European phenological response to climate change matches the warming pattern

Global climate change impacts can already be tracked in many physical and biological systems; in particular, terrestrial ecosystems provide a consistent picture of observed changes. One of the preferred indicators is phenology, the science of natural recurring events, as their recorded dates provide a high-temporal resolution of ongoing changes. Thus, numerous analyses have demonstrated an earlier onset of spring events for mid and higher latitudes and a lengthening of the growing season. However, published single-site or single-species studies are particularly open to suspicion of being biased towards predominantly reporting climate change-induced impacts. No comprehensive study or meta-analysis has so far examined the possible lack of evidence for changes or shifts at sites where no temperature change is observed. We used an enormous systematic phenological network data set of more than 125 000 observational series of 542 plant and 19 animal species in 21 European countries (1971–2000). Our results showed that 78% of all leafing, flowering and fruiting records advanced (30% significantly) and only 3% were significantly delayed, whereas the signal of leaf colouring/fall is ambiguous. We conclude that previously published results of phenological changes were not biased by reporting or publication predisposition: the average advance of spring/summer was 2.5 days decade⁻¹ in Europe. Our analysis of 254 mean national time series undoubtedly demonstrates that species' phenology is responsive to temperature of the preceding months (mean advance of spring/summer by 2.5 days°C⁻¹, delay of leaf colouring and fall by 1.0 day°C⁻¹). The pattern of observed change in spring efficiently matches measured national warming across 19 European countries (correlation coefficient r =−0.69, P <0.001).     

Spatial and temporal variability of the phenological seasons in Germany from 1951 to 1996

Various indications for shifts in plant and animal phenology resulting from climate change have been observed in Europe. This analysis of phenological seasons in Germany of more than four decades (1951–96) has several major advantages: (i) a wide and dense geographical coverage of data from the phenological network of the German Weather Service, (ii) the 16 phenophases analysed cover the whole annual cycle and, moreover, give a direct estimate of the length of the growing season for four deciduous tree species. After intensive data quality checks, two different methods – linear trend analyses and comparison of averages of subintervals – were applied in order to determine shifts in phenological seasons in the last 46 years. Results from both methods were similar and reveal a strong seasonal variation. There are clear advances in the key indicators of earliest and early spring (?0.18 to ?0.23 d y^?1) and notable advances in the succeeding spring phenophases such as leaf unfolding of deciduous trees (?0.16 to ?0.08 d y^?1). However, phenological changes are less strong during autumn (delayed by + 0.03 to + 0.10 d y^?1 on average). In general, the growing season has been lengthened by up to ?0.2 d y^?1 (mean linear trends) and the mean 1974–96 growing season was up to 5 days longer than in the 1951–73 period. The spatial variability of trends was analysed by statistical means and shown in maps, but these did not reveal any substantial regional differences. Although there is a high spatial variability, trends of phenological phases at single locations are mirrored by subsequent phases, but they are not necessarily identical. Results for changes in the biosphere with such a high resolution with respect to time and space can rarely be obtained by other methods such as analyses of satellite data.     

Bayesian analysis of climate change impacts in phenology

The identification of changes in observational data relating to the climate change hypothesis remains a topic of paramount importance. In particular, scientifically sound and rigorous methods for detecting changes are urgently needed. In this paper, we develop a Bayesian approach to nonparametric function estimation. The method is applied to blossom time series of Prunus avium L., Galanthus nivalis L. and Tilia platyphyllos SCOP. The functional behavior of these series is represented by three different models: the constant model, the linear model and the one change point model. The one change point model turns out to be the preferred one in all three data sets with considerable discrimination of the other alternatives. In addition to the functional behavior, rates of change in terms of days per year were also calculated. We obtain also uncertainty margins for both function estimates and rates of change. Our results provide a quantitative representation of what was previously inferred from the same data by less involved methods.     

Does flower phenology mirror the slowdown of global warming?

Although recent global warming trends in air temperature are not as pronounced as those observed only one decade ago, global mean temperature is still at a very high level. Does plant phenology – which is believed to be a suitable indicator of climate change – respond in a similar way, that is, does it still mirror recent temperature variations? We explored in detail long-term flowering onset dates of snowdrop, cherry, and lime tree and relevant spring temperatures at three sites in Germany (1901–2012) using the Bayesian multiple change-point approach. We investigated whether mean spring temperature changes were amplified or slowed down in the past decade and how plant phenology responded to the most recent temperature changes. Incorporating records with different end points (i.e., 2002 and 2012), we compared differences in trends and inferred possible differences caused by extrapolating phenological and meteorological data. The new multiple-change point approach is characterized by an enhanced structure and greater flexibility compared to the one change point model. However, the highest model probabilities for phenological (meteorological) records were still obtained for the one change point (linear) model. Marked warming trends in the recent decade were only revealed for mean temperatures of March to May, here better described with one or two change point models. In the majority of cases analyzed, changes in temperatures were well mirrored by phenological changes. However, temperatures in March to May were linked to less strongly advancing onset dates for lime tree flowering during the period 1901-2012, pointing to the likely influence of photoperiodic constraints or unfulfilled chilling requirements. Due to the slowdown of temperature increase, analyses conducted on records ending in 2002 demonstrated distinct differences when compared with records ending in 2012. Extrapolation of trends could therefore (along with the choice of the statistical method) lead to distinctly different results and most recent data should be integrated in order not to over- or underestimate future phenological changes.     

Trophic level asynchrony in rates of phenological change for marine,freshwater and terrestrial environments

Recent changes in the seasonal timing (phenology) of familiar biological events have been one of the most conspicuous signs of climate change. However, the lack of a standardized approach to analysing change has hampered assessment of consistency in such changes among different taxa and trophic levels and across freshwater, terrestrial and marine environments. We present a standardized assessment of 25 532 rates of phenological change for 726 UK terrestrial, freshwater and marine taxa. The majority of spring and summer events have advanced, and more rapidly than previously documented. Such consistency is indicative of shared large scale drivers. Furthermore, average rates of change have accelerated in a way that is consistent with observed warming trends. Less coherent patterns in some groups of organisms point to the agency of more local scale processes and multiple drivers. For the first time we show a broad scale signal of differential phenological change among trophic levels; across environments advances in timing were slowest for secondary consumers, thus heightening the potential risk of temporal mismatch in key trophic interactions. If current patterns and rates of phenological change are indicative of future trends, future climate warming may exacerbate trophic mismatching, further disrupting the functioning, persistence and resilience of many ecosystems and having a major impact on ecosystem services.     

植物物候对气候变化的响应

植物物候的变化可以直观地反映某些气候变化,尤其是气候变暖.植物生长节律的变化引起植物与环境关系的改变.生态系统的物质循环（如水和碳的循环）等过程将随物候而改变.不同种类植物物候对气候变化的响应的差异,会使植物间和动植物间的竞争与依赖关系也发生深刻的变化.目前欧洲、美洲、亚洲等许多地区均有关于春季植物物候提前,秋季物候推迟,使植物的生长季延长,从而提示气候变暖的趋势.植物物候的模拟模型构成生态系统生产力模型的重要部分.     

植物物候与气候研究进展

植物物候及其变化是多个环境因子综合影响的结果,其中气候是最重要、最活跃的环境因子。主要从气候环境角度分析了植物物候与气候以及气候变化间的相互关系,概述了国内外有关植物物候及物候模拟等方面的研究进展。表明,温度是影响物候变化最重要的因子;同时,水分成为胁迫因子时对物候的影响也十分重要。近50a左右,世界范围内的植物物候呈现出了春季物候提前,秋季物候推迟或略有推迟的特征,从而导致了多数植物生长季节的延长,并成为全球物候变化的趋势。全球气候变暖改变了植物开始和结束生长的日期,其中冬季、春季气温的升高使植物的春季物候提前是植物生长季延长的主要原因。目前对物候学的研究方向主要集中在探讨物候与气候变化之间的关系,而模型模拟是定量研究气候变化与植物物候之间关系的重要方式,国内外已经开发出多种物候模型来分析气候驱动与物候响应之间的因果关系。另外遥感资料的应用也为物候模型研究提供了新的方向。物候机理研究、物候与气候关系以及物候模型研究将是研究的重点。     

气候变化对桂林植物物候的影响

利用线性倾向估计、Mann-Kendall突变检测等方法,对桂林气候(1951～2009年)和3种植物物候(1983～2009年)的趋势变化特征进行了分析,并探讨了物候期与气温、日照、降水等气象因子的相关性及其对主要气候影响因子的响应情况。结果表明:在当地气候变化背景下,桂林市植物物候期发生了不同程度的变化,春季物候期提前,秋季物候期推迟,绿叶期延长;平均气温是影响植物物候期最为显著的气象因子,气温每增高1℃,春季物候平均提前5d左右,秋季物候平均推迟8d左右,绿叶期延长约27d;春季物候和绿叶期的突变一般发生在气温突变之后,但秋季物候期突变与其影响月份气温的突变并无关系。以上分析说明植物物候对气候变化响应比较敏感,通过分析气候和植物物候变化的规律,掌握气候对当地植物物候的可能影响,可为农业生产、生态环境监测和评估等提供理论依据。     

Forecasting phenology under global warming

As a consequence of warming temperatures around the world, spring and autumn phenologies have been shifting, with corresponding changes in the length of the growing season. Our understanding of the spatial and interspecific variation of these changes, however, is limited. Not all species are responding similarly, and there is significant spatial variation in responses even within species. This spatial and interspecific variation complicates efforts to predict phenological responses to ongoing climate change, but must be incorporated in order to build reliable forecasts. Here, we use a long-term dataset (1953–2005) of plant phenological events in spring (flowering and leaf out) and autumn (leaf colouring and leaf fall) throughout Japan and South Korea to build forecasts that account for these sources of variability. Specifically, we used hierarchical models to incorporate the spatial variability in phenological responses to temperature to then forecast species'' overall and site-specific responses to global warming. We found that for most species, spring phenology is advancing and autumn phenology is getting later, with the timing of events changing more quickly in autumn compared with the spring. Temporal trends and phenological responses to temperature in East Asia contrasted with results from comparable studies in Europe, where spring events are changing more rapidly than are autumn events. Our results emphasize the need to study multiple species at many sites to understand and forecast regional changes in phenology.     

Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006

Shifts in the timing of spring phenology are a central feature of global change research. Long‐term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start‐of‐spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8 km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16 000 individual measurements of ground‐based phenology, and two temperature‐driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day‐of‐year estimates by ±60 days and in standard deviation by ±20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground‐ or model‐based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS trends could be detected for only 12% of North America and were divided between trends towards both earlier and later spring.