[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2421":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":59},2421,"Characterizing non-permanent aquatic habitats using a Bayesian-curated remote sensing: insights from a rotifer case study","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10750-026-06348-z","Abstract Remote sensing is an indispensable tool for generating consistent, standardized time-series data across broad spatial scales. However, its usefulness for ecological and evolutionary research depends on how well it captures temporally relevant environmental variation. Non-permanent aquatic systems, such as Mediterranean shallow water bodies, are particularly challenging because their characteristic abrupt wet-dry transitions over short timescales increase noise and misclassification of hydrological states. Organisms inhabiting these systems are tightly coupled to the timing, duration, and predictability of inundation phases. Here, we developed a Bayesian state-space approach to curate Sentinel-2 time-series data, improving the reliability of hydrological state detection in a system of shallow ponds in the eastern Iberian Peninsula. The curated hydrological state data enabled the quantification of non-permanent regimes using ecologically meaningful metrics, revealing substantial differences in water availability and predictability among neighboring ponds under similar climatic conditions. We demonstrate how these metrics are relevant to evolutionary ecology. Specifically, we link hydrological unpredictability to bet-hedging strategies in rotifer populations. Rotifer clones originating from more unpredictable ponds exhibited lower hatching fractions, which is consistent with adaptive risk-spreading strategies. This study highlights the importance of characterizing environmental variability at temporal scales directly relevant to organismal life cycles when linking habitat dynamics to evolutionary responses.","摘要 遥感是生成跨大空间尺度一致、标准化时间序列数据不可或缺的工具。然而，其在生态学与进化研究中的实用性取决于其对时间相关环境变异的捕捉能力。非永久性水生系统，如地中海浅水水体，尤其具有挑战性，因为其在短时间尺度上特有的干湿突变转换会增加噪声和水文状态误分类。栖息于这些系统中的生物与淹水阶段的时机、持续时间和可预测性紧密耦合。在此，我们开发了一种贝叶斯状态空间方法，用于整理哨兵2号（Sentinel-2）时间序列数据，从而提高伊比利亚半岛东部浅塘系统水文状态检测的可靠性。经过整理的水文状态数据使得利用具有生态学意义的指标量化非永久性水文情势成为可能，揭示了在相似气候条件下相邻池塘之间在水体可利用性和可预测性方面的显著差异。我们展示了这些指标如何与进化生态学相关。具体而言，我们将水文不可预测性与轮虫种群的赌注对冲策略联系起来。来自更不可预测池塘的轮虫克隆表现出较低的孵化比例，这与适应性风险分散策略一致。本研究强调了在将栖息地动态与进化响应相联系时，在与生物生活史直接相关的时间尺度上刻画环境变异的重要性。",null,"Hydrobiologia","2026-09-11T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,20,18,13,1,"该研究提出贝叶斯状态空间方法优化Sentinel-2时序数据，提升非永久性水体水文状态识别精度，并关联轮虫种群风险分散策略，方法新颖、结论可靠，对农业遥感与生态监测有参考价值，但属基础生态学论文，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业信息化","生物多样性","遥感监测","水生生态","贝叶斯模型",0,"10.1007\u002Fs10750-026-06348-z",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":51,"card":52,"direction":56,"ingested_from":58},"W7212221912",[36,39,42,45,48],{"name":37,"orcid":38},"Carlota Solano Udina","https:\u002F\u002Forcid.org\u002F0000-0002-7272-8258",{"name":40,"orcid":41},"Eduardo M. García‐Roger","https:\u002F\u002Forcid.org\u002F0000-0001-7112-8464",{"name":43,"orcid":44},"Anabel Forte","https:\u002F\u002Forcid.org\u002F0000-0001-9534-1817",{"name":46,"orcid":47},"Javier Montero‐Pau","https:\u002F\u002Forcid.org\u002F0000-0002-0864-8157",{"name":49,"orcid":50},"María José Carmona","https:\u002F\u002Forcid.org\u002F0000-0002-4835-6933","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10750-026-06348-z.pdf",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"用贝叶斯状态空间方法校正Sentinel-2时序，刻画临时性水体水文动态并关联轮虫生活史策略。","贝叶斯状态空间模型校正Sentinel-2时序，提取水体淹没状态指标。","校正后水文指标揭示邻近池塘水分可用性与可预测性差异，且不可预测池塘轮虫克隆孵化率更低。","农业遥感与作物表型","可将该贝叶斯时序校正框架迁移至农业湿地\u002F灌溉塘遥感监测，并耦合生物生活史模型。","openalex","2026-09-14T23:30:18.412621Z"]