2022 ESA Annual Meeting (August 14 - 19)

LB 32-307 Using eBird as a long-term species monitor tool to evaluate spatiotemporal patterns in the San Juan Metropolitan Area

5:00 PM-6:30 PM
ESA Exhibit Hall
Luis E. Velázquez Román, University of Puerto Rico at Rio Piedras;Elvia J. Meléndez-Ackerman,University of Puerto Rico at Rio Piedras;
Background/Question/Methods

: Puerto Rico (PR) as much of the Antilles, is prone to hurricane events with the potential to alter the spatiotemporal patterns of its bird species. Understanding these patterns and its drivers can be a challenging task in developing countries. Nevertheless, eBird, offers a long-term alternative that can be resourceful for these cities and countries. Thus, the objective of this study was to assess the potential effects of Hurricane María in the bird species of San Juan Metropolitan Area (SJMA). To evaluate this, eBird data for PR (3,313,200 occurrences) and SJMA (624,922 occurrences) sampled over eight years was analyzed. Data was managed to reduce the error associated with citizen science and effort; and was classified according to canopy-cover, location, food guild and date.

Results/Conclusions

: Preliminary results indicate, that while the island wide data shows some species reducing its frequencies following the 2017 hurricane events, others do not. For example, the frequencies of the Scaly-naped Pigeon (Patagioenas squamosa) and White-crowned Pigeon (Patagioenas leucocephala) increased dramatically in urban areas with low-canopy-cover. Other species showed reduced frequencies over time in both data sets (PR and SJMA), but other factors may also be at play, following the hurricane events. The Adelaide Warbler (Setophaga adelaidae) showed reduced frequencies since 2016, first when a big drought affected the island and then even more after the 2017 hurricane season and have not returned to pre-hurricane levels. The patterns observed could be driven by spatiotemporal differences in the resources available for different species. Ongoing analyses are being done to explore the data using different landcover and land variables as a proxy for urbanization.