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Abstract. Monitoring leaf phenology tracks the progression ofclimate change and seasonal variations in a variety of organismal andecosystem processes. Networks of finite-scale remote sensing, such as thePhenoCam network, provide valuable information on phenological state at hightemporal resolution, but they have limited coverage. Satellite-based data withlower temporal resolution have primarily been used to more broadly measurephenology (e.g., 16 d MODIS normalizeddifference vegetation index (NDVI) product). Recent versions of the GeostationaryOperational Environmental Satellites (GOES-16 and GOES-17) can monitor NDVI attemporal scales comparable to that of PhenoCam throughout most of thewestern hemisphere. Here we begin to examine the current capacity of thesenew data to measure the phenology of deciduous broadleaf forests for thefirst 2 full calendar years of data (2018 and 2019) by fittingdouble-logistic Bayesian models and comparing the transition dates of the start, middle, and end of theseason to those obtained from PhenoCam and MODIS 16 dNDVI and enhanced vegetation index (EVI) products. Compared to these MODIS products, GOES was morecorrelated with PhenoCam at the start and middle of spring but had a largerbias (3.35 ± 0.03 d later than PhenoCam) at the end of spring.Satellite-based autumn transition dates were mostly uncorrelated with thoseof PhenoCam. PhenoCam data produced significantly more certain (allp values ≤0.013) estimates of all transition dates than any of thesatellite sources did. GOES transition date uncertainties were significantlysmaller than those of MODIS EVI for all transition dates (all p values ≤0.026), but they were only smaller (based on p value <0.05) than thosefrom MODIS NDVI for the estimates of the beginning and middle of spring. GOES willimprove the monitoring of phenology at large spatial coverages and providesreal-time indicators of phenological change even when the entire springtransition period occurs within the 16 d resolution of these MODISproducts. 

The opportunity to participate in and contribute to emerging fields is increasingly prevalent in science. However, simply thinking about stepping outside of your academic silo can leave many students reeling from the uncertainty. Here, we describe 10 simple rules to successfully train yourself in an emerging field, based on our experience as students in the emerging field of ecological forecasting. Our advice begins with setting and revisiting specific goals to achieve your academic and career objectives and includes several useful rules for engaging with and contributing to an emerging field. 

The newest version of the Geostationary Operational Environmental Satellite series (GOES-16 and GOES-17) includes a near infrared band that allows for the calculation of normalized difference vegetation index (NDVI) at a 1 km at nadir spatial resolution every five minutes throughout the continental United States and every ten minutes for much of the western hemisphere. The usefulness of individual NDVI observations is limited due to the noise that remains even after cloud masks and data quality flags are applied, as much of this noise is negatively biased due to scattering within the atmosphere. Fortunately, high temporal resolution NDVI allows for the identification of consistent diurnal patterns. Here, we present a novel statistical model that utilizes this pattern, by fitting double exponential curves to the diurnal NDVI data, to provide a daily estimate of NDVI over forests that is less sensitive to noise by accounting for both random observation errors and atmospheric scattering biases. We fit this statistical model to 350 days of observations for fifteen deciduous broadleaf sites in the United States and compared the method to several simpler potential methods. Of the days 60% had more than ten observations and were able to be modeled via our methodology. Of the modeled days 72% produced daily NDVI estimates with <0.1 wide 95% confidence intervals. Of the modeled days 13% were able to provide a confident NDVI value even if there were less than five observations between 10:00–14:00. This methodology provides estimates for daily midday NDVI values with robust uncertainty estimates, even in the face of biased errors and missing midday observations. 

An increasing number of studies have examined the effects of various biotic and abiotic factors on stemflow production. Of those that have ascribed the importance of canopy structure to stemflow production, there has been a bias towards field studies. Coupling Bayesian inference with the NIED (National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Japan) large‐scale rainfall simulator, this study leveraged a unique opportunity to control rainfall amounts and intensities to pinpoint the canopy structural metrics that differentially influence stemflow funnelling ratios for three common tree species between leafed and leafless canopy states. For the first time, we examined whether canopy structure metrics exert a static control on stemflow funnelling ratios or whether different elements of canopy structure are more or less important under leafed or leafless states, thereby allowing us to determine if tacit assumptions about the static influence of canopy structure on stemflow production (and funnelling) are valid (or not). Rainfall simulations were conducted at 15, 20, 30, 40, 50, and 100 mm h⁻¹under both leafed and leafless tree conditions (12 simulations in total) to detect any differential effects on the presence or absence of foliage on stemflow funnelling ratios. For leafed conditions, the highest percentages of best‐fitting models (ΔDIC ≤2) indicated that stemflow funnelling ratios were mainly controlled by total dry aboveground biomass (B_all), diameter at breast height (D_BH), total dry foliar biomass (B_f), tree height (H), and woody to foliar dry biomass ratio (BR). Whilst for the leafless state, the highest percentages of best‐fitting models (ΔDIC ≤2) indicated that total dry branch biomass (B_br) was relatively dominant as was the interaction effects between crown projection area and species (CPA:species). These results compel us to reject any assumption of a static effect of different elements of canopy structure on stemflow funnelling.

 

The decomposition of broadleaved tree leaves can contribute a substantial amount of energy to forested watershedsviadissolved organic matter (DOM), nutrients, and biological activity. Less is known about how these inputs may vary within a single tree species that is known to have two genetically distinct and geographically separate populations, or how these inputs may change throughout autumn senescence and abscission. It is often implicitly assumed that intraspecific differences in leaf‐litter leachate chemistry do not significantly differ geographically. We analyzed the morphological and chemical leaf traits and leachates fromFagus grandifolia(American beech) leaves (n= 360) during three phenophases: fresh green leaves, senescing leaves, and fallen leaves. During each phenophase, leaves were collected from four sites along a geographic transect stretching from Vermont to North Carolina (over 1400 km), with two sites representing each genetic population and differing climatic conditions. Leachates were analyzed for routine solutes and nutrients, as well as fluorescent and UV‐visible absorbance indices. Amounts of macro‐ and micronutrients were highly variable among sites and phenophases but tended to be lowest during the fallen‐leaf phase, while measured fluorescence and absorbance indices tended to increase during the senescing‐leaf phase and plateau. Results suggest significant differences in leached nutrients among sites, and optical properties and nutrients among phenophases. Aromaticity and molecular weight of DOM in leachates was generally low, and aromaticity and humification of leachates both increased over time with leaf age. These results also suggest that geographically (or genetically) separate populations of the same species do not experience senescence in the same way and that implicit assumptions of intraspecific uniformity of leaf‐litter leachate chemistry for a given tree species may be invalid.

 

Synthesis research in ecology and environmental science improves understanding, advances theory, identifies research priorities, and supports management strategies by linking data, ideas, and tools. Accelerating environmental challenges increases the need to focus synthesis science on the most pressing questions. To leverage input from the broader research community, we convened a virtual workshop with participants from many countries and disciplines to examine how and where synthesis can address key questions and themes in ecology and environmental science in the coming decade. Seven priority research topics emerged: (1) diversity, equity, inclusion, and justice (DEIJ), (2) human and natural systems, (3) actionable and use‐inspired science, (4) scale, (5) generality, (6) complexity and resilience, and (7) predictability. Additionally, two issues regarding the general practice of synthesis emerged: the need for increased participant diversity and inclusive research practices; and increased and improved data flow, access, and skill‐building. These topics and practices provide a strategic vision for future synthesis in ecology and environmental science.