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    Weather fluctuation can override the effects of integrated nutrient management on fungal disease incidence in the rice fields in Taiwan

    Plant materialRice (Oryza sativa L.) plants used for the experiment were from the collection of Taiwan Agricultural Research Institute. The rice variety (Tainan No. 11) used in this study has enhanced resistance to rice blast. The use of plant materials complies with international, national, and/or institutional guidelines and legislation.Field areaThis study was carried out in experimental rice fields under low-external-input and conventional farming in central Taiwan (23.5859 N, 120.4083 E; 8.0 ha). The annual average temperature ranged from 23 to 25 °C, the annual average relative humidity ranged from 75 to 92%, and the annual rainfall ranged from 1020 to 2873 mm year−1 (average data between 2006 and 2016 measured at a nearby weather station; Fig. 1). The experimental paddy plots were defined by considering the typical dimensions of the agricultural fields in Taiwan (0.5 to 1.0 ha). A long-term experiment was conducted from 2006 to 2016 to study the effects of different agronomic management on biodiversity, productivity, and environment, including traceability system, soil fertility, nitrogen leaching, production costs, disease incidence and severity, the abundance of pest and beneficial insects, and weed dynamics.The treatments consisted of conventional farming with high chemical fertilizer input (CF) and low-external-input farming with low fertilizer input (LF). In the CF farming, we followed the fertilizer recommendations that are constructed to meet the nutrient requirements of the crop. In the LF farming, the chemical fertilizers were largely reduced compared to the recommendation (see next paragraph for the details). The experiment was conducted as a randomized complete block design (RCBD) with four replicates. In agricultural experiments, the RCBD is a standard procedure by grouping experimental units into blocks. For example, the design can control variation in the experiments by considering spatial influences and adjusting the effects of target factors in fields. Each experimental unit consisted of a 0.58 ± 0.16 ha of the area of the field. Additional nutrient management in the LF system includes (1) nitrogen-fixing and cover crops, (2) manure and compost applications, (3) plant and soil nutrient analyses for adjusting fertilization, and (4) reduced tillage. Soil-available potassium gradually decreased during the 10-year study period in the area of the LF system. Over the study period, the LF system achieved the similar level of crop production as that of the CF system (Fig. S1).In our study area, there were two growing seasons within a year: one in the first half of the year (from February to June) and one in the second half (from August to December). The ground fertilizers were applied before rice seedlings were transplanted, followed by additional fertilizations during the tillering and boosting stages. The total amount of fertilizers used for the CF system included 140–180 and 120–140 kg N ha−1, 70–72 and 60 kg P2O5 ha−1, and 85 and 60 kg K2O ha−1 for the first and second seasons, respectively. For the LF system, 100 and 80 kg N ha−1, 30 and 30 kg P2O5 ha−1, and 30 and 30 kg K2O ha−1 were applied in the first and second seasons, respectively. The larger amount of fertilizers for the first season was due to its longer duration. For each rice growing season, fungicides were applied to both farming systems once during the boosting stage. During the fungicide application, a 10% mixture of Cartap plus Probenazole or 6% probenazole for rice blast (both 30 kg ha−1) and 1.5% Furametpyr for sheath blight (20 kg ha−1) were used.Rice disease monitoringThe major rice disease (rice blast; Fig. S2) was monitored biweekly in the CF and LF systems over the two growing seasons per year, with each growing season including (in chronological order) the tillering, flowering, and maturing stages. There was a total of 123 occasions during our study. The plants were disease free when planted out. When the lesion of the rice blast began to appear in the fields from the tillering stage to the maturing stage, the effects of the two treatments (CF and LF systems) in the paddy fields on the disease incidence of rice blast (caused by Pyricularia oryzae) were investigated. For each plot (or experimental unit), the incidence of rice disease was randomly examined at 5 points and for 25 plexuses (i.e., each derived from one primary tiller) per point. The disease incidence was quantified as the percentage of infected plexuses that were determined based on the presence of infected leaves.The area under the disease progress curve (AUDPC) was used to quantify disease incidences over time, and the relative AUDPC ((RAUDPC)) was used because of unequal sampling duration in the growing seasons during our study period. For each plot (or experimental unit), we used the (RAUDPC) to summarize the incidences of disease during each growing season as follows:$$RAUDPC=frac{sum_{i=1}^{n-1}frac{{y}_{i}+{y}_{i+1}}{2}times left({t}_{i+1}-{t}_{i}right)}{100 times left({t}_{n}-{t}_{1}right)},$$
    (1)
    where ({y}_{i}) and ({t}_{i}) are the disease incidence (%) and time (day) at the (i)th observation, respectively, and (n) is the total number of observations.Bayesian modelingWe built a mechanistic model that was applied to assess the interplay within a network of relationships among weather fluctuation, farming system, and disease incidence in the paddy fields. The model describes how (1) temperature and relative humidity together influence the development of primary inoculum, (2) rainfall detaches the fungal spores on the host tissues, and (3) rainfall and wind catch the airborne spores onto the leaf area. These environmental processes determine the disease incidence in the model. In addition, this model considers that farming systems can suppress or accelerate disease incidence. By fitting our model to the observed incidence, Bayesian inference was used as the parameter estimation technique for the models. In addition, we tested the alternative mechanistic hypotheses based on a model-selection criterion and cross vaidation (see subsequent paragraphs).With a linearity assumption, the incidences of disease (RAUDPC) were modeled as an inverse-logit function of the progress rate of the development of primary inoculum ((IP) with values between 0 and 1) and the net catchment of the airborne spores by rainfall and wind ((CT) with values between 0 and 1; when subtracting the detachment of spores by rainfall from the host tissue) as follows:$$RAUDPC=invLogitleft({a}_{f}+{b}_{1}bullet logitleft(avg_IPright)+{b}_{2}bullet logitleft(avg_IPbullet avg_CTright)right),$$
    (2)
    where ({a}_{f}), ({b}_{1}), and ({b}_{2}) describe the constant baseline for different farming systems ((f) = the CF or LF system), the direct effect size of (avg_IP), and the mediating effect size of (avg_CT) through (IP), respectively. The two parameters ((avg_IP) and (avg_CT)) are averaged (IP) and (CT) during the growing season, respectively (see below for details). The effect sizes ({b}_{1}) and ({b}_{2}) have values more than zero. The constant baseline allows the management-specific acting in the model when they can influence the disease incidence.The process rate (IP) was simulated as a function of the temperature response ((fleft(Tright)) with values between 0 and 1) and hourly air relative humidity ((RH,) %) as follows20:$$IP= left{begin{array}{ll}0& mathrm{if}, RH 0) are the steepness and midpoint parameters to control the portion of spores caught by the wind, respectively.The Bayesian framework ‘Stan’49 and its R interface ‘RStan’50 were used to construct and fit the models. There were two competing models: either considering the difference between the CF and LF systems by not fixed to the same values of the constant baseline ({a}_{f}) in Formula (2) or not. For each model, four Markov Chain Monte Carlo (MCMC) chains (for numerical approximations of Bayesian inference) ran, each with 5,000 iterations, and the first half of the iterations of each chain were discarded as burn-in. The R-hat statistic of each parameter approaches a value of 1, indicating model convergence. With a total of 2,000 samples, collected as one sample for every 5 iterations for each chain, the model parameters and their posterior distribution were estimated. To compare the two competing models, we calculated the widely applicable information criterion (WAIC) using the R package ‘loo’51. The best model was determined based on the lowest WAIC. By using the same R package, we also performed the approximate leave-one-out cross-validation (LOO-CV) to estimate the predictive ability of the two Bayesian models. Here, we used the expected log predictive density (ELPD) to be the predictive performance.Compliance with ethical standardsThe authors declare that they have no conflict of interest. This article does not contain any studies involving animals performed by any of the authors. This article does not contain any studies involving human participants performed by any of the authors. More

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    Global seasonal Sentinel-1 interferometric coherence and backscatter data set

    Sentinel-1 data selectionThe Copernicus Sentinel-1 mission was launched by the European Space Agency (ESA) in 2014 with the Sentinel-1A satellite, complemented with the second Sentinel-1B satellite in 2016. Each satellite has a 12-days repeat cycle. Continuity of the Sentinel-1 mission has been approved by ESA until 2030 and replacement satellites will be launched. The satellites operate in different acquisition modes over different parts of the globe. Land masses are covered primarily by the Interferometric Wide-Swath Mode (IW) with a 250 km swath width across-track. Single-look-complex (SLC) Level 1.1 data are required for interferometric processing. Along-track, Sentinel-1 data are sliced into consecutive frames (slices) of about 250 km length. Data are distributed via ESA’s Scientific Sentinel-1 Hub, which is mirrored at NASA’s Alaska Satellite Facility DAAC (ASF-DAAC). During production, Sentinel-1 SLC data were accessed on the ASF-DAAC data repository which resides on Amazon’s AWS S3 bucket in region us-west-2.Sentinel-1 satellites cover various parts of Earth in ascending and descending flight direction in a total of 175 relative orbits. ESA’s flight plan has some areas covered every six days and in both flight directions, predominantly over Europe. For the production of this data set, Sentinel-1 SLC frames were selected from all available scenes acquired between December 1st 2019 and November 30th 2020. Over the one-year timeframe, a maximum of 30 to 31 acquisitions at 12-days repeat, and 60 to 61 acquisitions at 6-days repeat intervals can be expected. The following selection criteria were applied consecutively to achieve global coverage with uniform distribution of acquisitions across seasons (Fig. 1):

    Global descending data (Fig. 1a) were selected where the one-year stack size had at least 25 acquisitions.

    Spatial gaps were filled with ascending data (Fig. 1a) where the one-year stack size had at least 25 acquisitions.

    For spatial consistency, over conterminous North America north of Panama, preference was given to ascending data where both ascending and descending data existed with stack sizes over 25 acquisitions.

    For stack sizes less than 25 acquisitions, preference was given to the flight direction with the larger number of acquisitions.

    Remaining gaps were filled with data from the flight direction available.

    Fig. 1Flight direction, polarization mode, and InSAR stack sizes of 6- and 12-days repeat coverage of Sentinel-1 data acquired between December 1st 2019 and November 30th 2020 selected for processing.Full size imageArctic and Antarctic regions are typically covered with polarization modes of horizontal transmit (HH single- or HH/HV dual-polarization). Figure 1b shows the global distribution of the processed data in horizontal and vertical polarization transmit modes, respectively. Table 1 summarizes the number of selected scenes in the two flight directions and various polarization modes. The total number of processed Sentinel-1 SLC frames came to ~205,000 scenes with a total raw input data volume of about 850 Terabytes. Figure 1c,d show the spatial distribution of the final scene selection with the number of 6- and 12-days repeat-pass image pairs. Consistent 6-days repeat coverage with about sixty image pairs from either ascending or descending orbits could be processed over Europe, the coastal areas of Greenland and Antarctica, and some smaller areas around the world; note that in some regions (e.g., India, interior Greenland, Northern Canada, Eastern China) 6-days repeat coverage was available in certain seasons only (Fig. 1c). A consistent coverage with 12-days repeat-pass imagery, instead, could be processed almost globally with the nominal maximum of about thirty repeat-pass pairs in areas where only one satellite, Sentinel-1A or Sentinel-1B, acquired data in all but few areas above 60° N in Canada, Greenland, or Russia (Fig. 1d). In some small areas in the Midwestern United States, the Khabarovsk region in Far-Eastern Russia, or in the Northern Sahara, neither Sentinel-1A nor Sentinel-1B acquire data in IW mode, leading to small gaps in the final data set.Table 1 Number of Sentinel-1 Single Look Complex scenes processed.Full size tableProcessing approachThe overall processing workflow was developed based on the interferometric processing software developed by GAMMA Remote Sensing and geared towards efficient processing in the Amazon Web Services (AWS) cloud environment utilizing Earth Big Data LLC’s cloud scaling solutions. The workflow is divided into three main blocks as illustrated in Fig. 2. Sentinel-1A and -1B acquire data along 175 relative orbits/orbital tracks. Blocks 1 and 2 were processed on a per relative orbit basis; block 3 was initiated after blocks 1 and 2 had been completed for all relative orbits.Fig. 2Implementation of the Sentinel-1 interferometric processor in the AWS cloud environment.Full size imageProcessing Block 1For each SLC of a given relative orbit, processing block 1 entailed:

    1.

    Conversion of SLC image files to a GAMMA specific format. Each Sentinel-1 SLC, covering an area of ~250 × 250 km, consists of six SLC image files (one SLC image file for each of the three sub-swaths in co- (VV or HH) and cross-polarizations (VH or HV).

    2.

    Compensation of the SLC amplitudes for the noise equivalent sigma zero (NESZ).

    3.

    The orbit state vectors provided with the original Sentinel-1 SLCs were updated with the precision state vectors (AUX_POEORB) distributed by the Sentinel-1 payload data ground segment 20 days after data take with a precision (3σ) generally of the order of 1 cm (target requirement  More

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    Pet-directed speech improves horses’ attention toward humans

    Jardat, P. & Lansade, L. Cognition and the human–animal relationship: a review of the sociocognitive skills of domestic mammals toward humans. Anim. Cogn. https://doi.org/10.1007/s10071-021-01557-6 (2021).Article 
    PubMed 

    Google Scholar 
    Knolle, F., Goncalves, R. P. & Jennifer Morton, A. Sheep recognize familiar and unfamiliar human faces from two-dimensional images. R. Soc. Open Sci. 4, 171228 (2017).Nawroth, C. & McElligott, A. G. Human head orientation and eye visibility as indicators of attention for goats (Capra hircus). PeerJ 5, e3073 (2017).Albuquerque, N. et al. Dogs recognize dog and human emotions. Biol. Lett. 12, 20150883 (2016).Article 

    Google Scholar 
    Albuquerque, N., Guo, K., Wilkinson, A., Resende, B. & Mills, D. S. Mouth-licking by dogs as a response to emotional stimuli. Behav. Processes 146, 42–45 (2018).Article 

    Google Scholar 
    Quaranta, A., D’ingeo, S., Amoruso, R. & Siniscalchi, M. Emotion recognition in cats. Animals 10, 1107 (2020).Sabiniewicz, A., Tarnowska, K., Świątek, R., Sorokowski, P. & Laska, M. Olfactory-based interspecific recognition of human emotions: Horses (Equus ferus caballus) can recognize fear and happiness body odour from humans (Homo sapiens). Appl. Anim. Behav. Sci. 230, 105072 (2020).Smith, A. V., Proops, L., Grounds, K., Wathan, J. & McComb, K. Functionally relevant responses to human facial expressions of emotion in the domestic horse (Equus caballus). Biol. Lett. 12, 20150907 (2016).Article 

    Google Scholar 
    Smith, A. V. et al. Domestic horses (Equus caballus) discriminate between negative and positive human nonverbal vocalisations. Sci. Rep. 8, 13052 (2018).ADS 
    Article 

    Google Scholar 
    Nakamura, K., Takimoto-Inose, A. & Hasegawa, T. Cross-modal perception of human emotion in domestic horses (Equus caballus). Sci. Rep. 8, 8660 (2018).ADS 
    Article 

    Google Scholar 
    Trösch, M. et al. Horses categorize human emotions cross-modally based on facial expression and non-verbal vocalizations. Animals 9, 862 (2019).Article 

    Google Scholar 
    Sankey, C., Henry, S., André, N., Richard-Yris, M. A. & Hausberger, M. Do horses have a concept of person? PLoS One 6, e18331 (2011).Trösch, M., Bertin, E., Calandreau, L., Nowak, R. & Lansade, L. Unwilling or willing but unable: can horses interpret human actions as goal directed?. Anim. Cogn. 23, 1035–1040 (2020).Article 

    Google Scholar 
    Warmuth, V. et al. Reconstructing the origin and spread of horse domestication in the Eurasian steppe. Proc. Natl. Acad. Sci. 109, 8202–8206 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    VanDierendonck, M. C. & Goodwin, D. Social contact in horses: implications for human-horse interactions. in The human-animal relationship. Forever and a day (eds. de Jonge, F. H. & van den Bos, R.) 65–81 (Royal van Gorcum, 2005).Saint-Georges, C. et al. Motherese in Interaction: At the Cross-Road of Emotion and Cognition? (A Systematic Review). PLoS ONE 8, 78103 (2013).ADS 
    Article 

    Google Scholar 
    Benjamin, A. & Slocombe, K. ‘Who’s a good boy?!’ Dogs prefer naturalistic dog-directed speech. Anim. Cogn. 21, 353–364 (2018).Article 

    Google Scholar 
    Ben-Aderet, T., Gallego-Abenza, M., Reby, D. & Mathevon, N. Dog-directed speech: Why do we use it and do dogs pay attention to it?. Proc. R. Soc. B Biol. Sci. 284, 20162429 (2017).Article 

    Google Scholar 
    Jeannin, S., Gilbert, C., Amy, M. & Leboucher, G. Pet-directed speech draws adult dogs’ attention more efficiently than Adult-directed speech. Sci. Rep. 7, 4980 (2017).ADS 
    Article 

    Google Scholar 
    Lesch, R. et al. Talking to dogs: Companion animal-directed speech in a stress test. Animals 9, 417 (2019).Article 

    Google Scholar 
    Lansade, L. et al. Horses are sensitive to baby talk : Pet-directed speech facilitates communication with humans in a pointing task and during grooming. Anim. Cogn. 5, 999–1006 (2021).Article 

    Google Scholar 
    Schachner, A. & Hannon, E. E. Infant-Directed Speech Drives Social Preferences in 5-Month-Old Infants. Dev. Psychol. 47, 19–25 (2011).Article 

    Google Scholar 
    Fernald, A. Approval and Disapproval: Infant Responsiveness to Vocal Affect in Familiar and Unfamiliar Languages. Child Dev. 64, 657–674 (1993).CAS 
    Article 

    Google Scholar 
    Slonecker, E. M., Simpson, E. A., Suomi, S. J. & Paukner, A. Who’s my little monkey? Effects of infant-directed speech on visual retention in infant rhesus macaques. Dev. Sci. 21, 12519 (2018).Article 

    Google Scholar 
    Kaplan, P. S., Goldstein, M. H., Huckeby, E. R. & Cooper, R. P. Habituation, sensitization, and infants’ responses to motherse speech. Dev. Psychobiol. 28, 45–57 (1995).CAS 
    Article 

    Google Scholar 
    Lansade, L. et al. Facial expression and oxytocin as possible markers of positive emotions in horses. Sci. Rep. 8, 14680 (2018).ADS 
    Article 

    Google Scholar 
    Hausberger, M. et al. Mutual interactions between cognition and welfare: The horse as an animal model. Neurosci. Biobehav. Rev. 107, 540–559 (2019).CAS 
    Article 

    Google Scholar 
    Fortin, M. et al. Emotional state and personality influence cognitive flexibility in horses (Equus caballus). J. Comp. Psychol. 132, 130–140 (2018).Article 

    Google Scholar 
    Trösch, M. et al. Horses feel emotions when they watch positive and negative horse–human interactions in a video and transpose what they saw to real life. Anim. Cogn. 23, 643–653 (2020).Article 

    Google Scholar 
    Forkman, B., Boissy, A., Meunier-Salaün, M. C., Canali, E. & Jones, R. B. A critical review of fear tests used on cattle, pigs, sheep, poultry and horses. Physiol. Behav. 92, 340–374 (2007).CAS 
    Article 

    Google Scholar 
    Lansade, L., Bouissou, M. F. & Erhard, H. W. Fearfulness in horses: A temperament trait stable across time and situations. Appl. Anim. Behav. Sci. 115, 182–200 (2008).Article 

    Google Scholar 
    Stomp, M. et al. An unexpected acoustic indicator of positive emotions in horses. PLoS One 13, e0197898 (2018).Briefer, E. F. et al. Segregation of information about emotional arousal and valence in horse whinnies. Sci. Rep. 5, 9989 (2015).ADS 
    Article 

    Google Scholar 
    Briefer, E. F., Tettamanti, F. & McElligott, A. G. Emotions in goats: Mapping physiological, behavioural and vocal profiles. Anim. Behav. 99, 131–143 (2015).Article 

    Google Scholar 
    Mendl, M., Burman, O. H. P. & Paul, E. S. An integrative and functional framework for the study of animal emotion and mood. in Proceedings of the Royal Society B: Biological Sciences vol. 277 2895–2904 (Royal Society, 2010).Siniscalchi, M., D’Ingeo, S. & Quaranta, A. Orienting asymmetries and physiological reactivity in dogs’ response to human emotional faces. Learn. Behav. 46, 574–585 (2018).Article 

    Google Scholar 
    Munsters, C. C. B. M., Visser, K. E. K., van den Broek, J. & Sloet van Oldruitenborgh-Oosterbaan, M. M. The influence of challenging objects and horse-rider matching on heart rate, heart rate variability and behavioural score in riding horses. Vet. J. 192, 75–80 (2012).Siniscalchi, M., D’Ingeo, S., Minunno, M. & Quaranta, A. Communication in dogs. Animals 8, 131 (2018).Article 

    Google Scholar 
    Call, J., Hare, B., Carpenter, M. & Tomasello, M. ‘Unwilling’ versus ‘unable’: Chimpanzees’ understanding of human intentional action. Dev. Sci. 7, 488–498 (2004).Article 

    Google Scholar 
    Kaminski, J., Schulz, L. & Tomasello, M. How dogs know when communication is intended for them. Dev. Sci. 15, 222–232 (2012).Article 

    Google Scholar 
    Pongrácz, P., Szapu, J. S. & Faragó, T. Cats (Felis silvestris catus) read human gaze for referential information. Intelligence 74, 43–52 (2019).Article 

    Google Scholar 
    Pongrácz, P. & Onofer, D. L. Cats show an unexpected pattern of response to human ostensive cues in a series of A-not-B error tests. Anim. Cogn. 23, 681–689 (2020).Article 

    Google Scholar 
    Proops, L., Grounds, K., Smith, A. V. & McComb, K. Animals remember previous facial expressions that specific humans have exhibited. Curr. Biol. 28, 1428-1432.e4 (2018).CAS 
    Article 

    Google Scholar 
    Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15, 155–163 (2016).Article 

    Google Scholar 
    von Borell, E. et al. Heart rate variability as a measure of autonomic regulation of cardiac activity for assessing stress and welfare in farm animals—A review. Physiol. Behav. 92, 293–316 (2007).Article 

    Google Scholar  More

  • in

    Confronting the water potential information gap

    Brutsaert, W. Hydrology: An Introduction (Cambridge Univ. Press, 2005).Philip, J. Plant water relations: some physical aspects. Annu. Rev. Plant Physiol. 17, 245–268 (1966).
    Google Scholar 
    Ghezzehei, T. A., Sulman, B., Arnold, C. L., Bogie, N. A. & Berhe, A. A. On the role of soil water retention characteristic on aerobic microbial respiration. Biogeosciences 16, 1187–1209 (2019).
    Google Scholar 
    Boyer, J. Differing sensitivity of photosynthesis to low leaf water potentials in corn and soybean. Plant Physiol. 46, 236–239 (1970).
    Google Scholar 
    Jarvis, P. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Phil. Trans. R. Soc. Lond. B 273, 593–610 (1976).
    Google Scholar 
    Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).
    Google Scholar 
    Tyree, M. T. & Sperry, J. S. Vulnerability of xylem to cavitation and embolism. Annu. Rev. Plant Biol. 40, 19–36 (1989).
    Google Scholar 
    Whalley, W., Ober, E. & Jenkins, M. J. J. Measurement of the matric potential of soil water in the rhizosphere. J. Exp. Biol. 64, 3951–3963 (2013).
    Google Scholar 
    Yu, H., Yang, P. & Lin, H. Spatiotemporal patterns of soil matric potential in the Shale Hills Critical Zone Observatory. Vadose Zone J. https://doi.org/10.2136/vzj2014.11.0167 (2015).Campbell, G. S. A simple method for determining unsaturated conductivity from moisture retention data. Soil Sci. 117, 311–314 (1974).
    Google Scholar 
    van Genuchten, M. T. A closed‐form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980).
    Google Scholar 
    Dorigo, W. et al. The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci. 15, 1675–1698 (2011).Scott, B. L. et al. New soil property database improves Oklahoma Mesonet soil moisture estimates. J. Atmos. Ocean. Technol. 30, 2585–2595 (2013).
    Google Scholar 
    Campbell, G. S. Soil water potential measurement: an overview. Irrig. Sci. 9, 265–273 (1988).
    Google Scholar 
    Van Looy, K. et al. Pedotransfer functions in Earth system science: challenges and perspectives. Rev. Geophys. 55, 1199–1256 (2017).
    Google Scholar 
    Clapp, R. B. & Hornberger, G. M. Empirical equations for some soil hydraulic properties. Water Resour. Res. 14, 601–604 (1978).
    Google Scholar 
    Cosby, B., Hornberger, G., Clapp, R. & Ginn, T. A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res. 20, 682–690 (1984).
    Google Scholar 
    Zhang, Y. & Schaap, M. G. Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). J. Hydrol. 547, 39–53 (2017).
    Google Scholar 
    Fatichi, S. et al. Soil structure is an important omission in Earth system models. Nat. Commun. 11, 522 (2020).
    Google Scholar 
    Ghezzehei, T. A. & Albalasmeh, A. A. Spatial distribution of rhizodeposits provides built-in water potential gradient in the rhizosphere. Ecol. Modell. 298, 53–63 (2015).
    Google Scholar 
    Leung, A. K., Garg, A. & Ng, C. W. W. Effects of plant roots on soil-water retention and induced suction in vegetated soil. Eng. Geol. 193, 183–197 (2015).
    Google Scholar 
    Caplan, J. S. et al. Decadal-scale shifts in soil hydraulic properties as induced by altered precipitation. Sci. Adv. 5, eaau6635 (2019).
    Google Scholar 
    Peña-Sancho, C., López, M., Gracia, R. & Moret-Fernández, D. Effects of tillage on the soil water retention curve during a fallow period of a semiarid dryland. Soil Res. 55, 114–123 (2017).
    Google Scholar 
    Stoof, C. R., Wesseling, J. G. & Ritsema, C. J. Effects of fire and ash on soil water retention. Geoderma 159, 276–285 (2010).
    Google Scholar 
    Gutmann, E. & Small, E. The effect of soil hydraulic properties vs. soil texture in land surface models. Geophys. Res. Lett. 32, L02402 (2005).
    Google Scholar 
    Weihermüller, L. et al. Choice of pedotransfer functions matters when simulating soil water balance fluxes. J. Adv. Model. Earth Syst. 13, e2020MS002404 (2021).
    Google Scholar 
    Shi, Y., Davis, K. J., Zhang, F. & Duffy, C. J. Evaluation of the parameter sensitivities of a coupled land surface hydrologic model at a critical zone observatory. J. Hydrometeorol. 15, 279–299 (2014).
    Google Scholar 
    Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J. & Yu, X. J. Parameter estimation of a physically-based land surface hydrologic model using an ensemble Kalman filter: a multivariate real-data experiment. Adv. Water Res. 83, 421–427 (2015).
    Google Scholar 
    Shi, Y. et al. Simulating high‐resolution soil moisture patterns in the Shale Hills watershed using a land surface hydrologic model. Hydrol. Process. 29, 4624–4637 (2015).
    Google Scholar 
    Sobol, I. M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55, 271–280 (2001).
    Google Scholar 
    Boucher, O. et al. Presentation and evaluation of the IPSL‐CM6A‐LR climate model. J. Adv. Model. Earth Syst. 12, e2019MS002010 (2020).
    Google Scholar 
    Lurton, T. et al. Implementation of the CMIP6 forcing data in the IPSL‐CM6A‐LR model. J. Adv. Model. Earth Syst. 12, e2019MS001940 (2020).
    Google Scholar 
    Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).
    Google Scholar 
    Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951–954 (2010).
    Google Scholar 
    Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).
    Google Scholar 
    Feldman, A. F., Short Gianotti, D. J., Trigo, I. F., Salvucci, G. D. & Entekhabi, D. Satellite‐based assessment of land surface energy partitioning–soil moisture relationships and effects of confounding variables. Water Resour. Res. 55, 10657–10677 (2019).
    Google Scholar 
    Stocker, B. D. et al. Quantifying soil moisture impacts on light use efficiency across biomes. N. Phytol. 218, 1430–1449 (2018).
    Google Scholar 
    Baldocchi, D. D., Xu, L. & Kiang, N. How plant functional-type, weather, seasonal drought, and soil physical properties alter water and energy fluxes of an oak–grass savanna and an annual grassland. Agric. For. Meteorol. 123, 13–39 (2004).
    Google Scholar 
    Trugman, A. T., Anderegg, L. D., Shaw, J. D. & Anderegg, W. R. Trait velocities reveal that mortality has driven widespread coordinated shifts in forest hydraulic trait composition. Proc. Natl Acad. Sci. USA 117, 8532–8538 (2020).
    Google Scholar 
    McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? N. Phytol. 178, 719–739 (2008).
    Google Scholar 
    Martínez-Vilalta, J. et al. Towards a statistically robust determination of minimum water potential and hydraulic risk in plants. New Phytol. 232, 404–417 (2021).Taiz, L., Zeiger, E., Møller, I. M. & Murphy, A. Plant Physiology and Development 6th edn (Sinauer Associates, 2015).Scholander, P. F., Bradstreet, E. D., Hemmingsen, E. & Hammel, H. Sap pressure in vascular plants: negative hydrostatic pressure can be measured in plants. Science 148, 339–346 (1965).
    Google Scholar 
    Martínez‐Vilalta, J., Poyatos, R., Aguadé, D., Retana, J. & Mencuccini, M. A new look at water transport regulation in plants. N. Phytol. 204, 105–115 (2014).
    Google Scholar 
    Grossiord, C. et al. Plant responses to rising vapor pressure deficit. N. Phytol. 226, 1550–1566 (2020).
    Google Scholar 
    Matheny, A. M. et al. Observations of stem water storage in trees of opposing hydraulic strategies. Ecosphere https://doi.org/10.1890/es15-00170.1 (2015).Wood, J. D., Knapp, B. O., Muzika, R.-M., Stambaugh, M. C. & Gu, L. The importance of drought–pathogen interactions in driving oak mortality events in the Ozark Border Region. Environ. Res. Lett. 13, 015004 (2018).
    Google Scholar 
    Hinckley, T. M., Lassoie, J. P. & Running, S. W. Temporal and spatial variations in the water status of forest trees. For. Sci. 24, a0001–z0001 (1978).
    Google Scholar 
    Marks, C. O. & Lechowicz, M. J. The ecological and functional correlates of nocturnal transpiration. Tree Physiol. 27, 577–584 (2007).
    Google Scholar 
    O’Keefe, K. & Nippert, J. B. Drivers of nocturnal water flux in a tallgrass prairie. Funct. Ecol. 32, 1155–1167 (2018).
    Google Scholar 
    Donovan, L., Linton, M. & Richards, J. Predawn plant water potential does not necessarily equilibrate with soil water potential under well-watered conditions. Oecologia 129, 328–335 (2001).
    Google Scholar 
    Kannenberg, S. A. et al. Opportunities, challenges and pitfalls in characterizing plant water‐use strategies. Funct. Ecol. 36, 24–37 (2022).Oliveira, R. S. et al. Linking plant hydraulics and the fast–slow continuum to understand resilience to drought in tropical ecosystems. New Phytol. 230, 904–923 (2021).Feng, X. et al. Beyond isohydricity: the role of environmental variability in determining plant drought responses. Plant Cell Environ. 42, 1104–1111 (2019).
    Google Scholar 
    Guo, J. S., Hultine, K. R., Koch, G. W., Kropp, H. & Ogle, K. Temporal shifts in iso/anisohydry revealed from daily observations of plant water potential in a dominant desert shrub. N. Phytol. 225, 713–726 (2020).
    Google Scholar 
    Hochberg, U., Rockwell, F. E., Holbrook, N. M. & Cochard, H. Iso/anisohydry: a plant–environment interaction rather than a simple hydraulic trait. Trends Plant Sci. 23, 112–120 (2018).
    Google Scholar 
    Novick, K. A., Konings, A. G. & Gentine, P. Beyond soil water potential: an expanded view on isohydricity including land–atmosphere interactions and phenology. Plant Cell Environ. 42, 1802–1815 (2019).
    Google Scholar 
    McCulloh, K. A. et al. A dynamic yet vulnerable pipeline: integration and coordination of hydraulic traits across whole plants. Plant Cell Environ. 42, 2789–2807 (2019).
    Google Scholar 
    Kennedy, D. et al. Implementing plant hydraulics in the Community Land Model, version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).
    Google Scholar 
    Mirfenderesgi, G., Matheny, A. M. & Bohrer, G. Hydrodynamic trait coordination and cost–benefit trade‐offs throughout the isohydric–anisohydric continuum in trees. Ecohydrology 12, e2041 (2019).
    Google Scholar 
    Xu, X., Medvigy, D., Powers, J. S., Becknell, J. M. & Guan, K. Diversity in plant hydraulic traits explains seasonal and inter‐annual variations of vegetation dynamics in seasonally dry tropical forests. N. Phytol. 212, 80–95 (2016).
    Google Scholar 
    De Kauwe, M. G. et al. Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe. Biogeosciences 12, 7503–7518 (2015).
    Google Scholar 
    Meinzer, F. C. et al. Converging patterns of uptake and hydraulic redistribution of soil water in contrasting woody vegetation types. Tree Physiol. 24, 919–928 (2004).
    Google Scholar 
    Scott, R. L., Cable, W. L. & Hultine, K. R. The ecohydrologic significance of hydraulic redistribution in a semiarid savanna. Water Resour. Res. 44, W02440 (2008).
    Google Scholar 
    Tyree, M. T. & Ewers, F. W. The hydraulic architecture of trees and other woody plants. N. Phytol. 119, 345–360 (1991).
    Google Scholar 
    Johnson, D. M. et al. A test of the hydraulic vulnerability segmentation hypothesis in angiosperm and conifer tree species. Tree Physiol. 36, 983–993 (2016).
    Google Scholar 
    Lehto, T. & Zwiazek, J. J. Ectomycorrhizas and water relations of trees: a review. Mycorrhiza 21, 71–90 (2011).
    Google Scholar 
    Bezerra-Coelho, C. R., Zhuang, L., Barbosa, M. C., Soto, M. A. & Van Genuchten, M. T. Further tests of the HYPROP evaporation method for estimating the unsaturated soil hydraulic properties. J. Hydrol. Hydromech. 66, 161–169 (2018).
    Google Scholar 
    Wullschleger, S., Dixon, M. & Oosterhuis, D. Field measurement of leaf water potential with a temperature‐corrected in situ thermocouple psychrometer. Plant Cell Environ. 11, 199–203 (1988).
    Google Scholar 
    Holtzman, N. M. et al. L-band vegetation optical depth as an indicator of plant water potential in a temperate deciduous forest stand. Biogeosciences 18, 739–753 (2021).
    Google Scholar 
    Nagy, R. C. et al. Harnessing the NEON data revolution to advance open environmental science with a diverse and data‐capable community. Ecosphere 12, e03833 (2021).
    Google Scholar 
    Novick, K. A. et al. The AmeriFlux network: a coalition of the willing. Agric. For. Meteorol. 249, 444–456 (2018).
    Google Scholar 
    Baldocchi, D. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56, 1–26 (2008).
    Google Scholar 
    Poyatos, R. et al. Global transpiration data from sap flow measurements: the SAPFLUXNET database. Earth Syst. Sci. Data 13, 2607–2649 (2021).Jackson, T. & Schmugge, T. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 36, 203–212 (1991).
    Google Scholar 
    Konings, A. G., Rao, K. & Steele‐Dunne, S. C. Macro to micro: microwave remote sensing of plant water content for physiology and ecology. N. Phytol. 223, 1166–1172 (2019).
    Google Scholar 
    Konings, A. G. et al. Detecting forest response to droughts with global observations of vegetation water content. Glob. Change Biol. https://doi.org/10.1111/gcb.15872 (2021).Momen, M. et al. Interacting effects of leaf water potential and biomass on vegetation optical depth. J. Geophys. Res. Biogeosci. 122, 3031–3046 (2017).
    Google Scholar 
    Simunek, J., Van Genuchten, M. T. & Sejna, M. The HYDRUS-1D Software Package for Simulating the One-Dimensional Movement of Water, Heat, and Multiple Solutes in Variably-Saturated Media (Dept Environ. Sci. Univ. California Riverside, 2005).Naylor, S., Letsinger, S., Ficklin, D., Ellett, K. & Olyphant, G. A hydropedological approach to quantifying groundwater recharge in various glacial settings of the mid‐continental USA. Hydrol. Process. 30, 1594–1608 (2016).
    Google Scholar 
    Urbanski, S. et al. Factors controlling CO2 exchange on timescales from hourly to decadal at Harvard Forest. J. Geophys. Res. Biogeosci. 112, G02020 (2007).
    Google Scholar 
    Thum, T. et al. Parametrization of two photosynthesis models at the canopy scale in a northern boreal Scots pine forest. Tellus B 59, 874–890 (2007).
    Google Scholar 
    Ardö, J., Mölder, M., El-Tahir, B. A. & Elkhidir, H. A. M. Seasonal variation of carbon fluxes in a sparse savanna in semi arid Sudan. Carbon Balance Manage. 3, 7 (2008).
    Google Scholar 
    Roman, D. T. et al. The role of isohydric and anisohydric species in determining ecosystem-scale response to severe drought. Oecologia 179, 641–654 (2015).
    Google Scholar 
    Fu, C. et al. Combined measurement and modeling of the hydrological impact of hydraulic redistribution using CLM4.5 at eight AmeriFlux sites. Hydrol. Earth Syst. Sci. 20, 2001–2018 (2016).
    Google Scholar 
    Liang, J. et al. Evaluating the E3SM land model version 0 (ELMv0) at a temperate forest site using flux and soil water measurements. Geosci. Model Dev. 12, 1601–1612 (2019).Herman, J. & Usher, W. SALib: an open-source Python library for sensitivity analysis. J. Open Source Softw. https://doi.org/10.21105/joss.00097 (2017). More

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    Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019

    Daily change in primary pollutantsTo elucidate the change trend of primary pollutants under the 13th Five-Year Plan, we calculated the daily primary pollutants in 2015 and 2019 based on formula (1) and formula (2). Such diurnal comparisons can reduce the effects of seasonal weather to some extent. From the 19 UAs (224 prefecture-level cities), the heat diagram of the daily change transfer matrix of primary pollutants from 2015 to 2019 is shown in Fig. 2, including six primary pollutants and clean day conditions.Figure 2Transfer change matrix heatmap of primary pollutants from 2015 to 2019.Full size imageFrom the sum of the diagonal numbers, 37% of the primary pollutants had no shift during the 13th Five-Year Plan period. PM2.5, PM10 and O3 were the main primary pollutants, especially PM2.5. More primary pollutants were diverted to ozone pollution, indicating that the proportion of O3 as the primary pollutant is gradually increasing. In addition, the proportion of clean air has increased significantly, which shows that pollution control has been effectively reflected during the 13th Five-Year Plan period. However, the proportion of NO2 before and after metastasis was approximately the same, with approximately 5% NO2 pollution. This may imply that the governance of NO2 pollution was rendered nonsignificant. It is noteworthy that ozone pollution in China has become an increasingly prominent task in recent years. Similar to Xiao’s16 research on ozone pollution, they argue that present-day ozone levels in major Chinese cities are comparable to or even higher than the 1980 levels in the United States. Taken together, ozone and PM2.5 have become the top two air pollution pollutants in China.Monthly distribution of primary pollutantsTo further explore the spatiotemporal distribution of the primary pollutants across the UAs, we obtained the most primary pollutants per month by dividing the number of days with the most pollutants by the number of cities in each UA from the 2019 data. In Fig. 3, the UAs location was plotted on the abscissa, and the monthly variance of the primary pollutant was plotted on the ordinate. As shown in Fig. 3, PM2.5 appeared as dark green, PM10 appeared as light green, O3 appeared as orange, NO2 appeared as yellow, and clean days appear as dark blue. The main pollutants in the 19 UAs are PM2.5, PM10 and O3. NO2, as the primary pollutant, only appeared in the HBOY UA in January. Ordos, located in HBOY, possess rich oil and coal resources, with coal mining as its leading industry38. According to the China Energy Statistical Yearbook 2019, nearly 250 million tons of raw coal were used for thermal power generation in Inner Mongolia Autonomous Region, making it the region with the largest amount of raw coal for thermal power generation in China39. To a certain extent, the increase of heating40 and the imperfect denitration technology41 are both contributing to the increase of NO2 pollution in the atmosphere. CO and SO2 did not become major pollutants. Clean days (where AQI  More

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    Mild chronic exposure to pesticides alters physiological markers of honey bee health without perturbing the core gut microbiota

    European Commission. Report from the commission to the European Parliament and the council on the implementation of the measures concerning the apiculture sector of Regulation (EU) No 1308/2013 of the European Parliament and of the Council establishing a common organisation of the markets in agricultural products. p. 1–16. https://eur-lex.europa.eu/legal-content/en/ALL/?uri=CELEX:52016DC0776 (2016).Motta, E. V. S. & Moran, N. A. Impact of glyphosate on the honey bee gut microbiota: Effects of intensity, duration, and timing of exposure. msystems 5, e00268-e1220. https://doi.org/10.1128/mSystems.00268-20 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B-Biol. Sci. 274, 303–313. https://doi.org/10.1098/rspb.2006.3721 (2007).Article 

    Google Scholar 
    Ollerton, J. Pollinator diversity: Distribution, ecological function, and conservation. Annu. Rev. Ecol. Evol. Syst. 48, 353–376. https://doi.org/10.1146/annurev-ecolsys-110316-022919 (2017).Article 

    Google Scholar 
    Greenleaf, S. S. & Kremen, C. Wild bees enhance honey bees’ pollination of hybrid sunflower. PNAS 103, 13890–13895. https://doi.org/10.1073/pnas.0600929103 (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Williams, I. H. The dependence of crop production within the European Union on pollination by honey bees. Agric. Zool. Rev. 20, 20 (1994).
    Google Scholar 
    Potts, S. G. et al. Declines of managed honey bees and beekeepers in Europe. J. Apic. Res. 49, 15–22. https://doi.org/10.3896/ibra.1.49.1.02 (2010).Article 

    Google Scholar 
    Vanengelsdorp, D., Hayes, J., Underwood, R. M. & Pettis, J. A survey of honey bee colony losses in the US, fall 2007 to spring 2008. PLoS One 3, 6. https://doi.org/10.1371/journal.pone.0004071 (2008).CAS 
    Article 

    Google Scholar 
    Chagnon, M. Fédération Canadienne de la Faune (Bureau régional du Québec, 2008).
    Google Scholar 
    Schreinemachers, P. & Tipraqsa, P. Agricultural pesticides and land use intensification in high, middle and low income countries. Food Policy 37, 616–626. https://doi.org/10.1016/j.foodpol.2012.06.003 (2012).Article 

    Google Scholar 
    Haber, A. I., Steinhauer, N. A. & vanEngelsdorp, D. Use of chemical and nonchemical methods for the control of Varroa destructor (Acari: Varroidae) and associated winter colony losses in US beekeeping operations. J. Econ. Entomol. https://doi.org/10.1093/jee/toz088 (2019).Article 
    PubMed 

    Google Scholar 
    Le Conte, Y., Ellis, M. & Ritter, W. Varroa mites and honey bee health: Can Varroa explain part of the colony losses?. Apidologie 41, 353–363. https://doi.org/10.1051/apido/2010017 (2010).Article 

    Google Scholar 
    Ellis, J. D., Evans, J. D. & Pettis, J. Colony losses, managed colony population decline, and colony collapse disorder in the United States. J. Apic. Res. 49, 134–136. https://doi.org/10.3896/IBRA.1.49.1.30 (2010).Article 

    Google Scholar 
    Chauzat, M. P. et al. Influence of pesticide residues on honey bee (Hymenoptera: Apidae) colony health in France. Environ. Entomol 38, 514–523. https://doi.org/10.1603/022.038.0302 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Juan-Borras, M., Domenech, E. & Escriche, I. Mixture-risk-assessment of pesticide residues in retail polyfloral honey. Food Control 67, 127–134. https://doi.org/10.1016/j.foodcont.2016.02.051 (2016).CAS 
    Article 

    Google Scholar 
    Kasiotis, K. M., Anagnostopoulos, C., Anastasiadou, P. & Machera, K. Pesticide residues in honeybees, honey and bee pollen by LC–MS/MS screening: Reported death incidents in honeybees. Sci. Total. Environ 485–486, 633–642. https://doi.org/10.1016/j.scitotenv.2014.03.042 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Mullin, C. A. et al. High levels of miticides and agrochemicals in north american apiaries: Implications for honey bee health. PLoS One 5, 19. https://doi.org/10.1371/journal.pone.0009754 (2010).CAS 
    Article 

    Google Scholar 
    Brandt, A., Gorenflo, A., Siede, R., Meixner, M. & Buchler, R. The neonicotinoids thiacloprid, imidacloprid, and clothianidin affect the immunocompetence of honey bees (Apis mellifera L.). J. Insect. Physiol. 86, 40–47. https://doi.org/10.1016/j.jinsphys.2016.01.001 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Alptekin, S. et al. Induced thiacloprid insensitivity in honeybees (Apis mellifera L.) is associated with up-regulation of detoxification genes. Insect Mol. Biol. 25, 171–180. https://doi.org/10.1111/imb.12211 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Tesovnik, T. et al. Exposure of honey bee larvae to thiamethoxam and its interaction with Nosema ceranae infection in adult honey bees. Environ. Pollut. 256, 113443. https://doi.org/10.1016/j.envpol.2019.113443 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Gregore, A. et al. Effects of coumaphos and imidacloprid on honey bee (Hymenoptera: Apidae) lifespan and antioxidant gene regulations in laboratory experiments. Sci. Rep. https://doi.org/10.1038/s41598-018-33348-4 (2018).Article 

    Google Scholar 
    Schneider, C. W., Tautz, J., Grunewald, B. & Fuchs, S. RFID tracking of sublethal effects of two neonicotinoid insecticides on the foraging behavior of Apis mellifera. PLoS One 7, 9. https://doi.org/10.1371/journal.pone.0030023 (2012).CAS 
    Article 

    Google Scholar 
    Vazquez, D. E., Ilina, N., Pagano, E. A., Zavala, J. A. & Farina, W. M. Glyphosate affects the larval development of honey bees depending on the susceptibility of colonies. PLoS One https://doi.org/10.1371/journal.pone.0205074 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vázquez, D. E., Latorre-Estivalis, J. M., Ons, S. & Farina, W. M. Chronic exposure to glyphosate induces transcriptional changes in honey bee larva: A toxicogenomic study. Environ. Pollut. https://doi.org/10.1016/j.envpol.2020.114148 (2020).Article 
    PubMed 

    Google Scholar 
    Farina, W. M., Balbuena, M., Herbert, L. T., Mengoni Goñalons, C. & Vázquez, D. E. Effects of the herbicide glyphosate on honey bee sensory and cognitive abilities: Individual impairments with implications for the hive. Insects 10, 354. https://doi.org/10.3390/insects10100354 (2019).Article 
    PubMed Central 

    Google Scholar 
    Wang, Y. H., Zhu, Y. C. & Li, W. H. Interaction patterns and combined toxic effects of acetamiprid in combination with seven pesticides on honey bee (Apis mellifera L.). Ecotox. Environ. Safe 190, 10. https://doi.org/10.1016/j.ecoenv.2019.110100 (2020).CAS 
    Article 

    Google Scholar 
    Kretschmann, A., Gottardi, M., Dalhoff, K. & Cedergreen, N. The synergistic potential of the azole fungicides prochloraz and propiconazole toward a short α-cypermethrin pulse increases over time in Daphnia magna. Aquat. Toxicol. 162, 94–101. https://doi.org/10.1016/j.aquatox.2015.02.011 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Yuan, X. et al. Gut microbiota: An underestimated and unintended recipient for pesticide-induced toxicity. Chemosphere https://doi.org/10.1016/j.chemosphere.2019.04.088 (2019).Article 
    PubMed 

    Google Scholar 
    Yang, Y. et al. Effects of three common pesticides on survival, food consumption and midgut bacterial communities of adult workers Apis cerana and Apis mellifera. Environ. Pollut. 249, 860–867. https://doi.org/10.1016/j.envpol.2019.03.077 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Martinson, V. G. et al. A simple and distinctive microbiota associated with honey bees and bumble bees. Mol. Ecol. 20, 619–628. https://doi.org/10.1111/j.1365-294X.2010.04959.x (2011).Article 
    PubMed 

    Google Scholar 
    Corby-Harris, V., Maes, P. & Anderson, K. E. The bacterial communities associated with honey bee (Apis mellifera) foragers. PLoS One 9, 13. https://doi.org/10.1371/journal.pone.0095056 (2014).CAS 
    Article 

    Google Scholar 
    Moran, N. A., Hansen, A. K., Powell, J. E. & Sabree, Z. L. Distinctive gut microbiota of honey bees assessed using deep sampling from individual worker bees. PLoS One https://doi.org/10.1371/journal.pone.0036393 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bonilla-Rosso, G. & Engel, P. Functional roles and metabolic niches in the honey bee gut microbiota. Curr. Opin. Microbiol. 43, 69–76. https://doi.org/10.1016/j.mib.2017.12.009 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384. https://doi.org/10.1038/nrmicro.2016.43 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kešnerová, L. et al. Gut microbiota structure differs between honeybees in winter and summer. ISME J. 14, 801–814. https://doi.org/10.1038/s41396-019-0568-8 (2020).Article 
    PubMed 

    Google Scholar 
    Killer, J., Dubná, S., Sedláček, I. & Švec, P. Lactobacillus apis sp. Nov., from the stomach of honeybees (Apis mellifera), having an in vitro inhibitory effect on the causative agents of American and European foulbrood. Int. J. Syst. Evol. Microbiol. 64, 152–157. https://doi.org/10.1099/ijs.0.053033-0 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Forsgren, E., Olofsson, T. C., Váasquez, A. & Fries, I. Novel lactic acid bacteria inhibiting Paenibacillus larvae in honey bee larvae. Apidologie 41, 99–108. https://doi.org/10.1051/apido/2009065 (2010).Article 

    Google Scholar 
    Schwarz, R. S., Huang, Q. & Evans, J. D. Hologenome theory and the honey bee pathosphere. Curr. Opin. Insect Sci. 10, 1–7. https://doi.org/10.1016/j.cois.2015.04.006 (2015).Article 
    PubMed 

    Google Scholar 
    Engel, P., Martinson, V. G. & Moran, N. A. Functional diversity within the simple gut microbiota of the honey bee. PNAS 109, 11002–11007. https://doi.org/10.1073/pnas.1202970109 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kešnerová, L. et al. Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol. 15, 28. https://doi.org/10.1371/journal.pbio.2003467 (2017).CAS 
    Article 

    Google Scholar 
    Kwong, W. K., Engel, P., Koch, H. & Moran, N. A. Genomics and host specialization of honey bee and bumble bee gut symbionts. PNAS 111, 11509–11514. https://doi.org/10.1073/pnas.1405838111 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lee, F. J., Rusch, D. B., Stewart, F. J., Mattila, H. R. & Newton, I. L. G. Saccharide breakdown and fermentation by the honey bee gut microbiome. Environ. Microbiol. 17, 796–815. https://doi.org/10.1111/1462-2920.12526 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Motta, E. V. S., Raymann, K. & Moran, N. A. Glyphosate perturbs the gut microbiota of honey bees. PNAS 115, 10305–10310. https://doi.org/10.1073/pnas.1803880115 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Blot, N., Veillat, L., Rouze, R. & Delatte, H. Glyphosate, but not its metabolite AMPA, alters the honeybee gut microbiota. PLoS One 14, 16. https://doi.org/10.1371/journal.pone.0215466 (2019).CAS 
    Article 

    Google Scholar 
    Raymann, K. et al. Imidacloprid decreases honey bee survival rates but does not affect the gut microbiome. Appl. Environ. Microbiol. 84, 13. https://doi.org/10.1128/aem.00545-18 (2018).CAS 
    Article 

    Google Scholar 
    Rouze, R., Mone, A., Delbac, F., Belzunces, L. & Blot, N. The honeybee gut microbiota is altered after chronic exposure to different families of insecticides and infection by Nosema ceranae. Microbes Environ. 34, 226–233. https://doi.org/10.1264/jsme2.ME18169 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    DeGrandi-Hoffman, G., Corby-Harris, V., DeJong, E. W., Chambers, M. & Hidalgo, G. Honey bee gut microbial communities are robust to the fungicide PristineA (R) consumed in pollen. Apidologie 48, 340–352. https://doi.org/10.1007/s13592-016-0478-y (2017).CAS 
    Article 

    Google Scholar 
    Liu, Y. J. et al. Thiacloprid exposure perturbs the gut microbiota and reduces the survival status in honeybees. J. Hazard. Mater. 389, 11. https://doi.org/10.1016/j.jhazmat.2019.121818 (2020).CAS 
    Article 

    Google Scholar 
    Syromyatnikov, M. Y., Isuwa, M. M., Savinkova, O. V., Derevshchikova, M. I. & Popov, V. N. The effect of pesticides on the microbiome of animals. Agriculture 10, 79. https://doi.org/10.3390/agriculture10030079 (2020).CAS 
    Article 

    Google Scholar 
    Thompson, H. M. et al. Evaluating exposure and potential effects on honeybee brood (Apis mellifera) development using glyphosate as an example. Integr. Environ. Assess. Manag. 10, 463–470. https://doi.org/10.1002/ieam.1529 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Motta, E. V. S. et al. Oral and topical exposure to glyphosate in herbicide formulation impact the gut microbiota and survival rates of honey bees. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01150-20 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Berg, C. J. et al. Glyphosate residue concentrations in honey attributed through geospatial analysis to proximity of large-scale agriculture and transfer off-site by bees. PLoS ONE 13, e0198876. https://doi.org/10.1371/journal.pone.0198876 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rubio, F., Guo, E. & Kamp, L. Survey of glyphosate residues in honey, corn, and soy products. Abstr. Pap. Am. Chem. Soc. https://doi.org/10.4172/2161-0525.1000249 (2015).Article 

    Google Scholar 
    El Agrebi, N. et al. Honeybee and consumer’s exposure and risk characterisation to glyphosate-based herbicide (GBH) and its degradation product (AMPA): Residues in beebread, wax, and honey. Sci. Total. Environ. 704, 135312. https://doi.org/10.1016/j.scitotenv.2019.135312 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Kubik, M. et al. Residues of captan (contact) and difenoconazole (systemic) fungicides in bee products from an apple orchard. Apidologie 31, 531–541 (2000).CAS 
    Article 

    Google Scholar 
    Lopez, S. H., Lozano, A., Sosa, A., Hernando, M. D. & Fernandez-Alba, A. R. Screening of pesticide residues in honeybee wax comb by LC-ESI-MS/MS. A pilot study. Chemosphere 163, 44–53. https://doi.org/10.1016/j.chemosphere.2016.07.008 (2016).CAS 
    Article 

    Google Scholar 
    Pettis, J. S. et al. Crop pollination exposes honey bees to pesticides which alters their susceptibility to the gut pathogen Nosema ceranae. PLoS One 8, 9. https://doi.org/10.1371/journal.pone.0070182 (2013).CAS 
    Article 

    Google Scholar 
    Abdallah, O. I., Hanafi, A., Ghani, S. B. A., Ghisoni, S. & Lucini, L. Pesticides contamination in Egyptian honey samples. J. Consum. Prot. Food Sci. 12, 317–327. https://doi.org/10.1007/s00003-017-1133-x (2017).CAS 
    Article 

    Google Scholar 
    Blaga, G. V. et al. Antifungal residues analysis in various Romanian honey samples analysis by high resolution mass spectrometry. J. Environ. Sci. Health Part B-Pestic. Contam. Agric. Wastes https://doi.org/10.1080/03601234.2020.1724016 (2020).Article 

    Google Scholar 
    Piechowicz, B., Wos, I., Podbielska, M. & Grodzicki, P. The transfer of active ingredients of insecticides and fungicides from an orchard to beehives. J. Environ. Sci. Health Part B-Pestic. Contam. Agric. Wastes 53, 18–24. https://doi.org/10.1080/03601234.2017.1369320 (2018).CAS 
    Article 

    Google Scholar 
    Almasri, H. et al. Mixtures of an insecticide, a fungicide and a herbicide induce high toxicities and systemic physiological disturbances in winter Apis mellifera honey bees. Ecotoxicol. Environ. Saf. 203, 111013. https://doi.org/10.1016/j.ecoenv.2020.111013 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Babendreier, D., Joller, D., Romeis, J., Bigler, F. & Widmer, F. Bacterial community structures in honeybee intestines and their response to two insecticidal proteins. FEMS Microbiol. Ecol. 59, 600–610. https://doi.org/10.1111/j.1574-6941.2006.00249.x (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    Emery, O., Schmidt, K. & Engel, P. Immune system stimulation by the gut symbiont Frischella perrara in the honey bee (Apis mellifera). Mol. Ecol. 26, 2576–2590. https://doi.org/10.1111/mec.14058 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Yanez, O., Gauthier, L., Chantawannakul, P. & Neumann, P. Endosymbiotic bacteria in honey bees: Arsenophonus spp. are not transmitted transovarially. FEMS Microbiol. Lett. https://doi.org/10.1093/femsle/fnw147 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tornisielo, V. L., Botelho, R. G., Alves, P. A. T., Bonfleur, E. J. & Monteiro, S. H. Pesticide tank mixes: an environmental point of view. in Herbicides-Current Research and Case Studies in Use. 473–487 (InTech, 2013).

    Google Scholar 
    Kanga, L. H., Siebert, S. C., Sheikh, M. & Legaspi, J. C. Pesticide residues in conventionally and organically managed Apiaries in South and North Florida. Curre. Investig. Agric. Curr. Res. https://doi.org/10.32474/CIACR.2019.07.000262 (2019).Article 

    Google Scholar 
    Lambert, O. et al. Widespread occurrence of chemical residues in beehive matrices from apiaries located in different landscapes of western France. PLoS One 8, 12. https://doi.org/10.1371/journal.pone.0067007 (2013).CAS 
    Article 

    Google Scholar 
    Mullins, J. W. Pest Control with Enhanced Environmental Safety, Vol 524 ACS Symposium Series, Vol. 13 183–198 (American Chemical Society, 1993).Book 

    Google Scholar 
    Nguyen, B. K. et al. Does imidacloprid seed-treated maize have an impact on honey bee mortality?. J. Econ. Entomol. 102, 616–623. https://doi.org/10.1603/029.102.0220 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pollak, P. Fine chemicals–the industry and the business. Chem. Int. 29, 22. https://doi.org/10.1515/ci.2007.29.5.22b (2007).Article 

    Google Scholar 
    Amrhein, N., Deus, B., Gehrke, P. & Steinrücken, H. C. The site of the inhibition of the shikimate pathway by glyphosate. II. Interference of glyphosate with chorismate formation in vivo and in vitro. Plant. Physiol. 66, 830–834. https://doi.org/10.1104/pp.66.5.830 (1980).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cao, G. et al. A novel 5-enolpyruvylshikimate-3-phosphate synthase shows high glyphosate tolerance in Escherichia coli and tobacco plants. PLoS One 7, e38718. https://doi.org/10.1371/journal.pone.0038718 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hitchcock, C. A., Dickinson, K., Brown, S. B., Evans, E. G. V. & Adams, D. J. Interaction of azole antifungal antibiotics with cytochrome P-450-dependent 14α-sterol demethylase purified from Candida albicans. Biochem. J. 266, 475–480. https://doi.org/10.1042/bj2660475 (1990).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alberoni, D., Favaro, R., Baffoni, L., Angeli, S. & Di Gioia, D. Neonicotinoids in the agroecosystem: In-field long-term assessment on honeybee colony strength and microbiome. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.144116 (2021).Article 
    PubMed 

    Google Scholar 
    Xu, C. et al. Changes in gut microbiota may be early signs of liver toxicity induced by epoxiconazole in rats. Chemotherapy 60, 135–142. https://doi.org/10.1159/000371837 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Yang, C., Hamel, C., Vujanovic, V. & Gan, Y. Fungicide: Modes of action and possible impact on nontarget microorganisms. ISRN Ecol. https://doi.org/10.5402/2011/130289 (2011).Article 

    Google Scholar 
    Coupe, R. H., Kalkhoff, S. J., Capel, P. D. & Gregoire, C. Fate and transport of glyphosate and aminomethylphosphonic acid in surface waters of agricultural basins. Pest Manag. Sci. 68, 16–30. https://doi.org/10.1002/ps.2212 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    Howe, C. M. et al. Toxicity of glyphosate-based pesticides to four North American frog species. Environ. Toxicol. Chem. 23, 1928–1938. https://doi.org/10.1002/etc.2268 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    Wagner, N., Reichenbecher, W., Teichmann, H., Tappeser, B. & Lötters, S. Questions concerning the potential impact of glyphosate-based herbicides on amphibians. Environ. Toxicol. Chem. 32, 1688–1700. https://doi.org/10.1002/etc.2268 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pareja, L. et al. Evaluation of glyphosate and AMPA in honey by water extraction followed by ion chromatography mass spectrometry. A pilot monitoring study. Anal. Methods 11, 2123–2128. https://doi.org/10.1039/c9ay00543a (2019).CAS 
    Article 

    Google Scholar 
    Thompson, T. S., van den Heever, J. P. & Limanowka, R. E. Determination of glyphosate, AMPA, and glufosinate in honey by online solid-phase extraction-liquid chromatography-tandem mass spectrometry.. Food. Addit. Contam. Part A Chem. Anal. Control. Expo. Risk. Assess 36, 434–446. https://doi.org/10.1080/19440049.2019.1577993 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Dai, P. et al. The herbicide glyphosate negatively affects midgut bacterial communities and survival of honey bee during larvae reared in vitro. J. Agric. Food Chem. 66, 7786–7793. https://doi.org/10.1021/acs.jafc.8b02212 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zheng, H., Powell, J. E., Steele, M. I., Dietrich, C. & Moran, N. A. Honeybee gut microbiota promotes host weight gain via bacterial metabolism and hormonal signaling. PNAS 114, 4775–4780. https://doi.org/10.1073/pnas.1701819114 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    du Rand, E. E. et al. Detoxification mechanisms of honey bees (Apis mellifera) resulting in tolerance of dietary nicotine. Sci. Rep. https://doi.org/10.1038/srep11779 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xiao, W. J. et al. Modulation of the pentose phosphate pathway alters phase I metabolism of testosterone and dextromethorphan in HepG2 cells. Acta Pharmacol. Sin. 36, 259–267. https://doi.org/10.1038/aps.2014.137 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Renzi, M. T. et al. Chronic toxicity and physiological changes induced in the honey bee by the exposure to fipronil and Bacillus thuringiensis spores alone or combined. Ecotox. Environ. Safe. 127, 205–213. https://doi.org/10.1016/j.ecoenv.2016.01.028 (2016).CAS 
    Article 

    Google Scholar 
    Singh, A., Gupta, V., Siddiqi, N., Tiwari, S. & Gopesh, A. Time course studies on impact of low temperature exposure on the levels of protein and enzymes in fifth instar larvae of Eri Silkworm, Philosamia ricini (Lepidoptera: satuniidae). Biochem. Anal. Biochem. 6, 6. https://doi.org/10.4172/2161-1009.1000321 (2017).CAS 
    Article 

    Google Scholar 
    Vlahović, M., Lazarević, J., Perić-Mataruga, V., Ilijin, L. & Mrdaković, M. Plastic responses of larval mass and alkaline phosphatase to cadmium in the gypsy moth larvae. Ecotox. Environ. Safe 72, 1148–1155. https://doi.org/10.1016/j.ecoenv.2008.03.012 (2009).CAS 
    Article 

    Google Scholar 
    Coleman, J. E. Structure and mechanism of alkaline-phosphatase. Annu. Rev. Biophys. Biomol. Struct. 21, 441–483. https://doi.org/10.1146/annurev.bb.21.060192.002301 (1992).CAS 
    Article 
    PubMed 

    Google Scholar 
    Bates, J. M., Akerlund, J., Mittge, E. & Guillemin, K. Intestinal alkaline phosphatase detoxifies lipopolysaccharide and prevents inflammation in zebrafish in response to the gut microbiota. Cell Host Microbe 2, 371–382. https://doi.org/10.1016/j.chom.2007.10.010 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kanost, M. R. & Gorman, M. J. Phenoloxidases in insect immunity. Insect Immunol. 1, 69–96. https://doi.org/10.1016/B978-012373976-6.50006-9 (2008).Article 

    Google Scholar 
    Collison, E., Hird, H., Cresswell, J. & Tyler, C. Interactive effects of pesticide exposure and pathogen infection on bee health—a critical analysis. Biol. Rev. 91, 1006–1019. https://doi.org/10.1111/brv.12206 (2016).Article 
    PubMed 

    Google Scholar 
    Helmer, S. H., Kerbaol, A., Aras, P., Jumarie, C. & Boily, M. Effects of realistic doses of atrazine, metolachlor, and glyphosate on lipid peroxidation and diet-derived antioxidants in caged honey bees (Apis mellifera). Environ. Sci. Pollut. Res. 22, 8010–8021. https://doi.org/10.1007/s11356-014-2879-7 (2015).CAS 
    Article 

    Google Scholar 
    Efferth, T., Schwarzl, S. M., Smith, J. & Osieka, R. Role of glucose-6-phosphate dehydrogenase for oxidative stress and apoptosis. Cell Death Differ. 13, 527–528. https://doi.org/10.1038/sj.cdd.4401807 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    Corona, M. & Robinson, G. E. Genes of the antioxidant system of the honey bee: Annotation and phylogeny. Insect Mol. Biol. 15, 687–701. https://doi.org/10.1111/j.1365-2583.2006.00695.x (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Field, L. M., Devonshire, A. L., Ffrench-Constant, R. H. & Forde, B. G. Changes in DNA methylation are associated with loss of insecticide resistance in the peach-potato aphid Myzus persicae (Sulz.). FEBS Lett. 243, 323–327. https://doi.org/10.1016/0014-5793(89)80154-1 (1989).CAS 
    Article 

    Google Scholar 
    Ma, M. et al. Isolation of carboxylesterase (esterase FE4) from Apis cerana cerana and its role in oxidative resistance during adverse environmental stress. Biochimie 144, 85–97. https://doi.org/10.1016/j.biochi.2017.10.022 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zou, F., Guo, Q., Shen, B. & Zhu, C. A cluster of CYP6 gene family associated with the major quantitative trait locus is responsible for the pyrethroid resistance in Culex pipiens pallen. Insect Mol. Biol. 28, 528–536. https://doi.org/10.1111/imb.12571 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lang, M. L., Braun, C. L., Kanost, M. R. & Gorman, M. J. Multicopper oxidase-1 is a ferroxidase essential for iron homeostasis in Drosophila melanogaster. PNAS 109, 13337–13342. https://doi.org/10.1073/pnas.1208703109 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Habineza, P. et al. The promoting effect of gut microbiota on growth and development of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier) (Coleoptera: Dryophthoridae) by modulating its nutritional metabolism. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01212 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kwong, W. K., Mancenido, A. L. & Moran, N. A. Immune system stimulation by the native gut microbiota of honey bees. R. Soc. Open Sci. 4, 170003. https://doi.org/10.1098/rsos.170003 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Paradis, D., Berail, G., Bonmatin, J. M. & Belzunces, L. P. Sensitive analytical methods for 22 relevant insecticides of 3 chemical families in honey by GC-MS/MS and LC-MS/MS. Anal. Bioanal. Chem 406, 621–633. https://doi.org/10.1007/s00216-013-7483-z (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Wiest, L. et al. Multi-residue analysis of 80 environmental contaminants in honeys, honeybees and pollens by one extraction procedure followed by liquid and gas chromatography coupled with mass spectrometric detection. J. Chromatogr. A 1218, 5743–5756. https://doi.org/10.1016/j.chroma.2011.06.079 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zufelato, M. S., Lourenco, A. P., Simoes, Z. L. P., Jorge, J. A. & Bitondi, M. M. G. Phenoloxidase activity in Apis mellifera honey bee pupae, and ecdysteroid-dependent expression of the prophenoloxidase mRNA. Insect Biochem. Mol. Biol. 34, 1257–1268. https://doi.org/10.1016/j.ibmb.2004.08.005 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    Gallup, J. M. qPCR inhibition and amplification of difficult templates. in PCR troubleshooting and optimization: the essential guide. 23–65 (Horizon Scientific Press, 2011).
    Google Scholar 
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. PNAS 108, 4516–4522. https://doi.org/10.1073/pnas.1000080107 (2011).Article 
    PubMed 

    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226. https://doi.org/10.1186/s40168-018-0605-2 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schliep, K. P. phangorn: Phylogenetic analysis in R. Bioinformatics 27, 592–593. https://doi.org/10.1093/bioinformatics/btq706 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363. https://doi.org/10.1002/bimj.200810425 (2008).MathSciNet 
    Article 
    PubMed 
    MATH 

    Google Scholar 
    Belzunces, L. P., Theveniau, M., Masson, P. & Bounias, M. Membrane acetylcholinesterase from Apis mellifera head solubilized by phosphatidylinositol-specific phospholipase-C interacts with an anti-CRD antibody. Comp. Biochem. Physiol. B-Biochem. Mol. Biol. 95, 609–612. https://doi.org/10.1016/0305-0491(90)90029-s (1990).Article 

    Google Scholar 
    Bergmeyer, H. U. & Gawehn, K. Principles of Enzymatic Analysis (Verlag Chemie, 1978).
    Google Scholar 
    Al-Lawati, H., Kamp, G. & Bienefeld, K. Characteristics of the spermathecal contents of old and young honeybee queens. J. Insect Physiol. 55, 117–122. https://doi.org/10.1016/j.jinsphys.2008.10.010 (2009).CAS 
    Article 

    Google Scholar 
    Habig, W. H., Pabst, M. J. & Jakoby, W. B. Glutathione s-transferases—first enzymatic step in mercapturic acid formation. J. Biol. Chem. 249, 7130–7139 (1974).CAS 
    Article 

    Google Scholar 
    Bounias, M., Kruk, I., Nectoux, M. & Popeskovic, D. Toxicology of cupric salts on honeybees. V. Gluconate and sulfate action on gut alkaline and acid phosphatases. Ecotox. Envirom. Safe 35, 67–76. https://doi.org/10.1006/eesa.1996.0082 (1996).CAS 
    Article 

    Google Scholar 
    Alaux, C. et al. Interactions between Nosema microspores and a neonicotinoid weaken honeybees (Apis mellifera). Environ. Microbiol. 12, 774–782. https://doi.org/10.1111/j.1462-2920.2009.02123.x (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Therneau, T. “Survival”: A Package for Survival Analysis in S. R package version 2.38. https://CRAN.R-project.org/package=survival. (2015).Kassambara, A. & Kosinski, M. “Survminer”: Drawing Survival Curves using “ggplot2”. R package version 0.4.2. https://CRAN.R-project.org/package=survminer. (2018).de Mendiburu, F. Statistical Procedures for Agricultural Research. Package “Agricolae” Version 1.44. Comprehensive R Archive Network. Institute for Statistics and Mathematics, Vienna, Austria. http://cran.r-project.org/web/packages/agricolae/agricolae.pdf (2013).Caraux, G. & Pinloche, S. PermutMatrix: A graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 21, 1280–1281. https://doi.org/10.1093/bioinformatics/bti141 (2004).Article 
    PubMed 

    Google Scholar  More

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    Publisher Correction: Natural selection for imprecise vertical transmission in host–microbiota systems

    AffiliationsDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USAMarjolein Bruijning, Lucas P. Henry, Simon K. G. Forsberg, C. Jessica E. Metcalf & Julien F. AyrolesLewis-Sigler Institute for Integrative Genomics, Princeton, NJ, USALucas P. Henry, Simon K. G. Forsberg & Julien F. AyrolesAuthorsMarjolein BruijningLucas P. HenrySimon K. G. ForsbergC. Jessica E. MetcalfJulien F. AyrolesCorresponding authorCorrespondence to
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    A comprehensive catalogue of plant-pollinator interactions for Chile

    In recent years there has been an increasing concern regarding the global decline of pollinators and pollination services1,2,3. Recent studies estimate that over 87% of the flowering plant species rely on biotic pollination4. Pollination is a mutualistic interaction, and plants provide pollinators with various rewards, including nectar, oil, or excess pollen to feed upon5,6. Although bees are the most well-known pollinator group, pollination can be performed by a wide variety of species, including mammals, birds, reptiles, and other insects.Plant-pollinator interactions are among the key processes that generate and maintain biodiversity7,8. The coevolutionary processes involved in animal pollination have helped maintain the structure and function of entire communities and species’ networks. Wild plant species and natural ecosystems provide several products and services, including nutrient cycling, medicine, food, a source of pollinators for domesticated crops, and alternative food and shelter sources for agricultural pollinators9. However, the complex web of interactions and the large number of species involved (ca. 400,000 species globally) makes it challenging to estimate pollinators’ value in natural ecosystems, particularly when the life history of so many pollinator species remains little studied and understood10.Pollinators also provide highly valuable ecosystem services to crops11,12. More than 70% of the world’s crops depend directly on insect pollination, making pollination key to food security11,13. The European honeybee (Apis mellifera) is likely the most economically important pollinator of crops worldwide13,14. Honeybees are adaptable, easy to manage, and cost-efficient. However, in recent years, ‘colony collapse’ caused by several factors, including parasitic mites and the excessive use of pesticides and herbicides, have led to a decline in managed honeybee colonies in many parts of the world15,16,17. Similarly, habitat loss and fragmentation have detrimental effects on both native and commercial pollinators. In degraded habitats, pollinators struggle to find resources and nesting sites18,19,20.In Chile, pollination represents a multimillion-dollar business. Between January and October 2020, the export of Chilean fruit reached USD 4.149 million, while fresh vegetables generated USD 347 million during the same period21. Although agricultural pollinators have been well studied, native pollinators remain largely unknown. With over 460 species of native bees in Chile, approximately 70% are endemic; researchers have only begun to understand the relationships between native plants and their pollinators22,23,24. Also, managed honeybees and bumblebees introduced to Chile for crop pollination are highly invasive and easily leave croplands to forage in neighbouring native ecosystems25,26, competing directly with native pollinators for the ever-diminishing resources in native grasslands and forests posing a threat to Chile’s unique ecoregions25,27.Because of the importance of pollination in the maintenance of biodiversity and the economic benefits of agricultural crop production, there is an urgent need to understand the causes behind the current decline in pollinator species. In this sense, collating and reviewing existing information on pollinators and making this information easily accessible in the form of a user-friendly database is of immeasurable value. In this study, we compiled the information available about pollination and pollinators (sensu lato) for Chile, aiming to understand plant-pollinator interactions, identify knowledge and geographic gaps, and provide a baseline from which to carry out further studies. We aimed to make a datasheet with a format that was adaptable to different regions and other countries by allowing it to be easily understood, easy to access and find and aiming to avoid duplicity of data. This study represents the first systematic effort to compile the available information on pollination and pollinators for Chile. This pollination catalogue for Chile adds to other international efforts of systematising this information as, for example, the Catalogue of Afrotropical Bees28 and the CPC Plant Pollinators Database29. More