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Influence of Local Wind Conditions on the Flight Speed of the Great Cormorant Phalacrocorax carbo

Influence of Local Wind Conditions on the Flight Speed of the Great Cormorant Phalacrocorax carbo Hindawi Publishing Corporation International Journal of Zoology Volume 2012, Article ID 187102, 7 pages doi:10.1155/2012/187102 Research Article Influence of Local Wind Conditions on the Flight Speed of the Great Cormorant Phalacrocorax carbo 1 1 1 2 3 Ken Yoda, Tadashi Tajima, Sachiho Sasaki, Katsufumi Sato, and Yasuaki Niizuma Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan International Coastal Research Center, Atmosphere and Ocean Research Institute, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8564, Japan Faculty of Agriculture, Meijo University, 1-501 Shiogamaguchi, Tenpaku-ku, Nagoya 468-9502, Japan Correspondence should be addressed to Ken Yoda, yoda.ken@nagoya-u.jp Received 13 June 2012; Revised 3 August 2012; Accepted 14 November 2012 Academic Editor: Inma Estevez Copyright © 2012 Ken Yoda et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In seabirds, the relationship between flight speed and wind direction/speed is thought to be particularly important for studying energy-saving strategy and foraging habitat selection. In this study, we examined whether the ground and calculated air speeds of four great cormorants (Phalacrocorax carbo)wereaffected by wind conditions using high-resolution GPS data loggers. The birds increased their ground flight speed in tailwinds, decreased it in headwinds, and changed their air speed in relation to wind components. However, they did not change their foraging sites according to the wind conditions. They were likely to respond to moderate wind conditions by adjusting their air speed without changing their foraging sites. 1. Introduction speed have been used in many bird studies (see papers cited in [9]), but this leads to erroneous interpretations of wind Understanding how environmental factors affect animal effects on bird flight [10]; statistically rigorous approaches movements is of central importance to movement ecology need to be used [9]. [1]. In seabirds, the relationship between flight speed and In this study, we deployed fine-scaled GPS data loggers wind direction/speed is thought to be particularly important on free-ranging great cormorants (Phalacrocorax carbo)to for studying energy-saving strategy and foraging habitat evaluate the effects of winds on their ground speed (i.e., selection and has been well examined [2, 3]. For example, the the speed of the bird with respect to the ground) and family Procellariiformes is likely to favour side or tailwinds calculated air speed (i.e., the speed relative to the air in at large scales (thousands of kilometres) [4, 5], which can which the bird is flying). Cormorants often commute in reduce their energy expenditure [4] or lead sexually dimor- a straight line to memorised foraging areas [11–13]and phic species to be segregated in foraging areas [6]. Thus, use powered flight with uninterrupted flapping without the effects of winds are expected to exert strong selection gliding; therefore, they are suitable for examining wind pressures on morphology, behaviour, and the life histories of effects on seabird movements and expected to follow the birds [7, 8]. However, several factors can bias the relationship theory of powered flight (e.g., [14]). In addition, because between wind and flight speed in seabirds, especially at fine the maximum foraging range of the great cormorant is spatial scales (several to tens of kilometres). First, the flight relatively small (less than 15 km; [15]), we could use local paths of seabirds are often convoluted, making it difficult wind measurements that would be nearly identical to the to relate flight paths with wind conditions. Second, because winds that the birds encountered. We examined whether wind conditions change dramatically during short time the ground and calculated air speeds of great cormorants periods, large-scale meteorology such as satellite-derived wind data is insufficient for detecting the effects of winds on were affected by wind conditions and we determined if they bird flight at fine scales. Third, conventional analyses of air changed foraging sites according to wind conditions. 2 International Journal of Zoology ◦  ◦ 2. Materials and Methods station: 34 44 N, 136 31 E, 39.6 m asl). For the outward and homing flights of each foraging trip, the absolute difference This study was conducted in June 2010 at the Unoyama in the angle between flight direction and wind direction (i.e., ◦  ◦ breeding colony (34 48 N, 136 53 E) and the Ishigakiike 0–180 ) was calculated. Wind speed in the direction of flight ◦  ◦ colony (34 51 N, 136 36 E) in the Tokai region of Japan. was calculated as the product of wind speed and the cosine of Great cormorants nest on trees in these regions [16]. Because the angle. We calculated air speed using ground speed, wind they temporally leave their nests when humans approach, we speed, and the angle between the ground and wind vectors. used alpha-chloralose (Tokyo Chemical Industry Co., Ltd., To examine the relationship between ground flight speed Tokyo, Japan) to capture specimens. Alpha-chloralose is an and wind speed in the direction of flight, a linear mixed anaesthetic and it allowed us to capture cormorants safely. model that treated individual birds as a random effect was We inserted 35–40 mg of alpha-chloralose powder into the run using the lme4 package in the statistical software R [17]. mouthofafish(e.g.,ayu Plecoglossus altivelis) and placed The significance of the fixed effects, as well as their 95% it on a nest that contained chicks using a fishing rod. After confidence intervals, was obtained from 100,000 Markov a bird ate the fish and was immobilised (within 1-2 h), we chain Monte Carlo (MCMC) simulations performed using captured it. In total, we captured eight birds in this way. the pvals.fnc function in R [18]. No birds were injured during the procedure. The procedures A two-dimensional generalised additive model (GAM) used in this study were approved by the Ministry of the was used to analyse the relationship between air speed and Environment, Japan. wind [9]. The GAM was used instead of the conventional We deployed GPS loggers on four breeding adults, we method that tests the relationship between air speed as a deployed acceleration data loggers on two other birds for function of ground speed and air speed (see papers cited different research purposes, and we did not deploy any in [9]) because the conventional analysis can produce erro- loggers on the remaining two birds. A small plastic base was neous correlations [9, 10]. We divided the wind variable into attached to the bird’s back feathers with adhesive tape (Tesa, two components (i.e., zonal and meridional components); Hamburg, Germany) and glue (Loctite 401). A data logger the eastern direction was represented by positive values of x was attached using a cable tie that could be cut remotely and the northern direction by positive values of y . The two (RC-150-150T, Little Leonard, Tokyo, Japan), which entered variables were implemented in a GAM by first transforming beneath the feathers that were glued to the base. To avoid them via a LOESS smoother (a locally weighted regression) having to recapture of birds using alpha-chloralose, we used with a maximum span of 80% and 2 degrees of freedom, a remote release system that could send a signal to cut the following [9]. To fit the GAM, we used the gam package in cable tie (RX-100N, Little Leonard). After several days of R[19]. attachment, we recovered the data loggers when the birds To examine the degree of overlap in foraging areas were on their nests. between successive trips, we calculated the proportion of the A GPS data logger consisted of a GPS receiver with foraging area of one trip that was covered by the foraging an antenna (GiPSy, Technosmart, Rome, Italy) and it was area of another trip, that is HR[i, j] = A[i, j]/A[i], where powered by a Li-SOCl battery (LS14500, SAFT, Paris, A[i, j] is the area of the intersection between the two foraging France). The GPS loggers were programmed to take posi- areas and A[i] is the foraging area of trip i [20]. These were tional fixes every 9 or 20 s. The overall mass was 59–84 g, calculated for two birds that conducted six successive trips. which corresponded to <5% of each bird’s body mass. The This analysis was performed using the adehabitat package in birds did not appear to be negatively affected by the loggers R[21]. We excluded one trip from this analysis because the or from being handled by the researchers. bird’s position was not obtained consistently in the foraging We calculated trip duration and trip range, which was area (Trip no. 4 in Table 1). defined as the maximum distance from the colony. The Data were analysed using Matlab version 7.1 (Math- cormorants commuted in a straight line without stopovers Works, Inc., Natick, MA, USA) and R version 2.14.1 [22]. (Figure 1); therefore, we could easily define outward and homing flights using the sudden initial decrease and the final increase in ground flight speed, which marked the first flight 3. Results and the final flight, respectively, during the trip (Figure 2). We defined foraging time as the period between the outward We obtained GPS data from four birds with body masses and homing flights; the foraging area was identified as the ranging from 1570 to 2140 g (mean = 1905 g). The birds area where the bird remained during that period. For each were named A, B (Unoyama colony), C, and D (Ishigakiike outward and homing flight, flight speed and direction were colony). The GPS loggers recorded 15 trips that consisted averaged. of 12 complete trips and 3 trips with truncated data due Wind direction and speed were recorded at two cli- to exhausted batteries. Mean trip duration and trip range matological stations operated by the Japan Meteorological were 5.1 ± 4.8 h (0.7–19.0 h, n = 12) and 13.1 ± 3.8km Agency (JMA). We used 10-min wind direction and speed (8.6–18.7 km, n = 12), respectively, for complete trips. For measurements for each outward and homing flight. For the truncated data, we only used the outward flights in our each flight, several wind measurements were averaged. The analysis. Finally, we obtained 27 outward and homing paths. weather stations were within 15 km of each colony (Figure 1; Average flight duration was 11 min (4–23 min, n = 27). ◦  ◦ Minamichita station: 34 44 N, 136 56 E, 6.5 m asl; Tsu Average wind speed on each path was 2.7 ± 1.4 m/s (0.8–5.3, International Journal of Zoology 3 34.9 N Chita Peninsula Ise Bay 5 km 34.74 N ◦ ◦ 136.5 E 137.2 E Figure 1: GPS tracks for four great cormorants during 15 foraging trips. The star and circle indicate the Unoyama and Ishigakiike breeding colonies, respectively. The two squares indicate the closest meteorological stations to each colony from which we derived wind information. Outward Homing Outward Homing 12:21 12:50 13:19 13:48 14:16 14:45 15:14 15:43 Time (h:m) Figure 2: Example of time-series data of ground flight speed and distance from the colony. The shadows indicate outward and homing flights. n = 27). Mean ground and air speeds on the paths were cormorants can be predicted by wind speed in the direction 14.4 ± 1.7 m/s (11.2–18.5, n = 27) and 14.6 ± 1.3 m/s (11.2– of flight (Figure 4(b)). Their ground flight speed increased 17.5, n = 27; Figure 4(a)), respectively. in tailwinds and decreased in headwinds. Qualitatively, this Ground flight speed increased significantly with wind matches very well with observations from visual surveys in strength relative to the birds (Figure 4(b)). A linear mixed which ground flight speeds of great cormorants in tailwinds model estimated S = 0.70 (±0.10, 0.48–0.91; SD, 95% CI)· were higher than in headwinds [23]. In addition, [23] W + 14.4 (±0.31, 13.4–15.1), where S and W were ground reported a mean ground speed of 18.8 m/s in a strong flight speed and wind speed, respectively, in the direction of tailwind (6.5 m/s), which agrees very well with our mixed- flight (P< 0.001). Air speed was significantly related to x model prediction of 19.0 m/s. On the other hand, [23] also (F = 5.2, P< 0.05), but not to y (F = 1.7, P = 0.2). Thus, reported a ground speed of 12.4 m/s in an extreme headwind air speed was only influenced by wind that was blowing east- (8.0 m/s), which was higher than our predicted value of west (Figure 5). 8.8 m/s in a similar wind. Great cormorants prefer to fly close Successive trips by two birds qualitatively showed high to the ground (i.e., ground effect; weak wind speeds closer to foraging-site fidelity and the foraging areas overlapped the ground ∼0.3 m) when flying into strong headwinds [23]; (Table 1; Figure 6). therefore, our predictive equation seems to underestimate their ground speed in such extreme headwinds, that is, outside our measured range (>5 m/s of wind speed). 4. Discussion The calculated air speeds of our birds (14.6 m/s) ranged between previously reported minimum power speeds (V ) Great cormorants encountered various wind speeds and mp for this species (13.5 [24] to 16.5 m/s [23]), which is the directions (Figure 3). The birds sometimes encountered contrasting wind conditions between outward and homing speed at which the least mechanical power is required from the flight muscles to maintain the bird flying at a constant air trips. Although sample size was small (four individuals), our speed [25]. Therefore, great cormorants might adopt V as data clearly demonstrated that the ground speed of great mp Distance from colony (km) Ground speed (m/s) 4 International Journal of Zoology Homing Homing 18 Outward Outward Wind speed (m/s) Wind speed (m/s) 3-4 1-2 3-4 1-2 2-3 2-3 0-1 0-1 (a) (b) Figure 3: Two examples of wind conditions and GPS positions. The Bird A encountered different wind conditions between outward and homing trips in (a) Trip no. 2 and (b) Trip no. 6, respectively. The star indicates the Unoyama breeding colony. The dots show the GPS positions that were interpolated at 1 min to produce a clearer display and the colour indicates the ground speed of the bird. Arrows indicate travel direction. The two rose plots on each panel show the frequency of occurrence of wind direction, which is the direction from which the wind was blowing (not where it was blowing to), and wind speed (colour) during outward and homing flights. Air Ground −4 −2 024 Flight speed Wind speed in the direction of flight (m/s) (a) (b) Figure 4: (a) Measured ground speeds and calculated air speeds for great cormorants and (b) the relationship between ground speed and wind speed in the direction of flight (tailwind component along the track direction). The solid line is the best fit that was estimated by a linear mixed model: the dashed lines indicate the 95% confidence intervals. was indicated for Kerguelen shag P. verrucosus [26]. However, decreases in tailwinds and increases in headwinds, this means the great cormorants showed changes in calculated air speed that the birds adopted maximum range speed (V ), which mr in relation to wind components (Figure 5). Optimal flight is the air speed where energy expenditure per distance theory predicts that V will be unaffected by winds [7]; travelled is minimal [27], and our data might support the mp therefore, air speed optimization [27] might explain changes optimality theory in relation to wind condition. Further in calculated air speed in relation to wind conditions, as study that incorporates morphological and actual air-speed found in this study. Because air and ground flight speed are measurements would be needed to clarify this issue. approximately equivalent in calm wind conditions, the actual Notably, the great cormorants showed strong foraging- air speed in this study might show the same relationship site fidelity (Table 1; Figure 6), which is common in to the wind as the ground speed (Figure 4(b)). If air speed cormorants [12, 28, 29], irrespective of wind conditions. This Flight speed (m/s) Ground flight speed (m/s) Ground speed (m/s) International Journal of Zoology 5 −1 −2 −1 −3 −2 P< 0.05 N.S. −4 −4 −2 0 2 −4 −2 0 2 4 x y w w (a) (b) Figure 5: The predicted relative influence of the wind components (x and y ) on the air speed of great cormorants. The eastern direction w w was represented by positive values of x and the northern direction by positive values of y . Calculated air speed was significantly related to w w x , but not to y . The solid lines are the fitted functional response and the broken lines represent standard error curves. w w 34.82 N ◦ 34.88 N Trip no.1 Trip no.2 Trip no.6 Trip no.6 Trip no.5 Trip Trip no.5 no.1 Trip no.2 Trip no.3 34.76 N Trip no.3 ◦ Trip no.4 34.72 N ◦ ◦ ◦ ◦ 137.00 E 137.12 E 136.50 E 136.65 E Wind speed (m/s) Wind speed (m/s) 2-3 2-3 5-6 5-6 1-2 1-2 4-5 4-5 3-4 0-1 3-4 0-1 (a) (b) Figure 6: Foraging area overlap in two great cormorants that conducted multiple foraging trips. The 95% contours of the kernel density estimates of the foraging areas are shown for (a) Bird A and (b) Bird C (see Table 1). The two rose plots show the frequency of occurrence of wind direction, which is the direction from which the wind was blowing (not where it was blowing to), and wind speed (colour) during outward and homing flights. might be related to low predictability of wind conditions in based on winds. The significant levels of foraging-site fidelity this study area, and therefore, foraging site selection based in successive foraging trips also suggest that their prey may be on wind would not be efficient in contrast to some seabirds predictable in space within a period of several days and that that live in “predictable” local wind conditions at large the cormorants are able to remember specific foraging sites spatial scales [3, 5, 29]. In addition, because the cormorants [12, 31, 32]. In general, seabirds with small foraging ranges using pure powered flight could not efficiently extract energy and corresponding short trip durations, as was the case in compared to gliders such as shearwaters [5], albatrosses [4], this study (5 h in mean trip duration), are likely to have boobies, and gannets [3, 30], they did not select foraging sites higher foraging site fidelity because the low probability of Effect on air speed 6 International Journal of Zoology Table 1: Foraging area overlap in two great cormorants, the boobies: adaptations to foraging in a tropical environment?” proportion of the foraging area of one trip that was covered by the Proceedings of the Royal Society B, vol. 272, no. 1558, pp. 53– foraging area of another trip, that is HR[i, j] = A[i, j]/A[i], where 61, 2005. A[i, j] is the area of the intersection between the two foraging areas [4] H. Weimerskirch, T. Guionnet, J. Martin, S. A. Shaffer, and D. and A[i] is the foraging area of trip i. 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Influence of Local Wind Conditions on the Flight Speed of the Great Cormorant Phalacrocorax carbo

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Copyright © 2012 Ken Yoda et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

Hindawi Publishing Corporation International Journal of Zoology Volume 2012, Article ID 187102, 7 pages doi:10.1155/2012/187102 Research Article Influence of Local Wind Conditions on the Flight Speed of the Great Cormorant Phalacrocorax carbo 1 1 1 2 3 Ken Yoda, Tadashi Tajima, Sachiho Sasaki, Katsufumi Sato, and Yasuaki Niizuma Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan International Coastal Research Center, Atmosphere and Ocean Research Institute, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8564, Japan Faculty of Agriculture, Meijo University, 1-501 Shiogamaguchi, Tenpaku-ku, Nagoya 468-9502, Japan Correspondence should be addressed to Ken Yoda, yoda.ken@nagoya-u.jp Received 13 June 2012; Revised 3 August 2012; Accepted 14 November 2012 Academic Editor: Inma Estevez Copyright © 2012 Ken Yoda et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In seabirds, the relationship between flight speed and wind direction/speed is thought to be particularly important for studying energy-saving strategy and foraging habitat selection. In this study, we examined whether the ground and calculated air speeds of four great cormorants (Phalacrocorax carbo)wereaffected by wind conditions using high-resolution GPS data loggers. The birds increased their ground flight speed in tailwinds, decreased it in headwinds, and changed their air speed in relation to wind components. However, they did not change their foraging sites according to the wind conditions. They were likely to respond to moderate wind conditions by adjusting their air speed without changing their foraging sites. 1. Introduction speed have been used in many bird studies (see papers cited in [9]), but this leads to erroneous interpretations of wind Understanding how environmental factors affect animal effects on bird flight [10]; statistically rigorous approaches movements is of central importance to movement ecology need to be used [9]. [1]. In seabirds, the relationship between flight speed and In this study, we deployed fine-scaled GPS data loggers wind direction/speed is thought to be particularly important on free-ranging great cormorants (Phalacrocorax carbo)to for studying energy-saving strategy and foraging habitat evaluate the effects of winds on their ground speed (i.e., selection and has been well examined [2, 3]. For example, the the speed of the bird with respect to the ground) and family Procellariiformes is likely to favour side or tailwinds calculated air speed (i.e., the speed relative to the air in at large scales (thousands of kilometres) [4, 5], which can which the bird is flying). Cormorants often commute in reduce their energy expenditure [4] or lead sexually dimor- a straight line to memorised foraging areas [11–13]and phic species to be segregated in foraging areas [6]. Thus, use powered flight with uninterrupted flapping without the effects of winds are expected to exert strong selection gliding; therefore, they are suitable for examining wind pressures on morphology, behaviour, and the life histories of effects on seabird movements and expected to follow the birds [7, 8]. However, several factors can bias the relationship theory of powered flight (e.g., [14]). In addition, because between wind and flight speed in seabirds, especially at fine the maximum foraging range of the great cormorant is spatial scales (several to tens of kilometres). First, the flight relatively small (less than 15 km; [15]), we could use local paths of seabirds are often convoluted, making it difficult wind measurements that would be nearly identical to the to relate flight paths with wind conditions. Second, because winds that the birds encountered. We examined whether wind conditions change dramatically during short time the ground and calculated air speeds of great cormorants periods, large-scale meteorology such as satellite-derived wind data is insufficient for detecting the effects of winds on were affected by wind conditions and we determined if they bird flight at fine scales. Third, conventional analyses of air changed foraging sites according to wind conditions. 2 International Journal of Zoology ◦  ◦ 2. Materials and Methods station: 34 44 N, 136 31 E, 39.6 m asl). For the outward and homing flights of each foraging trip, the absolute difference This study was conducted in June 2010 at the Unoyama in the angle between flight direction and wind direction (i.e., ◦  ◦ breeding colony (34 48 N, 136 53 E) and the Ishigakiike 0–180 ) was calculated. Wind speed in the direction of flight ◦  ◦ colony (34 51 N, 136 36 E) in the Tokai region of Japan. was calculated as the product of wind speed and the cosine of Great cormorants nest on trees in these regions [16]. Because the angle. We calculated air speed using ground speed, wind they temporally leave their nests when humans approach, we speed, and the angle between the ground and wind vectors. used alpha-chloralose (Tokyo Chemical Industry Co., Ltd., To examine the relationship between ground flight speed Tokyo, Japan) to capture specimens. Alpha-chloralose is an and wind speed in the direction of flight, a linear mixed anaesthetic and it allowed us to capture cormorants safely. model that treated individual birds as a random effect was We inserted 35–40 mg of alpha-chloralose powder into the run using the lme4 package in the statistical software R [17]. mouthofafish(e.g.,ayu Plecoglossus altivelis) and placed The significance of the fixed effects, as well as their 95% it on a nest that contained chicks using a fishing rod. After confidence intervals, was obtained from 100,000 Markov a bird ate the fish and was immobilised (within 1-2 h), we chain Monte Carlo (MCMC) simulations performed using captured it. In total, we captured eight birds in this way. the pvals.fnc function in R [18]. No birds were injured during the procedure. The procedures A two-dimensional generalised additive model (GAM) used in this study were approved by the Ministry of the was used to analyse the relationship between air speed and Environment, Japan. wind [9]. The GAM was used instead of the conventional We deployed GPS loggers on four breeding adults, we method that tests the relationship between air speed as a deployed acceleration data loggers on two other birds for function of ground speed and air speed (see papers cited different research purposes, and we did not deploy any in [9]) because the conventional analysis can produce erro- loggers on the remaining two birds. A small plastic base was neous correlations [9, 10]. We divided the wind variable into attached to the bird’s back feathers with adhesive tape (Tesa, two components (i.e., zonal and meridional components); Hamburg, Germany) and glue (Loctite 401). A data logger the eastern direction was represented by positive values of x was attached using a cable tie that could be cut remotely and the northern direction by positive values of y . The two (RC-150-150T, Little Leonard, Tokyo, Japan), which entered variables were implemented in a GAM by first transforming beneath the feathers that were glued to the base. To avoid them via a LOESS smoother (a locally weighted regression) having to recapture of birds using alpha-chloralose, we used with a maximum span of 80% and 2 degrees of freedom, a remote release system that could send a signal to cut the following [9]. To fit the GAM, we used the gam package in cable tie (RX-100N, Little Leonard). After several days of R[19]. attachment, we recovered the data loggers when the birds To examine the degree of overlap in foraging areas were on their nests. between successive trips, we calculated the proportion of the A GPS data logger consisted of a GPS receiver with foraging area of one trip that was covered by the foraging an antenna (GiPSy, Technosmart, Rome, Italy) and it was area of another trip, that is HR[i, j] = A[i, j]/A[i], where powered by a Li-SOCl battery (LS14500, SAFT, Paris, A[i, j] is the area of the intersection between the two foraging France). The GPS loggers were programmed to take posi- areas and A[i] is the foraging area of trip i [20]. These were tional fixes every 9 or 20 s. The overall mass was 59–84 g, calculated for two birds that conducted six successive trips. which corresponded to <5% of each bird’s body mass. The This analysis was performed using the adehabitat package in birds did not appear to be negatively affected by the loggers R[21]. We excluded one trip from this analysis because the or from being handled by the researchers. bird’s position was not obtained consistently in the foraging We calculated trip duration and trip range, which was area (Trip no. 4 in Table 1). defined as the maximum distance from the colony. The Data were analysed using Matlab version 7.1 (Math- cormorants commuted in a straight line without stopovers Works, Inc., Natick, MA, USA) and R version 2.14.1 [22]. (Figure 1); therefore, we could easily define outward and homing flights using the sudden initial decrease and the final increase in ground flight speed, which marked the first flight 3. Results and the final flight, respectively, during the trip (Figure 2). We defined foraging time as the period between the outward We obtained GPS data from four birds with body masses and homing flights; the foraging area was identified as the ranging from 1570 to 2140 g (mean = 1905 g). The birds area where the bird remained during that period. For each were named A, B (Unoyama colony), C, and D (Ishigakiike outward and homing flight, flight speed and direction were colony). The GPS loggers recorded 15 trips that consisted averaged. of 12 complete trips and 3 trips with truncated data due Wind direction and speed were recorded at two cli- to exhausted batteries. Mean trip duration and trip range matological stations operated by the Japan Meteorological were 5.1 ± 4.8 h (0.7–19.0 h, n = 12) and 13.1 ± 3.8km Agency (JMA). We used 10-min wind direction and speed (8.6–18.7 km, n = 12), respectively, for complete trips. For measurements for each outward and homing flight. For the truncated data, we only used the outward flights in our each flight, several wind measurements were averaged. The analysis. Finally, we obtained 27 outward and homing paths. weather stations were within 15 km of each colony (Figure 1; Average flight duration was 11 min (4–23 min, n = 27). ◦  ◦ Minamichita station: 34 44 N, 136 56 E, 6.5 m asl; Tsu Average wind speed on each path was 2.7 ± 1.4 m/s (0.8–5.3, International Journal of Zoology 3 34.9 N Chita Peninsula Ise Bay 5 km 34.74 N ◦ ◦ 136.5 E 137.2 E Figure 1: GPS tracks for four great cormorants during 15 foraging trips. The star and circle indicate the Unoyama and Ishigakiike breeding colonies, respectively. The two squares indicate the closest meteorological stations to each colony from which we derived wind information. Outward Homing Outward Homing 12:21 12:50 13:19 13:48 14:16 14:45 15:14 15:43 Time (h:m) Figure 2: Example of time-series data of ground flight speed and distance from the colony. The shadows indicate outward and homing flights. n = 27). Mean ground and air speeds on the paths were cormorants can be predicted by wind speed in the direction 14.4 ± 1.7 m/s (11.2–18.5, n = 27) and 14.6 ± 1.3 m/s (11.2– of flight (Figure 4(b)). Their ground flight speed increased 17.5, n = 27; Figure 4(a)), respectively. in tailwinds and decreased in headwinds. Qualitatively, this Ground flight speed increased significantly with wind matches very well with observations from visual surveys in strength relative to the birds (Figure 4(b)). A linear mixed which ground flight speeds of great cormorants in tailwinds model estimated S = 0.70 (±0.10, 0.48–0.91; SD, 95% CI)· were higher than in headwinds [23]. In addition, [23] W + 14.4 (±0.31, 13.4–15.1), where S and W were ground reported a mean ground speed of 18.8 m/s in a strong flight speed and wind speed, respectively, in the direction of tailwind (6.5 m/s), which agrees very well with our mixed- flight (P< 0.001). Air speed was significantly related to x model prediction of 19.0 m/s. On the other hand, [23] also (F = 5.2, P< 0.05), but not to y (F = 1.7, P = 0.2). Thus, reported a ground speed of 12.4 m/s in an extreme headwind air speed was only influenced by wind that was blowing east- (8.0 m/s), which was higher than our predicted value of west (Figure 5). 8.8 m/s in a similar wind. Great cormorants prefer to fly close Successive trips by two birds qualitatively showed high to the ground (i.e., ground effect; weak wind speeds closer to foraging-site fidelity and the foraging areas overlapped the ground ∼0.3 m) when flying into strong headwinds [23]; (Table 1; Figure 6). therefore, our predictive equation seems to underestimate their ground speed in such extreme headwinds, that is, outside our measured range (>5 m/s of wind speed). 4. Discussion The calculated air speeds of our birds (14.6 m/s) ranged between previously reported minimum power speeds (V ) Great cormorants encountered various wind speeds and mp for this species (13.5 [24] to 16.5 m/s [23]), which is the directions (Figure 3). The birds sometimes encountered contrasting wind conditions between outward and homing speed at which the least mechanical power is required from the flight muscles to maintain the bird flying at a constant air trips. Although sample size was small (four individuals), our speed [25]. Therefore, great cormorants might adopt V as data clearly demonstrated that the ground speed of great mp Distance from colony (km) Ground speed (m/s) 4 International Journal of Zoology Homing Homing 18 Outward Outward Wind speed (m/s) Wind speed (m/s) 3-4 1-2 3-4 1-2 2-3 2-3 0-1 0-1 (a) (b) Figure 3: Two examples of wind conditions and GPS positions. The Bird A encountered different wind conditions between outward and homing trips in (a) Trip no. 2 and (b) Trip no. 6, respectively. The star indicates the Unoyama breeding colony. The dots show the GPS positions that were interpolated at 1 min to produce a clearer display and the colour indicates the ground speed of the bird. Arrows indicate travel direction. The two rose plots on each panel show the frequency of occurrence of wind direction, which is the direction from which the wind was blowing (not where it was blowing to), and wind speed (colour) during outward and homing flights. Air Ground −4 −2 024 Flight speed Wind speed in the direction of flight (m/s) (a) (b) Figure 4: (a) Measured ground speeds and calculated air speeds for great cormorants and (b) the relationship between ground speed and wind speed in the direction of flight (tailwind component along the track direction). The solid line is the best fit that was estimated by a linear mixed model: the dashed lines indicate the 95% confidence intervals. was indicated for Kerguelen shag P. verrucosus [26]. However, decreases in tailwinds and increases in headwinds, this means the great cormorants showed changes in calculated air speed that the birds adopted maximum range speed (V ), which mr in relation to wind components (Figure 5). Optimal flight is the air speed where energy expenditure per distance theory predicts that V will be unaffected by winds [7]; travelled is minimal [27], and our data might support the mp therefore, air speed optimization [27] might explain changes optimality theory in relation to wind condition. Further in calculated air speed in relation to wind conditions, as study that incorporates morphological and actual air-speed found in this study. Because air and ground flight speed are measurements would be needed to clarify this issue. approximately equivalent in calm wind conditions, the actual Notably, the great cormorants showed strong foraging- air speed in this study might show the same relationship site fidelity (Table 1; Figure 6), which is common in to the wind as the ground speed (Figure 4(b)). If air speed cormorants [12, 28, 29], irrespective of wind conditions. This Flight speed (m/s) Ground flight speed (m/s) Ground speed (m/s) International Journal of Zoology 5 −1 −2 −1 −3 −2 P< 0.05 N.S. −4 −4 −2 0 2 −4 −2 0 2 4 x y w w (a) (b) Figure 5: The predicted relative influence of the wind components (x and y ) on the air speed of great cormorants. The eastern direction w w was represented by positive values of x and the northern direction by positive values of y . Calculated air speed was significantly related to w w x , but not to y . The solid lines are the fitted functional response and the broken lines represent standard error curves. w w 34.82 N ◦ 34.88 N Trip no.1 Trip no.2 Trip no.6 Trip no.6 Trip no.5 Trip Trip no.5 no.1 Trip no.2 Trip no.3 34.76 N Trip no.3 ◦ Trip no.4 34.72 N ◦ ◦ ◦ ◦ 137.00 E 137.12 E 136.50 E 136.65 E Wind speed (m/s) Wind speed (m/s) 2-3 2-3 5-6 5-6 1-2 1-2 4-5 4-5 3-4 0-1 3-4 0-1 (a) (b) Figure 6: Foraging area overlap in two great cormorants that conducted multiple foraging trips. The 95% contours of the kernel density estimates of the foraging areas are shown for (a) Bird A and (b) Bird C (see Table 1). The two rose plots show the frequency of occurrence of wind direction, which is the direction from which the wind was blowing (not where it was blowing to), and wind speed (colour) during outward and homing flights. might be related to low predictability of wind conditions in based on winds. The significant levels of foraging-site fidelity this study area, and therefore, foraging site selection based in successive foraging trips also suggest that their prey may be on wind would not be efficient in contrast to some seabirds predictable in space within a period of several days and that that live in “predictable” local wind conditions at large the cormorants are able to remember specific foraging sites spatial scales [3, 5, 29]. In addition, because the cormorants [12, 31, 32]. In general, seabirds with small foraging ranges using pure powered flight could not efficiently extract energy and corresponding short trip durations, as was the case in compared to gliders such as shearwaters [5], albatrosses [4], this study (5 h in mean trip duration), are likely to have boobies, and gannets [3, 30], they did not select foraging sites higher foraging site fidelity because the low probability of Effect on air speed 6 International Journal of Zoology Table 1: Foraging area overlap in two great cormorants, the boobies: adaptations to foraging in a tropical environment?” proportion of the foraging area of one trip that was covered by the Proceedings of the Royal Society B, vol. 272, no. 1558, pp. 53– foraging area of another trip, that is HR[i, j] = A[i, j]/A[i], where 61, 2005. A[i, j] is the area of the intersection between the two foraging areas [4] H. Weimerskirch, T. Guionnet, J. Martin, S. A. Shaffer, and D. and A[i] is the foraging area of trip i. 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International Journal of ZoologyHindawi Publishing Corporation

Published: Dec 10, 2012

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