Ecology

Experiment 1To study the acceptance and tag retention rates of honey bees under different introduction conditions, we set up three two-frame observation hives with (sim 1500) adult bees and a queen. Observation hives were set up in a shed, with an entrance tube that connected to the outside 4 ft above the ground so bees could freely forage in the surrounding fields (Fig. 1A). We put emerging brood from healthy source colonies in an incubator ((33.5^{circ }) C, (ge 55)% RH) and tagged individuals that emerged overnight. Each hive had two vent holes (1” Dia.) through which we could introduce bees (Fig. 1A).Figure 1(A) Observation hive with introduction holes (red), through which bees were introduced via funnel or introduction cage. (B) Plastic tags, silicon tags, and sucrose spray. (C) Photograph of a tagged bee foraging (Photo by Greg Yauney).Full size imageIn our initial experiment, there were seven treatment groups with 20 bees each per colony (n = 420 bees total). The seven treatments were: Control (C), No Sucrose (NS), Plastic (P), Wood Glue (WG), Not Incubated (NI), No Cage (NC), and Day (D). The Control treatment was designed to be a positive control, where we applied all the techniques we thought might increase acceptance of bees into a colony and tag retention rates. All other treatments had a single difference in the tag, tagging process, or introduction process to distinguish it from the control group, as detailed in Table 1. We glued a tag to the thorax of each bee (Fig. 1B) and marked the abdomen with a paint pen (Posca) to distinguish among treatment groups. In order to glue tags on, we picked each bee up, placed a small amount of glue on the thorax, and placed a tag on top of the glue with a pair of forceps (see Video 1 which details the tagging process). All bees except those in the Plastic group were tagged with 1.7 mm(^2) silicon tags (3.4 mm area). Silicon was chosen because it is a material representative of ASICs, which you would expect in a custom chip designed to track bee foraging flights. Plastic tags were 3 mm Dia. plastic discs (7.07 mm area), which are the commercially available bee tags commonly used in honey bee tracking and behavior experiments (Betterbee). All tags were glued on with shellac glue, the glue that comes with commerical honey bee marking kits (Betterbee), except for in the wood glue group, where they were glued on with wood glue (Titebond III). Next, bees were placed in a container with a bit of honey and stored until they were ready to be introduced. All bees except those in the Not Incubated group were placed in the incubator ((33.5^{circ }) C, (ge 55)% RH). The Not Incubated group was stored in a room environment, with variation between 21–27(^{circ }) C and 35–42% RH until introduction. Bees in the Day treatment spent 5 h in the incubator and then were sprayed with a light sucrose syrup (1 sucrose: 1 water (v/v)) and introduced at 4pm while the hives were still actively foraging. The rest of the bees spent between 5 and 8 h in the incubator or room environment before being introduced at 10:30 pm, after foraging had concluded. All except the No Sucrose group were sprayed with a light sucrose syrup before being introduced. The No Cage bees were rapidly introduced through one of the vent holes on the top of the hive using a funnel. The rest of the bees were placed in a cage together, which we connected to the introduction holes at the top of the colony, allowing them to move freely between the cage and the hive.Table 1 Experimental design used for preparation and introduction of treatment groups.Full size tableBeginning on day 2 (07/09/2020), we observed each hive in the morning on days 2-4 and 6-9 to see how many bees per group were present, hereinafter referred to as presence, and how many bees per group were present with tags, hereinafter referred to as success. We selected a random order in which to observe the three hives and a random order in which to observe the treatment groups for each hive. Each side of each hive had a grid drawn on it that divided it into nine squares. We scanned each side of each colony by eye for each treatment, starting with the lower left square of the grid on the first side, moving across the row, and then moving up to the next row, counting presence and success, using a tally counter when needed. We then moved rapidly to the other side and started at the top left of the grid, scanning row by row until we had observed each square in the grid. After an initial scan for each treatment, we placed the covers on the hives and shook for 10s to encourage bees to move around in the hive, and then waited for at least 15 minutes before a second observation. The maximum presence and success from the two daily observations were used for each treatment group and hive for analysis. Since we collected data by scanning each colony, we sometimes found more bees from a group in an observation hive than we had found in the same hive on previous day(s), even though more time had passed. Over the course of the experiment, our hives grew in size, and we believed we were seeing less tagged bees in part because they made up a smaller proportion of the hive population, and so decided to do a destructive sampling before the tagged bees reached foraging age. After dark on day 14 (7/21/2020), we made sure no tagged bees were dead on the bottom of the hives. We blocked the entrances, vacuumed all bees at the entrances into containers, and froze vacuumed bees and the three colonies, so that we could do a destructive sampling of all 3 colonies. This allowed us to get a final count of the presence and success for each of the seven treatment groups. We dissected each frozen colony, removing and inspecting each dead bee, and recorded the presence and success of each treatment group.Experiments 2 and 3We set up three two-frame observation hives in the same shed used for experiment 1 to conduct follow-up experiments in August 2020. The goal of experiment 2 was to compare Gorillaglue gel, an easily accessible Superglue (SG), to Titebond III, a readily accessible Wood Glue (WG2) used in experiment 1. We placed frames of capped brood in an incubator overnight to produce one day old nurse bees. We picked up each bee, placed a small dot of either superglue or wood glue on the thorax, and then placed 1.7 mm(^2) silicon tags on top of the glue. Bees were stored in the incubator with honey for 5–6 h until after dark. Then, we sprayed the bees with a light sucrose syrup and connected their cages to the vent holes at the top of the observation hives, allowing the bees to freely move between their cage and the hives. These details are summarized in Table 1.Some honey bee tagging projects may benefit from tagging foragers as opposed to nurse bees, because nurse bees are the youngest workers and if you tag them you must wait for them to reach foraging age, during which time they may lose their tags. Specifically, tagging foragers as opposed to nurses will be advantageous when the tag price is extremely high or the project is very time constrained, and knowing the exact age of tagged bees is not important for the project goals. Since foragers are older workers that have already acquired the colony scent and learned to navigate the area surrounding their hive, the optimal methods for introducing nurses and foragers may differ. It is not easy to use bees from a source colony, because if they are within foraging range of their maternal colony, they will attempt to fly back home. The goal of experiment 3 was to apply a treatment that had high success with nurse bees (Experiment 1: WG) to foragers, and compare with releasing foragers near their colony and allowing them to return freely. We call these treatments Hive Introduced (HI) and Natural Release (NR), respectively. All foragers for this experiment were collected from the observation hives and were introduced back to the same observation hive after tagging, either through the vent holes at the top of the hive or by releasing the bees near the entrance of the hive. We collected foragers from each colony entrance into a cage with an insect vacuum (Hand-Held DC Vac/Aspirator, Bioquip), specifically aspirating bees that were arriving from foraging trips or had nectar loads, and placed them in the fridge to anesthetize them. We then selected those with intact wings, placed a dot of wood glue on their thoraxes, and placed silicon tags on top of the glue. Both treatment groups were stored in the incubator ((33.5^{circ }) C, (ge 55)% RH) and given honey to feed on. After 2 h in the incubator, the containers with NR bees were sprayed with a light sucrose syrup and placed on the ground 5 ft in front of their respective hive entrances and opened, allowing the bees to fly back to their hives unaided. At 10PM, when it was dark and foraging had concluded, the HI bees were sprayed with a light sucrose syrup. Their cages were then connected to the vent holes at the top of the observation hives, allowing them to freely move between their cage and the hives.Experiments 2 and 3 were conducted in the same hives simultaneously, but were considered separate experiments because experiment 2 was conducted with nurses of known age and experiment 3 was conducted with foragers of unknown age. Nurses and foragers typically have an age difference and experience different levels of risk due to the behaviors they engage in, and so we analyzed these data separately in order to not confound our results. Beginning on day two (08/26/2020), we observed each hive on days 2–11 and 15–21 to determine introduction presence and success for experiment 2 and experiment 3. Forager observations (experiment 3) were always done early in the morning, before foraging activity commenced. As in experiment 1, we randomized observation order, scanned colonies for each treatment group before and after shaking, and used the maximum presence and success from the two observations for analysis. Since we collected data on multiple days by scanning each colony, we occasionally found more bees in a group than we had found on previous day(s), even though more time had passed.Statistical methodsStatistical analyses were performed in R 4.0.520. To determine which preparation and introduction techniques were associated with the highest presence and success, we built generalized linear mixed-effects models (glmms)21 for the proportion of present and success bees to introduced bees respectively, with treatment and sampling day as fixed effects, and colony as a random effect. For experiment 1, treatment was a categorical variable, where the Control bees were the reference group. We assessed the significance of the full models using Wald likelihood ratio chi-square tests on each glmm (‘Anova’ function in the ‘car’ package with test set to ‘Chisq’)22. In all statistical tests, (alpha) was set to 0.05. The destructive data from experiment 1 were analyzed separately from hive observation data. We ran a correlation test to determine the relationship between hive observation data from the final observation day, day 9, and the destructive sampling on day 14 using the ggpubr package23. More

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Time dynamics of the mollusk communitiesIn this section, the presence-absence of the species recorded in the three periods (T1, T2, T3) are analyzed in relation to time, habitat, and human impact. The list of the 28 species of freshwater mollusks (17 gastropods and 11 bivalves) in T1–T3, their codes, and origins are given in Table 1.The number of mollusk species has increased in time as the river has shifted from lotic to a mixture of flowing and stagnant sectors due to the building of reservoirs. T1 was characterized mainly by rheophilic elements and prosobranchs. Some species became extinct during the hydro-technical works (before or during T2) and are unlikely to recover, such as the rheophilic Theodoxus transversalis and Lithoglyphus naticoides. Other rheophilic species disappeared between T1 and T2 but managed to survive in tributaries and repopulated some sectors during the last years. The most remarkable recovery is that of the thick-shelled river mussel Unio crassus, a species protected by EU legislation. T2 was characterized by some extinctions but also colonization by lentiphilic pulmonates and tolerant, resistant species such as some clams. A few lotic species also survived in the river sectors between the dams. In T3, we encountered a rich and diverse community, including some newly established populations of AIS and the discontinuous presence of both lentic and lotic communities. Overall, the present-day fauna is richer than in former periods, consisting of 15 species of gastropods and 8 bivalves. The AIS included the gastropods Physa acuta and Ferrissia californica, which arrived in the area most likely during the XXth century, Viviparus acerosus, which is native to the Danube, but unknown until after 2000 in the upper-middle Olt River basin, the bivalves Dreissena polymorpha, also native in the Danube but an invader in the middle Olt since 2008–2010, Sinanodonta woodiana, first found in 2015, and Corbicula fluminea, which was first discovered in the Olt (and also in Transylvania) during our survey in February 2020. The mean number of native species per river’s sector increases almost linearly (2.8 species per sector in T1, 3.3 in T2, and 4.6 in T3), while the corresponding values for AIS increase non-linearly (no AIS in T1, 0.6 species per sector in T2 and 3.2 in T3).In the CCA of freshwater mollusk community changes through time (Period as predictor), the adjusted explained variation was 23.6% (test on all axes, pseudo-F = 5.9, p = 0.001). The polygons delimiting the positions of the sites during the three periods of time show no overlap, and they were distinct and separated in the ordination space (Fig. 1a). T2 (the period with maximum human impact) is distinctly placed and separated from the period without impact (T1) along both ordination axes. Meantime, T3 is closer to T1, having an intermediate position between the other two periods, showing a trend of recovery, such as the return of some species. In the CCA of T1–T3 species presence-absence predicted by the selected environmental descriptors (Period, Habitat, and Impact) (Fig. 1b), the adjusted explained variation was 28.36% (test on all axes, pseudo-F = 4.2, p = 0.001). FD(Rao) computed on all FT was plotted as isolines by GAM on the ordination space (model AIC = -17.19, model test F = 5.1, p = 0.003; tests of non-linearity in predictor effects: F = 3.9, p = 0.03). The functional diversity decreased from T1 to T2, then increased sharply to T3; it also decreased from rivers (R) to lakes (L) and along the human impact gradient (Impact).Figure 1Canonical correspondence analysis (CCA) of mollusk communities: (a) classification diagram of sampling sites based on period (as predictors): T1—XIXth century, T2—1995–2000, T3—2020 (adjusted explained variation 23.6%; first axis accounts for 17.6% the second for 6.0%, both axes are significant, p = 0.001); (b) CCA diagram of species occurrence constrained by environmental predictors (period, habitat: L—lakes, lentic sector in reservoirs, R—river, lotic sectors, and Impact—human impact) with functional diversity expressed as Rao quadratic entropy index (FD (Rao)) isolines plotted by generalized additive models (GAM) on the ordination space (adjusted explained variation 28.36%; first axis accounts for 16.3%, the second for 6.0%, both axes are significant, p = 0.001) .Full size imageIn the dc-CA with the selected predictors on T1–T3 presence-absence data, the first two axes separate the communities by period, each positioned in a distinct quadrant (Fig. 2). After a decrease in diversity from T1 to T2, in T3, there were more species and higher functional diversity. In time, there was a reduction in body size, a switch from species with separate sexes to hermaphrodites, a transition of oviposition towards ovo-viviparity (in snails), and external fecundation (in bivalves), and a switch of the feeding type. The dc-CA adjusted explained variation was 16.47%; tests based on sectors and species showed significant relationships (combined test for all axes, pseudo-F = 2.6, p = 0.006), the dimensionality test based on case scores was significant for the first axis (pseudo-F = 4.2, p = 0.001) and marginally significant for the second one (pseudo-F = 1.1, p = 0.053). In contrast, the dimensionality test based on species scores was significant only for the first axis (pseudo-F = 1.6, p = 0.004). The adjusted variation explained by environmental predictors (Hab, Impact, and Period) was 28.36%, and by the selected functional traits (Sexes, FeedT, SizeM, and Ovipos) was 14.64%.Figure 2Double-constrained correspondence analysis (dc-CA) with selected predictors on presence-absence data in T1–T3. The selected functional traits (in blue) are Sexes (circles: H—hermaphrodite, S—separate sexes), Feeding type (squares: SCR—scraper, SS—scraper and sediment, SF—scraper and filter, F—filter, SEDF—suspension and deposit feeder), Oviposition (diamonds: OV—ovo-viviparity, CAP—capsule/eggmass, BE—parental care, juveniles in brood pouches of demibranchs, No—no oviposition, external fecundation), and mean body size (SizeM); the selected environmental predictors (in red) are time (Period, with levels T1—XIXth century, T2—1995–2000, T3—2020), habitat (R—river, lotic sector; L—lake, a lentic sector in reservoirs) and human impact (Impact). Species are coded by the first three letters of the genus and species names. The adjusted explained variation was 16.47%, the first axis accounts for 12.7% and the second for 2.2%. Native species have black labels, while aliens (AIS) are written in green.Full size imageWe have split the binary data describing communities into two parts: natives and AIS, using the latter as predictors. We partitioned the variation in native species composition explained by the three predictor groups (Period, Environment, and AIS) (Fig. 3), subjecting the explanatory variables to an interactive forward selection procedure. We used RDA with centered response variables (CCA can not be used because the empty rows in some tables hinder the use of a proper hierarchical permutation scheme). The adjusted explained variation was 39.6% (the simple effects: time accounted for 22.33%, habitat and impact 24.73%, and the selected AIS 20.82%). All simple and unique effects were significant (p  More

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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