Authors: Halbritter AH1, Gillespie MAK2, Estiarte M3,4
Reviewers: Wohlgemuth T5, Peñuelas J3,4
Measurement unit: days, growing degree days, counts; Measurement scale: plot; Equipment costs: €-€€; Running costs: €; Installation effort: low to medium; Maintenance effort: high (frequent recording); Knowledge need: medium (species knowledge); Measurement mode: manual or data logger
Phenology refers to the timing of a species’ seasonal recurrent life-cycle events – the phenophases (see Table 4.5.1). Plants living in seasonal environments adjust the timing of their vegetative and reproductive phenophases in line with conditions favourable for each activity, be it growth, flowering, seed set, or other activity. Co-ordination to promote the optimal timing of each phenophase is important for survival, plant growth, and reproduction. Minimising the risk of freezing of newly formed leaves and flowers, adjusting leaf senescence to lengthen the active season but optimising nutrient resorption before frosts, or adjusting the timing of flowering in animal-pollinated plants with the peak occurrence of their pollinators to optimise fertilisation are examples of how this timing can be important. Thus phenology can affect demography (Inouye, 2008; Miller-Rushing et al., 2010; Scranton & Amarasekare, 2017), species distribution (Chuine, 2010), biodiversity and community composition (van Vliet et al., 2003; CaraDonna et al., 2014), trophic interactions such as plant–pollinator interactions (Thackeray et al., 2016) or outbreak of disease (van Vliet, 2010), and alter biochemical cycling (Keeling et al., 1996; Cleland et al., 2007; Peñuelas et al., 2009; Heberling et al., 2019). Understanding shifts in the phenology of different and interacting species provides researchers with more information to forecast the impacts of climate change on plant communities and ecosystem functioning. In Northern Hemisphere temperate and boreal zones, northern range limits are caused by the inability to finish fruit maturation and southern ranges are defined by lacking cold temperatures (chilling) in wintertime that are necessary to break bud dormancy (Chuine, 2010).
Plants use environmental cues as seasonal triggers and to control their development. The main cues are photoperiod, snow cover, timing of snowmelt, air and soil temperature, soil moisture, precipitation, and exposure to cold, which, excluding daylength, are likely to be modified under climate change. Consequently, phenology can be used as a proxy to document the effects of changes in climate. Over the last few decades, the phenology of many species across taxonomic groups and regions has shifted due to climate change (Peñuelas & Filella, 2001; Parmesan & Yohe, 2003; Menzel et al., 2006; Cleland et al., 2007; Wolkovich et al., 2012), with many species advancing their phenology due to warmer spring temperatures. However, species have reacted in varying ways, partly because different species respond to different environmental cues (e.g. Vitasse et al., 2009). Plant phenology can also be impacted by other global-change drivers, such as nitrogen deposition and elevated CO2 (Cleland et al., 2006; Stevens et al., 2018).
4.5.1 What and how to measure?
Visual phenological monitoring
Aboveground plant phenology is recorded by dating as accurately as possible the occurrence of the phenophases of interest related to the vegetative and/or reproductive events. Here, only aboveground phenological events visible to the naked eye are considered; not events that need instrumentation to be detected, such as stem and belowground growth or physiological events (see protocols 2.1.1 Aboveground plant biomass, 2.1.2 Belowground plant biomass and chapter 5 Stress physiology).
Phenological events are spread across the entire growing season and the data collection can be very time consuming. The most commonly recorded phenophases in climate-change studies are the first occurrence and maturation of the vegetative and reproductive phenophases listed in Table 4.5.1 (for a more detailed description of phenophases in trees see Table 1 in Finn et al., 2007). Phenophases can overlap (e.g. buds and flowers); for detailed studies, it is important to record all concurrent phases.
Table 4.5.1 The most commonly recorded vegetative and reproductive phenophases in climate-change studies.
| Vegetative phases
|
Dormant winterbuds | |
| Breaking leaf buds | ||
| Green leaves, needles, or rosette visible | ||
| Stem or shoot elongation | ||
| Lammas growth (second shoots grown in summer; e.g. Battey, 2003) | ||
| Senescence / leaf or needle colouring | ||
| Leaf or needle fall | ||
| Reproductive phases
|
Bud development ceased in autumn | |
| Budburst | ||
| Emergence of petals | ||
| Open flowers / anthesis | ||
| Flower withering | ||
| Fruit / seed maturation | ||
| Seed dispersal | ||
Defining beforehand when a certain phenophase is reached is important for consistency, especially when different species are compared, and different people are involved in the data collection. For example, flowering is generally defined when the petals are open, revealing the reproductive structure and the flower is ready to be pollination (if insect pollinated). Some species produce a single flower, while others produce several flowers together in a spike or umbel. For plants with a more complicated flowering structure, flowering is defined when the first flower on any spike or umble is has opened (see Haggerty & Mazer, 2008 for species with different flowering architectures and definitions of different phenophases). For large species, phenophases are better defined as a fraction of plant organs reaching a stage, for instance 50% yellow leaves is taken as date for leaf senescence, or alternatively, the proportion of branches at a certain stage, i.e. flowers open (Morellato et al., 2010).
Typically, data are recorded for plants within experimental plots and further expressed as plot aggregated values, although sometimes records are done at plot or ramet level. Phenological monitoring requires repeated assessment of the presence and absence of phenophases (Denny et al., 2014). Repeatedly recording the presence and absence of different phenophases provides more information than recording a single “event” (Diez et al., 2012). From repeated recordings, multiple events of each phenophase (e.g. onset, duration, end) can be assessed and extracted. This is also useful in habitats with no defined start of the growing season (e.g. tropics), where plants produce leaves, flowers, and fruits all year round.
The duration of the recording period will depend on the level of detail required by the phenophases under study and on the specific growth cycle and life form of the investigated species. For example, some species flower only for a couple of days, while other species flower for several months (Gentry, 1974; Opler et al., 1980) and require a different sampling duration and frequency. Ideally, the time intervals for phenological monitoring range from every day to once a week, depending on how fast species change their phenophases. For example, to accurately detect the onset and end of anthesis, recording must be more frequent in species flowering just for a few days than in species with longer flowering periods (see above).
Abundance of phenophases
In addition to the presence/absence of phenophases, it is recommended to record the abundance of specific life stages (e.g. number of open flowers for a species), especially for continuous records. This is a simple additional effort that provides information on the date of peak events, for example number of open flowers, which can be important for pollinators.
Field operation
For continuous observations, it is important to mark the plots and plants or ramets to make sure the same area and individuals are monitored each time. In some systems (e.g. heavily grazed areas), fencing might be preferable to protect the plants and monitoring plots from large herbivores.
Interpretation
Phenophases can be defined temporally as a point in time (i.e. onset or end) or a duration. The onset or end of a phenophase can be defined by its mean start date or end date. The duration of a phenophase can be defined as the period from the mean start date to the mean end date. The onset of a phenophase is one of the most commonly used variables in phenological studies (e.g. Oberbauer et al., 2013). However, the onset and end date or duration of a phenophase do not necessarily respond in the same way to climate change, suggesting that only focusing on the onset might be misleading (CaraDonna et al., 2014).
The onset or end date of a phenophase is calculated as the first or last day a phenophase is observed. Often, the mean date when the first 10–25% of plants start to flower or budburst is used (Jentsch et al., 2009). If the abundance of different phenophases is recorded, the peak of a phenophase can be calculated. For this, the date when 50% of the individuals are in a certain phenophase is commonly used (CaraDonna et al., 2014; Gugger et al., 2015).
Phenological dates on their own are often meaningless and it is the comparison between different species, treatments, time periods, or across time periods that make them useful. Since changes in phenophases are often triggered by environmental cues, it is highly recommended to complement the phenological monitoring with continuous meteorological records. The variability in the meteorology can be correlated to within- and among- year variations in the phenophases at site level and to compare with the sensitivity to experimental treatments (see protocol 1.5 Meteorological measurements). The most useful variables to record for phenological monitoring are: air temperature, soil temperature, and soil moisture (Carbognani et al., 2016; Theobald et al., 2017). In cold environments (i.e. alpine and arctic habitats), the timing of snowmelt should always be recorded because it is an important driver that defines the start of the growing season (Körner, 2003). Climate data can also be used to calculate the cumulative temperature to reach a phenophase (often as growing degree days above a temperature threshold), which is a measure of the temperature requirement (i.e. energy) of a species to reach a phenophase.
To study the phenological response to changes in temperature, often the temperature sensitivity of a species is calculated, which is the change of a phenological event (in days) per change in temperature, ΔT (Wolkovich et al., 2012). In a climate warming experiment, the temperature sensitivity can be calculated as:
(phenological event datei,warm − phenological event datei,control )/ ΔT
But see Kenan et al. (2019) for challenges using this simplistic metric for temperature sensitivity and a robust alternative.
Where to start
Denny et al. (2014), Elmendorf et al. (2016), Finn et al. (2007), Haggerty & Mazer (2008)
4.5.2 Special cases, emerging issues, and challenges
Automated phenological monitoring
Ground-based, observational phenological monitoring is labour-intensive and expensive and usually only applicable at a local scale. It is thus difficult to upscale to the community or ecosystem level. More recently, “near-surface” phenological monitoring has been undertaken with automated digital cameras (Sonnentag et al., 2012; Brown et al., 2016), which can provide a measure of greenness at a broad spatial and temporal scale containing valuable information on leaf phenology (Richardson et al., 2007). Digital cameras capture colour changes in the vegetation between green-up and senescence colours (red, green, blue, RGB) in the visible spectrum or infrared spectrum (Ide & Oguma, 2010; Sonnentag et al., 2012; Nijland et al., 2014).
Satellite image-aided analysis and satellite remote sensing measure the reflectance of the vegetation from which the normalised difference vegetation index (NDVI) can be calculated. NDVI is an index for the green biomass of the vegetation (Tucker, 1979; Gamon et al., 1995) and can be used to measure the green-up and senescence at a landscape scale. Ground-based observations and hemispherical photography can be used to verify the reliability of satellite data (Schwartz et al., 2002; Karlsen et al., 2009; Rautiainen et al., 2012; Zhang et al., 2015), and also highlight methodological limitations for NDVI data (e.g. noise in the satellite data because of clouds).
Different approaches, challenges, and emerging methods
A common source of long-term datasets to analyse past phenology are museum specimens and historical recordings (MacGillivray et al., 2010; Bartomeus et al., 2011). More recently, citizen-science has contributed to the collection of large phenology datasets (e.g. Miller-Rushing & Primack, 2008; Crimmins et al., 2009).
Comparative studies manipulate plants or plots over a short time period (1–4 years) and the species’ response in phenology is measured. Such experiments allow direct manipulation of environmental variables to help disentangle potentially interacting factors (Rafferty et al., 2013). The limitations of such experiments are that they are often accompanied by unwanted or unrecorded side effects (e.g. warming can be correlated with a drying effect).
Alternative approaches are to combine long-term datasets with experimental approaches and/or models to provide a more complete picture of species responses to future climate change (Rafferty et al., 2013). A useful approach is to systematically replicate experiments along environmental gradients to understand the underlying variation of species- and site-specific patterns that many studies show (Dunne et al., 2003; Delnevo et al., 2017). More recently, many national and international phenology networks (van Vliet et al., 2003; Denny et al., 2014; Elmendorf et al., 2016), have developed standardised protocols for phenological observations that allow comparisons across species, environments, phenophases, and time.
Phenology is highly linked with physiology, and interdisciplinary studies combining these two fields can improve the mechanistic and evolutionary understanding of phenology (Forrest & Miller-Rushing, 2010). To study the effect of climate change on plant–pollinator interactions (e.g. Bartomeus et al., 2011; Rafferty & Ives, 2011; Kudo & Ida, 2013; Gillespie et al., 2016), data from broad species networks are required, i.e. counting the number of insect visits to flowers (also see protocol 4.13 Pollinator visitation). Setting up such networks is time consuming and species identification skills for both plants and insects are needed. It is also challenging to combine species networks with climate manipulations, because animals are mobile and use a larger spatial area compared to plants. A useful supplement to phenological studies, is to investigate the consequences of changing phenologies on plant fitness (i.e. quantify survival and/or reproductive output; see protocol 4.1 Sexual reproduction) at the population level (Miller-Rushing et al., 2010; Kudo & Ida, 2013; Forrest, 2015), which could improve our understanding of potential future species distributions. Finally, phenological data are often sparsely and unevenly sampled, there are uncertainties around observations (e.g. the exact date a flower opens is not captured), and forecasting phenology is affected by multiple factors. Novel approaches and statistical methods from other fields can provide more robust tools to analyse phenological data (Diez et al., 2014; Pearse et al., 2017).
4.5.3 References
Theory, significance, and large datasets
More on theory: Hudson & Keatley (2010); Large data sets such as the European Phenology Network: van Vliet et al. (2003) and the USA National Phenology Network: Schwartz et al. (2012)
More on methods and existing protocols
Beuker et al. (2016), Denny et al. (2014), Elmendorf et al. (2016), Haggerty & Mazer (2008), Law et al. (2008)
All references
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Authors: Halbritter AH1, Gillespie MAK2, Estiarte M3,4
Reviewers: Wohlgemuth T5, Peñuelas J3,4
Affiliations
1 Department of Biological Sciences and Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
2 Department of Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
3 CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
4 CREAF, Cerdanyola del Vallès, Spain
5 Forest Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland