Wearable reproductive trackers: quantifying a key life history event remotely

Abstract

Advancements in biologging technology allow terabytes of data to be collected that record the location of individuals but also their direction, speed and acceleration. These multi-stream data sets allow researchers to infer movement and behavioural patterns at high spatiotemporal resolutions and in turn quantify fine-scale changes in state along with likely ecological causes and consequences. The scope offered by such data sets is increasing and there is potential to gain unique insights into a suite of ecological and life history phenomena. We use multi-stream data from global positioning system (GPS) and accelerometer (ACC) devices to quantify breeding events remotely in an Arctic breeding goose. From a training set of known breeders we determine the movement and overall dynamic body acceleration patterns indicative of incubation and use these to classify breeding events in individuals with unknown reproductive status. Given that researchers are often constrained by the amount of biologging data they can collect due to device weights, we carry out a sensitivity analysis. Here we explore the relative merits of GPS vs ACC data and how varying the temporal resolution of the data affects the accuracy of classifying incubation for birds. Classifier accuracy deteriorates as the temporal resolution of GPS and ACC are reduced but the reduction in precision (false positive rate) is larger in comparison to recall (false negative rate). Precision fell to 94.5%, whereas recall didn’t fall below 98% over all sampling schedules tested. Our data set could have been reduced by c.95% while maintaining precision and recall > 98%. The GPS-only classifier generally outperformed the ACC-only classifier across all accuracy metrics but both performed worse than the combined GPS and ACC classifier. GPS and ACC data can be used to reconstruct breeding events remotely, allowing unbiased, 24-h monitoring of individuals. Our resampling-based sensitivity analysis of classifier accuracy has important implications with regards to both device design and sampling schedules for study systems, where device size is constrained. It will allow researchers with similar aims to optimize device battery, memory usage and lifespan to maximise the ability to correctly quantify life history events.

Time-varying effects of local weather on behavior and probability of breeding deferral in two Arctic-nesting goose populations

“…Yet, advances in miniaturized tracking technologies such as accelerometers allows for a substantial amount of information to be collected from each individual. The method demonstrated in this paper may increase our capacity to link animal behavior and individual reproductive output, as new methods are being derived for identifying reproductive events remotely (e.g., Schreven et al 2021 ; Ozsanlav-Harris et al 2022 ), with environmental conditions (Valletta et al 2017 ) and consequences of climate change (e.g., Lameris et al 2018 ). Further, these methods could be used in the context of varying landscapes to better understand linkages among behavior, environmental conditions, and reproductive success, allowing practitioners to pinpoint critical periods of the annual cycle to ascribe priority areas for improved conservation efforts.…”

Differing drivers of decline within a migratory metapopulation has implications for future conservation

“…In addition, biologging data from four males and one immature female, which are known not to incubate (Stroud, 1982), were not classified with a single day of incubation. Only breeding seasons with a GPS fix frequency <60 min and an Acc burst frequency <24 min were included because coarser resolutions reduced incubation classification accuracy (Ozsanlav‐Harris et al, 2022).…”

“…We classified incubation events from biologging data using the methodology described in Ozsanlav-Harris et al (2022). This and an Acc burst frequency <24 min were included because coarser resolutions reduced incubation classification accuracy (Ozsanlav-Harris et al, 2022).…”

“…This makes it difficult to identify the point at which breeding fails and thus identify contributing factors. Biologging devices provide a unique opportunity to remotely assess productivity at the individual‐level much earlier in the reproductive period, for example, nesting success (Ozsanlav‐Harris et al, 2022). Using a set of known tracked breeders it is possible to derive movement metrics from GPS data and energy expenditure from accelerometer (Acc) data (Qasem et al, 2012) to determine patterns indicative of incubation.…”

“…Using a set of known tracked breeders it is possible to derive movement metrics from GPS data and energy expenditure from accelerometer (Acc) data (Qasem et al, 2012) to determine patterns indicative of incubation. A rule‐based classification system can then be used to classify days as ‘incubation’ or ‘not’ using a method that could be applied to other species with uniparental incubation (Ozsanlav‐Harris et al, 2022).…”