1. Factors affecting the relative abundance index of low mobility
species: Spiny lobster in the Galapagos Islands
Murillo-Posada J.C.* , Salas S. & Velázquez-Abunader I.
Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Mérida *
Introduction
One approach to estimate relative abundance has been the use of standardization methods, especially in the cases where resources have heterogeneous spatial distribution, such as lobster.
The aim of this study was to assess how factors such as sea surface temperature (SST), distance to port, region, origin of fishermen, daily timing and the season fishing, affect CPUE of lobsters Panulirus penicillatus and P. gracilis. Based in data obtained from Galapagos Islands, Ecuador.
Methods
Study area
Galapagos
Islands
Figure 1. Division of the Galapagos in biogeographic regions. Red circles indicate occurrence of P. penicillatus (red lobster), green circles occurrence of P. gracilis (green lobster).
Statistic analysis
Results and discussion
GAM model was selected because it had the lowest AOC value for both lobster species.
The temperature, distance to port and region were the factors that contributed most to the variability of CPUE in both species (Table 2).
CPUE varied inversely to change in SST, which can be explained due to the fact that temperature influences growth, reproduction, foraging behavior and recruitment, and hence resource availability to be captured (Kanciruk, 1980, Phillips & Smith 2006) (Fig. 2, left)
Distance to port was also a significant factor associated to CPUE. Lobsters had relatively high CPUE in areas near to ports; above of 87% of its fishing effort was allocated to sites 74 km away from the base Port (Fig. 2, right).
Table 2. Forward Stepwise analysis for both species (GAM model); k = number of knots; Dexp = deviance explained
Figure 2. Left: Association between SST and standardized CPUE with GAM for both lobster species. Rigth: Distance to Port Association with standardized CPUE, for the two spiny lobster species. Black spots represent the residuals of each sampling unit (fishing trip).
The CPUE of P. penicillatus was higher at night than during daytime (Fig. 3A). In contrast, P. gracilis had a significantly higher CPUE during the day (Fig. 3A).
For P. penicillatus the CPUE remained steadily in the first three months of the fishing season, decreasing in December (with the increase of SST). In contrast, P. gracilis increased its abundance in December (Fig. 3B).
Finally, the origin of the fisher and the region on the CPUE, had differential effect in both species. These differences are explained because both species have different distribution and abundance in Galapagos.
P. penicillatus was abundant in the south-central part of the Archipelago (Puerto Ayora and Baquerizo M.), while P. gracilis was more abundant in the western region, where fishermen live in Puerto Villamil (Fig. 3C and 3D).
Figure 3. Effect of daily timing, month, origin of fisherman and region in the CPUE of the two species of lobster of the Galapagos Islands.
References
Bi, J., and Bennett, P.K., Regression error characteristic curves. In: 20thInternational Conference on Machine Learning (ICML), Washington, DC, .
Kansiruk, P Ecology of juvenile and adult Palinuridae (Spiny Lobster), Chapter II. Pp In: Cobb and Phillips(Eds). The Biology and Management of Lobsters 2.
Phillips, B.F. and Melville Smith,R.S Panulirus Species. In: Lobster: Biology, Management, Acuculture and Ficheries.
B.F. Phillips (ed.). Blackwell Publishing, Oxford,
Variables
P. penicillatus
Dexp. acum. (%)
Dexp.
AIC
CPUE ~1: (Null model)
14079
CPUE ~ s(DIST) = A
5.15
13591
A + s(SST) = B (k = 25 )
13.7
8.55
13072
B + REGION = C
16.8
3.1
12866
C + MACROZONA = D
17.8 1
12832
D + DAILY TIMING = E
18.7
0.9
12760
E + MONTH = F 19
0.3
12740
F + SOURCE_DATA = G
23.1
4.1
12441
G + ORIGIN
23.5
0.4
12419
FINAL MODEL
-0.5
Variables
P. gracilis
Dexp. acum.
(%)
Dexp
AIC
CPUE~ 1 (Null model)
6292
CPUE ~ s(DIST) = I
4.21
4.2
6099
I + s(SST) = J (k=25)
19.1
14.9
5534
J + REGION = K
29.4
10.3
5051
K + ILAND = L
30.1
0.7
5031
L + SOURCE_DATA = M
31.7
1.6
4939
M + TIMING = N 32
0.3
4927
N + ORIGIN = O
33.1
1.1
4881
O + MONTH
34.1
1.0
4834
FINAL MODEL
34.3
0.2
4825
Factors contributing to variability in CPUE
Data:
Colected by
Charles Darwin Fundation and
Galapagos National Park
Enviromental
Temperature
(SST)
Temporal
Daily timing
Month
Year
Spatial
Region
Distance to Port
Origin of fisherman
CPUE probability distribution (GAMMA)
Resource behavior
Fisher behavior
Fitted of model
Akaike Information Criterion (AIC)
Model Selection
Generalized linear Model (GLM) O
Generalized additive models (GAM)
Perfomance of models
Regression Error Characteristics Curve (REC) (Bi and Bennett, 2003)
Area Over the Curve (AOC)
Acknowledgments:
CONACYT, CINVESTAV and GALAPAGOS NATIONAL PARK