Herbarium specimens show patterns of fruiting phenology in native and invasive plant species across New England.

PREMISE OF THE STUDY: Patterns of fruiting phenology in temperate ecosystems are poorly understood, despite the ecological importance of fruiting for animal nutrition and seed dispersal. Herbarium specimens represent an under-utilized resource for investigating geographical and climatic factors affecting fruiting times within species, patterns in fruiting times among species, and differences between native and non-native invasive species. METHODS: We examined over 15,000 herbarium specimens, collected and housed across New England, and found 3159 specimens with ripe fruits, collected from 1849-2013. We examined patterns in fruiting phenology among 37 native and 18 invasive woody plant species common to New England. We compared fruiting dates between native and invasive species, and analyzed how fruiting phenology varies with temperature, space, and time. KEY RESULTS: Spring temperature and year explained a small but significant amount of the variation in fruiting dates. Accounting for the moderate phylogenetic signal in fruiting phenology, invasive species fruited 26 days later on average than native species, with significantly greater standard deviations. CONCLUSIONS: Herbarium specimens can be used to detect patterns in fruiting times among species. However, the amount of intraspecific variation in fruiting times explained by temporal, geographic, and climatic predictors is small, due to a combination of low temporal resolution of fruiting specimens and the protracted nature of fruiting. Later fruiting times in invasive species, combined with delays in autumn bird migrations in New England, may increase the likelihood that migratory birds will consume and disperse invasive seeds in New England later into the year.

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States.

Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model-based phenology representations fail to capture local- to regional-scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground-based observations to estimate models that better represent how community-level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA National Phenology Network, and satellite remote sensing-based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the eastern United States. Specifically, we evaluated two different approaches: (i) using species-specific models in combination with species composition information to 'upscale' model predictions and (ii) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species-specific models. More generally, results from this analysis demonstrate how in situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models.

A new seasonal-deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios.

A spring phenology model that combines photoperiod with accumulated heating and chilling to predict spring leaf-out dates is optimized using PhenoCam observations and coupled into the Community Land Model (CLM) 4.5. In head-to-head comparison (using satellite data from 2003 to 2013 for validation) for model grid cells over the Northern Hemisphere deciduous broadleaf forests (5.5 million km2 ), we found that the revised model substantially outperformed the standard CLM seasonal-deciduous spring phenology submodel at both coarse (0.9 × 1.25°) and fine (1 km) scales. The revised model also does a better job of representing recent (decadal) phenological trends observed globally by MODIS, as well as long-term trends (1950-2014) in the PEP725 European phenology dataset. Moreover, forward model runs suggested a stronger advancement (up to 11 days) of spring leaf-out by the end of the 21st century for the revised model. Trends toward earlier advancement are predicted for deciduous forests across the whole Northern Hemisphere boreal and temperate deciduous forest region for the revised model, whereas the standard model predicts earlier leaf-out in colder regions, but later leaf-out in warmer regions, and no trend globally. The earlier spring leaf-out predicted by the revised model resulted in enhanced gross primary production (up to 0.6 Pg C yr-1 ) and evapotranspiration (up to 24 mm yr-1 ) when results were integrated across the study region. These results suggest that the standard seasonal-deciduous submodel in CLM should be reconsidered, otherwise substantial errors in predictions of key land-atmosphere interactions and feedbacks may result.

Determining past leaf-out times of New England's deciduous forests from herbarium specimens.

UNLABELLED: • PREMISE OF THE STUDY: There is great interest in studying leaf-out times of temperate forests because of the importance of leaf-out in controlling ecosystem processes, especially in the face of a changing climate. Remote sensing and modeling, combined with weather records and field observations, are increasing our knowledge of factors affecting variation in leaf-out times. Herbarium specimens represent a potential new source of information to determine whether the variation in leaf-out times observed in recent decades is comparable to longer time frames over past centuries.• METHODS: Here we introduce the use of herbarium specimens as a method for studying long-term changes in leaf-out times of deciduous trees. We collected historical leaf-out data for the years 1834-2008 from common deciduous trees in New England using 1599 dated herbarium specimens with young leaves.• KEY RESULTS: We found that leaf-out dates are strongly affected by spring temperature, with trees leafing out 2.70 d earlier for each degree C increase in mean April temperature. For each degree C increase in local temperature, trees leafed out 2.06 d earlier. Additionally, the mean response of leaf-out dates across all species and sites over time was 0.4 d earlier per decade. Our results are of comparable magnitude to results from studies using remote sensing and direct field observations.• CONCLUSIONS: Across New England, mean leaf-out dates varied geographically in close correspondence with those observed in studies using satellite data. This study demonstrates that herbarium specimens can be a valuable source of data on past leaf-out times of deciduous trees.

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery.

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including "canopy greenness", processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the "greenness rising" and end of the "greenness falling" stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.