A spatio-temporal comparison of avian migration phenology using Citizen Science data

Abstract

Please cite this article as: Arab, A., Courter, J.R., Zelt, J., A spatio-temporal comparison of avian migration phenology using Citizen Science data. Spatial Statistics (2016), http://dx. AbstractThe effects of climate change have wide-ranging impacts on wildlife species and recent studies indicate that birds' spring arrival dates are advancing in response to changes in global climates.In this paper, we propose a spatio-temporal approach for comparing avian first arrival data for multiple species. As an example, we analyze spring arrival data for two long-distance migrants (Ruby-throated Hummingbird Archilochus colubris; and Purple Martin Progne subis) in eastern North America from 2001-2010 using Citizen Science data. The proposed approach provides re-1 *Manuscript Click here to view linked References searchers with a tool to compare mean arrival dates while accounting for spatial and temporal variability. Our results show that on average, Purple Martins arrive 29.95 to 31.84 days earlier than Ruby-throated Hummingbirds, but after accounting for this overall difference, spatial nuances exist whereby martins arrive earlier in the southern United States and migrate northward at a slower rate than hummingbirds. Differences were also noted in how climate and weather variables such as the North Atlantic Oscillation index, winter temperature, winter-spring spring precipitation, sampling effort, and altitude impacted migration dates. Our method may easily be generalized to analyze a broad range of temporal and spatial Citizen Scientists data to help better understand the ecological impacts of climate change.

Opportunities and challenges for big data ornithology

“…For this reason, big data are greatly advancing our understanding of avian phenology, including the timing of migration and breeding phenophases. Some big data resources, such as the citizen science project Journey North, focus exclusively on aspects of phenology such as migration arrival (Arab et al 2016). Likewise, eBird is increasingly being used to understand migration phenology, particularly how the timing of migration has been shifting in response to recent climate change, and the extent to which these shifts vary geographically and with species traits (Hurlbert and Liang 2012, Mayor et al 2017).…”

Correcting Misclassification Errors in Crowdsourced Ecological Data: A Bayesian Perspective

“…Several models that account for spatial dependency have also been developed within the Bayesian framework for citizen science data (Humphreys et al., 2019). Conditional autoregressive (CAR) priors have been found to adequately capture spatial variability in some studies (Arab & Courter, 2015; Arab et al., 2016; Pagel et al., 2014; Purse et al., 2015), while Gaussian random fields (Humphreys et al., 2019) and stochastic partial differential equations (SPDE) (Peterson et al., 2020) have been successfully used in others. To our knowledge, no one has addressed the issue of misclassification in citizen science accounting for spatial dependence.…”

Historical Phenology of West Virginia: 130 Years of Spring Avian Migration and Wildflower Blooming

“…Several biases from the data sources have been considered in the results. It has been shown that increased observers result in seemingly earlier arrival dates Arab et al, 2016). However, an argument for the integrity of the sources in this study can be made.…”

“…For both analyses, avian first arrival dates and wildflower blooming dates, the data sources were evaluated for credibility and accuracy before combining the datasets. Specifically for first arrival dates, more observers may result in seemingly earlier arrival dates Arab et al 2016). We reconciled our avian sources with these issues by arguing that all observers were experienced naturalists who visited a variety of habitats to observe migrating species and that the data from each source were gathered by groups of people more often than by single individuals, which improved consistency among sources.…”

“…Archival records have been used to collect first arrival dates of migrants for temporal analyses in the past Butler, 2003;. Evidence exists that spring arrival is advancing due to a number of variables, including climatic factors such as increasing spring temperatures (Swanson & Palmer, 2009), increased spring precipitation (Arab et al, 2016), the North Atlantic Oscillation (Marra et al, 2005), increased temperatures in wintering grounds (Cotton, 2003), and changes in the length of the growing season . Avian migrants that have advanced their spring arrival have been shown to correlate with trends of increased abundance, which demonstrates survival benefits for species with high adaptability (Newson et al, 2016).…”