Poisson et al. : Effects of lunar cycle and fishing operations on longline catches 
271 
40 ° E 
45 ° E 
50° E 
5” S 
10 ° S 
15 0 S 
20 ” S 
25” S 
30 1 S 
5°S 
15 ” S 
20 ” S 
25 ° S 
30” S 
40 ° E 
45 ” E 
55 ” E 
Figure 2 
Location of Reunion Island in the southwestern Indian Ocean. The 
areas shown with diagonal lines are the three fishing grounds 
of the fleet between 1998 and 2000, the Seychelle waters (area 
A), the Mozambique Channel (area B), the west and southwest 
of Reunion Island within the EEZ in association with deep sea- 
mounts and sea surface temperature (SST) fronts (area C). 
Logbook data used to analyse the effect of operational and gear 
setting practices on catches where located in area D (20°-23°S 
lat. and 53°-57°E long.) 
represented other longline field trials where 
CPUE and catch compositions were com- 
pared by using gear with various densities 
of chemical lightsticks. 
Environment (database 4) 
Database 4 consisted of two environmental 
databases that were linked to experimen- 
tal databases to investigate cyclical lunar 
influence on CPUE. Lunar days were coded 
in chronological order with values between 
1 and 30. A lunar phase index was allo- 
cated to each fishing day according to the 
four phases of the moon (new moon, first 
quarter, full moon, and last quarter). Full 
and new moons refer to the day of each 
full or new moon ±2 days. Lunar illumina- 
tion was calculated for each fish caught, 
according to its hooking hour, based on the 
angle of elevation of the moon (on a 24-h 
cycle). These data were obtained by using 
an astronomical software package called 
LunarPhase (http://www.nightskyobserver. 
com/LunarPhase/index.htm, accessed Janu- 
ary 2000) and were stratified into three 
classes: 1) angle <45° (low illumination); 2) 
high illumination (angle >45°-90°); and 3) 
dark (new moon). An important underlying 
assumption inherent in this approach is 
that the cloud coverage is not considered 
when calculating the index. An index for tidal 
phase was assigned to each hooking time and 
location by the French Service Hydrographique 
et Oceanographique de la Marine (SHOM). The 
tidal index consisted of four nominal phases 
(ebb, high, flood, and low) and is thus a good 
approximation of the theoretical sea level height 
changes, although it does not account for current 
velocity. The fishery operates throughout the 
year and no temporal discontinuities in fishing 
effort were evident because effort was quite homoge- 
neous for indices of lunar illumination and tide phase. 
Statistical methods 
Lunar effect on fishing performance was investigated 
at two different scales. At a larger scale, we conducted 
a between-class analysis (Doledec and Chessel, 1990, 
1994; Gaertner et al., 2005; Bigot et al., 2008) in order 
to approximate the influence of lunar days (phase) on the 
CPUE of all the species recorded in logbooks (database 
1), where “lunar days” was a categorical factor. For that 
purpose, we sought axes for the between-group analysis 
that would best discriminate the centers of gravity of 
each lunar day and allow us to investigate associations 
between lunar phases and the variability of CPUE for 
each species. In addition, a permutation test, which 
extended the test of Romesburg (1985) to all kinds of 
variables (Manly, 1991), was carried out to test the 
significance of the between-group variability. We used 
nonparametric LOESS regression to further investigate 
variability in CPUE for species that were most affected 
by the lunar day. On a finer scale, we carried out a 
multiple correspondence analysis (MCA) — equivalent 
to normalized principal component analysis (PCA) to 
determine the most favourable catch factors on a daily 
scale (Tenenhaus and Young, 1985; Mazouni et al., 
1996). MCA allowed us to visualize the associations 
between each of the five major species caught during 
experimental sets and three lunar-related factors stud- 
ied on a daily scale (tide phase, capture time, lunar 
illumination). 
A between-group centered principal component anal- 
ysis (CPCA, R-mode) and a between-group factorial 
correspondence analysis (FCA) were conducted to ex- 
amine the influence of lightstick density on CPUE and 
catch composition of experimental sets (database 3). 
The ADE-4 software (Thioulouse et al., 1997, http//pbil. 
