Characterizing and determining factors that explain variation in North American bird natal dispersal

Joanathan J. Chu, Santiago Claramunt

Dispersal is an important aspect of ecology; fundamental to many processes such as community connectivity, gene flow, speciation, and macroevolution. Natal dispersal, defined as the movement of an animal from birth site to first established breeding site, it is usually the longest dispersal event among birds and thus it is influential to range changes. Evidently there exists variation in dispersal within species and across species, as past research has shown age- and sex-specific differences in philopatry. More recent studies have investigated the distribution of dispersal distances and characterized the variation in dispersal distances, rather than the level of philopatry. Empirical evidence that strongly suggests causality between a predictor and dispersal variation is lacking due to the multicausality of the dispersal process and difficulty in estimating dispersal parameters. There have been contrasting results regarding factors that control dispersal variation; population size, geographic range size, migratory behaviour, sex feeding guild and morphology are all parameters that have shown correlation with dispersal distances.

I plan to use large-scale mark-recapture data (bird banding) to estimate natal dispersal distances in North American birds. Using museum specimens I plan to measure various wing morphology (aspect ratio, wing area, hand-wing index etc.) associated with flight efficiency, a parameter lacking in past analyses. In addition to ecological variables I plan to build models to determine which wing measurement best explains the variation in dispersal distances across species.

Using large-scale citizen science data to model phenology of bird migration

Jonathan J. Chu1, Daniel Gillis1, Santiago Claramunt1,2 and Shelby H. Riskin1, (1)Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada, (2)Department of Natural History, Royal Ontario Museum, Toronto, ON, Canada


Substantial global data show shifting phenologies across many organisms in response to climate change. For birds, migration arrival dates in breeding regions have been shifting earlier and there is evidence both adaptation and plasticity influence these shifts. We hypothesize that flight efficiency, as measured by morphology of the flight apparatus, may be associated with larger shifts, as better fliers may be able to better respond to changing conditions during migration. The use of large-scale citizen science data, such as eBird, presents a powerful tool to investigate arrival date shift, as data are analogous to amateur birding checklists historically used in migration tracking. As the usage of these programs has increased, a methodology addressing variable sampling effort needs to be applied, as looking at early arrivals on data with increasing sampling effort over time may bias arrival date trends. We applied a logistic model to 12 years of eBird data for 35 common passerines in the Greater Toronto Area to estimate mean arrival date (MAD). We then used linear regression to analyze changes in MAD with time, migratory distance, and flight ability.


We find that of the 35 species analysed, 22 showed earlier arrivals (β = -0.03 to -0.80) and 13 showed later arrivals (β = 0.01 to 0.32) during the study period. Overall, birds arrived approximately 3.5 days earlier in 2019 than 2008 (β = -0.29, p-value = 0.07, r= 0.29), a rate of change similar to those reported globally. Short distance migrants had a stronger shift (β = -0.44, p-value = 0.12, r2 = 0.22) than long distance migrants (β = -0.21, r2 = 0.20, p=0.15). A weak relationship existed between flight efficiency proxies and MAD, suggesting that plasticity in migration timing may be affected by morphology. In an increasingly warming world, tracking such phenological changes will be instrumental in understanding which species are most at risk, particularly as phenological mismatches are associated with population declines. Large-scale citizen science data presents the ability to model and track these changes across greater geographic ranges and with greater sampling effort than past data collection schemes.

Project is currently being worked on for publication. Will be presented as a poster for ESA 2020

Fig 1. Plots of logistic model fitting for Gray Catbird arrivals to Toronto in 2018. Right graph shows daily total number of checklists (blue line), compared to number of checklist containing sightings of Gray Catbirds. Left graph shows model fitting, inflection point can be interpreted as the mean arrival date of birds in 2018.