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Remote sensing methods enable detection of solar-induced chlorophyll a fluorescence. However, to unleash the full potential of this signal, intensive cross-disciplinary work is required to harmonize biophysical and ecophysiological studies. For decades, the dynamic nature of chlorophyll a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods enable detection of ChlaF induced by sunlight across a range of larger scales, from using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as solar-induced fluorescence (SIF) and its application promises to overcome spatial constraints on studies of photosynthesis, opening new research directions and opportunities in ecology, ecophysiology, biogeochemistry, agriculture and forestry. However, to unleash the full potential of SIF, intensive cross-disciplinary work is required to harmonize these new advances with the rich history of biophysical and ecophysiological studies of ChlaF, fostering the development of next-generation plant physiological and Earth-system models. Here, we introduce the scale-dependent link between SIF and photosynthesis, with an emphasis on seven remaining scientific challenges, and present a roadmap to facilitate future collaborative research towards new applications of SIF.

The phenology of montane conifer forests is likely to shift in response to climate change and altered seasonal dynamics of light, temperature, and moisture. Solar-induced fluorescence (SIF) is expected to provide substantial improvement for mapping temporal changes in evergreen gross primary production (GPP) over greenness-based remote sensing indices. The utility of SIF to monitor seasonal changes in the phenology of conifer photosynthesis depends on the degree to which GPP and SIF respond in synchrony to key environmental drivers. However, to what extent SIF and GPP become decoupled by responding differently to the combined effects of light and other environmental conditions remains unknown. The goal of this study was to characterize the responses of GPP and SIFred to a suite of environmental drivers at the half-hour time scale and determine how these relationships change across seasons. We analyzed one year of tower-based SIFred and eddy covariance-derived GPP data from a conifer forest at Niwot Ridge, Colorado. We compared the light responses of GPP and SIFred across the year, finding that SIFred increased in response to light earlier in the year than did GPP. The light response of GPP had a positive temperature dependence in spring, and this dependency reversed in summer due to increased evaporative demand, while the light response of SIFred was less temperature dependent. Using artificial neural network ensemble analysis, we found that from spring to summer, SIFred did not exhibit a parallel response to the seasonally dynamic temperature and moisture controls on GPP. In summer SIFred was not correlated with canopy conductance, suggesting that SIF is less sensitive to stomatal control than GPP. Our results suggest that, in conifers, photosystems begin to activate in spring prior to when water becomes available for photosynthesis, presenting a challenge for the use of SIF as a phenological indicator in conifer forests.

Understanding the pronounced seasonal and spatial variation in leaf carboxylation capacity (Vc,max ) is critical for determining terrestrial carbon cycling in tropical forests. However, an efficient and scalable approach for predicting Vc,max is still lacking. Here the ability of leaf spectroscopy for rapid estimation of Vc,max was tested. Vc,max was estimated using traditional gas exchange methods, and measured reflectance spectra and leaf age in leaves sampled from tropical forests in Panama and Brazil. These data were used to build a model to predict Vc,max from leaf spectra. The results demonstrated that leaf spectroscopy accurately predicts Vc,max of mature leaves in Panamanian tropical forests (R2 = 0.90). However, this single-age model required recalibration when applied to broader leaf demographic classes (i.e. immature leaves). Combined use of spectroscopy models for Vc,max and leaf age enabled construction of the Vc,max -age relationship solely from leaf spectra, which agreed with field observations. This suggests that the spectroscopy technique can capture the seasonal variability in Vc,max , assuming sufficient sampling across diverse species, leaf ages and canopy environments. This finding will aid development of remote sensing approaches that can be used to characterize Vc,max in moist tropical forests and enable an efficient means to parameterize and evaluate terrestrial biosphere models.

The association between increased lineage diversification rates and the evolution of latex and resin canals is widely cited as a paradigmatic example of Ehrlich and Raven’s ‘escape‐and‐radiate’ hypothesis of co‐evolution. However, it has been over a quarter‐century since the original study, and updates to phylogenetic comparative methods, plant molecular systematics, and phenotypic data warrant a reassessment of this classic finding. We gathered data on latex and resin canals across 345 families and 986 genera of vascular plants and conducted a multi‐scale test of the association between these traits and lineage diversification rates. At a broad scale (across clades), we used sister‐clade comparisons to test whether 28 canal‐bearing clades had higher net diversification rates than their canal‐lacking sister clades. At a finer scale (within clades), we used ancestral state reconstructions and phylogenetic models of lineage diversification rates to examine the relationship between trait evolution and the timing of diversification rate shifts in two better‐characterized clades – Araceae and Papaveraceae. At both scales of our analyses we found poor support for the predicted relationship between diversification and the evolution of latex and resin canals. Follow‐up analyses clarified that the qualitative change between our results and those of the Farrell et al.’s classic study is not the result of different phylogenetic comparative methods. Instead, the differences are attributable to updates to plant systematic hypotheses and new data on laticifers and resin canal presence/absence. Synthesis. Our updated study reveals that there is no longer strong evidence for latex or resin canals as general, consistently replicable drivers of species diversity across plants. However, we cannot rule out a relationship in all groups. We therefore argue that theoretical and empirical work aimed at understanding ecological factors that condition ‘escape‐and‐radiate’ dynamics will allow for more nuanced tests of the hypothesis in the future.

The connection between solar-induced fluorescence (SIF) and vegetation gross primary productivity is being widely investigated across spatial, temporal, and biological scales, including: a) studies at the leaf [1], [2], plant canopy [2]–[4] or satellite pixel scale [5], [6], b) temporally with studies spanning from diurnal [7] to seasonal scales [1], [3], [5], and b) biologically with studies covering various plant functional types (PFTs), e.g., crops [4], [7], deciduous [8] or evergreen forests [1], [3], in response to different sources of stress.

Carbon allocation to non-structural carbohydrates (NSC) is essential for plant metabolism playing an important role in tree responses to drought. It is still unclear if and how interspecific hydraulic trait variation modulates NSC concentration dynamics in different plant organs, particularly in tropical tree species. We investigated whether drought-resistance strategies (inferred from hydraulic traits) explain seasonal and interannual NSC dynamics in leaves, branches, trunks, and roots in seasonal eastern Amazon tree species in Brazil. We measured NSC concentration in eight abundant species during three years, including the end of the wet and dry seasons of the typical regular years (2013-2014) and the extreme drought induced by El Niño–Southern Oscillation in 2015 (ENSO). Organs have an important contribution to explain the starch (ST), soluble sugar (SS), and NSC variance among trees. We showed seasonal and year-to-year homeostasis in ST and SS concentrations in a majority of organs during 2013 and 2014, but SS increased in all organs during the extreme ENSO drought, while the ST concentration did not. The increase in SS concentration was more evident in woody organs from species with intermediate and tolerant drought strategies. The drought-tolerant species maintain higher root starch concentrations and mobilize more SS during extreme drought.

As a preparatory study for future hyperspectral missions that can measure canopy chemistry, we introduce a novel approach to investigate whether multi-angle Moderate Resolution Imaging Spectroradiometer (MODIS) data can be used to generate a preliminary database with long-term estimates of chlorophyll. MODIS monthly chlorophyll estimates between 2000 and 2015, derived from a fully coupled canopy reflectance model (ProSAIL), were inspected for consistency with eddy covariance fluxes, tower-based hyperspectral images and chlorophyll measurements. MODIS chlorophyll estimates from the inverse model showed strong seasonal variations across two flux-tower sites in central and eastern Amazon. Marked increases in chlorophyll concentrations were observed during the early dry season. Remotely sensed chlorophyll concentrations were correlated to field measurements (r2 = 0.73 and r2 = 0.98) but the data deviated from the 1:1 line with root mean square errors (RMSE) ranging from 0.355 μg cm−2 (Tapajos tower) to 0.470 μg cm−2 (Manaus tower). The chlorophyll estimates were consistent with flux tower measurements of photosynthetically active radiation (PAR) and net ecosystem productivity (NEP). We also applied ProSAIL to mono-angle hyperspectral observations from a camera installed on a tower to scale modeled chlorophyll pigments to MODIS observations (r2 = 0.73). Chlorophyll pigment concentrations (ChlA+B) were correlated to changes in the amount of young and mature leaf area per month (0.59 ≤ r2 ≤ 0.64). Increases in MODIS observed ChlA+B were preceded by increased PAR during the dry season (0.61 ≤ r2 ≤ 0.62) and followed by changes in net carbon uptake. We conclude that, at these two sites, changes in LAI, coupled with changes in leaf chlorophyll, are comparable with seasonality of plant productivity. Our results allowed the preliminary development of a 15-year time series of chlorophyll estimates over the Amazon to support canopy chemistry studies using future hyperspectral sensors.

Leaf quantity (i.e., canopy leaf area index, LAI), quality (i.e., per-area photosynthetic capacity), and longevity all influence the photosynthetic seasonality of tropical evergreen forests. However, these components of tropical leaf phenology are poorly represented in most terrestrial biosphere models (TBMs). Here, we explored alternative options for the representation of leaf phenology effects in TBMs that employ the Farquahar, von Caemmerer & Berry (FvCB) representation of CO2 assimilation. We developed a two-fraction leaf (sun and shade), two-layer canopy (upper and lower) photosynthesis model to evaluate different modeling approaches and assessed three components of phenological variations (i.e., leaf quantity, quality, and within-canopy variation in leaf longevity). Our model was driven by the prescribed seasonality of leaf quantity and quality derived from ground-based measurements within an Amazonian evergreen forest. Modeled photosynthetic seasonality was not sensitive to leaf quantity, but was highly sensitive to leaf quality and its vertical distribution within the canopy, with markedly more sensitivity to upper canopy leaf quality. This is because light absorption in tropical canopies is near maximal for the entire year, implying that seasonal changes in LAI have little impact on total canopy light absorption; and because leaf quality has a greater effect on photosynthesis of sunlit leaves than light limited, shade leaves and sunlit foliage are more abundant in the upper canopy. Our two-fraction leaf, two-layer canopy model, which accounted for all three phenological components, was able to simulate photosynthetic seasonality, explaining ~90% of the average seasonal variation in eddy covariance-derived CO2 assimilation. This work identifies a parsimonious approach for representing tropical evergreen forest photosynthetic seasonality in TBMs that utilize the FvCB model of CO2 assimilation and highlights the importance of incorporating more realistic phenological mechanisms in models that seek to improve the projection of future carbon dynamics in tropical evergreen forests.

Eddy covariance (EC) datasets have provided insight into climate determinants of net ecosystem productivity (NEP) and evapotranspiration (ET) in natural ecosystems for decades, but most EC studies were published in serial fashion such that one study's result became the following study's hypothesis. This approach reflects the hypothetico-deductive process by focusing on previously derived hypotheses. A synthesis of this type of sequential inference reiterates subjective biases and may amplify past assumptions about the role, and relative importance, of controls over ecosystem metabolism. Long-term EC datasets facilitate an alternative approach to synthesis: the use of inductive data-based analyses to re-examine past deductive studies of the same ecosystem. Here we examined the seasonal climate determinants of NEP and ET by analyzing a 15-year EC time-series from a subalpine forest using an ensemble of Artificial Neural Networks (ANNs) at the half-day (daytime/nighttime) time-step. We extracted relative rankings of climate drivers and driver-response relationships directly from the dataset with minimal a priori assumptions. The ANN analysis revealed temperature variables as primary climate drivers of NEP and daytime ET, when all seasons are considered, consistent with the assembly of past studies. New relations uncovered by the ANN approach include the role of soil moisture in driving daytime NEP during the snowmelt period, the nonlinear response of NEP to temperature across seasons, and the low relevance of summer rainfall for NEP or ET at the same daytime/nighttime time step. These new results offer a more complete perspective of climate-ecosystem interactions at this site than traditional deductive analyses alone.

© 2019 John Wiley & Sons Ltd Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are “cryptic”—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

New commercial-off-the-shelf imaging spectrometers promise the combination of high spatial and spectral resolution needed to retrieve solar induced fluorescence (SIF). Imaging at multiple wavelengths for individual plants and even individual leaves from low-altitude airborne or ground-based platforms has applications in agriculture and carbon-cycle science. Data from these instruments could provide insight into the status of the photosynthetic apparatus at scales of space and time not observable with tools based on gas exchange, and could support the calibration and validation activities of current and forthcoming space missions to quantify SIF. High-spectral resolution enables SIF retrieval from regions of strong telluric absorption by molecular oxygen, and also within numerous solar Fraunhofer lines in atmospheric windows not obscured by oxygen or water absorptions. Because the SIF signal can be < 5 % of background reflectance, rigorous instrument characterization and reduction of systematic error is necessary. Here we develop a spectral stray-light correction algorithm for a commercial off-the-shelf imaging spectrometer designed to quantify SIF. We use measurements from an optical parametric oscillator laser at 44 wavelengths to generate the spectral line-spread function and develop a spectral stray-light correction matrix using a novel exposure-bracketing method. The magnitude of spectral stray light in this instrument is small, but spectral stray light is detectable at all measured wavelengths. Examination of corrected line-spread functions indicates that the correction algorithm reduced spectral stray-light by 1 to 2 orders of magnitude.

Background: Global climate change has the potential to shape evolutionary trajectories of invasive species via many routes, including through changes in mating systems. Many cleistogamous (CL) plants adjust investment in CL (selfed) vs. chasmogamous (CH, potentially outcrossed) progeny across environmental gradients. However, the details of such adjustments are lacking for highly invasive plant species.Aims: We used a highly invasive grass, Microstegium vimineum, as a model for understanding how changes in water-induced stress (including potential associated changes in soil nutrient availability) might affect mating systems and thus evolutionary change in invasive species. We predicted that plants would respond to increased water-induced stress through a relative reduction in investment in CL vs. CH reproduction (i.e., a decrease in the CL:CH ratio).Methods: Under greenhouse conditions, we measured fecundity (number of inflorescences and florets per plant) as well as relative investment in CL vs. CH flore...

Satellite and tower-based metrics of forest-scale photosynthesis generally increase with dry season progression across central Amazônia, but the underlying mechanisms lack consensus. We conducted demographic surveys of leaf age composition, and measured the age dependence of leaf physiology in broadleaf canopy trees of abundant species at a central eastern Amazon site. Using a novel leaf-to-branch scaling approach, we used these data to independently test the much-debated hypothesis – arising from satellite and tower-based observations – that leaf phenology could explain the forest-scale pattern of dry season photosynthesis. Stomatal conductance and biochemical parameters of photosynthesis were higher for recently mature leaves than for old leaves. Most branches had multiple leaf age categories simultaneously present, and the number of recently mature leaves increased as the dry season progressed because old leaves were exchanged for new leaves. These findings provide the first direct field evidence that branch-scale photosynthetic capacity increases during the dry season, with a magnitude consistent with increases in ecosystem-scale photosynthetic capacity derived from flux towers. Interactions between leaf age-dependent physiology and shifting leaf age-demographic composition are sufficient to explain the dry season photosynthetic capacity pattern at this site, and should be considered in vegetation models of tropical evergreen forests. © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust

To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuana CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jaru RJA) exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.

Historically, the persistence of mixed mating (reproduction via both self- and cross-fertilization) has presented a puzzle because classic theory predicts that mixed mating should be evolutionarily unstable. One mechanism that could contribute to the maintenance of mixed mating in cleistogamous species is the ability to invest in the appropriate type of reproduction (outcrossing vs. selfing) for a given pollination environment. We tested whether Collomia grandiflora plants responded to pollen limitation by plastically shifting relative investment in cleistogamous (obligately self-fertilizing) versus chasmogamous (potentially outcrossing) reproduction. We estimated reproductive effort (seed production) for chasmogamous and cleistogamous reproductive modes in hand-pollinated and unpollinated plants. We also investigated whether plastic responses to pollen limitation could be constrained by water availability or early nutrient fertilization. Pollen limitation significantly increased cleistogamous rep...

Hybridization plays an important role in the evolution of many taxonomic groups, but large-scale phylogenetic patterns of hybridization are poorly known. Here, we investigate patterns of hybridization in vascular plants. Our dataset included 282 families, 3212 genera and ≈37,000 species accounts from eight regional floras covering continental Europe, two island regions, and parts of North America and Australia. Interspecific hybrids were common in the wild, occurring in 40% of families and 16% of genera, with an overall frequency of 0.09 hybrids per nonhybrid species. Taxon species richness explained a large amount of variation in the number of hybrids, but taxon bias (study effort) did not. We accounted for species richness in calculating hybridization propensities, and found that both families and genera differed in hybridization propensity. Hybridization propensity of a given group was generally consistent across regions (with the exception of Hawaii), suggesting that hybridization behavior may be determined more by intrinsic properties of a group than by environmental conditions. We found evidence of a strong phylogenetic signal ( λ =0.93) in hybridization propensity as hybrids were not uniformly distributed across orders of vascular plants. Characterization of the hybridization behavior of groups should lead to increased predictive power regarding their traits and evolutionary trajectories, and will allow comparative tests of the traits driving differences in hybridization propensity.

Leaf seasonality in Amazon forests Models assume that lower precipitation in tropical forests means less plant-available water and less photosynthesis. Direct measurements in the Amazon, however, show that production remains constant or increases in the dry season. To investigate this mismatch, Wu et al. use tower-based cameras to detect the phenology (i.e., the seasonal patterns) of leaf dynamics in tropical tree crowns in Amazonia, Brazil, and relate this to patterns of CO 2 flux. Accounting for age-dependent variation among individual leaves and crowns is necessary for understanding the seasonal dynamics of photosynthesis in the entire ecosystem. Leaf phenology regulates seasonality of the carbon flux in tropical forests across a gradient of climate zones. Science , this issue p. 972

We briefly introduce R2G2, an R CRAN package to visualize spatially explicit biological data within the Google Earth interface. Our package combines a collection of basic graph-editing features, including automated placement of dots, segments, polygons, images (including graphs produced with R), along with several complex three-dimensional (3D) representations such as phylogenies, histograms and pie charts. We briefly present some example data sets and show the immediate benefits in communication gained from using the Google Earth interface to visually explore biological results. The package is distributed with detailed help pages providing examples and annotated source scripts with the hope that users will have an easy time using and further developing this package. R2G2 is distributed on http://cran.r-project.org/web/packages.