SEDIMENTARY CHARACTERISTICS THAT INFLUENCE ON THE LOGGERHEAD TURTLE NESTING BEHAVIOR IN CALHETA DE PAU BEACH, BOA VISTA ISLAND (CAPE VERDE ARCHIPELAGO) López, O.1, Alonso, I.2, López-Jurado, L.F.1 1Department
of Biology, University of Las Palmas de Gran Canaria 2Department of Physics, University of Las Palmas de Gran Canaria, 35017 Las Palmas, Spain E-mail:
[email protected] (a)
(a)
INTRODUCTION The nest site selection is an adaptive mechanism that balances the cost of searching the nest site (in terms of energy and predation risk) and benefit to select a suitable place for incubation (Wood and Bjorndal, 2000) (Fig. 1. a). Physical-chemical characteristics of nests influences in the hatching success, in the sex ratio of hatchlings, in embryonic development and fitness offspring (Miller et al., 2003). Furthermore the position of the nest directly influences in the risk of flooding of the nests, in the risk of predation of eggs and hatchlings and the orientation of hatchlings into the sea. (Wood and Bjorndal, 2000, Spencer and Thompson, 2003; Kamel and Mrosovsky, 2005; Karavas et al., 2005) (Fig. 1.b). Although the nest site selection is not a random process (Tiwari et al., 2005; Weishampel, 2003), the drivers of this behavior, that would be influenced by genetic or (b) environmental factors, are unknown (Weishampel et al., 2006). Among the environmental parameters that influence in this Figure 1.- (a) Lost turtle, which can not return to the sea by process, we have a wide range of physical, chemical and biotic parameters, such as salinity, temperature and pH of the soil, themselves. (b) Nest dug by the erosive action of waves. porosity and compaction of the sand, the slope of the beach, coastal dynamics, etc. (Miller et al., 2003). So the turtles must be able to use these multiple environmental factors during the nesting process through the integration of environmental information or the use of critical thresholds to be reached by any of the environmental factors (Wood and Bjorndal, 2000; Mazaris et al., 2006).
(b) Figure 2.- Area study. (a) Archipelago of Cape Verde. (b) Boa Vista Island. In red, Calheta de Pau Beach.
The Cape Verde Archipelago is located on the western coast of Africa, (between 14° 48’-17° 18’ N and 22° 42’-25° 18’ W, see Fig. 2.a.), and constitutes one of the main nesting areas of loggerhead sea turtle, Caretta caretta (Varo-Cruz et al., 2007; Marco et al., 2008), estimating 10000 nest per season in Boa Vista Island and 15000 throughout the archipelago (Varo et al., 2005). (b)
The goal of this work is the analysis of possible relationships between physical and sedimentary characteristics of the study beach and the nest site selection for loggerhead sea turtle.
MATERIALS AND METHODS Study Site. The Calheta de Pau Beach is located on the east coast of the Boa Vista island (Fig. 2.b.) and is approximate 386 m. of length (Fig. 3). The beach is easily accessible from the sea, presented in the north end a shelter at the predominant wave NNE. Likewise, the backshore is limited by the formation of a small area of dunes and the foreshore, approximately in the middle of the beach, we found remains of a calcareous rocky whose main portion is about 14 m. for wide and 11 m. for long. The southern end of the beach is bounded by a small rocky platform. Sedimentological and Physical Data. During the summer of 2007 were three samplings of sand and the study of the profiles of the beach. The first campaign was held on 29 July, the second on 12 September and the last was held on 2 October. Among the physical-chemical properties have been studied are the characteristics of the sand as grain size, (Mz), the sorting (σl) and the skewness (SKl), its porosity and carbon and water content. The beach was divided into 4 zones by conducting profiles 5 of the beach in the intertidal zone (Fig. 3.b.), which is 102 meters between each other, except between profiles 4 and 5, which separated 73.34 m. The date and time of each campaign was based on the expected low maximum for the current month as the annual tide developed by the Portuguese Hydrographic Institute for the zone (IHP, 2007). The top of the profiles and the 64 samples that were taken were georeferenced using GPS. For the realization of (a) the profiles, we used the method of Emery (1961) (Fig. 4.a.). Samples were taken at the surface and 40 cm. of depth. Deep samples were obtained only in the Figure 3.- (a) Satellite image of the Calheta de Pau Beach, where we indicate the tops of profiles and sampling points located in the center of each zone. Once collected the samples of sand and took his wet weight were prepared for shipment to 5 profiles and 64 samples points. (b) Calheta de Pau Beach. the Laboratory of Geology, University of Las Palmas de Gran Canaria, where it held its drying and analysis (Fig. 4. b.) The particle size determination was performed by screening and the analysis of grain size parameters using the statistical program GRADISTAT © (Blott and Pye, 2001). For the determination of carbon content, we used the volumetric method of Beltrand calcimeter, by comparison with a standard of CaCO3 every 14 samples (Guitián and Carballo, 1976). Also, the porewater content (PC) of samples was obtained by gravimetric method. Finally, the value of porosity was carried out using the methodology described by Kikukawa et al. in 1999 (Fig. 5). (a)
(b)
(c)
Nesting Behavior. Nesting behavior of loggerheads was studied from 1 July to 2 October. During this time we sampled the beach to include the nesting activity of the previous night. All turtle tracks were noted and determined to presence of nest o non-nest in the track. (methodology in Schroeder and Murphy, 1999 and Varo et al. 2006) (Fig. 4.c.). The nesting success was calculated as the percentage of nest respect the total tracks registered in the night (Schroeder and Murphy, 1999).
Statistical Analysis. For statistical analysis we used the Poisson regression model. To analyze the Figura 4.- (a) Measurement of a profile using the method of Emery (1961). (b) Collecting a sample. (c) Track marked using relationship exists between the sampling periods and areas, applied the Chi-square Test and the the methodology described by Varo et al. in 2006. Tukey Multiple Comparisons. While to analyze the type of correlation between the data we used Pearson correlation. All data were analyzed with statistical software R (R Development Core Team, 2007).
RESULTS AND DISCUSSION We have established two distinct areas of study at our beach. The first, in the extreme north, is characterized by soft slopes. By contrast, the southern end of the beach has a slope more abruptly. If we analyze the temporal evolution of the profiles, we can see that in second sampling there was an erosive process along the beach. However, in third sampling there was some recovery of material (Fig. 6)
Figura 5.- Prototype to measure in situ the porosity of the sand using the methodology described by Kikukawa et al. in 1999.
Granulometric characteristics were classified according to the scale of Folk and Ward (1957), whereby the sediment of the beach was sands of size medium and thick (mean value in surface of 1.31ø and 1.5ø in depth). The sorting is between moderate and moderately good, which some small changes to other points as well as surface (mean value 0.78ø) and depth (0.76ø). The values of skewness are symmetrical (mean value on the surface -0.01ø and in depth -0.07ø). Regarding the carbon content, most of the samples exceeded 95% without major changes in zone and temporal. The behavior of water content in the samples is consistent; in the area near the backshore and surface samples the humidity is lower that in depth (mean value on surface 1.6% and in depth 5%). During the 2007 season on the beach of Calheta Pau there were 2110 tracks and the nesting success was 46.6%, what is similar to those obtained Figure 6.- Temporal evolution of the profiles. for the beach during the seasons 2001 and 2002 (Varo et al. 2007) and is within the limits of 10 to 75% of tracks without nest recorded for this species in other areas (Dood, 1988). If we look at each area, we can see that the zone 3 and 4 are where the number the tracks is lower (22,3% and 6%, respectively), but the nesting success is highest (53.2% and 46.5% ) (Table I). We found differences between the number of tracks by area (p-value | t |) 2.36 e-14). Regarding the number of nests, and applying a similar analysis, we see that the variables that are most significant are the granulometric characteristics. The number of nests increases with decreasing grain size (t value 2758; Pr (> | t |) 0.0064). and increases the sorting, (t value 3283; Pr (> | t |) 0.0012). Finally, the number of nests also increases when decreasing the skewnes ( t value -3312; Pr (> | t |) 0.0011). Figure 8.- Distribution of the number of nests/m2 in each zone and period of observation. One can see that the mean number of nest/m2 in zone 1 is significantly higher that in zone 3 (p-value
