Ecology

Mixing fire occurrence with wildfire activity is problematic also when trying to draw policy conclusions. Fang et al.1 examined the temporal pattern of fire numbers between 2005-18 and concluded that the application of a fire suppression policy after 1987 has contributed to decreases in fire occurrences after 2007. However, fire suppression is an effort to mitigate the results of a fire once it has started10. Consequently, fire suppression strictly affects the burned area, and not fire occurrence. Other aspects associated with fire planning, like awareness campaigns or fire bans, may act on fire occurrence. However, any relationship between fire occurrence and fire suppression will necessarily be artefactual because the latter does not affect the former.We acknowledge that part of the discrepancy with Fang et al.1 may lie in the different scales used in these analyses. However, fire activity is a term that currently lacks a rigorous definition and should be used with caution. Fire occurrence depends primarily on the number of ignitions (along with other factors affecting fire detection such as climate, topography or vegetation), which, in turn, results from human activity1 and, in some areas, lightning11. Using fire occurrence as an indicator for fire activity is particularly problematic when comparing multiple biomes that show marked differences in fire regime, as we demonstrate here. Additionally, ENSO and fire suppression may both affect burned area, but there is currently no mechanism that can explain a mechanistic link between either of these processes and the number of fire events. Consequently, fire occurrence should not be used as a sole metric of fire activity.We additionally note that burned area is not necessarily a reliable metric of fire impacts on ecosystems and society. Significant variation in severity and intensity may occur within a fire perimeter12. Additionally, damage to people and property are not captured by this metric13. While we caution against the use of a single metric to evaluate fire activity, we hope to have demonstrated that using fire occurrence alone is particularly problematic, and that the picture it paints is rather unrealistic. More

Species composition of vegetation in the WLFZIn this survey, a total of 44 species in 43 genera of 21 families of vascular plants were found and confirmed in the reservoir WLFZ of the Jinsha River basin, among which, 13 genera and 13 species of Compositae, 4 genera and 4 species of Gramineae, 3 genera and 3 species of Amaranthaceae, 2 genera and 2 species of Verbenaceae, Labiatae, Umbelliferae, Cruciferae and Convolvulaceae, 1 genus and 2 species of Polygonaceae, and the remaining 12 families were all single genera. Compositae had the highest number of species, followed by Gramineae and Amaranthaceae, accounting for 29.55%, 9.09% and 6.82% of the total number of species in this survey, respectively, which are the main dominant families in the region.According to the life type classification system of the Flora of China, the plants in the WLFZ of this survey can be classified into five life types: annual herbs, perennial herbs, annual or biennial herbs, annual or perennial herbs, and biennial herbs. The community is overwhelmingly dominated by annuals with a high proportion of 54.55%, followed by perennials with 34.09% and the rest of all life types with a total of 11.36%.The higher number of annual plants indicates that the environmental conditions in the WLFZ are harsher after inundation by water storage, and plants that can complete their entire life cycle in a short period of time after receding water are more likely to survive compared to plants that take a long time to complete their entire life cycle.The vegetation types in each study area of the WLFZ are shown in Table 3, among which 17 species, including S. subulatum, E. humifusa, C. bonariensis, V. officinalis, O. biennis, S. plebeia, U. fissa, B. juncea, S. orientalis, D. repens, A. lividus, T. mongolicum, G. parviflora, P. praeruptorum, P. hys-terophorus, D. stramonium and Ph. Nil, are newly discovered species in the reservoir WLFZ, which are rarely reported in other reservoir WLFZ studies so far. Among the study areas, the Longkou study area was the richest in vegetation types, with the most families, species and life types among all study areas, and the number of perennial herb species was comparable to that of annual herb species, while all other study areas were mainly dominated by annual herbs. The vegetation composition of the remaining study areas averaged 6–8 families and 11–12 species, except for the Ludila study area with no plants growing and the Liyuan study area with only 5 families and 5 species. In general, each study area was dominated by Compositae and Gramineae.Table 3 Vegetation composition in each study area.Full size tableVegetation area, coverage, and percentage of the WLFZAccording to the vegetation classification in the WLFZ of each study area (Fig. 5 and Table 4), the vegetation coverage of the study areas of the Liyuan, Ahai, Ludila and Guanyinyan reservoir WLFZ were all less than 5%. The study area of Ludila was completey devoid of vegetation in the WLFZ. The coverage in Liyuan was only 0.02%, with mostly individual herbaceous plants sporadically distributed on the upper boundary of the WLFZ. In Ahai, C. dactylon grow concentratly in patches at the top of the WLFZ together with some other sparsely growing vegetation, with a coverage of 1.47%. The vegetation coverage of Guanyinyan was 3.21%, mainly distributed in the upper part of the WLFZ and expanding towards the middle. In this area, 30.39% of the vegetation was X. sibiricum, growing in large tracts as low seedlings; 21.03% was A. sessilis growing in patches, 10.87% was C. dactylon growing mainly on the upper boundary of the WLFZ, and 37.71% was a mixture of plants growing in clusters with only a few of each.Figure 5The results of vegetation classification in the WLFZ of each study area. (a) Liyuan, (b) Ahai (c) Longkaikou, (d) Ludila, (e) Guanyinyan, (f) Xiluodu. Note: Non-Veg (Non-vegetation), Other-Veg (Other vegetation), C. Dac (Cynodon dactylon), A. Ses (Alternanthera sessilis), C. Bon (Conyza bonariensis), Ch. Amb (Chenopodium ambrosioides), C. Can (Conyza canadensis), D. Rep (Dichondra repens), H. Sib (Hydrocotyle sibthorpioides), V. Off (Verbena officinalis), X. Sib (Xanthium sibiricum). (Generated with eCognition Developer, and the URL is https://www.ecognition.com).Full size imageTable 4 Vegetation area, vegetation coverage and vegetation classification accuracy of WLFZ in each study area.Full size tableThe vegetation coverage of Longkaikou and Xiluodu WLFZ was more abundant, 46.47% and 55.81% respectively. In Longkaikou, vegetation mainly covered the middle and upper parts of the WLFZ. Of the vegetation, 66.38% was C. dactylon, 26.50% was A. sessilis, 2.35% was H. sibthorpioides, 1.68% was Ch. ambrosioides, and 3.09% was a variety of vegetation species, only a few of each, divided into Other-Veg class.Due to weather and equipment constraints, we were unable to photograph the upper and lower boundaries of the WLFZ in Xiluodu study area, but we still obtained the images of the main part of the WLFZ, which consisted mainly of 58.4% X. sibiricum, 28.04% C. dactylon, 10.59% S. viridis, and 2.97% other vegetation.The vegetation coverage in the WLFZ of different reservoirs of the Jinsha River basin varied significantly, but in terms of quantity, most of them were absolutely dominated by 1–4 species, which were distributed in patches and strips, and covered an area and proportion far more than the rest of the vegetation, while the rest of the vegetation was sparse in quantity each and was sporadically distributed. C. dactylon, A. sessilis, X. sibiricum, S. viridis, H. sibthorpioides, Ch. Ambrosioides were the main dominant and pioneer species for vegetation restoration in the reservoir WLFZ of the Jinsha River basin.Spatial distribution pattern of vegetation in fluctuating zoneSince no vegetation survived in the Ludila study area, and the vegetation in the Liyuan, Ahai and Guanyinyan study areas was sparse, with less than 5% coverage, and all of them were concentrated in the upper part of the WLFZs (Fig. 5), this paper mainly analyzed the spatial distribution pattern of vegetation in the Longkou and Xiluodu study areas, which had better vegetation coverage.Landscape patternCA is a basic index for landscape pattern study, and LPI reflects the proportion of the largest patch in the landscape type to the total landscape area, which is an expression of patch dominance. The SHAPE and PAFRAC describe the complexity of patch shape, the larger the SHAPE value indicates the more complex patch shape; the closer the PAFRAC value to 1 indicates the more regular patch shape. PROX reflects the degree of proximity of each landscape type, the larger its value indicates the higher degree of patch aggregation and the lower degree of fragmentation; ENN describes the degree of physical connection of the landscape types, the larger its value indicates the greater distance between patches and the greater degree of fragmentation.From the overall landscape level (Fig. 6), in the Longkaikou study area, CA and LPI showed that the areas of vegetation patches were large, less spatially fluctuating and uniform distribution, with obvious patch dominance, reflecting characteristics of patchy distribution; PROX and ENN showed that the vegetation patches were clustered and the landscape was well connected; SHAPE and PAFRAC showed that there was little variation in the shape complexity of vegetation patches in most areas of the WLFZ.Figure 6Spatial characteristics of vegetation landscape pattern index in the Longkaikou study area (Generated with ArcGIS 10.5 software, and the URL is: https://www.esri.com/en-us/home).Full size imageAt the level of landscape types (Table 5), the vegetation landscape types in the Longkou study area included C. dactylon, A. sessilis, H. sibthorpioides and other vegetation, among which, C. dactylon showed significant advantages in patch area, patch dominance, patch aggregation and connectivity; followed by A. sessilis and H. sibthorpioides, A. sessilis was significantly better than H. sibthorpioides in patch area, but in patch shape, H. sibthorpioides was more aggregated than A. sessilis and had better patch connectivity; Other-Veg showed significant weaknesses in patch area and aggregation; there were no significant differences among the landscape types in patch shape.Table 5 Landscape index of patch types in the Longkaikou study area.Full size tableThe spatial characteristics of the vegetation landscape pattern index in the Xiluodu study area were shown in Fig. 7. From the overall level of the landscape, the area of vegetation patches and the dominance of patches were spatially variable, the vegetation was well connected, with obvious characteristics of patchy distribution, and the shape of vegetation patches did not show obvious spatial characteristics.Figure 7Spatial characteristics of vegetation landscape pattern index in the Xiluodu study area (Generated with ArcGIS 10.5 software, and the URL is:https://www.esri.com/en-us/home).Full size imageFrom the level of landscape types (Table 6), the vegetation landscape types in Xiluodu study area included four categories: X. sibiricum, C. dactylon, S. viridis and Other-Veg type. Among them, X. sibiricum showed obvious advantages in patch area, patch dominance, patch aggregation and connectivity, followed by C. dactylon, both of which were significantly better than S. viridis and Other-Veg, and the differences in patch shape complexity among landscape types were small.Table 6 Landscape index of patch types in the Xiluodu study area.Full size tableDistribution characteristics along terrainAccording to the statistics (Fig. 8), the vegetation area share of Longkaikou study area in the upper, middle and lower elevation gradients of the WLFZ was 54.61%, 26.62% and 18.77%, respectively, indicating that the vegetation was mostly in the upper part of the WLFZ, with a coverage of 83.80%, while the vegetation in the lower part was the least, with a coverage of less than 1%. From the viewpoint of each vegetation species, in the upper part of the WLFZ, C. dactylon had the largest area, accounting for 66.9% of the total vegetation area, followed by A. sessilis, accounting for 25.9%, while H. sibthorpioides and Other-Veg only survived in the upper part, accounting for 2.3% and 4.9% each. From the distribution of each slope class, the vegetation of the WLFZ gradually decreased with the increase of slope, and the vegetation was mainly concentrated in the range of slope 35°, and the coverage of each vegetation decreased significantly when the slope exceeded 35°. In the aspect, the distribution of vegetation in the WLFZ did not show any obvious preference. The surface relief in the study area of Longkou was generally low, and C. dactylon was mainly distributed in the range of surface relief less than 0.84 m. When the surface relief is greater than 2.52 m, the vegetation coverage tends to be close to 0. The vegetation showed no obvious distribution preference in terms of surface roughness and topographic wetness index.Figure 8Changes in vegetation coverage with topographic factors in the Longkaikou study area (Drawn with Origin 2018_64Bit, and the URL is https://www.OriginLab.cn/).Full size imageThe spatial distribution of vegetation in the study area of Xiluodu was shown in Fig. 9. The maximum drop in water level at Xiluodu study area can reach 60 m, but only the half of the upper part of the subsidence zone with a drop of about 30 m was photographed. The coverage rate of C. dactylon was the largest in this elevation gradient, S. viridis was mainly distributed in the uppermost part of the zone, while X. strumarium was well covered in all elevation gradients. From the distribution of surface relief, the overall vegetation coverage decreases with the increase of surface relief, with X. strumarium and S. viridis mainly distributed in the area of 0–3.45 m, while both the coverage of C. dactylon and Other-Veg were not much different across the surface relief . The distribution of vegetation showed no obvious preference in terms of slope, aspect, surface roughness and topographic wetness index.Figure 9Changes in vegetation coverage with topographic factors in the Xiluodu study area (Drawn with Origin 2018_64Bit, and the URL is https://www.OriginLab.cn/).Full size imageInfluence of topographic factors on the spatial distribution pattern of vegetation in the WLFZAccording to the results of species distribution modeling, the number of samples in the study area of Longkaikou was 39,321, and the overall accuracy of the model was 88.2%. The terrain factors, in descending order of importance, were elevation  > slope  > surface relief  > surface roughness  > aspect  > topographic wetness index, with values of 0.681, 0.146, 0.091, 0.042, 0.033 and 0.007, respectively (Fig. 10). It can be seen that the vegetation distribution in the WLFZ was mainly influenced by elevation, followed by slope and surface relief, and is less influenced by surface roughness, aspect and topographic wetness index. This was consistent with the results of typical correlation analysis.Figure 10Ranking of important values of topographic factors in the Longkaikou study area (Drawn with Origin 2018_64Bit, and the URL is https://www.OriginLab.cn/).Full size imageA total of six pairs of typical variables were calculated in the Longkou study area, and standardized typical coefficients were used due to the inconsistency of each landscape pattern index as well as topographic factor units. According to the results of significance test (Table 7), the first four pairs of typical p-values were less than 0.05, indicating that the correlations reached a significant level, and their correlation coefficients were 0.565, 0.262, 0.142, and 0.034, among which the correlation coefficient of the first pair was the largest, so the first pair was selected for analysis. The topographic factors and landscape indices highly correlated with the first pair of typical variables were elevation, surface relief and CA and SHAPE, respectively. According to Tables 8 and 9, their mechanism of action was that the greater the elevation, the smaller the surface relief, resulting in a larger patch size and more complex shape of the vegetation, and therefore a more frequent exchange of energy with the outside world and a greater ability to survive.Table 7 Significance test of typical correlation coefficient in the Longkaikou study area.Full size tableTable 8 Standardized canonical correlation coefficients of terrain factors in the Longkaikou study area.Full size tableTable 9 Standardized typical correlation coefficients of landscape pattern in the Longkaikou study area.Full size tableThe number of samples in the study area of Xiluodu was 41,010, and the overall accuracy of the model was 61.4%. The terrain factors, in descending order of importance, were elevation  > surface relief  > ground roughness  > aspect  > slope  > terrain moisture index, with values of 0.395, 0.209, 0.157, 0.123, 0.073, and 0.043, respectively (Fig. 11). It can be seen that the vegetation distribution in the WLFZ was most influenced by the elevation, followed by the surface relief.According to the typical correlation analysis, six pairs of typical variables were calculated for the Xiluodu study area, of which the first four pairs had typical P values less than 0.05 (Table 10), indicating that the correlation reached a significant level, and their correlation coefficients were 0.299, 0.208, 0.102, and 0.033, and the first pair was the largest, so the first pair was selected for analysis.The topographic factors and landscape indices with high correlation with the first pair of typical variables were elevation,surface relief and CA, PAFRAC, respectively, and according to Tables 11 and 12, their mechanism of action was that the greater the elevation, the greater the surface relief, leading to a smaller patch area and simpler shape of the vegetation.Figure 11Ranking of important values of topographic factors in the Xiluodu study area (Drawn with Origin 2018_64Bit, and the URL is https://www.OriginLab.cn/).Full size imageTable 10 Significance test of typical correlation coefficient in the Xiluodu study area.Full size tableTable 11 Standardized canonical correlation coefficients of terrain factors in the Xiluodu study area.Full size tableTable 12 Standardized typical correlation coefficients of landscape pattern in the Xiluodu study area.Full size tableLimiting factors of vegetation restoration in WLFZPreliminary studies showed that after long-term water level fluctuations in the cascade reservoirs, most of the vegetation in the WLFZs of the cascade reservoirs in the Jinsha River basin could be restored to different degrees, however, the restored species types were relatively simple, all of them were herbaceous plants, and mainly annual herbaceous plants. The restoration of the WLFZs of different reservoirs varied significantly, with vegetation coverage of more than 46% and 27 species types in the better restored areas, such as the Longkou study area, while the vegetation coverage of the less restored areas was usually less than 5% and 5–12 species types, and some areas even had no grass, such as the Ludila study area. According to the statistics (Fig. 12), the habitats in the study area of different reservoirs in the Jinsha River basin were significantly heterogeneous, with significant differences in climate, soil conditions, topography, and water level drop, etc. Because of the inconsistent range of values and units of different environmental factors, comparative analysis was performed by normalization, as shown in Fig. 12, vegetation cover was significantly correlated with the average soil Ph and the average thickness of the subsurface 30 cm soil layer, and the two study areas with average soil Ph greater than 8, Pear Garden and Rudyra, were almost completely bare. These two study areas were almost dominated by sand and gravel, with thin soils averaging  8 and soil thickness  More

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Resco de Dios et al. claim that the modulation of ENSO on fire in China is weak. They base their claim on the insignificant correlations they find between gridded area and ENSO indices on individual grid points in China. Unlike their analysis of individual grid points, our analyses were based on the covariance of data on these grid points. Combining all grid points, our correlation analysis increases the degree of freedom, raises the likelihood of a significance test, and therefore is reliable and robust. Fire in individual grid points can be noisy on a local scale, while climate plays a more critical role in modulating large-scale fires.Many previous studies revealed the dominant impacts of ENSO in different regions of China7, 8. Resco de Dios et al. stated that the ENSO could only influence the ignitions and thus has little effect on fire activity. In fact, fuel availability and flammability are also key factors in fire occurrence, particularly for large-scale fires9. This is evidenced by the strong correlations between fire occurrence and interannual climate variability.China’s fire policy not only suppresses existing fires but also prevents human-ignited fire occurrences. As revealed in previous studies, the fire suppression policy since 1987 decreased not only burnt areas but also fire occurrences10.The study by Resco de Dios et al. was based on MODIS-derived annual area burned, which differs from our ground-truthed WFAC fire occurrence dataset. The MODIS cannot sufficiently distinguish the wildfire from the frequent crop fires and thus vastly misinterrupt the crop fires as wildfire, especially over the northern plains where forests are rare. Here, we show that the EOF analyses of the WFAC can also reveal the dipole fire pattern between southwestern and southeastern China. We highlight that the dipole fire pattern and ENSO modulation are on large scales. The fire control policy not only suppresses existing fires but also prevents human-ignited fire occurrences, and thus plays an effective role in reducing five activities in China. More

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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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