Curves for Curlew: Identifying Curlew breeding status from GPS tracking data

Abstract

Identifying the breeding status of cryptic bird species has proved problematic without intense or inherently expensive monitoring. Most, if not all, intensive bird monitoring comes with disturbance risks and many projects now rely on tagging of individuals to provide remote information on movements. Given the importance of breeding status when targeting conservation interventions novel methods are needed. This study aimed to identify breeding status in Eurasian Curlew ( Numenius arquata ) from GPS tag movement patterns using the “ recurse” package in R. This package identifies foci of activity (using K ‐means clustering) based on revisitations. Using a training data set from an individual of known breeding status, we visually assessed the frequency of revisits to particular locations to identify prebreeding, incubation, chick guarding, and post‐breeding stages to an accuracy of a within at most half a day and thus breeding outcomes. Limited validation was provided by additional field observations. Based on our results, we estimate a low daily nest survival rate of 0.935 during incubation, that only a small proportion of individuals successfully raised young, and that there was a high proportion (26%) of non‐breeders in the population. The Eurasian Curlew is a species of high conservation concern across Europe, and our results concur with recent studies highlighting that population declines are likely to be driven by low levels of productivity. The acquisition of improved knowledge on the behaviors of individuals at each stage of breeding enables more targeted conservation efforts and reduces the need for additional monitoring visits that may cause increased disturbance and risk of nest failure. We hope that the approach outlined may be developed to provide practitioners who have detailed knowledge of the behavior of their study species with a practical means of assessing the breeding status and outcomes of their study populations.

Monitoring wader breeding productivity

“…Robust evaluation of the effectiveness of survey-based productivity monitoring has been carried out for Eurasian Curlew and Northern Lapwing (Grant et al 2000, Bolton et al 2011) and similar evaluation for other species may facilitate wider adoption of these methods. Projects involving either adult or brood tracking may provide opportunities to ground-truth assumptions made during survey visits (Bowgen et al 2022). Finally, we would suggest that globally there are many wader species for which the approaches described here would provide valuable information and be broadly suited and we hope this review encourages further development or adoption of these methods.…”

“…Projects involving either adult or brood tracking may provide opportunities to ground‐truth assumptions made during survey visits (Bowgen et al . 2022). Finally, we would suggest that globally there are many wader species for which the approaches described here would provide valuable information and be broadly suited and we hope this review encourages further development or adoption of these methods.…”

“…Monitoring Eurasian Curlew breeding productivity is particularly challenging, especially in high-density areas (Grant et al 2000). Curlews will exhibit territorial behaviour over a far larger area than the immediate nesting site (Bowgen et al 2022) but pairs can nest in close proximity, making separating individuals or pairs very difficult. This can be a challenge for monitoring the productivity of many wader species (although for European Golden Plover, individuals can often be identified by plumage variation).…”

Extracting reproductive parameters from GPS tracking data -- a new tool for a nesting raptor in Europe

“…The widespread adoption of miniaturized automated tracking devices has facilitated the study of seasonal migration patterns of many animal species in great detail (Kays et al 2015, Panuccio et al 2021. So far, however, relatively few studies have used these large-scale GPS tracking data to extract information on survival (Klaassen et al 2014, Sergio et al 2019, Swift et al 2020, Buechley et al 2021 or reproduction (Picardi et al 2020, Schreven et al 2021, Bowgen et al 2022, Overton et al 2022, Ozsanlav-Harris et al 2022, Eisaguirre et al 2023.…”

“…Information on reproduction can be derived from animal tracking data if individuals follow repeatable patterns that can be reliably identified and distinguished. For example, the nest as a central location will be visited frequently during the breeding phase, resulting in typical central-place foraging movement patterns that can be detected in tracking data (van der Wal et al 2015, Picardi et al 2020, Bowgen et al 2022, Overton et al 2022, Eisaguirre et al 2023. The identification of nesting behaviour can be further improved by using both GPS and accelerometery data, as periods of incubation lead to unique patterns with near-zero movement (Schreven et al 2021, Ozsanlav-Harris et al 2022).…”