Anglerfishes are widely distributed from shallow to deep-water habitats occupying different ecological niches. To explain this adaptability, we performed a morpho-functional study on common benthic anglerfishes inhabiting the Indian deep-sea waters. Sensory capabilities of species were examined using the morphology and morphometry of
Las especies del orden Lophiiformes habitan desde las aguas superficiales hasta las más profundas de los océanos ocupando diferentes nichos ecológicos. Con el fin de explicar esta adaptabilidad se llevó a cabo un estudio mofo-funcional de especies bentónicas comunes en aguas profundas del Océano Índico. La capacidad sensorial de las especies se analizó a partir de la morfología y morfometría del otolito
The order Lophiiformes, commonly known as anglerfishes, is a diverse group of benthic and pelagic species inhabiting shallow to deep-sea waters. This order comprises approximately 358 extant species in five suborders (
The Indian Ocean, and especially the region around the Andaman and Nicobar Islands, is characterized by their rich deep-sea fishery resources (
The aim of this work is to understand better the coexistence of the most common benthic species of lophiiforms occurring at the Andaman and Nicobar Islands (
Specimens were collected during the deep-sea fishery exploratory surveys of the Fishery Oceanographic Research Vessel (FORV)
The lophiiforms were identified following standard identification keys (
Otoliths were collected and washed with distilled water to remove exogenous matter, dried and kept in plastic vials for further analysis. Otoliths from the right side of each fish were oriented with the inner side (
Sixteen morphological variables were measured on each specimen using a Vernier calliper (0.1 mm precision): total length (
– Oral gape surface (
– Oral gape shape (
– Oral gape position (
– Eye size (
– Eye position (
– Body transversal shape (
– Caudal peduncle throttling (
– Fin surface ratio (
– Fin surface to body size ratio (
– Aspect ratio of the pectoral fin (
– Aspect ratio of the caudal fin (
To estimate the functional traits, the morphological data were standardized to remove the allometric effect using the total weight (
The Kolmogorov-Smirnov and Levene tests were used to check normality of the data distributions and variance homogeneity, respectively. The intraspecific variability was analysed considering the fish size-otolith measurement relationships as a tool in the feeding ecology to estimate fish size and biomass (
To order species in the functional space, a principal component analysis (PCA) based on the correlation matrix of the functional traits was performed. The choice of which principal components to interpret was based on a broken-stick model, which constructs a null distribution of eigenvalues and compares it with observed ones (
The degree of functional niche overlap among species was performed using a non-parametric kernel density function (
(1) |
(2) |
All species shared otolith features such as dorsal lobes and the lightly marked
All otolith morphometric variables showed a statistically significant relationship with fish length for all species (
Relationship | |
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n | |
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se ( |
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n | |
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se ( |
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n | |
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se ( |
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10 | 0.170 | 1.06 | 0.081 | 0.955 | 16 | 0.150 | 1.05 | 0.148 | 0.785 | 16 | 0.070 | 1.07 | 0.127 | 0.835 | |
10 | 0.490 | 0,48 | 0,039 | 0.951 | 16 | 0.800 | 0.36 | 0.077 | 0.606 | 16 | 0.260 | 0.52 | 0.089 | 0.711 | |
10 | 0.572 | 0.53a | 0.049 | 0.925 | 16 | 0.341 | 0.63ac | 0.075 | 0.833 | 16 | 0.337 | 0.58a | 0.069 | 0.838 | |
10 | 0.220 | 0.59a,b | 0.042 | 0.959 | 16 | 1.220 | 0.57a,b | 0.083 | 0.768 | 16 | 3.370 | 0.33b | 0.169 | 0.240 | |
10 | 0.049 | 1.50ac | 0.110 | 0.958 | 16 | 0.020 | 1.64ab | 0.281 | 0.708 | 16 | 0.047 | 1.24a | 0.109 | 0.902 | |
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n | |
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se ( |
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n | |
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se ( |
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15 | 0.007 | 1.53 | 0.209 | 0.804 | 12 | 0.220 | 0,89 | 0.213 | 0.636 | ||||||
15 | 0.238 | 0,53 | 0.098 | 0.690 | 12 | 1.220 | 0.29 | 0.147 | 0.287 | ||||||
15 | 0.049 | 0.96bc | 0.121 | 0.829 | 12 | 0.300 | 0.53ac | 0.100 | 0.740 | ||||||
15 | 0.270 | 0.82a | 0.111 | 0.805 | 12 | 1.680 | 0.46a,b | 0.076 | 0.787 | ||||||
15 | 0.001 | 2.01bc | 0.233 | 0.851 | 12 | 0.004 | 2.62b | 0.171 | 0.959 |
The ANOVA tests revealed significant differences for all relative variables (
Variable | Species | |
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0.1333 | 0.000 | 0.000 | 1.000 | ||
0.312 | 0.000 | 0.467 | |||
0.492 | 0.001 | ||||
0.000 | |||||
1.000 | 0.018 | 0.000 | 1.000 | ||
0.262 | 0.000 | 0.021 | |||
0.543 | 0.000 | ||||
0.000 | |||||
0.969 | 0.156 | 0.000 | 0.000 | ||
1.000 | 0.000 | 0.006 | |||
0.000 | 0.167 | ||||
0.999 | |||||
0.066 | 1.000 | 0.000 | 1.000 | ||
0.206 | 0.024 | 0.315 | |||
0.000 | 1.000 | ||||
0.000 | |||||
0.443 | 0.002 | 0.000 | 1.000 | ||
0.371 | 0.000 | 0.052 | |||
0.543 | 0.000 | ||||
0.000 |
The first five PCA axes explained 97.9% of the total variance and the first three explained 93.8%. The PC1 axis alone contributed 63.7% of the total variance and was mainly correlated with
The functional traits
Traits | Species | |
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0.86 | 0.73 | 0.50 | 0.72 | ||
0.69 | 0.45 | 0.62 | |||
0.54 | 0.61 | ||||
0.66 | |||||
0.56 | 0.44 | 0.26 | 0.13 | ||
0.82 | 0.64 | 0.48 | |||
0.75 | 0.57 | ||||
0.81 | |||||
0.61 | 0.00 | 0.04 | 0.00 | ||
0.00 | 0.00 | 0.00 | |||
0.80 | 0.47 | ||||
0.45 | |||||
0.61 | 0.72 | 0.66 | 0.71 | ||
0.77 | 0.40 | 0.47 | |||
0.61 | 0.67 | ||||
0.82 | |||||
0.28 | 0.48 | 0.10 | 0.00 | ||
0.71 | 0.28 | 0.04 | |||
0.36 | 0.10 | ||||
0.54 | |||||
0.22 | 0.45 | 0.36 | 0.04 | ||
0.67 | 0.53 | 0.40 | |||
0.80 | 0.47 | ||||
0.51 | |||||
0.42 | 0.00 | 0.06 | 0.00 | ||
0.00 | 0.01 | 0.00 | |||
0.78 | 0.52 | ||||
0.62 | |||||
0.72 | 0.49 | 0.78 | 0.75 | ||
0.74 | 0.91 | 0.91 | |||
0.68 | 0.72 | ||||
0.90 | |||||
0.20 | 0.83 | 0.57 | 0.25 | ||
0.21 | 0.38 | 0.56 | |||
0.57 | 0.29 | ||||
0.38 | |||||
0.73 | 0.91 | 0.81 | 0.20 | ||
0.75 | 0.74 | 0.27 | |||
0.76 | 0.14 | ||||
0.22 | |||||
0.65 | 0.71 | 0.64 | 0.68 | ||
0.87 | 0.38 | 0.40 | |||
0.46 | 0.49 | ||||
0.84 |
Species | |
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0.53 (0.22) | 0.52 (0.30) | 0.44 (0.29) | 0.32 (0.33) |
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0.57 (0.33) | 0.43 (0.28) | 0.38 (0.28) | |
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0.65 (0.15) | 0.46 (0.20) | ||
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0.62 (0.22) |
Most studies performed on deep-sea fish species from Indian waters have focused on taxonomy and biology (
The relative size of fish otoliths tends to increase with depth (
Given that common anglerfishes from the Indian Ocean had different functional niches and can coexist in some bathymetries, the slight variations in their functional traits suggest that functional variability is linked to competence for similar resource requirements (theory of limiting similarity,
Overall, anglerfishes with higher swimming capability and oral gape surface (e.g.,
In conclusion, anglerfishes have evolved functionally towards different ecological strategies to live in low-energy habitats. Hence, morpho-functional traits seem to be good ecological predictors for explaining the coexistence of species. Functional traits associated with feeding habits, locomotion and manoeuvrability help us to understand the ecology of these species (
The authors express their sincere thanks and gratitude to the Secretary of the Ministry of Earth Sciences (MoES), New Delhi and the Director of the Centre for Marine Living Resources and Ecology (MoES), Government of India, for supporting the work and providing the facilities onboard FORV
Species | Variables | n | min | max | mean | sd |
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10 | 12.3 | 35 | 19.15 | 6.54 |
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3.47 | 5.53 | 4.19 | 0.62 | ||
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4.86 | 7.93 | 5.88 | 0.94 | ||
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13.2 | 23.4 | 16.58 | 2.98 | ||
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0.022 | 0.099 | 0.0395 | 0.022 | ||
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59 | 155 | 85.1 | 27.64 | ||
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16 | 12.9 | 22.1 | 17.83 | 2.64 |
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3.59 | 4.53 | 4.07 | 0.24 | ||
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1.62 | 4.81 | 5.76 | 0.5 | ||
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13.3 | 17.7 | 15.86 | 1.28 | ||
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0.021 | 0.048 | 0.0325 | 0.007 | ||
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68 | 109 | 92 | 11.02 | ||
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16 | 3.47 | 10.21 | 7.3 | 1.78 |
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1.75 | 3.13 | 2.51 | 0.36 | ||
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2.89 | 5.19 | 4.25 | 0.59 | ||
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11.1 | 22.1 | 15.13 | 3.28 | ||
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0.006 | 0.015 | 0,01 | 0.003 | ||
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49 | 110 | 75.12 | 17.46 | ||
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15 | 6.31 | 15 | 9.98 | 3.18 |
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2.46 | 3.52 | 2.99 | 0.36 | ||
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3.69 | 6,3 | 4.73 | 0.93 | ||
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10.3 | 16.6 | 13.13 | 2.2 | ||
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0.006 | 0,03 | 0.134 | 0.007 | ||
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91 | 170 | 118.5 | 22.6 | ||
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12 | 6.33 | 9.69 | 8 | 1.18 |
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3.48 | 4.63 | 4.02 | 0,3 | ||
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2.19 | 2.91 | 2.59 | 0.22 | ||
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9,6 | 11.96 | 10.73 | 0.75 | ||
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0.006 | 0.021 | 0.016 | 0.004 | ||
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41 | 67 | 57.25 | 7.16 |
Variable | df | Sum Sq | Mean Sq | F value |
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1 | 0.760 | 0.760 | 371.152 | <0.001 | |
Species | 4 | 1.873 | 0.470 | 229.436 | <0.001 | |
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4 | 0.018 | 0.004 | 2.158 | 0.085 | |
Residuals | 59 | 0.121 | 0.002 | |||
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1 | 0.011 | 0.101 | 15.474 | <0.001 | |
Species | 4 | 0.657 | 0.164 | 238.848 | <0.001 | |
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4 | 0.002 | 0.001 | 0.879 | 0.482 | |
Residuals | 59 | |||||
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1 | 0.660 | 0.660 | 1074.370 | <0.001 | |
Species | 4 | 0.531 | 0.133 | 216.269 | <0.001 | |
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4 | 0.012 | 0.003 | 4.764 | 0.002 | |
Residuals | 59 | 0.036 | 0.001 | |||
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1 | 0.157 | 0.157 | 127.371 | <0.001 | |
Species | 4 | 0.240 | 0.060 | 48.657 | <0.001 | |
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4 | 0.013 | 0.003 | 2.706 | 0,039 | |
Residuals | 57 | 0.070 | 0.001 | |||
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1 | 0.957 | 0.957 | 350.515 | <0.001 | |
Species | 4 | 3.746 | 0.936 | 343.147 | <0.001 | |
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4 | 0.074 | 0.019 | 6.787 | <0.001 | |
Residuals | 59 | 0.161 | 0.003 |
Traits | PC 1 | PC 2 | PC 3 | PC 4 | PC 5 | PC 6 |
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–0.030 | –0.068 | 0.153 | 0.739 | 0.253 | 0.306 | |
–0.034 | 0.019 | 0.012 | 0.120 | 0.653 | –0.101 | |
0.050 | –0.084 | 0.168 | 0.499 | –0.548 | –0.066 | |
0.075 | 0.037 | –0.051 | –0.149 | –0.302 | 0.041 | |
–0.018 | –0.022 | 0,039 | 0.233 | –0.166 | 0.061 | |
–0.236 | –0.012 | –0.169 | 0.056 | –0.077 | 0.215 | |
–0.084 | 0.274 | 0.162 | 0.182 | 0.160 | –0.686 | |
0.013 | 0.021 | 0.137 | –0.139 | 0.212 | 0.570 | |
0.868 | 0.130 | 0.360 | –0.045 | 0.041 | 0.035 | |
0.350 | 0.309 | –0.834 | 0.236 | 0.028 | 0.026 | |
–0.225 | 0.893 | 0.212 | –0.014 | –0.109 | 0.205 |
Species |
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Total |
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10 (100) | 0 | 0 | 0 | 0 | 10 |
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0 | 16 (100) | 0 | 0 | 0 | 16 |
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0 | 0 | 19 (95) | 1 | 0 | 20 |
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0 | 0 | 1 | 17 (89.5) | 3 | 21 |
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0 | 0 | 0 | 1 | 14 (82.3) | 15 |