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. 2015 May 11;116(6):875–888. doi: 10.1093/aob/mcv055

Changes in autumn senescence in northern hemisphere deciduous trees: a meta-analysis of autumn phenology studies

Allison L Gill 1, Amanda S Gallinat 1, Rebecca Sanders-DeMott 1, Angela J Rigden 2, Daniel J Short Gianotti 2, Joshua A Mantooth 2, Pamela H Templer 1,*
PMCID: PMC4640124  PMID: 25968905

Abstract

Background and Aims Many individual studies have shown that the timing of leaf senescence in boreal and temperate deciduous forests in the northern hemisphere is influenced by rising temperatures, but there is limited consensus on the magnitude, direction and spatial extent of this relationship.

Methods A meta-analysis was conducted of published studies from the peer-reviewed literature that reported autumn senescence dates for deciduous trees in the northern hemisphere, encompassing 64 publications with observations ranging from 1931 to 2010.

Key Results Among the meteorological measurements examined, October temperatures were the strongest predictors of date of senescence, followed by cooling degree-days, latitude, photoperiod and, lastly, total monthly precipitation, although the strength of the relationships differed between high- and low-latitude sites. Autumn leaf senescence has been significantly more delayed at low (25° to 49°N) than high (50° to 70°N) latitudes across the northern hemisphere, with senescence across high-latitude sites more sensitive to the effects of photoperiod and low-latitude sites more sensitive to the effects of temperature. Delays in leaf senescence over time were stronger in North America compared with Europe and Asia.

Conclusions The results indicate that leaf senescence has been delayed over time and in response to temperature, although low-latitude sites show significantly stronger delays in senescence over time than high-latitude sites. While temperature alone may be a reasonable predictor of the date of leaf senescence when examining a broad suite of sites, it is important to consider that temperature-induced changes in senescence at high-latitude sites are likely to be constrained by the influence of photoperiod. Ecosystem-level differences in the mechanisms that control the timing of leaf senescence may affect both plant community interactions and ecosystem carbon storage as global temperatures increase over the next century.

Keywords: Autumn phenology, climate change, growing season, leaf senescence, temperature, deciduous tree, woody plants

INTRODUCTION

Air temperatures around the globe are increasing (Jones et al., 2012), which has measureable effects on a variety of plant processes. Specifically, the effects of climate change on the phenology of vegetation have received increased attention over the past several decades as any factor that alters the timing of early growing season leaf-out and senescence has the potential to affect a variety of ecosystem properties. For example, the timing of leaf-out and senescence of deciduous plants has been shown to affect plant competition (Fridley, 2012), plant growth (Myneni et al., 1997) and ecosystem carbon uptake (Barichivich et al., 2012). Changes in phenology also affect surface albedo through differences in reflectance between closed and bare canopies (Richardson et al., 2013). While leaf-out has been shown to advance over the past century as a result of increasing air temperatures (Linderholm, 2006; Polgar and Primack, 2011), the relationship between temperature and leaf senescence remains less well understood (García-Plazaola et al., 2003; Richardson et al., 2013). Leaf senescence completes the growing season for deciduous trees and therefore factors that delay leaf-off can lengthen the period of plant photosynthesis and increase rates of gross primary productivity (Richardson et al., 2010). Thus, an extension of the growing season can contribute to reduced atmospheric CO2 concentrations due to enhanced carbon sequestration in terrestrial plants (Penuelas et al., 2009; Richardson et al., 2013). However, the increase in carbon uptake may be partially offset by increased rates of ecosystem respiration (Piao et al., 2008).

Efforts to examine the effects of increasing air temperatures on leaf senescence have employed a wide array of methodologies and have measured senescence across a range of scales, from ground-based observations of individual trees to regional-scale remote sensing. Ge et al. (2014) conducted a meta-analysis of ground-based phenological data sets spanning >20 years throughout China and found that leaf senescence in trees and woody shrubs was delayed by 1·98 d per decade from 1960 to 2011. Menzel et al. (2006) used long-term, ground-based observations to determine that autumn senescence in Europe was delayed by 1·3 d per decade and 1·0 d °C–1 between 1971 and 2000; however, these trends were not unidirectional as only 15 % of individual sites demonstrated significantly delayed senescence, 12 % showed senescence dates that advanced and 83 % did not change significantly over time. Through the use of 8 × 8 km2 grid remote-sensing data, Piao et al. (2006) demonstrated that senescence was delayed by 0·37 d year–1 over the 27 year period between 1982 and 2009 in deciduous forests in the northern hemisphere. Jeong et al. (2011) found evidence of advanced senescence in temperate North America using satellite data from 1982 to 2011. Several additional studies also show weak or insignificant relationships between the timing of autumn senescence and air temperatures (Menzel, 2003; Menzel et al., 2006; Pudas et al., 2008).

The lack of consistent relationships between air temperature and timing of leaf senescence suggests that autumn senescence may be influenced by a variety of factors that obscure its relationship with temperature. The date of first frost in the autumn months in temperate and boreal ecosystems is highly variable and does not always follow a linear cooling trend (Way and Montgomery, 2014). It is therefore likely that the timing of leaf senescence responds to a suite of environmental factors, although the genetic basis of senescence activity and physiology remains unknown (Tuskan et al., 2006; Way and Montgomery, 2014). For example, photoperiod (Hanninen and Tanino 2011; Jeong et al., 2011; Way, 2011) and local meteorological factors such as wind and humidity (Staelens et al., 2003; Travers and Eldridge, 2013) have all been shown to affect dates of senescence. The factors that most strongly influence the onset of senescence may also differ across ecosystems, with high-latitude plants considered to be more responsive to photoperiod and low-latitude plants more responsive to temperature (Stinziano and Way, 2014; Way and Montgomery, 2014). In addition to site-level meteorological factors, recent studies also demonstrate a relationship between the timing of spring leaf-out and the timing of autumn leaf senescence (Fu et al., 2014; Keenan and Richardson, 2015).

Changes in vegetation dynamics have major implications for the global carbon cycle, yet the current generation of terrestrial biosphere models often fail to simulate changes in autumn phenology accurately (Richardson et al., 2012). Many terrestrial biosphere models use temperature as a primary predictor of leaf senescence despite its inconsistency as a strong driver in observational studies (Menzel, 2002; Menzel et al., 2006; Richardson et al., 2013). For example, the Integrated Biosphere Simulator (IBIS) model induces leaf drop when mean daily air temperatures fall below 5 °C (Foley et al., 2010), while other models use combinations of both mean air temperature and threshold temperatures to predict leaf shedding (Estrella and Menzel, 2006). Growing degree-days or cooling degree-days are employed in some models, and provide an integrated measure of temperature history over a growing season (White and Nemani, 2003; Jolly et al., 2005; Richardson et al., 2012). Other models predict leaf drop through an integration of photoperiod and mean air temperature thresholds (BIOME-BGC; White et al., 1997), or photoperiod and cooling degree-day summation (Delpierre et al., 2009). The lack of consensus in phenology modelling schemes reflects the fact that the mechanisms that drive autumn phenology remain poorly understood (Vitasse et al., 2011).

Leaf senescence data are widely collected, although integrating these observations into an interpretable framework poses a challenge due to the variability in measurement methods and criteria used to determine the date of senescence. There is no consistent definition of leaf senescence, nor a standard methodology used for monitoring the process across studies (Gallinat et al. 2015). Some data networks standardize methodologies over specific regions or habitat types in an effort to synthesize senescence trends over time and space (e.g. Ibanez et al., 2010; Panchen et al., 2015). Despite these large-scale efforts, there are numerous individual studies that have examined controls on the timing of leaf senescence at local and regional scales that vary in their methodology. In many cases the definition of leaf senescence depends on the plant species examined, as some species’ life history traits inherently lend themselves to particular definitions. For instance, the genus Quercus typically holds senescing leaves longer than other genera, which may make the date of 50 % leaf colour change the best description of leaf senescence, while members of the genus Paulownia drop green leaves, making leaf drop a preferable metric to characterize the end of the growing season. Some studies integrate physiological measurements such as photosynthetic rate and chlorophyll content to delineate the date of senescence (Nagai et al., 2011). While the metrics chosen by individual research groups often optimize senescence estimates for a particular genus or location, the variation in methodology across ecosystems makes large-scale generalization challenging.

Here we synthesize publications from the peer-reviewed literature to evaluate larger scale trends in dates of leaf senescence across leaf senescence studies in the northern hemisphere. We present a meta-analysis to describe patterns of leaf senescence in boreal and temperate deciduous trees and examine relationships between dates of leaf senescence and meteorological measurements to assess physical drivers of leaf senescence. The primary goals of this synthesis were to: (1) characterize the methodologies employed in observations of autumn senescence in boreal and temperate deciduous trees in the northern hemisphere; (2) determine the influence of temperature, precipitation, latitude and photoperiod on the timing of leaf senescence in high- and low-latitude ecosystems; (3) identify how senescence trends differ among taxa and regions; and (4) provide recommendations for future research by identifying biologically relevant methods for monitoring leaf senescence and characterizing key areas of uncertainty in predicting senescence phenology.

MATERIALS AND METHODS

Data collection

We assembled a database of peer-reviewed publications using the Web of Science search engine. We used all combinations of the following search terms as ‘topics’: ‘autumn’ or ‘fall’; ‘phenology’ or ‘senescence’; ‘tree’ or ‘plant’ or ‘forest’; and ‘leaf’. We limited the search to English-language, peer-reviewed journal articles published before 25 February 2013. The search resulted in 760 publications, but we limited our subsequent analysis to all publications that report either (1) the date of tree leaf senescence in a given year (for one year or a series of years) or (2) a rate of change in tree leaf senescence dates over time or temperature for a reported measurement period. We limited the data set to publications that report estimates of autumn leaf senescence for ecosystems with one annual senescence period. To optimize data resolution, we included only those publications in which researchers monitored phenology more frequently than 14 day intervals throughout the senescence period. We included measurements from both young and mature trees and, in the cases of experimental studies, used data from reference treatments only. Within publications, we considered unique sites and plant species to represent independent data. We extracted data from tables and text when available and used DataThief (version 1.6; Tummers, 2006) to extract values presented in the figures.

Studies were distributed throughout North and South America, Europe and Asia, with data concentrated in the eastern USA, Europe and Japan (Fig. 1). The distribution of studies was heavily skewed toward the northern hemisphere, with only two studies located in the southern hemisphere. Due to lack of sufficient replication in the southern hemisphere, we focus our analysis on senescence trends in the northern hemisphere alone. We identified 64 publications and 1121 independent measurements that met our criteria.

Fig. 1.

Fig. 1.

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Distribution of sites used in the meta-analysis. Studies that do not report a specific site location or co-ordinates are not presented on the map.

We compiled data into two categories based on the response type: (1) date of senescence (DOS) and (2) change in senescence. The DOS data set includes 56 publications (Appendix 1), all of which report a date of leaf senescence for a single year or a single mean date of leaf senescence over multiple years. The change in senescence data set includes 21 publications (Appendix 2), all of which report a rate of change in senescence over time or temperature (d year–1 or d °C–1, respectively). The change in senescence data set includes five publications that did not self-report a rate of change for which we calculated the change in senescence over time from the dates of senescence for more than five continuous years (Appendix 2). Twelve of the 64 publications reported data that fell into both DOS and change in senescence categories.

Analyses

Date of senescence (DOS).

In order to make meaningful comparisons across studies reporting the date of leaf senescence and to assess autumn leaf senescence trends accurately on a large scale, we characterized the metrics used to measure the date of leaf senescence and identified the metrics most widely presented in the literature (Table 1). In some cases, publications reported DOS using multiple metrics (e.g. both 50 and 100 % leaf fall), and we report these to demonstrate the full spectrum of metrics used to estimate date of leaf senescence in the literature (Table 1). For cases in which the authors report a mean date of leaf senescence over a range of years, we used the median year of the measurement period to approximate leaf fall year. We assessed the relationship of colour change-based metrics and abscission-based metrics (leaf fall), the two main classes of senescence measurements, with site latitude.

Table 1.

Metrics used to estimate date of senescence across publications reporting the date of fall leaf senescence in the northern hemisphere (DOS studies only, n = 56 publications and 861 independent measurements total)

Leaf senescence metric No. of publications Fraction of publications No. of observations Fraction of observations
50 % leaf fall 20 0·36 291 0·34
100 % leaf fall 12 0·21 162 0·19
NDVI 6 0·11 83 0·1
80 % leaf fall 4 0·07 62 0·07
50 % green 3 0·05 3 0
50 % colour change 2 0·04 7 0·01
0 % green 2 0·04 2 0
50 % yellow 2 0·04 3 0
PAR transmission plateau 2 0·04 66 0·08
Peak colour change 2 0·04 94 0·11
Start of leaf fall 2 0·04 68 0·08
10 % colour change in 10 % of trees 1 0·02 8 0·01
10 % colour change in 90 % of trees 1 0·02 8 0·01
10 % leaf fall 1 0·02 48 0·06
50 % Amax 1 0·02 6 0·01
50 % colour change or leaf fall 1 0·02 103 0·12
75 % leaf fall 1 0·02 9 0·01
90 % leaf fall 1 0·02 9 0·01
Eddy covariance 1 0·02 2 0
Landsat 50 % cover 1 0·02 1 0
More yellow than green leaves 1 0·02 4 0
Photo ‘greenness’ index 1 0·02 1 0
Photosynthetic rate = 0 1 0·02 16 0·02
Peak red 1 0·02 1 0
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The number and total percentage of published studies and independent observations that used each metric are reported.

Note that many studies employed multiple metrics, therefore the number of entries in the table is greater than the total number of publications included in Appendix 1.

See Appendix 1 for list of publications included here.

LAI, leaf area index; PAR, photosnthetically acive radiation; NDVI, normalized difference vegetation index.

Meteorological data collection.

We examined relationships of air temperature, cooling degree-days (CDDs), precipitation, photoperiod and site latitude to the date of leaf senescence. In particular, we sought to determine the temporal window (e.g. 1 month or 3 months prior to leaf fall) over which meteorological data (i.e. air temperature and precipitation) were averaged that explained the greatest variation in leaf fall dates. Based on the latitude and longitude of the individual sites, we obtained daily meteorological data for each study location from either the National Climate Data Center’s (NCDC) Global Summary of the Day (GSOD) database (National Climate Data Center, 2013) or the Global Historical Climatology Network (GHCN) Daily database (Menne et al., 2012). We excluded observations that did not report location co-ordinates or a specific site location, and those which did not have station-based meteorological data reported within 2 ° latitude/longitude for the reported period. For each study site, we used the closest meteorological station (median distance 14 km) reporting daily minimum temperature, maximum temperature and precipitation with >70 % temporal coverage (22 days per month) for the years referenced in the corresponding study. Mean temperature was calculated by taking the mean of the daily minimum and maximum temperatures (World Meteorological Organization, 2011). At each site, we calculated the mean temperature (°C), latitude-based photoperiod (h) and total precipitation (mm) for three periods: 1–31 August, 1–30 September and 1–31 October. CDDs were calculated using mean daily temperature from 1 August to 31 October. CDDs represent the thermal sum below a base temperature of 20°C based on the method of Richardson et al. (2006).

We examined the relationships between meteorological variables and date of 50 % leaf fall, the most common metric employed across DOS studies (Table 1). We binned senescence observations by site latitude (low latitude, 25–49°N; high latitude, 50–70°N; roughly corresponding to the distribution of temperate and boreal forests, respectively) and regressed mean DOS at each site against mean August, September and October temperature (°C), cumulative CDDs (°C), photoperiod on the date of leaf senescence (h), site latitude and total monthly precipitation (mm) for August, September and October. In order to assess the relative effect of temperature and photoperiod on the date of senescence across high and low latitude sites, we then used partial correlation analysis to assess the relationship between the DOS and temperature (mean August, September, October and cumulative CDDs) while removing the effect of photoperiod.

Change in senescence.

While all studies completed their observation period between 1993 and 2010, the start of senescence observations varied widely across studies from 1931 to 2005 (Appendix 2). In order to evaluate the influence of when observations were initiated and the study duration on study-specific estimates of change in senescence over time or temperature, we binned studies by decades based on the start of the measurement period. We further binned studies by continent (North America, Europe and Asia), major plant order (all orders with more than five independent measurements) and site latitude (low latitude, 25–49°N; high latitude, 50–70°N) to evaluate the relationships between spatial and taxonomic factors and the date of senescence. We computed the mean change in the date of senescence over time (years) and temperature (°C) across all studies and used one-way analyses of variance (ANOVAs; P < 0·05) and Tukey’s tests of post-hoc intergroup comparison to assess differences in the change in senescence across continents, plant orders, site latitude range and time periods. All statistical analyses were conducted in R (version 3.1.1; R Development Core Team, 2013).

RESULTS

Date of senescence

Among DOS studies, we identified 24 distinct methods of estimating date of autumn leaf senescence across 56 publications (Table 1; Appendix 1). These publications utilized a variety of metrics, including leaf fall, leaf colour change and annual changes in plant physiology as measures of leaf senescence. Date of senescence was significantly earlier at higher latitudes, regardless of whether colour change or percentage leaf fall was used as a measure of date of senescence (P < 0·01 for both; Fig. 2). However, latitude explained more variation in the date of leaf senescence in measurements utilizing colour change (R2 = 0·55; e.g. 50 % colour change, 50 % green, peak colour change, more yellow than green leaves; Fig. 2A) compared to percentage leaf fall (R2 = 0·26; e.g. 10, 50, 80, 90 and 100 % leaf fall; Fig. 2B).

Fig. 2.

Fig. 2.

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Relationship between latitude and the date of leaf senescence by studies that estimate date of senescence using (A) leaf colour change and (B) percentage leaf fall. Points represent all studies that estimate the date of leaf senescence using either method. Measurements using leaf colour change explain more variation in the relationship between latitude and the date of leaf senescence than measurements using percentage leaf fall (colour change, R2 = 0·55, P < 0·001; leaf fall, R2 = 0·26, P < 0·001).

We limited our analysis of the relationship between meteorological variables and the date of leaf senescence to studies reporting the date of 50 % leaf fall in order to compare a common metric of leaf senescence among published studies (20 publications). The date of 50 % leaf fall was the most common metric utilized and accounted for 36 % of publications and 34 % of individual observations of DOS. Thirteen of the other 24 methods used to characterize the date of leaf senescence were unique to a single study; it was therefore not feasible to examine effects of meteorological measurements across these studies together.

Simple linear regression models highlighted relationships between site-level meteorological data and the date of 50 % leaf fall across all sites examined in the northern hemisphere (Fig. 3). Compared with other meteorological variables, mean monthly temperature explained the most variation in the date of 50 % leaf fall, and the relationship strengthened as the temperature window examined shifted later in the year, with a delay of 2·49 ± 0·48 (mean ± s.e.) d °C–1 in mean October temperature (R2 = 0·49, P < 0·001; Fig. 3C). Sites with greater CCDs senesced earlier (R2 = 0·37, P < 0·001; Fig. 3D). Latitude and photoperiod on the date of 50 % leaf fall explained 25 and 22 % of the variation in the date of 50 % leaf fall, respectively (Fig. 3E, F). Greater precipitation in August through October weakly delayed or did not change leaf fall timing significantly, with precipitation alone explaining ≤16 % of the variability in the date of 50 % leaf fall (Fig. 3G–I).

Fig. 3.

Fig. 3.

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Relationship between monthly averaged site temperature (°C), cooling degree-days (CDDs, °C), photoperiod (h) on date of senescence (DOS), site latitude, total monthly precipitation (mm) and the date of 50 % leaf fall. Points represent the average date of 50 % leaf fall by site. Low-latitude and high-latitude sites are as indicated in the key in (A).

The relationships between date of 50 % leaf fall and all measures of temperature (August, September, October mean temperature and cumulative CDDs) were weaker in high-latitude compared with low-latitude sites (Table 2; Fig. 3A–D). The relationship between date of 50 % leaf fall and each measure of temperature weakened (i.e. P-values increased) once the effect of photoperiod through partial correlation analysis was removed for high-latitude sites (CDD: R = –0·54, P = 0·068; Rpartial = –0·064, P = 0·87; mean October temperature: R = 0·59, P = 0·068; Rpartial = 0·25, P = 0·50; Table 2). In contrast, the relationship between each measure of temperature and date of 50 % leaf fall in low-latitude sites remained statistically significant when the effect of photoperiod was removed (CDD: R = –0·69, P = 0·001; Rpartial = –0·49, P = 0·02; mean October temperature: R = 0·81, P < 0·001; Rpartial = 0·73, P < 0·001; Table 2).

Table 2.

Partial correlation coefficients associated with simple linear regression models

Parameter Effect removed Low latitude
 High latitude
R P-value R P-value
Cooling degree-days (°C) None –0·69 0·001 –0·54 0·068
Photoperiod –0·49 0·02 –0·064 0·87
August temperature (°C) None 0·39 0·098 0·25 0·48
Photoperiod –0·31 0·19 –0·072 0·85
September temperature (°C) None 0·63 0·4 0·55 0·097
Photoperiod 0·35 0·14 0·012 0·96
October temperature (°C) None 0·81 <0·001 0·59 0·068
Photoperiod 0·73 <0·001 0·25 0·5
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Change in senescence

Six publications reported changes in autumn leaf senescence with temperature (109 independent observations) and 18 publications (291 independent observations) reported changes in autumn leaf senescence with time (Appendix 2). Examining all studies together, we found that leaf senescence was delayed by 0·33 ± 0·03 d year–1 and 1·20 ± 0·17 d °C–1 (mean ± 1 s.e.). Date of senescence did not vary over time at high-latitude sites, but was significantly more delayed at low-latitude sites over time (P = 0·040; Fig. 4A). The relationship between date of senescence and temperature did not differ significantly between high and low latitudes (P = 0·96; Fig 4B). In all sites (low- and high-latitude), date of senescence was delayed with increased temperatures (Fig. 4B). Senescence was delayed with time across all three northern hemisphere continents examined, but the delay was significantly larger with time in North America than in Europe and Asia (P <0·001; Fig. 4C). Both Europe and Asia showed delayed leaf senescence with increased temperatures (Fig. 4D), but there was not a statistically significant difference in response to temperature between continents (P = 0·095, Fig. 4B). Both Rosales and Fagales were significantly more delayed per °C than Ginkogales (P < 0·0001; Fig. 4F). The rate of senescence delay (d year–1 and d degree–1) was higher in studies initiated in recent decades (1990s–2000s) than those initiated in 1930–1980, although the difference was not statistically significant (Fig. 4G, P = 0·069; Fig. 4H, P = 0·095, respectively).

Fig. 4.

Fig. 4.

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Change in date of senescence by latitude range, continent, plant order and decade. Shown are orders with more than five independent observations. Time periods in (G) and (H) show the decade in which senescence observations began, with all studies ending between 1993 and 2010. Values are means ± s.e.; different letters indicate statistically significant differences between groups. Values below the bars show the number of independent observations.

DISCUSSION

Drivers of autumn leaf senescence

Among the meteorological measurements examined, October temperatures were the strongest predictors of date of 50 % leaf fall, followed by CDDs, latitude, photoperiod and, lastly, total monthly precipitation, although the strength of the relationships differed between high- and low-latitude sites (Figs 3 and 4; Table 2). Temperature continued to explain significant variation in the date of 50 % leaf fall in low-latitude sites after the effect of photoperiod was removed, but did not explain significant variation in high-latitude leaf fall timing following removal of photoperiod from regression models (Table 2). These results suggest that while temperature alone may be a reasonable predictor of the date of 50 % leaf fall in general (mean October temperature: R2 = 0·49 across all sites), the factors that most strongly control leaf fall may differ by site location. The lack of change in date of 50 % leaf fall over time at high latitudes despite large changes in temperature (Fig. 4A; Jones et al., 2012) is possibly due to the strong constraint photoperiod imposes on the timing of leaf fall in these latitudes (Stinziano and Way, 2014; Way and Montgomery, 2014). Our results suggest that while temperature and photoperiod are two important variables that could be incorporated in models to predict the date of leaf senescence, it is unlikely that model parameters can be applied uniformly at the global scale. Temperature alone may provide a reasonable predictor for the date of 50 % leaf fall at low latitudes, but the inclusion of the effect of photoperiod in models may be important to assess the timing of leaf-off at high latitudes.

Compared with low-latitude ecosystems, high-latitude ecosystems may continue to experience the largest increases in temperature over the next century (Jones et al., 2012), while photoperiod will remain the same over time. These asynchronous changes in meteorological factors may affect plant communities and their role in carbon sequestration (Stinziano and Way, 2014). For example, as the ranges of more southerly plant species migrate north in response to rising temperatures (Beck et al., 2011), these temperature-responsive species may lengthen their photosynthetic period, while native, photoperiod-dependent northerly species may show less flexibility in the timing of leaf senescence, affecting competitive dynamics within plant communities (Way and Montgomery, 2014). The strong influence of photoperiod on leaf senescence at high latitudes could also influence ecosystem-scale carbon uptake. Photoperiod-induced leaf senescence will end an individual growing season’s period of ecosystem carbon uptake, but carbon losses through soil respiration may remain high with warmer temperatures (Piao et al., 2008). While warming-induced changes in leaf senescence delay the end of the growing season and result in enhanced net carbon uptake in temperate forests (Keenan et al., 2014), these patterns may not hold true in higher latitude boreal forests if trees do not extend their period of photosynthesis at the end of the growing season. In other words, the stronger constraint of photoperiod on date of leaf senescence in high-latitude boreal deciduous forests may limit the response of forest carbon uptake to increases in temperature.

There was a statistically significant delay in leaf senescence with time (days per year) across all three continents in the northern hemisphere, but delays were stronger in North America than in Europe and Asia. This pattern is similar to the results of Piao et al. (2007), who used large-scale remote-sensing techniques and quantified senescence delays of 0·28 d year–1 in North America but only 0·11 d year–1 in Eurasia over the last two decades. Some of the differences observed across continents may be due to differences in tree species composition and the number of species assessed. Differences in geographical distribution of studies within each continent may also explain some of the variation observed. For example, while European studies were distributed throughout the continent, studies tended to be concentrated in coastal regions in Asia and North America (Fig. 1). In addition, our analysis found that high-latitude regions have smaller changes in senescence over time than low-latitude regions (Fig. 4). As the majority of high-latitude studies included in this analysis were conducted in Europe, it is not surprising that we found larger delays in North America and Asia than in Europe. Unlike satellite-derived measurements of senescence that show an intensifying delay in senescence in recent decades (Jeong et al., 2011), we did not find a statistically significant change in the rate of delay over time (Fig. 4G).

Recommendations for future research

Looking forward, as the study of leaf senescence phenology garners additional research interest, one important take-away from this meta-analysis is the need to identify and use common, biologically relevant definitions of the date of leaf senescence. Among the publications we examined in our meta-analysis, date of senescence was estimated by a variety of metrics, including leaf fall, colour change and chlorophyll content, and was measured on a variety of scales, including leaf, branch, individual tree and total canopy. Fifty per cent leaf fall comprised over one-third of senescence measurements, and we show that it is a useful metric to compare date of senescence observations across multiple studies. Leaf fall is easily characterized in the field and contributes to measurements of forest productivity, making it a practical ecological measure. While 50 % leaf fall is the metric most commonly used across studies of fall leaf senescence, our results demonstrate that senescence dates estimated using leaf colour change are more strongly correlated with latitude than percentage leaf fall (Fig. 2). Because the senescence process is a combination of biodegradation and nutrient resorption, deciduous plants often show a large decline in photosynthetic activity long before they drop their leaves (Wilson et al., 2001). Additionally, species-specific relationships between the end of the photosynthetic period and date of leaf fall may vary, adding bias to patterns not accounted for in this analysis. Therefore, metrics involving leaf colour change may represent more biologically meaningful measures of the end of the growing season for deciduous plants compared with percentage leaf fall. We suggest that researchers consider the biological relevance of senescence metrics when designing future studies to allow for robust cross-study comparisons and enable understanding of large-scale trends.

Deciduous trees in the northern hemisphere are well represented by leaf senescence studies, especially in eastern North America, Europe and parts of Asia (Fig. 1). Additional studies in other regions in North America and the southern hemisphere are needed. This enlarged geographic range of studies would allow researchers to disentangle differences in the response of autumn leaf senescence to changes in temperature at finer resolution across space and deciduous forest biomes (temperate vs. boreal). Due to small sample sizes, we found it was not feasible to estimate senescence trends at the species level. We therefore focused on the level of plant order and found that plants in the order Rosales and Fagales respond to increases in air temperature with later dates of senescence compared with Ginkgoales.

Regional, national and global networks of on-the-ground and remotely sensed data provide important information about changes in autumn phenology, but there is an opportunity to augment these data through smaller scale, independent studies as well. In particular, we recommend that global change experiments that are currently underway to examine the effects of elevated CO2, warmer temperatures, drought, elevated tropospheric ozone or other global change factors, as well as multifactor investigations, include measurements of leaf senescence. Despite the noted underprediction of changes in spring leaf-out by experimental manipulations compared with observational studies (Wolkovich et al., 2012), ongoing or new experiments could provide additional critical information by disentangling the various environmental and physiological drivers of autumn phenology.

Conclusions

To our knowledge, this is the first study to synthesize existing senescence data derived from individual studies of deciduous trees throughout the northern hemisphere. Our findings demonstrate that across published studies, leaf senescence is delayed over time and in response to temperature, although low-latitude sites show significantly stronger delays in senescence over time than high-latitude sites. We find that while temperature alone may be a reasonable predictor of the date of leaf senescence when examining a broad suite of sites, it is important to consider that temperature-induced changes in senescence at high-latitude sites are likely to be constrained by the influence of photoperiod. Ecosystem-level differences in the mechanisms that control the timing of leaf senescence may affect both plant community interactions and ecosystem carbon storage as global temperatures increase over the next century.

ACKNOWLEDGEMENTS

We thank Richard Primack, Michael Hanley and two anonymous reviewers for helpful comments on earlier drafts of this manuscript. We thank Amanda Vieillard for help with the early stages of this work, and the Boston University Biogeoscience Program for providing a venue to bring our group together. This work was supported by awards from the United States Department of Energy Office of Science Graduate Fellowship Program administered by the Oak Ridge Institute for Science and Education (DE-AC05-06OR23100 to A.L.G.) and the United States National Science Foundation Graduate Fellowship Program (DGE-1247312 to A.J.R. and A.S.G.) and CAREER program (DEB 1149929 to P.H.T.).

Appendix 1 Publications included in date of senescence analysis

This table includes all publications that met search criteria for date of senescence studies and were included in the senescence metric tabulation in Table 1. Studies highlighted in bold used the most common senescence metric (50 % leaf fall) and were included in the regression and partial correlation analyses. (*) indicates a paper that reported a mean date of leaf senescence over a range of years, and (**) indicates that the range of years varied by site within the study.

No. Paper Observations Continent Site Latitude Longitude Biome Order Year range Senescence metric
1 Asshoff et al. (2006) 9 Europe Swiss Canopy Crane Reserve 47·46667 7·5 Temperate Fagales 2002–2004 75 % LF
2 Augspurger et al. (2005) 5 North America Trelease Woods, understorey 40·15 –88·16667 Temperate Sapindales 1996–1998* 0 % LAI
Trelease Woods, edge
Trelease Woods, canopy Mixed
3 Barr et al. (2004) 16 North America Old Aspen Site 53·7 –106·2 Boreal Fagales 1997–2003 PAR transmission plateau, Px = 0
Malpighiales 1994, 1996–2003
4 Blanke and Kunz (2011) 3 Europe Klein-Altendorf Research Centre 50·62 6·988 Temperate Mixed 1988–2010* 50 % LF
1958–2007*
1958–1987*
5 Brown et al. (1997) 4 North American Orland E. White Arboretum 39·0638 –78·0646 Temperate Fagales 1994 50 % LF
6 Chen et al. (2005) 6 Asia Beijing 40·01667 116·3333 Temperate Mixed 1982–1993* NDVI
Gaixian 40·4333 122·3333 1982–1993*
Harbin 45·7 126·6667 1982–1993*
Luoyang 34·6667 112·41667 1982–1993*
Mudanjiang 44·4333 129·6667 1982–1993*
Xi'an 34·21667 108·96667 1982–1993*
Xingtai 37·0667 114·5 1982–1993*
7 Chmielewski and Rötzer (2001) 11 Europe Dinaric Mountain Region 42·9 20·3 Temperate Mixed 1969–1984* 50 % LF
Great Hungarian Lowlands 46 19·6
North Alpine Foreland 47·8 10·9
Southern Central European Highlands 48·2 8·2
Bohemian Highlands 48·6 15·5
Northern Central European Highlands 50·4 8·7
British Isles 51·6 –3·8 Temperate
North Sea/Central European Lowlands 52·9 10·1 Temperate
Baltic Sea Region 57·2 15·2 Temperate
North Atlantic Mountain Region 61·9 8·1 Boreal
North Scandinavia 67·1 26·7 Boreal
8 Chmielewski et al. (2005) 3 Europe Saxony NA NA Temperate Sapindales 1961–2000* 50 % CC
Fagales 1961–2000*
9 Connor et al. (1994) 6 North America Orland E. White Arboretum 39·0638 −78·0646 Temperate Fagales 1989–1991 50 % LF
10 Delbart et al. (2005) 10 Asia Turuhansk 65·8 88 Boreal Fagales 1999 NDVI
Taseevo 57·18333 94·833333 Boreal
Vehme 63·08333 88 Boreal
Podtsesovo 58·51666 92·216666 Boreal
Aksarka 66·5 67·816666 Boreal
Ilir 50·5 100·7 Boreal
Kuragino 53·9 92·7 Boreal
Krasnoyarsk 56 93 Boreal
Tachtip 52·733 90 Boreal
Volchno 54 81 Boreal
11 Dillen et al. (2012) 2 North America Harvard Forest 42·533 –72·183 Temperate Fagales 2010 50 % LF
Fagales
12 Doi (2012) 53 Asia Japan meteorological sites (53 sites) NA NA Temperate Rosales 1953–2005 80 % LF
13 Doi and Takashi (2008) 2 Asia Japan meteorological sites 35·4 135·8 Temperate Ginkgoales 1953–2005* 80 % LF
Sapindales
14 Dragoni et al. (2011) 10 North America Morgon Monroe State Forest 39·317 –86·417 Temperate Mixed 1999–2008 0 % LAI
15 Dragoni and Rahman (2012) 48 North America 28·25°N 28·25 NA Temperate Mixed 1989–1999 NDVI
35·75°N 35·75 NA
48·25°N 48·25 NA
16 Elmore et al. (2012) 1 North America Cheasapeake Bay 39·87 –77·75 Temperate Mixed 2004–2008* Landsat 50 % cover
17 Fridley (2012) 6 North America Syracuse, NY 43·05 –76·15 Temperate Mixed 2008–2010 50 % LF, 90 % LF
18 Gibson et al. (2001) 1 North America Clemson, SC 34·68 –82·84 Temperate Rosales 1998 50 % LF
19 Gill et al. (1998) 4 North America Hubbard Brook Experimental Forest 43·94 –71·72 Temperate Fagales 1989 50 % LF
Lamiales
Sapindales
20 Glinwood and Pettersson (2000) 1 Uppsala 59·816667 17·633333 Temperate Rosales 1998 50 % LF
21 Grassi and Magnani (2005) 6 Europe Nonantola Experimental Site 44·683 11·033 Temperate Lamiales 2001–2003 50 % Amax
Fagales
22 Heide (2003) 7 Europe Agricultural University of Norway 60 10·783 Boreal Fagales 1996–2002* 50 % LF
23 Isaacson et al. (2012) 8 North America Flambeau River State Forest 45·733 –90·75 Temperate Lamiales 2009 NDVI
Kettle Moraine State Forest 43·598 –88·280 Sapindales
24 Juknys et al. (2011) 92 Europe Vytautus Magnus University 54·083 23·083 Temperate Malvales 1956–60, 1968–1997, 2000–2010 50 % LF, 50 % CC
25 Kafaki et al. (2009) 4 Asia Guilan Province NA NA Temperate Fagales 2003–2006 100 % LF
26 Keskitalo (2005) 1 Europe Umea University 63·820539 20·303591 Boreal Malpighiales 2003 100 % LF
27 Kodani et al. (2002) Japan Tohoku Research Center 36·76667 141·13333 Temperate Fagales 1999 0 % LAI
28 Kozlov and Berlina (2002) 60 Europe Lapland Biosphere Reserve 67·65 32·61667 Boreal Mixed 1931–1941, 1946–1952, 1957–1998 Start of LF
29 Kramer (1995) 9 Europe International Phenological Gardens NA NA Temperate Malpighiales 1955–1987 50 % LF
Malvales
Pinales
Fagales
30 Laurila et al. (2001) 2 Europe Petsikko 69·47 27·23 Boreal Fagales 1997–1998 Eddy covariance
31 Lebourgeois et al. (2010) 8 Europe 102 sites NA NA Temperate Pinales 1997–2006 10 % CC in 10 % of trees, 10 % CC in 90 % of trees
Fagales
32 Lee et al. (2003) 88 North America Harvard Forest 42·53 –72·18 Temperate Fagales 1991–1999 50 % LF
Cornales
Lamiales
Fagales
Sapindales
Rosales
33 Nagai et al. (2011) 9 Asia Takayama 36·1466 137·423 Temperate Fagales 2005–2007 0 % LAI
Sapindales
34 Nakamura et al. (2010) 1 Asia Tomakomai Experimental Forest 42·667 141·6 Temperate Fagales 2008 80 % LF
35 Nicolai (2010) 8 Europe Marburg, Hesse 50·816667 8·6 Temperate Fabales 2002–2009 Start of LF
36 Niinemets and Tamm (2005) 6 Europe Karkna 58·46667 26·633333 Temperate Sapindales 1997 50 % LF, 80 % LF
Fagales
Malpighiales
37 Norby et al. (2003) 4 North America Oak Ridge 35·9 –84·333333 Temperate Sapindales 1995–1996 50 % LF
38 Parker and Tibbs (2004) 11 North America Anapolis, MD 38·883333 –76·55 Temperate Fagales 1994 50 % LF
Cornales
Fagales
Saxifragales
Magnoliales
39 Pataki and Oren (2003) 6 North America Duke Forest 36·017 –79·982 Temperate Fagales 1997 50 % LF, 100 % LF
Saxifragales
Magnoliales
40 Pellis et al. (2004) 50 Europe Antwerp 51·083 4·366 Temperate Malpighiales 1996–1998, 2001 100 % LF, plateau PAR ratio
41 Richardson et al. (2009) 1 North America Bartlett Experimental Forest 44·064 –71·288 Temperate Mixed 2007 Photo ‘greenness’ index, peak red colour
42 Rolshausen and Schaefer (2007) 2 Europe Freiberg 48 8 Temperate Rosales 2004 Peak CC
43 Schaberg et al. (2003) 1 North America USDA Forest Service Northern Research Station 44·5 –73·2 Temperate Sapindales 1999 50 % green, 50 % yellow, 0 % green
44 Schreiber et al. (2013) 4 North America Alberta-Pacific Forest Industries – –113 Temperate Malpighiales 2010 More yellow than green leaves
45 Sonnentag et al. (2012) 1 North America Harvard Forest 42·5 –72·2 Temperate Mixed 2010 100 % LF, 50 % green, 0 % green
46 Soolanayakana-hally et al. (2013) 2 North America Vancouver 49·26 –123·25 Temperate Malpighiales 2009 100 % LF, 50 % yellow
Indian Head, Saskatchewan
47 Soudani et al. (2012) 9 Europe Fontainebleau 48 2 Temperate Fagales 2005–2009** NDVI
Hesse 48 7
Fougeres 48 1
48 Tartachnyk and Blanke (2001) 3 Europe Bonn 50·733 7·0833 Temperate Rosales 1997–1999 90 % LF
49 Tateno et al. (2005) 48 Asia Ashui Forest Research Station 35·3 135·72 Temperate Cornales 2001 10 % LF
Ericales
Sapindales
Aquifoliales
Apiales
mixed
Fagales
Rosales
Lamiales
Magnoliales
Laurales
50 Uddling et al. (2005) 2 Europe Ostad Field Station 57·9 12·4 Temperate Fagales 1997–1998 100 % LF
51 Vitasse et al. (2009) 103 Europe Pyrenees 46 6 Temperate Fagales 2005–2007 50 % CC or LF
Lamiales
Sapindales
52 Waddell et al. (2001) 3 North America Sesquicentennial State Park (high elevation) 34·091 –80·907 Temperate Fagales 1994 50 % LF
Sesquicentennial State Park (low elevation) 34·091 –80·907
Clemson University 34·132 –80·869
53 Wang et al. (1992) 23 North America Mont St. Hilaire Nursery 45·533 –73·133 Temperate Fagales 1988–1989* 50 % LF
Lamiales
Malvales
Rosales
Malpighiales
Sapindales
54 White et al. (1997) 2 North America Hubbard Brook 43·933 –71·67 Temperate Mixed 1991–1992 NDVI
55 Zhang et al. (2011) 4 North America Harvard Forest 42·533 –72·183 Temperate Mixed 2001–2004 50 % LF, 50 % colour change
56 Zhao et al. (2006) 71 Asia Henan Meteorological Bureau sites NA NA Temperate Malpighiales 1986–2003 100 % LF
Fabales
Sapindales
Open in a new tab

CC, colour change, LAI, leaf area index; LF, leaf fall; PAR, photosynthetically acive radiation; NA, not available; NDVI, normalized difference vegetation index.

Appendix 2 Publications included in change in senescence analysis

(*) indicates that the slope was calculated based on >5 years of DOS data reported in the study, and (**) indicates that the range of years for which data are reported is less than the 5 year threshold period, but the study reported senescence change in d °C–1 across an elevation gradient. All studies were included in the analysis regardless of senescence metric.

No. Paper Observations Continent Biome Order Year range Senescence metric Regression type
1 Barr et al. (2004)* 1 North America Boreal Fagales 1994–2003 PAR transmission plateau Time
2 Chen and Xu (2012) 46 Asia Temperate Rosales 1986–2005 “Almost all leaves fallen” Time and temperature
3 Chen et al. (2005) 5 Asia Temperate Mixed 1982–1993 NDVI Time
4 Chmielewski and Rötzer. (2005) 11 Europe Temperate/boreal Mixed 1969–1998 50 % LF Time
5 Chmielewski et al. (2005) 3 Europe Temperate Sapindales 1961–2000 50 % CC Time
Fagales
6 Dragoni et al. (2011) 3 North America Temperate Mixed 1998–2008 0 % LAI Time
7 Dragoni and Rahman (2012) 9 North America Temperate Mixed 1989–2008 NDVI Time
8 Ibanez et al. (2010) 183 Asia Temperate Ginkgoales 1953–2005 80 % LF Time
Rosales
9 Juknys et al. (2012a) 3 Europe Temperate Sapindales 1956–2010 50 % LF Time
Fagales
Malvales
10 Juknys et al. (2012b) 2 Europe Temperate Fagales 1956–2010 50 % LF Temperature
Malvales
11 Kozlov and Berlina (2002)* 1 Europe Boreal mixed 1931–1941, 1946–1952, 1957–1998 ‘Begininning of leaf fall’ Time
12 Kunz and Blanke (2011) 1 Europe Temperate Rosales 1958–2008 50 % LF Time
13 Lee et al. (2003)* 11 North America Temperate Rosales 1991–1998 50 % LF Time
Sapindales
Lamiales
Fagales
14 Matsumoto (2010) 55 Asia Temperate Ginkgoales 1961–2000 80 % LF Temperature
15 Menzel et al. (2001) 4 Europe Temperate Sapindales 1951–1996 Start of CC Time
Fagales
16 Menzel et al. (2006) 1 Europe Temperate mixed 1971–2000 50 % CC Time and temperature
17 Nicolai (2010)* 1 Europe Temperate Fabales 2002–2009 Start of LF Time
18 Pudas et al. (2008) 3 Europe Boreal Fagales 1997–2006 50 % colour change Time and temperature
19 Richardson et al. (2006) 1 North America Temperate Sapindales 1957–2004 Photo greenness index Time
20 Vitasse et al. (2009) 2 Europe Temperate Fagales 2005–2007** 50 % CC or LF Temperature
21 Zhao et al. (2006)* 4 Europe Temperate Sapindales 1986–2003 100% LF Time
Malpighiales
Fabales
Open in a new tab

LAI, leaf area index; LF, leaf fall; PAR, photosnthetically acive radiation; NDVI, normalized difference vegetation index.

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