seaturtle.org : MTN : ARCHIVES : Sign In
Figure 1. Map of El Cardoncito beach.
We investigated the possible influence of beach sand cycles in the nesting preferences of the olive ridley turtle. We presumed that changes of beach morphology due to littoral transport and sand input from dry arroyos may modify the conditions for sea turtles nesting. We divided El Cardoncito beach into four sections from north to south (Fig. 2). Section 1 contains the highest number of olive ridley nests. Section 4 is characterized by a rocky zone of the coast. Sections 2, 3 and 4 are also influenced by nearby streams. We analyzed the morphodynamics of the beach using computer simulations of the beach topography measured at the four sections (dots in Fig. 1). The Emery method was used to profile the beach with two 1.8 m stadia rods joined with a 4 m cord (Krause, 2004), with independent benchmarks for each section at the starting point (dune base). The olive ridley nesting season runs from July to November in the East coast of the Southern Baja Peninsula, with a seasonal peak of nests in September each year (35-50%). In order to compare the beach morphology during the main nesting season, beach profiles were measured in July, September and October of 2005. During the hurricane season (from July to September), associated wave activity normally erodes the beach creating scarps that make it difficult for turtles to nest. This is usually followed by a period of beach accretion. However, hurricane activity in the area during our study was low and none struck the peninsula.
Figure 2. Beach Morphodynamics from computer simulations. Measured beach profiles are indicated by dots. Distances are referred from the coast.
We developed a MATLAB 7.0 computer program for the 3D simulation of the beach topography from irregularly spaced points (Appendix I). Orthogonal distances (m) from coastline and elevations (cm) were used in the simulation. In the simulation for July, the beach was widest during the early part of the nesting season, when the lowest elevations were found in section 1 and the berm was not evident. In the simulation for September, a higher elevation of the nesting zone in section 1 was observed due to increased wave swells that in turn lead to berm development on the northeastern side of the beach; this berm persisted until October, when the beach begins another cycle and the berm disappeared. No beach scarp formation was observed at site 1 during the three surveys (Fig. 2).
These results show that olive ridley turtle nesting overlaps with the occurrence of higher beach elevations where the sand thickness is greater and perhaps more favorable for nesting activity. At these elevated sites, the nests are laid beyond the wave swash zone, and apparently have suitable conditions and appropriate humidity levels for excavation and embryonic development. There is no information about hatch success since all clutches are relocated to the protected areas. However, these results contribute to understand the nesting site selection process. The beach condition during September could be related to the high percentages of nests laid during August and September (usually more than 60%).
KRAUSE, G. 2004. The “Emery-Method” revisited—performance of an inexpensive method of measuring beach profiles and modifications. Journal of Coastal Research 20: 340-346.
MONROY-GARCIA, Y. 2004. Determinación de zonas de importancia para la anidación de la tortuga marina Lepidochelys olivacea, en la región de San José del Cabo, Baja California Sur, temporada 2004. Tesis de licenciatura, Universidad Autónoma Metropolitana, Unidad Xochimilco, México. 25 pp.
TIBURCIO-PINTOS, G. 2005. Anidación de Tortugas Marinas en el Municipio de Los Cabos, Baja California Sur, México y Propuestas para su Conservación. Tesis de maestría, Universidad Autónoma de Baja California Sur, México. 121 pp.
function playaP(np)
% np= Number of profiles, each one captured in an ascii file data with .dat extension
x=[]; y=[]; z=[];
for ip=1:np
[filename, pathname, filterindex] = uigetfile(‘*.dat’, ‘Select a profile data file’);
playa=filename(7:8); dd=filename(13:14);mm=filename(11:12);aa=filename(9:10);
aaaa=[‘20’ aa];
fecha=datestr(datenum(str2num(aaaa),str2num(mm),str2num(dd)),1);
matriz=load([pathname filename]);
[n,n2]=size(matriz);
yy=[]; zz=[];
yrel(2:n+1)=matriz(:,1); xrel(2:n+1)=matriz(:,2);
yrel(1)=0;xrel(1)=0;
for i=1:n+1;
zz(i)=sum(yrel(1:i));
yy(i)=sum(xrel(1:i));
end
y=[y yy]; z=[z zz];
x=[x ip*ones(1,n+1)];
end
yi = 0:0.5:30; xi=1:0.2:np; % grid density to interpolate elevation values
[XI,YI] = meshgrid(xi,yi);
ZI = griddata(x,y,z,XI,YI);
figure; hold on
mesh(XI,YI,ZI)
plot3(x,y,z,’ok’)
xlabel(‘Profile’,’FontSize’,14);set(gca,’XTick’,1:np);
ylabel(‘Distance (m)’,’FontSize’,14);
zlabel(‘Elevation (cm)’,’FontSize’,14)
grid on
view(130,70)
title([playa ‘, ‘ fecha],’FontSize’,14);
axis([1 np 0 30 -400 0]); %Adjust scales depending of length of the beach and maximum elevation.
colormap copper