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Data from: Reproductive losses due to climate change-induced earlier flowering are not the primary threat to plant population viability in a perennial herb

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DataONE2021-11-29 更新2024-06-08 收录
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Abstract1. Despite a global footprint of shifts in flowering phenology in response to climate change, the reproductive consequences of these shifts are poorly understood. Furthermore, it is unknown whether altered flowering times affect plant population viability. 2. We examine whether climate change-induced earlier flowering has consequences for population persistence by incorporating reproductive losses from frost damage (a risk of early flowering) in population models of a subalpine sunflower (Helianthella quinquenervis). Using long-term demographic data for three populations that span the species’ elevation range (8–15 years, depending on the population), we first examine how snowmelt date affects plant vital rates. To verify vital rate responses to snowmelt date experimentally, we manipulate snowmelt date with a snow removal experiment at one population. Finally, we construct stochastic population projection models and Life Table Response Experiments for each population. 3. We find that populations decline (λs < 1) as snowmelt dates become earlier. Frost damage to flower buds, a consequence of climate change-induced earlier flowering, does not contribute strongly to population declines. Instead, we find evidence that negative effects on survival, likely due to increased drought risk during longer growing seasons, drive projected population declines under earlier snowmelt dates. 4. Synthesis. Shifts in flowering phenology are a conspicuous and important aspect of biological responses to climate change, but here we show that the phenology of reproductive events can be unreliable measures of threats to population persistence, even when earlier flowering is associated with substantial reproductive losses. Evidence for shifts in reproductive phenology, along with scarcer evidence that these shifts actually influence reproductive success, are valuable but can paint an incomplete and even misleading picture of plant population responses to climate change., Usage notesHEQU_demography dataLong-term demographic vital rates for Helianthella quinquenervis, across three study populations. The csv file was made in Excel. See README.txt file for detailed description of all variables.snowmeltData on first day of bare ground (snowmelt date, shown as day of year; 1 Jan = 1) from a long-term snow monitoring plot monitored by billy barr, near the mid-elevation study population described in the paper (The Rocky Mountain Biological Laboratory). snowmeltT1 refers to the date of melt in year t+1 and snowmeltTm1 refers to the date of melt in year t-1.snow.removal.experimentVital responses to experimental snow removal at one study population. The csv file was made in Excel. Variables are described in the associated README.txt file.

1. 尽管全球范围内植物开花物候(flowering phenology)因气候变化发生偏移已被广泛观测,但这类物候变化所带来的繁殖后果仍知之甚少。此外,开花时间的改变是否会影响植物种群生存力(population viability),目前尚无定论。 2. 我们通过将霜冻损害导致的繁殖损失(早开花的风险之一)纳入亚高山向日葵(Helianthella quinquenervis)的种群模型中,探究气候变化诱导的早开花是否会对种群存续产生影响。我们利用覆盖该物种海拔分布范围的3个种群的长期种群统计数据(每个种群的监测时长为8~15年不等),首先分析融雪日期如何影响植物生命率(vital rates)。为了通过实验验证生命率对融雪日期的响应,我们在其中一个种群开展了融雪去除实验,以此操控融雪日期。最后,我们为每个种群构建了随机种群投影模型(stochastic population projection models)与生命表响应实验(Life Table Response Experiments)。 3. 我们发现,随着融雪日期提前,种群数量呈下降趋势(随机种群增长率λₛ < 1)。由气候变化诱导的早开花所带来的后果——花芽霜冻损害,并未对种群下降起到显著推动作用。相反,我们的研究结果显示,种群下降主要由生存状况的负面影响所驱动,这一负面影响大概率源于更长生长季中加剧的干旱风险,而该风险会在融雪日期提前的情况下出现。 4. 总结:开花物候偏移是植物响应气候变化的显著且重要的生物学特征,但本研究表明,繁殖事件的物候变化或许无法可靠反映种群存续面临的威胁,即便早开花与显著的繁殖损失存在关联。现有关于繁殖物候偏移的研究证据,以及更少的、能证明这类偏移确实会影响繁殖成功的证据,固然具有价值,但它们可能无法完整反映,甚至会误导我们对植物种群响应气候变化的认知。 使用说明 HEQU_demography data 该数据集为亚高山向日葵(Helianthella quinquenervis)3个研究种群的长期种群统计生命率数据。csv文件由Excel制作,所有变量的详细说明请参见README.txt文件。 snowmelt 该数据来自落基山生物实验室(Rocky Mountain Biological Laboratory)附近中海拔研究样地的长期融雪监测样方,记录了裸地首次出现的日期(即融雪日期,以年积日表示;1月1日记为1),该监测由Billy Barr开展。其中snowmeltT1代表t+1年的融雪日期,snowmeltTm1代表t-1年的融雪日期。 snow.removal.experiment 该数据集记录了单个研究种群的融雪去除实验所得到的生命响应数据。csv文件由Excel制作,相关变量的详细说明请参见配套的README.txt文件。
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2024-03-16
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