Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate

Fall migration observation data Étude des populations d'oiseaux du Québec (ÉPOQ) is a database of bird observations managed by QuébecOiseaux (Larivée, 2001). It is North America’s longest-running, and prior to eBird, largest checklist program. Data are collected according to guidelines designed to maximize scientific rigor (Dunn et al., 1996). ÉPOQ consists of more than six million observations that have been collected since the early 1900s by birders from all around Québec, as well as many retro-active contributions from other published works that date back to the 1700s. Although both professional and amateur birders are able to submit their observations to ÉPOQ, 90% of the data is collected by 10% of the observers, called ‘expert observers’ (Francoeur, 2012). ÉPOQ is an invaluable source of historical bird abundance information in Québec, particularly for data compiled from the late 1960s to 2012. After 2012, the growing popularity of eBird largely replaced ÉPOQ, and the reduction in the number of observations reduced our ability to draw inference for multiple species. Unlike standardized bird surveys, ÉPOQ observations are mostly opportunistic, variable in effort and there is no explicit indication of a species’ absence. However, they are well suited to provide information on trajectory and timing trends (Dunn et al., 1996). We compiled all ÉPOQ records from 1970 to 2012 for waterfowl species from August 15 to January 31 of the subsequent year to ensure we captured a buffer period before and after fall migration. To minimize potential biases with opportunistic data collection, we restricted our analyses to complete checklists (as indicated by the observers) that had an effort of ≥ 30 mins and ≤ 8 hrs. We restricted the analysis to a focal area within 100 km of the St. Lawrence River, starting near Cornwall, ON at the Québec border and extending eastward to Riviere-du-Loup, QC. After applying all the filters, we identified 15 focal species that had a minimum of 10 years in which a species was reported in ≥ 40 checklists per year, which we identified as our minimal threshold for inclusion (Table S1, Supplemental Tables) and restricted our analyses to these years. To spatially aggregate the ÉPOQ records, we georeferenced each observation to one of the 12 economic regions in the study area (Fig. 1) and aggregated all observations of species/day of year (hereafter DOY) per year for each of the economic regions. Because ÉPOQ does not explicitly indicate a species’ absence, we assumed the species was absent if not included on a complete checklist. We did not include checklists with geese only for duck species, as they typically represent observations of large flocks flying overhead, and therefore do not necessarily represent effort in suitable waterfowl stopover habitat. Climate data and spatio-temporal scales To estimate the influence of climate to the north of each economic region, which broadly reflects conditions from where birds would be migrating from, we delineated six spatial scales. Starting at the mid-latitude for each economic region and spanning the width of the region, the spatial scales moved northward at the following distances: 0- 50 km, 50 – 150 km, 150 – 250 km, 250 – 400 km, 400 – 600 km, and 600 – 1000 km. The spatial scales also widened beyond the width of the region at a logistic growth scale (0, 4, 45, 118, 496, 800 km) so that the scales would capture possible east-west migration of birds as they traveled southwards (Fig. 1). As the initial width of the first (and smallest) spatial scale is set to match with the width of the economic region, which differ in size, there is some variation in spatial scales for each economic region. This variation is small compared to the size differences between the scales (Fig. S1), with the largest difference occurring at the largest scales which, proportionally, would be the smallest comparative difference across the economic regions. Expert waterfowl researchers and biologists across the study region guided the decision for the spatial scale sizes and the funnel-like shape of the scales. Natural Resources Canada provided spatiotemporal data of minimum temperature and precipitation from interpolated weather station data (McKenney et al., 2011). This included 5-day averages (pentads) for minimum temperature and precipitation. The only other variable available from this dataset (maximum temperature) was not used in the analysis due to its correlations with minimum temperature, and the consensus among waterfowl experts was that minimum temperature during the fall was the most ecologically relevant variable. We extracted minimum temperature and precipitation at six spatial scales for each of the 12 economic regions (Fig.1). To assess how climatic conditions had changed during migration, we identified the time-period during which 90% of the EPOQ observations were made and calculated the seasonal (fall migration period) mean minimum temperature and mean precipitation during this time-period for each spatial scale. We then used a linear model (LM) to test seasonal trends in minimum temperature and precipitation with effects for year, spatial scale, and their interaction that differed across spatial scales.