Ecology

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

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

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

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

Beaulieu, J. et al. Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits. Evol. Appl. 13, 2704–2722 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lenz, P. et al. Multi-trait genomic selection for weevil resistance, growth and wood quality in Norway spruce. Evol. Appl. 13, 76–94 (2020).CAS 
PubMed 

Google Scholar 
Lebedev, V. G., Lebedeva, T. N., Chernodubov, A. I. & Shestibratov, K. A. Genomic selection for forest tree improvement: Methods, achievements and perspectives. Forests 11, 1190 (2020).
Google Scholar 
Mullin, T. J. et al. Economic importance, breeding objectives and achievements. In Genetics, Genomics and Breeding of Conifers (eds Plomion, C. et al.) (Science Publishers & CRC Press, 2011).
Google Scholar 
Zhang, J., Peter, G. F., Powell, G. L., White, T. L. & Gezan, S. A. Comparison of breeding values estimated between single-tree and multiple-tree plots for a slash pine population. Tree Genet. Genomes 11, 48 (2015).CAS 

Google Scholar 
Martínez-García, P. J. et al. Predicting breeding values and genetic components using generalized linear mixed models for categorical and continuous traits in walnut (Juglans regia). Tree Genet. Genomes 13, 109 (2017).
Google Scholar 
Weng, Y., Ford, R., Tong, Z. & Krasowski, M. Genetic parameters for bole straightness and branch angle in Jack pine estimated using linear and generalized linear mixed models. For. Sci. 63, 111–117 (2017).
Google Scholar 
Mrode, R. A. Linear Models for the Prediction of Animal Breeding Values 2nd edn. (CAB International, 2005).
Google Scholar 
Henderson, C. R. Theoretical bias and computational methods for a number of different animal models. J. Dairy Sci. 71, 1–16 (1988).
Google Scholar 
Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics 4th edn. (Longman Publishing Group, 1996).
Google Scholar 
Henderson, C. R. A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32, 69–83 (1976).MATH 

Google Scholar 
Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 56, 330–338 (1922).
Google Scholar 
Hill, W. G. & Weir, B. S. Variation in actual relationship as a consequence of Mendelian sampling and linkage. Genet. Res. 93, 47–64 (2011).CAS 

Google Scholar 
Doerksen, T. K. & Herbinger, C. M. Male reproductive success and pedigree error in red spruce open-pollinated and polycross mating systems. Can. J. For. Res. 38, 1742–1749 (2008).
Google Scholar 
Godbout, J. et al. Development of a traceability system based on SNP array for the large-scale production of high-value white spruce (Picea glauca). Front. Plant Sci. 8, 1264 (2017).PubMed 
PubMed Central 

Google Scholar 
Galeano, E., Bousquet, J. & Thomas, B. R. SNP-based analysis reveals unexpected features of genetic diversity, parental contributions and pollen contamination in a white spruce breeding program. Sci. Rep. 11, 4990 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lenz, P. et al. Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: An example from white spruce. Heredity 124, 562–578 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Askew, G. R. & El-Kassaby, Y. A. Estimation of relationship coefficients among progeny derived from wind-pollinated orchard seeds. Theor. Appl. Genet. 88, 267–272 (1994).CAS 
PubMed 

Google Scholar 
Doerksen, T. K., Bousquet, J. & Beaulieu, J. Inbreeding depression in intra-provenance crosses driven by founder relatedness in white spruce. Tree Genet. Genomes 10, 203–212 (2014).
Google Scholar 
Meuwissen, T. H. E., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Heffner, E. L., Lorenz, A. J., Jannink, J.-L. & Sorrels, M. E. Plant breeding with genomic selection: Gain per unit time and cost. Crop Sci. 50, 1681–1690 (2010).
Google Scholar 
Grattapaglia, D. & Resende, M. D. V. Genomic selection in forest tree breeding. Tree Genet. Genomes 7, 241–255 (2011).
Google Scholar 
Beaulieu, J., Doerksen, T., Clément, S., MacKay, J. & Bousquet, J. Accuracy of genomic selection models in a large population of open-pollinated families in white spruce. Heredity 113, 342–352 (2014).
Google Scholar 
Habier, D., Tetens, J., Seefried, F.-R., Lichtner, P. & Thaller, G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Gen. Select. Evol. 42, 5 (2010).
Google Scholar 
Perkel, J. SNP genotyping: six technologies that keyed a revolution. Nat. Methods 5, 447–454 (2008).CAS 

Google Scholar 
Pavy, N. et al. Development of high-density SNP genotyping arrays for white spruce (Picea glauca) and transferability to subtropical and nordic congeners. Mol. Ecol. Res. 13, 324–336 (2013).CAS 

Google Scholar 
Thomson, M. J. High-throughput genotyping to accelerate crop improvement. Plant Breed. Biotechnol. 2, 195–212 (2014).
Google Scholar 
Beaulieu, J., Doerksen, T., MacKay, J., Rainville, A. & Bousquet, J. Genomic selection accuracies within and between environments and small breeding groups in white spruce. BMC Genomics 15, 1048 (2014).PubMed 
PubMed Central 

Google Scholar 
Liu, L., Chen, R., Fugina, C. J., Siegel, B. & Jackson, D. High-throughput and low-cost genotyping method for plant genome editing. Curr. Prot. 1, e100 (2021).CAS 

Google Scholar 
Lenz, P. et al. Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana). BMC Genomics 18, 335 (2017).PubMed 
PubMed Central 

Google Scholar 
de los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D. & Calus, M. P. L. Whole-genome regression and prediction models applied to plant and animal breeding. Genetics 193, 327–345 (2013).PubMed Central 

Google Scholar 
Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for non-orthogonal problems. Technometrics 12, 55–67 (1970).MATH 

Google Scholar 
Tibshirani, R. Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. Series B. 58, 267–288 (1996).MathSciNet 
MATH 

Google Scholar 
VanRaden, P. M. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414–4423 (2008).CAS 
PubMed 

Google Scholar 
Legarra, A., Aguilar, I. & Misztal, I. A relationship matrix including full pedigree and genomic information. J. Dairy Sci. 92, 4656–4663 (2009).CAS 
PubMed 

Google Scholar 
Zapata-Valenzuela, J., Whetten, R. W., Neale, D., McKeand, S. & Isik, F. Genomic estimated breeding values using genomic relationship matrices in a cloned population of loblolly pine. Genes Genomes Genet. 3, 909–916 (2013).
Google Scholar 
Muñoz, P. R. et al. Unraveling additive from non-additive effects using genomic relationship matrices. Genetics 198, 1759–1768 (2014).PubMed 
PubMed Central 

Google Scholar 
Ratcliffe, B. et al. Single-step BLUP with varying genotyping effort in open-pollinated Picea glauca. Genes Genomes Genet. 7, 935–942 (2017).
Google Scholar 
Gamal El-Dien, O. et al. Multienvironment genomic variance decomposition analysis of open-pollinated Interior spruce (Picea glauca x engelmannii). Mol. Breed. 38, 26 (2018).
Google Scholar 
Zobel, B. J. & Sprague, J. R. Juvenile Wood in Forest Trees (Springer, 1988).
Google Scholar 
Osorio, L. F., White, T. L. & Huber, D. A. Age trends of heritabilities and genotype-by-environment interactions for growth traits and wood density from clonal trials of Eucalyptus grandis Hill ex Maiden. Silv. Genet. 50, 108–117 (2000).
Google Scholar 
Baltunis, B. S., Gapare, W. J. & Wu, H. X. Genetic parameters and genotype by environment interaction in radiata pine for growth and wood quality traits in Australia. Silv. Genet. 59, 113–124 (2010).
Google Scholar 
Gamal El-Dien, O. et al. Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing. BMC Genomics 16, 370 (2015).PubMed 
PubMed Central 

Google Scholar 
Resende, M. D. V. et al. Genomic selection for growth and wood quality in Eucalyptus: Capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol. 194, 116–128 (2012).PubMed 

Google Scholar 
Chen, Z.-Q. et al. Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce. BMC Genomics 19, 946 (2018).PubMed 
PubMed Central 

Google Scholar 
Beaulieu, J., Perron, M. & Bousquet, J. Multivariate patterns of adaptive genetic variation and seed source transfer in Picea mariana. Can. J. For. Res. 34, 531–545 (2004).
Google Scholar 
Li, P., Beaulieu, J. & Bousquet, J. Genetic structure and patterns of genetic variation among populations in eastern white spruce (Picea glauca). Can. J. For. Res. 27, 189–198 (1997).
Google Scholar 
Namkoong, G. Inbreeding effects on estimation of genetic additive variance. For. Sci. 12, 8–13 (1966).
Google Scholar 
Squillace, A. E. Average genetic correlations among offspring from open-pollinated forest trees. Silv. Genet. 23, 149–156 (1974).
Google Scholar 
Muñoz, P. R. et al. Genomic relationship matrix for correcting pedigree errors in breeding populations: impact on genetic parameters and genomic selection accuracy. Crop Sci. 53, 1115–1123 (2014).
Google Scholar 
Tan, B. et al. Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids. BMC Plant Biol. 17, 110 (2017).PubMed 
PubMed Central 

Google Scholar 
Weigel, K. A., VanRaden, P. M., Norman, H. D. & Grosu, H. A 100-year review: Methods and impact of genetic selection in dairy cattle—From daughter-dam comparisons to deep learning algorithms. J. Dairy Sci. 100, 10234–10250 (2017).CAS 
PubMed 

Google Scholar 
Grattapaglia, D. et al. Quantitative genetics and genomics converge to accelerate forest tree breeding. Front. Plant Sci. 9, 1693 (2018).PubMed 
PubMed Central 

Google Scholar 
Park, Y.-S., Beaulieu, J. & Bousquet, J. Multi-varietal forestry integrating genomic selection and somatic embryogenesis. In Vegetative Propagation of Forest Trees (eds Park, Y.-S. et al.) 302–322 (National Institute of Forest Science, 2016).
Google Scholar 
Bousquet, J. et al. Spruce population genomics. In Population Genomics: Forest Trees (ed. Rajora, O. P.) (Springer Nature, 2021).
Google Scholar 
Chamberland, V. et al. Conventional versus genomic selection for white spruce improvement: A comparison of costs and benefits of plantations on Quebec public lands. Tree Genet. Genomes 16, 17 (2020).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
MacFarland, T. W. & Yates, J. M. Wilcoxon matched-pairs signed-ranks test. In Introduction to Nonparametric Statistics for the Biological Sciences Using R 133–175 (Springer, 2016) https://doi.org/10.1007/978-3-319-30634-6_5.Li, Y. et al. Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information. BMC Genomics 20, 1026 (2019).PubMed 
PubMed Central 

Google Scholar 
Calleja-Rodriguez, A. et al. Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine. BMC Genomics 21, 796 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ukrainetz, N. K. & Mansfield, S. D. Assessing the sensitivities of genomic selection for growth and wood quality traits in lodgepole pine using Bayesian models. Tree Genet. Genomes 16, 14 (2020).
Google Scholar 
Ukrainetz, N. K. & Mansfield, S. D. Prediction accuracy of single-step BLUP for growth and wood quality traits in the lodgepole pine breeding program in British Columbia. Tree Genet. Genomes 16, 64 (2020).
Google Scholar 
Thistlethwaite, F. R. et al. Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform. BMC Genomics 18, 930 (2017).PubMed 
PubMed Central 

Google Scholar 
Suontama, M. et al. Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories. Heredity 122, 370–379 (2019).CAS 
PubMed 

Google Scholar 
Müller, B. S. F. et al. Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus. BMC Genomics 18, 524 (2017).PubMed 
PubMed Central 

Google Scholar 
Thavamanikumar, S., Arnold, R. J., Luo, J. & Thumma, B. R. Genomic studies reveal substantial dominant effects and improved genomic predictions in an open-pollinated breeding population of Eucalyptus pellita. Genes Genomes Genet. 10, 3751–3763 (2020).CAS 

Google Scholar 
Resende, R. T. et al. Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model. Heredity 119, 245–255 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Marco de Lima, B. et al. Quantitative genetic parameters for growth and wood properties in Eucalyptus “urograndis” hybrid using near-infrared phenotyping and genome-wide SNP-based relationships. PLoS ONE 14, e0218747 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bouvet, J.-M., Makouanzi, G., Cros, D. & Vigneron, Ph. Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: Prediction accuracy implications. Heredity 116, 146–157 (2016).CAS 
PubMed 

Google Scholar 
Pégard, M. et al. Favorable conditions for genomic evaluation to outperform classical pedigree evaluation highlighted by a proof-of-concept study in poplar. Front. Plant Sci. 11, 581954 (2020).PubMed 
PubMed Central 

Google Scholar  More

Literature search and study selectionA systematic literature search was conducted in the ISI Web of Science database for observational and experimental studies published from 1975 to 13 January 2020 using the following search terms: TOPIC: (grassland* OR prairie* OR steppe* OR shrubland* OR scrubland* OR bushland*) AND TOPIC: (drought* OR ‘dry period*’ OR ‘dry condition*’ OR ‘dry year*’ OR ‘dry spell*’) AND TOPIC: (product* OR biomass OR cover OR abundance* OR phytomass). The search was refined to include the subject categories Ecology, Environmental Sciences, Plant Sciences, Biodiversity Conservation, Multidisciplinary Sciences and Biology, and the document types Article, Review and Letter. This yielded a total of 2,187 peer-reviewed papers (Supplementary Fig. 1). At first, these papers were screened by title and abstract, which resulted in 197 potentially relevant full-text articles. We then examined the full text of these papers for eligibility and selected 87 studies (43 experimental, 43 observational and 1 that included both types) on the basis of the following criteria:

(1)

The research was conducted in the field, in natural or semi-natural grasslands or shrublands (for example, artificially constructed (seeded or planted) plant communities or studies using monolith transplants were excluded). We used this restriction because most reports on observational droughts are from intact ecosystems, and experiments in disturbed sites or using artificial communities would thus not be comparable to observational drought studies.

(2)

In the case of observational studies, the drought year or a multi-year drought was clearly specified by the authors (that is, we did not arbitrarily extract dry years from a long-term dataset). Please note that some observational data points are from control plots of experiments (of any kind), where the authors reported that a drought had occurred during the study period. We did not involve gradient studies that compare sites of different climates, which are sometimes referred to as ‘observational studies’.

(3)

The paper reported the amount or proportion of change in annual or growing-season precipitation (GSP) compared with control conditions. We consistently use the term ‘control’ for normal precipitation (non-drought) year or years in observational studies and for ambient precipitation (no treatment) in experimental studies hereafter. Similarly, we use the term ‘drought’ for both drought year or years in observational studies and drought treatment in experimental studies. In the case of multi-factor experiments, where precipitation reduction was combined with any other treatment (for example, warming), data from the plots receiving drought only and data from the control plots were used.

(4)

The paper contained raw data on plant production under both control and drought conditions, expressed in any of the following variables: ANPP, aboveground plant biomass (in grassland studies only) or percentage plant cover. In 79% of the studies that used ANPP as a production variable, ANPP was estimated by harvesting peak or end-of-season AGB. We therefore did not distinguish between ANPP and AGB, which are referred to as ‘biomass’ hereafter. We included the papers that reported the production of the whole plant community, or at least that of the dominant species or functional groups approximating the abundance of the whole community.

(5)

When multiple papers were published on the same experiment or natural drought event at the same study site, the most long-term study including the largest number of drought years was chosen.

In addition to the systematic literature search, we included 27 studies (9 experimental, 17 observational and 1 that included both types) meeting the above criteria from the cited references of the Web of Science records selected for our meta-analyses, and from previous meta-analyses and reviews on the topic. In total, this resulted in 114 studies (52 experimental, 60 observational and 2 that included both types; Supplementary Note 9, Supplementary Fig. 2 and ref. 25).Data compilationData were extracted from the text or tables, or were read from the figures using Web Plot Digitizer26. For each study, we collected the study site, latitude, longitude, mean annual temperature (MAT) and precipitation (MAP), study type (experimental or observational), and drought length (the number of consecutive drought years). When MAT or MAP was not documented in the paper, it was extracted from another published study conducted at the same study site (identified by site names and geographic coordinates) or from an online climate database cited in the respective paper. We also collected vegetation type—that is, grassland when it was dominated by grasses, or shrubland when the dominant species included one or more shrub species (involving communities co-dominated by grasses and shrubs). Data from the same study (that is, paper) but from different geographic locations or environmental conditions (for example, soil types, land uses or multiple levels of experimental drought) were collected as distinct data points (but see ‘Statistical analysis’ for how these points were handled). As a result, the 114 published papers provided 239 data points (112 experimental and 127 observational)25.For the observational studies, normal precipitation year or years specified by the authors was used as the control. If it was not specified in the paper, the year immediately preceding the drought year(s) was chosen as the control. When no data from the pre-drought year were available, the year immediately following the drought year(s) (14 data points) or a multi-year period given in the paper (22 data points) was used as the control. For the experimental studies, we also collected treatment size (that is, rainout shelter area or, if it was not reported in the paper, the experimental plot size).For the calculation of drought severity, we used yearly precipitation (YP), which was reported in a much higher number of studies than GSP. We extracted YP for both control (YPcontrol) and drought (YPdrought). For the observational studies, when a multi-year period was used as the control or the natural drought lasted for more than one year, precipitation values were averaged across the control or drought years, respectively. Consistently, in the case of multi-year drought experiments, YPcontrol and YPdrought were averaged across the treatment years. When only GSP was published in the paper (63 of 239 data points), we used this to obtain YP data as follows: we regarded MAP as YPcontrol, and YPdrought was calculated as YPdrought = MAP − (GSPcontrol − GSPdrought). From YPcontrol and YPdrought data, we calculated drought severity as follows: (YPdrought − YPcontrol)/YPcontrol × 100.For production, we compiled the mean, replication (N) and, if the study reported it, a variance estimate (s.d., s.e.m. or 95% CI) for both control and drought. In the case of multi-year droughts, data only from the last drought year were extracted, except in five studies (17 data points) where production data were given as an average for the drought years. When both biomass and cover data were presented in the paper, we chose biomass. For each study, we consistently considered replication as the number of the smallest independent study unit. When only the range of replications was reported in a study, we chose the smallest number.To quantify climatic aridity for each study site, we used an aridity index (AI), calculated as the ratio of MAP and mean annual PET (AI = MAP/PET). This is a frequently used index in recent climate change research27,28. AI values were extracted from the Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v.2 for the period of 1970–2000 (aggregated on annual basis)29.Because we wanted to prevent our analysis from being distorted by a strongly unequal distribution of studies between the two study types regarding some potentially important explanatory variables, we left out studies from our focal meta-analysis in three steps. First, we left out studies that were conducted at wet sites—that is, where site AI exceeded 1. The value of 1 was chosen for two reasons: above this value, the distribution of studies between the two study types was extremely uneven (22 experimental versus 2 observational data points with AI  > 1)25, and the AI value of 1 is a bioclimatically meaningful threshold, where MAP equals PET. Second, we left out shrublands, because we had only 14 shrubland studies (out of 105 studies with AI  More

Lampert, W. Zooplankton research: The contribution of limnology to general ecological paradigms. Aquat. Ecol. 31, 19–27. https://doi.org/10.1023/A:1009943402621 (1997).Article 

Google Scholar 
Sotton, B. et al. Trophic transfer of microcystins through the lake pelagic food web: Evidence for the role of zooplankton as a vector in fish contamination. Sci. Total Environ. 466–467, 152–163. https://doi.org/10.1016/j.scitotenv.2013.07.020 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
St-Gelais, F. N., Sastri, A. R., del Giorgio, P. A. & Beisner, B. E. Magnitude and regulation of zooplankton community production across boreal lakes. Limnol. Oceanogr. Lett. 2(6), 210–217. https://doi.org/10.1002/lol2.10050 (2017).Article 

Google Scholar 
Dejen, E., Vijverberg, J., Nagelkerke, L. A. J. & Sibbing, F. A. Temporal and spatial distribution of microcrustacean zooplankton in relation to turbidity and other environmental factors in a large tropical lake (L. Tana, Ethiopia). Hydrobiologia 513(1), 39–49. https://doi.org/10.1023/b:hydr.0000018163.60503.b8 (2004).Article 

Google Scholar 
Arendt, K. E. et al. Effects of suspended sediments on copepods feeding in a glacial influenced sub-Arctic fjord. J. Plankton Res. 33, 1526–1537. https://doi.org/10.1093/plankt/fbr054 (2011).CAS 
Article 

Google Scholar 
Carrasco, N. K., Perissinotto, R. & Jones, S. Turbidity effects on feeding and mortality of the copepod Acartiella natalensis (Connell and Grindley, 1974) in the St Lucia Estuary, South Africa. J. Exp. Mar. Biol. Ecol. 446, 45–51. https://doi.org/10.1016/j.jembe.2013.04.016 (2013).Article 

Google Scholar 
Goździejewska, A. et al. Effects of lateral connectivity on zooplankton community structure in floodplain lakes. Hydrobiologia 774, 7–21. https://doi.org/10.1007/s10750-016-2724-8 (2016).CAS 
Article 

Google Scholar 
Zhou, J., Qin, B. & Han, X. The synergetic effects of turbulence and turbidity on the zooplankton community structure in large, shallow Lake Taihu. Environ. Sci. Pollut. Res. 25, 1168–1175. https://doi.org/10.1007/s11356-017-0262-1 (2018).CAS 
Article 

Google Scholar 
Chou, W.-R., Fang, L.-S., Wang, W.-H. & Tew, K. S. Environmental influence on coastal phytoplankton and zooplankton diversity: A multivariate statistical model analysis. Environ. Monit. Assess. 184(9), 5679–5688. https://doi.org/10.1007/s10661-011-2373-3 (2011).CAS 
Article 
PubMed 

Google Scholar 
Du, X. et al. Analyzing the importance of top-down and bottom-up controls in food webs of Chinese lakes through structural equation modeling. Aquat. Ecol. 49(2), 199–210. https://doi.org/10.1007/s10452-015-9518-3 (2015).CAS 
Article 

Google Scholar 
Feitosa, I. B. et al. Plankton community interactions in an Amazonian floodplain lake, from bacteria to zooplankton. Hydrobiologia 831, 55–70. https://doi.org/10.1007/s10750-018-3855-x (2019).CAS 
Article 

Google Scholar 
Kruk, M. & Paturej, E. Indices of trophic and competitive relations in a planktonic network of a shallow, temperate lagoon. A graph and structural equation modeling approach. Ecol. Indic. 112, 106007. https://doi.org/10.1016/j.ecolind.2019.106007 (2020).Article 

Google Scholar 
Kruk, M., Paturej, E. & Artiemjew, P. From explanatory to predictive network modeling of relationships among ecological indicators in the shallow temperate lagoon. Ecol. Indic. 117, 106637. https://doi.org/10.1016/j.ecolind.2020.106637 (2020).Article 

Google Scholar 
Kruk, M., Paturej, E. & Obolewski, K. Zooplankton predator–prey network relationships indicates the saline gradient of coastal lakes. Machine learning and meta-network approach. Ecol. Indic. 125, 107550. https://doi.org/10.1016/j.ecolind.2021.107550 (2021).Article 

Google Scholar 
Oh, H.-J. et al. Comparison of taxon-based and trophi-based response patterns of rotifer community to water quality: Applicability of the rotifer functional group as an indicator of water quality. Anim. Cells Syst. 21, 133–140. https://doi.org/10.1080/19768354.2017.1292952 (2017).Article 

Google Scholar 
Sodré, E. D. O. & Bozelli, R. L. How planktonic microcrustaceans respond to environment and affect ecosystem: A functional trait perspective. Int. Aquat. Res. 11, 207–223. https://doi.org/10.1007/s40071-019-0233-x (2019).Article 

Google Scholar 
Simões, N. R. et al. Changing taxonomic and functional β-diversity of cladoceran communities in Northeastern and South Brazil. Hydrobiologia 847, 3845–3856. https://doi.org/10.1007/s10750-020-04234-w (2020).Article 

Google Scholar 
Goździejewska, A. M., Koszałka, J., Tandyrak, R., Grochowska, J. & Parszuto, K. Functional responses of zooplankton communities to depth, trophic status, and ion content in mine pit lakes. Hydrobiologia 848, 2699–2719. https://doi.org/10.1007/s10750-021-04590-1 (2021).CAS 
Article 

Google Scholar 
Hart, R. C. Zooplankton feeding rates in relation to suspended sediment content: Potential influences on community structure in a turbid reservoir. Fresh. Biol. 19, 123–139. https://doi.org/10.1111/j.1365-2427.1988.tb00334.x (1988).Article 

Google Scholar 
Gliwicz, Z. M. & Pijanowska, J. The role of predation in zooplankton succession. In Plankton Ecology. Succession in Plankton Communities (ed. Sommer, U.) 253–296 (Springer Verlag, 1989).Chapter 

Google Scholar 
Gardner, M. B. Effects of turbidity on feeding rates and selectivity of bluegills. Trans. Am. Fish. Soc. 110(3), 446–450. https://doi.org/10.1577/1548-8659(1981)110%3c446:EOTOFR%3e2.0.CO;2 (1981).Article 

Google Scholar 
Zettler, E. R. & Carter, J. C. H. Zooplankton community and species responses to a natural turbidity gradient in Lake Temiskaming, Ontario-Quebec. Can. J. Fish. Aquat. Sci. 43, 665–673. https://doi.org/10.1139/f86-080 (1986).Article 

Google Scholar 
APHA. Standard Methods for the Examination of Water and Wastewater 20th edn. (American Public Health Association, 1999).
Google Scholar 
Lind, O. T., Chrzanowski, T. H. & D’avalos-Lind, L. Clay turbidity and the relative production of bacterioplankton and phytoplankton. Hydrobiologia 353, 1–18. https://doi.org/10.1023/A:1003039932699 (1997).CAS 
Article 

Google Scholar 
Boenigk, J. & Novarino, G. Effect of suspended clay on the feeding and growth of bacterivorous flagellates and ciliates. Aquat. Microb. Ecol. 34, 181–192. https://doi.org/10.3354/ame034181 (2004).Article 

Google Scholar 
Noe, G. B., Harvey, J. W. & Saiers, J. E. Characterization of suspended particles in Everglades wetlands. Limnol. Oceanogr. 52, 1166–1178. https://doi.org/10.4319/lo.2007.52.3.1166 (2007).ADS 
CAS 
Article 

Google Scholar 
Bilotta, G. S. & Brazier, R. E. Understanding the influence of suspended solids on water quality and aquatic biota. Water Res. 42, 2849–2861. https://doi.org/10.1016/j.watres.2008.03.018 (2008).CAS 
Article 
PubMed 

Google Scholar 
Fernandez-Severini, M. D., Hoffmeyer, M. S. & Marcovecchio, J. E. Heavy metals concentrations in zooplankton and suspended particulate matter in a southwestern Atlantic temperate estuary (Argentina). Environ. Monit. Assess. 185, 1495–1513. https://doi.org/10.1007/s10661-012-3023-0 (2013).CAS 
Article 
PubMed 

Google Scholar 
Paaijmans, K. P., Takken, W., Githeko, A. K. & Jacobs, A. F. G. The effect of water turbidity on the near-surface water temperature of larval habitats of the malaria mosquito Anopheles gambiae. Int. J. Biometeorol. 52(8), 747–753. https://doi.org/10.1007/s00484-008-0167-2 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Asrafuzzaman, M., Fakhruddin, A. N. M. & Hossain, M. A. Reduction of turbidity of water using locally available natural coagulants. ISRN Microbiol. 1–6, 2011. https://doi.org/10.5402/2011/632189 (2011).Article 

Google Scholar 
Kirk, K. L. & Gilbert, J. J. Suspended clay and the population dynamics of planktonic rotifers and cladocerans. Ecology 71(5), 1741–1755. https://doi.org/10.2307/1937582 (1990).Article 

Google Scholar 
Kirk, K. L. Effects of suspended clay on Daphnia body growth and fitness. Freshwater Biol. 28, 103–109. https://doi.org/10.1111/j.1365-2427.1992.tb00566.x (1992).Article 

Google Scholar 
Levine, S. N., Zehrer, R. F. & Burns, C. W. Impact of resuspended sediment on zooplankton feeding in Lake Waihola, New Zealand. Freshw. Biol. 50, 1515–1536. https://doi.org/10.1111/j.1365-2427.2005.01420 (2005).Article 

Google Scholar 
Moreira, F. W. A. et al. Assessing the impacts of mining activities on zooplankton functional diversity. Acta Limn. Bras. 28, e7. https://doi.org/10.1590/S2179-975X0816 (2016).Article 

Google Scholar 
Kerfoot, W. C. & Sih, A. Predation. Direct and Indirect Impacts on Aquatic Communities Vol. 160 (University Press of New England, 1987).
Google Scholar 
Schou, M. O. et al. Restoring lakes by using artificial plant beds: Habitat selection of zooplankton in a clear and a turbid shallow lake. Freshw. Biol. 54(7), 1520–1531. https://doi.org/10.1111/j.1365-2427.2009.02189.x (2009).Article 

Google Scholar 
Goździejewska, A. M., Gwoździk, M., Kulesza, S., Bramowicz, M. & Koszałka, J. Effects of suspended micro- and nanoscale particles on zooplankton functional diversity of drainage system reservoirs at an open-pit mine. Sci. Rep. 9, 16113. https://doi.org/10.1038/s41598-019-52542-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ribeiro, F. et al. Silver nanoparticles and silver nitrate induce high toxicity to Pseudokirchneriella subcapitata, Daphnia magna and Danio rerio. Sci. Total Environ. 466–467, 232–241. https://doi.org/10.1016/j.scitotenv.2013.06.101 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Vallotton, P., Angel, B., Mccall, M., Osmond, M. & Kirby, J. Imaging nanoparticle-algae interactions in three dimensions using Cytoviva microscopy. J. Microsc. 257(2), 166–169. https://doi.org/10.1111/jmi.12199 (2015).CAS 
Article 
PubMed 

Google Scholar 
Shanthi, S. et al. Biosynthesis of silver nanoparticles using a probiotic Bacillus licheniformis Dahb1 and their antibiofilm activity and toxicity effects in Ceriodaphnia cornuta. Microb. Pathogenesis 93, 70e77. https://doi.org/10.1016/j.micpath.2016.01.014 (2016).CAS 
Article 

Google Scholar 
Vijayakumar, S. et al. Ecotoxicity of Musa paradisiaca leaf extract-coated ZnO nanoparticles to the freshwater microcrustacean Ceriodaphnia cornuta. Limnologica 67, 1–6. https://doi.org/10.1016/j.limno.2017.09.004 (2017).CAS 
Article 

Google Scholar 
Hart, R. C. Zooplankton distribution in relation to turbidity and related environmental gradients in a large subtropical reservoir: Patterns and implications. Freshw. Biol. 24(2), 241–263. https://doi.org/10.1111/j.1365-2427.1990.tb00706.x (1990).Article 

Google Scholar 
Pollard, A. I., González, M. J., Vanni, M. J. & Headworth, J. L. Effects of turbidity and biotic factors on the rotifer community in an Ohio reservoir. In Rotifera VIII: A Comparative Approach. Developments in Hydrobiology, Hydrobiologia Vol. 387388 (eds Wurdak, E. et al.) 215–223 (Springer, 1998).
Google Scholar 
Roman, M. R., Holliday, D. V. & Sanford, L. P. Temporal and spatial patterns of zooplankton in the Chesapeake Bay turbidity maximum. Mar. Ecol. Prog. Ser. 213, 215–227. https://doi.org/10.3354/meps213215 (2001).ADS 
Article 

Google Scholar 
Young, I. R. & Ribal, A. Multiplatform evaluation of global trends in wind speed and wave height. Science 364(6440), 548–552. https://doi.org/10.1126/science.aav9527 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Goździejewska, A. M., Skrzypczak, A. R., Paturej, E. & Koszałka, J. Zooplankton diversity of drainage system reservoirs at an opencast mine. Knowl. Manag. Aquat. Ecosyst. 419, 33. https://doi.org/10.1051/kmae/2018020 (2018).Article 

Google Scholar 
Goździejewska, A. M., Skrzypczak, A. R., Koszałka, J. & Bowszys, M. Effects of recreational fishing on zooplankton communities of drainage system reservoirs at an open-pit mine. Fish. Manag. Ecol. 00, 1–13. https://doi.org/10.1111/fme.12411 (2020).Article 

Google Scholar 
Allesina, S., Bodini, A. & Bondavalli, C. Ecological subsystems via graph theory: The role of strongly connected components. Oikos 110, 164–176. https://doi.org/10.1111/j.0030-1299.2005.13082.x (2005).Article 

Google Scholar 
D’Alelio, D. et al. Ecological-network models link diversity, structure and function in the plankton food-web. Sci. Rep. 6, 21806. https://doi.org/10.1038/srep21806 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Krebs, C. J. Ecology: The Experimental Analysis of Distribution and Abundance 6th edn. (Benjamin Cummings, 2009).
Google Scholar 
Ejsmont-Karabin, J., Radwan, S. & Bielańska-Grajner, I. Rotifers. Monogononta–Atlas of Species. Polish Freshwater Fauna (Univ of Łódź, 2004).
Google Scholar 
Streble, H. & Krauter, D. Das Leben im Wassertropfen. Mikroflora und Mikrofauna des Süβwassers (Kosmos Gesellschaft der Naturfreunde Franckhsche Verlagshandlung Stuttgart, 1978).
Google Scholar 
Ejsmont-Karabin, J. The usefulness of zooplankton as lake ecosystem indicators: Rotifer trophic state index. Pol. J. Ecol. 60, 339–350 (2012).
Google Scholar 
Gutkowska, A., Paturej, E. & Kowalska, E. Rotifer trophic state indices as ecosystem indicators in brackish coastal waters. Oceanologia 55(4), 887–899. https://doi.org/10.5697/oc.55-4.887 (2013).Article 

Google Scholar 
Dembowska, E. A., Napiórkowski, P., Mieszczankin, T. & Józefowicz, S. Planktonic indices in the evaluation of the ecological status and the trophic state of the longest lake in Poland. Ecol. Indic. 56, 15–22. https://doi.org/10.1016/j.ecolind.2015.03.019 (2015).Article 

Google Scholar 
Sousa, W., Attayde, J. L., Rocha, E. D. S. & Eskinazi-Sant’Anna, E. M. The response of zooplankton assemblages to variations in the water quality of four man-made lakes in semi-arid northeastern Brazil. J. Plankton Res. 30(6), 699–708. https://doi.org/10.1093/plankt/fbn032 (2008).Article 

Google Scholar 
Kak, A. & Rao, R. Does the evasive behavior of H. exarthra influence its competition with cladocerans? In Rotifera VIII: A Comparative Approach. Developments in Hydrobiology, Hydrobiologia Vol. 387/388 (eds Wurdak, E. et al.) 409–419 (Springer, 1998).
Google Scholar 
Hochberg, R., Yang, H. & Moore, J. The ultrastructure of escape organs: Setose arms and crossstriated muscles in Hexarthra mira (Rotifera: Gnesiotrocha: Flosculariaceae). Zoomorphology 136, 159–173. https://doi.org/10.1007/s00435-016-0339-2 (2017).Article 

Google Scholar 
Brooks, J. L. & Dodson, S. I. Predation, body size, and composition of plankton. Science 150, 28–35 (1965).ADS 
CAS 
Article 

Google Scholar 
Connell, J. H. Intermediate-disturbance hypothesis. Science 204(4399), 1345 (1979).CAS 
Article 

Google Scholar 
Martín González, A. M., Dalsgaard, B. & Olesen, J. M. Centrality measures and the importance of generalist species in pollination networks. Ecol. Complex. 7(1), 36–43. https://doi.org/10.1016/j.ecocom.2009.03.008 (2010).Article 

Google Scholar 
Paine, R. T. A note on trophic complexity and community stability. Am. Nat. 104, 91–93 (1969).Article 

Google Scholar 
Schmitz, O. J. & Trussell, G. C. Multiple stressors, state-dependence and predation risk—Foraging trade-offs: Toward a modern concept of trait-mediated indirect effects in communities and ecosystems. Curr. Opin. Behav. Sci. 12, 6–11. https://doi.org/10.1016/j.cobeha.2016.08.003 (2016).Article 

Google Scholar 
Burns, C. W. & Gilbert, J. J. Effects of daphnid size and density on interference between Daphnia and Keratella cochlearis. Limnol. Oceanogr. 31(4), 848–858. https://doi.org/10.4319/lo.1986.31.4.0848 (1986).ADS 
Article 

Google Scholar 
Gilbert, J. J. Suppression of rotifer populations by Daphnia: A review of the evidence, the mechanisms, and the effects on zooplankton community structure. Limnol. Oceanogr. 33(6), 1286–1303. https://doi.org/10.4319/lo.1988.33.6.1286 (1988).ADS 
Article 

Google Scholar 
Conde-Porcuna, J. M., Morales-Baquero, R. & Cruz-Pizarro, L. Effects of Daphnia longispina on rotifer populations in a natural environment: Relative importance of food limitation and interference competition. J. Plankton Res. 16(6), 691–706. https://doi.org/10.1093/plankt/16.6.691 (1994).Article 

Google Scholar 
Ladle, R. J. & Whittaker, R. J. (eds) Conservation Biogeography (Wiley–Blackwell, 2011).
Google Scholar 
Cottee-Jones, H. E. W. & Whittaker, R. J. The keystone species concept: A critical appraisal. Front. Biogeogr. 4(3), 117–127. https://doi.org/10.21425/F5FBG12533 (2012).Article 

Google Scholar 
Remane, A. Die Brackwasserfauna. Verhandlungen Der Deutschen Zoologischen Gesellschaft 36, 34–74 (1934).
Google Scholar 
Skrzypczak, A. R. & Napiórkowska-Krzebietke, A. Identification of hydrochemical and hydrobiological properties of mine waters for use in aquaculture. Aquac. Rep. 18, 100460. https://doi.org/10.1016/j.aqrep.2020.100460 (2020).Article 

Google Scholar 
von Flössner, D. & Krebstiere, C. Kiemen-und Blattfüsser, Branchiopoda, Fischläuse, Branchiura Vol. 382 (VEB Gustav Fischer Verlag, 1972).
Google Scholar 
Koste, W. Rotatoria. Die Rädertiere Mitteleuropas. Überordnung Monogononta. I Textband, II Tafelband 52–570 (Gebrüder Borntraeger, 1978).
Google Scholar 
Rybak, J. I. & Błędzki, L. A. Freshwater Planktonic Crustaceans (Warsaw University Press, 2010).
Google Scholar 
Błędzki, L. A. & Rybak, J. I. Freshwater Crustacean Zooplankton of Europe: Cladocera & Copepoda (Calanoida, Cyclopoida). Key to Species Identification with Notes on Ecology, Distribution, Methods and Introduction to Data Analysis (Springer, 2016).Book 

Google Scholar 
Bottrell, H. H. et al. A review of some problems in zooplankton production studies. Norw. J. Zool. 24, 419–456 (1976).
Google Scholar 
Ejsmont-Karabin, J. Empirical equations for biomass calculation of planktonic rotifers. Pol. Arch. Hydr. 45, 513–522 (1998).
Google Scholar 
Kovach, W. L. MVSP—A Multivariate Statistical Package for Windows, ver. 3.2 (Kovach Computing Services Pentraeth, 2015).
Google Scholar 
Borgatti, S. P. Centrality and network flow. Soc. Netw. 27, 55–71. https://doi.org/10.1016/j.socnet.2004.11.008 (2005).Article 

Google Scholar 
Kamada, T. & Kawai, S. An algorithm for drawing general undirected graphs—Inform. Process Lett. 31, 7–15 (1989).MathSciNet 
Article 

Google Scholar 
Pavlopoulos, G. A. et al. Using graph theory to analyze biological networks. BioData Min 4, 10 (2011).Article 

Google Scholar 
Newman, M. E. J. A measure of betweenness centrality based on random walks. Soc. Netw. 27, 39–54. https://doi.org/10.1016/j.socnet.2004.11.009 (2005).Article 

Google Scholar 
Brandes, U. A. faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177. https://doi.org/10.1080/0022250X.2001.9990249 (2001).Article 
MATH 

Google Scholar  More

Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science (80- ). 359, 80–83 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Eakin, C. M., Sweatman, H. P. A. & Brainard, R. E. The 2014–2017 global-scale coral bleaching event: Insights and impacts. Coral Reefs 38, 539–545 (2019).ADS 

Google Scholar 
Glynn. Coral reef bleaching: Facts, hypotheses and implications. Glob. Chang. Biol. 2, 495–509 (1996).ADS 

Google Scholar 
Brown, B. E. Coral bleaching: Causes and consequences. Coral Reefs 16, 129–138 (1997).
Google Scholar 
Maynard, J. A. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nat. Clim. Chang. 5, 688–694 (2015).ADS 

Google Scholar 
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S. & Hoegh-Guldberg, O. Ocean acidification causes bleaching and productivity loss in coral reef builders. Proc. Natl. Acad. Sci. U. S. A. 105, 17442–17446 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang, H. et al. Positive and negative responses of coral calcification to elevated pCO2: Case studies of two coral species and the implications of their responses. Mar. Ecol. Prog. Ser. 502, 145–156 (2014).ADS 
CAS 

Google Scholar 
Hoadley, K. D. et al. Physiological response to elevated temperature and pCO2 varies across four Pacific coral species: Understanding the unique host + symbiont response. Sci. Rep. 5, 1–15 (2015).
Google Scholar 
Schoepf, V. et al. Coral energy reserves and calcification in a high-CO2 world at two temperatures. PLoS One. 8, e75049 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
IPCC. In IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, (eds. Pörtner, H.-O. et al.) 1–36 (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2019).Bahr, K. D., Jokiel, P. L. & Rodgers, K. S. Relative sensitivity of five Hawaiian coral species to high temperature under high-pCO2 conditions. Coral Reefs 35, 729–738 (2016).ADS 

Google Scholar 
Dove, S. G., Brown, K. T., Van Den Heuvel, A., Chai, A. & Hoegh-Guldberg, O. Ocean warming and acidification uncouple calcification from calcifier biomass which accelerates coral reef decline. Commun. Earth Environ. 1, 1–9 (2020).
Google Scholar 
Chow, M. H., Tsang, R. H. L., Lam, E. K. Y. & Ang, P. O. Quantifying the degree of coral bleaching using digital photographic technique. J. Exp. Mar. Bio. Ecol. 479, 60–68 (2016).
Google Scholar 
Amid, C. et al. Additive effects of the herbicide glyphosate and elevated temperature on the branched coral Acropora formosa in Nha Trang, Vietnam. Environ. Sci. Pollut. Res. 25, 13360–13372 (2018).CAS 

Google Scholar 
Anthony, K. R. N., Connolly, S. R. & Willis, B. L. Comparative analysis of energy allocation to tissue and skeletal growth in corals. Limnol. Oceanogr. 47, 1417–1429 (2002).ADS 

Google Scholar 
Edmunds, P. J. & Davies, P. S. An energy budget for Porites porites (Scleractinia). Mar. Biol. 92, 339–347 (1986).
Google Scholar 
Stimson, J. S. Location, quantity and rate of change in quantity of lipids in tissue of Hawaiian hermatypic corals. Bull. Mar. Sci. 41, 889–904 (1987).ADS 

Google Scholar 
Harland, A. D., Navarro, J. C., Spencer Davies, P. & Fixter, L. M. Lipids of some Caribbean and Red Sea corals: Total lipid, wax esters, triglycerides and fatty acids. Mar. Biol. 117, 113–117 (1993).CAS 

Google Scholar 
Grottoli, A. G., Tchernov, D. & Winters, G. Physiological and biogeochemical responses of super-corals to thermal stress from the northern gulf of Aqaba, Red Sea. Front. Mar. Sci. 4, 1–12 (2017).
Google Scholar 
Rodrigues, L. J. & Grottoli, A. G. Energy reserves and metabolism as indicators of coral recovery from bleaching. Limnol. Oceanogr. 52, 1874–1882 (2007).ADS 

Google Scholar 
Anthony, K. R. N., Hoogenboom, M. O., Maynard, J. A., Grottoli, A. G. & Middlebrook, R. Energetics approach to predicting mortality risk from environmental stress: A case study of coral bleaching. Funct. Ecol. 23, 539–550 (2009).
Google Scholar 
Baumann, J. H., Grottoli, A. G., Hughes, A. D. & Matsui, Y. Photoautotrophic and heterotrophic carbon in bleached and non-bleached coral lipid acquisition and storage. J. Exp. Mar. Bio. Ecol. 461, 469–478 (2014).CAS 

Google Scholar 
Hughes, A. D. & Grottoli, A. G. Heterotrophic compensation: A possible mechanism for resilience of coral reefs to global warming or a sign of prolonged stress?. PLoS ONE 8, 1–10 (2013).
Google Scholar 
Grottoli, A. G. et al. The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Glob. Chang. Biol. 20, 3823–3833 (2014).ADS 
PubMed 

Google Scholar 
Grottoli, A. G., Rodrigues, L. J. & Palardy, J. E. Heterotrophic plasticity and resilience in bleached corals. Nature 440, 1186–1189 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Levas, S. J. et al. Can heterotrophic uptake of dissolved organic carbon and zooplankton mitigate carbon budget deficits in annually bleached corals?. Coral Reefs 35, 495–506 (2016).ADS 

Google Scholar 
Jury, C. P., Delano, M. N. & Toonen, R. J. High heritability of coral calcification rates and evolutionary potential under ocean acidification. Sci. Rep. 9, 1–13 (2019).
Google Scholar 
Jury, C. P. & Toonen, R. J. Adaptive responses and local stressor mitigation drive coral resilience in warmer, more acidic oceans. Proc. R. Soc. B Biol. Sci. 286, 20190614 (2019).
Google Scholar 
Concepcion, G. T., Polato, N. R., Baums, I. B. & Toonen, R. J. Development of microsatellite markers from four Hawaiian corals: Acropora cytherea, Fungia scutaria, Montipora capitata and Porites lobata. Conserv. Genet. Resour. 2, 11–15 (2010).

Google Scholar 
Gorospe, K. D. & Karl, S. A. Genetic relatedness does not retain spatial pattern across multiple spatial scales: Dispersal and colonization in the coral, Pocillopora damicornis. Mol. Ecol. 22, 3721–3736 (2013).PubMed 

Google Scholar 
Wall, C. B., Ritson-Williams, R., Popp, B. N. & Gates, R. D. Spatial variation in the biochemical and isotopic composition of corals during bleaching and recovery. Limnol. Oceanogr. 64, 2011–2028 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bahr, K. D., Tran, T., Jury, C. P. & Toonen, R. J. Abundance, size, and survival of recruits of the reef coral Pocillopora acuta under ocean warming and acidification. PLoS ONE 15, 1–13 (2020).
Google Scholar 
Rogelj, J. et al. Paris agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).ADS 
CAS 
PubMed 

Google Scholar 
McLachlan, R. H., Price, J. T., Solomon, S. L. & Grottoli, A. G. Thirty years of coral heat-stress experiments: A review of methods. Coral Reefs 39, 885–902 (2020).
Google Scholar 
Grottoli, A. G. et al. Increasing comparability among coral bleaching experiments. Ecol. Appl. 31, e02262 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Grottoli, A. G. Variability of stable isotopes and maximum linear extension in reef-coral skeletons at Kaneohe Bay, Hawaii. Mar. Biol. 135, 437–449 (1999).
Google Scholar 
McLachlan, R. H., Dobson, K. L., Grottoli, A. G. Quantification of Total Biomass in Ground Coral Samples. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bdyai7se.McLachlan, R. H., Muñoz-Garcia, A., Grottoli, A. G. Extraction of Total Soluble Lipid from Ground Coral Samples. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bc4qiyvw.McLachlan, R. H., Price, J. T., Dobson, K. L., Weisleder, N. & Grottoli, A. G. Microplate Assay for Quantification of Soluble Protein in Ground Coral Samples. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bdc8i2zw.McLachlan, R. H., Juracka, C. & Grottoli, A. G. Symbiodiniaceae Enumeration in Ground Coral Samples Using Countess™ II FL Automated Cell Counter. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bdc5i2y6.McLachlan, R. H. & Grottoli, A. G. Geometric Method for Estimating Coral Surface Area Using Image Analysis. Protocols.io https://doi.org/10.17504/protocols.io.bdyai7se(2021).Muscatine, L., McCloskey, L. R. & Marian, R. E. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611 (1981).ADS 
CAS 

Google Scholar 
Levas, S. J. et al. Organic carbon fluxes mediated by corals at elevated pCO2 and temperature. Mar. Ecol. Prog. Ser. 519, 153–164 (2015).ADS 
CAS 

Google Scholar 
Perry, C. T. et al. Loss of coral reef growth capacity to track future increases in sea level. Nature 558, 396–400 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Woodley, C. M., Burnett, A. & Downs, C. A. Epidemiological Assessment of Reproductive Condition of ESA Priority Coral (2013).Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Glob. Chang. Biol. 20, 125–139 (2014).ADS 
PubMed 

Google Scholar 
Rodrigues, L. J., Grottoli, A. G. & Lesser, M. P. Long-term changes in the chlorophyll fluorescence of bleached and recovering corals from Hawaii. J. Exp. Biol. 211, 2502–2509 (2008).PubMed 

Google Scholar 
Rowan, H. et al. Environmental gradients drive physiological variation in Hawaiian corals. Coral Reefs 40(5), 1505–1523. https://doi.org/10.1007/s00338-021-02140-8 (2021).Article 

Google Scholar 
Houlbrèque, F. & Ferrier-Pagès, C. Heterotrophy in tropical scleractinian corals. Biol. Rev. 84, 1–17 (2009).PubMed 

Google Scholar 
J. T. Price, thesis, The Ohio State University (2020). More