White-capped albatrosses alter fine-scale foraging behavior patterns when associated with fishing vessels

Abstract

Incidental bycatch of seabirds in commercial fishing activities is known to cause declines in seabird populations. However, the full impacts on the ecology of seabirds, including effects on seabird distribution and behavior, through the association with fisheries are not fully understood. We developed a novel method to integrate fine-scale GPS tracking data from the foraging trips of 25 white-capped albatross Thalassarche steadi within sub-Antarctic New Zealand with fishing effort distribution data to (1) quantify fine-scale overlap between individual albatrosses and individual vessels and (2) characterize behavioral changes in albatrosses when they are associated with fishing vessels. Overlap between vessels and albatrosses occurred in 68% of tracks. However, albatrosses demonstrated high variability in foraging trip destinations and association rates with fishing activity, both between and within individuals. Eight tracks never overlapped a fishing vessel. Of the 17 tracks that did overlap, a range of 2 to 73% of foraging effort on each trip occurred while overlapping a fishing vessel. Albatross foraging behavior was characterized by a significantly slower and straighter path when overlapping vessels. This study highlights the utility of GPS tags to examine the fine-scale distribution of seabirds in relation to fishing activity, revealing how effects of fisheries on marine megafauna may extend beyond mortality and injury as well as population numbers. However, results are currently constrained, not only by limited tracking data sets, but also by the quality (spatio-temporal resolution) and availability of fishing effort data. Critical conservation issues related to the effects of fisheries on threatened marine megafauna cannot be fully addressed without comparative data sets with resolution equal to GPS tags.

Fisheries Bycatch as an Inadvertent Human-Induced Evolutionary Mechanism

“…The observed heterogeneity in survival may correspond to two types of individuals that reflect behavioral syndromes, such as those strongly attracted by fishing vessels and therefore susceptible to capture and mortality by longlines, and those less attracted by fishing vessels and less susceptible to capture. Indeed, recent studies showed that some individuals appear to be more attracted to fishing vessels than others on a handful of seabird species [32], including albatrosses [33], [34]. Harvesting, fishing or trapping can produce within-species differential vulnerability in target species [6], [12], [14].…”

“…Based on the observation that within a population of a given seabird species some individuals appear to be more attracted to fishing vessels than others [32], including albatrosses [33], [34], we hypothesize that this held for our study population of albatrosses, and can account for the paradoxical population trend. The population is assumed to be heterogeneous, with two types of individuals that reflect behavioral syndromes (animal personalities): those strongly attracted by fishing vessels and therefore susceptible to capture and mortality by longlines; and those less attracted by fishing vessels and therefore less susceptible to capture.…”

Individual consistency in the foraging behaviour of Northern Gannets: Implications for interactions with offshore renewable energy developments

“…The repeatability of an individual's foraging behaviour has been demonstrated in previous studies [35,54] whereas others report changes in foraging behaviour. For example, Torres et al [55] found that the white-capped albatross Thalassarche steadi had highly variable and adaptable foraging destinations in response to fisheries.…”

Classification of Animal Movement Behavior through Residence in Space and Time

“…We recorded incubation foraging trips of adult albatross (n = 24) after securing the GPS tag to back feathers using Tesa ® tape. To focus on at-sea behaviors we removed all points within 5 km of the colony [16]. We completed all analysis in R [17] and implemented in C, with adapted code from Chirico [18] and Kahle and Wickham [19].…”