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Site selectionWe selected 104 sites covering a range of ecological, land cover, and climate conditions across North America (Table 1). These sites were selected because they are part of either the National Ecological Observatory Network (NEON) or AmeriFlux network, all have PhenoCams, and each has at least one year of available flux data between 2017 and 2021. Among the included sites, 44 are part of the NEON.Table 1 List of AmeriFlux and NEON sites included in the dataset. .Full size tablePlanetScope image database compilationThe LSP metrics included in the dataset are derived from a database of daily 3 m PlanetScope imagery. To compile this database, a Python script was created to search, request, and download imagery using Planet’s RESTful API interface (https://developers.planet.com/docs/apis/data/). For each site, the area of interest (AOI) was defined using a GeoJSON file that prescribed a 10 by 10 km box centered over the flux tower at each site. Each GeoJSON was then used to submit search requests to the API. As part of the search process, the following filters were applied to ensure that good quality images with adequate clear sky views and high-accuracy geolocation were downloaded: (1) quality category identified as ‘standard’; (2) cloud cover less than or equal to 50%; and (3) ground control is ‘true’. Filtering was performed using all available PlanetScope ‘PSScene4Band’ imagery from 2016 to 2022. Once the API completed the search, the Python script read the search results, submitted orders, and the selected imagery was downloaded from Planet’s cloud-based system to local storage. During execution of the Python script, a log file was created to keep track of successful and failed orders. If an order failed, the script was run again targeting the specific order that failed. The resulting dataset included over 1.8 million unique files with, on average, 3,885 scene images for each site (i.e., the number of images, on average, that overlap part of each 10 by 10 km site), and had a total volume of 62.2 TB.Image processingTo ensure that high-quality image time series were used to generate LSP metrics, we used PlanetScope per-pixel quality assurance information to exclude pixels that had low quality in all 4 bands (i.e., blue, green, red, and near-infrared). Specifically, we excluded pixels where the Unusable Data Mask (layer ‘umd’) was not 0 (i.e., we retained pixels that were not cloud contaminated or located in non-image areas) and pixels where the Usable Data Mask (layer ‘umd2’) is 0 (i.e., we retained pixels that were not contaminated by snow, shadow, haze, or clouds). We then cropped all the images to exclude pixels outside of the 10 by 10 km window centered over each tower. We selected this window size based on published results showing that 80% of the average monthly footprint at eddy covariance towers ranges from 103 to 107 square meters22. Note that the swath for PlanetScope imagery often did not cover entire sites and some sites (e.g., the tall tower at US-Pfa) have larger footprints than other sites. Similarly, most sites had multiple PlanetScope image acquisitions on the same day. To create image time series, we mosaiced all available imagery at each site on each date, and, under the assumption that geolocation error was non-systematic and modest, we created a single image for each date using the mean surface reflectance for pixels with multiple values on the same day. The resulting database of daily surface reflectance images were sorted in chronological order, sub-divided into 200 sub-areas at each site (i.e., 0.5 km2 each), and then stored as image stacks to facilitate parallel processing to estimate LSP metrics, where each image stack included all images from July 1, 2016 through January 31, 2022.Creation of daily EVI2 time seriesTo estimate LSP metrics we adapted the algorithm described by Bolton et al.19, which was originally implemented to estimate LSP metrics from harmonized Landsat and Sentinel-2 (HLS) imagery, for use with PlanetScope imagery. Prior to LSP estimation, daily images of the two-band Enhanced Vegetation Index30 (EVI2) data were generated from PlanetScope imagery and then interpolated to create smooth time series of daily EVI2 values at each pixel in three main steps. First, sources of variation related to clouds, atmospheric aerosols, and snow were detected and removed from the EVI2 time series at each pixel based on data masks provided with PlanetScope imagery (described above) and outlier detection criteria (i.e., de-spiking and removing negative EVI2 values). Second, we identified the ‘background’ EVI2 value (the minimum EVI2 value outside of the growing season) based on the 10th percentile of snow-free EVI2 values at each pixel. Any dates with EVI2 values below the background value were replaced with the background EVI2. Third, penalized cubic smoothing splines were used to gap-fill and smooth the data to create daily EVI2 time series across all years of available data. Complete details on these steps are given in Bolton et al.19. This approach has been tested and shown to yield PlanetScope EVI2 time series that are consistent with both EVI2 time series from HLS imagery and time series of the Green Chromatic Coordinate (GCC) from PhenoCam imagery26. We used the EVI2 instead of other vegetation indices such as the Enhanced Vegetation Index (EVI) or the Normalized Difference Vegetation Index (NDVI) because EVI2 is less sensitive to noise from atmospheric effects relative to EVI and is less prone to saturation over dense canopies and noise from variation in soil background reflectance over sparse canopies relative to the NDVI30,32. Thus, phenological metrics from EVI2 time series tend to have better agreement with PhenoCam observations than corresponding metrics from NDVI33.Identifying phenological cyclesPrior to estimating LSP metrics, we first identity unique growth cycles by searching the period before and after each local peak in the daily PlanetScope EVI2 time series. To be considered a valid growth cycle, the difference in EVI2 between the local minimum and maximum was required to be at least 0.1 and greater than 35% of the total range in EVI2 over the 24-month period centered on the target year ± 6 months. The start of each growth cycle is restricted to occur within 185 days before the peak of the cycle and at least 30 days after the previous peak. The same procedure was applied in reverse at the end of the cycle to constrain the range of end dates for each growth cycle. This procedure is applied recursively over the time series until each local peak has been assessed and all growth cycles (with associated green-up period, peak greenness, and green-down period) are identified in the time series at each pixel. As part of this process, the algorithm provides the number of growth cycles identified for each year in the time series.Retrieving LSP metricsLSP metrics are estimated for each pixel in up to two growth cycles in each year. If no growth cycles are detected, the algorithm returns fill values for all timing metrics, but does report values for the four annual metrics: EVImax, EVIamp, EVIarea, and numObs (see below). If more than two growth cycles are detected, which is rare but does occur (e.g., alfalfa, which is harvested and regrows multiple times in a year), the algorithm records 7 LSP metrics for each of the two growth cycles with the largest EVI2 amplitudes. The resulting dataset includes seven ‘timing’ metrics that identify the timing of greenup onset, mid-greenup, maturity, peak EVI2, greendown onset, mid-greendown, and dormancy. These metrics record the day of year (DOY) when the EVI2 time series exceeds 15%, 50%, and 90% of EVI2 amplitude during the greenup phase, reaches its maximum, and goes below 90%, 50%, and 15% of EVI2 amplitude during the greendown phase. In addition, the algorithm records three complementary metrics that characterize the magnitude of seasonality and total ‘greenness’ at each pixel in each growth cycle: the EVI2 amplitude, the maximum EVI2, and the growing season integral of EVI2, which is calculated as the sum of daily EVI2 values between the growth cycle start- and end-dates (i.e., from greenup onset to dormancy).Quality assurance flagsQuality Assurance (QA) values are estimated at each pixel based on the density of observations and the quality of spline fits during each phenophase of the growing season. A QA value of 1 (high quality) is assigned if the correlation between observed versus fitted daily EVI2 values is greater than 0.75 and the maximum gap during each phase is less than 30 days. A QA value of 2 (moderate quality) is assigned if the correlation coefficient is less than 0.75 or the length of the maximum gap over the segment is greater than 30 days. A QA value of 3 (low quality) is assigned if the correlation coefficient is less than 0.75 and the length of the maximum gap over the segment is greater than 30 days. A QA value of 4 is assigned if no growth cycles were detected or insufficient data were available to run the algorithm. More

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Ethical statementEthics approval was obtained for this study from the ethics committee of the University of Goettingen (Chair: Prof. Dr. Peter-Tobias Stoll) under the reference number 17./04.22Wurz.Study areaAll plots were situated in northeastern Madagascar in the SAVA region (Supplementary Fig. 1). The natural vegetation is tropical lowland rainforest, but deforestation rates are high30,67.The region is globally and nationally one of the most biodiverse places with high levels of endemism17,68. Forest loss is mainly driven by slash-and-burn shifting hill rice cultivation58. The region is characterized by a warm and humid climate with an annual rainfall of 2255 mm and a mean annual temperature of 23,9 °C (mean value of 60 plots extracted from CHELSA climatology69). Vanilla is the main cash crop in the SAVA region, making Madagascar the main vanilla producer globally21,22. Vanilla prices have shown strong fluctuations over the past years, with a price boom between 2014 and 2019 triggering an expansion of vanilla agroforestry in the region22,23.Study designWe selected 10 villages based on the 60 villages selected within the Diversity Turn in Land Use Science project22 (Supplementary Fig. 1). We selected the villages based on the list of villages for our study region from official election lists which listed all villages within a fokontany individually22. Village boundaries, demographics, infrastructure were defined based on a rapid survey with the village chief. Among the 60 villages, we considered all villages without coconut plantations, with less than 40% water (river, sea, and lakes) to avoid a strong influence of water elements and with forest fragments and shifting cultivation present within a 2 km radius around the village. Two of these 17 villages overlapped within a 2 km radius of the villages, thus we randomly selected one of them, resulting in 14 villages. We visited these 14 villages in a randomized order and stopped after we found 10 villages which fulfilled the necessary criteria (all land-use types present, willing to participate). In each of the 10 villages, we selected three vanilla agroforests, one forest fragment, and two fallows. Overall, we studied 60 plots across 10 villages and 10 plots in one protected old-growth forest (Marojejy National Park). All plots had a minimum distance of 260 m and a mean minimum distance of 794 m (SD = 468 m) to each other. Plot elevation ranged between 10 and 819 m.a.s.l. (mean  = 205 m, SD = 213 m; Supplementary Table 20).Plot selectionIn each of the 10 villages, we selected three vanilla agroforests with low, medium, and high canopy closure, respectively, covering a within village canopy cover gradient. To refine our vanilla agroforest classification, we used interviews with the plot owners to categorize all vanilla agroforests based on land-use history into fallow- and forest-derived agroforests15. Forest-derived vanilla agroforests are established within forest fragments, which have been manually thinned of dense understory vegetation. Fallow-derived vanilla agroforests are established on formerly slashed and burned plots, where vegetation has been cleared for hill rice production (shifting cultivation system locally called tavy). Out of our 30 vanilla agroforests, 20 vanilla agroforests were fallow-derived and 10 vanilla agroforests were forest-derived, roughly matching the proportion of fallow- and forest-derived vanilla agroforests across the study region (70% are fallow-derived vanilla agroforests, 27% are forest-derived vanilla agroforests and 3% of unknown origin22.In addition to vanilla agroforests, we selected one forest fragment in each village. Forest fragments were located inside the agricultural landscape and were remnants of the once continuous forest; these fragments are frequently used for natural product extraction. Forest fragments have not been burned or clear cut in living memory, yet the ongoing resource extraction results in a much simplified stand structure and fewer large trees compared to old-growth forest12. Furthermore, we chose one herbaceous and one woody fallow in each of the 10 study villages. Both fallow types form part of the shifting hill rice production cycle and represent the fallow period at different stages after the crop production. Herbaceous fallows have been slashed and burned multiple times with the last cultivation cycle at the end of 2016, one year prior to the first species data collection in 2017, and thereafter left fallow11. The continuous succession of herbaceous fallows turns them into woody fallows with the domination of woody plants including shrubs, trees, and sometimes bamboo. Our 10 woody fallows have last burned 4–16 years before data collection. In this study, we combine both herbaceous and woody fallows into the category “fallow”. Generally, fallows occur in different forms in the study region. The characteristics of fallows depend on the frequency of past fires and the length of fallow periods in between crop cultivation11. Frequent burning results in a loss of native and woody species and a dominance of exotic species and grasses11. In later fallow cycles, fern species increasingly appear11.Due to the commonly repeated slashing and burning, secondary forests are very rare in the study region. Shifting cultivation prevails in Madagascar70, because it is an important option for people to grow food because means for agricultural intensification are scarce. According to our baseline survey (performed in 60 villages in our study region), 90% of the interviewed farmers grow rice for subsistence in addition to growing vanilla22. Out of this sample, 64% of farmers grow rice in irrigated paddies and 26% of farmers use shifting cultivation.We also studied 10 plots at two sites in Marojejy National Park, the only remaining, continuous old-growth forest at a low altitude in our study area71. We chose accessible old-growth forest plots with a minimum distance of 250 m from the forest edge. Five of the 10 old-growth forest plots were located in Manantenina Valley, the other five old-growth forest plots were situated in the eastern part of Marojejy National Park, called Bangoabe area. Illegal selective logging has occurred in some parts of the park. During our plot selection, we avoided sites with traces of selective logging.Land-use history classificationTo collect information on the land-use history or farm history, interviews with farmers are common72,73. We did interviews with the plot owner. Questions on land-use history were binary (forest-derived or fallow-derived) and did not include information on the detailed land-use history (e.g. frequency of burning, past crop systems). Thus, we consider this selfreported data very reliable. The land-use categorization derived by farmers was confirmed by our visual plot inspections (forest-derived vanilla agroforests do have a quite distinctive vegetation structure compared to fallow-derived vanilla agroforests). Additionally, data on tree species composition and soil characteristics show evident differences between the categories and back up the binary land-use history categorization. Analysis of tree species composition showed that fallow- and forest-derived vanilla agroforests differ significantly in tree species composition12. Soil analysis (see Fig. S9) showed that our fallow-derived vanilla agroforests are associated with fertility-related variables such as an increase in calcium, pH, nitrogen, and phosphorus, which is common after slas-and-burn agriculture74,75.Plot designWe collected species data on plots with a radius of 25 m (1964 m2, 0.1964 ha). We established our circular plots in a homogeneous area of the land-use type or forest. Adjacent land uses were usually different because farmers generally own small-scale land with a mean size of 0.66 ha (mean size of agroforests). We assessed vanilla plant data (yield, vine length, vine age, planting density) on 36 vanilla pieds on each of 30 circular vanilla plots (Supplementary Fig. 8). We defined one vanilla pied (foot in French) as the combination of a vanilla vine and a minimum of one support tree. The 36 vanilla pieds were evenly selected in each of the circular plots based on a sampling protocol to ensure comprehensive and unbiased sampling. We chose vanilla pieds independent of age, length or health condition. We marked the 36 selected vanilla pieds per plot with a unique barcode to assess vanilla yield (April 2018) and other plant health variables on the same plant (not used in this study). However, for 37 vanilla pieds (out of a total of 1080 marked vanilla pieds), the barcodes were lost or unreadable and we selected a new plant closest to the original position (independent of age, length, or condition) and marked it with a new unique barcode. We measured the size of the vanilla agroforest by walking with the agroforest owner and a hand-held GPS device at the perimeter of the plot.Vanilla planting densityWe counted each vanilla pied on each 25 m circular plot by dividing the plot in four-quarter segments. We calculated the area of each 25 m radius plot including slope correction and calculated vanilla planting density (vanilla pieds per hectare) by dividing the number of vanilla pieds by the slope-corrected plot area.Vanilla yieldWe measured yield on 30 vanilla plantations (10 forest-derived vanilla plantations and 20 fallow-derived vanilla plantations); three in each of our 10 study villages. We measured vanilla yield on a total of 36 vanilla pieds between March and April 2018. We assessed the vanilla yield before harvest to ensure an accurate yield assessment due to two reasons. Firstly, vanilla pods are commonly harvested successively due to their differing pollination date and maturity requiring multiple visits over several weeks. Secondly, theft of vanilla pods is commonplace around harvest time. We, therefore, estimated the weight of the on-plant-hanging vanilla pods by measuring pod volume and relating this to a prior established volume–weight correlation. This is possible because vanilla pods only grow in length and width in the first 8 weeks of their development76. Our yield assessment consisted of one interview part with the plot owner and one measurement part. The interview part included questions about the occurrence of theft and early harvest on the plantation. During the measurement part, we assessed the number, diameter, and length of all vanilla pods. We measured vanilla pod length with a ruler starting at the junction of stem and pod until the tip of the pod without considering the bending of the pod. We measured the diameter at the widest part of the pod using a caliper. We firstly calculated pod volume based on the standard volume cylinder formula using the measured diameter (cm) and length (cm): V = πr2h.Secondly, we calculated the weight (g) of each pod by using the linear regression equation (y = bx + a) of a weight–volume correlation of 114 vanilla pods from 114 different agroforests (weight, length, and diameter of these 114 green vanilla was assessed post-harvest in 2017). We calculated the weight of all measured pods of the harvest in 2018 based on the formula:$${{{{{rm{volume}}}}}}={{{{{rm{pi }}}}}}({{{{{rm{diameter}}}}}}({{{{{rm{mm}}}}}})/20)^wedge 2ast {{{{{rm{length}}}}}}({{{{{rm{cm}}}}}})$$Here, we divided the pod diameter (mm) by 20 to obtain the radius and to transform millimeters to centimeters. Weight was defined as volume*0.5662 + 0.9699. No vanilla pods were stolen or already harvested on our 36 vanilla pieds and hence we did not need to account for it in our vanilla yield calculation.Vanilla vine lengthWe assessed vanilla vine length for all 36 vanilla pieds (same vanilla pieds as used for the yield assessment) on each plot by measuring the total length of the vine from the lowest to the highest part with a measuring stick. If the vanilla vine was looped on the support tree (= vanilla vine is hanging in multiple loops on the support tree), we measured from the top height of the looping of the vanilla vine until the lowest height of the vine. At the medium height of the vanilla vine, we counted the number of times the vanilla vine passed through. We calculated the total length of the liana by multiplying the maximum height of the vanilla vine by the number of times the vine passed through the middle. In some cases, the vanilla vine looped at two different heights, we thus considered the middle between the two looping heights as the top height. If vanilla vines grew on two different support trees, we considered them as one vanilla pieds if support trees were More

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