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The rates of ingestion, egestion, ammonium excretion, and CO2 respiration of Tripneustes gratilla (Echinodermata: Echinoidea) from a shallow water embayment at eastern Mactan Island, central Philippines were examined. There was no significant difference in weight-specific feeding rate with the three macrophytes (Sargassum polycystum, Thalassia hemprichii, and Kappaphycus alvarezii). The power function equation, M=KWb fitted well with the weight-specific egestion rates ((r2=0·93), weight-specific CO2 respiration rates (r2=0·92) and ammonium excretion rates (r2=0·86). Weight-specific egestion rates, CO2 respiration and ammonium excretion were indirectly proportional to body weight. For weight-specific egestion rate (in μg DW faeces/g DW urchin-h), the regression coefficient, b and constant, K were −2·31±0·68 (mean±SE) (P=0·02) and 6359·55±5394·31 (P=0·29), respectively. The b and K for CO2 respiration were −1·28±0·48 (P=0·04) and 39·72±10·59 (P=0·01), respectively. For ammonium excretion, the b and K were −1·03±0·46 (P=0·11) and 262·51±56·89 (P=0·02), respectively.

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ABSTRACT:Traditional remote sensing approach for mapping aquaculture ponds typically involves the use of aerial photography and high resolution images. The current study demonstrates the use of object-based image processing and analyses of LiDAR-data-generated derivative images with 1-meter resolution, namely: CHM (canopy height model) layer, DSM (digital surface model) layer, DTM (digital terrain model) layer, Hillshade layer, Intensity layer, NumRet (number of returns) layer, and Slope layer. A Canny edge detection algorithm was also performed on the Hillshade layer in order to create a new image (Canny layer) with more defined edges. These derivative images were then used as input layers to perform a multi-resolution segmentation algorithm best fit to delineate the aquaculture ponds. In order to extract the aquaculture pond feature, three major classes were identified for classification, including land, vegetation and water. Classification was first performed by using assign class algorithm to classify Flat Surfaces to segments with mean Slope values of 10 or lower. Out of these Flat Surfaces, assign class algorithm was then performed to determine Water feature by using a threshold value of 63.5. The segments identified as Water were then merged together to form larger bodies of water which comprises the aquaculture ponds. The present study shows that LiDAR data coupled with object-based classification can be an effective approach for mapping coastal aquaculture ponds. The workflow currently presented can be used as a model to map other areas in the Philippines where aquaculture ponds exist.