Results
Publications
Frog Find
Using a cognitive model to understand crowdsourced data from citizen scientists
ABSTRACT: Threatened species monitoring can produce enormous quantities of acoustic and visual recordings which must be searched for
animal detections. Data coding is extremely time-consuming for humans and even though machine algorithms are emerging
as useful tools to tackle this task, they too require large amounts of known detections for training. Citizen scientists are often
recruited via crowd-sourcing to assist. However, the results of their coding can be difficult to interpret because citizen scientists
lack comprehensive training and typically each codes only a small fraction of the full dataset. Competence may vary between
citizen scientists, but without knowing the ground truth of the dataset, it is difficult to identify which citizen scientists are
most competent. We used a quantitative cognitive model, cultural consensus theory, to analyze both empirical and simulated
data from a crowdsourced analysis of audio recordings of Australian frogs. Several hundred citizen scientists were asked
whether the calls of nine frog species were present on 1260 brief audio recordings, though most only coded a fraction of these
recordings. Through modeling, characteristics of both the citizen scientist cohort and the recordings were estimated. We then
compared the model’s output to expert coding of the recordings and found agreement between the cohort’s consensus and
the expert evaluation. This finding adds to the evidence that crowdsourced analyses can be utilized to understand large-scale
datasets, even when the ground truth of the dataset is unknown. The model-based analysis provides a promising tool to screen
large datasets prior to investing expert time and resources. Thorpe et al., 2023 Behaviour Research Methods
BIOMON
A call to scale up biodiversity monitoring from idiosyncratic, small-scale programmes to coordinated, comprehensive and continuous monitoring across large scales
ABSTRACT: Conservation managers cannot manage what they don’t know about, yet our existing biodiversity monitoring is idiosyncratic and small in scale. One of Australia’s commitments to the Convention for Biological Diversity in 2015 was the creation of a national biodiversity monitoring programme. This has not yet occurred despite the urgent need to monitor common and threatened species, as highlighted by the challenges of determining the biodiversity impacts of the Black Summer fires of 2019/20. In light of improvements to automation, miniaturisation and powering devices, the world urgently needs to scale-up biodiversity monitoring to become coordinated, comprehensive and continuous across large scales. We propose the BIOMON project that could achieve this where individual sensor nodes use machine learning models to identify biodiversity via sound or photos onboard. This could be coupled with abiotic data on temperature and humidity, plus factors such as bushfire smoke. Nodes would be set within networks that transmit the results back to a central cloud repository where robust analyses are conducted and provided free to the public (along with the raw data). Network arrays could be set up across entire continents to measure the change in biodiversity. No one has achieved this yet, and significant challenges remain associated with training the algorithms, low power cellular network coverage, sensor power versus memory trade-offs, and sensor network placement. Much work is still needed to achieve these goals; however we are living in the 21st Century and such lofty goals cannot be achieved unless we start working towards them. Hayward et al., 2022. Australian Zoologist