Proximal remote sensing of tree physiology at northern treeline: Do late-season changes in the photochemical reflectance index (PRI) respond to climate or photoperiod?
Introduction
The Forest Tundra Ecotone (FTE) is the world's largest ecological transition zone spanning 13,400 km across the northern hemisphere (Callaghan et al., 2002). This vast circumpolar transition zone between the boreal forest and the Arctic tundra is experiencing warming more rapidly than any other ecoregion on earth (Kattsov et al., 2005; Serreze et al., 2000). Potential changes in the FTE could have far reaching consequences for global biogeochemical cycling (Zhang et al., 2013), regional energy balance (Harding et al., 2002), biodiversity of flora and fauna (Skre et al., 2002), as well as ecosystem services and socioeconomics of Arctic-Boreal regions (Callaghan et al., 2002) – yet, relatively little is known about how the FTE is responding to warming.
A promising approach for improved understanding of the effects of climate change on inaccessible vegetation in the FTE is the study of seasonal changes in photosynthesis, or “photosynthetic phenology”. To track photosynthetic phenology at the landscape scale, many studies have been relying on time-series of spectral indices that can be derived from satellite imagery (e.g., Reed et al., 1994; Zeng et al., 2011; Jeong et al., 2011; Piao et al., 2011; Wang et al., 2015; Park et al., 2016). The spectral indices used by these studies (e.g., the Normalized Difference Vegetation Index or NDVI; EVI or the Enhanced vegetation index) employ parts of the electromagnetic spectrum that are sensitive to changes in leaf area and to a much lesser degree changes in leaf pigment pools (e.g., Eitel et al., 2011; Gamon et al., 1995). Consequently, these indices mainly track seasonal transitions in deciduous plant-assemblages associated with leaf development in the spring and leaf-abscission during late-season which closely track changes in photosynthetic phenology (e.g., Gamon et al., 1995). However, much of the FTE is dominated by evergreen species such as white spruce (Picea glauca) and or black spruce (Picea mariana) characterized by negligible seasonal changes in leaf area. This limits the use of these spectral indices for remotely tracking photosynthetic phenology. To overcome this limitation, spectral vegetation indices could be used that are sensitive to seasonal changes in pigment pools. These changes are controlled by a complex interaction between genetics and environmental factors such as photoperiod, temperature and incident light (Niinemets et al., 2003; Heide, 1974; Salisbury, 1981; Thomas and Vince-Prue, 1997; Rosenthal and Camm, 1997; Busch et al., 2008; Cooke et al., 2012; Ensminger et al., 2015).
The complex interaction between genetics and environmental factors may be particularly important to consider at the FTE where harsh growth conditions may have caused unique evolutionary adaptations with important implications on which of these environmental factors control changes in pigment pools and to what degree (Howe et al., 2003; Tang et al., 2016). In particular, relying on photoperiod as a deterministic signal to control changes in chlorophyll carotenoid ratios could be advantageous as opposed to relying on more stochastic environmental controls such as air temperature. An increase in the latter may temporarily increase carbon gain but at the same time would pose excessive physiological risk (e.g., to a sudden late-season freeze event) for trees at the FTE where drastic weather shifts are common (Körner, 2012; Körner et al., 2016).
Physiologically, changes in pigment pools enable plants to adapt their light use efficiency (LUE) to changing environmental conditions to optimize carbon gain during suitable growth conditions while minimizing the likelihood of damage to their photosystems (Adams and Demmig-Adams, 1994; Adams and Demmig-Adams, 1995; Adams et al., 2002; Adams et al., 2004; Verhoeven, 2014). Seasonal changes in LUE occur primarily via shifts in the relative pool sizes of chlorophyll and carotenoid pigments. Chlorophyll pigments absorb light that provides the energy needed to drive photosynthesis, and as such, chlorophyll concentration tends to be higher during the growing season when warm temperatures enable catalyzed reactions to occur, and lower outside of the growing season when temperatures and/or light limit/inhibit photosynthetic activity (Sofronova et al., 2016). Carotenoid pigments, including xanthophylls and carotenes, play an important role in dissipating excess light energy as heat (i.e., thermal dissipation) to avoid damage to the photosystems. Thus, seasonal shifts in ratios of chlorophyll to carotenoid pools offers an important photoprotective mechanism by which plants can regulate the absorption and dissipation of incoming energy. We contend this is likely particularly important for plants in the FTE during spring and late-season when temperatures are often too low for photosynthetic activity, yet the light environment is often extreme due to long days (12+ hours of daylight), high albedo of snow amplifying solar irradiance, and sparse canopy cover limiting shade (Tranquillini, 1979).
To remotely detect seasonal changes in the ratios of chlorophyll to carotenoid pools, the photochemical reflectance index (PRI) has been used (Wong and Gamon, 2015b). This spectral vegetation index is sensitive to plant physiological processes and relies on reflectance values at 531 nm and 570 nm. The 531 nm wavelength falls in the absorption region of both chlorophyll and carotenoids, whereas only chlorophyll absorbs the incoming light at 570 nm (Gamon et al., 1992, Gamon et al., 1997). Hence, PRI is sensitive to seasonal changes in the ratio of chlorophyll to carotenoid concentrations. However, it is also sensitive to diurnal variations in the concentration of xanthophyll cycle pigments which make up a portion of the total carotenoid pool (Garrity et al., 2011; Gamon et al., 2016; Filella et al., 2009; Wong and Gamon, 2015a). Over seasonal timescales, chlorophyll to carotenoid ratios have been shown to dominate the diurnal signal caused by light induced changes of xanthophyll-cycle pigments (Filella et al., 2009; Porcar-Castell et al., 2012; Fréchette et al., 2016; Gamon et al., 2015) making it a promising index for tracking photosynthetic phenology in evergreen species (Wong and Gamon, 2015a, Wong and Gamon, 2015b). For example, work by Wong and Gamon (2015b) showed that PRI from ground-based reflectance measurements closely tracked the onset of photosynthetic activation in evergreen conifer seedlings.
Despite these recent findings, the availability of satellite derived PRI time-series to study photosynthetic phenology at the FTE has been limited. This recently changed for North America and Europe when NASA made available a 16-year time-series of multi-angle implementation of atmospheric correction (MAIAC) processed MODIS data (https://e4ftl01.cr.usgs.gov/MOTA/; Lyapustin et al., in review). These atmospherically corrected datasets of surface reflectance allow PRI to be calculated using a green band centered around 531 nm (band 11, 526–536 nm) and a red reference band (band 1, 620–670 nm). Gamon et al. (2016) demonstrated that a MODIS derived version of PRI - the Chlorophyll Carotenoid Index (CCI) – tracked seasonal changes in photosynthetic carbon uptake at sites dominated by conifer species including eastern hemlock (Tsuga canadensis), red spruce (Picea rubens), loblolly pine (Pinus taeda), Douglas fir (Pseudotsuga menziesii) and western hemlock (Tsuga heterophylla). Similarly, Ulsig et al. (2017) showed promising relationships (R2 = 0.80) between the start of the growing season in Scots pine (Pinus sylvestris) determined by ground-based flux tower measurements of net ecosystem exchange (NEE) and MODIS derived PRI time-series.
The results from the above research by Gamon et al. (2016) and others (e.g., Ulsig et al., 2017; Middleton et al., 2016) suggest that PRI-based vegetation indices may reveal links between environmental variables and photosynthetic phenology. The goal of this study is to gain a more mechanistic understanding on which environmental factors drive changes in PRI at the FTE during late-season phenological transitions – including those susceptible to climate change (e.g., air- and soil temperature) and those not susceptible to climate change (e.g., photoperiod) – and the degree to which these relationships exist. We hypothesized that gradual late-season phenological changes captured by PRI are largely driven by changes in photoperiod as opposed to more stochastic drivers such as temperature. Support for this hypothesis would limit the suitability of late-seasonal PRI time-series for understanding climate change effects on the FTE.
Section snippets
Study area
Field data were collected during the late-growing season (June 30th–September 20th) in 2016 and 2017 along an approximately 5.5 km long south-north transect across the FTE near the Dalton Highway (67°59′ 40.92″ N latitude, 149°45′ 15.84″ W longitude), Alaska, USA (Fig. 1). There, trees are increasingly sparse and the landscape eventually transitions into treeless tundra. Along this transect, six study plots (P1 to P6, see Fig. 1) were established, each with six study trees:three with diameter
Time-series of environmental variables and PRI0
The time-series of the collected environmental data varied along a general late-season-seasonal trend though there were distinct differences in their progression between the two years (Fig. 2). For instance, Tamean showed pronounced differences between 2016 and 2017; July and August Tamean temperatures were warmer in 2017 but considerably colder than in 2016 between mid-August to early September. This trend reversed again through much of September when growing season temperatures in 2017 were
Environmental controls of temporal variation in late-season PRI0 time-series
The results of this study partly support our hypothesis that trees at the FTE predominantly rely on a deterministic photoperiod signal to drive pigment pool changes in the late-season that is largely unaffected by intra- and inter-seasonal variability in environmental variables (Fig. 5). The physiological explanation for this is likely that enhanced sensitivity to more intra-seasonally variable environmental signals such as Ta might pose excessive physiological risk for trees at the FTE where
Conclusion
The results from this study provide novel insights on how to interpret late-season PRI time-series at the FTE in the context of climate change. Photoperiod, unaffected by environmental change, exhibited decidedly the strongest effect on PRI0 partly supporting our hypothesis that photoperiod is the predominant driver of the PRI0 signal at the FTE. However, environmental variables SRmean and Tsmean, both of which are expected to shift with a changing climate, also exhibited statistically
Acknowledgments
The funding for this work came from NASA Above grant NNX15AT86A. We thank Sarah Sackett and Jyoti Jennewein for their help with some of our fieldwork and logistical support. We would also like to thank Steven Garrity for some helpful discussions and advice. Lastly, we would like to thank three anonymous reviewers and associate editor Dr. John Gamon for their helpful comments and feedback that greatly improved the quality of the manuscript.
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