1. Bats Use Path Integration Rather Than Acoustic Flow to Assess Flight Distance along Flyways 
Gal Aharon, Meshi Sadot, Yossi Yovel 
Current Biology 
Volume 27, Issue 23, Pages e3 (December 2017)
DOI: /j.cub
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2. Figure 1
Bats Do Not Use Statistical Acoustic Flow for Estimating Traveled Distance
(A) Top view schematic of the experimental setup. Note that the x and y axes have different scales.
(B and C) Prediction of bats’ searching behavior if they used statistical acoustic flow (B) or other navigation strategies such as path integration (C) under the different testing conditions (different colors). Each rectangle depicts a top view of the corridor, with colored bars showing the predicted searching location in each condition.
(D) Searching behavior (position over time) of two individual bats under different acoustic-flow conditions. Panels show examples of one trial in each condition (for two bats). Turning points in the corridor are depicted by black circles.
(E) The center and width of the searching location of four bats under different flow conditions. The center was defined as the mean of all turning points, and the width was defined as the mean distance between two consecutive turning points (STAR Methods).
The vertical gray dotted line marks the original platform position at 20 m in all panels. See also Figure S1 and Tables S1 and S2.
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3. Figure 2 Bats Use Path Integration for Estimating Traveled Distance
(A–C) The experimental setup (A), predictions (B), and results (C) of three bats in the paradigm in which all poles were removed from the corridor. Note that the x and y axes have different scales. Green lines in (B) depict the prediction for the path-integration strategy in the corridor without poles.
(D–F) The experimental setup (D), predictions (E), and results (F) of four bats in the paradigm in which the corridor was shifted by 10 m. The circles and arrows show the bats’ release points for the experiments with the original (black) and shifted corridor (green). Green lines in (E) depict the prediction for the path-integration strategy in the shifted corridor.
In both (C) and (F), the black bars depict the same control trials as in Figure 1, which serve as a reference for where the bat learned the position of the platform. The center of the searching location was defined as the mean of all turning points, and the width was defined as the mean distance between two consecutive turning points (STAR Methods). The vertical gray dotted line marks the original platform position at 20 m in all panels. See also Table S2.
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4. Figure 3 Bats Overestimate Target Location When Flying Faster
(A) Searching location as a function of flight speed for three bats with additional weight (circles) and four bats without additional weight (asterisks). The mean speed in each trial was calculated by taking the mean of the momentary speed (STAR Methods). Black dashed line represents the mean ± SEM of all four bats.
(B) Points of deceleration as a function of flight speed for three bats with additional weight (circles) and four bats without additional weight (asterisks). Black dashed line represents the mean ± SEM of all four bats.
(C) Prediction of bats’ searching and deceleration locations when comparing the 50% slowest flights (black) and 50% fastest flights (gray). The width of the turning is predicted to be larger in the fast flights because deceleration takes more time when flying faster.
(D) Deceleration and searching locations (mean ± turning width) of four bats are shown (deceleration location: t test, p = 0.03 for the group, and permutation t test, p < 0.02 for three individuals; p = 0.13 for the bat that died and did not complete the experiment; searching location: t test, p = 0.03 for the group, and permutation t test, p < 0.05 for all four individuals). Colors are the same as in (C); solid lines indicate searching locations, and dashed lines indicate deceleration locations. The vertical gray dotted line marks the original platform position at 20 m. Only the deceleration points toward the end of the corridor were used in this figure (as averaging the deceleration points in both directions would nullify the effect). Thus, the deceleration points appear after the mean searching points but before the end of the searching segment. The same pattern was obtained for the deceleration points in the opposite direction—there was a significant correlation between speed and deceleration in all bats (Pearson correlation: p < 0.05, R > 0.46 in all four bats). All bats decelerated later in this direction too, although two bats were only nearly significant (t test: p = 0.06 for the group; permutation t test: p < 0.05 for two individuals, p = 0.08 for the other two).
See also Table S2.
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5. Figure 4
Bats Underestimated the Location of the Target in Wind and Used Path Integration over 70 m
(A) Two examples of flight trajectories in wind (same presentation as in Figure 1). Note how the turning positions gradually drift toward the beginning of the corridor—the opposite of the typical behavior. Arrows show the direction of the wind. The vertical gray dotted line marks the original platform position at 20 m.
(B) The center and width of the searching location of four bats in the wind (gray) and the control (black) conditions. Arrows show the direction of the wind. The vertical gray dotted line marks the original platform position at 20 m.
(C) The mean searching location of four individual bats as a function of accumulated flight distance is shown for the control condition (poles every 150 cm with no platform). See Figure S3 for the other conditions. Two cases are shown: the dashed line indicates when the bat came within the detection range of the start or end tulle walls, and the solid line indicates when the bat did not encounter the tulle walls. The mean ± SEM of all four bats is shown. To ascertain that the bats could sense the end tulle walls, we ran the same analysis but this time assumed a 5 m sensing range, and we got the same results of a gradually shifting searching location.
See also Figure S3 and Table S2.
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