Time-in-area represents foraging activity in a wide-ranging pelagic forager

Abstract

Successful Marine Spatial Planning depends upon the identification of areas with high importance for particular species, ecosystems or processes. For seabirds, advancements in biologging devices have enabled us to identify these areas through the detailed study of at-sea behaviour. However, in many cases, only positional data are available and the presence of local biological productivity and hence seabird foraging behaviour is inferred from these data alone, under the untested assumption that foraging activity is more likely to occur in areas where seabirds spend more time. We fitted GPS devices and accelerometers to northern gannets Morus bassanus and categorised the behaviour of individuals outside the breeding colony as plunge diving, surface foraging, floating and flying. We then used the locations of foraging events to test the efficiency of 2 approaches: time-in-area and kernel density (KD) analyses, which are widely employed to detect highly-used areas and interpret foraging behaviour from positional data. For KD analyses, the smoothing parameter (h) was calculated using the ad hoc method (KD ad hoc ), and KD h=9.1 , where h = 9.1 km, to designate core foraging areas from location data. A high proportion of foraging events occurred in core foraging areas designated using KD ad hoc , KD h=9.1 , and time-inarea. Our findings demonstrate that foraging activity occurs in areas where seabirds spend more time, and that both KD analysis and the time-in-area approach are equally efficient methods for this type of analysis. However, the time-in-area approach is advantageous in its simplicity, and in its ability to provide the shapes commonly used in planning. Therefore, the time-in-area approach can be used as a simple way of using seabirds to identify ecologically important locations from both tracking and survey data.

Seasonal shifts in foraging distribution due to individual flexibility in a tropical pelagic forager, the Ascension frigatebird

“…S1 in the Supplement). We calculated the time-in-area using the function 'tripGrid' in R package 'trip' (Sumner 2015), which adequately represents the foraging intensity in a given area for pelagic seabirds (Warwick-Evans et al 2015). We then downloaded environmental variables corresponding both spatially and temporally to the tracking data from the Env-DATA system in Movebank (Dodge et al 2013) and from the Copernicus Marine Environmental Monitoring Service (http:// marine.…”

Search and foraging behaviors from movement data: A comparison of methods

“…Gannets are visual predators (Cronin, ) and undertake plunge‐diving from height, entering the water at speeds of up to 24 m/s (Chang et al., ). Prior to diving, gannets typically slow their flight and increase their path sinuosity (Wakefield et al., ; Bodey et al., ; Patrick et al., ; Warwick‐Evans et al., ). The relationship between slow speed during search and prey capture attempts has been established both theoretically (Bartoń & Hovestadt, ; Benhamou, ) and empirically in a variety of mobile marine and terrestrial species (Anderson & Lindzey, ; Byrne & Chamberlain, ; Edwards, Quinn, Wakefield, Miller, & Thompson, ; McCarthy, Heppell, Royer, Freitas, & Dellinger, ; Towner et al., ; Wakefield et al., ; Williams et al., ).…”

Predicting the impacts of wind farms on seabirds: An individual‐based model

“…Individuals were defined as searching where GPS points had a tortuosity index of <0.9 and a speed >1 m/s. The cells in this grid comprising the top 25% of time spent undertaking searching behaviour for all individuals combined were identified as key foraging areas (Warwick-Evans et al, 2015;Figure 1).…”

“…Areas where gannets spend more time represent areas of increased foraging, and hence areas of higher fish availability (Warwick-Evans et al, 2015). The distribution of fish among the patches was therefore assigned by multiplying the TIA grid by a numerical constant (Appendix S5).…”

“…This was used to determine the probability of foraging in each cell, rather than flying straight through it. All grids were created independently for Ethographer for IGOR Pro (Sakamoto et al, 2009) was used to extract behaviours from the acceleration data automatically, based on unsupervized cluster analysis of the acceleration signals as described in full in Warwick-Evans et al (2015). We were able to classify all periods within the first 5 days of data per bird as foraging, flying, resting on the water and diving.…”