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
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 discuss with the Awardees about this project.
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 discuss with the Awardees about this project.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
26 March 2024 5:17pm
Lars Holst Hansen
Aarhus University
26 March 2024 10:42pm
Big congratulations to all grant awardees! Looking much forward to following the various projects!
Adrien Pajot
WILDLABS
Fauna & Flora
29 March 2024 3:38pm
In reply to Lars_Holst_Hansen 26 March 2024 10:42pm
Big congratulations to all grant awardees! Looking much forward to following the various projects!
They will soon present their project on the website, and you will be able to discuss it with them!
27 March 2024 7:01am
Congratulations! I can´t wait so see what you are building. TimeLord sounds great @Rob_Appleby , @Alasdair I love the "low-difficulty" specification ;-)
Alasdair Davies
Arribada Initiative
31 March 2024 11:50pm
In reply to capreolus 27 March 2024 7:01am
Congratulations! I can´t wait so see what you are building. TimeLord sounds great @Rob_Appleby , @Alasdair I love the "low-difficulty" specification ;-)
Thanks Robin. The community can certainly help tweak just how low difficulty / easy we can make it by being the testers :)
Mauricio Akmentins
National Scientific and Technical Research Council of Argentina (CONICET)
27 March 2024 6:04pm
Thanks to WILDLABS (Fauna & Flora and Arm) for this incredible opportunity to test new technologies for conservation of threatened Andean water frogs! We will share our results to get the feedback of the WILDLABS community
Adrien Pajot
WILDLABS
Fauna & Flora
1 April 2024 10:36am
Discuss with @JorgeLizarazo and his team about their project
WILDLABS AWARDS 2024 - BoutScout: Monitoring System for Avian Nesting Behavior Studies | WILDLABS
First things first, our team, @jcguerra10, @hefca, and myself, is thrilled to share with immense pride and gratitude towards the WILDLABS Community and their partners at Arm. Being able to contribute to Avian Studies and its conservation through our awarded BoutScout project is a true honor.In a nutshell, BoutScout seeks to overcome the limitations faced by traditional avian study methods, such as the high costs associated with Hobo dataloggers and the time-consuming post-data interpretation processes. By integrating a multi-sensor datalogger with AI analytics, it will bring up more opportunities on how we monitor bird nests. It not only collects critical environmental data but also enables faster post-data interpretation and processing. This approach is going to allow for a detailed analysis of nest temperature variations, thereby identifying crucial parental activities that are vital for the health and survival of bird species. To address this, we are developing a datalogger equipped with sensors to capture detailed data on light, temperature, and humidity around bird nests. Two prototypes based on Arduino MKR Zero have shown effectiveness in preliminary data collection. We plan to enhance these prototypes by expanding sensor capabilities, including movement detection and improved environmental monitoring. Then to achieve a pronto analysis on the extensive future data, our project leverages over 400 nest data sets for the development of a 1D CNN and LSTM models. This model is integral for interpreting complex nesting behaviors, such as on- and off-bouts, offering unprecedented insights into avian behavior interpretation. We've entered into a partnership with the '4U' project, collaboratively led by Universidad Icesi and Universidad del Norte. This collaboration provides the perfect testing grounds for our prototypes across distinct Colombian habitats: a dry tropical forest in the Guajira region at the north, and a cloud forest in the western Andean mountain range. These diverse environments are ideal for our development efforts.@wildlabs Gratitude:This achievement reflects not only our dedication but also the vital support we've received. Special thanks to Universidad Icesi, as well Professors Gustavo Londoño and Juan Pablo Gomez (At Universidad del Norte) for their invaluable backing and guidance during the submission and the procces ahead on the ground.
wildlabsnetAdrien Pajot
WILDLABS
Fauna & Flora
1 April 2024 10:37am
Discuss with @Mauricio_Akmentins and his team about their project:
WILDLABS AWARDS 2024 - Underwater Passive Acoustic Monitoring (UPAM) for threatened Andean water frogs | WILDLABS
In our project awarded with the "2024 WILDLABS Awards", we will develop the first Underwater Passive Acoustic Monitoring (UPAM) program to assess the conservation status and for the long-term monitoring of population trends of an Andean water frog in the high-altitude arid environment of the Central Andean Puna ecoregion of northwestern Argentina.The main objective of our project is testing the existing technologies of automated underwater-recording devices (HydroMoth - GoupGets) combined with automated species recognition software (Kaleidoscope Pro - Wildlife Acoustics) and environmental sensors (HOBO data loggers) in the extreme environment of high-Andes Puna to develop a standardized monitoring tool for scientific-based management decisions towards the conservation of the threatened Pozuelos’ rusted frog (Telmatobius rubigo) in the core area of the MAB-UNESCO Laguna de Pozuelos Biosphere Reserve in Jujuy province in NW Argentina. The output of our work is the development of a UPAM protocol based on open-source automated recording units for survey aquatic biodiversity as a response to the increasing environmental problems that freshwater ecosystems face. This protocol will be destinated to protected areas management, community-based monitoring, and to measure the impact of economic activities on freshwater resources
wildlabsnetAdrien Pajot
WILDLABS
Fauna & Flora
4 April 2024 1:51pm
Discuss with @Abigail and her team about their project!
Adrien Pajot
WILDLABS
Fauna & Flora
5 April 2024 4:02pm
Discuss with @Rob_Appleby and his team about their project!
Adrien Pajot
WILDLABS
Fauna & Flora
8 April 2024 8:52am
Discuss with @emilydorne and her team about their project!
Adrien Pajot
WILDLABS
Fauna & Flora
10 April 2024 9:04am
Discuss with @MattyD797 and his team about their project!
Adrien Pajot
WILDLABS
Fauna & Flora
10 April 2024 9:05am
Discuss with @eferguson and her team about their project!
WILDLABS AWARDS 2024 - Enhancing Pollinator Conservation through Deep NeuralNetwork Development | WILDLABS
Greetings Everyone, We are so excited to share details of our WILDLABS AWARDS project "Enhancing Pollinator Conservation through Deep Neural Network Development" and establish a place to share details along the way and gain input from this phenomenal collective in conservation tech! To start, below is a brief description of the project and our objectives. We look forward to sharing more details as they unfold!--------Project Description: Ocean Science Analytics (emerging from underwater environments to assist soil-side) in collaboration with The San Diego Pollinator Monitoring Program (SDPMP), led by Christina Simokat, will be developing deep neural network to detect pollinators within video data. The SDPMP conducts surveys of plant-pollinator networks in San Diego County's sage scrub communities, aiming to understand pollinator species and their interactions. Using camera traps and the deep learning technology VIAME (originally developed for underwater applications), the program collects video data on pollinator activity, particularly focusing on endangered species like Encinitas baccharis and post-fire recovery networks (check out our ArcGIS Dashboard from a recently conducted post-fire network composition study wherein we collected video data in summer 2023). This project's objectives include annotating tracks of insects in video data meticulously and developing neural networks for accurate detection of pollinator activity in large datasets, aiming to advance understanding of insect-plant interactions and potentially influencing policy changes and scientific advancements in pollinator conservation. Success entails optimizing technology for wider use, comprehensively evaluating pollinator-plant interactions, and uncovering insights into pollinator resilience and adaptation to environmental disturbances. Our WILDLABS AWARD Team (from left to right): Christina Simokat, Mia Lorence (@mia_lorence), Yuli Martinez (@martiyu), and Liz Ferguson
wildlabsnetAdrien Pajot
WILDLABS
Fauna & Flora
11 April 2024 8:35am
Discuss with @Andrew_Hill and his team about their project!
Adrien Pajot
WILDLABS
Fauna & Flora
11 April 2024 8:35am
Discuss with @Andrew_Hill and his team about their project!
Adrien Pajot
WILDLABS
Fauna & Flora
11 April 2024 8:35am
Discuss with @Andrew_Hill and his team about their project!
Adrien Pajot
WILDLABS
Fauna & Flora
12 April 2024 9:28am
Discuss with @JeremyFroidevaux and @DarrylCox about their project!
WILDLABS AWARDS 2024 - BumbleBuzz: automatic recognition of bumblebee species and behaviour from their buzzing sounds | WILDLABS
The 'BumbleBuzz' team (@JeremyFroidevaux, @DarrylCox, @RichardComont, @TBFBumblebee, @KJPark, @yvesbas, @ilyassmoummad, @nicofarr) is very pleased to have been awarded the WILDLABS Awards 2024. Here is a brief description of what we intend to do. The primary aim of this work is to create and develop a bioacoustic toolbox for surveying UK bumblebee species and behaviour in a cost-effective and non-invasive way. This project will build on emergent technologies that are currently revolutionising insect ecology and monitoring (here bioacoustics and recent deep learning innovations). The objectives are threefold: (i) create an open-access reference library of buzzing sounds from bumblebee species, (ii) develop, train and test AI algorithms for automatically recognizing bumblebee species and behaviour from their buzzes, and (iii) field testing the bioacoustic approach before adoption of this tool by conservation practitioners. (c) Jérémy Froidevaux - Great yellow bumblebee (Bombus distinguendus) on Benbecula island (Scotland). The project started last year (thanks to the support from the Eva Crane Trust, Wildlife Acoustics, and the Leverhulme Trust) and the WILDLABS Award will allow us to continue this project in 2024. More specifically, by partnering with the Bumblebee Conservation Trust (BBCT) for the 2024 campaign, we aim to collect/annotate additional recordings of bumblebee species that are not, or poorly, represented in our database, with a particular focus on the Shrill Carder, a priority species for conservation in the UK. In parallel, we will continue to develop the AI algorithm for species/behaviour recognition. We will also deploy acoustic recorders in the field to optimise the bioacoustic toolbox for monitoring bumblebees over large spatio-temporal scales. Our focus will be to refine the toolbox and sampling strategy for smooth integration within BBCT and its monitoring and citizen science projects.
wildlabsnetAdrien Pajot
WILDLABS
Fauna & Flora
15 April 2024 9:24am
Discuss with @Hubertszcz and his team about their project!
WILDLABS AWARDS 2024 – MothBox | WILDLABS
We are incredibly thankful to WILDLABS and Arm for selecting the MothBox for the 2024 WILDLABS Awards. The MothBox is an automated light trap that attracts and photographs moths and other nocturnal insects. A raspberry pi (mini-computer) controls a super high-resolution camera and lights, so that the MothBox can be deployed and programmed to collect data at a pre-defined schedule. A computer vision model then processes the images and automatically identifies the insects captured by the trap. Moths are one of the most diverse orders of insects and a great indicator of the overall diversity in an area because moth larva (caterpillars) are often highly specialized, feeding exclusively on single host-plant species, or living in narrow niches like rock faces, moss, or decaying wood. As adults they are important pollinators, and their populations support birds and other small predators. However, despite the diversity and ecological importance of moths, they are seldom included in conservation planning due to the difficulty in including them in monitoring systems. Conventional monitoring of moths involves moth-sheeting or setting out bucket traps, which require many hours of tedious work by scientists with specialized taxonomic knowledge. This is the problem the MothBox and other automated camera traps for insects seek to address. These are many groups working on automated camera trapping of insects (as is evident from the WILDLABS group), and we intend to continue and strengthen our collaboration with these groups to make insect monitoring more accessible and scalable. What sets the MothBox apart is our focus on a low-cost, low-weight, jungle-proof solution that can easily be deployed en masse by non-scientists in remote tropical locations. Currently, parts for one complete MothBox cost approximately $450, though we are working on ways to decrease this cost further. The MothBox is fully open source with in-depth documentation on Github: https://github.com/Digital-Naturalism-Laboratories/Mothbox My partner @hikinghack has been documenting the MothBox project on WILDLABS for a while, see our past posts/updates here:Introducing the MothBox: https://wildlabs.net/discussion/cheap-automated-mothboxUpdate 2: https://wildlabs.net/discussion/update-2-cheap-automated-mothboxUpdate 3: https://wildlabs.net/discussion/update-3-cheap-automated-mothboxUpdate 4: https://wildlabs.net/discussion/mothbox-mothbeam-update-4Also, checkout the recording of @hikinghack k presenting the MothBox during a wildlabs event back in November: https://wildlabs.net/event/automated-moth-monitoring-you With the WILDLABS Awards funding this project can really take off. Some of the things we plan on doing in the coming year: Adding capacity for a solar panel or additional batteries to extend deployment time.Deploy MothBoxes at sites around Panama, doing a national moth inventory and collecting big data for training AI identification models.Hire AI and taxonomic experts to create, validate, and refine computer vision models.Work with the Panamanian reforestation NGO Pro Eco Azuero to quantify biodiversity uplift associated with native-tree planting and natural regeneration of tropical forests. We would also like to thank all of the people and organizations that helped get us here: Earthshot Labs for the initial funding to get V1 off the ground, Michigan State University which helped develop a drone-deployable version of the MothBox for the Rainforest XPRIZE competition in Singapore last year, Experiment.com which funded development of V3 of the MothBox, ETH Zurich which provided funds for construction of 11 MothBoxes in late 2023, and of course our partner @mothyash for his valuable insight on moth behaviour and raspberry pi programming.
wildlabsnet
Alex Rood
WILDLABS
World Wide Fund for Nature/ World Wildlife Fund (WWF)