Asynchronous Amazon forest canopy phenology indicates adaptation to both water and light availability

The GRACE mission provides measurements of spatio-temporal changes in Earth's gravity field which can be used to derive changes in TWS, and has been implemented in previous studies over the Amazon (Chen et al 2010, Frappart et al 2013, Thomas and Reager 2014). We use the GRACE GRCTellus Monthly Mass Grids-Land product (Swenson and Wahr 2006, Landerer and Swenson 2012) at 1.0 degree resolution from January 2003 to December 2011 provided by the Jet Propulsion Laboratory. Monthly grids represent the mass deviation relative to the January 2004–December 2009 baseline and are implemented here as a proxy for water availability to vegetation. Four missing months of data were filled by taking the mean of the previous and subsequent months.

The Advanced Microwave Scanning Radiometer—EOS (AMSR-E) is a microwave radiometer deployed on the polar-orbiting Aqua satellite. The VOD parameter derived from AMSR-E brightness temperatures, defines the frequency dependent extinction of land surface microwave emissions by the intervening vegetation layer (Jackson and Schmugge 1991, Van de Griend and Wigneron 2004, Jones and Kimball 2012). VOD is sensitive to changes in canopy biomass and water content, including both photosynthetic and woody elements (hereafter referred to as canopy biomass), and has been used to track both Amazon (Jones et al 2011) and African (Guan et al 2012) forest phenology. The VOD retrieval algorithm minimizes the potential influence of atmosphere precipitable water vapor, temperature, surface water inundation and soil moisture effects, providing near-daily global observations posted to a 25 km resolution global EASE grid projection (Jones et al 2011, Jones and Kimball 2012). For this application, the 10.7 GHz, descending orbit (UTC 1:30 A.M. equatorial crossings) VOD record from January 2003 to September 2011 was composited to a monthly mean time series consistent with the GRACE TWS record.

The (MODIS) MOD15A2 8-day L4 Collection five LAI and an EVI (Huete et al 2002) derived from MODIS multiangle implementation of atmospheric correction (MAIAC) data (Lyapustin et al 2011a, 2011b, 2012) are used to track canopy leaf area and greenness, respectively, from January 2003 to December 2011 at 1 km resolution. The LAI product was spatially resampled to match the VOD 25 km EASE grid format by taking the mean of all highest quality LAI pixels with pixel centers in the spatial extent of each 25 km pixel. The 8-day LAI record was then temporally resampled to monthly means.

Gridded monthly 1.0 degree resolution diffuse and direct PAR data (CERES_SYN1deg_Ed3A) was used from the Clouds and the Earth's Radiant Energy System (CERES) (Wielicki et al 1996) record from January 2003 to December 2011. Total PAR was calculated as the sum of direct and diffuse PAR.

TRMM 3B43 version seven Accumulated Rain monthly (cm/month) gridded data at 0.25 degree resolution from January 1998 to December 2011 was acquired from the Goddard Earth Sciences Data and Information Services Center. The pixel-wise number of months per year with less than 100 mm precipitation (dry months) were calculated and summarized by ecoregion (figure 1). Pearson product-moment cross-correlations between TRMM precipitation and GRACE TWS were calculated to examine the temporal offset between precipitation inputs and water availability.

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We used the World Wildlife Fund (WWF) Terrestrial Ecoregions of the World data set (Olson et al 2001) to distinguish ecoregion level changes in canopy seasonality. The WWF data contains 867 global ecoregions, each representing distinct assemblages of natural communities sharing a large majority of species, dynamics and environmental conditions. Nine terrestrial ecoregions spanning the west-to-east regional moisture gradient encompassing southern equatorial Amazon evergreen broadleaf forest were used in the analysis (figure 1). Monthly ecoregion means of the remotely sensed datasets were used to calculate monthly climatology and as inputs to a 4-parameter sinusoidal model (detailed in section 3.8) for estimating ecoregion scale phase shift differences between water availability (TWS), vegetation canopy biomass (VOD) and canopy leaf area (LAI) phenology.

An extensive literature review located vegetation phenology field data within the ecoregions and general period of this study. Site specific field measurements within intact forests and without experimental manipulation (table S1) were found in two ecoregions (Tapajos–Xinghu, Xinghu–Tocantins–Araguaia). Although the temporal span of the field data was incomplete relative to the remote sensing record, with some data collected in prior years (1999–2002), these data constituted the only publicly available field observation record. Tower flux data of aggregated monthly GEP and Pc (table S1) from the Large-Scale Biosphere–Atmosphere (LBA) Experiment in Amazonia was acquired from the LBA flux tower integrated database (Restrepo-Coupe et al 2013). The GEP data was calculated as the difference between day and night tower estimates of net ecosystem exchange of CO₂ (NEE), assuming no temperature effect on ecosystem respiration as no within-month correlation was observed between nighttime NEE and nighttime temperature at any sites studied. Pc (Pc not constrained by light) was calculated as GEP for a fixed range of PAR (725 < PAR < 925 μmol CO₂ m⁻² s⁻¹). In this study, monthly GEP and Pc climatologies were calculated at four sites (RJA, K67, K83, CAX) within three ecoregions (figure 1). The field and flux tower observations are not implemented in the formal analysis, but are presented for comparison and interpretation of the satellite record.

Monthly climatologies were calculated over the 2003–2011 record for TWS, VOD, LAI, EVI, and both total and direct PAR by ecoregion. We also implemented a 4-parameter sinusoidal fit (1) to the full TWS, VOD and LAI data records,

where y_t is the fitted monthly climatology value, y₀ is the mean value, a is the amplitude of the sinusoidal curve, x is the monthly increment, b is the frequency, and c is the phase shift. Although this method gives a generalized monthly climatology interpretation, it provides a phase shift parameter (c) quantifying the temporal offset between the three data sets within each ecoregion. The temporal offset in months was calculated as the phase shift difference divided by 2π/b; b in all cases was equivalent to 12 months (table S3), confirming a yearly cycle. Model results, specifically the temporal offset between the three data sets, was confirmed by a more robust pairwise bispectral analysis (see appendix 1 of supplementary information).

The satellite observations, when examined together over ecoregions and supported by available field and flux tower data, confirm that Amazon forest canopies exhibit seasonality (figure 2). The observed TWS seasonality is consistent with basin-wide model and field measurement based observations of PAW (Asner et al 2004, Brando et al 2010) and soil moisture (Fisher et al 2007). The observed VOD seasonality is synchronous with woody biomass increment growth (Saleska et al 2003, Goulden and Miller 2004, Rice et al 2004) and sap flow (Fisher et al 2007) from available field measurements. Observed LAI and VOD seasonality is well aligned with field-level leaf production increases and peaks in litterfall rates (Rice et al 2004, Barlow et al 2007, Doughty and Goulden 2008, Brando et al 2010), while peaks in MAIAC EVI, a well-established proxy for photosynthetic C fixation, are temporally aligned with Pc and GEP from regional flux tower observations.

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Pearson product-moment cross-correlations between TRMM precipitation and GRACE TWS at the ecoregion scale were greatest at a two months lag (0.78 < R < 0.91) for seven ecoregions and a three months lag (0.89 < R < 0.91) for two ecoregions (table S2). These results confirm a reported two months lag between precipitation inputs and TWS across eight Amazon sub-basins (Frappart et al 2013) and a 2–3 months lag between peak precipitation and river level and stream flow observations (Marengo et al 2008). Thus, seasonal minima in plant-available moisture indicated from bulk TWS changes occur months after the initial dry season (precipitation <100 mm/month) onset, which is also confirmed by estimates and measures of plant-available water at variable soil depths (Asner et al 2004, Fisher et al 2007, Brando et al 2010). Therefore, equating dry months to periods of minimum water availability represents an oversimplification of ecosystem water dynamics and neglects hydraulic transport processes and water residence times, which can be considerable in the Amazon basin.

The resulting coefficients and statistics from the sinusoidal model fits are displayed in (table S3). The sinusoidal model ecoregion climatology of TWS, VOD, and LAI each displayed seasonal phase shifts along the west-to-east moisture gradient (figure 3). GRACE TWS displayed a two months shift in seasonal peak over the Amazon, ranging from March in the west to May in the east. VOD displayed a one month shift, peaking in June in the west and July in the east; LAI displayed the greatest shift of 2.5 months, with western peaks occurring in August and eastern peaks in mid-May.

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The ecoregion phase shift difference between the three datasets decreased with increasing dry season across the west-to-east moisture gradient (figure 3). VOD seasonality lagged TWS by a maximum of 2.8 months to a minimum of 1 month. LAI seasonality lagged TWS by 4.9–0.7 months. LAI seasonality lagged VOD for 5 of 9 ecoregions by 2.1–0.8 months, was equivalent (no lag) for one ecoregion, and preceded VOD by 0.3–0.8 months for the remaining two ecoregions. The phase shift differences all displayed significant negative linear relationships (0.67 < r² < 0.73; p < 0.01) with dry season length by ecoregion (figure 4), where VOD, LAI and TWS seasonal patterns are more synchronous for ecoregions with longer dry seasons.

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The linear relationship between dry season length and the phase shift difference between VOD and LAI (figure 4) was used to estimate relative canopy phenology adaptations to light and water availability. The mean number of dry months per pixel from the 0.25 degree TRMM data was input to the linear model to estimate expected pixel-wise phase shifts between VOD and LAI. The resulting phase shifts provide a proxy for the degree to which the timing of canopy growth and development has adapted primarily to light (maximal phase shift) or water (minimal phase shift) availability (figure 5).

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The southern equatorial Amazon ecoregions represent a west-to-east transition from predominantly light-adapted to water-adapted canopy phenology (figure 5). The observed canopy behavior supports current theories that net leaf flush is responsive to solar irradiance, but also demonstrates an adaptive response to limit drought susceptibility, even in relatively precipitation rich regions, for efficient temporal allocation of resources for wood or leaf growth. In more light-adapted western ecoregions (figure 3, row 1), available water (TWS) reaches maximum levels during seasonal lows in direct PAR and allocation of resources to non-photosynthetic canopy elements is prioritized, displayed as a rise in VOD and confirmed by field measures of wet season woody growth (Saleska et al 2003, Goulden and Miller 2004, Rice et al 2004). The VOD rise is also consistent with canopy water content increases required for cellular expansion and new leaf formation (Pantin et al 2012). Increases in LAI (i.e. the net flush of new leaves) coincides with seasonal increases in direct PAR, representing a shift in resource allocation from woody to leaf growth. Decreasing LAI (i.e. peaks in net leaf senescence and abscission) is then observed as available water (TWS) reaches seasonal lows and canopy biomass (VOD) declines. Canopy greenness (EVI) and productivity (Pc and GEP) then rise as the new cohort of leaves reach photosynthetic maturity, which has been reported to occur up to approximately 40 days following full leaf expansion for tropical species (Niinemets et al 2012).

As dry season length increases, a general transition from light-adapted to water-adapted canopy phenology becomes apparent (figure 5). For longer dry seasons (figure 3, row 2), seasonal net leaf flush (LAI) becomes more synchronous with canopy biomass (VOD) increases, yet maintains comparable seasonality with direct PAR and remains somewhat decoupled from water availability (TWS). As dry season increases further (figure 3, row 3) and water becomes more limiting, net leaf flush (LAI) and canopy biomass (VOD) become tightly coupled to water availability (TWS) and begin to lag direct PAR seasonality, characteristics attributable to drought-tolerant systems where water deficits, increasing temperatures and inhibitory atmosphere vapor pressure deficits coincide with seasonal peaks in direct solar irradiance.

Top-of-atmosphere irradiance seasonality varies with latitude and solar azimuth angle, but total PAR seasonality at the ecoregion scale displays relatively small seasonal increases, on the order of 11–18% (figure 2), due to cloud cover seasonality. The low PAR seasonality perhaps counters the theory that leaf growth is responsive to increases in irradiance, which is not obvious when examining the climatology of total PAR and LAI (figure 2). However, the amount of quanta, which ultimately drives photosynthesis, can vary with no change in total PAR. Diffuse solar irradiance, enriched by blue quanta with higher energy, contains less quanta than direct irradiance with a more uniform spectral distribution (Valladares et al 2012); the photosynthetically active photon-flux density (PPFD) of diffuse irradiance will be lower than direct irradiance at equal PAR. The endogenous response that drives synchronous net leaf flush with increases in direct PAR in light-adapted systems (figure 3) may be responsive to increases in PPFD associated with the transition from diffuse-dominated to direct-dominated PAR.

In ecoregions that experience short dry seasons, net leaf flush is synchronized with direct PAR resulting in canopy leaf area peaks during periods of lowest water availability. If water availability were to decline below critical thresholds following leaf flush (i.e. drought years) would ecoregions that exhibit asynchrony between net leaf flush and water availability display greater detrimental canopy effects (e.g. lack of available water to support a new cohort of leaves) than ecoregions that exhibit net leaf flush synchronous with water availability? A study of the 2005 and 2010 Amazon droughts (Xu et al 2011), which spanned approximately 2.5 and 3.2 million km², respectively (Lewis et al 2011, Xu et al 2011), and were among the driest years of record based on river observations dating to 1902 (Xu et al 2011), provides context for addressing this question. Of the vegetated area that experienced significant declines in precipitation in 2010, 51% displayed significant declines in satellite observed greenness (NDVI) during July, August, September (JAS) and October, November, December (OND) composite periods. The areas affected in 2010 were located in all ecoregions used in this study, indicating that even eastern ecoregions with drought tolerant phenology (net leaf flush synchronous with available water) could not withstand the extreme water limitations. In contrast, of the vegetated area that experienced significant declines in precipitation in 2005, 14% displayed significant declines in greenness during the JAS composite period. However, during the OND composite period western ecoregions displayed an increase in area of significant greenness decline while eastern ecoregions displayed some recovery to normal greenness. Although further research is needed, western ecoregions displaying light-adapted canopy phenology (net leaf flush asynchronous with water availability) may be more sensitive to detrimental drought effects.

Seasonal phase differences between canopy phenology and light and water availability indicators documented in this study may improve understanding, and parameterizations and representations, of canopy processes in regional carbon and climate models. Regions that display light-adapted canopy phenology can be weighted for greater sensitivity to solar irradiance seasonality, while regions displaying water-adapted canopy phenology can be driven primarily by water availability and estimates of rooting depth (Nepstad et al 1994) and hydraulic redistribution (Da and Goulden 2004). Investigating these phase differences at moderate spatial scales may allow for a classification scheme, whereby the driving factor of canopy phenology (whether it be water or light, or a combination thereof) is based on the phase difference between satellite derived water availability, canopy biomass, and leaf flush, as displayed in figure 5. Such an effort would allow for more specific modeling of canopy phenology across contiguous tropical forests.