A resampling-based method for effort correction in abundance trend analyses from opportunistic biological records

Zbinden, Niklaus; Kéry, Marc; Häfliger, Guido; Keller, Véréna

doi:10.1080/00063657.2014.969679

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…Non‐matching trends for such species highlight the potential value of unstructured online databases even in countries where structured monitoring schemes exist: casual observations for some species may provide a better basis for population trend estimation than structured monitoring routes that are suboptimal for certain species. However, trends derived from unstructured data would have to be validated with relevant monitoring schemes such as specific wetland bird counts (Zbinden et al ., ). Identifying the species that are poorly covered by structured monitoring schemes and communicating this knowledge gap to casual observers may enhance the value of data contributed to online databases.…”

Section: Discussionmentioning

confidence: 97%

Unstructured citizen science data fail to detect long‐term population declines of common birds in Denmark

Kamp

Oppel

Heldbjerg

et al. 2016

Diversity and Distributions

126

118

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Aim Long‐term monitoring of biodiversity is necessary to identify population declines and to develop conservation management. Because long‐term monitoring is labour‐intensive, resources to implement robust monitoring programmes are lacking in many countries. The increasing availability of citizen science data in online public databases can potentially fill gaps in structured monitoring programmes, but only if trends estimated from unstructured citizen science data match those estimated from structured monitoring programmes. We therefore aimed to assess the correlation between trends estimated from structured and unstructured data. Location Denmark. Methods We compared population trends for 103 bird species estimated over 28 years from a structured monitoring programme and from unstructured citizen science data to assess whether trends estimated from the two data sources were correlated. Results Trends estimated from the two data sources were generally positively correlated, but less than half the population declines identified from the structured monitoring data were recovered from the unstructured citizen science data. The mismatch persisted when we reduced the structured monitoring data from count data to occurrence data to mimic the information content of unstructured citizen science data and when we filtered the unstructured data to reduce the number of incomplete lists reported. Mismatching trends were especially prevalent for the most common species. Worryingly, more than half the species showing significant declines in the structured monitoring showed significant positive trends in the citizen science data. Main conclusions We caution that unstructured citizen science databases cannot replace structured monitoring data because the former are less sensitive to population changes. Thus, unstructured data may not fulfil one of the most critical functions of structured monitoring programmes, namely to act as an early warning system that detects population declines.

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Section: Discussionmentioning

confidence: 97%

Unstructured citizen science data fail to detect long‐term population declines of common birds in Denmark

Kamp

Oppel

Heldbjerg

et al. 2016

Diversity and Distributions

126

118

View full text Add to dashboard Cite

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“…The problems associated with the involvement of volunteers of mixed background in data generation are becoming increasingly well known (Dennis & Hardy, ; Dennis et al ., ; Boakes et al ., ; Hochachka et al ., ), as are the solutions for dealing with them (e.g. Kremen et al ., ; Snäll et al ., , ; Breed et al ., ; Dennis et al ., ; Zbinden et al ., ; Thomas et al ., ). Overall, using information generated by mixed volunteers requires a good understanding of what potential biases and noise may be generated by the sampling process (Dennis & Thomas, ; Isaac & Pocock, ).…”

Section: Discussionmentioning

confidence: 99%

A rapid assessment of a poorly known insect group

Hardwick

Kaartinen

Koponen³

et al. 2015

Insect Conserv Diversity

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'Conservation planning and ecological analyses require large‐scale data on species’ abundance and distribution. We describe a national effort to quickly establish the distributions of taxa with hard‐to‐identify adults but easily identifiable larval stages: cynipid gall wasps (Hymenoptera: Cynipidae, Cynipini) on oak (Quercus robur). The survey was implemented by volunteers across 239 sites over 687 km of latitude. It yielded 10 523 galls, which were identified by specialists. The material revealed two species new to Finland and the national distributions and local abundances of 16 species. The likelihood of finding a given species was significantly affected by the visual apparency of its galls. This suggests that some cryptic generations may have been missed in the sampling. At the national level, our study exposed systematic changes in the species richness of insect taxa associated with a patchily distributed host plant towards its range margin. Species’ incidences decline as host trees grow increasingly scattered within the landscape. From a conservation perspective, the data increased eight‐fold the known records of gall wasps from Finland, and informed the first assessment of national threat status of these taxa. Our study reveals how the efforts of the public may be efficiently harnessed for revealing the current status of poorly known taxa. In addition to delivering a first survey of the target taxa, and of factors affecting their distribution, such ‘citizen science’ may generate a permanent base of enthusiasts for the future monitoring of changes and trends.

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“…In general, we expect the impact of spatial subsampling would vary in different situations and with different subsampling parameters. For example, there may be a greater impact of spatial subsampling when estimating population trends or other processes that show spatial non‐stationarity (Kamp et al., 2016; Zbinden et al., 2014).…”

Section: Discussionmentioning

confidence: 99%

Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions

Johnston

Hochachka

Strimas‐Mackey

et al. 2021

Diversity and Distributions

145

194

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Aim Ecological data collected by the general public are valuable for addressing a wide range of ecological research and conservation planning, and there has been a rapid increase in the scope and volume of data available. However, data from eBird or other large‐scale projects with volunteer observers typically present several challenges that can impede robust ecological inferences. These challenges include spatial bias, variation in effort and species reporting bias. Innovation We use the example of estimating species distributions with data from eBird, a community science or citizen science (CS) project. We estimate two widely used metrics of species distributions: encounter rate and occupancy probability. For each metric, we critically assess the impact of data processing steps that either degrade or refine the data used in the analyses. CS data density varies widely across the globe, so we also test whether differences in model performance are robust to sample size. Main conclusions Model performance improved when data processing and analytical methods addressed the challenges arising from CS data; however, the degree of improvement varied with species and data density. The largest gains we observed in model performance were achieved with 1) the use of complete checklists (where observers report all the species they detect and identify, allowing non‐detections to be inferred) and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists. Improvements in model performance with data refinement were more evident with larger sample sizes. In general, we found that the value of each refinement varied by situation and we encourage researchers to assess the benefits in other scenarios. These approaches will enable researchers to more effectively harness the vast ecological knowledge that exists within CS data for conservation and basic research.

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A resampling-based method for effort correction in abundance trend analyses from opportunistic biological records

Cited by 10 publications

References 29 publications

Unstructured citizen science data fail to detect long‐term population declines of common birds in Denmark

Unstructured citizen science data fail to detect long‐term population declines of common birds in Denmark

A rapid assessment of a poorly known insect group

Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions

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