Outstanding challenges and future directions for biodiversity monitoring using citizen science data

Johnston, Alison; Matechou, Eleni; Dennis, Emily B.

doi:10.1111/2041-210x.13834

Cited by 114 publications

(88 citation statements)

References 165 publications

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“…In large-scale monitoring programs, data often hail from both structured and unstructured or opportunistic sampling (Altwegg and Nichols, 2019; Bischof et al, 2020a; Isaac et al, 2020). In certain extreme cases (e.g., citizen science data), large portions of the study area may be left unsampled, unbeknownst to the investigator (Johnston et al, 2022; Bird et al, 2014). The three SCR-GLMMs tested here (SARE, RE and FM) allow modelling unknown spatial variation in detection probability in the absence of known fixed effects.…”

Section: Discussionmentioning

confidence: 99%

Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects

Dey

Moqanaki

Milleret

et al. 2022

Preprint

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Spatial capture-recapture (SCR) models are now widely used for estimating density from repeated individual spatial encounters. SCR accounts for the inherent spatial autocorrelation in individual detections by modelling detection probabilities as a function of distance between the detectors and individual activity centres. However, additional spatial heterogeneity in detection probability may still creep in due to environmental or sampling characteristics. if unaccounted for, such variation can lead to pronounced bias in population size estimates.Using simulations, we describe and test three Bayesian SCR models that use generalized linear mixed models (GLMM) to account for latent heterogeneity in baseline detection probability across detectors using: independent random effects (RE), spatially autocorrelated random effects (SARE), and a twogroup finite mixture model (FM).Overall, SARE provided the least biased population size estimates (median RB: -9 – 6%). When spatial autocorrelation was high, SARE also performed best at predicting the spatial pattern of heterogeneity in detection probability. At intermediate levels of autocorrelation, spatially-explicit estimates of detection probability obtained with FM where more accurate than those generated by SARE and RE. In cases where the number of detections per detector is realistically low (at most 1), all GLMMs considered here may require dimension reduction of the random effects by pooling baseline detection probability parameters across neighboring detectors (“aggregation”) to avoid over-parameterization.The added complexity and computational overhead associated with SCR-GLMMs may only be justified in extreme cases of spatial heterogeneity. However, even in less extreme cases, detecting and estimating spatially heterogeneous detection probability may assist in planning or adjusting monitoring schemes.

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Section: Discussionmentioning

confidence: 99%

Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects

Dey

Moqanaki

Milleret

et al. 2022

Preprint

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“…in the case of species of species-rich genera which may be confused by observers with less experience. It may be useful to include the existence of false positives in future modelling work (Johnston et al 2022).…”

Section: Model Assumptionsmentioning

confidence: 99%

Improving citizen science data for long-term monitoring of plant species in the Netherlands

Strien

Zweden

Sparrius³

et al. 2022

Biodivers Conserv

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In 2012, a new volunteer-based recording scheme for vascular plants was launched in the Netherlands. Its purpose is to track the changes in the number of occupied 1-km grid cells for as many native plant species as possible between survey rounds of 8 years. We did not prescribe a strict field protocol to minimize variation in observer effort, but instead chose to statistically correct for this variation with occupancy models. These models require replicated visits to a grid cell per season, which was implemented by having two independent observers survey grid cells and record all plant species observed. Now that a first survey round has ended (2012–2019), we evaluate our approach, i.e. we tested whether the scheme has the potential to produce proper trend estimates. The number of occupied grid cells in the first round was estimated per species, using an occupancy model with day of year, visit duration and observer experience as covariates for detection. The detection probability, which was 0.43 on average, strongly depended on visit duration and day of year. It was possible to estimate the number of occupied grid cells quite precisely for several hundreds of species, such that the statistical power is expected to be high enough to detect changes of 10% between survey rounds. For rare species, however, the power to detect changes is expected to be quite low. We conclude that the approach works well, but further improvements are suggested.

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“…Many citizen science projects prioritize collecting and analyzing data that are tailored for a particular purpose, rather than prioritizing collecting, analyzing, and sharing data for eventual reuse. The former situation leads to inconsistent data standards and structures, and to technical obstacles to obtaining, merging, and analyzing [ 34 ] disparate datasets. Such challenges drove the inception of the Global Mosquito Alert Consortium [ 11 ], which seeks to connect those in the international citizen science community through common protocols involving real-time monitoring of breeding habitats, mosquitoes, and bites, along with mosquito biodiversity approaches—all of which are put into place by a bundle of apps that can be customized to a specific locality via language options.…”

Section: Introductionmentioning

confidence: 99%

Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes

Carney

Mapes

Low

et al. 2022

Insects

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Mosquito-borne diseases continue to ravage humankind with >700 million infections and nearly one million deaths every year. Yet only a small percentage of the >3500 mosquito species transmit diseases, necessitating both extensive surveillance and precise identification. Unfortunately, such efforts are costly, time-consuming, and require entomological expertise. As envisioned by the Global Mosquito Alert Consortium, citizen science can provide a scalable solution. However, disparate data standards across existing platforms have thus far precluded truly global integration. Here, utilizing Open Geospatial Consortium standards, we harmonized four data streams from three established mobile apps—Mosquito Alert, iNaturalist, and GLOBE Observer’s Mosquito Habitat Mapper and Land Cover—to facilitate interoperability and utility for researchers, mosquito control personnel, and policymakers. We also launched coordinated media campaigns that generated unprecedented numbers and types of observations, including successfully capturing the first images of targeted invasive and vector species. Additionally, we leveraged pooled image data to develop a toolset of artificial intelligence algorithms for future deployment in taxonomic and anatomical identification. Ultimately, by harnessing the combined powers of citizen science and artificial intelligence, we establish a next-generation surveillance framework to serve as a united front to combat the ongoing threat of mosquito-borne diseases worldwide.

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Outstanding challenges and future directions for biodiversity monitoring using citizen science data

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 114 publications

References 165 publications

Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects

Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects

Improving citizen science data for long-term monitoring of plant species in the Netherlands

Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes

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