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In autumn, the dissolved organic matter (DOM) contribution of leaf litter leachate to streams in forested watersheds changes as trees undergo resorption, senescence, and leaf abscission. Despite its biogeochemical importance, little work has investigated how leaf litter leachate DOM changes throughout autumn and how any changes might differ interspecifically and intraspecifically. Since climate change is expected to cause vegetation migration, it is necessary to learn how changes in forest composition could affect DOM inputs via leaf litter leachate. We examined changes in leaf litter leachate fluorescent DOM (FDOM) from American beech (Fagus grandifolia Ehrh.) leaves in Maryland, Rhode Island, Vermont, and North Carolina and from yellow poplar (Liriodendron tulipifera L.) leaves from Maryland. FDOM in leachate samples was characterized by excitation-emission matrices (EEMs). A six-component parallel factor analysis (PARAFAC) model was created to identify components that accounted for the majority of the variation in the data set. Self-organizing maps (SOM) compared the PARAFAC component proportions of leachate samples. Phenophase and species exerted much stronger influence on the determination of a sample's SOM placement than geographic origin. As expected, FDOM from all trees transitioned from more protein-like components to more humic-like components with senescence. Percent greenness of sampled leaves and the proportion of tyrosine-like component 1 were found to be significantly different between the two genetic beech clusters, suggesting differences in photosynthesis and resorption. Our results highlight the need to account for interspecific and intraspecific variations in leaf litter leachate FDOM throughout autumn when examining the influence of allochthonous inputs to streams.

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.

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.

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 À1 under 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 Shin'ichi Iida and Kathryn I. Wheeler contributed equally to this study.