Modelling collective navigation via non-local communication

Abstract

Collective migration occurs throughout the animal kingdom, and demands both the interpretation of navigational cues and the perception of other individuals within the group. Navigational cues orient individuals towards a destination, while it has been demonstrated that communication between individuals enhances navigation through a reduction in orientation error. We develop a mathematical model of collective navigation that synthesizes navigational cues and perception of other individuals. Crucially, this approach incorporates uncertainty inherent to cue interpretation and perception in the decision making process, which can arise due to noisy environments. We demonstrate that collective navigation is more efficient than individual navigation, provided a threshold number of other individuals are perceptible. This benefit is even more pronounced in low navigation information environments. In navigation ‘blindspots’, where no information is available, navigation is enhanced through a relay that connects individuals in information-poor regions to individuals in information-rich regions. As an expository case study, we apply our framework to minke whale migration in the northeast Atlantic Ocean, and quantify the decrease in navigation ability due to anthropogenic noise pollution.

Collective movement as a solution to noisy navigation and its vulnerability to population loss

“…Migrating animals often move together. In fact, recent research suggests that even animals that appear to migrate alone may be more social than they seem-for example, flight calls made by nocturnal bird migrants [9], or the vocalizations of migratory whales [35] may allow for collective navigation even when individuals appear to be alone. The prevalence of group migration highlights the importance of better understanding collective navigation for conservation, and not just for the migrating species in question.…”

Intent matters: how flow and forms of information impact collective navigation

“…The arguments of the resultant vectors are denoted ϕj′=false0argRfalse¯jθand ϕjδ′=false0argRfalse¯jδ.The parameter α is a weighting between the headings based on the group and inherent information. Here, we use an equal weighting ( α = 1/2) which, following a sensitivity analysis, was shown to broadly give the fastest successful migration times under a variety of information fields [31]. The combination is the same irrespective of the heading type (actual, (A); intended, (I)) and sensing mechanism (nearest-neighbour, (N); fixed-ranged, (F)).…”

“…To choose an appropriate value for the concentration parameter, κ ′ i , given the number of neighbours, we preferentially use a pre-calculated lookup table of the likelihood. This method is expanded upon in [31]; we only note here that it avoids biases in the estimates of κ ′, calculated by repeatedly generating samples for fixed falsefalse|Nfalsefalse| and κ and determining the uncorrected κ ′ value for each sample. Algorithms for this method are given in the electronic supplementary material.…”

Challenges of mismatching timescales in longitudinal studies of collective behaviour

“…Finally, the selective pressures leading to the evolution of aggregations of individuals influence the relevant domains in which conflicts of interest might occur. For example, forming and maintaining groups can benefit individuals by increasing their ability to detect predators [49], increasing their ability to find food [50], improving their navigation ability [51][52][53] or reducing the energetic costs of movements [54]. However, differences in preferences can arise across all of these domains, including whether risk is present [55], what food patches to choose [56], which direction to move in [2] or the speed of locomotion [19].…”