ML model pipelines using low energy consumption to analyze camera trap data for pre-construction monitoring of a proposed wildlife crossing of Interstate-5 in southern Oregon.

Click HERE to know more about this project and discuss with the Awardees. 

Innovative Sensor Technologies for Sustainable Coexistence: Advancing Crocodilian Conservation and Ecosystem Monitoring in Costa Rica (@maggiemcgreal, @YoungHo_Shin, @Christian4800, @JoshuaLasseigne)

This project aims to develop custom multisensor satellite trackers to monitor the behaviour of crocodiles in Costa Rica, utilizing machine learning algorithms to generate a sensor ethogram based on motion data collected by the trackers, thereby evaluating how crocodiles respond to human disturbances.

Click HERE to know more about this project and discuss with the Awardees. 

Enhancing Pollinator Conservation through Deep Neural Network Development (@eferguson)

Our project employs cutting-edge technology, deep neural networks, and open-access sharing to monitor and understand pollinator-plant interactions, benefiting wider conservation tech efforts.

Click HERE to know more about this project and discuss with the Awardees. 

Fostering Bat conservation and Citizen science in Zimbabwe (@Abigail, @Ropafadzo, @Ronald)

The project aims to promote bat conservation and citizen science in Zimbabwe by establishing three bat groups in three provinces and training individuals to use bat detectors and Kaleidoscope software effectively. This project will enhance monitoring efforts and generate valuable data on bat species distribution, population size, and behavior.

Click HERE to know more about this project and discuss with the Awardees. 

30k Awardees 

Developing AudioMoth for the detection of infrasonic elephant rumbles (@Andrew_Hill, @alex_rogers, @Anthony_Dancer, @LydiaKatsis, @LewisRowden)

This collaborative project between Open Acoustic Devices (OAD) and the Zoological Society of London (ZSL) will extend the frequency range of AudioMoth to detect low-frequency sounds, using the case study of the forest elephant; a cryptic and endangered species that produces a low-frequency rumble. 

Click HERE to know more about this project and discuss with the Awardees. 

TimeLord: A low-cost, low-power and low-difficulty timer board to control battery-powered devices (@Rob_Appleby, @Alasdair, @ClaireP, @bucknall, @gfo974)

A <US$50, 1-4 channel timer PCB designed around powerful Arm architecture, capable of sophisticated timing (on/off) schedules for a wide variety of battery-powered sensors and recording systems including animal-borne devices.

Click HERE to know more about this project and discuss with the Awardees. 

FinDrop: Accessible Acoustic Monitoring for Mesophotic Marine Environments (@MattyD797, @stefpap, @Rhinecanthus, @dantzker, @aldemar) 

We will innovate on an open-source and affordable fish sound recorder (0-10kHz) for deep reef environments, empowering effective monitoring and exploration of marine protected areas, including biodiversity hotspots, spawning grounds, and species occupancy.

Click HERE to know more about this project and discuss with the Awardees. 

60k Awardees 

No-code custom AI for camera trap species classification (@emilydorne, @pbull, @katie_wetstone, @dmorris)  

This project will provide conservationists with a code-free way to train custom species classification models on camera trap image data, saving countless hours of human review time currently used to process the data and getting conservationists to the ecological outputs they care about faster.

Click HERE to know more about this project and discuss with the Awardees.  

MothBox (@Hubertszcz, @hikinghack, @mothyash)

We are developing the MothBox: a tool that uses open science hardware and computer vision to make monitoring moths and other nocturnal insects more accessible to non-specialists, and more scalable for scientific and conservation purposes.

Click HERE to know more about this project and discuss with the Awardees. 

Mobilizing Motus (@asmith, @smackenzie, @CatherineJardine, @dlepage, @sarahdavidson)

We aim to provide innovative software solutions to sustain exceptional growth of the Motus Wildlife Tracking System, and ensure that data flow limitations, database size, and complexity does not present a barrier to practitioners making conservation breakthroughs on the ground.

Click HERE to know more about this project and discuss with the Awardees.