- 著者
- Akiyuki Kenmochi Hiroyuki Matsuura Takashi Yoshikawa Rumi Sohrin Yumiko Obayashi Jun Nishikawa
- 出版者
- The Plankton Society of Japan, The Japanese Association of Benthology
- 雑誌
- Plankton and Benthos Research (ISSN:18808247)
- 巻号頁・発行日
- vol.17, no.1, pp.57-65, 2022-02-22 (Released:2022-02-23)
- 参考文献数
- 53
- 被引用文献数
- 1
Seasonal occurrences of marine cladocerans in offshore Suruga Bay, Japan, were studied from 2014 to 2019. Zooplankton samples were collected monthly from a station (depth: ca. 1000 m) located in the offshore area of the bay. Cladocerans appeared each year from February to December, and disappeared in January. Their abundance increased drastically from April to September and comprised a substantial portion of the offshore mesozooplankton community during this period. Maximum cladoceran abundance occurred from June to August, ranging from 65.9–1341.9 individuals m−3. These patterns in cladoceran abundances were basically repeated each year throughout the study period. This result suggests that mass occurrences of cladocerans in offshore regions of the bay during the spring-summer are regular events rather than sporadic. Seven species, which have previously been reported from Japanese waters, were identified, and successional changes in the dominant species were observed. The most abundant species, Penilia avirostris, carried parthenogenetic embryos in brood pouches, suggesting that they were not simply transported from coastal areas, but that they also reproduce in the offshore waters. Regular mass occurrences of marine cladocerans in offshore Suruga Bay could have an important impact on the offshore ecosystem of the bay, and factors enabling these population cycles need to be determined.
- 著者
- Nobuharu Kami Teruyuki Baba Satoshi Ikeda Takashi Yoshikawa Hiroyuki Morikawa
- 出版者
- 一般社団法人 情報処理学会
- 雑誌
- Journal of Information Processing (ISSN:18826652)
- 巻号頁・発行日
- vol.20, no.3, pp.757-766, 2012 (Released:2012-07-15)
- 参考文献数
- 19
We present a fast algorithm for probabilistically extracting significant locations from raw GPS data based on data point density. Extracting significant locations from raw GPS data is the first essential step of algorithms designed for location-aware applications. Most current algorithms compare spatial/temporal variables with given fixed thresholds to extract significant locations. However, the appropriate threshold values are not clearly known in priori, and algorithms with fixed thresholds are inherently error-prone, especially under high noise levels. Moreover, they do not often scale in response to increase in system size since direct distance computation is required. We developed a fast algorithm for selective data point sampling around significant locations based on density information by constructing random histograms using locality-sensitive hashing. Theoretical analysis and evaluations show that significant locations are accurately detected with a loose parameter setting even under high noise levels.