Additional file 1 of Prey ingestion rates revealed by back-mounted accelerometers in Eurasian spoonbills
Creators: name: Lok, Tamar
name: van der Geest, Matthijs
name: Bom, Roeland A.
name: de Goeij, Petra
name: Piersma, Theunis
name: Bouten, Willem
Publisher: name: figshare
Year: 2024
Subjects: subject: Ecology
subject: FOS: Biological sciences

description: Additional file 1: Figure S1. To improve the classification of passive (soaring/gliding) flight, we graphically inspected the acceleration data during autumn migration flights of three individuals (6283, 6287 and 6291). Here, the migratory flight of spoonbill 6291 on 25 September 2016, crossing Brittany (between the red bars) and then following the Atlantic coast of France (left panel), and the associated acceleration segments during the crossing of Brittany (right panel) are plotted. The light blue dots on the itinerary reflect the positions during which the bird performed passive flight based on visual inspection of the plotted acceleration segments on the right. We only selected samples that consisted entirely of passive flight, which are outlined in bold in the right panel. Figure S2. Duration of behaviours expressed during continuous sampling bouts of 10 s, which is a subset of all annotated data as shown in Table S2 for (a) all unique behaviours as distinguished during the video annotation and (b) pooled behaviours as described in Table S2. Sample sizes indicated on top reflect the number of times a behaviour was expressed for a certain amount of time, summed over the five observed birds. Figure S3. Distribution of segment lengths resulting from applying the flexible segmentation method to the annotated data set with different values for the ARL0 parameter. Prior to applying the flexible segmentation method, the annotated data set was cut into samples of 1.6 s. The minimum number of measurements in a segment was set at 3 (i.e., 0.15 s for 20 Hz data). Figure S4. Distribution of segment lengths per assigned behaviour (i.e., the behaviour occurring during most of that segment) resulting from applying the flexible segmentation method to the annotated data set with ARL0 = 100. Prior to applying the flexible segmentation method, the annotated data set was cut into samples of 1.6 s. The minimum number of measurements in a segment was set at 3 (i.e., 0.15 s for 20 Hz data). Figure S5. Explorative analysis of the effects of reducing the number of predictor variables and of down- and up-sampling the amount of annotated data used to train the random forest model for certain behaviours that were over- versus underrepresented in the data (search and stand versus drink, handle, walk and fly-passive). Down-sampling was done on the entire data set (to maintain the 70% train and 30% test data set), while up-sampling was only performed on the training data set (as identical (copied) segments would otherwise appear in the training and testing data set). The full predictor set contains 32 predictor variables, whereas the reduced predictor set uses only the predictor variables that were estimated to result in &gt; 20% decrease in overall accuracy when removed from the model, as calculated by the function importance of the randomForest R-package. Figure S6. Proportion of observed behaviours classified as a certain behaviour (top row) and the proportion of predicted behaviours that were actually a certain observed behaviour (bottom row). Figure S7. Seasonal variation in prey ingestion rates of adult female spoonbills foraging in the Wadden Sea, as estimated by the fixed segmentation method with segments of 0.4 s (in blue) and the flexible segmentation method using ARL0 = 100 (in orange). Although 95% confidence intervals are mostly overlapping, the flexible segmentation method estimates consistently—on average 33%—lower prey ingestion rates than the fixed segmentation method. Figure S8. Patterns of sensitivity, precision and F-measure when reducing the number of segments to the number of 2.0 s segments available for each behaviour (i.e. the smallest sample size). By doing so, we remove the potential effect of smaller samples sizes at longer segment lengths on classification performance. While this reduced the sensitivity and precision of particularly ingesting prey, the patterns in relation to segment length remained similar to those reported in Fig. 3 of the main paper. Figure S9. Estimated percentage of time spent searching and ingesting prey by adult female spoonbills, overall (left panels) and when in the Wadden Sea (right panels), and the resulting prey ingestion rates when using the fixed segmentation method with segments of 0.4 s (in blue) and the flexible segmentation method using ARL0 = 100 (in orange). From these graphs, it becomes clear that the lower estimated prey ingestion rates by the flexible segmentation method are mainly driven by the lower estimated proportion of time spent ingesting prey. Another remarkable difference is that the flexible segmentation method estimates a higher proportion of searching than the fixed segmentation method, but only early in the breeding season (i.e. in the months April–May) when the birds are attached to the nest for a considerable part of their time. This suggests that the flexible segmentation method performs worse in distinguishing incubating (sitting on the nest) from searching than the fixed segmentation method (see also Figure S10) and may as such overestimate the proportion of time spent searching, resulting in an underestimation of prey ingestion rates. Figure S10. Proportion of time performing different behaviours throughout the season. Adult females are known to spent about 50% of their time sitting on the nest during the incubation and early chick rearing phase (April–June). This graph thus shows that sitting is rather poorly distinguished from standing, as the birds were estimated to be sitting maximally 10% of the time. It is thus advised to pool sitting and standing into a single “inactive” category and use additional information to determine whether the bird was incubating (e.g., using the location of the nest). Figure S11. Classification performance as a function of acceleration sampling frequency (number of acceleration measurements per second) using a fixed segment length of 0.8 s. Segments of 0.8 s were used to still have two measurements in the sample with a sampling frequency of 2 Hz (which is needed to estimate ODBA, an important predictor variable). Table S1. Information on captured and tracked adult Eurasian spoonbills in the colony on Schiermonnikoog. Note that one individual (with colourcode LaG/RBYf) was caught three times (in different years) to replace its malfunctioning tracker. Table S2. Amount of annotated acceleration data (in seconds) per behavioural class recorded per bird (with the sex indicated in brackets) and in total. Note that for 6283, 6287 and 6291, passive flight data were graphically validated (based on visual inspection of acceleration graphs) instead of based on visual observation of the bird (see Fig. S1). Foraging behaviour was video-recorded for three birds at three different sites: 760 in the Wadden Sea (marine, N53.477° E6.284°), 763 in the Lauwersmeer (freshwater, N53.358° E6.256°) and 1608 in the Bantpolder (freshwater, N53.405° E6.151°). Table S3. Number of days per bird per month included in the analysis (i.e., with at least 20 s of acceleration data classified as foraging in the Wadden Sea) of seasonal variation in prey ingestion rates of adult female spoonbills foraging in the Wadden Sea.