Combining Citizen Science Data and Satellite Descriptors of Ecosystem Functioning to Monitor the Abundance of a Migratory Bird during the Non-Breeding Season

Abstract

Migratory birds are particularly exposed to habitat changes in their breeding and non-breeding grounds. Remote sensing technologies offer an excellent opportunity to monitor species’ habitats from space at unprecedented spatiotemporal scales. We analyzed if remotely sensed ecosystem functioning attributes (EFAs) adequately predict the spatiotemporal variation of the Woodcock’s (Scolopax rusticola) relative abundance in southwest Europe, during autumn migration and wintering periods. We used data gathered from Woodcock monitoring through citizen science (N = 355,654 hunting trips) between 2009 and 2018. We computed a comprehensive set of EFAs on a weekly basis from three MODIS satellite products: enhanced vegetation index (EVI), tasseled cap transformation (TCT), and land surface temperature (LST). We developed generalized linear mixed models to explore the predictive power of EFAs on Woodcock’s abundance during the non-breeding season. Results showed that Woodcock abundance is correlated with spatiotemporal dynamics in primary productivity (measured through the EVI), water cycle dynamics (wetness component of TCT), and surface energy balance (LST) in both periods. Our findings underline the potential of combining citizen science and remote sensing data to monitor migratory birds throughout their life cycles—an issue of critical importance to ensure adequate habitat management in the non-breeding areas.

Monitoring Eurasian Woodcock (Scolopax rusticola) with Pointing Dogs in Italy to Inform Evidence-Based Management of a Migratory Game Species

“…The findings of our study appear to support previous research that the pre-nuptial migration toward nesting sites occurs over a shorter period of time, primarily between the second ten days of February and the third ten days of March (on average 40 days), as opposed to the autumn-winter migration, which occurs over a relatively long period of time (on average 70 days)., as evidenced by recent studies [21,36], and this supports our data derived from the post-hunting period, shorter than hunting ones.…”

“…The estimates we obtained in our study show a similar trend to those observed in independent data, which were obtained during night trips to capture Woodcock presence [27]. Additionally, our results regarding the variation within and between seasons align with findings from other studies in European countries with similar data [13,36].…”

“…In all monitoring years, Woodcock registered ICA values for the month of March that were greater than those for the months of the wintering phase. This higher Woodcock concentration may result from opportunistic movements linked to climatic conditions, which are well-documented in other Mediterranean nations [36,54] but may be linked to migratory patterns. This specific, poorly understood component of the phenology of pre-nuptial migration will need to be clarified in future research and requires more in-depth analyses than is possible here.…”

“…Nonetheless, it is important to note that this monitoring program may be affected by biases arising from non-stratified and/or random sampling methods and collaborations with non-technical personnel, including hunters [10]. However, the methodology adopted to flush the birds, namely the use of pointing dogs, and/or the data of hunting trips is well established to evaluate the abundance of several species among which the Woodcock is included [13,27,36]. The estimates we obtained in our study show a similar trend to those observed in independent data, which were obtained during night trips to capture Woodcock presence [27].…”

Satellite-based environmental variables complement traditional variables in spatio-temporal models of purple martin migration

“…Research teams comprising individuals with different specialisations (e.g. statistics, spatio-temporal modelling, climatology, remotesensing, Citizen Science data applications) can often help ecologists develop and interpret complex spatio-temporal models, particularly those that assess the impacts of environmental variables on migration phenology of birds (for examples, see Tombre et al 2008;Si et al 2015;Gibb et al 2017;Youngflesh et al 2021;Moreira et al 2022;Zhu and Guo 2022). In our case, our research team quickly recognised the correlation between mean January temperature and green-up, and decided to remove January temperature from our final model (Table 2).…”