Insectivorous bats form mobile sensory networks to optimize prey localization: The case of the common noctule bat

Abstract

Animals that depend on ephemeral, patchily distributed prey often use public information to locate resource patches. The use of public information can lead to the aggregation of foragers at prey patches, a mechanism known as local enhancement. However, when ephemeral resources are distributed over large areas, foragers may also need to increase search efficiency, and thus apply social strategies when sampling the landscape. While sensory networks of visually oriented animals have already been confirmed, we lack an understanding of how acoustic eavesdropping adds to the formation of sensory networks. Here we radio-tracked a total of 81 aerial-hawking bats at very high spatiotemporal resolution during five sessions over 3 y, recording up to 19 individuals simultaneously. Analyses of interactive flight behavior provide conclusive evidence that bats form temporary mobile sensory networks by adjusting their movements to neighboring conspecifics while probing the airspace for prey. Complementary agent-based simulations confirmed that the observed movement patterns can lead to the formation of mobile sensory networks, and that bats located prey faster when networking than when relying only on local enhancement or searching solitarily. However, the benefit of networking diminished with decreasing group size. The combination of empirical analyses and simulations elucidates how animal groups use acoustic information to efficiently locate unpredictable and ephemeral food patches. Our results highlight that declining local populations of social foragers may thus suffer from Allee effects that increase the risk of collapses under global change scenarios, like insect decline and habitat degradation.

Keywords: automated radio tracking; ephemerality; group foraging; simulation; sociality.

Fig. 1.

Concept of a mobile sensory network, illustrated for insect-feeding bats. Three insectivorous bats align their flight paths (simplified as two-dimensional trajectories) while flying from left to right. Bats can detect insect patches using echolocation at distances of up to 15 m, but can eavesdrop on conspecifics as far away as 160 m. When a bat finds a food patch and starts hunting for single prey items (red bat), it uses specific hunting calls, so-called feeding buzzes (25), which alert its direct neighbor (yellow bat) to the patch location. The yellow bat would then abruptly change its flight direction to approach the patch, leading its other neighbor (blue bat) to reorient as well, and so on, until all bats in the network get close enough to the food patch to eavesdrop on the feeding buzzes of conspecifics that indicate active insect pursuit and therefore the presence of food. Thus, the information can slowly propagate across the network through changes in flight and echolocation behavior of chains of neighboring individuals. Searching for food in a network is most effective when interindividual distance and patch diameter match, and therefore, all food patches that lie in the flight direction of the network will be found, even though individual bats actively scan only a small portion of the total area. In this example, every additional bat may increase the zone scanned by the network by 160 m, while only actively sampling a zone of 30 m. Thus, a mobile sensory network consisting of only six bats may create a moving scanning zone of more than 1 km in width.

Fig. 2.

Flight behavior of bats close to conspecifics. Relative selection strength of a focal bat (black bat) for distance to its nearest conspecific (white bat) and distance-dependent angle persistence of complete flights, as calculated from the iSSA. Deviation from random behavior concerning selection for the distance to conspecifics is indicated by symbol and color. During 53% of the flights yielding significant selection coefficients, bats flew closer to conspecifics than expected and decreased their directional persistence as they got closer (Lower Right quadrant). In an additional 23% of the flights yielding significant selection coefficients, bats flew away from conspecifics and decreased their directional persistence (Upper Left quadrant). For graphical reasons, coefficients for seven flights that fell outside the plotted range are not shown (four in Upper Left quadrant, two in Upper Right quadrant, one in Lower Right quadrant, six of them being significant on the 0.1 level).

Fig. 3.

Movement properties of interacting bats during search. Short-term response (32 s) of a focal bat searching for insects to its nearest (searching or hunting) tagged conspecific as a function of the initial interindividual distance. Blue lines depict the effect from the model on simultaneously flying bat dyads; yellow lines depict the effect of the null model based on pseudodyads where bats flew in the same area, but during different days. Bands represent the 95% confidence intervals. (A and B) Difference in bearings. Positive values indicate that bats moved in diverging directions, negative values indicate that bats moved in converging directions. (C and D) Change of interindividual distance.

Fig. 4.

Distributions of the mean network sizes, calculated for one randomly chosen focal bat per model run, shown for different numbers of modeled bats and aggregated over the nine different levels of resource distribution (between 1 and 213 patches). A number of three means, that the focal bat was connected with a conspecific that was itself connected with another conspecific (chain of three bats). The Inset in the Lower Left panel shows the network size distribution for a model scenario with four food patches and 80 bats. Inset is to scale with the large panels.

Fig. 5.

Time until simulated bats found unoccupied food cells (median per model run), depending on whether bats used empirically derived movement strategies: that is, mobile sensory networking behavior, aligned their flights during prey search, used local enhancement or foraged solitarily. The modeled scenarios included different numbers of bats (whereas a number of 80 bats is the estimated colony size from the empirical study) and levels of spatial resource aggregation, as reflected by the number of food patches. The number of food cells in the model was fixed at 213 cells measuring 75 × 75 m, which represented the median area used for hunting per night by tracked bats in our study (based on kernel density estimates from localizations recorded during hunting). Tracked bats used ∼5 ± 3 (mean ± SD) distinct patches for hunting per day, for which all scenarios showed an advantage of the mobile sensory network strategy over the other foraging strategies.