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    <title>DSpace 集合:</title>
    <link>http://scholars.ntou.edu.tw/handle/123456789/203</link>
    <description />
    <pubDate>Mon, 24 Aug 2026 18:25:46 GMT</pubDate>
    <dc:date>2026-08-24T18:25:46Z</dc:date>
    <image>
      <title>DSpace 集合:</title>
      <url>https://scholars.ntou.edu.tw:443/retrieve/85/海洋生物研究所.jpg</url>
      <link>http://scholars.ntou.edu.tw/handle/123456789/203</link>
    </image>
    <item>
      <title>Use of Artificial Intelligence (AI) in Meso - zooplankton (Copepod) research: Applications, advances and future perspectives</title>
      <link>http://scholars.ntou.edu.tw/handle/123456789/26799</link>
      <description>標題: Use of Artificial Intelligence (AI) in Meso - zooplankton (Copepod) research: Applications, advances and future perspectives
作者: Thirunavukkarasu, Subramani; Rajendran, Poovazhagi; Watts, Andrew Gift; Epinoux, Clemence; Molinero, Juan-Carlos; Liao, Bo-Kai; Hwang, Jiang-Shiou
摘要: Copepods dominate meso-zooplankton communities in marine and freshwater systems and form a critical trophic bridge between primary producers and higher consumers, thereby regulating secondary production, carbon flux, and nutrient cycling. Escalating marine pollution, including microplastics, petroleum hydrocarbons, heavy metals, and eutrophication-driven harmful algal blooms and their byproducts, threatens these keystone organisms by impairing feeding, reproduction, and survival, with cascading effects on ecosystem stability and fisheries resources. Yet, conventional monitoring based on net sampling, microscopy, and expert taxonomy remains laborintensive, time-consuming, and insufficient for resolving large-scale, long-term pollution impacts. Artificial intelligence (AI) has emerged as a transformative solution, enabling rapid, automated, and scalable assessment of copepod communities and their environmental drivers. This review synthesizes recent advances in machine learning and deep learning for automated species identification, image-based classification, ecological modeling, and distribution forecasting, alongside AI-powered spatiotemporal imputation and prediction of chlorophyll-a and water-quality indicators to diagnose habitat quality and pollution stress. Approaches such as convolutional neural networks, ensemble learning, generative models, and real-time imaging systems substantially improve detection accuracy, data completeness, and predictive performance. Despite challenges related to training data, sensor integration, and model generalization, AI-driven frameworks offer unprecedented capacity for continuous monitoring and early warning. Integrating these technologies into marine pollution management will strengthen biodiversity conservation, ecosystem resilience, and evidence-based coastal governance.</description>
      <pubDate>Thu, 01 Jan 2026 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://scholars.ntou.edu.tw/handle/123456789/26799</guid>
      <dc:date>2026-01-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>Phenological shifts in plankton dynamics reshape pressures on anchovy recruitment in the southern coastal waters of the Korean peninsula</title>
      <link>http://scholars.ntou.edu.tw/handle/123456789/26800</link>
      <description>標題: Phenological shifts in plankton dynamics reshape pressures on anchovy recruitment in the southern coastal waters of the Korean peninsula
作者: Lee, Sun-Hee; Molinero, Juan Carlos; Ramirez-Romero, Eduardo; Park, Joo Myun; Scotti, Marco; Tseng, Li-Chun; Kang, Jung-Hoon
摘要: The East Asian marginal seas support one of the world's highest levels of fish production and consumption, yet their coastal ecosystems are increasingly threatened by rapid warming and degradation driven by cumulative anthropogenic pressures, including habitat modification and overexploitation. Using a comprehensive dataset (2010-2020) from a major fishing ground in the southern coastal region of the Korean Peninsula, we investigate phenological changes in plankton communities across three statistically supported thermal regimes identified during the 2010s. Phytoplankton exhibited a progressive decline in the magnitude of the annual bloom, while the peak of the Nemopilema nomurai jellyfish bloom shifted earlier by similar to 1.5 months (from day of year [DOY] 268 to DOY 225), increasing temporal overlap with the anchovy (Engraulis japonicus) spawning season after 2014. During the 2010s, annual production of anchovy declined markedly from 113.7 &amp; times; 10(3) metric tons in 2010-2013 to 85.01 &amp; times; 10(3) metric tons in 2017-2020. Structural equation modeling revealed that these declines were associated with the combined effects of weakened phytoplankton productivity and earlier jellyfish blooms, suggesting interacting bottom-up (phytoplankton-zooplankton-anchovy) and top-down processes affecting anchovy early life stages. These patterns are consistent with a phenology-driven mechanism linking environmental variability to recruitment conditions, rather than a direct causal attribution. Our findings highlight the importance of phenological shifts in shaping forage fish productivity and underscore the need to incorporate jellyfish dynamics into ecosystem-based fisheries management in the region.</description>
      <pubDate>Thu, 01 Jan 2026 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://scholars.ntou.edu.tw/handle/123456789/26800</guid>
      <dc:date>2026-01-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>Species diversity and molecular phylogeny of tubular Ulva (Ulvaceae, Ulvophyceae) in Taiwan, including U. taiwanifretensis sp. nov</title>
      <link>http://scholars.ntou.edu.tw/handle/123456789/26791</link>
      <description>標題: Species diversity and molecular phylogeny of tubular Ulva (Ulvaceae, Ulvophyceae) in Taiwan, including U. taiwanifretensis sp. nov
作者: Lin, Showe-Mei; Huang, Teng-Yi; Guiry, Michael D.; Chiou, Yu-Shan; Liu, Li-Chia; Shyu, Jeng-Feng
摘要: Tubular Ulva species are a prominent component of marine macroalgal communities along the coasts of Taiwan. However, their species diversity remains poorly known. Here, we sequenced and examined collections made over the past 15 years from various habitats in Taiwan. RbcL phylogenetic analyses identified 12 species, and these were largely confirmed by tufA analyses. These included a new species (U. taiwanifretensis sp. nov.), four species recorded for the first time from Taiwan (U. flexuosa subsp. linziformis, U. tepida, U. torta and U. tentaculosa), four undescribed taxa, and three previously reported species [U. arago &amp; euml;nsis (previously recorded as U. linza), U. prolifera, U. meridionalis]. In contrast, three tubular species previously recorded in Taiwan (U. clathrata, U. compressa and U. intestinalis) were not detected in this study. These species may have been misidentified as their tubular morphology superficially resembles that of U. arago &amp; euml;nsis or U. prolifera. Notably, U. taiwanifretensis sp. nov. from the Taiwan Strait is morphologically similar to U. intestinalis.</description>
      <pubDate>Thu, 01 Jan 2026 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://scholars.ntou.edu.tw/handle/123456789/26791</guid>
      <dc:date>2026-01-01T00:00:00Z</dc:date>
    </item>
    <item>
      <title>Population density and starvation as key drivers of cannibalism in the hydrothermal vent crab Xenograpsus testudinatus</title>
      <link>http://scholars.ntou.edu.tw/handle/123456789/26776</link>
      <description>標題: Population density and starvation as key drivers of cannibalism in the hydrothermal vent crab Xenograpsus testudinatus
作者: Ayyappan, Jishnu Panamoly; Thirunavukkarasu, Subramani; Vasanthakumaran, Murugan; Mammel, Mubarak; Hwang, Jiang-Shiou
摘要: Cannibalism is reported here for the first time in the hydrothermal vent crab Xenograpsus testudinatus, an endemic species inhabiting sulfur-rich shallow vent ecosystems. We examined how food deprivation and population density influence cannibalistic behavior under controlled laboratory conditions. Size-matched crabs were maintained at three initial densities (low: 15; medium: 45 (control group); high: 90 individuals per tank) and subjected to a 28-day starvation period. Cannibalism and mortality were recorded daily. Weekly cumulative counts were summarized descriptively and analyzed using Poisson generalized linear mixed models (GLMMs), with tank included as a random effect and effective density (accounting for mortality over time) modeled as a continuous covariate. GLMMs showed significant effects of initial density, week, and effective density on cannibalism (all p &lt; 0.001). Relative to medium density, low-density tanks showed higher baseline cannibalism (log coefficient = 0.883 +/- 0.332; incidence rate ratio [IRR] approximate to 2.42), whereas high-density tanks showed lower baseline rates (-0.870 +/- 0.106; IRR approximate to 0.42). Cannibalism increased markedly over time, becoming significant in Weeks 2-4 and peaking in Week 4 (log coefficient = 1.978 +/- 0.154; IRR approximate to 7.23). Effective density independently amplified realized cannibalism (log coefficient = 1.776 +/- 0.080; IRR approximate to 5.91 per 1 SD increase), indicating that encounter-driven processes intensified as remaining individuals became concentrated. Consistent with model predictions, survival declined as cannibalism increased; patterns suggest that early removal of weaker individuals may transiently reduce mortality risk, followed by intensified competition that promotes further cannibalism during prolonged starvation. Overall, our results suggest that cannibalism in X. testudinatus is influenced by both population density and food limitation. However, because no fed control group was included, the independent contributions of starvation and density cannot be fully disentangled. Future studies should address this limitation for a clearer understanding of the underlying drivers.</description>
      <pubDate>Thu, 01 Jan 2026 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://scholars.ntou.edu.tw/handle/123456789/26776</guid>
      <dc:date>2026-01-01T00:00:00Z</dc:date>
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