TY  - BOOK
AU  - Sara, Cherry Joy N.
TI  - Morphometry and descriptive characterization of Gobiidae species in Lake Mainit , Surigao del Norte
PY  - 2009///
KW  - Morphometry
KW  - Family Gobiidae
KW  - Mensural characters
KW  - Hierarchical clustering
KW  - Lake Mainit
KW  - Surigao del Norte
KW  - Mindanao
KW  - Philippines
KW  - Gobies
KW  - Sampling
KW  - Glossogobius species
KW  - Freshwater fishery
KW  - Von Bertalanffy's growth function (VBGF)
KW  - Length at first maturity
KW  - One-way ANOVA  (Analysis of Variance)
KW  - Pairing testing techniques
KW  - Dendrogram
KW  - Undergraduate Thesis
KW  - BIO200,
KW  - BSB
N1  - Thesis (BS Biology) -- University of the Philippines Mindanao, 2009
N2  - This study aimed to use morphometry in classifying and characterizing gobies in Lake Mainit, Surigao del Norte. Three (3) statistical approaches were used: descriptive statistics, one-way ANOVA (ananlysis of Variance) along with the Turkey?s HSD post hoc multiple comparison pairwise test, and the Hierarchical clustering using the Pearson Correlation for the dendogram. The von Bertalanffy?s Growth Factor was also used to determine the adult samples with regards to their length at first maturity. Three (3) samplings were made during the course of the study. They were initially grouped according to their color patterns. Seventeen (17) groups were generated using this method. (Twenty-four (24) mensural characters were used as parameters in this study. Results using the descriptive statistics showed that Glossogobius sp. F. had the largest mean in most of the parameters while Glossogobius sp. L. had the least. With regards to sampling period, samples in sampling 2 had the largest mean, while samples in sampling 3 had the least. Using one-way ANOVA, results showed that the different groups of Glossogobius species using the 24 parameters are significantly different at 0.05 level of significance. With regards to the sampling period, samples in sampling 1 were similar to those in sampling 2, and samples in sampling 3 were significantly different from the other two. The dendogram using Hierarchical clustering has generated eleven (11) groups, lesser than the number generated using the color patterns. The use of color patterns may not be enough to classify stocks of fishes
ER  -