Species-specific spring and autumn leaf phenology captured by time-lapse digital cameras

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1 Species-specific spring and autumn leaf phenology captured by time-lapse digital cameras YINGYING XIE, 1,2, DANIEL L. CIVCO, 3 AND JOHN A. SILANDER JR. 1 1 Department of Ecology and Evolutionary Biology, University of Connecticut, 75 North Eagleville Road, Unit 3043, Storrs, Connecticut USA 2 Department of Geography, University at Buffalo, 105 Wilkeson Quadrangle, Buffalo, New York USA 3 Department of Natural Resources and the Environment, University of Connecticut, 1376 Storrs Road, Unit 4087, Storrs, Connecticut USA Citation: Xie, Y., D. L. Civco, and J. A. Silander Jr Species-specific spring and autumn leaf phenology captured by time-lapse digital cameras. Ecosphere 9(1):e /ecs Abstract. Plant leaf phenology is typically observed either via ground-based visual observations on individuals or via remote sensing of land surface vegetation. To integrate phenological information from both data sources, collected at different spatial scales using different observational protocols, digital cameras were deployed spanning canopy areas with enough spatial resolution to identify temporal changes in individual deciduous tree species with continuous observations. Comparisons of phenology between camera photography and in situ observations have been reported in prior studies; however, it is still unclear that how these camera images relate to field observations at individual and species levels, and how the metrics from those images provide comparable species-specific phenological responses to environmental variation. We set a suite of digital time-lapse cameras to acquire continuous photographs of deciduous tree canopies and conducted ground-based visual observations in Connecticut, USA, from 2012 to Comparisons between image-derived dates and observed phenological dates showed that both green and red color indices could be matched to ground observations, and red color indices showed good performance in matching autumn phenology across our group of eight tree species that dominate the southern New England forests. Linear mixed-effects models were applied to investigate the relationships between climatic/ weather conditions and the timing of peak and of intensity of red color in fall foliage for each species. Model results suggested that temperature, precipitation, drought stress in autumn, and heat stress in summer are all important factors to the timing of peak fall foliage color and that higher minimum temperatures (or lower cold degree-day accumulation) in the autumn are linked to higher intensity of red coloration at least in sugar maples. This study improves our understanding of temporal and spatial variation in the phenology of deciduous trees captured by digital cameras. As well, this provides insights into relating speciesspecific information on phenology from visual observations in the field to near-surface remote sensing and points to the need for further research on autumn phenology using the change in redness of tree canopies. Key words: model. color index; deciduous trees; drought and heat stress; fall foliage color; leaf senescence; mixed-effects Received 15 September 2017; revised 30 November 2017; accepted 4 December Corresponding Editor: Theresa M. Crimmins. Copyright: 2018 Xie et al. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. yingying.xie1@gmail.com 1 January 2018 Volume 9(1) Article e02089

2 INTRODUCTION Plant leaf phenology is the study of annual life-cycle events in plants from bud burst and leaf expansion in the spring to leaf coloration and leaf drop in the fall, which is typically observed through ground-based, visual observation on individuals in the field, or remote sensing of land surface vegetation by satellites (Zhang et al. 2003, Polgar and Primack 2011, Xie et al. 2015a, b). However, the different observation scales and protocols make the integration of ground-based observations and remotely sensed data difficult (Keenan et al. 2014). To build a bridge linking ground-based species phenology to remotely sensed multi-species land surface phenology, digital cameras have been used as a near-surface remote sensing tool to record seasonal change in forest canopy phenology (Richardson et al. 2007, 2009, Crimmins and Crimmins 2008, Sonnentag et al. 2012, Ide and Oguma 2013). One advantage of digital pheno-cameras is that they may compensate for time-consuming and necessarily periodic field observations and provide fine-scale spatiotemporal information within the large spatial extent of satellite remote sensing (Brown et al. 2016). Moreover, digital pheno-cameras cover areas with enough spatial resolution to identify temporal changes in individuals within canopies as well as groups of individuals forming a continuous canopy with unbroken daily observations. Comparisons between vegetation indices from satellite remote sensing and color indices from digital cameras suggest significant agreement of these two metrics (Hufkens et al. 2012, Keenan et al. 2014, Kobayashi et al. 2016). Phenological metrics derived via color indices from camera images have been compared in prior studies to in situ observations in a few sites (Keenan et al. 2014, Berra et al. 2016, Nagai et al. 2017) and visual assessment on camera images (Klosterman et al. 2014, Kosmala et al. 2016). However, how pheno-camera images relate to phenology observations in the field at individual and species levels has rarely been studied. More specifically, it is unclear whether species-specific phenological information is adequately captured by digital camera images, and whether or how the metrics from pheno-camera images are comparable to visually observed phenology among tree species and sites that can improve our understanding overall of species-specific phenological responses to environmental factors. Pheno-camera images typically record information in three color channels (red, green, and blue), which allows the detection of changes in greenness and redness of tree canopies across the growing season (Richardson et al. 2007, Bater et al. 2011). Previous studies applied the metrics derived from the changes in greenness and redness to indicate the timing of spring leafing out and fall leaf senescence (Sonnentag et al. 2012, Ide and Oguma 2013, Keenan et al. 2014) and the timing of fall foliage peak color during autumn senescence (Klosterman et al. 2014, Kosmala et al. 2016). The changes in leaves during autumn senescence include declining greenness and the accumulation (or uncovering) of red pigments (primarily anthocyanins and some carotenoids; Sanger 1971, Archetti et al. 2009). Chlorophyll is the visually dominant pigment in leaves showing green colors that mask yellow and orange colors of carotenoids in spring and summer. In the fall, with the beginning of leaf senescence, chlorophyll is degraded (chlorophyll a to b forms giving leaves a darker green color) and carotenoids are uncovered (various forms expressing yellow, orange, and sometimes reddish colors in the senescent leaves; Lee et al. 2003, Feild et al. 2011). At the same time, anthocyanin concentration (expressing red to purple colors) may build up in leaves typically in response to various abiotic and biotic stresses (e.g., nutrient deficiency, low ph, drought stress, insect damage, high UV light, cool temperatures; Christie et al. 1994, Dixon and Paiva 1995, Chalker-Scott 1999). Different forms of anthocyanins tend to be intensely red or even purple in color. These pigments also break down as leaves continue to senesce, nutrients are reabsorbed, and leaves turn brown with just tannins remaining as leaves drop (Close and Beadle 2003). These color changes of tree canopies are difficult to measure simply using ground-based, visual, phenological observations (Menzel 2002, Cleland et al. 2007). Ground visual observations on autumn phenology typically only report whether or not green leaves change color, though metrics of amount of autumn color have been developed based on ground observations (cf. Archetti et al. 2013). In contrast, pheno-cameras may have the ability to directly capture the 2 January 2018 Volume 9(1) Article e02089

3 changes in intensity of red or yellow colors providing more information to help quantify and understand fall foliage color changes (Klosterman et al. 2014, Kosmala et al. 2016). Quantifying timing and intensity of fall foliage color changes is important since fall leaf color changes have both ecological and economic impacts. The timing of leaf color change in fall is associated with other species activities via ecological synchrony, since both plant and animal lifecycling events may be trigged by similar abiotic and biotic factors, or they are directly or indirectly associated with each other. For example, migratory birds may use the visual cue of colored leaves in fall or the associated cues of declining fruit and insect abundance, along with decrease in temperature (Ellwood et al. 2015). The timing of leaf yellowing in trees appears to be more effective in tracking the phenology of brook trout spawning than models based only on abiotic factors (Pepino et al. 2013). The striking color of leaves displayed in autumn is a key element of fall foliage ecotourism (Rustad et al. 2011, Archetti et al. 2013, Morse and Smith 2015). Moreover, how climate and weather variation affects the leaf colors and the timing of fall foliage has gotten considerable public and scientific attentions recently (Tang et al. 2016). However, we know too little about the effects of environmental conditions on the timing or the change in brightness of fall foliage color because of inconsistencies in ground observation protocols and the complicated mechanisms associated with autumn phenology (Gallinat et al. 2015). Previous studies have focused primarily on the effects of temperature and photoperiod on timing of leaf coloration and leaf drop, but few studies have identified the relative importance of other contributing factors (e.g., drought, heat stress, rainfall events) or quantified their effects (Gallinat et al. 2015, Xie et al. 2015b). In addition, species differ in their phenological responses to environmental cues (Richardson et al. 2006, Primack et al. 2009, Polgar et al. 2014) and their fall foliage colors. To understand spatiotemporal phenological responses of forest communities to environmental conditions at a large spatial scale, it is necessary to integrate species-level responses (Diez et al. 2012, Xie et al. 2015a). To date, the application of phenocamera images on fall foliage color has been rare, though good correlations have recently been found between redness derived from camera images and anthocyanin indices (Liang et al. 2011, Yang et al. 2014, Liu et al. 2015). Thus, it is important to explore the capability of using metrics observed via pheno-cameras on foliage color changesinautumnfordifferentspeciestohelp understand how environmental factors affect autumn leaf senescence and coloration. This study aims to (1) capture seasonal leaf phenological changes in a suite of deciduous canopy tree species using time-lapse digital phenocameras; (2) examine the relationship between signals captured by digital cameras compared with ground-based, visual observation of leaf phenology; (3) examine different patterns of phenological change among different species using digital camera images; (4) investigate the relationships between climatic/weather conditions and the timing and intensity of fall foliage color derived from camera images across species and sites. MATERIALS AND METHODS Time-lapse camera monitoring and image pre-processing We set up time-lapse cameras (Wingscapes, that we term pheno-cameras to take digital photographs of deciduous forest tree canopies in nine sites in and around the forested landscapes of the University of Connecticut campus and Mansfield, Connecticut, USA, for three years from 2012 to 2014; these sites spanned a range of representative soil and topographic positions and forest community types. One camera facing the forest canopy was mounted on a metal pole at each site (e.g., Fig. 1a, b). Cameras faced west or north to avoid direct sun light in the camera lens as much as possible. Photographs were automatically taken hourly during the daytime across the whole growing season with a fixed white balance. Photographs were downloaded from the memory cards, and batteries were replaced once every two to three months. The resolution of the images, containing 4 8 canopytreesinthefield of view, was pixels (Fig. 1c f). This resolution allows one to identify species in the canopy and the associated ground-based phenophases (leaf out, leaf coloration, and leaf drop; cf. Appendix S1: Fig. S1) for each individual in the image (see a video showing seasonal change of tree canopies at one site in 2013, 3 January 2018 Volume 9(1) Article e02089

4 XIE ET AL. Fig. 1. Camera system and examples of ROI (region of interest) selection for four canopy trees at one site, Turf1, in (a) and (b) show the time-lapse camera and temperature data logger under the radiation shield. (c f) Four photographs show the seasonal change of tree canopies through the growing season from spring to autumn. (c) 10 May; (d) 18 July; (e) 2 October; (f) 21 October. ROI was selected to avoid overlap of multiple tree canopies. Numbers of ROI from the image indicate tree species. 1: red maple, 2: sugar maple, 3: white ash, 4: pignut hickory. Images were processed using PhenoCam GUI software developed by Andrew Richardson s research group in Harvard University ( unh.edu/webcam/tools/, Richardson et al. 2007, 2009). Regions of interest (ROIs) in each image series were designed to include specific areas for image processing, such as the whole canopy of the forest in the image, or individual canopies of specific trees (Fig. 1). Sky and shadow areas were avoided as much as possible from including in ROI, and the region with the purest canopy was chosen as the ROI for each individual tree in the image. Reflectance information in the images was represented by three color channels (red, green, and blue) digital numbers (DNs). The DN of each color channel was calculated using the PhenoCam software (Richardson et al. 2009) and averaged for all the pixels within the ROI for each image. Three color chromatic coordinates of red, green, and blue (rcc, gcc, and bcc) were calculated in each ROI in each image over time (Gillespie et al. 1987, Sonnentag et al. 2012) to represent relative brightness of three color channels. As the weather conditions (e.g., fog, rain, and snow) varied or darker times during the day or between days (e.g., clouds and shadows) lead to low values of DN producing spikes and variations in the time series of color chromatic coordinates, we used 90% quantile values of three-day moving window smoothed data to optimally extract daily color information and reduce the effects from dark or shadow areas in ROI with very low values (Richardson et al. 2009, Yang et al. 2014). This method calculated 90% quantile values of DN within every 3 d moving by 1 d to extract daily DN time series. The equations of three color chromatic coordinates are as follows: rcc ¼ R DN=ðR DN þ G DN þ B DNÞ (1) gcc ¼ G DN=ðR DN þ G DN þ B DNÞ (2) bcc ¼ B DN=ðR DN þ G DN þ B DNÞ (3) We also calculated time series of modified visible atmospherically resistant vegetation index 4 January 2018 Volume 9(1) Article e02089

5 (VARI; Vi~na and Gitelson 2011) for each ROI in the images by using three color channels as: VARI ¼ ðg DN R DNÞ ðr DN þ G DN B DNÞ (4) It has been shown that VARI is a good indicator of the relative content of anthocyanin in plant leaves, and this index may be used to detect plant phenological change over time in the autumn (Vi~na and Gitelson 2011). We used the same three-day moving window data smoothing method to obtain daily VARI time series. Phenological date determination and comparison from images and ground observations Time series of color indices from the phenocamera images were analyzed for eight dominant deciduous tree species of southern New England forests at nine sites (Table 1). Eight species (with total number of replicates) were as follows: Acer rubrum (red maple, 8), Acer saccharum (sugar maple, 7), Betula lenta (black birch, 1), Carya glabra (pignut hickory, 3), Carya ovata (shagbark hickory, 3), Fraxinus americana (white ash, 4), Quercus alba (white oak, 2), and Quercus rubra (red oak, 14). Based on the time series of g cc and r cc, leaf phenology in spring and fall were determined for each canopy tree. Abnormally high values in winter time, probably cause by snow, were removed, when we determined phenological dates, to reduce error. The start of season (SOS) was determined as the day on which g cc started to increase by at least 5% of the amplitude of g cc (i.e., difference between minimum and maximum values) on next day in spring (Fig. 2a). The end of season (EOS) was determined as the day on which g cc reached the minimum value in autumn (Fig. 2a). To capture more information on changes of tree foliage color in autumn, we used time series of r cc to determine the day on which r cc reached to the maximum value, which indicates the timing of the peak of redness (POR) of fall foliage (Fig. 2b). We calculated the deviation of redness (DOR), which is the difference between maximum and minimum values of r cc in autumn, to represent how much redness was expressed (i.e., intensity of redness) in each tree in each year (Fig. 2b). We determined the day after POR date on which r cc decreased from the maximum value to at most 5% of amplitude of r cc in autumn (i.e., DOR) as the end of redness date (EOR; Fig. 2b). We also used the minimum value of VARI in autumn as another indicator to determine the day of the peak of redness (POR.vari) in tree canopies. This is because the value of VARI has been shown to be negatively correlated with relative content of anthocyanins (Vi~na and Gitelson 2011), which reflects a decrease in VARI during autumn when anthocyanins are produced. By comparing POR (peak of redness) derived from rcc with POR.vari, we tested how POR matched with the apparent Table 1. Replicate locations of tree canopies captured by time-lapse cameras in nine sites in and around UCONN Forest and nearby sites. Site name Latitude Longitude Elevation (m) Species and number of replicates ASP Red maple (Acer rubrum, 1), sugar maple (Acer saccharum, 1), white ash (Fraxinus americana,2) CRP Pignut hickory (Carya glabra, 2), red oak (Quercus rubra,2) HBP Shagbark hickory (Carya ovata, 3), red oak (Quercus rubra,3) RMP Red maple (Acer rubrum, 4), red oak (Quercus rubra,2) SMP Sugar maple (Acer saccharum,4) JohnC Red maple (Acer rubrum, 2), red oak (Quercus rubra,3) Fenton Red oak (Quercus rubra,2) Turf Red maple (Acer rubrum, 1), sugar maple (Acer saccharum, 1), white ash (Fraxinus americana, 1), pignut hickory (Carya glabra, 1), white oak (Quercus alba,1) Turf Sugar maple (Acer saccharum, 1), black birch (Betula lenta,1), white ash (Fraxinus americana, 1), white oak (Quercus alba, 1), red oak (Quercus rubra, 2) Note: Ground phenology observation at four sites (CRP, HBP, RMP, and SMP) parallel time-lapse camera monitoring. Sites with parallel phenology ground observations on trees. 5 January 2018 Volume 9(1) Article e02089

6 Fig. 2. Example of determining end of season (EOS) and start of season (SOS) from time series of g cc (a), peak of redness (POR) and end of redness (EOR) from r cc (b). Data were from one red maple tree at one site (Turf1) in peak timing of relative anthocyanin in leaves and how POR.vari may provide a slightly different metric of autumn leaf coloration than POR. We had parallel, ground-based visual observations on leaf phenology for each of these eight tree species measured twice weekly in spring and autumn from 2012 to 2014 replicated at four sites following the modified protocols from the USA National Phenology Network (Denny et al. 2014; see Appendix S1: Table S1; Fig. 1; and Xie et al. 2017). Proportion of canopy in leaf unfolding, leaf coloration, and leaf drop for each tree canopy were observed and scored visually. Phenological transition dates (i.e., onset, peak, and end dates) of leaf unfolding, leaf coloration, and leaf drop of each tree were determined using the method modified from Zhang et al. (2003). We fitted logistic curve for the percentage value over time and identified the day with the maximum (in increasing curve) or minimum (in decreasing curve) change of curvature as the onset and end phenological dates (Appendix S2: Fig. S1). The onset and end dates indicated the start of increase in observed proportion of canopy and the end of decrease in observed proportion during the change period in spring and autumn (see an example in Appendix S2: Fig. S1). We also determined the day at the inflection point of the logistic curve as the peak phenological date (Appendix S2: Fig. S1). We compared the visually observed leaf phenology of each tree in the field with the phenological dates derived from pheno-camera images to help us understand how ground-based visual observations relate to the dates derived from phenocamera monitoring of the same canopy trees. Observed leaf phenology dates included the onset, peak, and end dates for each of: leaf unfolding, leaf coloration, and leaf drop. Imagederived dates included SOS, EOS, POR, and EOR. Comparison for spring phenology was between three phenological transition dates of leaf unfolding and SOS. For autumn phenology, we conducted comparisons between the onset, peak, and end dates of leaf coloration and EOS and POR, and between the onset, peak, and end dates of leaf drop and EOS and EOR. For each pair of comparisons, an intercept-only model was fitted and the average date offset from the 1:1 line was calculated between dates derived from time series of color indices and visually scored observations to show how comparable the dates were for all tree species and sites sampled using visual scoring vs. continuous pheno-camera observation methods. Environmental factors We set shielded temperature data loggers ( ua ) on wood stakes (about 1 m height) in the field with radiation shields (Fig. 1b), to record hourly air temperature at the nine sites, then calculated daily mean, minimum, and maximum temperatures. Daily precipitation data were obtained from the nearest weather station at Storrs, Connecticut, as part of the United States Historical Climatology Network ( gov/epubs/ndp/ushcn/ushcn.html), which is about 2 5 km away from the pheno-camera sites. To investigate the relationships between environmental factors and leaf phenology, we developed a list of climatic/weather variables across three seasons (spring, summer, and autumn) to represent chill, frost, wet conditions, and extreme 6 January 2018 Volume 9(1) Article e02089

7 weather events (e.g., heat- and drought stress, and heavy rainfall events; cf. Table 2; Xie et al. 2015b). We used monthly minimum temperature (T min ) and cold degree-day (CDD and CDDi) to represent the chill conditions (Richardson et al. 2006, Archetti et al. 2013). Cold degree-day was calculated based on daily mean temperature, and CDDi was calculated using daily minimum temperature. We calculated monthly (August, September, and October) accumulative CDD and CDDi using three different base temperatures (10, 15, and 20 C; Richardson et al. 2006, Dragoni and Rahman 2012) to determine which period of CDD and CDDi with what base temperature may best explain leaf phenology (cf. Xie et al. 2015b). Our hypothesis is that in addition to chilling conditions in early fall period, other weather conditions throughout the whole growing season from spring to autumn may all have effects on fall phenology. Thus, we also developed frost days (FD), hot days (HD), growing season drought (GDR), rainy days (RD), heavy rainy days (ECA) to represent plant-relevant environmental stressors, which potentially affect tree performance (Niinemets 2010, Duque et al. 2013, Wilson and Silander 2014, Xie et al. 2015b). We calculated three sets of weather variables, GDR, RD, and heavy rainy days, for three periods (1 May to 30 June, 1 July to 31 August, and 1 September to 31 October) as proxies for spring, summer, and autumn weather conditions (cf. Xie et al. 2015b). Hot days only occurred in July and August in the study sites. Frost days were calculated for two months, September and October. Statistical analysis We applied linear mixed-effects models to investigate how climatic/weather factors affect the timing of the peak of fall foliage color of canopy trees. The mixed-effects modeling framework allowed us to fit both inter-annual variation and species/site variations in one model. The linear mixed-effects model is written as: Y i ¼ X i b þ Z i c i þ e i (5) In Eq. 5, Y i represents response variable for the ith subject. X i b is the fixed component of the model, and Z i c i is the random component of the model. X i is matrix including fixed variables. b contains fixed parameters of fixed variables. Z i is a design matrix that represents the known values, which have effects on the continuous response variable that varies randomly across subjects. c i and e i are assumed to be independent and normally distributed (West et al. 2007, Zuur et al. 2009, Greven and Kneib 2010). In this study, the response variable is the phenological date, POR, for each individual tree. The fixed variables are climatic/weather variables, which explained interannual variation in phenology. The random components include random intercept at species and site levels, and random slopes at the species level. Random intercept explained variation in phenology among species (i.e., early species vs. late species) and sites (e.g., variation related to wet site vs. dry site). Random slopes at the species level explained species-specific phenological responses in autumn to variation in climate/weather factors (i.e., sensitive species vs. non-sensitive species). We used a top-down strategy (Diggle et al. 2002, West et al. 2007, Zuur et al. 2009) to build models and selected the best models using marginal Akaike Information Criterion (maic) for fixed effects and conditional AIC (caic) for random effects (Greven and Kneib 2010, see model selection procedures detailed in Appendix S2). Data Table 2. Weather/climate variables developed to summarize and explain phenological responses in tree leaf canopies. Name (abbreviation) Description Unit Cold degree-day (CDD & CDDi) (T b T i )& (T b T min ) Degree-day T min Monthly mean minimum temperature C Hot days (HD 35 ) Number of days with T max 35 C Days Frost days (FD) Number of days with T min 0 C Days Growing season drought (GDR) Number of events when 7 consecutive days without precipitation Number of events Rainy days (RD) Number of days with precipitation 2 mm Days Heavy rainy days (ECA) Number of days with precipitation 20 mm Days Note: T b, base temperature; T i, daily mean temperature; T max, daily maximum temperature; T min, daily minimum temperature. 7 January 2018 Volume 9(1) Article e02089

8 were analyzed using R software (R Core Team 2015). Best models based on AIC criteria were reported. We also applied linear mixed-effects models to DOR (deviation of redness) to investigate the relationships between climatic/weather factors and intensity of redness for each tree canopy. We used random intercept at the individual tree level in the linear mixed-effects models for DOR and used climatic/weather factors as fixed variables. Models were fitted for eight species together and three species separately (red maple, sugar maple, and red oak) that had most replicates in the dataset and are known to display red-hued foliage in the autumn (Coder 2008). RESULTS Time series and phenological dates derived from images for eight deciduous tree species Color indices generated from pheno-camera images represent seasonal changes of deciduous trees species over the growing season. All tree species had similar patterns of change in g cc (Fig. 3) as the seasonal change of greenness in tree canopies. Increases in g cc in spring reflect the developmental stages of canopy tree leaves from bud burst and leaf unfolding with chlorophyll development through to full leaf expansion, while the decrease in g cc in autumn indicated the process of leaf coloration change associated with leaf senescence and chlorophyll degradation through to leaf drop. During the summer, g cc decreased slightly with the green color of leaves becoming darker as chlorophyll concentrations changed with maturity over the summer (Shull 1929). Among eight dominant deciduous tree species, their start of seasons (SOSs) differed by 0 9 d, but the differences in EOSs were much larger, varying from 3 to 30 d (Table 3; Appendix S1: Table S2). The period between SOS and EOS can be used as a proxy of growing season length for these deciduous tree species. Consistent differences of growing season length among deciduous tree species (e.g., shorter growing season length of maples [ d on average] than oaks [ d on average]) were found. Moreover, these differences were mostly attributed to the large differences among species in the end of growing season (EOS). All tree species showed similar patterns in change of r cc across the growing season. While r cc started to increase in spring matching the increase in g cc, r cc increased most dramatically in the fall reaching to the maximum value corresponding to the peak of red color (POR) of fall foliage. After the peak, r cc decreased as leaves turned brown and dropped (Fig. 4). The changes of r cc during the growing season also reflect species-specific differences in leaf pigment production. In our eight species, PORs and EORs of maples, white ash, and black birch were always earlier than oaks and hickories. The differences of PORs and EORs between white ash and other species were more than 20 d (Table 3; Appendix S2: Table S1). In addition, we found that the changes in VARI for all tree species were strongest in autumn (Appendix S2: Fig. S2) reflecting the leaf color change primarily associated with the production of anthocyanin in leaves. We also found that POR from r cc time series data was highly correlated (Pearson s r = 0.92) with POR.vari derived from VARI (Appendix S2: Fig. S3); this suggests that r cc and POR may be good proxy of changes of anthocyanins in leaves. Comparisons of image-derived and visually observed phenological dates Comparisons of spring phenology between leaf unfolding based on visual scoring and SOS based on pheno-camera g cc metrics showed that SOS was 3.3 d later on average than the onset date of visually based leaf unfolding, but 1.6 d earlier on average than the peak date of leaf unfolding observed visually across all tree species (Fig. 5). In comparing autumn phenophases, we found that EOS derived from g cc metrics was, respectively, 24.3 d and 11.5 d later on average than the visually observed onset and peak of leaf coloration, but 1.2 d earlier than the visually based end date of leaf coloration (Fig. 6a c). Peak of redness derived from r cc metrics was, respectively, 21.5 and 8.2 d later on average than the visually observed onset and peak of leaf coloration, but 4.3 d earlier than the visually based end date of leaf coloration (Fig. 6d f). Both EOS and POR match well with the end date of leaf coloration, but POR had more data points falling on the 1:1 line than EOS based on the distributions of data in the figures (Fig. 6c, f). In the comparisons of timing of leaf drop, both the intercept-only regression lines and the values of averaged distance to 1:1 line suggested that, while EOS matched well with the peak date of 8 January 2018 Volume 9(1) Article e02089

9 Fig. 3. Time series of g cc for one example of an individual tree from each of eight species in one growing season shown along with ground observation for five of these eight species. (a) red maple (RMP 2014), (b) sugar maple (SMP 2012), (c) white oak (Turf1 2013), (d) red oak (RMP 2014), (e) pignut hickory (CRP 2014), (f) shagbark hickory (HBP 2014), (g) white ash (Turf1 2013), (h) black birch (Turf2 2012). Small gray dots are hourly raw data, and bigger black dots are smoothed data. Variation in g cc in raw data is due to variation in weather and light conditions. The two vertical dashed lines indicate SOS and EOS. Ground observation data (colored circles), and the associated fitted lines, were converted to the scale of g cc for each individual for direct comparison with g cc time series, showing leaf unfolding (green), leaf coloration (red), and leaf drop (orange). The conversion scale was slightly different for each tree. Colored curves are fitted logistic curves from ground observations. leaf drop, EOR matched well with the end of leaf drop (Fig. 7b, f). End of season was 10.5 d later than the onset of leaf drop, and was 12.2 d earlier than the end of leaf drop, but it was only 0.9 d earlier than the peak of leaf drop (Fig. 7a c). End of redness was 19.9 and 8.5 d later than the onset and peak of leaf drop, but was 2.8 d earlier than the end of leaf drop (Fig. 7d f). 9 January 2018 Volume 9(1) Article e02089

10 Table 3. Mean values (and standard deviation) of start of season (SOS), end of season (EOS), peak of redness (POR), and end of redness (EOR) in Julian calendar days derived from camera images for all replicates of eight deciduous tree species ( ). Species SOS EOS POR EOR Red maple 123 (5.2) 291 (11.3) 285 (9.2) 301 (6.3) Sugar maple 123 (3.7) 288 (11.6) 282 (10.0) 296 (10.5) Black birch 122 (5.2) 296 (3.0) 287 (3.0) 303 (7.5) Pignut hickory 123 (5.1) 300 (5.6) 289 (7.7) 305 (4.4) Shagbark hickory 131 (3.3) 291 (4.7) 291 (4.0) 304 (3.4) White ash 128 (5.0) 283 (11.2) 266 (7.5) 282 (6.7) White oak 129 (4.5) 301 (7.7) 293 (9.2) 313 (5.9) Red oak 129 (3.8) 303 (7.8) 296 (7.7) 312 (9.0) Though POR and POR.vari were highly correlated, comparison between POR and POR.vari suggested that there is an offset (about 2 d) between the two dates (Appendix S2: Fig. S3). The reason for the offset could be that the time series of r cc and VARI were measuring slightly different colors, though both of them may be proxies for relative content of anthocyanins. Data uncertainties could also be another reason leading to the offset. We found that r cc -based POR has a high correlation with POR.vari likely reflecting the timing of the peak of relative anthocyanins in leaves (Appendix S2: Figs. S2, S3). Effects from climatic/weather conditions on fall foliage peak color and intensity of red color in eight deciduous tree species Linear mixed-effects models for POR suggested that chill, frost, rainfall, and drought in the autumn were the most important factors affecting the timing of peak of red color in fall foliage. The best models of POR included frost in October (FDOct) and rainfall in autumn (RD [1 September 1 31 October]) in the fixed effects, drought in autumn (GDR [1 September 31 October]) in the random slopes component (i.e., species-level effects), and random intercepts component (i.e., site-level effects). This model explained about 69% of variance in POR dates for all species (R 2 = 0.69). The model suggested that more FDOct and rainfall in autumn (RD [1 September 31 October]) caused 0.8 and 0.7 d earlier peak of red color in fall foliage on average for all species, respectively. But different species showed different responses in POR to drought in autumn (GDR [1 September 31 October]; Table 4). More autumn drought delayed POR for two species (A. rubrum and Q. alba), but lead to earlier POR for five other species (A. saccharum, B. lenta, C. ovata, F. americana, and Q. rubra). The effects of autumn drought on POR for pignut hickory (C. glabra) were minimal (coefficient is 0.002). In addition, the model selection procedures showed that a few models had very similar values of AIC and Bayesian Information Criterion (BIC) with differences smaller than 2 (Appendix S2: Table S2), which means these models had very similar performances in explaining the peak date of fall foliage color. This suggested that all variables in these models may be important in explaining variation in autumn phenology. Summer heat stress, minimum temperature, and chill (CDDi) in September may also be factors affecting the timing of peak color in fall foliage. More summer heat, lower minimum temperature in September, or more chill in September leads to earlier peak of color in fall foliage. Large variation in DOR was found among species and individual replicate trees. Deviation of redness is one measurement of the intensity of redness in autumn foliage. Black birch, maples, and oaks had larger DORs than other species, and within each species, the variation of DOR among years was much smaller than among replicate trees in different sites (Table 5), which indicates that there is a strong individual tree (i.e., genotype or site) effect. Linear mixed-effects models revealed no significant relationships with environmental explanatory variables except for sugar maples. Cold degree-day (CDDi) and monthly minimum temperatures in September and October (T min -Sep; T min -Oct), and FD in October (FDOct) did have significant effects in explaining variation in DOR for sugar maples (Table 6). The coefficients in the models suggested that lower levels of cold degree-day accumulations and higher minimum temperatures in September and October lead to higher intensity of red leaves (DOR values) in sugar maples in the fall, while more frosts in October decreased DOR values in sugar maples. DISCUSSION This study used digital pheno-cameras to monitor seasonal changes of deciduous tree canopies 10 January 2018 Volume 9(1) Article e02089

11 Fig. 4. Time series of r cc for one example of an individual tree from each of eight species in one growing season shown along with ground observation for five of these eight species. (a) red maple (RMP 2014), (b) sugar maple (SMP 2012), (c) white oak (Turf1 2013), (d) red oak (RMP 2014), (e) pignut hickory (CRP 2014), (f) shagbark hickory (HBP 2014), (g) white ash (Turf1 2013), (h) black birch (Turf2 2012). Small gray dots are hourly raw data, and bigger black dots are smoothed data. The two vertical dashed lines indicate POR and EOR. Ground observation data (colored circles), along with the associated fitted lines, were converted to the scale of r cc for each individual for direct comparison with r cc time series, showing leaf coloration (red) and leaf drop (orange). The conversion scale was slightly different for each tree. Colored curves are fitted logistic curves from ground observations. foliage color from spring through autumn. The objectives were to investigate how foliage color indices derived from digital camera images compared with ground-based visual observations of phenology on multiple tree species, and to examine how timing and intensity of fall foliage color may be affected by a suite of environmental factors. We found the seasonal changes in greenness 11 January 2018 Volume 9(1) Article e02089

12 Fig. 5. Comparison plots between three phenological transition dates of leaf unfolding and start of season (SOS). The dashed line is 1:1 line. The solid line is fitted line by intercept-only model. Y-axis is pheno-cameraderived SOS, and X-axis is visually observed leaf unfolding dates. (a) the onset date of leaf unfolding, (b) the peak date of leaf unfolding, and (c) the end date of leaf unfolding. Values of i and d in each panel are the intercept value of solid line (days later [+] or earlier [ ]) and the averaged perpendicular distance from dots to 1:1 line. Shapes indicate five tree species: pignut hickory, red maple, + red oak, M shagbark hickory, sugar maple. Fig. 6. Comparison plots between three phenological transition dates of leaf coloration and end of season (EOS) and peak of redness (POR). The dashed line is 1:1 line. The solid line is fitted line by intercept-only model. X-axis is visually observed leaf coloration date, and Y-axis is the pheno-camera-derived EOS or POR. (a) EOS vs. the onset date of leaf coloration, (b) EOS vs. the peak date of leaf coloration, (c) EOS vs. the end date of leaf coloration, (d) POR vs. the onset date of leaf coloration, (e) POR vs. the peak date of leaf coloration, and (f) POR vs. the end date of leaf coloration. Values of i and d in each panel are the intercept value of solid line (days later [+] or earlier [ ]) and the averaged perpendicular distance from dots to 1:1 line. Shapes indicate five tree species: pignut hickory, red maple, + red oak, M shagbark hickory, sugar maple January 2018 Volume 9(1) Article e02089

13 Fig. 7. Comparison plots between three phenological transition dates of leaf drop and end of season (EOS) and end of redness (EOR). The dashed line is 1:1 line. The solid line is fitted line by intercept-only model. X-axis is visually observed leaf drop date, and Y-axis is the pheno-camera-derived EOS or EOR. (a) EOS vs. the onset date of leaf drop, (b) EOS vs. the peak date of leaf drop, (c) EOS vs. the end date of leaf drop, (d) EOR vs. the onset date of leaf drop, (e) EOR vs. the peak date of leaf drop, (f) EOR vs. the end date of leaf drop. Values of i and d in each panel are the intercept value of solid line (days later [+] or earlier [ ]) and the averaged perpendicular distance from dots to 1:1 line. Shapes indicate five tree species: pignut hickory, red maple, + red oak, M shagbark hickory, sugar maple. (g cc ) and redness (r cc ) seen in time series across the growing season reflect what has been found in changes in leaf chlorophyll, carotenoids, and anthocyanin pigments measured over time in Table 4. Coefficient values of growing season drought (GDR; 1 September 31 October) as random slopes for eight species in the best models of peak of redness (POR). Species Coefficient of GDR (1 September 31 October) Red maple 0.09 Sugar maple 0.61 Black birch 0.04 Pignut hickory Shagbark hickory 0.12 White ash 0.18 White oak 0.06 Red oak 0.10 some of these same species by others researchers published elsewhere (Sanger 1971, Dixon and Paiva 1995, Lee et al. 2003, Coder 2008) and are directly comparable with pheno-camera indices developed by others (Keenan et al. 2014, Yang et al. 2014). The slight decrease in g cc during summer may be related to the changes in chlorophyll a and b concentrations and the associated reflectance differences that are known to change over the growing season (Shull 1929, Sanger 1971, Lee et al. 2003). The increase in r cc in spring may be mainly caused by the synthesis of reddish carotenoids (Sanger 1971, Garcia-Plazaola et al. 1997). But the most dramatic increases in redness occurred in the fall, most likely reflected elevated anthocyanin levels in response to environmentally driven genetic regulation of anthocyanin biosynthesis by associated response gene in plants (Dixon and Paiva 1995, Coder 2008). We also found important environmental factors that 13 January 2018 Volume 9(1) Article e02089

14 Table 5. Mean values (and standard deviation) of deviation of redness (DOR), an intensity of redness metric derived from camera images for all replicates of eight deciduous species ( ), and the range of variation of DOR among replicate trees in each year and among years for each individual tree within each species. Species DOR Range of variation among trees (910 3 ) Range of variation among years (910 3 ) Red maple 0.13 (0.11) Sugar maple 0.16 (0.10) Black birch 0.21 (0.08) 6 6 Pignut hickory 0.08 (0.05) Shagbark hickory 0.12 (0.05) White ash 0.10 (0.07) White oak 0.16 (0.07) Red oak 0.13 (0.07) Note: Due to the missing data and small sample size, there is no range value for black birch. affected timing and color intensity of fall foliage, including temperature, rainfall, frost, and drought stress in autumn. Diverse species-specific sensitivities of fall leaf coloration to drought stress may reflect the diversity in physiology and adaptation strategy among deciduous tree species in temperate forest (Diez et al. 2012). Our findings suggested more complex response mechanisms for fall phenology at species level, than spring phenology, which is mainly driven by thermal conditions (Cleland et al. 2007, Polgar et al. 2014, Xie et al. 2015a). Comparison of phenological dates between camera images and ground observation We found that color indices and phenological dates derived from digital camera images can be Table 6. Fixed variables in the random intercept models for deviation of redness (DOR) of sugar maples. Fixed structure of LME Coefficient Standard error P-value DOR ~ T min -Sep DOR ~ T min -Oct DOR ~ CDDiSep DOR ~ FDOct Notes: Random effects are intercepts at the individual tree level. For variables names, refer to Table 2. Month abbreviations and numbers in each variable indicate the time period for each variable or the base temperature in the calculations. used to determine plant phenological stages at the species level. The time series of green and red color indices reflect the differences in timing of developmental stages in spring and autumn among species (Inoue et al. 2014). Our comparisons between image-derived dates and visually observed phenological dates provide insights into how digital camera monitoring relates to ground-based visual observations. Digital cameras captured similar patterns of changes in forest canopy as visual observations, but derived slightly different metrics for leaf phenology in spring and autumn. We found that imagederived SOSs (i.e., increase in g cc values form a minimum level in early spring) correspond to the development stage of trees between the onset and the peak of leaf unfolding. The reasons for this temporal offset are likely that digital cameras can only detect the change of greenness above some color sensitivity threshold of the sensors in the camera at a given pixel resolution. The onset of leaf unfolding and bud burst seen up close visually probably does not provide sufficiently large enough change in greenness for the cameras to detect. This would result in a later, camera-detected SOS than is seen visually close up at the start of leaf unfolding (Fig. 5a). Similarly, the SOS detected by camera will occur before the end of leaf unfolding detected visually; one is measuring when most buds unfolded the first leave, hence the offset from the 1:1 line (Fig. 5c). Among the eight tree species, red maple and red oak had the bigger offsets than other species (Fig. 5). This may be related to the reddish-green new leaf colors seen in red maples and red oaks, and the cameras may not capture the small change in greenness from new leaves. In addition, both red maples and red oaks have flushtype leaf emergence, which means that almost all leaves flush simultaneously and immediately after bud break (Kikuzawa 1983). Another factor causing deviations from the 1:1 line would be the uncertainties in ground observations. With only two observations per week, one may not adequately capture the exact timing of fast changes in bud burst and leaf unfolding in spring dates, which are estimated from interpolation (Appendix S2: Fig. S1). This may cause some deviation from the real phenological dates that may be more accurately captured via continuous visual or digital monitoring January 2018 Volume 9(1) Article e02089

15 For autumn phenology, we developed a set of new color indices (POR and EOR) to capture the change in redness of tree foliage canopies over time. In comparisons with ground-based visual observation, we found that both POR and EOS matched well with visually scored observations on the end of leaf coloration, and EOS matched well with the peak of leaf drop, while EOR matched well with the end of leaf drop. In autumn, when onset of leaf coloration was detected visually, most leaves on tree canopies were still green. The visually observed peak of leaf coloration represented the timing when leaves changed color most quickly. However, POR and EOS measured the timing of fully changed leaf color with the highest proportion of redness and lowest proportion of greenness present. These factors likely contributed to the temporal offset between the visually scored onset and peak of leaf coloration and the image-based POR and EOS (Fig. 6). In terms of leaf drop, when leaves were observed to start dropping (onset), color changes in greenness and redness were probably not great enough to be captured by the cameras integrating across the entire canopy ROI; leaves can start dropping when most or all of the leaves are still green. But when about half of canopy lost leaves (peak of leaf drop), this corresponded to the EOS with the lowest proportion of greenness. When the canopy trees were seen to drop almost all of their leaves (end of leaf drop), this corresponded to the timing of EOR, since very low quantities of red colored leaves would have been detected by the camera. These are probably the main causes for the temporal offset shown in Fig. 7. Among eight tree species, sugar maple always had the biggest offset between ground-based and camerabased phenological dates (Figs. 6, 7). One explanation for the large offset seen in sugar maples may be related to the leaf senescence patterns that this species typically shows: Leaf senescence generally starts from either the top or outer canopies of sugar maple trees (Koike et al. 2001). However, the ROI of our cameras mainly captured the upper parts of the tree canopy (cf. Fig. 1) to avoid the overlap among tree canopies in the images, while visual observations focus on the whole tree crown. That is probably why camera-based dates were always earlier than ground-based fall phenological dates for sugar maple. Our methodology provides a direct, quantitative means of comparison between visual observations of phenological changes in forest tree species over the course of the growing season and corresponding changes detected with remotely sensed camera images. Potential biases may exist in visually scoring autumn phenophases. For example, considerable challenges are afforded in visually scoring trees consistently over time with continuously varying proportions of the canopy that are green and/or colored and/ or with dropped leaves. Also there will be ambiguities in visually scoring leaf drop for individuals of those species that may retain some leaves throughout the winter (e.g., beech and some oaks). In contrast, the camera is consistent and objective in providing continuous, quantitatively based phenological metrics over time. Of course, there are also some biases in the image-based leaf phenology metrics, as discussed above. The offsets between visually based phenological observations and camera-based observation provides a direct means of relating phenological metrics based on two different systems while accounting for the bias and errors in each (cf. Figs. 5 7). The comparison on the peak foliage color date in fall has been done between visual assessment on camera images and time series estimate from time-lapse imagery, which suggested good matches (Klosterman et al. 2014, Kosmala et al. 2016). To our knowledge, our study for the first time compares leaf coloration from ground observation and peak color date derived from time-lapse imagery at the species and individual levels. Our findings point out the potentially valuable application of redness measures in autumn phenology and that the change in redness in tree canopies may provide an informative measure of the biological processes of autumn phenology. The application of redness changes in leaf phenology to study fall phenology and the environmental control thereof at species level remains rare (Sonnentag et al. 2012, Ide and Oguma 2013, Keenan et al. 2014). Thus, while we suggest using g cc -based SOS and EOS indices as proxies of the start and end of growing season, we also encourage the use of r cc -based, POR and EOR as continuous, objective, quantitative indices to investigate autumnal changes of foliage senescence in temperate deciduous trees January 2018 Volume 9(1) Article e02089