Search

Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea‐Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best‐performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea, the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO2 parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type. [ABSTRACT FROM AUTHOR]

In deciduous forests, spring leaf development and fall leaf senescence regulate the timing and duration of photosynthesis and transpiration. Being able to model these dates is therefore critical to accurately representing ecosystem processes in biogeochemical models. Despite this, there has been relatively little effort to improve internal phenology predictions in widely used biogeochemical models. Here, we optimized the phenology algorithms in a regionally developed biogeochemical model (PnET-CN) using phenology data from eight mid-latitude PhenoCam sites in eastern North America. We then performed a sensitivity analysis to determine how the optimization affected future predictions of carbon, water, and nitrogen cycling at Bartlett Experimental Forest, New Hampshire. Compared to the original PnET-CN phenology models, our new spring and fall models resulted in shorter season lengths and more abrupt transitions that were more representative of observations. The new phenology models affected daily estimates and interannual variability of modeled carbon exchange, but they did not have a large influence on the magnitude or long-term trends of annual totals. Under future climate projections, our new phenology models predict larger shifts in season length in the fall (1.1–3.2 days decade−1) compared to the spring (0.9–1.5 days decade−1). However, for every day the season was longer, spring had twice the effect on annual carbon and water exchange totals compared to the fall. These findings highlight the importance of accurately modeling season length for future projections of carbon and water cycling. [ABSTRACT FROM AUTHOR]

Copyright of Botany is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is currently limited by a lack of standardized, high‐quality, empirical data that span large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data that are generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the United States and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be maximally accessible and easily integrated into investigators' workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format that were available through the ecocomDP R package; we then provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included in this effort. Our workflows are open‐source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks. [ABSTRACT FROM AUTHOR]

Large-scale changes in the state of the land surface affect the circulation of the atmosphere and the structure and function of ecosystems alike. As global temperatures increase and regional climates change, the timing of key plant phenophase changes are likely to shift as well. Here we evaluate a suite of phenometrics designed to facilitate an "apples to apples" comparison between remote sensing products and climate model output. Specifically, we derive day-of-year (DOY) thresholds of leaf area index (LAI) from both remote sensing and the Community Land Model (CLM) over the Northern Hemisphere. This systematic approach to comparing phenologically relevant variables reveals appreciable differences in both LAI seasonal cycle and spring onset timing between model simulated phenology and satellite records. For example, phenological spring onset in the model occurs on average 30 days later than observed, especially for evergreen plant functional types. The disagreement in phenology can result in a mean bias of approximately 5% of the total estimated Northern Hemisphere NPP. Further, while the more recent version of CLM (v5.0) exhibits seasonal mean LAI values that are in closer agreement with satellite data than its predecessor (CLM4.5), LAI seasonal cycles in CLM5.0 exhibit poorer agreement. Therefore, despite broad improvements for a range of states and fluxes from CLM4.5 to CLM5.0, degradation of plant phenology occurs in CLM5.0. Therefore, any coupling between the land surface and the atmosphere that depends on vegetation state might not be fully captured by the existing generation of the model. We also discuss several avenues for improving the fidelity between observations and model simulations. [ABSTRACT FROM AUTHOR]

Linking biometric measurements of stand‐level biomass growth to tower‐based measurements of carbon uptake—gross primary productivity and net ecosystem productivity—has been the focus of numerous ecosystem‐level studies aimed to better understand the factors regulating carbon allocation to slow‐turnover wood biomass pools. However, few of these studies have investigated the importance of previous year uptake to growth. We tested the relationship between wood biomass increment (WBI) and different temporal periods of carbon uptake from the current and previous years to investigate the potential lagged allocation of fixed carbon to growth among six mature, temperate forests. We found WBI was strongly correlated to carbon uptake across space (i.e., long‐term averages at the different sites) but on annual timescales, WBI was much less related to carbon uptake, suggesting a temporal mismatch between C fixation and allocation to biomass. We detected lags in allocation of the previous year's carbon uptake to WBI at three of the six sites. Sites with higher annual WBI had overall stronger correlations to carbon uptake, with the strongest correlations to carbon uptake from the previous year. Only one site had WBI with strong positive relationships to current year uptake and not the previous year. Forests with low rates of WBI demonstrated weak correlations to carbon uptake from the previous year and stronger relationships to current year climate conditions. Our work shows an important, but not universal, role of lagged allocation of the previous year's carbon uptake to growth in temperate forests. Plain Language Summary: We compared the interannual variability of stand‐level biomass growth (estimated from annual tree‐level measurements) to carbon uptake (measured from towers monitoring gas exchange over forest canopies) to identify temporal mismatches between the two processes. We used data from multiple forested sites with long‐term measurements of carbon uptake to ask the question: is there a consistent temporal offset between the uptake of carbon and the allocation to plant biomass? We found that the relationship between growth and carbon uptake varies among sites, and there was no consistent temporal offset between the uptake of carbon and allocation to biomass growth. Sites with higher growth rates had higher interannual variability, and more apparent coupling between biomass growth and carbon uptake from the previous year. Forests with lower growth rates had weaker relationships with carbon uptake and stronger coupling with current year environmental conditions. We demonstrate that temporal lags between carbon uptake and allocation to growth are not universal among these temperate forests, and the carry‐over of uptake stored from the previous year is not as critical in slow‐growing forests, compared to fast‐growing forests, likely due to lower demands for growth. This work helps to clarify the limitations on the growth of slow‐turnover wood biomass. Key Points: We found evidence for lags in allocation of previous year's carbon uptake to wood biomass increment (WBI) at three of six sites studiedSites with high annual WBI demonstrated strong correlations to carbon uptake, with the strongest correlations including previous year uptakeWBI was less tightly coupled to carbon uptake in less productive forests that also exhibited low interannual variability in WBI [ABSTRACT FROM AUTHOR]

In recent decades, the use of satellite sensors, near‐surface cameras, and other remote methods for monitoring vegetation phenology at landscape and higher scales has become increasingly common. These technologies provide a means to determine the timing of phenophases and growing season length at different spatial resolutions; coverage that is not attainable by human observers. However, in situ ground observations are required to validate remotely derived phenometrics. Despite increased knowledge and expertise there still remains the persistent challenge of reconciling ground observations at the individual plant level with remotely sensed (RS) phenometrics at landscape or larger scales. We compared the timing of in situ phenophase estimates (spring and autumn) with a range of corresponding remote sensing (moderate resolution imaging spectroradiometer [MODIS], visible infrared imaging radiometer suite [VIIRS], PhenoCam) phenometrics across five terrestrial sites in the United States' NEON (Harvard Forest [MA] [HARV], Onaqui [UT] [ONAQ], Abby Road [WA] [ABBY], Disney Wilderness Preserve [FL] [DSNY], and Ordway‐Swisher Biological Station [FL] [OSBS]) focusing on the 3‐year period from 2017 to 2019. Our main objective was to explore potential reasons for the observed discrepancies between in situ and RS phenometrics and to determine which technologies were better able to capture ground observations. Statistically significant relationships were strongest (p < 0.001) for spring phenophases, while the only RS phenometrics significantly correlated with in situ estimates of autumn phenophases were leaf fall (p < 0.01) and leaves (p < 0.000). In particular, root mean square error (RMSE) (mean bias error [MBE]) for MODIS‐Enhanced Vegetation Index‐2 band (EVI2), VIIRS‐EVI2, and PhenoCam‐green chromatic coordinate (GCC) derived early spring transition dates indicated overall differences of 21.7 days (−4.6 days), 28.4 days (−1.2 days), and 24.1 days (11.9 days) from in situ estimates of early leaf‐out dates. In autumn, RMSE/MBE was smallest (10.9 days/−2.2 days) between phenesse estimates (95th percentile date) of the latest date of in situ leaf fall and VIIRS derived end of senescence, compared to the equivalent phenometric derived from MODIS (13.5 days/7.7 days) and PhenoCam (GCC greenness‐falling) (13.8 days/−5.1 days). Overall, discrepancies between in situ and RS phenology related to scale, species availability, and the short duration of the time series (3 years). However, as the NEON project progresses these challenges are expected to be reduced as more data become available. [ABSTRACT FROM AUTHOR]

Ecological data are collected and shared at an increasingly rapid pace, but it is often shared in inconsistent and untraceable processed forms. Images of wood contain a wealth of information such as colours and textures but are most commonly reduced to ring‐width measurements before they can be shared in various common file formats. Archiving digital images of wood samples in libraries, which have been developed for ecological analysis and are publicly available, remains the exception.We developed the Wood Image Analysis and Dataset (WIAD), an open‐source application including a web interface to integrate basic visual analysis of wood samples, such as increment cores, thin sections or X‐ray films, basic data processing, and archiving of the images and derived data to facilitate transparency and reproducibility in studies using visual characteristics of wood.WIAD provides user‐friendly tools to manipulate images of wood samples, mark and measure wood characteristics such as growth increments, density fluctuations, early‐ and latewood widths and fire scars, and to visualise, process and archive images, metadata, and the derived data.WIAD constitutes a step towards the reproducible automation of tree‐ring analysis while establishing an open‐source foundation to create improved community‐developed repositories which would enable novel ecological studies harnessing the wealth of existing visual data. [ABSTRACT FROM AUTHOR]

How variations in carbon supply affect wood formation remains poorly understood in particular in mature forest trees. To elucidate how carbon supply affects carbon allocation and wood formation, we attempted to manipulate carbon supply to the cambial region by phloem girdling and compression during the mid‐ and late‐growing season and measured effects on structural development, CO2 efflux and nonstructural carbon reserves in stems of mature white pines. Wood formation and stem CO2 efflux varied with a location relative to treatment (i.e., above or below the restriction). We observed up to twice as many tracheids formed above versus below the treatment after the phloem transport manipulation, whereas the cell‐wall area decreased only slightly below the treatments, and cell size did not change relative to the control. Nonstructural carbon reserves in the xylem, needles and roots were largely unaffected by the treatments. Our results suggest that low and high carbon supply affects wood formation, primarily through a strong effect on cell proliferation, and respiration, but local nonstructural carbon concentrations appear to be maintained homeostatically. This contrasts with reports of decoupling of source activity and wood formation at the whole‐tree or ecosystem level, highlighting the need to better understand organ‐specific responses, within‐tree feedbacks, as well as phenological and ontogenetic effects on sink‐source dynamics. Wood formation and carbon allocation in mature forest trees are two important processes of the terrestrial carbon cycle, which both lack a mechanistic understanding. By girdling and temporarily compressing the phloem of mature white pine at Harvard Forest, we found that carbon supply variation due to these restrictions in phloem transport affected wood formation and stem respiration without substantially changing local nonstructural carbon reserves. We also observed large changes in the number of cells formed, correlating strongly with the presumed carbon supply gradient. In contrast, changes in cell‐wall thickness along the presumed carbon supply gradient were relatively small and cell size was not detectably affected. The presented carbon sensitivity of cambial cell division, cell elongation and cell‐wall thickening, as well as the effects on stem CO2 efflux and local nonstructural carbon reserves present significant advances in our understanding of wood formation and carbon allocation in forest trees with repercussions on the debate of source‐ versus sink‐limitation of growth in trees. [ABSTRACT FROM AUTHOR]

Summary: Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated.Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics.We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color.These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices. [ABSTRACT FROM AUTHOR]

Rising temperatures with increased drought pose two challenges for management of future biodiversity. First, are the most vulnerable species concentrated in specific regions and habitats? Second, where can landscape heterogeneity potentially mitigate impacts? We conducted a comprehensive trait analysis of forest plots spanning the eastern United States to quantify how resource‐acquisitive species respond to moisture‐soil‐climate interactions. We found that resource‐acquisitive species, including nutrient‐acquisitive and moisture‐acquisitive species, respond disproportionately to environmental gradients, and their response is largely explained by soil variation. We showed that the strong boundary of resource‐acquisitive species occurs near the last glacial limit, highlighting one of the clearest indicators of soil controls. Although local soil moisture may reduce drought‐induced stress for moisture‐acquisitive species, nutrient‐acquisitive species remain vulnerable on wet soils in dry climates. The results suggest that theories explaining species distributions should devote close attention to the combination of local drainage and soil type. Plain Summary language: Global warming and drought may cause harm to the biodiversity on the planet. But we may be able to protect species if we can identify vulnerable habitats and regions. To address this challenge, we analyzed a large data set of forest plots across the eastern United States. Our study showed that soil type and local soil moisture play important roles in species response to dry and warm conditions. Our results revealed a sharp change in the population of resource‐acquisitive species near the boundary of the last glaciation, indicating a strong soil control. While nutrient‐acquisitive species did not benefit from local moisture availability, moisture‐acquisitive species favored these conditions. Key Points: Nutrient‐acquisitive species tend to locate on the north of the last glacial limitNutrient‐acquisitive species derive limited benefit from local moisture availabilityHeterogeneity in moisture availability may provide drought refuges for moisture‐acquisitive species [ABSTRACT FROM AUTHOR]

Projected changes in temperature and precipitation are expected to influence spring and autumn vegetation phenology and hence the length of the growing season in many ecosystems. However, the sensitivity of green‐up and senescence to climate remains uncertain. We analyzed 488 site years of canopy greenness measurements from deciduous forest broadleaf forests across North America. We found that the sensitivity of green‐up to temperature anomalies increases with increasing mean annual temperature, suggesting lower temperature sensitivity as we move to higher latitudes. Furthermore, autumn senescence is most sensitive to moisture deficits at dry sites, with decreasing sensitivity as mean annual precipitation increases. Future projections suggest North American deciduous forests will experience higher sensitivity to temperature in the next 50 years, with larger changes expected in northern regions than in southern regions. Our study highlights how interactions between long‐term and short‐term changes in the climate system influence green‐up and senescence. Plain Summary language: Vegetation activity is sensitive to climate change, but relatively few studies have quantified this across large spatial extents. We used a large dataset of leaf color derived from digital images to look at how the timing of green‐up and fall senescence vary in relation to environmental factors across North America. We found that green‐up is more sensitive to temperature at warmer sites, but fall is more sensitive to moisture availability. If the climate continues to change as currently projected, we predict that the timing of spring will exhibit more dramatic swings in onset, but the timing of autumn will likely become less sensitive to moisture availability in the next 50 years. Key Points: Green‐up is controlled by energy‐related drivers, but senescence is controlled by a combination of moisture‐ and energy‐related driversSpring green‐up is more sensitive to warming in warmer areasAutumn senescence is more sensitive to moisture deficit in dry areas [ABSTRACT FROM AUTHOR]

Shifts in vegetation phenology are a key example of the biological effects of climate change1-3. However, there is substantial uncertainty about whether these temperature-driven trends will continue, or whether other factors—for example, photoperiod—will become more important as warming exceeds the bounds of historical variability4,5. Here we use phenological transition dates derived from digital repeat photography6 to show that experimental whole-ecosystem warming treatments7 of up to +9 °C linearly correlate with a delayed autumn green-down and advanced spring green-up of the dominant woody species in a boreal Picea-Sphagnum bog. Results were confirmed by direct observation of both vegetative and reproductive phenology of these and other bog plant species, and by multiple years of observations. There was little evidence that the observed responses were constrained by photoperiod. Our results indicate a likely extension of the period of vegetation activity by 1-2 weeks under a ‘CO2 stabilization’ climate scenario (+2.6 ± 0.7 °C), and 3-6 weeks under a ‘high-CO2 emission’ scenario (+5.9 ± 1.1 °C), by the end of the twenty-first century. We also observed severe tissue mortality in the warmest enclosures after a severe spring frost event. Failure to cue to photoperiod resulted in precocious green-up and a premature loss of frost hardiness8, which suggests that vulnerability to spring frost damage will increase in a warmer world9,10. Vegetation strategies that have evolved to balance tradeoffs associated with phenological temperature tracking may be optimal under historical climates, but these strategies may not be optimized for future climate regimes. These in situ experimental results are of particular importance because boreal forests have both a circumpolar distribution and a key role in the global carbon cycle11. Experimental whole-ecosystem warming treatments of a Picea-Sphagnum peat bog reveal the likely phenological consequences of future temperature increases that exceed those of historical climate regimes. [ABSTRACT FROM AUTHOR]

Probabilistic forecasts of species distribution and abundance require models that accommodate the range of ecological data, including a joint distribution of multiple species based on combinations of continuous and discrete observations, mostly zeros. We develop a generalized joint attribute model ( GJAM), a probabilistic framework that readily applies to data that are combinations of presence-absence, ordinal, continuous, discrete, composition, zero-inflated, and censored. It does so as a joint distribution over all species providing inference on sensitivity to input variables, correlations between species on the data scale, prediction, sensitivity analysis, definition of community structure, and missing data imputation. GJAM applications illustrate flexibility to the range of species-abundance data. Applications to forest inventories demonstrate species relationships responding as a community to environmental variables. It shows that the environment can be inverse predicted from the joint distribution of species. Application to microbiome data demonstrates how inverse prediction in the GJAM framework accelerates variable selection, by isolating effects of each input variable's influence across all species. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO2 and H2O flux data, and to study climate change impacts on the terrestrial biosphere. Measurement(s) plant phenological trait • plant greenness Technology Type(s) digital camera Sample Characteristic - Organism Embryophyta Sample Characteristic - Environment terrestrial biome Sample Characteristic - Location Earth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9897089 [ABSTRACT FROM AUTHOR]