Robustness of simple avian population trend models for semi-structured citizen science data is species-dependent

Boersch‐Supan, Philipp H.; Trask, Amanda E.; Baillie, Stephen R.

doi:10.1016/j.biocon.2019.108286

Cited by 38 publications

(53 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…34 A similar comparison between relatively structured and unstructured datasets for UK birds showed more consistent agreement in trends calculated with simple statistical techniques (90 out of 141 species' trends positively correlated for the two sets), such that agreement was more likely for common and widespread species. 35…”

Section: Ll Open Accessmentioning

confidence: 99%

Making Messy Data Work for Conservation

et al. 2020

View full text Add to dashboard Cite

Section: Ll Open Accessmentioning

confidence: 99%

Making Messy Data Work for Conservation

et al. 2020

View full text Add to dashboard Cite

“…Robust population monitoring is constrained by the availability of biodiversity data that varies considerably over space, time and with regard to the taxa covered (Amano et al 2016). Long-term, systematic survey programs that employ a formal sampling design and a rigorous protocol often allow the estimation of accurate species population trends (Gregory et al 2005, Boersch-Supan et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Model-based integration of citizen-science data from disparate sources increases the precision of bird population trends

Hertzog

Frank

Boehner³

et al. 2020

Preprint

View full text Add to dashboard Cite

AimTimely and accurate information on population trends is a prerequisite for effective biodiversity conservation. Structured biodiversity monitoring programs have been shown to track population trends reliably, but require large financial and time investment. The data assembled in a large and growing number of online databases are less structured and suffer from bias, but the number of observations is much higher compared to structured monitoring programs. Model-based integration of data from these disparate sources could capitalize on their respective strengths.LocationGermany.MethodsAbundance data for 26 farmland bird species were gathered from the standardized Common Breeding Bird Survey (CBBS) and three online databases that varied with regard to their degree of survey standardization. Population trends were estimated with a benchmark model that included only CBBS data, and five Bayesian hierarchical models integrating all data sources in different combinations. Across models, we compared consistency and precision of the predicted population trends, and the accuracy of the models. Bird species body mass, prevalence in the dataset and abundance were tested as potential predictors of the explored quantities.ResultsConsistency in predicted annual abundance indices was generally high especially when comparing the benchmark models to the integrated models without unstructured data. The accuracy of the estimated population changes was higher in the hierarchical models compared to the benchmark model but this was not related to data-integration. Precision of the predicted population trends increased as more data sources were integrated.Main conclusionsModel-based integration of data from different sources can lead to improved precision of bird population trend estimates. This opens up new opportunities for conservation managers to identify declining populations earlier. Integrating data from online databases could substantially increase sample size and thus allowing to derive trends for currently not well-monitored species, especially at sub-national scales.

show abstract

“…time of the observations that include measures of expertise of the observer [26][27][28][29] , measures of effort 26,28,[30][31][32][33] (e.g. duration of the survey) and survey completeness 26,28,30,[32][33][34] (i.e. did the participant recorded every species they saw?).…”

mentioning

confidence: 99%

“…In some studies these variables are used to filter the data in an effort to remove biases that will hide signals of interest in the data. Survey completeness is commonly used to only include data where absences can be inferred because all species observed-within the taxonomic group of interest-were recorded 26,30,32 . It has also been shown that filtering data by a measure of effort can improve species distribution models 26 .…”

mentioning

confidence: 99%

“…As an addition or alternative to filtering, these metadata can be included in models to account for biases. Expertise and effort metrics have been used as covariates in occupancy models 27,29 , species distribution models 29 , mixed effects models 30,33 , logistic regressions 32 , and generalised additive models (GAMs) 31 where they have been found to improve model performance. All these approaches require metadata to be collected by participants during the collection process, so are applicable to only a minority of citizen science projects where these metadata are collected.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias

August

Fox

Roy

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Around the world volunteers and non-professionals collect data as part of environmental citizen science projects, collecting wildlife observations, measures of water quality and much more. However, where projects allow flexibility in how, where, and when data are collected there will be variation in the behaviour of participants which results in biases in the datasets collected. We develop a method to quantify this behavioural variation, describing the key drivers and providing a tool to account for biases in models that use these data. We used a suite of metrics to describe the temporal and spatial behaviour of participants, as well as variation in the data they collected. These were applied to 5,268 users of the iRecord Butterflies mobile phone app, a multi-species environmental citizen science project. in contrast to previous studies, after removing transient participants (those active on few days and who contribute few records), we do not find evidence of clustering of participants; instead, participants fall along four continuous axes that describe variation in participants' behaviour: recording intensity, spatial extent, recording potential and rarity recording. Our results support a move away from labelling participants as belonging to one behavioural group or another in favour of placing them along axes of participant behaviour that better represent the continuous variation between individuals. Understanding participant behaviour could support better use of the data, by accounting for biases in the data collection process. Human activities are causing changes in biodiversity at both global and local scales 1. Climate change, land use change, and globalisation have led to wide scale loss of biodiversity 2 and the translocation of species around the globe 3 , with negative impacts on ecosystem functioning 4,5. Citizen science offers a mechanism to monitor the status and change of biodiversity, identify drivers of change, and report on progress in reversing declines 6. Citizen science engages volunteers and non-professionals in scientific research 7 and has grown and diversified rapidly in recent years, due in part to technological advances 8,9. It provides benefits both in the engagement of people and in the cost-effective collection of environmental data 10 and so offers a mechanism for large scale and long term data collection that would not be practical otherwise. Citizen science data are increasingly used to understand large scale processes in ecological and conservation biology such as the impacts of climate change 11,12 , pesticides 13 , invasive species 14,15 , and poaching 16. The diversity of citizen science projects is exemplified by projects that focus on the recording of wildlife observations. These projects range from highly structured recording schemes that require taxonomic expertise to participate, to simple opportunistic projects that encourage mass participation 17-19. Allowing flexibility in the data collection process allows participants to choose how they engage with a project. This has...

show abstract

Robustness of simple avian population trend models for semi-structured citizen science data is species-dependent

Cited by 38 publications

References 53 publications

Making Messy Data Work for Conservation

Making Messy Data Work for Conservation

Model-based integration of citizen-science data from disparate sources increases the precision of bird population trends

Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias

Contact Info

Product

Resources

About