Density-depended Acoustical Identification of Two Common Seaweeds (Posidonia Oceanica and Cymodocea Nodosa) in the Mediterranean Sea

Abstract

The non-destructive samplings are very important in not damaging seagrasses and seaweed under protection, at the eld studies. The grasses are prominent in the assessment of the ecological status of the marine environments. One of the effective non-destructive samplings was the acoustical methods which need a low level of the sea and atmospheric conditions as compared to the other remote sensing system. Like the others, acoustic data alone are inherently ambiguous concerning the identities of the scatterers and need sea-truthing at the eld studies. Considering the requirements above and the advantages of the acoustical methods, an acoustical in situ study was conducted to discriminate two dominant seagrasses along the western Turkish Mediterranean coast in August (in the year 2012) when biometrics of both species was at maxima. Four different regions were involved in the study during data collection using a split beam echosounder operated at 206 kHz since each region had different strengths of their biometrics re ecting regional variations of the acoustical measurements. For discrimination, a statistical approach based on the acoustical parameters and their statistics was enriched and validated with multivariate analyses (Silhouette, k-means, PCA and CAP analyses). Posidonia oceanica was correlated with mean, median, SD and maximum value of Sa, whereas Cymodocea nodosa was characterized by hardness and roughness of leaf echo, followed by skewness and kurtosis of Sa. The acoustics of C. nodosa were related to regional differences, but P. oceanica was related to the depthwise difference. Both species had highly different densities (g/cm 3 ), followed by biomass. The present study will interactively help acoustical studies to be more common and comprehensible and the other previous studies published did as well.Recently, in/ex situ acoustical studies have increased to relate the biometrics to the Elementary Distance Sampling Unit (EDSU, Simmonds and

A Package of Script Codes, POSIBIOM for Vegetation Acoustics;<em> POSIdonia</em> BIOMass

“…The flower and fruit which occurred often in the West Mediterranean Sea [84,85] would change the acoustical traits of the meadow in other parts of the Mediterranean coasts. The main factor producing the variation in the estimates is the strength and magnification of the physiological activities of the meadow, calcification, oxygen gas releasing, beam pattern (orientation), age/type (juvenile, middle and adult leaves) and hardness of the leaves, and bottom depth of the occurrence of the meadow [60][61][62][86][87][88][89][90]. The relationships used in the present study were established by means of an in/ex situ study at a bottom depth of 15 for each season.…”

“…After completing to scan the sheaths and leaves, strong scatterers like fish individuals among the leaves are removed according range of the expected threshold of the Posidonia [60]. The next step is the biomass estimates of the leaves which are regressed with seasonal equations according the current sampling month [60], followed by saving the output data in a format of "*.xls" without variable names into an automatically created folder "PosiBiom" The variable names are in the order of each column as follows; geographical coordinates (latitude, longitude), yearday, bottom depth, canopy tip depth, leaf height (canopy height since orientation of leaves standing on the bottom; right, semi-flat and flat position, [62]), and three different successive biomasses (g/m 2 ) estimated by Sv and Sa ("cut experiment"), and leaf calibration ("leaf experiment") [60]. There is an auxiliary script, namely "PosiDrwTool" in "Tools" of the main menu, POSIBIOM to draw distribution of the estimates in a format of trackline or contour (Appendices 7 and 8).…”

“…Recently, in/ex situ acoustical studies have increased to relate the biometrics of the seaweeds and seagrasses to the Elementary Distance Sampling Unit (EDSU) [52], for instance, Depew et al [53] focused on Cladophora sp., Monpert et al [54] on P. oceanica, and Zostera marina, Llorens-Escrich et al [55] on P. oceanica, Shao et al [56] on Saccharina japonica, and Minami et al [57] on Sargassum horneri. There were some studies to calibrate the acoustical data versus the biometrics of two seagrasses, P. oceanica and Cymodocea nodosa [58][59][60][61] and to identify the seagrass, P. oceanica and C. nodosa [62].…”

A Deep Learning Approach to Estimate Halimeda incrassata Invasive Stage in the Mediterranean Sea

“…In the domain of data acquisition, three general types of data can be distinguished: acoustic images, hyper or multi-spectral satellite images, and RGB images. Although some research has been conducted in the field of acoustic seagrass monitoring [10][11][12], its prevalence is overshadowed by detection methods involving hyperspectral or RGB images, primarily due to the high cost associated with its requisite sensors. Moreover, while successful in detecting meadow-like seagrass, acoustic monitoring faces challenges in distinguishing between seagrass types and exhibits a dependency on depth ranges.…”

A Package of Script Codes, POSIBIOM for Vegetation Acoustics: POSIdonia BIOMass

“…Llorens-Escrich et al [55] working on P. oceanica; Shao et al [56] working on Saccharina japonica; and Minami et al [57] studying Sargassum horneri. There have also been some studies calibrating acoustic data with the biometrics of two seagrasses, P. oceanica and Cymodocea nodosa [58][59][60][61], and identifying the seagrasses, P. oceanica and C. nodosa [62].…”