There is increasing availability and use of unstructured and semi‐structured citizen science data in biodiversity research and conservation. This expansion of a rich source of ‘big data’ has sparked numerous research directions, driving the development of analytical approaches that account for the complex observation processes in these datasets. We review outstanding challenges in the analysis of citizen science data for biodiversity monitoring. For many of these challenges, the potential impact on ecological inference is unknown. Further research can document the impact and explore ways to address it. In addition to outlining research directions, describing these challenges may be useful in considering the design of future citizen science projects or additions to existing projects. We outline challenges for biodiversity monitoring using citizen science data in four partially overlapping categories: challenges that arise as a result of (a) observer behaviour; (b) data structures; (c) statistical models; and (d) communication. Potential solutions for these challenges are combinations of: (a) collecting additional data or metadata; (b) analytically combining different datasets; and (c) developing or refining statistical models. While there has been important progress to develop methods that tackle most of these challenges, there remain substantial gains in biodiversity monitoring and subsequent conservation actions that we believe will be possible by further research and development in these areas. The degree of challenge and opportunity that each of these presents varies substantially across different datasets, taxa and ecological questions. In some cases, a route forward to address these challenges is clear, while in other cases there is more scope for exploration and creativity.

https://research-repository.st-andrews.ac.uk/bitstream/10023/25066/1/Johnston_2022_MEE_biodiversitymonitoring_CC.pdf

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

There is increasing availability and use of unstructured and semi-structured citizen science data in biodiversity research and conservation. This expansion of a rich source of ‘big data’ has sparked numerous research directions, driving the development of analytical approaches that account for the complex observation processes in these datasets. We review outstanding challenges in the analysis of citizen science data for biodiversity monitoring. For many of these challenges, the potential impact on ecological inference is unknown. Further research can document the impact and explore ways to address it. In addition to outlining research directions, describing these challenges may be useful in considering the design of future citizen science projects or additions to existing projects. We outline challenges for biodiversity monitoring using citizen science data in four partially overlapping categories: challenges that arise as a result of (a) observer behaviour; (b) data structures; (c) statistical models; and (d) communication. Potential solutions for these challenges are combinations of: (a) collecting additional data or metadata; (b) analytically combining different datasets; and (c) developing or refining statistical models. While there has been important progress to develop methods that tackle most of these challenges, there remain substantial gains in biodiversity monitoring and subsequent conservation actions that we believe will be possible by further research and development in these areas. The degree of challenge and opportunity that each of these presents varies substantially across different datasets, taxa and ecological questions. In some cases, a route forward to address these challenges is clear, while in other cases there is more scope for exploration and creativity. 

Citizen science helps predictions of climate change impact on flowering phenology: A study on Anemone nemorosa

Citizen science plant observations encode global trait patterns

Abstract Citizen scientists play an increasingly important role in biodiversity monitoring. Most of the data, however, are unstructured—collected by diverse methods that are not documented with the data. Insufficient understanding of the data collection processes presents a major barrier to the use of citizen science data in biodiversity research. We developed a questionnaire to ask citizen scientists about their decision-making before, during and after collecting and reporting species observations, using Germany as a case study. We quantified the greatest sources of variability among respondents and assessed whether motivations and experience related to any aspect of data collection. Our questionnaire was answered by almost 900 people, with varying taxonomic foci and expertise. Respondents were most often motivated by improving species knowledge and supporting conservation, but there were no linkages between motivations and data collection methods. By contrast, variables related to experience and knowledge, such as membership of a natural history society, were linked with a greater propensity to conduct planned searches, during which typically all species were reported. Our findings have implications for how citizen science data are analysed in statistical models; highlight the importance of natural history societies and provide pointers to where citizen science projects might be further developed. 

/pdf/decision-making-of-citizen-scientists-when-recording-species-3w4cjsmr.pdf

Decision-making of citizen scientists when recording species observations

Observing the Observers: How Participants Contribute Data to iNaturalist and Implications for Biodiversity Science

Anthropogenic change has altered the composition and function of ecological communities across the globe. As a result, there is a need for studies examining observed community compositional change and determining whether and how anthropogenic change drivers may be influencing that turnover. In particular, it is also important to determine to what extent community turnover is idiosyncratic or if turnover can be explained by predictable responses across species based on traits or niche characteristics. Here, we measured turnover in avian communities across North America from 1990 to 2016 in the Breeding Bird Survey using an ordination method, and modeled turnover as a function of land use and climate change drivers from local to regional scales. We also examined how turnover may be attributed to species groups, including foraging guilds, trophic groups, migratory distance, and breeding biomes. We found that at local scales, land use change explained a greater proportion of variance in turnover than climate change variables, while as scale increased, trends in temperature explained a greater proportion of variance in turnover. We also found across the study region, turnover could be attributed to one of a handful of species undergoing strong expansions or strong declines over the study time period. We did not observe consistent patterns in compositional change in any trait groups we examined except for those that included previously identified highly influential species. Our results have two important implications: First, the relative importance of different anthropogenic change drivers may vary with scale, which should be considered in studies' modeling impacts of global change on biodiversity. Second, in North American avian communities, individual species undergoing large shifts in population may drive signals in compositional change, and composite community turnover metrics should be carefully selected as a result.

Anthropogenic drivers of avian community turnover from local to regional scales.

One of the primary methods for classifying a species range is the species distribution model (SDM), which predicts where species are most likely to occur based on a set of environmental factors (Freeman & Mason, 2015; Guisan & Thuiller, 2005). These model outputs have important implications for ecological research and conservation management (Elith & Leathwick, 2009; Engler et al., 2017; Franklin, 2010; Guisan et al., 2013), but depend critically on methods that produce accurate model predictions by minimizing false positives and false Received: 24 August 2020 | Revised: 10 March 2021 | Accepted: 7 April 2021 DOI: 10.1111/ddi.13296

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.13296

Grace J. Di Cecco

Papers

Observing the Observers: How Participants Contribute Data to iNaturalist and Implications for Biodiversity Science

Anthropogenic drivers of avian community turnover from local to regional scales.

Using temporal occupancy to predict avian species distributions