Philippa Kaur

1.Bâki Iz, H. Is the global sea surface temperature rise accelerating?. Geod. Geodyn. 9, 432–438 (2018).Article 

Google Scholar 
2.Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).ADS 
Article 

Google Scholar 
3.Sarmiento, J. L. et al. Response of ocean ecosystems to climate warming. Global Biogeochem. Cycles 18 (2004).4.Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1–12 (2018).CAS 
Article 

Google Scholar 
5.Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).ADS 
PubMed 
Article 
CAS 

Google Scholar 
6.Beaugrand, G. & Kirby, R. R. How do marine pelagic species respond to climate change? Theories and observations. Ann. Rev. Mar. Sci. 10, 169–197 (2018).PubMed 
Article 

Google Scholar 
7.Verity, P. G., Smetacek, V. & Smayda, T. J. Status, trends and the future of the marine pelagic ecosystem. Environ. Conserv. 29, 207–237 (2002).Article 

Google Scholar 
8.Palko, B. J., Beardsley, G. L. & Richards, W. J. Synopsis of the biological data on dolphin-fishes, Coryphaena hippurus Linnaeus and Coryphaena equiselis Linnaeus. NOAA Tech. Rep. NMFS Circ. 443, 1–28 (1982).
Google Scholar 
9.Oxenford, H. A. Biology of the dolphinfish (Coryphaena hippurus) in the western central Atlantic: A review. Sci. Mar. 63, 277–301 (1999).Article 

Google Scholar 
10.Moltó, V. et al. A global review on the biology of the dolphinfish (Coryphaena hippurus) and its fishery in the Mediterranean Sea: advances in the last two decades. Rev. Fish. Sci. Aquac. (2020).11.FAO. Coryphaena hippurus (Linnaeus, 1758). Species fact sheets. http://www.fao.org/fishery/species/3130/en (2019).12.Morales-Nin, B., Cannizzaro, L., Massuti, E., Potoschi, A. & Andaloro, F. An overview of the FADs fishery in the Mediterranean Sea. Proc. Tuna Fish. Fish Aggreg. Dev. Symp. 184–207 (2000).13.Morales-Nin, B. Mediterranean FADs fishery: An overview. In Second International Symposium on Tuna Fisheries and Fish Aggregating Devices (2011).14.Giorgi, F. Climate change hot-spots. Geophys. Res. Lett. 33, L08707 (2006).ADS 
Article 

Google Scholar 
15.Durrieu de Madron, X. et al. Marine ecosystems’ responses to climatic and anthropogenic forcings in the Mediterranean. Prog. Oceanogr. 91, 97–166 (2011).16.Adloff, F. et al. Mediterranean sea response to climate change in an ensemble of twenty first century scenarios. Clim. Dyn. 45, 2775–2802 (2015).Article 

Google Scholar 
17.Darmaraki, S. et al. Future evolution of marine heatwaves in the mediterranean sea. Clim. Dyn. 53, 1371–1392 (2019).Article 

Google Scholar 
18.Bignami, S., Sponaugle, S. & Cowen, R. K. Effects of ocean acidification on the larvae of a high-value pelagic fisheries species, mahi-mahi Coryphaena hippurus. Aquat. Biol. 21, 249–260 (2014).Article 

Google Scholar 
19.Norton, J. G. Apparent habitat extensions of dolphinfish (Coryphaena hippurus) in response to climate transients in the California current*. Sci. Mar. 63, 239–260 (1999).Article 

Google Scholar 
20.Chang, S.-K. & Maunder, M. N. Aging material matters in the estimation of von Bertalanffy growth parameters for dolphinfish (Coryphaena hippurus). Fish. Res. 119–120, 147–153 (2012).Article 

Google Scholar 
21.Furukawa, S. et al. Age, growth, and reproductive characteristics of dolphinfish Coryphaena hippurus in the waters off west Kyushu, northern East China Sea. Fish. Sci. 78, 1153–1162 (2012).CAS 
Article 

Google Scholar 
22.Asch, R. G., Stock, C. A. & Sarmiento, J. L. Climate change impacts on mismatches between phytoplankton blooms and fish spawning phenology. Glob. Chang. Biol. 25, 2544–2559 (2019).ADS 
PubMed 
Article 

Google Scholar 
23.Shoji, J. et al. Possible effects of global warming on fish recruitment: shifts in spawning season and latitudinal distribution can alter growth of fish early life stages through changes in daylength. ICES J. Mar. Sci. 68, 1165–1169 (2011).Article 

Google Scholar 
24.R Core Team. R: A Language and Environment for Statistical Computing. Version 3.6.2. https://www.R-project.org/ (R Foundation for Satistical Computing, 2019).25.Wickham, H. ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).Article 

Google Scholar 
26.Morrongiello, J. R., Thresher, R. E. & Smith, D. C. Aquatic biochronologies and climate change. Nat. Clim. Chang. 2, 849–857 (2012).ADS 
Article 

Google Scholar 
27.Schismenou, E. et al. Seasonal changes in otolith increment width trajectories and the effect of temperature on the daily growth rate of young sardines. Fish. Oceanogr. 25, 362–372 (2016).Article 

Google Scholar 
28.Schismenou, E. et al. Disentangling the effects of inherent otolith growth and model-simulated ecosystem parameters on the daily growth rate of young anchovies. Mar. Ecol. Prog. Ser. 515, 227–237 (2014).ADS 
Article 

Google Scholar 
29.Catalán, I. A. et al. Daily otolith growth and ontogenetic geochemical signatures of age-0 anchovy (Engraulis encrasicolus) in the gulf of cádiz (SW Spain). Mediterr. Mar. Sci. 15, 781–789 (2014).Article 

Google Scholar 
30.Tanner, S. E. et al. Regional climate, primary productivity and fish biomass drive growth variation and population resilience in a small pelagic fish. Ecol. Indic. 103, 530–541 (2019).Article 

Google Scholar 
31.Ito, S., Okunishi, T., Kishi, M. J. & Wang, M. Modelling ecological responses of Pacific saury (Cololabis saira) to future climate change and its uncertainty. ICES J. Mar. Sci. 70, 980–990 (2013).Article 

Google Scholar 
32.Vinagre, C., Ferreira, T., Matos, L., Costa, M. J. & Cabral, H. N. Latitudinal gradients in growth and spawning of sea bass, Dicentrarchus labrax, and their relationship with temperature and photoperiod. Estuar. Coast. Shelf Sci. 81, 375–380 (2009).ADS 
Article 

Google Scholar 
33.Suthers, I. M. & Sundby, S. Role of the midnight sun: Comparative growth of pelagic juvenile cod (Gadus morhua) from the Arcto-Norwegian and a Nova Scotian stock. ICES J. Mar. Sci. 53, 827–836 (1996).Article 

Google Scholar 
34.Pepin, P. et al. Once upon a larva: Revisiting the relationship between feeding success and growth in fish larvae. ICES J. Mar. Sci. 72, 359–373 (2015).Article 

Google Scholar 
35.Fablet, R. et al. Shedding light on fish otolith biomineralization using a bioenergetic approach. PLoS ONE 6, e27055 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Lorenzen, K. Toward a new paradigm for growth modeling in fisheries stock assessments: Embracing plasticity and its consequences. Fish. Res. 180, 4–22 (2016).Article 

Google Scholar 
37.Campos-Candela, A., Palmer, M., Balle, S., Álvarez, A. & Alós, J. A mechanistic theory of personality-dependent movement behaviour based on dynamic energy budgets. Ecol. Lett. 22, 213–232 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Payne, M. R. et al. Uncertainties in projecting climate-change impacts in marine ecosystems. ICES J. Mar. Sci. 73, 1272–1282 (2016).Article 

Google Scholar 
39.Fernandes, J. A. et al. Can we project changes in fish abundance and distribution in response to climate? Glob. Chang. Biol. (2020).40.Ramírez-Romero, E. et al. Assessment of the skill of coupled physical-biogeochemical models in the NW Mediterranean. Front. Mar. Sci. (2020).41.Rountrey, A. N., Coulson, P. G., Meeuwig, J. J. & Meekan, M. Water temperature and fish growth: Otoliths predict growth patterns of a marine fish in a changing climate. Glob. Chang. Biol. 20, 2450–2458 (2014).ADS 
PubMed 
Article 

Google Scholar 
42.Moltó, V., Ospina-Alvarez, A., Gatt, M., Palmer, M. & Catalán, I. A. A Bayesian approach to recover the theoretical temperature-dependent hatch date distribution from biased samples: The case of the common dolphinfish (Coryphaena hippurus). Preprint at: https://arxiv.org/abs/2004.01000 (2020).43.Catalán, I. A. et al. Critically examining the knowledge base required to mechanistically project climate impacts: A case study of Europe’s fish and shellfish. Fish Fish. 1–17 (2019).44.Morrongiello, J. R., Walsh, C. T., Gray, C. A., Stocks, J. R. & Crook, D. A. Environmental change drives long-term recruitment and growth variation in an estuarine fish. Glob. Chang. Biol. 20, 1844–1860 (2014).ADS 
PubMed 
Article 

Google Scholar 
45.Baudron, A. R., Needle, C. L., Rijnsdorp, A. D. & Tara Marshall, C. Warming temperatures and smaller body sizes: Synchronous changes in growth of North Sea fishes. Glob. Chang. Biol. 20, 1023–1031 (2014).46.Pauly, D. & Cheung, W. W. L. Sound physiological knowledge and principles in modeling shrinking of fishes under climate change. Glob. Chang. Biol. 24, e15–e26 (2018).ADS 
PubMed 
Article 

Google Scholar 
47.Wenger, A. S., Whinney, J., Taylor, B. & Kroon, F. The impact of individual and combined abiotic factors on daily otolith growth in a coral reef fish. Sci. Rep. 6, 1–10 (2016).Article 
CAS 

Google Scholar 
48.García, A. et al. Climate-induced environmental conditions influencing interannual variability of Mediterranean bluefin (Thunnus thynnus) larval growth. Fish. Oceanogr. 22, 273–287 (2013).Article 

Google Scholar 
49.Pimentel, M., Pegado, M., Repolho, T. & Rosa, R. Impact of ocean acidification in the metabolism and swimming behavior of the dolphinfish (Coryphaena hippurus) early larvae. Mar. Biol. 161, 725–729 (2014).CAS 
Article 

Google Scholar 
50.FAO-CopeMed II. Report of the CopeMed II-MedSudMed Workshop on the Status of Coryphaena hippurus Fisheries in the Western-Central Mediterranean, Cádiz, Spain, 8–9 October 2019. CopeMed Technical Documents No. 54 (GCP/INT/028SPA-GCP/INT/362/EC). 1–22 (2019).51.Tittensor, D. P. et al. A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1. 0. Geosci. Model Dev. 11, 1421–1442 (2018).52.Massutí, E. & Morales-Nin, B. Reproductive biology of dolphin-fish (Coryphaena hippurus L.) off the island of Majorca (western Mediterranean). Fish. Res. 30, 57–65 (1997).53.Massutí, E. & Morales-Nin, B. Seasonality and reproduction of dolphin-fish (Coryphaena hippurus) in the Western Mediterranean*. Sci. Mar. 59, 357–364 (1995).
Google Scholar 
54.Potoschi, A., Reñones, O. & Cannizzaro, L. Sexual development, maturity and reproduction of dolphinfish (Coryphaena hippurus) in the western and central Mediterranean*. Sci. Mar. 63, 367–372 (1999).Article 

Google Scholar 
55.Alemany, F. et al. Influence of physical environmental factors on the composition and horizontal distribution of summer larval fish assemblages off Mallorca island (Balearic archipelago, western Mediterranean). J. Plankton Res. 28, 473–487 (2006).Article 

Google Scholar 
56.Torres, A. P. et al. Decapod crustacean larval communities in the Balearic Sea (western Mediterranean): Seasonal composition, horizontal and vertical distribution patterns. J. Mar. Syst. 138, 112–126 (2014).Article 

Google Scholar 
57.Massutí, E., Deudero, S., Sánchez, P. & Morales-Nin, B. Diet and Feeding of Dolphin (Coryphaena hippurus) in Western Mediterranean Waters. Bull. Mar. Sci. 63, 329–341 (1998).
Google Scholar 
58.Merten, W., Appeldoorn, R., Rivera, R. & Hammond, D. Diel vertical movements of adult male dolphinfish (Coryphaena hippurus) in the western central atlantic as determined by use of pop-up satellite archival transmitters. Mar. Biol. 161, 1823–1834 (2014).Article 

Google Scholar 
59.D’Ortenzio, F. & D’Alcalà, M. R. On the trophic regimes of the Mediterranean Sea: A satellite analysis. Biogeosciences 5, 2959–2983 (2008).
Google Scholar 
60.IPCC. Climate change 2014: Synthesis report. In Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (Core Writing Team, Pachauri, R.K., Meyer, L.A. eds.). (IPCC, 2014).61.Grazzini, F. & Viterbo, P. Record-breaking warm sea surface temperature of the Mediterranean Sea. ECMWF Newsl. 98, 30–31 (2003).
Google Scholar 
62.Olita, A., Sorgente, R., Ribotti, A., Natale, S. & Gaberšek, S. Effects of the 2003 European heatwave on the Central Mediterranean Sea surface layer: a numerical simulation. Eur. Geosci. Union 3, 85–125 (2006).
Google Scholar 
63.Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: Effects of the 2003 heat wave. Glob. Chang. Biol. 15, 1090–1103 (2009).ADS 
Article 

Google Scholar 
64.Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).ADS 
Article 

Google Scholar 
65.Hobday, A. J. et al. Categorizing and naming marine heatwaves. Oceanography 31, 162–173 (2018).Article 

Google Scholar 
66.Schlegel, R. W. Marine Heatwave Tracker. http://www.marineheatwaves.org/tracker. https://doi.org/10.5281/zenodo.3787872 (2020).67.Ricker, W. E. Computation and interpretation of biological statistics of fish populations. Bull. Fish. Res. Bd. Can. 191, 1–382 (1975).68.Solano-Fernández, M., Montoya-Márquez, J. A., Gallardo-Cabello, M. & Espino-Barr, E. Age and growth of the Dolphinfish Coryphaena hippurus in the coast of Oaxaca and Chiapas, Mexico. Rev. Biol. Mar. Oceanogr. 50, 491–505 (2015).Article 

Google Scholar 
69.Höhne, L. et al. Environmental determinants of perch (Perca fluviatilis) growth in gravel pit lakes and the relative performance of simple versus complex ecological predictors. Ecol. Freshw. Fish 00, 1–17 (2020).
Google Scholar 
70.Kuhn, M. caret: Classification and Regression Training. R package. Version 6.0-86. https://CRAN.R-project.org/package=caret (2020).71.Su, Y.-S. & Yajima, M. R2jags: Using R to Run ‘JAGS’. R Package Version 0.5-7. https://CRAN.R-project.org/package=R2jags (2015).72.Plummer, M. rjags: Bayesian Graphical Models Using MCMC. R Package Version 4-10. https://CRAN.R-project.org/package=rjags (2015).73.Then, A. Y., Hoenig, J. M., Hall, N. G. & Hewitt, D. A. Evaluating the predictive performance of empirical estimators of natural mortality rate using information on over 200 fish species. ICES J. Mar. Sci. 72, 82–92 (2015).Article 

Google Scholar 
74.Massutí, E., Morales-Nin, B. & Moranta, J. Otolith microstructure, age, and growth patterns of dolphin, Coryphaena hippurus, in the western Mediterranean. Fish. Bull. 97, 891–899 (1999).
Google Scholar 
75.Copemed II. Report of the CopeMed II-MedSudMed Workshop on Stock Assessment of Coryphaena hippurus in the Western-Central Mediterranean. Málaga, Spain 13–15 September 2016. Copemed II Technical Documents No. 44 (GCP/INT/028/SPA – GCP/INT/006/EC). Málaga, 2016. 1–31. http://www.faocopemed.org/pdf/publications/CopeMedII_TD44.pdf (2016). More

Selection and initial metabolic profiling of organismsIn order to maximize the chance of obtaining communities with diverse taxonomic profiles from different environmental compositions, the organisms selected were drawn from a number of bacterial taxa known to employ varying metabolic strategies. In addition, given the growing relevance of synthetic microbial communities to industrial and biotechnological applications73,74,75,76, we chose to employ bacterial species that have previously been used as model organisms and have well-characterized metabolic capabilities. This criterion, paired with the availability of flux-balance models associated with a majority of these organisms, allows us to explore the metabolic mechanisms observed in our various experimental conditions with higher confidence. These selection principles resulted in a set of 15 candidate bacterial organisms (Acinetobacter baylyi, Bacillus licheniformis, Bacillus subtilis, Corynebacterium glutamicum, Escherichia coli, Lactococcus lactis, Methylobacterium extorquens, Pseudomonas aeruginosa, Pseudomonas fluorescens, Pseudomonas putida, Salmonella enterica, Streptomyces coelicolor, Shewanella oneidensis, Streptococcus thermophilus, and Vibrio natriegens) spanning three bacterial phyla (Actinobacteria, Firmicutes, and Proteobacteria, Supplementary Table 1, Supplementary Fig. 1a).A microtiter plate-based phenotypic assay was used to assess the metabolic capabilities of each of the 15 candidate organisms. Each organism, stored in glycerol at −80 °C, was initially grown in 3 mL of Miller’s LB broth (Sigma–Aldrich, St. Louis, MO) for 18 h with shaking at 300 rpm at each organism’s recommended culturing temperature (Supplementary Table 1). To maximize oxygenation of the cultures and prevent biofilm formation, culture tubes were angled at 45° during this initial growth phase. Candidate organism Streptococcus thermophilus was found to have produced too little biomass in this time period and was grown for an additional 8 h. Each culture was then separately washed three times by centrifuging at 6000 × g for 2 min, removing the supernatant, suspending the pellet in 1 mL of M9 minimal medium with no carbon source, and vortexing or triturating to homogenize. The cultures were then diluted to OD600 0.5 ± 0.1 as read by a microplate reader (BioTek Instruments, Winooski, VT) and distributed into each well of three PM1 Phenotype MicroArray Plates (Biolog Inc., Hayward, CA) per organism at final OD600 of 0.05 ± 0.01. The carbon sources in the PM1 plates (Supplementary Table 2, Supplementary Fig. 1b) were resuspended in 150 µl of M9 minimal media prepared from autoclaved M9 salts (BD, Franklin Lakes, NJ) and filter-sterilized MgSO4 and CaCl2 prior to inoculation. The cultures in each PM1 plate were incubated at each organism’s recommended culturing temperature with shaking at 300 rpm for 48 h. After this growing period, the OD600 of each culture was measured by a microplate reader to quantify growth. To account for evaporation in the outer wells of the plates, which could yield in inflated OD readings, three ‘evaporation control’ plates with no carbon source were inoculated with bacteria at a final OD600 of 0.05 and incubated at 30 °C for 48 h. The averaged OD600 readings of these plates were subtracted from the readings of the bacterial growth plates to correct for evaporation. A one-tailed t-test was performed using these corrected OD600 values to determine significance of growth above the value of the negative controls (p  More

California has a vast area and spans ten latitudes, and its internal geographical conditions and climate conditions vary widely13. Therefore, the California wildfires in history differed greatly in their frequency, size, intensity and extent of damage8. As the California wildfires are growing fiercer, they have become a hot topic worldwide. However, there is still a long way to go before the general conclusions from the wildfire literature can be applied in practice. For example, how the analyses of which types of wildfires are increasing the fastest can be used to guide the amendment of wildfire management policies? and how to guide fire fighting methods based on the results of the wildfire dominant factor model? To provide some practical reference for wildfire management work, we grouped the wildfires according to size (large fires, small fires) and ignition cause (natural fires and human-caused fires), and discussed their distribution characteristics separately using the administrative units from CAL FIRE and the weather division of California from the National Oceanic and Atmospheric Administration (NOAA) as the base map. While focusing on wildfires in the past two decades, the distribution of wildfires from 1920 to 1999 was used as prior information for comparison.Wildfire size distributionThe burned area of wildfires is an important indicator of their destructive power. Several studies have shown that 1% of large and extreme wildfires are responsible for 90% of the total damage caused by wildfires14,15. Besides, the Probability density distribution of wildfire burned area, that is the wildfire size, has an obvious heavy tail feature. Research from Strauss et al.16 and Holmes et al.17 indicate that the wildfire size distribution fits the Pareto distribution well. Based on their conclusions, five common heavy-tailed distributions were selected (which are Gamma, Lognormal, Pareto, Truncated Pareto and Weibull distribution) to fit the wildfire size distribution throughout California within the eighty years before year 2000 and twenty years after year 2000s, seeking the best description of the California wildfire size distribution. The estimated parameters and the goodness of fit test results are shown in Tables 1 and 2. The empirical wildfire size distribution and the fitting curve are shown in Figure. 1. Fig. 1 shows that the wildfire size distribution did not change much from the last century to the present. Also, all these fitting curves can capture the main feature of the empirical distribution. Table 1 lists the estimated shape and scale parameters for each distribution. It can be found that the shape parameter of current wildfire size distribution ((alpha)) decrease compared to the historical wildfires. The value of shape determines the thickness of the tail. A smaller shape value means a thicker tail. In the context of wildfires, it means the probability density of large wildfires increase. Table 2 shows the goodness of fit for each distribution by Akaike Information Criterion (AIC), Kolmogorov-Smirnov (K-S) and Cramer-VonMises (CvM) test score. For all the tests, the smaller the value of the test score, the better the fit. Among these five fits, the lognormal distribution is the best for wildfire size description in 1920–1999, following by the Pareto distribution; while the best fitting distribution in 2000–2019 changes to the truncated Pareto, the second-best fitting result is still from the Pareto distribution. Therefore, Pareto is appropriate to summarize the general feature of wildfire size distribution in California.
Table 1 Heavy-tailed distribution fitting results of wildfire size distribution.Full size tableTable 2 Goodness-of-fit test results of Akaike Information Criterion (AIC), Kolmogorov–Smirnov (K–S) test, and Cramer-Von Mises (CvM) test for heavy-tailed distribution fitting.Full size tableTo further explore the variation of wildfire size distribution within the entire state of California, the probability density of the logarithm of wildfire size was plotted for 1920 to 1999 and 2000 to 2019. As shown in Fig. 2, wildfires in 1920–1999 were mostly about 100–1000 acre (0.40–4.05 km(^2)) in size; while during 2000–2019, the number of small fires increased significantly,Figure 1The empirical histogram of wildfire size and the typical heavy tailed distribution fitting curves for wildfires in (a) 1920–1999 and (b) 2000–2019. The wildfire sizes are in acres (1000 acre = 4.05 km(^2)). The curve with different colors represent different types of distribution, the black, yellow, red, green, and blue curves represent the fitting result of Gamma, Log-normal, Pareto, Truncated Pareto, and Weibull distribution, separately. The tail of the distribution was truncated from the burned area of 2000 acres to show the fitting difference between different distributions.Full size image the majority of wildfire sizes were in the range of 10–100 acres (0.04–0.40 km(^2)). Wildfires were also divided into natural wildfires and human-caused wildfires based on their ignition causes. The red, green and blue dashed lines in the figure delineate the fitting results of Gamma, Lognormal and Weibull distribution separately, which capture the distribution characteristics for each type of the wildfires. The fitting parameters and the goodness of test results were attached in the supplementary information (Table S1). Figure 2b,e show that although the overall shape of the distribution of natural wildfires in 1920–1999 and 2000–2019 are similar, the proportion of extreme wildfires larger than 10,000 acres (40.47 km(^2)) has increased significantly in the last two decades. From Fig. 2c,f, it can be found that the shape of the fire size distribution of human-caused wildfires differs greatly, which is the result of the rapid increase of the proportion of small fires. Although human activity directly or indirectly ignited 44(%) of wildfires in the United States18 and 39(%) of wildfires in California (as shown in the statistical summary in Table 3), they are generally easily contained in the initial attack19. The rapidly growing population in California has led to increased human activities and community coverage, which has increased the incidence of human-caused wildfires20. However, the expansion of human land has reduced the continuity, which is essential for the spread of wildfires21. Also, the improvement of wildfire monitoring and fire fighting ability has made most of the small human-caused wildfires able to be extinguished during the first 24 h after discovery19. Together, these reasons lead to the rapid increase in the frequency of small human-caused fires in the past two decades.Figure 2Logarithm of California wildfire size empirical distribution in 1920–1999 and 2000–2019. The Gamma, Lognormal and Weibull distribution fitting results are indicated by the red, green and blue dash lines. The wildfire sizes are in acres (1000 acre = 4.05 km(^2)). (a–c) are the historical wildfires from 1920 to 1999, (d–f) are the wildfires from 2000 to 2019; (a,d) are the distribution of all wildfires, (b,e) are the distribution of natural wildfires, (c,f) are the distribution of human-caused wildfires.Full size imageTable 3 Statistical summary of wildfire ignition causes in CA from 2000 to 2019.Full size tableLarge and small fires are not only very different in the probability density distribution characteristics but also in prevention measures, response methods, and resources needed to be invested in fire fighting22,23. In order to discuss the spatiotemporal distribution of large and small wildfires, it is critical to determine the threshold of large wildfires. Therefore, the mean excess plot shown in Fig. 3 was used to determine the threshold of the large fire. The linear part’s starting point is the threshold of the extreme value in the original distribution17,24. As shown in Fig. 3, 500 acres (2.02 km(^2)) would be appropriate to separate the large fires and small fires for the entire California. Also, as shown in Fig. 1, 500 acres is an appropriate starting point of the heavy tail. Based on the historical record from CAL FIRE, the frequency of large wildfires accounted for 19.68 (%) of the total (1247 out of 6336 wildfires), while the burned area of large wildfires accounted for 97.04 (%) of the total burned area (13,089.68 out of 13,488.19 thousand acres, that is 52,972.05 out of 54,584.77 km(^2)) in the past two decades. According to the size class of fire defined by national wildfire coordinating group (NWCG), the large fire in this study refers to the wildfires of or larger than class E.Figure 3Mean excess plot for wildfires burned areas.Full size imageTemporal variation of wildfires in CA from 1920–1999 and 2000 to 2019Based on the wildfire history records provided by the CAL FIRE Fire Perimeter database, the frequency and burned area of wildfires in CA from 1920 to 2019 were extracted, and separated into two time periods: 1920–1999 and 2000–2019. California has seen an average of 317 wildfires a year over the past 20 years, which were included in the Fire Perimeter database, burning an average of 674,410 acres (2,729.24 km(^2)). Figure 4 shows the changes in the annual wildfire frequency (a–e) and burned area (f–j) over time. The red lines represent the segmented linear regression trend in 1920–1999 and 2000–2019, separately. The grey areas depicted the 95(%) confidence interval. Comparing the slope of the fitting line, it is apparent that in most cases, the frequency and burned area growth of wildfires in the past two decades are much higher than that during the 80 years in history, if the breakpoint is fixed to the year 2000. Also, the 95(%) confidence intervals of the regression lines over the past two decades are generally larger than that between 1920 and 1999. Although the sample size in these two time periods is different, it can be seen from the spread of data points that the uncertainty of wildfire frequency and burned area have increased significantly in the past two decades. From the view of fire frequency, the rapid increase in the number of small fires brings greater uncertainty than that of large fires, and the uncertainty of natural fires is higher than that of human-caused fires. In terms of the burned area, the uncertainty comes mainly from large wildfires and natural wildfires. When it comes to the increase rate, Fig. 4b,c,g,h show that in the large and small wildfire group, the accelerated increase of wildfire frequency was mainly contributed by the small fires, while the accelerated increase of burned area was from the large fires. The frequency of large wildfires and the burned area of small wildfires in the recent 20 years even have the trend of decrease. This trend suggests that it would be efficient for the fire management department to pay more attention to the regions with the potential risk of extreme fires and prevent small fires from burning continuously and becoming large fires. Figure 4d,e,i,j display the trend for the natural and human-caused wildfires. The increase of the human-caused wildfire frequency is much faster than that of the natural wildfires in both time periods. However, the increases in the burned area due to the increasing frequency of wildfires with different causes are similar. It shows that the human-caused small wildfires have the strongest growth trend in the recent twenty years. In the view of wildfire management, while human activities increase the likelihood of wildfires ignition, large natural fires are more threatening in terms of size and destruction.Figure 4Temporal distribution of wildfire frequency and burned area from 1920 to 2019. The red line indicates the segmented linear regression results for 1920–1999 and 2000–2019. The gray areas indicate the 95(%) confidence interval. (R^2) represents the coefficient of determination and p represents the p-value. (a–e) are the temporal distribution of wildfire frequency, (f–j) are the temporal distribution of the burned area of wildfires; (a,f) are the distribution for all wildfires; (b,g) are plots of large fires, which have the burned area larger than 500 acres (2.02 km(^2)), while (c,h) are plots of small fires, which have the burned area in the range of 10 acres (0.04 km(^2)) to 500 acres (2.02 km(^2)); (d,i,e,j) divided wildfires into natural fires and human-caused fires. The small plot in (h) zooms in to the burned area of 0–50 thousand acres.Full size imageCalifornia’s Mediterranean climate is characterized by hot and dry summers, which leads to a high wildfire ignition risk25,26. Also, the hot and dry Santa Ana wind events have accelerated the spread of wildfires each fall27. The precipitation in California was concentrated in the winter, and the temperature was moderate28, allowing wildland vegetation to grow fast and storing fuel for next year. However, the significant climate change after the year 2000 has affected the seasonal distribution of wildfires.Figure 5 compiles box plots of the seasonal variation of wildfire frequency and burned area distribution in 1920–1999 and 2000–2019, which were divided into different groups by size and ignition cause as well. The boxes and points in the plots represent the wildfire frequency or total burned area in this month each year. In general, the peak season for wildfires was late summer and early autumn. In terms of the frequency, from 1920 to 1999, the wildfire season started in June, and the most frequent occurrence was observed in August. In most years, the number of wildfires in July and August were similar, followed by June and September. However, from 2000 to 2019, the frequency of wildfires in July increased significantly and became much more considerable than in other months. Meanwhile, the start of the wildfire season has also advanced to May, and the duration has extended. From May to September, the overall fire frequency of all wildfires, large wildfires, and small wildfires increased each month. The number of natural fires also increased between June to September. The frequency of human-caused wildfires, on the other hand, increased each month. Similar to the previous discussions, the increase of wildfire frequency in July in the past two decades mainly came from small fires and human-caused wildfires. It is worth noting that there has been a major increase in the natural wildfires in July in the past two decades. In terms of the burned area, the month with the largest total burned area of wildfires in 2000–2019 has been advanced to July, compared to August in 1920–1999. Natural wildfires and human-caused wildfires contributed similarly to the burned area growth. There is no noticeable change in the total burned area in months other than the wildfire season.Figure 5Seasonal variation of wildfire frequency and burned area from 1920 to 2019. The threshold of large and small wildfires is 500 acre (2.02 km(^2)). (a–j show the seasonal variation of fire frequency, (k–t) show the seasonal variation of burned area; (a,b,k,l) are plots for all CA wildfires, (c–f) and (m–p) divided fires into large and small fire size group, (g–j) and (q–t) divided fires into natural and human-caused wildfire groups. The small plots in (o) and (p) zoom in to the burned area of 0–10 thousand acres.Full size imageSpatial distribution of wildfires in CA from 2000 to 2019CAL FIRE has 21 operational units throughout the state that are designated to address fire suppression over a certain geographic area and six ‘Contract Counties’ (Kern, Los Angeles, Marin, Orange, Santa Barbara and Ventura) for fire protection services. Due to the complex environmental and terrain conditions in California, the risk of wildfires varies significantly from region to region, and the causes of extreme wildfires are also completely different. In order to provide fire managers with more effective fire suppression measures, this study used kernel density estimation (KDE) to analyze hot spot regions of all the wildfires, natural fires and human-caused fires from 2000 to 2019, the KDE for wildfires in 1920–1999 were also added for comparison. The resolution of KDE analyses was 500 m. The results are shown in Figs. 6 and 7. Figure 6 treated all the fires equally, and shows the spatial density of wildfire numbers; while Fig. 7 weighted the wildfires with their burned area, and represents the burned area-weighted spatial density of wildfire occurrence.Comparing the spatial density distribution of all wildfires in different time periods in this study, as shown in Fig. 6a,d, it is evident that the coverage of wildfire occurrence has increased significantly. From 1920 to 1999, the only hot spot with a very high wildfire density was Los Angeles County (LAC). In the past two decades, not only did the hot spot of LAC expand to Ventura county (VNC) but also the wildfire density in the southwest corner of Riverside Unit (RRU) and San Diego Unit (MVU) on the south coast and the southwest corner of San Bernardino Unit (BDU) have grown to a very high level. In the eastern part of the San Joaquin Drainage under the central California climate division, namely the Sierra Nevada Mountains (identified in Fig. 10), wildfire density has increased from very low to very high. Among them, Nevada-Yuba-Placer Unit (NEU) and Tuolumne-Calaveras Unit (TCU) are the newly emerged high-density wildfire regions. Moreover, the spatial density distributions were grouped by causes, and Fig. 6b,e represent the natural wildfires, and c,f represent the human-caused wildfires. It can be found that while the high-density areas of natural wildfires have not shifted in both time periods, the density has increased. In contrast, the density of human-caused wildfires has increased notably in western and central California in the past two decades. Before the year 2000, there were almost no human-caused wildfires along the west coastline, but almost every county along the west-coast is characterized by an increase of human-caused wildfires in the past two decades. San Benito-Monterey Unit (BEU) and San Luis Obispo Unit (SLU) even became the new hot spots. Meanwhile, the coverage area of the original human-caused wildfire hot spots on the south coast has been further expanded. From 1920 to 1999, the density of human-caused wildfires in the Sierra Nevada Mountain was very low in central California. Still, in the past two decades, it has become a new wildfire ignition hot spot. The counties in northern California, such as Siskiyou Unit (SKU), Shasta-Trinity Unit (SHU), Tehama-Glenn Unit (TGU), etc., have been almost no human-caused wildfires from 1920 to 1999, but widespread human-caused wildfires have emerged in the past two decades.After inducing the wildfire burned area into the KDE calculation, the spatial density distribution has changed significantly. In general, as shown in 7a,d, the regions where large wildfires are concentrated are SKU and Sonoma-Lake-Napa Unit (LNU) in Northern California and MVU in the South Coast. Although the number of wildfires in the central Sierra Nevada Mountains has increased significantly, the total burned area did not significantly change. Thereafter, the wildfires with different causes were separated, and it can be found from 7b,e that natural wildfires with large burned areas were concentrated in northern California. In the past two decades, the region with a very high-density of wildfire occurrence in the northernmost SKU has expanded significantly, and a new hot spot of wildfires has also appeared in Lassen-Modoc Unit (LMU). However, the high-density wildfire area between Tuolumne-Calaveras Unit (TCU) and Madera-Mariposa-Merced Unit (MMU) did not arise in the past two decades. In the distribution of human-caused wildfires, as shown in 7c,f, the density of wildfires in MVU in the southernmost part of California has surpassed that of historical hot spots, VNC and LAC. Meanwhile, the density of wildfires at the junction of TCU and MMU in the central region has also increased.Comparing 6 and 7, it is obvious that the spatial distribution of wildfire density and burned area-weighted wildfire density are not entirely consistent. CAL FIRE Units along the South Coast, which are in the climate division of South Coast Drainage, are prominent in both densities, and are mainly composed of human-caused wildfires. The SKU and LMU units in the northernmost part of North Coast Drainage are the areas where natural wildfires were concentrated, and the distribution of SKU wildfires is relatively wider. The Units adjacent to the Sierra Nevada Mountains in central California, which are the units in the northeast of San Joaquin Drainage, show a low wildfire density when the burned area was added to the calculation, even though the number of wildfires has increased rapidly in the past two decades. This distribution is related to the vegetation cover and land use in California. In northern California, the evergreen and deciduous forests are the dominant vegetation, the forests are dense and less developed by human, and the population density is relatively low28,29. Wildfires are difficult to be detected early-on in these remote areas, and there is enough fuel to keep them burning and spreading. On the other hand, shrubs are the dominant vegetation in southern California. Also, most of the southern CA areas have been developed and associated with a higher level of human activity, leading to wildfires in southern California has a greater social and economic impact on human lives and society30.Figure 6Kernel density distribution of wildfire occurrence in CA during 1920–1999 (a–c), and 2000–2019 (d–f). (a–f) are wildfire density distribution maps for all wildfires, natural wildfires and human-caused wildfires in CA, separately.Full size imageFigure 7Kernel density distribution of burned area weighted wildfire occurrence in CA during 1920–1999 (a–c), and 2000–2019 (d–f). (a–f) are wildfire density distribution maps for all wildfires, natural wildfires and human-caused wildfires in CA, separately.Full size imageFrom the discussion above, it can be found that while the frequency and spatial density distribution of human-caused wildfires have changed significantly in the past two decades, the changes in burned area were relatively small because of the high proportion of small wildfires. Also, unlike natural fires, human-caused fires can be prevented or controlled in the early stage by taking effective measures19. Therefore, the human-caused wildfires were further classified to generate a more detailed spatial density distribution map. The anthropogenic causes were subdivided by CAL FIRE into 15 types. The spatial distribution of wildfires with different causes are shown in the supplementary figures (Supplementary Fig. 1). In this study, human-caused wildfires were classified into three categories: transportation (railroad, vehicle, aircraft), human activity (equipment use, smoking, campfire, debris, arson, playing with fire, firefighter training, non-firefighter training, escaped prescribed fire, illegal alien campfire) and construction (powerline, structure). As shown in Fig. 8, hot spots for all three broad types of wildfires include areas along the Sierra Nevada Range and along the southern coast. However they differ in the density level and coverage. Among them, the number and coverage of wildfires caused by human subjective behavior are larger than those caused by traffic and construction. Besides, the wildfires caused by human activities also led to the emergence of a unique hot spot in the northernmost edge of CA, which is the SKU county. Therefore, for the wildfire management purpose, it would be proactive to provide wildfire education to residents in regions with high wildfire risk, update the wildfire risk map in time, and issue early warnings of wildfire risk to the public during the fire season, to increase the public’s awareness of wildfire prevention.Figure 8KDE Analysis of human-caused wildfires in CA from 2000 to 2019. (a) Transportation (railroad, vehicle, aircraft); (b) Human Activity (equipment use, smoking, campfire, debris, arson, playing with fire, firefighter training, non-firefighter training, escaped prescribed fire, illegal alien campfire); (c) Human Construction (power line, structure).Full size imageMultivariate analysis of California wildfiresThe occurrence and spread of wildfires are related to human activities and environmental variables. In order to formulate effective suppression and control policies for wildfire management, it is essential to understand the relationship between the spatial distribution of wildfires and various variables. From the KDE analysis, the spatial distributions of the wildfire density calculated with and without burned area were obtained, which also shows the areas with high wildfire risk from 2000 to 2019. According to the research from Faivre et al.7, 12 variables that have potential correlations with wildfires, involving human-related variables, geographic conditions, fuel, and climate variables were selected to conduct the subsequent analyses.Table 4 calculated the spatial correlation between the burned area-weighted wildfire density and potential anthropogenic and environmental variables within the wildfire perimeters, as well as the interrelation between each variable. It can be derived from the first column that among the human-related variables, except for the distance to the road, other variables are positively correlated with the wildfire occurrence density. It means that in areas where wildfires have occurred in the last two decades, the farther away from the power line, the higher the wildfire density; the closer to the road, the higher the wildfire density; and the greater the density of houses and population, the higher the density of wildfires. Among environmental variables such as topography, vegetation cover, and climate, only elevation is negatively correlated with wildfire density. That is, the higher the elevation, the lower the wildfire density. From the correlations among various variables, it can be found that there is a strong correlation between the distance from the wildfire perimeter to the road and power line, population, and house density, as well as elevation and two climate variables. For further analyses, one variable would be removed between the two variables whose correlation is greater than 0.5. Therefore, the distance to power line, population density and elevation were removed in the multivariate analysis.Table 4 Spatial Correlation Analysis between 12 selected variables wildfire occurrence density: distance to power line (DP), distance to road (DR), housing density (DH), population density (DP), elevation, aspect, slope, tree, shrub, grass, maximum temperature (Tmax), maximum vapor pressure deficit (VPDmax).Full size tableThe principal component analysis (PCA) was implemented on the remaining variables and the two types of wildfire spatial densities obtained from KDE, to classify the variables and evaluate their relationships. The eigenvalue matrix was attached in the supplement information (Supplementary Table S3.). Both PCA results require five principal components to explain at least 80(%) of the data variance. The interrelations of the variables and the fire occurrence density decomposed by PC1 and PC2 are shown in Fig. 9. There is a strong and similar interrelationship between the two types of fire densities and the driver variables. The length and orientation of the variables indicate that the wildfire densities have the strongest correlation with the grass cover and the other two variables of vegetation cover (shrub and tree), namely fuel cover in general. Meanwhile, the correlation between the climate variables and the wildfire densities is also significant, especially for the maximum vapor pressure deficit (VPDmax). Besides, the human-related variables are moderately correlated with the wildfire densities, while topographic variables are almost orthogonal with the wildfire densities, which means their correlations are weak.Figure 9PCA loading plots with (a) fire occurrence density, (b) burned area weighted fire occurrence density. The variables include distance to road (DR), housing density (DH), aspect, slope, tree, shrub, grass, maximum temperature (Tmax), maximum vapor pressure deficit (VPDmax), wildfire density (FOD) and burned area weighted wildfire density ((FOD_A)).Full size imageBased on the analyses above, the Logistic Regression (LR) was implemented on the selected nine variables to further determine their relationship with wildfire occurrence. The coefficient, standard error and the significance level for each variable were shown in Table 5. The positive and negative sign of the coefficient represents the positive or negative correlation with the wildfire occurrence, and the p-value indicates whether the correlation is significant. The results reveal that the climate variables are the most critical in whether the wildfires can be ignited or not, followed by the variables of distance to road, and the cover of grass. The sign of the coefficient of the human-related variables is negative, which means that in general, wildfires ignited far from the human communities. Similarly, the areas where trees are dominant vegetation cover have fewer wildfire ignitions. Overall, logistic regression results show that the areas with high temperature, high VPD, grass as the dominant vegetation cover, and away from human communities have a higher risk of wildfire ignition.Table 5 Logistic regression results of uncorrelated explanatory variables for California wildfires occurrence (2000–2019).Full size table More

1.Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Hurricanes and caribbean coral reefs: impacts, recovery patterns, and role in long-term decline. Ecology 86, 174–184 (2005).Article 

Google Scholar 
2.Harvell, D. et al. Coral disease, environmental drivers, and the balance between coral and microbial associates. Oceanography 20, 172–195 (2007).Article 

Google Scholar 
3.Silverman, J., Lazar, B., Cao, L., Caldeira, K. & Erez, J. Coral reefs may start dissolving when atmospheric CO2 doubles. Geophys. Res. Lett. 36, 1–5 (2009).Article 

Google Scholar 
4.Jackson, J., Donovan, M., Cramer, K. & Lam, W. Status and Trends of Caribbean Coral Reefs 1970–2012 (2012).5.IPCC. Climate Change 2014 Synthesis Report. IPCC Fifth Assessment Report 151 (2014).6.Zaneveld, J. R. et al. Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales. Nat. Commun. 7, 1–12 (2016).Article 

Google Scholar 
7.Bruno, J. F., Petes, L. E., Drew Harvell, C. & Hettinger, A. Nutrient enrichment can increase the severity of coral diseases. Ecol. Lett. 6, 1056–1061 (2003).Article 

Google Scholar 
8.Danovaro, R. et al. Sunscreens cause coral bleaching by promoting viral infections. Environ. Health Perspect. 116, 441–447 (2008).CAS 
Article 

Google Scholar 
9.Díaz, M. & Madin, J. Macroecological relationships between coral species’ traits and disease potential. Coral Reefs 30, 73–84 (2011).ADS 
Article 

Google Scholar 
10.Bruno, J. F. The coral disease triangle. Nat. Clim. Chang. 5, 302–303 (2015).ADS 
Article 

Google Scholar 
11.Muller, E. M. et al. Low pH reduces the virulence of black band disease on Orbicella faveolata. PLoS ONE 12, e0178869 (2017).Article 

Google Scholar 
12.Thurber, R. V., Payet, J. P., Thurber, A. R. & Correa, A. M. S. Virus-host interactions and their roles in coral reef health and disease. Nat. Rev. Microbiol. 15, 205–216 (2017).CAS 
Article 

Google Scholar 
13.Pollock, F. J., Morris, P. J., Willis, B. L. & Bourne, D. G. The urgent need for robust coral disease diagnostics. PLoS Pathog. 7 (2011).14.Beeden, R., Maynard, J. A., Marshall, P. A., Heron, S. F. & Willis, B. L. A framework for responding to coral disease outbreaks that facilitates adaptive management. Environ. Manag. 49, 1–13 (2012).ADS 
Article 

Google Scholar 
15.Walton, C. J., Hayes, N. K. & Gilliam, D. S. Impacts of a regional, multi-year, multi-species coral disease outbreak in Southeast Florida. Front. Mar. Sci. 5 (2018).16.Harvell, C. D. et al. Emerging marine diseases: Climate links and anthropogenic factors. Manter Lab. 580 (1999).17.Wilkinson, C. Status of Coral Reefs of the World: 2008. (2008).18.Ruiz-Moreno, D. et al. Global coral disease prevalence associated with sea temperature anomalies and local factors. Dis. Aquat. Organ. 100, 249–261 (2012).Article 

Google Scholar 
19.Bruckner, A. W. Proceedings of the Caribbean Acropora Workshop: Potential Application of the U.S. Endangered Species Act as a Conservation Strategy. in Proceedings of the Caribbean Acropora Workshop 199 (2003).20.Casas, V. et al. Widespread association of a Rickettsiales-like bacterium with reef-building corals. Environ. Microbiol. 6, 1137–1148 (2004).Article 

Google Scholar 
21.Aronson, R. B. & Precht, W. F. White-band disease and the changing face of Caribbean coral reefs. Hydrobiologia 460, 25–38 (2001).Article 

Google Scholar 
22.Gladfelter, W. B. White-band disease in Acropora palmata: Implications for the structure and growth of shallow reefs. Bull. Mar. Sci. 32, 639–643 (1982).
Google Scholar 
23.Richardson, L. L. Coral diseases: What is really known?. TREE 13, 438–443 (1998).CAS 
PubMed 

Google Scholar 
24.Richardson, L. L. et al. Florida’s mystery coral-killer identified. Sci. Corresp. 392, 557–558 (1998).CAS 

Google Scholar 
25.Richardson, L. & Voss, J. Changes in a coral population on reefs of the northern Florida Keys following a coral disease epizootic. Mar. Ecol. Prog. Ser. 297, 147–156 (2005).ADS 
Article 

Google Scholar 
26.Berkelmans, R., De’ath, G., Kininmonth, S. & Skirving, W. J. A comparison of the 1998 and 2002 coral bleaching events on the Great Barrier Reef: Spatial correlation, patterns, and predictions. Coral Reefs 23, 74–83 (2004).Article 

Google Scholar 
27.Burge, C. A. et al. Climate change influences on marine infectious diseases: Implications for management and society. Ann. Rev. Mar. Sci. 6, 249–277 (2014).Article 

Google Scholar 
28.Maynard, J. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nat. Clim. Chang. 5, 688–694 (2015).ADS 
Article 

Google Scholar 
29.Roth, L., Kramer, P. R., Doyle, E. & and O’Sullivan, C. Caribbean SCTLD Dashboard. ArcGIS Online (2020). https://www.agrra.org/coral-disease-outbreak/.30.Alvarez-Filip, L., Estrada-Saldívar, N., Pérez-Cervantes, E., Molina-Hernández, A. & González-Barrios, F. J. A rapid spread of the stony coral tissue loss disease outbreak in the Mexican Caribbean. PeerJ https://doi.org/10.7717/peerj.8069 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Muller, E. M., Sartor, C., Alcaraz, N. I. & van Woesik, R. Spatial epidemiology of the stony-coral-tissue-loss disease in Florida. Front. Mar. Sci. 7, 163 (2020).Article 

Google Scholar 
32.Aeby, G. S. et al. Pathogenesis of a tissue loss disease affecting multiple species of corals along the florida reef tract. Front. Mar. Sci. 6, 1–18 (2019).ADS 
Article 

Google Scholar 
33.Weil, E. & Rogers, C. S. Coral reef diseases in the Atlantic-Caribbean. In Coral Reefs: An Ecosystem in Transition (eds Dubinsky, Z. & Stambler, N.) 465–491 (Springer, 2011). https://doi.org/10.1007/978-94-007-0114-4.
Google Scholar 
34.Rippe, J. P., Kriefall, N. G., Davies, S. W. & Castillo, K. D. Differential disease incidence and mortality of inner and outer reef corals of the upper Florida Keys in association with a white syndrome outbreak. Bull. Mar. Sci. 95, 305–316 (2019).Article 

Google Scholar 
35.Neely, K. Ex-Situ Disease Treatment Trials. 1–3 (2018). https://floridadep.gov/sites/default/files/Ex-Situ-Disease-Treatment-Trials.pdf.36.Neely, K. Ex Situ Disease Treatment Trials Final Report. 1–3 (2019). Available at: https://floridadep.gov/sites/default/files/DEPLabTrialsFINALReport2019.01508comp_0.pdf.37.Miller, C. V., May, L. A., Moffitt, Z. J. & Woodley, C. M. Exploratory Treatments for Stony Coral Tissue Loss Disease: Pillar Coral (Dendrogyra cylindrus). (2020). https://doi.org/10.7289/V5/TM-NOS-NCCOS-24538.Favero, M., Balut, K., Levine, M. & Circle, M. Amoxicillin Trihydrate Stability in Correlation with Coral Ointment Batch #18006-B and Simulated Seawater. 1–9 (2019). https://floridadep.gov/sites/default/files/AmoxicillinStabilityinBothSeawaterBatch18006-B_FINAL_508C_0.pdf.39.Aeby, G. S. et al. First record of black band disease in the Hawaiian archipelago: Response, outbreak status, virulence, and a method of treatment. PLoS ONE 10, 1–17 (2015).Article 

Google Scholar 
40.Walker, B. K. & Brunelle, A. Southeast Florida large ( >2 meter) diseased coral colony intervention summary report. 1–164 (2018). https://floridadep.gov/sites/default/files/Large-Coral-Disease-Intervention-Summary-Report.pdf.41.Combs, I. Characterizing the Impacts of Scleractinian Tissue Loss Disease Outbreak on Corals in Southeast Florida. (2019).42.Combs, I. R., Studivan, M. S., Eckert, R. J. & Voss, J. D. Quantifying impacts of stony coral tissue loss disease on corals in Southeast Florida through surveys and 3D photogrammetry. PLoS One (In the press).43.Voss, J. D., Shilling, E. N. & Combs, I. R. Intervention and fate tracking for corals affected by stony coral tissue loss disease in the northern Florida Reef Tract. 1–23 (2019). Available at: https://floridadep.gov/sites/default/files/VossSEFLDiseaseReport2018_FINAL_508compliant.pdf.44.Veron, J. E. N. Corals of the World. (2000).45.NOAA. Stony Coral Tissue Loss Disease Case Definition. Florida Keys National Marine Sanctuary (2018).46.Banks, K. W. et al. The Reef Tract of Continental Southeast Florida (Miami-Dade, Broward and Palm Beach Counties, USA). in Coral Reefs of the USA 175–220 (2008).47.González-Barrios, F. J. & Álvarez-Filip, L. A framework for measuring coral species-specific contribution to reef functioning in the Caribbean. Ecol. Indic. 95, 877–886 (2018).Article 

Google Scholar 
48.R Core Team. R: A language and environment for statistical computing. (2020).49.Wickham, H. Package ‘ggplot2’: Create Elegant Data Visualizations Using the Grammar of Graphics. 277 (2020).50.Hope, R. M. Package ‘ Rmisc ’: Ryan Miscellaneous. (2016).51.Kassambara, A. Package ‘ rstatix ’: Pipe-Friendly Framework for Basic Statistical Tests. (2020).52.Derek, O., Wheeler, P. & Dinno, A. Package ‘ FSA ’: Simple Fisheries Stock Assessment Methods. (2020).53.Sweet, M. J., Croquer, A. & Bythell, J. C. Experimental antibiotic treatment identifies potential pathogens of white band disease in the endangered Caribbean coral Acropora cervicornis. Proc. R. Soc. B Biol. Sci. 281, 20140094–20140094 (2014).CAS 
Article 

Google Scholar 
54.Neely, K. L., Macaulay, K. A., Hower, E. K. & Dobler, M. A. Effectiveness of topical antibiotics in treating corals affected by Stony Coral Tissue Loss Disease. PeerJ 8, e9289 (2020).Article 

Google Scholar 
55.Voss, J. D., Mills, D. K., Myers, J. L., Remily, E. R. & Richardson, L. L. Black band disease microbial community variation on corals in three regions of the wider Caribbean. Microb. Ecol. 54, 730–739 (2007).CAS 
Article 

Google Scholar 
56.Sekar, R., Kaczmarsky, L. & Richardson, L. Microbial community composition of black band disease on the coral host Siderastrea siderea from three regions of the wider Caribbean. Mar. Ecol. Prog. Ser. 362, 85–98 (2008).ADS 
CAS 
Article 

Google Scholar 
57.Sato, Y., Willis, B. L. & Bourne, D. G. Successional changes in bacterial communities during the development of black band disease on the reef coral, Montipora hispida. ISME J. 4, 203–214 (2010).Article 

Google Scholar 
58.Miller, A. W. & Richardson, L. L. A meta-analysis of 16S rRNA gene clone libraries from the polymicrobial black band disease of corals. FEMS Microbiol. Ecol. 75, 231–241 (2010).Article 

Google Scholar 
59.Hudson, H. First Aid for Massive Corals Infected With Black Band Disease, Phormidium corallyticum: An Underwater Aspirator and Post-Treatment Sealant to Curtail Reinfection. In AAUS 20th Symposium Proceedings 2000 (2000).60.Randall, C. J. et al. Testing methods to mitigate Caribbean yellow-band disease on Orbicella faveolata. PeerJ 2018, 1–20 (2018).
Google Scholar 
61.Walker, B. K. & Pitts, K. SE FL Reef-building-coral Response to Amoxicillin Intervention and Broader-scale Coral Disease Intervention. 1–17 (2019). https://floridadep.gov/sites/default/files/WalkerMCAVDiseaseExperimentSummaryReportJune2019_final_14Aug2019.pdf.62.Neely, K. Florida Keys Coral Disease Strike Team: FY 2019/2020 Final Report. 1–17 (2020). Available at: https://floridadep.gov/sites/default/files/FloridaKeysCoralDiseaseStrikeTeam_FY19-20FinalReport.pdf.63.Paterson, I. K., Hoyle, A., Ochoa, G., Baker-Austin, C. & Taylor, N. G. H. Optimising antibiotic usage to treat bacterial infections. Sci. Rep. 6, 1–10 (2016).Article 

Google Scholar  More

1.Roth-Schulze, A. J. et al. Functional biogeography and host specificity of bacterial communities associated with the Marine Green Alga Ulva spp. Mol. Ecol. 27, 1952–1965 (2018).PubMed 
Article 

Google Scholar 
2.Teagle, H., Hawkins, S. J., Moore, P. J. & Smale, D. A. The role of kelp species as biogenic habitat formers in coastal marine ecosystems. J. Exp. Mar. Biol. Ecol. 492, 81–98 (2017).Article 

Google Scholar 
3.Goecke, F., Labes, A., Wiese, J. & Imhoff, J. F. Chemical interactions between marine macroalgae and bacteria. Mar. Ecol. Prog. Ser. 409, 267–300 (2010).ADS 
CAS 
Article 

Google Scholar 
4.Singh, R. P. & Reddy, C. R. K. Seaweed-microbial interactions: Key functions of seaweed-associated bacteria. FEMS Microbiol. Ecol. 88, 213–230 (2014).CAS 
PubMed 
Article 

Google Scholar 
5.Ramanan, R., Kim, B. H., Cho, D. H., Oh, H. M. & Kim, H. S. Algae-bacteria interactions: Evolution, ecology and emerging applications. Biotechnol. Adv. 34, 14–29 (2016).CAS 
PubMed 
Article 

Google Scholar 
6.Ismail, A. et al. Antimicrobial activities of bacteria associated with the brown alga padina pavonica. Front. Microbiol. 7, 1–13 (2016).
Google Scholar 
7.Sañudo-Wilhelmy, S. A., Gómez-Consarnau, L., Suffridge, C. & Webb, E. A. The role of B vitamins in marine biogeochemistry. Ann. Rev. Mar. Sci. 6, 339–367 (2014).PubMed 
Article 

Google Scholar 
8.Karthick, P. & Mohanraju, R. Antimicrobial potential of epiphytic bacteria associated with seaweeds of little Andaman, India. Front. Microbiol. 9, 1–11 (2018).Article 

Google Scholar 
9.El Shafay, S. M., Ali, S. S. & El-Sheekh, M. M. Antimicrobial activity of some seaweeds species from Red sea, against multidrug resistant bacteria. Egypt. J. Aquat. Res. 42, 65–74 (2016).Article 

Google Scholar 
10.Dobretsov, S. V. & Qian, P. Y. Effect of bacteria associated with the green alga Ulva reticulata on marine micro- and macrofouling. Biofouling 18, 217–228 (2002).Article 

Google Scholar 
11.Mieszkin, S., Callow, M. E. & Callow, J. A. Interactions between microbial biofilms and marine fouling algae: A mini review. Biofouling 29, 1097–1113 (2013).CAS 
PubMed 
Article 

Google Scholar 
12.Burke, C., Thomas, T., Lewis, M., Steinberg, P. & Kjelleberg, S. Composition, uniqueness and variability of the epiphytic bacterial community of the green alga Ulva australis. ISME J. 5, 590–600 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Tujula, N. A. et al. Variability and abundance of the epiphytic bacterial community associated with a green marine Ulvacean alga. ISME J. 4, 301–311 (2010).PubMed 
Article 

Google Scholar 
14.Burke, C., Steinberg, P., Rusch, D., Kjelleberg, S. & Thomas, T. Bacterial community assembly based on functional genes rather than species. Proc. Natl. Acad. Sci. 108, 14288–14293 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
15.Roth-Schulze, A. J., Zozaya-Valdés, E., Steinberg, P. D. & Thomas, T. Partitioning of functional and taxonomic diversity in surface-associated microbial communities. Environ. Microbiol. 18, 4391–4402 (2016).PubMed 
Article 

Google Scholar 
16.Selvarajan, R. et al. Distribution, interaction and functional profiles of epiphytic bacterial communities from the rocky intertidal seaweeds, South Africa. Sci. Rep. 9, 1–13 (2019).ADS 
Article 
CAS 

Google Scholar 
17.Aires, T., Serrão, E. A. & Engelen, A. H. Host and environmental specificity in bacterial communities associated to two highly invasive marine species (genus Asparagopsis). Front. Microbiol. 7, 1–14 (2016).Article 

Google Scholar 
18.Lachnit, T., Fischer, M., Künzel, S., Baines, J. F. & Harder, T. Compounds associated with algal surfaces mediate epiphytic colonization of the marine macroalga Fucus vesiculosus. FEMS Microbiol. Ecol. 84, 411–420 (2013).CAS 
PubMed 
Article 

Google Scholar 
19.Nylund, G. M. et al. The red alga Bonnemaisonia asparagoides regulates epiphytic bacterial abundance and community composition by chemical defence. FEMS Microbiol. Ecol. 71, 84–93 (2010).CAS 
PubMed 
Article 

Google Scholar 
20.Campbell, A. H., Marzinelli, E. M., Gelber, J. & Steinberg, P. D. Spatial variability of microbial assemblages associated with a dominant habitat-forming seaweed. Front. Microbiol. 6, 1–10 (2015).Article 

Google Scholar 
21.Munday, P. L. Competitive coexistence of coral-dwelling fishes: The lottery hypothesis revisited. Ecology 85, 623–628 (2004).Article 

Google Scholar 
22.Geange, S. W., Poulos, D. E., Stier, A. C. & McCormick, M. I. The relative influence of abundance and priority effects on colonization success in a coral-reef fish. Coral Reefs 36, 151–155 (2017).ADS 
Article 

Google Scholar 
23.Stratil, S. B., Neulinger, S. C., Knecht, H., Friedrichs, A. K. & Wahl, M. Temperature-driven shifts in the epibiotic bacterial community composition of the brown macroalga Fucus vesiculosus. Microbiologyopen 2, 338–349 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Stratil, S. B., Neulinger, S. C., Knecht, H., Friedrichs, A. K. & Wahl, M. Salinity affects compositional traits of epibacterial communities on the brown macroalga Fucus vesiculosus. FEMS Microbiol. Ecol. 88, 272–279 (2014).CAS 
PubMed 
Article 

Google Scholar 
25.Zhang, Y. et al. Effect of salinity on the microbial community and performance on anaerobic digestion of marine macroalgae. J. Chem. Technol. Biotechnol. 92, 2392–2399 (2017).CAS 
Article 

Google Scholar 
26.Liao, L. & Xu, Y. Effects of nitrogen nutrients on growth and epiphytic bacterial composition in sea weed Gracilaria lemaneiformis. Fish. Sci. 28, 130–135 (2009).ADS 
CAS 

Google Scholar 
27.Zozaya-Valdés, E., Roth-Schulze, A. J. & Thomas, T. Effects of temperature stress and aquarium conditions on the red macroalga Delisea pulchra and its associated microbial community. Front. Microbiol. 7, 1–10 (2016).Article 

Google Scholar 
28.Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. Biol. Rev. 77, 342–356 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Liu, X. et al. Isolation and pathogenicity identification of bacterial pathogens in bleached disease and their physiological effects on the red macroalga Gracilaria lemaneiformis. Aquat. Bot. 153, 1–7 (2019).ADS 
CAS 
Article 

Google Scholar 
30.Xie, X. et al. Large-scale seaweed cultivation diverges water and sediment microbial communities in the coast of Nan’ao Island, South China Sea. Sci. Total Environ. 598, 97–108 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
31.Yang, Y. et al. Cultivation of seaweed Gracilaria in Chinese coastal waters and its contribution to environmental improvements. Algal Res. 9, 236–244 (2015).Article 

Google Scholar 
32.Lindström, E. S. & Langenheder, S. Local and regional factors influencing bacterial community assembly. Environ. Microbiol. Rep. 4, 1–9 (2012).PubMed 
Article 

Google Scholar 
33.Hellweger, F. L., Van Sebille, E. & Fredrick, N. D. Biogeographic patterns in ocean microbes emerge in a neutral agent-based model. Science (80-. ). 345, 1346–1349 (2014).34.Longford, S. R. et al. Comparisons of diversity of bacterial communities associated with three sessile marine eukaryotes. Aquat. Microb. Ecol. 48, 217–229 (2007).Article 

Google Scholar 
35.Lachnit, T., Meske, D., Wahl, M., Harder, T. & Schmitz, R. Epibacterial community patterns on marine macroalgae are host-specific but temporally variable. Environ. Microbiol. 13, 655–665 (2010).PubMed 
Article 

Google Scholar 
36.Pei, P. et al. Effects of biological water purification grid on microbial community of culture environment and intestine of the shrimp Litopenaeus vannamei. Aquac. Res. 50, 1300–1312 (2019).CAS 
Article 

Google Scholar 
37.Shade, A. & Handelsman, J. Beyond the Venn diagram: The hunt for a core microbiome. Environ. Microbiol. 14, 4–12 (2012).CAS 
PubMed 
Article 

Google Scholar 
38.Spoerner, M., Wichard, T., Bachhuber, T., Stratmann, J. & Oertel, W. Growth and thallus morphogenesis of Ulva mutabilis (chlorophyta) depends on a combination of two bacterial species excreting regulatory factors. J. Phycol. 48, 1433–1447 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Kessler, R. W., Weiss, A., Kuegler, S., Hermes, C. & Wichard, T. Macroalgal–bacterial interactions: Role of dimethylsulfoniopropionate in microbial gardening by Ulva (Chlorophyta). Mol. Ecol. 27, 1808–1819 (2018).CAS 
PubMed 
Article 

Google Scholar 
40.Malmstrom, R. R., Kiene, R. P. & Kirchman, D. L. Identification and enumeration of bacteria assimilating dimethylsulfoniopropionate (DMSP) in the North Atlantic and Gulf of Mexico. Limnol. Oceanogr. 49, 597–606 (2004).ADS 
CAS 
Article 

Google Scholar 
41.Holmström, C., Egan, S., Franks, A., McCloy, S. & Kjelleberg, S. Antifouling activities expressed by marine surface associated Pseudoalteromonas species. FEMS Microbiol. Ecol. 41, 47–58 (2002).PubMed 
Article 

Google Scholar 
42.Holmström, C. & Kjelleberg, S. The effect of external biological factors on settlement of marine invertebrate and new antifouling technology. Biofouling 8, 147–160 (1994).Article 

Google Scholar 
43.Lachnit, T., Blümel, M., Imhoff, J. F. & Wahl, M. Specific epibacterial communities on macroalgae : Phylogeny matters more than habitat. Aquat. Biol. 5, 181–186 (2009).Article 

Google Scholar 
44.Fan, X. et al. The effect of nutrient concentrations, nutrient ratios and temperature on photosynthesis and nutrient uptake by Ulva prolifera : Implications for the explosion in green tides. J. Appl. Phycol. 26, 537–544 (2014).CAS 
Article 

Google Scholar 
45.Van Alstyne, K. L. Seawater nitrogen concentration and light independently alter performance, growth, and resource allocation in the bloom-forming seaweeds Ulva lactuca and Ulvaria obscura ( Chlorophyta ). Harmful Algae 78, 27–35 (2018).PubMed 
Article 
CAS 

Google Scholar 
46.Lachnit, T., Wahl, M. & Harder, T. Isolated thallus-associated compounds from the macroalga Fucus vesiculosus mediate bacterial surface colonization in the field similar to that on the natural alga. Biofouling 26, 247–255 (2010).CAS 
PubMed 
Article 

Google Scholar 
47.Su, H. et al. Persistence and spatial variation of antibiotic resistance genes and bacterial populations change in reared shrimp in South China. Environ. Int. 119, 327–333 (2018).CAS 
PubMed 
Article 

Google Scholar 
48.Ekwanzala, M. D., Dewar, J. B. & Momba, M. N. B. Environmental resistome risks of wastewaters and aquatic environments deciphered by shotgun metagenomic assembly. Ecotoxicol. Environ. Saf. 197, 110612 (2020).CAS 
PubMed 
Article 

Google Scholar 
49.Numberger, D. et al. Characterization of bacterial communities in wastewater with enhanced taxonomic resolution by full-length 16S rRNA sequencing. Sci. Rep. 9, 1–14 (2019).CAS 
Article 

Google Scholar 
50.Teklehaimanot, G. Z., Genthe, B., Kamika, I. & Momba, M. N. B. Prevalence of enteropathogenic bacteria in treated effluents and receiving water bodies and their potential health risks. Sci. Total Environ. 518–519, 441–449 (2015).ADS 
PubMed 
Article 
CAS 

Google Scholar 
51.Kelley, S. E. Experimental studies of the evolutionary significance of sexual reproduction. V. A field test of the sib-competition hypotheses. Evolution (N. Y). 43, 1066 (1989).52.Browne, L. & Karubian, J. Rare genotype advantage promotes survival and genetic diversity of a tropical palm. New Phytol. 218, 1658–1667 (2018).PubMed 
Article 

Google Scholar 
53.Gressler, V. et al. Lipid, fatty acid, protein, amino acid and ash contents in four Brazilian red algae species. Food Chem. 120, 585–590 (2010).CAS 
Article 

Google Scholar 
54.Gu, D. et al. Purification of R-phycoerythrin from Gracilaria lemaneiformis by centrifugal precipitation chromatography. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 1087–1088, 138–141 (2018).55.Su, Y. bin et al. Pyruvate cycle increases aminoglycoside efficacy and provides respiratory energy in bacteria. Proc. Natl. Acad. Sci. U. S. A. 115, E1578–E1587 (2018).56.Hollants, J., Leliaert, F., De Clerck, O. & Willems, A. What we can learn from sushi: A review on seaweed-bacterial associations. FEMS Microbiol. Ecol. 83, 1–16 (2013).CAS 
PubMed 
Article 

Google Scholar 
57.AQSIQ. Specifications for Oceanographic Survey. Part 4: Survey of Chemical Parameters in Sea Water. 16–26 (Standards Press of China, 2007).58.Burke, C., Kjelleberg, S. & Thomas, T. Selective extraction of bacterial DNA from the surfaces of macroalgae. Appl. Environ. Microbiol. 75, 252–256 (2009).CAS 
PubMed 
Article 

Google Scholar 
59.Xu, Y., Le, G. & Zhang, Y. Comparison with several methods to isolate epiphytic bacteria from Gracilaria lemaneiformis (Rhodophyta). Microbiol. China 34, 123–126 (2007).
Google Scholar 
60.Pei, P. et al. Analysis of the bacterial community composition of the epiphytes on diseased Gracilaria lemaneiformis using PCR-DGGE fingerprinting technology. J. Fish. Sci. China 25 (2018).61.Takahashi, S., Tomita, J., Nishioka, K., Hisada, T. & Nishijima, M. Development of a prokaryotic universal primer for simultaneous analysis of bacteria and archaea using next-generation sequencing. PLoS One 9 (2014).62.Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).CAS 
PubMed 
Article 

Google Scholar 
63.Liu, T. et al. Joining Illumina paired-end reads for classifying phylogenetic marker sequences. BMC Bioinform. 21, 1–13 (2020).Article 

Google Scholar 
64.Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
PubMed 
Article 

Google Scholar 
66.Cole, J. R. et al. Ribosomal database project: Data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, 633–642 (2014).Article 
CAS 

Google Scholar 
67.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).Article 
CAS 

Google Scholar 
68.Wang, Y. et al. Comparison of the levels of bacterial diversity in freshwater, intertidal wetland, and marine sediments by using millions of illumina tags. Appl. Environ. Microbiol. 78, 8264–8271 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Somerfield, P. J. Identification of the Bray-Curtis similarity index: Comment on Yoshioka (2008). Mar. Ecol. Prog. Ser. 372, 303–306 (2008).ADS 
Article 

Google Scholar 
70.Higgins, M. A., Robbins, G. A., Maas, K. R. & Binkhorst, G. K. Use of bacteria community analysis to distinguish groundwater recharge sources to shallow wells. J. Environ. Qual. 49, 1530–1540 (2020).CAS 
PubMed 
Article 

Google Scholar 
71.Yang, J., Ma, L., Jiang, H., Wu, G. & Dong, H. Salinity shapes microbial diversity and community structure in surface sediments of the Qinghai-Tibetan Lakes. Sci. Rep. 6, 6–11 (2016).ADS 
Article 
CAS 

Google Scholar 
72.Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Richardson, D. M. Fifty Years of Invasion Ecology: The Legacy of Charles Elton. (John Wiley & Sons, 2011).4.Soberón, J. Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10, 1115–1123 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Brown, J. H. Patterns, modes and extents of invasions by vertebrates. Biological Invasions: A Global Perspective. 85–110 (John Wiley & Sons, 1989).6.Holt, R. D., Barfield, M. & Gomulkiewicz, R. Theories of niche conservatism and evolution: could exotic species be potential tests. in: Species Invasions: Insights into Ecology, Evolution and Biogeography (eds. Sax, Stachowicz & Gaines) 259–290 (Sinauer Associates, Mass, 2005).7.Brown, J. H., Stevens, G. C. & Kaufman, D. M. The geographic range: size, shape, boundaries, and internal structure. Annu. Rev. Ecol. Syst. 27, 597–623 (1996).Article 

Google Scholar 
8.Sagarin, R. D., Gaines, S. D. & Gaylord, B. Moving beyond assumptions to understand abundance distributions across the ranges of species. Trends Ecol. Evol. 21, 524–530 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Guisan, A., Petitpierre, B., Broennimann, O., Daehler, C. & Kueffer, C. Unifying niche shift studies: insights from biological invasions. Trends Ecol. Evol. 29, 260–269 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Forsyth, D. M., Duncan, R. P., Bomford, M. & Moore, G. Climatic suitability, life-history traits, introduction effort, and the establishment and spread of introduced mammals in Australia. Conserv. Biol. 18, 557–569 (2004).Article 

Google Scholar 
11.Bomford, M., Kraus, F., Barry, S. C. & Lawrence, E. Predicting establishment success for alien reptiles and amphibians: a role for climate matching. Biol. Invasions 11, 713–724 (2009).Article 

Google Scholar 
12.Petitpierre, B. et al. Climatic niche shifts are rare among terrestrial plant invaders. Science 335, 1344–1348 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Liu, C., Wolter, C., Xian, W. & Jeschke, J. M. Most invasive species largely conserve their climatic niche. Proc. Natl Acad. Sci. U.S.A. 117, 23643–23651 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Pearman, P. B., Guisan, A., Broennimann, O. & Randin, C. F. Niche dynamics in space and time. Trends Ecol. Evol. 23, 149–158 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
15.González-Suárez, M., Bacher, S. & Jeschke, J. M. Intraspecific trait variation is correlated with establishment success of alien mammals. Am. Nat. 185, 737–746 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Redding, D. W. et al. Location-level processes drive the establishment of alien bird populations worldwide. Nature https://doi.org/10.1038/s41586-019-1292-2 (2019).17.Titeux, N. et al. The need for large-scale distribution data to estimate regional changes in species richness under future climate change. Divers. Distrib. 23, 1393–1407 (2017).Article 

Google Scholar 
18.Chevalier, M., Broennimann, O., Cornuault, J., & Guisan, A. Data integration methods to account for spatial niche truncation effects in regional projections of species distribution. Ecol. Appl. (in press).19.Blackburn, T. M. & Duncan, R. P. Determinants of establishment success in introduced birds. Nature 414, 195–197 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Bacon, S. J., Aebi, A., Calanca, P. & Bacher, S. Quarantine arthropod invasions in Europe: the role of climate, hosts and propagule pressure. Divers. Distrib. 20, 84–94 (2014).Article 

Google Scholar 
21.Abellán, P., Tella, J. L., Carrete, M., Cardador, L. & Anadón, J. D. Climate matching drives spread rate but not establishment success in recent unintentional bird introductions. Proc. Natl Acad. Sci. 114, 9385–9390 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
22.Long, J. L. Introduced Mammals of the World: Their History, Distribution and Influence. (CSIRO PUBLISHING, 2003).23.Pulliam, H. R. On the relationship between niche and distribution. Ecol. Lett. 3, 349–361 (2000).Article 

Google Scholar 
24.Godsoe, W., Jankowski, J., Holt, R. D. & Gravel, D. Integrating biogeography with contemporary niche theory. Trends Ecol. Evol. 32, 488–499 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Sax, D. F., Early, R. & Bellemare, J. Niche syndromes, species extinction risks, and management under climate change. Trends Ecol. Evol. 28, 517–523 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Csergő, A. M. et al. Less favourable climates constrain demographic strategies in plants. Ecol. Lett. 20, 969–980 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
27.Capellini, I., Baker, J., Allen, W. L., Street, S. E. & Venditti, C. The role of life history traits in mammalian invasion success. Ecol. Lett. 18, 1099–1107 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Sol, D., Bacher, S., Reader, S. M., & Lefebvre, L. Brain size predicts the success of mammal species introduced into novel environments. American Naturalist 172(S1), S63–S71 (2008).Article 

Google Scholar 
29.Duncan, R. P., Blackburn, T. M., Rossinelli, S. & Bacher, S. Quantifying invasion risk: the relationship between establishment probability and founding population size. Methods Ecol. Evol. 5, 1255–1263 (2014).Article 

Google Scholar 
30.Allen, C. R. et al. Predictors of regional establishment success and spread of introduced non-indigenous vertebrates. Glob. Ecol. Biogeogr. 22, 889–899 (2013).Article 

Google Scholar 
31.Cassey, P., Delean, S., Lockwood, J. L., Sadowski, J. S. & Blackburn, T. M. Dissecting the null model for biological invasions: A meta-analysis of the propagule pressure effect. PLoS Biol. 16, e2005987 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Buswell, J. M., Moles, A. T. & Hartley, S. Is rapid evolution common in introduced plant species? J. Ecol. 99, 214–224 (2011).Article 

Google Scholar 
33.Broennimann, O., Mráz, P., Petitpierre, B., Guisan, A. & Müller-Schärer, H. Contrasting spatio-temporal climatic niche dynamics during the eastern and western invasions of spotted knapweed in North America. J. Biogeogr. 41, 1126–1136 (2014).Article 

Google Scholar 
34.Shea, K. & Chesson, P. Community ecology theory as a framework for biological invasions. Trends Ecol. Evol. 17, 170–176 (2002).Article 

Google Scholar 
35.Facon, B. et al. A general eco-evolutionary framework for understanding bioinvasions. Trends Ecol. Evol. 21, 130–135 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Escobar, L. E., Qiao, H., Cabello, J. & Townsend Peterson, A. Ecological niche modeling re-examined: a case study with the Darwin’s fox. Ecol. Evolut. 8, 4757–4770 (2018).Article 

Google Scholar 
37.Petitpierre, B. et al. Will climate change increase the risk of plant invasions into mountains? Ecol. Appl. 26, 530–544 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Pheloung, P. C., Williams, P. A. & Halloy, S. R. A weed risk assessment model for use as a biosecurity tool evaluating plant introductions. J. Environ. Manag. 57, 239–251 (1999).Article 

Google Scholar 
39.Pluess, T. et al. Which factors affect the success or failure of eradication campaigns against alien species? PLoS One 7, e48157 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Pyšek, P. et al. MAcroecological Framework for Invasive Aliens (MAFIA): disentangling large-scale context dependence in biological invasions. NeoBiota 62, 407–461 (2020).Article 

Google Scholar 
41.Lonsdale, W. M. Global patterns of plant invasions and the concept of invasibility. Ecology 80, 1522 (1999).Article 

Google Scholar 
42.Leung, B. et al. TEASIng apart alien species risk assessments: a framework for best practices. Ecol. Lett. 15, 1475–1493 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Fourcade, Y. Comparing species distributions modelled from occurrence data and from expert-based range maps. Implication for predicting range shifts with climate change. Ecol. Inform. 36, 8–14 (2016).Article 

Google Scholar 
44.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
45.Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agriculture. Ecosyst. Environ. 126, 67–80 (2008).Article 

Google Scholar 
46.Bellard, C. et al. Will climate change promote future invasions? Glob. Chang. Biol. 19, 3740–3748 (2013).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).Article 

Google Scholar 
48.Cola, V. D. et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).Article 

Google Scholar 
49.Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. U.S.A. 104, 13384–13389 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Plummer, M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. in Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), March 20–22, Vienna, Austria. (2003).51.R Core Team. R: a language and environment for statistical computing. (2014).52.Su, Y.-S. & Yajima, M. R2jags: a package for running jags from R. (2013).53.Gelman, A. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1, 515–534 (2006).MathSciNet 
MATH 

Google Scholar 
54.Little, R. & Rubin, D. Statistical Analysis with Missing Data, Second Edition. (Wiley Series in Probability and Statistics, 2002).55.Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).MATH 

Google Scholar 
56.Gelman, A., Meng, X.-L. & Stern, H. Posterior predictive assessment of model fitness via realized discrepancies. Stat. Sin. 6, 733–760 (1996).MathSciNet 
MATH 

Google Scholar 
57.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
58.Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R. (Cambridge University Press, 2017).59.Broennimann, O., et al. Distance to native climatic niche margins explains establishment success of alien mammals. ecospat/NMI: NMI v1.0. Zenodo. https://doi.org/10.5281/zenodo.4588999. (2021). More

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1.Savci, S. An agricultural pollutant: chemical fertilizer. Int. J. Environ. Sci. Dev. 3, 77–80 (2012).CAS 

Google Scholar 
2.Guo, J. H. et al. Significant acidification in major Chinese croplands. Science 327, 1008–1010 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Raza, S. et al. Dramatic loss of inorganic carbon by nitrogen‐induced soil acidification in Chinese croplands. Glob. Change Biol. 26, 3738–3751 (2020).ADS 
Article 

Google Scholar 
4.Jez, J. M., Lee, S. G. & Sherp, A. M. The next green movement: plant biology for the environment and sustainability. Science 353, 1241–1244 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Cordovez, V., Dini-Andreote, F., Carrion, V. J. & Raaijmakers, J. M. Ecology and evolution of plant microbiomes. Annu. Rev. Microbiol. 73, 69–88 (2019).CAS 
PubMed 
Article 

Google Scholar 
6.Duran, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983.e914 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Dini-Andreote, F. & Raaijmakers, J. M. Embracing community ecology in plant microbiome research. Trends Plant Sci. 23, 467–469 (2018).CAS 
PubMed 
Article 

Google Scholar 
8.de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
9.Hubbard, C. J. et al. The effect of rhizosphere microbes outweighs host plant genetics in reducing insect herbivory. Mol. Ecol. 28, 1801–1811 (2019).CAS 
PubMed 
Article 

Google Scholar 
10.Oldroyd, G. E. D. & Leyser, O. A plant’s diet, surviving in a variable nutrient environment. Science 368, eaba0196 (2020).CAS 
PubMed 
Article 

Google Scholar 
11.Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, eaba1223 (2020).CAS 
PubMed 
Article 

Google Scholar 
12.Martín‐Robles, N. et al. Impacts of domestication on the arbuscular mycorrhizal symbiosis of 27 crop species. New Phytol. 218, 322–334 (2018).PubMed 
Article 

Google Scholar 
13.Genre, A., Lanfranco, L., Perotto, S. & Bonfante, P. Unique and common traits in mycorrhizal symbioses. Nat. Rev. Microbiol. 18, 649–660 (2020).CAS 
PubMed 
Article 

Google Scholar 
14.Liu, X. et al. Partitioning of soil phosphorus among arbuscular and ectomycorrhizal trees in tropical and subtropical forests. Ecol. Lett. 21, 713–723 (2018).PubMed 
Article 

Google Scholar 
15.Varoquaux, N. et al. Transcriptomic analysis of field-droughted sorghum from seedling to maturity reveals biotic and metabolic responses. Proc. Natl Acad. Sci. USA 116, 27124–27132 (2019).CAS 
Article 

Google Scholar 
16.Lazcano, C., Barrios-Masias, F. H. & Jackson, L. E. Arbuscular mycorrhizal effects on plant water relations and soil greenhouse gas emissions under changing moisture regimes. Soil Biol. Biochem. 74, 184–192 (2014).CAS 
Article 

Google Scholar 
17.Sprent, J. I. Evolving ideas of legume evolution and diversity: a taxonomic perspective on the occurrence of nodulation. New Phytol. 174, 11–25 (2007).CAS 
PubMed 
Article 

Google Scholar 
18.Soltis, D. E. et al. Chloroplast gene sequence data suggest a single origin of the predisposition for symbiotic nitrogen fixation in angiosperms. Proc. Natl Acad. Sci. USA 92, 2647–2651 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.Young, N. D. et al. The Medicago genome provides insight into the evolution of rhizobial symbioses. Nature 480, 520–524 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.van Velzen, R. et al. Comparative genomics of the nonlegume Parasponia reveals insights into evolution of nitrogen-fixing Rhizobium symbioses. Proc. Natl Acad. Sci. USA 115, E4700–E4709 (2018).PubMed 
Article 
CAS 

Google Scholar 
21.Smil, V. Nitrogen in crop production: an account of global flows. Glob. Biogeochem. Cycles 13, 647–662 (1999).ADS 
CAS 
Article 

Google Scholar 
22.O’Hara, G. W. The role of nitrogen fixation in crop production. J. Crop Prod. 1, 115–138 (1998).Article 

Google Scholar 
23.Remigi, P., Zhu, J., Young, J. P. W. & Masson-Boivin, C. Symbiosis within symbiosis: evolving nitrogen-fixing legume symbionts. Trends Microbiol. 24, 63–75 (2016).CAS 
PubMed 
Article 

Google Scholar 
24.Garcia, K., Delaux, P. M., Cope, K. R. & Ané, J. M. Molecular signals required for the establishment and maintenance of ectomycorrhizal symbioses. New Phytol. 208, 79–87 (2015).PubMed 
Article 

Google Scholar 
25.Fisher, R. F. & Long, S. R. Rhizobium–plant signal exchange. Nature 357, 655–660 (1992).ADS 
CAS 
PubMed 
Article 

Google Scholar 
26.Cao, Y., Halane, M. K., Gassmann, W. & Stacey, G. The role of plant innate immunity in the legume–Rhizobium symbiosis. Annu. Rev. Plant Biol. 68, 535–561 (2017).CAS 
PubMed 
Article 

Google Scholar 
27.Ferguson, B. J. et al. Legume nodulation: the host controls the party. Plant Cell Environ. 42, 41–51 (2019).CAS 
PubMed 
Article 

Google Scholar 
28.Remans, R. et al. Effect of Rhizobium–Azospirillum coinoculation on nitrogen fixation and yield of two contrasting Phaseolus vulgaris L. genotypes cultivated across different environments in Cuba. Plant Soil 312, 25–37 (2008).CAS 
Article 

Google Scholar 
29.Cassán, F. & Diaz-Zorita, M. Azospirillum sp. in current agriculture: from the laboratory to the field. Soil Biol. Biochem. 103, 117–130 (2016).Article 
CAS 

Google Scholar 
30.Han, Q. et al. Variation in rhizosphere microbial communities and its association with the symbiotic efficiency of rhizobia in soybean. ISME J. 14, 1915–1928 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Saharan, B. S. & Nehra, V. Plant growth promoting rhizobacteria: a critical review. Life Sci. Med. Res. 21, 30 (2011).
Google Scholar 
32.Cheng, Y. T., Zhang, L. & He, S. Y. Plant–microbe interactions facing environmental challenge. Cell Host Microbe 26, 183–192 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Dini-Andreote, F. Endophytes: the second layer of plant defense. Trends Plant Sci. 25, 319–322 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Carrión, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019).ADS 
Article 
CAS 

Google Scholar 
35.Sieber, M. et al. Neutrality in the metaorganism. PLoS Biol. 17, e3000298 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).CAS 
PubMed 
Article 

Google Scholar 
37.Burns, A. R. et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 10, 655–664 (2016).CAS 
PubMed 
Article 

Google Scholar 
38.Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8, 732–740 (2006).PubMed 
Article 

Google Scholar 
39.Ning, D., Deng, Y., Tiedje, J. M. & Zhou, J. A general framework for quantitatively assessing ecological stochasticity. Proc. Natl Acad. Sci. USA 116, 16892–16898 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Purugganan, M. D. & Fuller, D. Q. The nature of selection during plant domestication. Nature 457, 843–848 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
42.Chen, Y. H., Gols, R. & Benrey, B. Crop domestication and its impact on naturally selected trophic interactions. Annu. Rev. Entomol. 60, 35–58 (2015).CAS 
PubMed 
Article 

Google Scholar 
43.Szoboszlay, M. et al. Comparison of root system architecture and rhizosphere microbial communities of Balsas teosinte and domesticated corn cultivars. Soil Biol. Biochem. 80, 34–44 (2015).CAS 
Article 

Google Scholar 
44.Perez-Jaramillo, J. E., Mendes, R. & Raaijmakers, J. M. Impact of plant domestication on rhizosphere microbiome assembly and functions. Plant Mol. Biol. 90, 635–644 (2016).CAS 
PubMed 
Article 

Google Scholar 
45.Perez-Jaramillo, J. E., Carrion, V. J., de Hollander, M. & Raaijmakers, J. M. The wild side of plant microbiomes. Microbiome 6, 143 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Emmett, B. D., Buckley, D. H., Smith, M. E. & Drinkwater, L. E. Eighty years of maize breeding alters plant nitrogen acquisition but not rhizosphere bacterial community composition. Plant Soil 431, 53–69 (2018).CAS 
Article 

Google Scholar 
47.Mutch, L. A. & Young, J. P. W. Diversity and specificity of Rhizobium leguminosarum biovar viciae on wild and cultivated legumes. Mol. Ecol. 13, 2435–2444 (2004).CAS 
PubMed 
Article 

Google Scholar 
48.Kiers, E. T., Hutton, M. G. & Denison, R. F. Human selection and the relaxation of legume defences against ineffective rhizobia. Proc. R. Soc. B 274, 3119–3126 (2007).CAS 
PubMed 
Article 

Google Scholar 
49.Pérez-Jaramillo, J. E. et al. Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits. ISME J. 11, 2244–2257 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Zachow, C., Müller, H., Tilcher, R. & Berg, G. Differences between the rhizosphere microbiome of Beta vulgaris ssp. maritima—ancestor of all beet crops—and modern sugar beets. Front. Microbiol. 5, 415 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Coleman‐Derr, D. et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. New Phytol. 209, 798–811 (2016).PubMed 
Article 
CAS 

Google Scholar 
52.Warschefsky, E., Penmetsa, R. V., Cook, D. R. & von Wettberg, E. J. Back to the wilds: tapping evolutionary adaptations for resilient crops through systematic hybridization with crop wild relatives. Am. J. Bot. 101, 1791–1800 (2014).PubMed 
Article 

Google Scholar 
53.Brozynska, M., Furtado, A. & Henry, R. J. Genomics of crop wild relatives: expanding the gene pool for crop improvement. Plant Biotechnol. J. 14, 1070–1085 (2016).CAS 
PubMed 
Article 

Google Scholar 
54.Zhang, H., Mittal, N., Leamy, L. J., Barazani, O. & Song, B. H. Back into the wild—apply untapped genetic diversity of wild relatives for crop improvement. Evol. Appl. 10, 5–24 (2017).PubMed 
Article 

Google Scholar 
55.Maxted, N. & Kell, S. P. Establishment of a Global Network for the In Situ Conservation of Crop Wild Relatives: Status and Needs (FAO Commission on Genetic Resources for Food and Agriculture, 2009).56.Stenberg, J. A., Heil, M., Åhman, I. & Björkman, C. Optimizing crops for biocontrol of pests and disease. Trends Plant Sci. 20, 698–712 (2015).CAS 
PubMed 
Article 

Google Scholar 
57.Heil, M. & Baldwin, I. T. Fitness costs of induced resistance: emerging experimental support for a slippery concept. Trends Plant Sci. 7, 61–67 (2002).CAS 
PubMed 
Article 

Google Scholar 
58.Liu, H. & Brettell, L. E. Plant defense by VOC-induced microbial priming. Trends Plant Sci. 24, 187–189 (2019).CAS 
PubMed 
Article 

Google Scholar 
59.Schulz-Bohm, K. et al. Calling from distance: attraction of soil bacteria by plant root volatiles. ISME J. 12, 1252–1262 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Ehlers, B. K. et al. Plant secondary compounds in soil and their role in belowground species interactions. Trends Ecol. Evol. 35, 716–730 (2020).PubMed 
Article 

Google Scholar 
61.Preece, C. & Penuelas, J. A return to the wild: root exudates and food security. Trends Plant Sci. 25, 14–21 (2020).CAS 
PubMed 
Article 

Google Scholar 
62.Rasmann, S. et al. Recruitment of entomopathogenic nematodes by insect-damaged maize roots. Nature 434, 732–737 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
63.Köllner, T. G. et al. A maize (E)-β-caryophyllene synthase implicated in indirect defense responses against herbivores is not expressed in most American maize varieties. Plant Cell 20, 482–494 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.Lebeis, S. L. et al. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349, 860–864 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
65.Vorholt, J. A., Vogel, C., Carlstrom, C. I. & Muller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).CAS 
PubMed 
Article 

Google Scholar 
66.Zhang, J. et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 37, 676–684 (2019).CAS 
PubMed 
Article 

Google Scholar 
67.Hatzenpichler, R., Krukenberg, V., Spietz, R. L. & Jay, Z. J. Next-generation physiology approaches to study microbiome function at single cell level. Nat. Rev. Microbiol. 18, 241–256 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Cui, L., Zhang, D., Yang, K., Zhang, X. & Zhu, Y. G. Perspective on surface-enhanced Raman spectroscopic investigation of microbial world. Anal. Chem. 91, 15345–15354 (2019).CAS 
PubMed 
Article 

Google Scholar 
69.Wang, Y., Huang, W. E., Cui, L. & Wagner, M. Single cell stable isotope probing in microbiology using Raman microspectroscopy. Curr. Opin. Biotechnol. 41, 34–42 (2016).CAS 
PubMed 
Article 

Google Scholar 
70.Cui, L. et al. Functional single-cell approach to probing nitrogen-fixing bacteria in soil communities by resonance Raman spectroscopy with 15N2 labeling. Anal. Chem. 90, 5082–5089 (2018).CAS 
PubMed 
Article 

Google Scholar 
71.Yang, K. et al. Rapid antibiotic susceptibility testing of pathogenic bacteria using heavy-water-labeled single-cell Raman spectroscopy in clinical samples. Anal. Chem. 91, 6296–6303 (2019).CAS 
PubMed 
Article 

Google Scholar 
72.Li, H. Z. et al. D2O-isotope-labeling approach to probing phosphate-solubilizing bacteria in complex soil communities by single-cell Raman spectroscopy. Anal. Chem. 91, 2239–2246 (2019).CAS 
PubMed 
Article 

Google Scholar 
73.Moutia, J.-F. Y., Saumtally, S., Spaepen, S. & Vanderleyden, J. Plant growth promotion by Azospirillum sp. in sugarcane is influenced by genotype and drought stress. Plant Soil 337, 233–242 (2010).CAS 
Article 

Google Scholar 
74.Bashan, Y. & De-Bashan, L. E. How the plant growth-promoting bacterium Azospirillum promotes plant growth—a critical assessment. Adv. Agron. 108, 77–136 (2010).CAS 
Article 

Google Scholar 
75.Figueiredo, M. V. B., Burity, H. A., Martínez, C. R. & Chanway, C. P. Alleviation of drought stress in the common bean (Phaseolus vulgaris L.) by co-inoculation with Paenibacillus polymyxa and Rhizobium tropici. Appl. Soil Ecol. 40, 182–188 (2008).Article 

Google Scholar 
76.Uma, C., Sivagurunathan, P. & Sangeetha, D. Performance of bradyrhizobial isolates under drought conditions. Int. J. Curr. Microbiol. App. Sci. 2, 228–232 (2013).
Google Scholar 
77.Tank, N. & Saraf, M. Salinity-resistant plant growth promoting rhizobacteria ameliorates sodium chloride stress on tomato plants. J. Plant Interact. 5, 51–58 (2010).CAS 
Article 

Google Scholar 
78.Tahir, H. A. et al. Plant growth promotion by volatile organic compounds produced by Bacillus subtilis SYST2. Front. Microbiol. 8, 171 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Vardharajula, S., Zulfikar Ali, S., Grover, M., Reddy, G. & Bandi, V. Drought-tolerant plant growth promoting Bacillus spp.: effect on growth, osmolytes, and antioxidant status of maize under drought stress. J. Plant Interact. 6, 1–14 (2011).CAS 
Article 

Google Scholar 
80.Santoyo, G., Orozco-Mosqueda, M. D. C. & Govindappa, M. Mechanisms of biocontrol and plant growth-promoting activity in soil bacterial species of Bacillus and Pseudomonas: a review. Biocontrol Sci. Technol. 22, 855–872 (2012).Article 

Google Scholar 
81.Leclere, V. et al. Mycosubtilin overproduction by Bacillus subtilis BBG100 enhances the organism’s antagonistic and biocontrol activities. Appl. Environ. Microbiol. 71, 4577–4584 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Hu, J. et al. Probiotic Pseudomonas communities enhance plant growth and nutrient assimilation via diversity-mediated ecosystem functioning. Soil Biol. Biochem. 113, 122–129 (2017).CAS 
Article 

Google Scholar 
83.Kohler, J., Hernández, J. A., Caravaca, F. & Roldán, A. Plant-growth-promoting rhizobacteria and arbuscular mycorrhizal fungi modify alleviation biochemical mechanisms in water-stressed plants. Funct. Plant Biol. 35, 141–151 (2008).CAS 
PubMed 
Article 

Google Scholar 
84.Nassar, A. H., El-Tarabily, K. A. & Sivasithamparam, K. Growth promotion of bean (Phaseolus vulgaris L.) by a polyamine-producing isolate of Streptomyces griseoluteus. Plant Growth Reg. 40, 97–106 (2003).CAS 
Article 

Google Scholar 
85.Gopalakrishnan, S. et al. Evaluation of Streptomyces strains isolated from herbal vermicompost for their plant growth-promotion traits in rice. Microbiol. Res. 169, 40–48 (2014).CAS 
PubMed 
Article 

Google Scholar 
86.Kwak, M.-J. et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat. Biotechnol. 36, 1100–1109 (2018).CAS 
Article 

Google Scholar 
87.Sang, M. K. & Kim, K. D. The volatile‐producing Flavobacterium johnsoniae strain GSE09 shows biocontrol activity against Phytophthora capsici in pepper. J. Appl. Microbiol. 113, 383–398 (2012).CAS 
PubMed 
Article 

Google Scholar 
88.Naznin, H. A. et al. Systemic resistance induced by volatile organic compounds emitted by plant growth-promoting fungi in Arabidopsis thaliana. PLoS ONE 9, e86882 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
89.Kiss, L., Russell, J. C., Szentiványi, O., Xu, X. & Jeffries, P. Biology and biocontrol potential of Ampelomyces mycoparasites, natural antagonists of powdery mildew fungi. Biocontrol Sci. Technol. 14, 635–651 (2004).Article 

Google Scholar 
90.Lee, S., Yap, M., Behringer, G., Hung, R. & Bennett, J. W. Volatile organic compounds emitted by Trichoderma species mediate plant growth. Fungal Biol. Biotechnol. 3, 1–14 (2016).CAS 
Article 

Google Scholar 
91.Zhang, S., Gan, Y. & Xu, B. Application of plant-growth-promoting fungi Trichoderma longibrachiatum t6 enhances tolerance of wheat to salt stress through improvement of antioxidative defense system and gene expression. Front. Plant Sci. 7, 1405 (2016).PubMed 
PubMed Central 

Google Scholar 
92.van der Meij, A., Worsley, S. F., Hutchings, M. I. & van Wezel, G. P. Chemical ecology of antibiotic production by Actinomycetes. FEMS Microbiol. Rev. 41, 392–416 (2017).PubMed 
Article 
CAS 

Google Scholar 
93.Bhatti, A. A., Haq, S. & Bhat, R. A. Actinomycetes benefaction role in soil and plant health. Microb. Pathog. 111, 458–467 (2017).CAS 
PubMed 
Article 

Google Scholar 
94.Chaurasia, A. et al. Actinomycetes: an unexplored microorganisms for plant growth promotion and biocontrol in vegetable crops. World J. Microbiol. Biotechnol. 34, 1–16 (2018).Article 

Google Scholar 
95.Ercoli, L., Schüßler, A., Arduini, I. & Pellegrino, E. Strong increase of durum wheat iron and zinc content by field-inoculation with arbuscular mycorrhizal fungi at different soil nitrogen availabilities. Plant Soil 419, 153–167 (2017).CAS 
Article 

Google Scholar 
96.Xu, L. et al. Arbuscular mycorrhiza enhances drought tolerance of tomato plants by regulating the 14-3-3 genes in the ABA signaling pathway. Appl. Soil Ecol. 125, 213–221 (2018).Article 

Google Scholar 
97.Ghorchiani, M., Etesami, H. & Alikhani, H. A. Improvement of growth and yield of maize under water stress by co-inoculating an arbuscular mycorrhizal fungus and a plant growth promoting rhizobacterium together with phosphate fertilizers. Agric. Ecosyst. Environ. 258, 59–70 (2018).CAS 
Article 

Google Scholar 
98.Meeds, J. A. et al. Phosphorus deficiencies invoke optimal allocation of exoenzymes by ectomycorrhizas. ISME J. https://doi.org/10.1038/s41396-020-00864-z (2021). More