Phenological Research Research Articles

Emerging opportunities and challenges in phenology: a review

AbstractPlant phenology research has gained increasing attention because of the sensitivity of phenology to climate change and its consequences for ecosystem function. Recent technological development has made it possible to gather invaluable data at a variety of spatial and ecological scales. Despite our ability to observe phenological change at multiple scales, the mechanistic basis of phenology is still not well understood. Integration of multiple disciplines, including ecology, evolutionary biology, climate science, and remote sensing, with long‐term monitoring data across multiple spatial scales is needed to advance understanding of phenology. We review the mechanisms and major drivers of plant phenology, including temperature, photoperiod, and winter chilling, as well as other factors such as competition, resource limitation, and genetics. Shifts in plant phenology have significant consequences on ecosystem productivity, carbon cycling, competition, food webs, and other ecosystem functions and services. We summarize recent advances in observation techniques across multiple spatial scales, including digital repeat photography, other complementary optical measurements, and solar‐induced fluorescence, to assess our capability to address the importance of these scale‐dependent drivers. Then, we review phenology models as an important component of earth system modeling. We find that the lack of species‐level knowledge and observation data leads to difficulties in the development of vegetation phenology models at ecosystem or community scales. Finally, we recommend further research to advance understanding of the mechanisms governing phenology and the standardization of phenology observation methods across networks. With the opportunity for “big data” collection for plant phenology, we envision a breakthrough in process‐based phenology modeling.

The model of analysis of variance of plant phenology on the example of spring rape

An example of analysis of variance of phenology development of spring rape plants in the conditions of Podolia (middle Transnistria; geographical coordinates are latitude – 48°40', longitude 26°35') of South West forest-steppe of Ukraine is given. The topicality of the topic is that because of their nature, the results of phenological research (observations) aren’t mathematically processed in virtue of its features. Filling this gap is the aim of the proposed article

Casting your network wide: a plea to scale-up phenological research

Accumulating scientific evidence has demonstrated widespread shifts in the biological seasons. These shifts may modify seasonal interspecific interactions, with consequent impacts upon reproductive success and survival. However, current understanding of these impacts is based upon a limited number of studies that adopt a simplified ‘bottom-up’ food-chain paradigm, at a local scale. I argue that there is much insight to be gained by widening the scope of phenological studies to incorporate food-web interactions and landscape-scale processes across a diversity of ecosystem types, with the ultimate goal of developing a generic understanding of the systems most vulnerable to synchrony effects in the future. I propose that co-location of predator and prey phenological monitoring at sentinel sites, acting as research platforms for detailed food-web studies, experimentation and match-up with earth observation data, would be an important first step in this endeavour.

The plant phenology monitoring design for The National Ecological Observatory Network

AbstractPhenology is an integrative science that comprises the study of recurring biological activities or events. In an era of rapidly changing climate, the relationship between the timing of those events and environmental cues such as temperature, snowmelt, water availability, or day length are of particular interest. This article provides an overview of the observer‐based plant phenology sampling conducted by the U.S. National Ecological Observatory Network (NEON), the resulting data, and the rationale behind the design. Trained technicians will conduct regular in situ observations of plant phenology at all terrestrial NEON sites for the 30‐yr life of the observatory. Standardized and coordinated data across the network of sites can be used to quantify the direction and magnitude of the relationships between phenology and environmental forcings, as well as the degree to which these relationships vary among sites, among species, among phenophases, and through time. Vegetation at NEON sites will also be monitored with tower‐based cameras, satellite remote sensing, and annual high‐resolution airborne remote sensing. Ground‐based measurements can be used to calibrate and improve satellite‐derived phenometrics. NEON's phenology monitoring design is complementary to existing phenology research efforts and citizen science initiatives throughout the world and will produce interoperable data. By collocating plant phenology observations with a suite of additional meteorological, biophysical, and ecological measurements (e.g., climate, carbon flux, plant productivity, population dynamics of consumers) at 47 terrestrial sites, the NEON design will enable continental‐scale inference about the status, trends, causes, and ecological consequences of phenological change.

Modeling plant phenology database: Blending near-surface remote phenology with on-the-ground observations

Phenology research handles multifaceted information that needs to be organized and made promptly accessed by scientific community. We propose the conceptual design and implementation of a database to store, manage, and manipulate phenological time series and associated ecological information and environmental data. The database was developed in the context of the e-phenology project and integrates ground-based conventional plant phenology direct observations with near-surface remote phenology using repeated images from digital cameras. It also includes site-base information, sensor derived data from the study site weather station and plant ecological traits (e.g., pollination and dispersal syndrome, flower and fruit color, and leaf exchange strategy) at individual and species level. We validated the database design through the implementation of a Web application that generates the time series based on queries, exemplified in two case studies investigating: the relationship between flowering phenology and local weather; and the consistency between leafing patterns derived from ground-based phenology on leaf flush and from vegetation image indices (%Green). The database will store all the information produced in the e-phenology project, monitoring of 12 sites from cerrado savanna to rainforest, and will aggregate the legacy information of other studies developed in the Phenology Laboratory (UNESP, Rio Claro, Brazil) over the last 20 years. We demonstrate that our database is a powerful tool that can be widely used to manage complex temporal datasets, integrating legacy and live phenological information from diverse sources (e.g., conventional, digital cameras, seed traps) and temporal scales, improving our capability of producing scientific and applied information on tropical phenology.

Synphenology of Herb Layer of Carpinion betuli Community in the Báb Forest

The submitted paper presents the results of a phenological research of herb layer of Carpinion betuli community which was realized on the locality of the Báb forest in 2012 and 2013. During 2012, there were 25 species recorded in an herbal layer and for a phenological research in 2013, I was able to document 24 taxa. In 2012, the highest number of present species in a phenological spectrum occurred. Especially, it was during March and October. In 2013, the highest number of species in a phenological spectrum was found on March and April and also later from September to October which is a period when forest species start to grow because of a temperature increase and it also occurred in the autumn period when a repeated growth of forest species took place. In 2012, species in generative phases occurred from 11th March until 1st September and after that, taxa in vegetative phases were found only. In 2013, because of low March temperatures, it was possible to observe generative phases only since 22nd March until the last date of a phenological observation – 11th December. A change in the onset, interphase duration and ending of the monitored phenophases between the years 2012 and 2013 was observed. Specifically, a shift in the onset of the vegetative and generative phenophases was noted. This distinction was caused by a different average week temperature measured in 2012 and 2013. In March 2013 a significant temperature decrease and even ground frost occurred.

From observations to experiments in phenology research: investigating climate change impacts on trees and shrubs using dormant twigs.

Climate change is advancing the leaf-out times of many plant species and mostly extending the growing season in temperate ecosystems. Laboratory experiments using twig cuttings from woody plant species present an affordable, easily replicated approach to investigate the relative importance of factors such as winter chilling, photoperiod, spring warming and frost tolerance on the leafing-out times of plant communities. This Viewpoint article demonstrates how the results of these experiments deepen our understanding beyond what is possible via analyses of remote sensing and field observation data, and can be used to improve climate change forecasts of shifts in phenology, ecosystem processes and ecological interactions. The twig method involves cutting dormant twigs from trees, shrubs and vines on a single date or at intervals over the course of the winter and early spring, placing them in containers of water in controlled environments, and regularly recording leaf-out, flowering or other phenomena. Prior to or following leaf-out or flowering, twigs may be assigned to treatment groups for experiments involving temperature, photoperiod, frost, humidity and more. Recent studies using these methods have shown that winter chilling requirements and spring warming strongly affect leaf-out and flowering times of temperate trees and shrubs, whereas photoperiod requirements are less important than previously thought for most species. Invasive plant species have weaker winter chilling requirements than native species in temperate ecosystems, and species that leaf-out early in the season have greater frost tolerance than later leafing species. This methodology could be extended to investigate additional drivers of leaf-out phenology, leaf senescence in the autumn, and other phenomena, and could be a useful tool for education and outreach. Additional ecosystems, such as boreal, southern hemisphere and sub-tropical forests, could also be investigated using dormant twigs to determine the drivers of leaf-out times and how these ecosystems will be affected by climate change.

Models for forecasting the flowering of Cornicabra olive groves

This study examined the impact of weather-related variables on flowering phenology in the Cornicabra olive tree and constructed models based on linear and Poisson regression to forecast the onset and length of the pre-flowering and flowering phenophases. Spain is the world's leading olive oil producer, and the Cornicabra variety is the second largest Spanish variety in terms of surface area. However, there has been little phenological research into this variety. Phenological observations were made over a 5-year period (2009-2013) at four sampling sites in the province of Toledo (central Spain). Results showed that the onset of the pre-flowering phase is governed largely by temperature, which displayed a positive correlation with the temperature in the start of dormancy (November) and a negative correlation during the months prior to budburst (January, February and March). A similar relationship was recorded for the onset of flowering. Other weather-related variables, including solar radiation and rainfall, also influenced the succession of olive flowering phenophases. Linear models proved the most suitable for forecasting the onset and length of the pre-flowering period and the onset of flowering. The onset and length of pre-flowering can be predicted up to 1 or 2 months prior to budburst, whilst the onset of flowering can be forecast up to 3 months beforehand. By contrast, a nonlinear model using Poisson regression was best suited to predict the length of the flowering period.

Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment.

Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof. We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008–2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery. Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.

Back to the future for plant phenology research.

Back to the future for plant phenology research.

Phenology research for natural resource management in the United States

Natural resource professionals in the United States recognize that climate-induced changes in phenology can substantially affect resource management. This is reflected in national climate change response plans recently released by major resource agencies. However, managers on-the-ground are often unclear about how to use phenological information to inform their management practices. Until recently, this was at least partially due to the lack of broad-based, standardized phenology data collection across taxa and geographic regions. Such efforts are now underway, albeit in very early stages. Nonetheless, a major hurdle still exists: phenology-linked climate change research has focused more on describing broad ecological changes rather than making direct connections to local to regional management concerns. To help researchers better design relevant research for use in conservation and management decision-making processes, we describe phenology-related research topics that facilitate "actionable" science. Examples include research on evolution and phenotypic plasticity related to vulnerability, the demographic consequences of trophic mismatch, the role of invasive species, and building robust ecological forecast models. Such efforts will increase phenology literacy among on-the-ground resource managers and provide information relevant for short- and long-term decision-making, particularly as related to climate response planning and implementing climate-informed monitoring in the context of adaptive management. In sum, we argue that phenological information is a crucial component of the resource management toolbox that facilitates identification and evaluation of strategies that will reduce the vulnerability of natural systems to climate change. Management-savvy researchers can play an important role in reaching this goal.

The utility of distribution data in predicting phenology

Summary The phenology of many species has been shown to shift under climate change. However, because species respond at different rates, ecological communities may be disrupted leading to species extinctions and loss of ecosystem services. Hence, there is a need to monitor and understand phenological change. Population data, gathered by standardised monitoring schemes, can be used to this end. However, such schemes require significant organisation and financial resources. Distribution data (georeferenced biological records with dates) are easier and cheaper to collect and may be an unexploited resource for phenology analyses. This would allow analysis of more taxa from more regions of the world. However, distribution data are potentially biased due to the unstandardised behaviour of biological recorders. Here, the ability of distribution data record dates to accurately predict phenology is investigated by using the British butterfly fauna as a model system. We used the total number of distribution records per unit time across Great Britain as a proxy for butterfly abundance. Phenology metrics of mean flight date and flight period length were then calculated from the resulting abundance–time relationships for each year in a 15‐year time series. These estimates were validated against those generated from a standardised‐effort population monitoring scheme. We analysed 1 078 328 records from 30 British butterflies and found that distribution data accurately predicted the mean flight date for 22 of the 30 species tested. Flight period length was only predicted accurately for seven of 30 species. We found a nonlinear but consistent positive relationship between the accuracy of mean flight date estimates and sample size (number of records) at both inter‐ and intraspecific scales. Our results suggest that a threshold sample size of c. 6500 distribution records (430 per year) is a pragmatic compromise between accuracy and recording effort, leading to little loss of accuracy in phenology predictions (an average decrease in accuracy of 2·9 days was observed). The results suggest that distribution data are a potentially useful resource for phenology research. This may allow practitioners to monitor particular regions and previously unstudied species relatively cheaply using existing mapping schemes.

Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM + data

Observations of vegetation phenology provide valuable information regarding ecosystem responses to climate variability and change. Phenology is also a first-order control on terrestrial carbon and energy budgets, and remotely sensed observations of phenology are often used to parameterize seasonal vegetation dynamics in ecosystem models. Current land surface phenology products are only available at moderate spatial resolution and possess considerable uncertainty. Higher resolution products that resolve finer spatial detail are therefore needed. A need also exists for data sets and methods that link ground-based observations of phenology to moderate resolution land surface phenology products. Data from the Landsat TM and ETM+ sensors have the potential to meet these needs, but have been largely unexplored by the phenology research community. In this paper we present a method for characterizing both long-term average and interannual dynamics in the phenology of temperate deciduous broadleaf forests using multi-decadal time series of Landsat TM/ETM+ images. Results show that spring and autumn phenological transition dates estimated from Landsat data agree closely with in-situ measurements of phenology collected at the Harvard Forest in central Massachusetts, and that Landsat-derived estimates for the start and end of the growing season in Southern New England varied by as much as 4weeks over the 30-year record of Landsat images. Application of this method over larger scales has the potential to provide valuable information related to landscape-scale patterns and long term dynamics in phenology, and for bridging the gap between in-situ phenological measurements collected at local scales and land surface phenology metrics derived from moderate spatial resolution of instruments such as MODIS and AVHRR.

Variability within the 10-Year Pollen Rain of a Seasonal Neotropical Forest and Its Implications for Paleoenvironmental and Phenological Research

Tropical paleoecologists use a combination of mud-water interface and modern pollen rain samples (local samples of airborne pollen) to interpret compositional changes within fossil pollen records. Taxonomic similarities between the composition of modern assemblages and fossil samples are the basis of reconstructing paleoclimates and paleoenvironments. Surface sediment samples reflect a time-averaged accumulation of pollen spanning several years or more. Due to experimental constraints, modern pollen rain samples are generally collected over shorter timeframes (1–3 years) and are therefore less likely to capture the full range of natural variability in pollen rain composition and abundance. This potentially biases paleoenvironmental interpretations based on modern pollen rain transfer functions. To determine the degree to which short-term environmental change affects the composition of the aerial pollen flux of Neotropical forests, we sampled ten years of the seasonal pollen rain from Barro Colorado Island, Panama and compared it to climatic and environmental data over the same ten-year span. We establish that the pollen rain effectively captured the strong seasonality and stratification of pollen flow within the forest canopy and that individual taxa had variable sensitivity to seasonal and annual changes in environmental conditions, manifested as changes in pollen productivity. We conclude that modern pollen rain samples capture the reproductive response of moist tropical plants to short-term environmental change, but that consequently, pollen rain-based calibrations need to include longer sampling periods (≥7 years) to reflect the full range of natural variability in the pollen output of a forest and simulate the time-averaging present in sediment samples. Our results also demonstrate that over the long-term, pollen traps placed in the forest understory are representative samples of the pollen output of both canopy and understory vegetation. Aerial pollen traps, therefore, also represent an underutilized means of monitoring the pollen productivity and reproductive behavior of moist tropical forests.

Fenologi Mangrove (Rhizophora apiculata, R. mucronata dan R.stylosa) di Pulau Unggas, Air Bangis Pasaman Barat, Sumatera Barat

Phenology of tropical forest plant is a population factor and does not include community factor, this is caused by a genetic determinationand also the influence of the environment. Phenology of plants in mangrove correlated with the time of flowering, fruiting, and productionof fruits or propagules. Phenology research in mangrove plants in the Unggas Island, West Pasaman, done with ‘tagging’ technique method.Signs made of plastic and are numbered. Data collection included from the formation of flower blooming, up to the formation of fruit orpropagules ripe. Phase of the development is categorized to two categories: (a) flower, and (b) fruit. The analysis method is the analysis ofvariance (ANOVA, p<0.01) and post-hoc test, Duncans Multiple Range Test (DMRT, p<0.01) to obtain comparison and differencesbetween the components of flower, flower fall, fruit, and fruit maturity.

Detecting plant seasonality from webcams using Bayesian multiple change point analysis

Phenological observations have a long tradition. By contrast, digital webcam-based phenological research has only developed in recent years, prompted by the development of cheaper user-friendly digital camera systems and by higher staff costs. Webcam photography provides spectral information in red, green and blue (RGB) wavelengths which mirror the seasonal colour changes in trees during bud burst, leaf unfolding, senescence and leaf fall.Recent publications have mainly used two types of image data analysis to define onset dates of certain phenological stages and to compare species and growing seasons. These methods work well, but require high quality of the webcam images. However, changing light and weather conditions complicate data analysis particularly at increasing camera-to-subject distances. We investigated a series of images providing colour information on different tree species, e.g. Fagus sylvatica, Populus tremula, as well as of trees at different altitudes (700–1200m) in the Bavarian Forest National Park, Germany. Webcam images were analysed by the two previously published methods and compared with results derived from a newly developed Bayesian multiple change point analysis. In particular, transition dates of leaf development were identified in the green, as well as the red, colour channel.The Bayesian analysis described phenological transition dates in spring and autumn and specified the uncertainties of the model fit. By contrast, previously employed methods have shortcomings associated with unrealistic asymptotic assumptions in logistic model fits or inability to cope with noise in data series.The change point analysis at different elevations showed how the Bayesian approach coped with increasingly degraded image quality. A delay in green-up of about 2.5 days per 100m of altitude was estimated for Fagus sylvatica in the study area. Autumn phenology at different altitudes did not show clear patterns.The Bayesian model approach allows not only the calculation of phenological change points during the year but also estimates the probability of those changes occurring on a particular day. This method appears to estimate reliably phenological events in the growing season, especially when handling low quality webcam data, either from poor weather conditions or when the subject is at a considerable distance from the camera.

Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis

Phenological changes are closely related to the carbon cycle of terrestrial ecosystems, and satellite data have been widely used in large scale phenological research. Numerous methods have been developed to reconstruct distinct satellite derived vegetation signals from continuous vegetation index time series and to track the points corresponding to important phenological events. In this study, we perform a multiple-method investigation of the spring vegetation growth onset phenology in temperate China north of 30°N with NDVI (normalized difference vegetation index) data produced from SPOT satellites. The results indicated that the spring onset dates estimated from five different methods show similar spatial pattern along latitudinal or altitudinal gradients, but with significant variances in the dates. In most areas (76% of pixels), the standard deviation (SD) of the estimated spring onset dates from different methods is within one month, while in a few places (2% of pixels), mostly agricultural cropland, the SD is more than 60 days. Comparing the satellite derived green-up onset dates against the gross primary productivity (GPP) time series curves acquired from six eddy covariance towers further highlights the strengths and limits of different methods in estimating phenological events. In a vast region with diverse vegetation types and physical environment, it is critical to choose the “right” method for the “right” place. Generally climate factors such as temperature and precipitation play an important role in controlling the start of vegetation growth in temperate China. Yet this climate-phenology relationship varies largely across different regions and vegetation types.

Evaluation of recent trends in Australian pome fruit spring phenology

Temporal and temperature driven analyses were conducted for eight spring phenology datasets from three Australian pome fruit growing regions ranging from 24 to 43 years in length. This, the first such analysis for Australia, indicated significant temporal change in phenophase timing for only one of the datasets. To determine relationships to temperature, a sequential chill and growth method as well as mean springtime temperatures were used to estimate phenophase timing. Expected advancement of phenophase ranged from 4.1 to 7.7 days per degree Celsius increase in temperature. The sequential chill and growth approach proved superior, with coefficients of determination between 0.49 and 0.85, indicating the inclusion of chill conditions are important for spring phenology modelling. Compared to similar phenological research in the Northern Hemisphere, the changes in response variables were often shallower in Australia, although significance of observed hemispheric differences were not found.

The phenology of Rubus fruticosus in Ireland: herbarium specimens provide evidence for the response of phenophases to temperature, with implications for climate warming

To date, phenological research has provided evidence that climate warming is impacting both animals and plants, evidenced by the altered timing of phenophases. Much of the evidence supporting these findings has been provided by analysis of historic records and present-day fieldwork; herbaria have been identified recently as an alternative source of phenological data. Here, we used Rubus specimens to evaluate herbaria as potential sources of phenological data for use in climate change research and to develop the methodology for using herbaria specimens in phenological studies. Data relevant to phenology (collection date) were recorded from the information cards of over 600 herbarium specimens at Ireland's National Herbarium in Dublin. Each specimen was assigned a score (0-5) corresponding to its phenophase. Temperature data for the study period (1852 - 2007) were obtained from the University of East Anglia's Climate Research Unit (CRU); relationships between temperature and the dates of first flower, full flower, first fruit and full fruit were assessed using weighted linear regression. Of the five species of Rubus examined in this study, specimens of only one (R. fruticosus) were sufficiently abundant to yield statistically significant relationships with temperature. The results revealed a trend towards earlier dates of first flower, full flower and first fruit phenophases with increasing temperature. Through its multi-phenophase approach, this research serves to extend the most recent work-which validated the use of herbaria through use of a single phenophase-to confirm herbarium-based research as a robust methodology for use in future phenological studies.

Digital repeat photography for phenological research in forest ecosystems

Digital repeat photography has the potential to become an important long-term data source for phenological research given its advantages in terms of logistics, continuity, consistency and objectivity over traditional assessments of vegetation status by human observers. Red-green-blue ( RGB) color channel information from digital images can be separately extracted as digital numbers, and subsequently summarized through color indices such as excess green ( ExG = 2 G − [ R + B]) or through nonlinear transforms to chromatic coordinates or other color spaces. Previous studies have demonstrated the use of ExG and the green chromatic coordinate ( g cc = G/[ R + G + B]) from digital landscape image archives for tracking canopy development but several methodological questions remained unanswered. These include the effects of diurnal, seasonal and weather-related changes in scene illumination on ExG and g cc , and digital camera and image file format choice. We show that g cc is generally more effective than ExG in suppressing the effects of changes in scene illumination. To further reduce these effects we propose a moving window approach that assigns the 90th percentile of all daytime values within a three-day window to the center day ( per90), resulting in three-day ExG and g cc . Using image archives from eleven forest sites in North America, we demonstrate that per90 is able to further reduce unwanted variability in ExG and g cc due to changes in scene illumination compared to previously used mean mid-day values of ExG and g cc . Comparison of eleven different digital cameras at Harvard Forest (autumn 2010) indicates that camera and image file format choice might be of secondary importance for phenological research: with the exception of inexpensive indoor webcams, autumn patterns of changes in g cc and ExG from images in common JPEG image file format were in good agreement, especially toward the end of senescence. Due to its greater effectiveness in suppressing changes in scene illumination, especially in combination with per90, we advocate the use of g cc for phenological research. Our results indicate that g cc from different digital cameras can be used for comparing the timing of key phenological events (e.g., complete leaf coloring) across sites. However, differences in how specific cameras “see” the forest canopy may obscure subtle phenological changes that could be detectable if a common protocol was implemented across sites.