Using Semistructured Surveys to Improve Citizen Science Data for Monitoring Biodiversity

Kelling, Steve; Johnston, Alison; Bonn, Aletta; Fink, Daniel; Ruiz‐Gutiérrez, Viviana; Bonney, Rick; Fernández, Miguel; Hochachka, Wesley M.; Julliard, Romain; Kraemer, Roland; Guralnick, Robert

doi:10.1093/biosci/biz010

Cited by 165 publications

(203 citation statements)

References 36 publications

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“…Our results show that the use of semi‐structured (Kelling et al. ) citizen science data with analyses designed to deal with the biases in these data can accurately estimate complex patterns of species’ distribution, abundance, and trends at landscape spatial scales across continental extents, and at weekly temporal scales across the full annual cycle. The resolution, extent, and completeness of the information that can be generated with this approach have the potential to increase our understanding of the processes that affect species populations and improve monitoring and conservation planning across a range of spatial and temporal scales (Runge et al.…”

Section: Discussionmentioning

confidence: 81%

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Modeling avian full annual cycle distribution and population trends with citizen science data

Fink

Auer

Johnston

et al. 2020

Ecological Applications

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156

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Information on species’ distributions, abundances, and how they change over time is central to the study of the ecology and conservation of animal populations. This information is challenging to obtain at landscape scales across range‐wide extents for two main reasons. First, landscape‐scale processes that affect populations vary throughout the year and across species’ ranges, requiring high‐resolution, year‐round data across broad, sometimes hemispheric, spatial extents. Second, while citizen science projects can collect data at these resolutions and extents, using these data requires appropriate analysis to address known sources of bias. Here, we present an analytical framework to address these challenges and generate year‐round, range‐wide distributional information using citizen science data. To illustrate this approach, we apply the framework to Wood Thrush (Hylocichla mustelina), a long‐distance Neotropical migrant and species of conservation concern, using data from the citizen science project eBird. We estimate occurrence and abundance across a range of spatial scales throughout the annual cycle. Additionally, we generate intra‐annual estimates of the range, intra‐annual estimates of the associations between species and characteristics of the landscape, and interannual trends in abundance for breeding and non‐breeding seasons. The range‐wide population trajectories for Wood Thrush show a close correspondence between breeding and non‐breeding seasons with steep declines between 2010 and 2013 followed by shallower rates of decline from 2013 to 2016. The breeding season range‐wide population trajectory based on the independently collected and analyzed North American Breeding Bird Survey data also shows this pattern. The information provided here fills important knowledge gaps for Wood Thrush, especially during the less studied migration and non‐breeding periods. More generally, the modeling framework presented here can be used to accurately capture landscape scale intra‐ and interannual distributional dynamics for broadly distributed, highly mobile species.

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Section: Discussionmentioning

confidence: 81%

“…, Kelling et al. ). There has been considerable work over the past decade developing methods to minimize the effects of these biases, including biases due to heterogeneous and imperfect observation processes (Kéry and Royle , Johnston et al.…”

Section: Introductionmentioning

confidence: 99%

Modeling avian full annual cycle distribution and population trends with citizen science data

Fink

Auer

Johnston

et al. 2020

Ecological Applications

Self Cite

156

176

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“…The advantages of these data are that they are available across large extents and for the entire year. Citizen science data can be highly variable, although analytical methods can account for much of this variation (Johnston et al 2018, 2019. Although citizen science data may be spatially biased and or data deficient for particular places (e.g., the Arctic for breeding shorebirds), in some cases, this shortfall could be addressed by combining citizen science data with more formal surveys (Skagen et al 2003).…”

Section: Discussionmentioning

confidence: 99%

Comparing abundance distributions and range maps in spatial conservation planning for migratory species

Johnston

Auer

Fink

et al. 2020

Ecological Applications

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Most spatial conservation planning for wide‐ranging or migratory species is constrained by poor knowledge of species’ spatiotemporal dynamics and is only based on static species’ ranges. However, species have substantial variation in abundance across their range and migratory species have important spatiotemporal population dynamics. With growing ecological data and advancing analytics, both of these can be estimated and incorporated into spatial conservation planning. However, there is limited information on the degree to which including this information affects conservation planning. We compared the performance of systematic conservation prioritizations for different scenarios based on varying the input species’ distributions by ecological metric (abundance distributions versus range maps) and temporal sampling resolution (weekly, monthly, or quarterly). We used the example of a community of 41 species of migratory shorebirds that breed in North America, and we used eBird data to produce weekly estimates of species’ abundances and ranges. Abundance distributions at a monthly or weekly resolution led to prioritizations that most efficiently protected species throughout the full annual cycle. Conversely, spatial prioritizations based on species’ ranges required more sites and left most species insufficiently protected for at least part of their annual cycle. Prioritizations with only quarterly species ranges were very inefficient as they needed to target 40% of species’ ranges to include 10% of populations. We highlight the high value of abundance information for spatial conservation planning, which leads to more efficient and effective spatial prioritization for conservation. Overall, we provide evidence that spatial conservation planning for wide‐ranging migratory species is most robust and efficient when informed by species’ abundance information from the full annual cycle.

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“…The information provided by the presence records is combined with the information of the non-detections, but because the outputs are so reliant on non-detections it is valuable to increase the rigour of recording non-detections. This could be achieved through semisystematic recording approaches, including complete list recording and reporting recording effort (Kelling et al, 2019). Increasing the number of visited sites is also valuable and sites must be visited in multiple years in order to be included in our Bayesian occupancy model : simulations show that including sites visited in only one year can lead to biased estimates of occupancy .…”

Section: Discussionmentioning

confidence: 99%

“…Increasingly, projects encourage recorders to submit complete lists or 'checklists' of species (Isaac & Pocock, 2015;B. L. Sullivan et al, 2014), to help reduce bias arising from incomplete reporting of species that were sighted (Kelling et al, 2019;Szabo, Vesk, Baxter, & Possingham, 2010). Occupancy models are now considered to be a valuable approach for estimating trends in occupancy from presence-only data van Strien et al, 2013), and are widely-used for this purpose (Burns et al, 2018;Cole et al, 2019;Powney et al, 2019;Termaat, van Grunsven, Plate, & van Strien, 2015), although they have received some criticism (Kamp, Oppel, Heldbjerg, Nyegaard, & Donald, 2016).…”

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confidence: 99%

Rapid assessment of the suitability of multi-species citizen science datasets for occupancy trend analysis

Pocock

Logie

Isaac

et al. 2019

Preprint

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Species records from volunteers are a vast and valuable source of information on biodiversity for a wide range of taxonomic groups. Although these citizen science data are opportunistic and unstructured, occupancy analysis can be used to quantify trends in distribution. However, occupancy analysis of unstructured data can be resourceintensive and requires substantial expertise. It is valuable to have simple 'rules of thumb' to efficiently assess the suitability of a dataset for occupancy analysis prior to analysis.• Our analysis was possible due to the production of trends, from our Bayesian occupancy analysis, for 10 967 species from 34 multi-species recording schemes in Great Britain.These schemes had an average of 500 visits to sites per year, and an average of 20% of visited sites received a revisit in a year. Occupancy trend outputs varied in their precision and we used expert elicitation on a subset of outputs to determine a precision threshold above which trends were suitable for further consideration. We then used classification trees with seven metrics to define simple rules explaining when the data would result in outputs that met the precision threshold.• We found that the suitability of a species' data was best described by (i) the number of records of the focal species in the 10% best-recorded years, and (ii) the proportion of recording visits for that taxonomic group with non-detections of the focal species.Surprisingly few data were required to be predicted to meet the precision threshold.Specifically, for 98% confidence that our Bayesian occupancy models would produce outputs meeting the precision threshold, there needed to be ≥29 records of the focal species in the 10% best-recorded years (equivalent to an average of 12.5 records per year in our dataset), although only ≥10 records (equivalent to 4.5 records per year) were required for species recorded in less than 1 in 25 visits.• We applied these rules to regional species data for Great Britain. Data from 32% of the species:region combinations met the precision threshold with 80% confidence, and 14% with 98% confidence. There was great variation between taxonomic groups (e.g.butterflies, moths and dragonflies were well recorded) and region (e.g. south-east England was best recorded).• These simple criteria provide no indication of the accuracy or representativeness of the trend outputs: this is vital, but needs to be assessed individually. However our criteria do provide a rapid, quantitative assessment of the predicted suitability of existing data for occupancy analysis and could be used to inform the design and implementation of multispecies citizen science recording projects elsewhere in the world.

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Using Semistructured Surveys to Improve Citizen Science Data for Monitoring Biodiversity

Cited by 165 publications

References 36 publications

Modeling avian full annual cycle distribution and population trends with citizen science data

Modeling avian full annual cycle distribution and population trends with citizen science data

Comparing abundance distributions and range maps in spatial conservation planning for migratory species

Rapid assessment of the suitability of multi-species citizen science datasets for occupancy trend analysis

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