Abstract
Detailed information acquired using tracking technology has the potential to provide accurate pictures of the types of movements and behaviors performed by animals. To date, such data have not been widely exploited to provide inferred information about the foraging habitat. We collected data using multiple sensors (GPS, time depth recorders, and accelerometers) from two species of diving seabirds, razorbills (Alca torda, N = 5, from Fair Isle, UK) and common guillemots (Uria aalge, N = 2 from Fair Isle and N = 2 from Colonsay, UK). We used a clustering algorithm to identify pursuit and catching events and the time spent pursuing and catching underwater, which we then used as indicators for inferring prey encounters throughout the water column and responses to changes in prey availability of the areas visited at two levels: individual dives and groups of dives. For each individual dive (N = 661 for guillemots, 6214 for razorbills), we modeled the number of pursuit and catching events, in relation to dive depth, duration, and type of dive performed (benthic vs. pelagic). For groups of dives (N = 58 for guillemots, 156 for razorbills), we modeled the total time spent pursuing and catching in relation to time spent underwater. Razorbills performed only pelagic dives, most likely exploiting prey available at shallow depths as indicated by the vertical distribution of pursuit and catching events. In contrast, guillemots were more flexible in their behavior, switching between benthic and pelagic dives. Capture attempt rates indicated that they were exploiting deep prey aggregations. The study highlights how novel analysis of movement data can give new insights into how animals exploit food patches, offering a unique opportunity to comprehend the behavioral ecology behind different movement patterns and understand how animals might respond to changes in prey distributions.
Highlights
Food resources are generally aggregated over a range of scales in hierarchical patch systems where high-density patches at small scales are nested within low-density patches at larger scales (Fauchald, Erikstad, & Skarsfjord, 2000; Regular, Hedd, & Montevecchi, 2013; Weimerskirch, Gault, & Cherel, 2005)
We propose that I) the number of PCE in individual dives can be used as an indication of prey encountered through the water column, and II) the time spent pursuing and catching prey (PCT) in dive bouts can reveal different foraging strategies performed in response to changes in prey availability to the seabirds in the area visited
We propose that the observed species-specific foraging strategies are the result of species-specific optimal foraging decisions taken according to perceived availability and profitability of foraging patches encountered as a response to dynamic changes in both prey availability and characteristics of heterogeneous environments
Summary
Food resources are generally aggregated over a range of scales in hierarchical patch systems where high-density patches at small scales are nested within low-density patches at larger scales (Fauchald, Erikstad, & Skarsfjord, 2000; Regular, Hedd, & Montevecchi, 2013; Weimerskirch, Gault, & Cherel, 2005) Foragers should adjust their foraging patterns in such complex environments to maximize foraging efficiency. Optimal foraging theory (OFT) is widely used to explain the foraging behavior of animals According to this theory, predators foraging in patchy environments make decisions to maximize the rate of energy intake while minimizing energy costs (Pyke, Pulliam, & Charnov, 1977; Stephens & Charnov, 1982). Technical advances over the last 50 years have enabled the collection of data which can capture the types of movement, feeding behavior, and physiological processes in environments where direct observations of behavior are difficult or impossible (Evans, Lea, & Patterson, 2013). The analysis and interpretation of these types of data involve the classification of different behavioral patterns, reconstructed time– depth profiles, and quantification of costs and benefits of different movement patterns (Brown, Kays, Wikelski, Wilson, & Klimley, 2013; Hays et al, 2016; Hussey et al, 2015; Kays, Crofoot, Jetz, & Wikelski, 2015)
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