Ecology

Plant and fungal materials, maintenance, and preparation
The winter wheat cultivar Zheng 9023 and the spring wheat cultivar Yongliang 4 were purchased from the commercial seed market in Wuhan City and Minqin County in China, respectively. The barley cultivar Huadamai 14 was provided from Prof. Dongfa Sun in Huazhong Agricultural University, Wuhan, China. The oat cultivar Mengmai 2 was donated by Prof. Jun Zhao in Neimeng Agricultural University. The maize cultivar Zhengdan 958 was purchased from the commercial seed market in Wuhan City. The rice cultivar LTH was donated by Prof. Youliang Peng of China Agricultural University. All seeds were surface-sterilized with a 0.5% sodium hypochlorite solution (NaClO) before sowing or S. sclerotiorum treatment.
S. sclerotiorum strain DT-8, which was originally isolated from a sclerotium collected from a diseased rapeseed, is a hypovirulent strain infected by a DNA mycovirus. Strain DT-8VF, a virulent strain, is a virus-free derivative of DT-8 [21]. Strain DT-8VFRFP is a derivative of DT-8VF labeled with the mCherry fluorophore by DNA transformation; it shows normal virulence. The wheat Fusarium head blight (FHB) pathogen, F. graminearum strain PH-1, was used to inoculate wheat spikes. An S. sclerotiorum virulent strain Ep-1PNA367 and three hypovirulent strains, AH98, SCH941, and T1-1-20, were also used to investigate their potential endophytic growth on wheat. AH98 is infected by a negative-stranded RNA virus [22], while SCH941 and T1-1-20 are infected by various other mycoviruses. All S. sclerotiorum strains and F. graminearum strain PH-1 were grown on potato dextrose agar (PDA) or potato dextrose broth (PDB) at 20 °C; Magnaporthe oryzae strain 131 was grown on PDA at 28 °C, or grown on tomato–oat medium to produce conidia, and was stored on PDA slants at 4 °C.
Microscopic observation
To observe the growth of S. sclerotiorum in wheat by confocal microscopy, seeds were surface-sterilized with NaClO and sown on half-strength Murashige and Skoog (MS) agarose medium amended with 25 mM sucrose for 8 days, then root crowns were inoculated with mycelia of the strain DT-8VFRFP. Wheat seedlings were maintained at 20 °C for 4 days under a 12-h photoperiod. Seedlings were subsequently washed three times with PBS for 10 min each, and then root sections were separated and incubated in a 1:100 dilution of wheat germ agglutinin conjugated to FITC (Sigma) for 1 h at room temperature according to the manufacturer’s instructions. The roots were visualized with a LEICA confocal microscope (LEICA SP8) using the 488-nm line of a 25-mW Argon ion laser for FITC and the 561-nm line of a 20-mW solid-state laser for mCherry.
For further observation with a transmission electron microscopy (TEM), 5-mm root segments from wheat seedlings grown on MS medium for 15 days after inoculation with strain DT-8VFRFP were fixed in 0.4% (v/v) glutaraldehyde solution overnight at 4 °C. After washing in PBS buffer, roots were dehydrated with a graded ethanol series. Samples were then embedded in Epon-821 and polymerized at 60 °C. Thin sections (50 nm) were cut using a Leica ULTRACUT UCT ultramicrotome with a diamond knife.
For TEM immunodetection, wheat seedling roots were fixed in 4% (w/v) freshly depolymerized paraformaldehyde and 0.4% (v/v) glutaraldehyde in 1× PBS, pH 7.4, for 1 h at 4 °C. The samples were then embedded using an LR white embedding kit (Fluka) and polymerized at 50 °C for 24 h. Immunogold labeling specificity was detected by displacing the anti-mCherry antibodies with rabbit preimmune serum. The method for TEM immunodetection was performed as previously described [23].
For scanning electron microscopy (SEM) observation, 2-mm root segments from the wheat, barley, oat, maize, and rice seedlings treated with different strains of S. sclerotiorum grown on MS medium for 15 days were used. All segments were fixed in a 0.4% (v/v) glutaraldehyde solution overnight at 4 °C. For SEM analysis, the sections were allowed to air-dry overnight in a desiccator at room temperature, sputter-coated with gold, and prepared for SEM analysis (EVO MA 10 Carl Zeiss SMT AG, Germany). Root segments from nontreated wheat were sampled and observed as a control comparison.
For confocal microscopy observations, 45 days after the root crown inoculation with mycelia of the DT-8VFRFP strain, stems from wheat plants growing in soil in the greenhouse were carefully washed with distilled water and embedded in Tissue-Tek O.C.T. compound medium (Sakura Finetek USA, Inc., Torrance, CA) at −23 °C overnight. Microtome sections (25 µm thick) were sliced using a freezing microtome (LEICA SP8, Germany). The stem microtome sections were then visualized with a LEICA confocal microscope using the 561-nm excitation wavelength for mCherry.
Re-isolation of S. sclerotiorum
To further probe whether S. sclerotiorum can go to aerial parts of wheat when inoculated on root of wheat seedling, plants were grown in sterile nutrient soil for 45 days. The samples for re-isolation were taken from the second segment near the base of wheat stem of eleven individual DT-8VFRFP-treated wheat plants, and were cut into 5 mm long segments, then surface-sterilized by dipping them in 70% EtOH for 2 min and then in 0.5% NaClO for 2 min, followed by rinsing three times with sterile distilled water. The sterilized stem segments were placed on hygromycin-amended PDA medium plates (50 µg/mL) and incubated at 20 °C for 8 days. Then, the emerging colonies were identified as S. sclerotiorum based on colony morphology, and PCR amplification [24].
PCR determination of S. sclerotiorum and mycoviruses
DNA samples of either wheat or fungi were extracted using a cetyltrimethylammonium bromide method. A primer pair XJJ21/XJJ222 (GTTGCTTTGGCGTGCTGCTC/CTGACATGGACTCAATACCAATCTG) was used for detection of S. sclerotiorum [24], and a primer pair pRep-F/pRep-R (GTCACCACCCAAACATTACAAAGAGCGTATTCC/ACGTCA GGTGC) was used for detection of viral DNA of SsHADV-1. A procedure described by Yu et al. [21] was used for PCR amplification.
Seed treatment of wheat with S. sclerotiorum and sowing
To determine whether S. sclerotiorum could promote growth and disease resistance of wheat in the greenhouse, wheat seeds were washed with tap water and surface-sterilized with 0.5% NaClO for 10 min, then washed three times with sterile water. Surface-sterilized seeds were soaked in sterile water for 4 h, and then collected and blotted dry. Meanwhile, fresh mycelium of strains DT-8 and DT-8VF were collected from PDA medium and then cultured in PDB medium in 250-mL flasks in a shaker at 20 °C for 4 days to obtain hyphal fragment suspensions; the number of viable fungal fragments was adjusted to 1.4 × 105 cfu/mL before inoculation of wheat seeds. The hyphal fragment suspensions were then used to inoculate the prepared seeds (100 mL of hyphal fragment suspension/kg wheat seeds) by thoroughly mixing the hyphal fragment suspension and wheat seeds for 6 h. The inoculated seeds were further dried using an electric fan for 12 h at room temperature. Wheat seeds soaked only in sterile water for 6 h and then dried with the same method were used as a control.
To test whether the treated seeds were successfully colonized by S. sclerotiorum, S. sclerotiorum-treated seeds were surface-sterilized with 0.5% NaClO for 10 min, rinsed three times with sterile distilled water, and then cut in half and placed on PDA plates and incubated at 20 °C for 7 days. All emerging colonies from S. sclerotiorum-treated seeds were confirmed as S. sclerotiorum based on colony morphology and PCR amplification. In addition, a small number of S. sclerotiorum-treated seeds were randomly picked and sown into soil taken from the field and grown in the greenhouse. The seedlings were then tested after 21 days for the presence of S. sclerotiorum by PCR amplification; all seedlings tested positive. Hence, the seed treatment was confirmed as an efficient method for inoculation of wheat seeds with S. sclerotiorum. Consequently, we used this method to treat wheat seeds for the rest of the study, and kept treated seeds under dry conditions at room temperature for up to 7 days before sowing.
Seeds treated either with strains DT-8VF or DT-8 were sown in pots in a greenhouse. In a laboratory test, treated seeds were allowed to germinate in a Petri dish on a layer of wet filter paper. Germinating seeds were then sown either into soil that was taken from the field or sterile nutrient soil. Twelve plants were grown in each pot. Wheat seedlings were maintained under greenhouse conditions at 20 °C. Strain DT-8-treated seeds were also tested in the field. Those seeds were sown as rows in the wheat field in exactly the same way as farmers normally do in each sowing season. Field management was conducted as per normal farmer practice, except that no fungicide was applied.
In order to investigate whether S. sclerotiorum colonization can spread to aerial parts of wheat plants originating from DT-8-treated seeds, ten plants from each plot were randomly sampled from the field. A total of 30 wheat plants from the strain DT-8-treated group and 20 plants from the nontreated group were sampled. First, the roots were rinsed thoroughly with tap water. Then the roots, flag leaves, and spikes were given three brief rinses in distilled water. Each wheat plant was given a number, from 1 to 30 for DT-8-inoculated plants, and from 1 to 20 for nontreated plants. DNA samples were extracted individually from the root, flag leaf, and spike of each plant and used to determine the presence of S. sclerotiorum and mycovirus.
Evaluating the growth of treated wheat in the greenhouse and field
To evaluate the growth of S. sclerotiorum-treated wheat in greenhouse experiments, plant height was measured at the seedling and anthesis stages, while flag leaf and spike lengths were evaluated only at the anthesis stage. There were 60 plants in each group treated with strain DT-8 and strain DT-8VF and 60 plants in the nontreated group. Determination of 1000-grain weight was repeated, four in each group. Measurement data for each group were calculated for statistical analysis.
To investigate whether other strains could promote wheat growth, strains Ep-1PNA367, AH98, SCH941, and T1-1-20 were used instead of strains DT-8 and DT-8VF. Wheat seeds were treated and then sown in a sterile mixture of vermiculite and perlite at the ratio of 3:1 in pots and placed in greenhouse, with ~50 seedlings in each pot. Seedling shoot fresh weight was measured at 25 days after planting. There were 30 plants in each treatment and control, and the average weight of ten seedlings was calculated.
For field tests, plant height and the length, width, and thickness of flag leaves at the early flowering stage were measured in the field at EZhou. Forty plants from each plot were randomly measured from a total of 120 plants in the DT-8-treated group and from 120 plants in the nontreated group. Measurement data for each group were calculated for statistical analysis. All the results were confirmed with independent lines and over two planting seasons.
Field experiments and wheat yield tests
To examine whether S. sclerotiorum treatment could enhance wheat yield under natural field conditions, DT-8-treated seeds were sown in a wheat field located at EZhou in late October of 2016 and harvested in mid-May of 2017. This experiment was repeated at EZhou, Jingmen City, and Xiangyang City in late October of 2017 and 2018 and harvested in mid-May of 2018 and 2019. Furthermore, seeds of the spring wheat cultivar Yongliang 4 were also treated with strain DT-8 and sown in mid-April of 2017 at Minqin and Tianzhu Counties in Gansu province and harvested in late July of 2017. All wheat was managed as per normal farmer practice, except that no fungicide was applied. The treatments with or without strain DT-8 were replicated four times at Tianzhu County and Jingmen City in 2017 and five times at other places and the wheat yield from 5 m2 was measured in each plot and used for statistical analysis.
Analysis of chlorophyll content and photosynthetic rate in flag leaves
To determine chlorophyll content, leaf tissues were harvested using a circular punch that yields 0.5-cm diameter leaf discs. There were four flag leaf replicates for each treatment. Chlorophyll was extracted from wheat flag leaves obtained from the field at EZhou using 95% (v/v) ethanol (analytically pure, Sinopharm Chemical Reagent Co., Ltd) and the extracted chlorophyll concentration was determined using a spectrophotometer (UV2102, Unico, Shanghai, China) [25].
For the photosynthetic rate, flag leaf samples were obtained from the field at EZhou. Each treatment had three flag leaf replicates. Photosynthetic rate determination was performed as previously described [26].
Assay of plant hormones
Five frozen flag leaf and spike replicates from each treatment (~100 mg for each flag leaf and spike sample) were ground to a fine powder in liquid nitrogen using a mortar and pestle. Each sample was weighed into a 1.5-mL tube, mixed with 750 μL of cold extraction buffer (methanol: water: acetic acid, 80:19:1, v/v/v) supplemented with internal standards, 10 ng of 2H6ABA, 10 ng of DHJA, and 5 μg of NAA, vigorously shaken on a shaking bed for 16 h at 4 °C in the dark, and then centrifuged at 12,000 rpm for 15 min at 4 °C. Supernatant was carefully transferred to a new 1.5-mL tube and pellets remixed with 400 μL of extraction buffer, shaken for 4 h at 4 °C, and centrifuged. The two supernatants were combined and filtered using a syringe-facilitated 13-mm diameter nylon filter with a pore size of 0.22 μm (Nylon 66; Jinteng Experiment Equipment Co., Ltd, Tianjing, China). The filtrate was dried by evaporation under nitrogen gas flow for ~5 h at room temperature and then dissolved in 200 μL of methanol. Aliquots of dissolved samples were further diluted 40 times using methanol for jasmonic acid (JA), abscisic acid (ABA), and indole-3-acetic acid (IAA) quantification. Liquid chromatography was carried out using an ultrafast liquid chromatography with an autosampler (Shimadzu Corporation, Kyoto, Japan). The method used for hormone determination was as previously described [27].
Inoculation of F. graminearum and rating of disease
Infection assays on flowering wheat spikes were performed as previously described [28]. At the early flowering stage, a conidial suspension of F. graminearum strain PH-1 was collected from 5-day-old cultures growing in carboxymethylcellulose medium, then filtered through three layers of lens-wiping paper and then mixed with 0.01% (v/v) Tween 20. Ten microliters of 1 × 105 conidia mL−1 conidial suspension was inoculated individually onto the fourth spikelet from the bottom using a micropipette. The inoculated wheat spikes were maintained at a relative humidity of 95% for 72 h. Symptomatic spikes were examined and images captured after 14 days.
For the greenhouse test, 15 spikes from each treatment were inoculated and the spikelet infection rate of each spike was calculated; then, the average spikelet infection rate for each treatment was calculated for statistical analysis. For the field test, ten spikes from each plot were inoculated from a total of 30 inoculated spikes in the strain DT-8-treated plots and 30 spikes in the nontreated plots. The spikelet infection rate for each plot was calculated and the average spikelet infection rate for strain DT-8 treatment and nontreated control were then calculated for statistical analysis. The field test was conducted twice, once in 2017 and repeated in 2018.
FHB survey in a natural, noninoculated field
To investigate natural FHB infection in strain DT-8-treated wheat, an FHB field survey was conducted in experimental fields located at EZhou City, Jingmen City, and Xiangyang City in 2018. The field survey protocol described by the National Agricultural Technology Extension Service Center of China was adopted for the FHB survey with minor modifications. A total of 500 spikes were sampled randomly in each plot; in total, 1500 spikes were collected from DT-8-treated plots, with the same number of spikes being collected from nontreated plots to calculate disease incidence (spikelet infection rate) and severity (disease index). The number of infected and noninfected spikelets on each spike was counted and the average spikelet infection rate for each plot was calculated and used for statistical analysis. To calculate the disease index, the infected spikes were divided into five grades, namely: grade 0, no spikelet was infected; grade 1, the spike was infected, but less than 25% of spikelets were infected; grade 2, more than 25%, but less than 50%, of the spikelets were infected; grade 3, more than 50%, but less than 75%, of the spikelets were infected; and grade 4, more than 75% of the spikelets were infected. Finally, the disease severity for each plot was calculated using a formula for disease index, DI = ∑(nX/4 N) × 100, where “X” is the scale value of each spike, “n” is the number of spikes in the category, and “N” is the total number of spikes assessed for each plot. The disease index for each group was used for statistical analysis.
Inoculation of M. oryzae on barley and rice
To probe if S. sclerotiorum could enhance resistance against the rice blast fungus (M. oryzae) in barley and rice, an inoculation test was carried out according to a method described by Kong et al. [29]. Conidia of M. oryzae were collected with sterile water from 4-day-old cultures growing on tomato–oat medium and then filtered through three layers of lens-wiping paper. Infection assays were performed in whole plant leaves by spray inoculation using an airbrush nebulizer compressor. Strain DT-8-treated seedlings, which were grown in a sterile mixture of vermiculite and perlite at the ratio of 3:1 in pots and placed in greenhouse for 9 days for barley at 20 °C and 20 days for rice at 28 °C, were sprayed with a conidial suspension [105 conidia/mL mixed with 0.02% (v/v) Tween 20], using 4 mL suspension for each pot. The plants were further incubated at 28 °C, 80% relative humidity, under a 16 h light/8 h darkness photoperiod. Then, we assessed the presence of S. sclerotiorum in aerial parts of DT-8-treated barley and rice by PCR amplification in greenhouse plants; 92% of samples were confirmed as positive for S. sclerotiorum. For barley, lesions of leaves with the same leaf age (bottom leaves) were examined and typical infected leaves were photographed with a digital camera at 5 days post inoculation (dpi); for rice, the leaves were examined and photographed at 7 dpi.
Detection of toxins (DON)
To assay point-inoculated spikelets from strain DT-8-treated and nontreated plants, each sample was placed in a 50-mL centrifuge tube and mixed with 400 μL of a mixed isotope internal standard. The solution was remixed with 20 mL of an acetonitrile–water solution after standing for 30 min, vigorously shaken on a shaking bed for 4 h at 4 °C, and then centrifuged at 10,000 rpm for 5 min. The supernatants were carefully transferred to a new tube. The supernatants were combined and filtered using a syringe-facilitated 13-mm diameter nylon filter with a pore size of 0.22 μm (Nylon 66; Jinteng Experiment Equipment Co., Ltd, Tianjing, China). The filtrate was dried by evaporation under the nitrogen gas flow for ~5 h at room temperature, and then dissolved in 200 μL of methanol. The method for DON determination was performed as described by the National Agricultural Technology Extension Service Center of China (GB5009.111-2016).
RNA sequencing and analysis
Sterilized wheat seeds were inoculated with strain DT-8, and then were sown in experimental fields located at EZhou City in 2017. Wheat flag leaves and spikes were collected from this field during the initial bloom stage, with three spikes and leaves being randomly sampled from each of the three replicate plots. The flag leaves and spikes of nontreated wheat plants in the same field were randomly taken from each of the three control replicate plots. The samples were immediately placed in liquid nitrogen and ground into powder. Total RNA samples were extracted with a TRIzol Plus RNA Purification Kit (Takara, Dalian, China) and treated with RNase-free DNase I (Takara, Dalian, China) according to the manufacturer’s instructions. The RNA quality was checked using a Nanodrop Spectrophotometer (Thermo Fisher Scientific Inc., Wilmington, DE, USA). Then, mRNA was enriched with magnetic beads Oligo (dT) (TransGen Biotech, Beijing, China). Subsequently, cDNA was synthesized using the mRNA as template. The cDNA fragments were linked with adapters, and suitable fragments were selected for PCR amplification. Agilent 2100 Bioanalyzer and ABI StepOnePlus Real-Time PCR Systems were used in the quantification and qualification of the sample library. Subsequently, the library was sequenced for raw data using an Illumina HiSeq X sequencer at BGI (The Beijing Genomics Institute, China). Then, adapters, low-quality sequences, and reads with high content of unknown base (N) reads were removed to obtain clean reads. The clean reads were then mapped to the wheat genome or S. sclerotiorum genome and the sequence results evaluated in terms of read quality, alignment, saturation, and the distribution of reads on reference genes [30]. Mismatches of no more than two bases were accepted in the alignment. Gene expression was calculated by the number of reads mapped to the reference genomes using the fragments per kilobase of transcript per million mapped reads method [31]. Subsequently, differentially expressed genes (DEGs) were selected with FDR  More

1.
FAO. Global forest resources assessment 2015: How are the world’s forests changing? 2nd edn, (Food and Agriculture Organization of the United Nations, 2015).
2.
Keenan, R. J. et al. Dynamics of global forest area: Results from the FAO global forest resources assessment. For. Ecol. Manag. 2015(352), 9–20 (2015).
Google Scholar 

3.
Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600 (2016).
ADS  CAS  PubMed  Google Scholar 

4.
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558–12558 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

5.
Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. 9, 679 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

6.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

7.
Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

8.
Morales-Hidalgo, D., Oswalt, S. N. & Somanathan, E. Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the global forest resources assessment 2015. For. Ecol. Manag. 352, 68–77 (2015).
Google Scholar 

9.
Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).
PubMed  Google Scholar 

10.
Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827 (2015).
ADS  CAS  PubMed  Google Scholar 

11.
Potapov, P. et al. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

12.
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108 (2018).
ADS  CAS  PubMed  Google Scholar 

13.
Achard, F. et al. Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Glob. Change Biol. 20, 2540–2554 (2014).
ADS  Google Scholar 

14.
Sloan, S. & Sayer, J. A. Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries. For. Ecol. Manag. 352, 134–145 (2015).
Google Scholar 

15.
Leimgruber, P. et al. Forest cover change patterns in Myanmar (Burma) 1990–2000. Environ. Conserv. 32, 356–364 (2005).
Google Scholar 

16.
Bhagwat, T. et al. Losing a jewel—Rapid declines in Myanmar’s intact forests from 2002–2014. PLoS ONE 12, e0176364 (2017).
PubMed  PubMed Central  Google Scholar 

17.
FAO. Forests and tree supporting rural livelihoods: Case studies from Myanmar and Viet Nam by Kollert, W. Thuy, L.T.T., Voan, V.L, Oo, T.S. and Khaing, N. Planted Forests and Trees Working Paper FP/50/E. Rome, Italy (available at https://www.fao.org/3/a-i6710e.pdf) (2017).

18.
Kyaw, W. W., Sukchai, S., Ketjoy, N. & Ladpala, S. Energy utilization and the status of sustainable energy in Union of Myanmar. Energy Proc. 9, 351–358 (2011).
Google Scholar 

19.
Mon, M. S., Mizoue, N., Htun, N. Z., Kajisa, T. & Yoshida, S. Factors affecting deforestation and forest degradation in selectively logged production forest: A case study in Myanmar. For. Ecol. Manag. 267, 190–198 (2012).
Google Scholar 

20.
Woods, K. Timber trade flows and actors in Myanmar: The political economy of Myanmar’s timber trade. (2013).

21.
Lim, C. L., Prescott, G. W., De Alban, J. D. T., Ziegler, A. D. & Webb, E. L. Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar. Conserv. Biol. 31, 1362–1372 (2017).
PubMed  Google Scholar 

22.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

23.
Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. Global Biodiversity Conservation: The critical role of hotspot in Biodiversity Hotspots (eds F.E. Zachos & J.C. Habel) 3–22 (Springer, Berlin, 2011).

24.
Schaefer, H., Bartholomew, B. & Boufford, D. E. Indofevillea jiroi (Cucurbitaceae), a new floral oil producing species from Northeastern Myanmar. Bione 17, 323–332 (2012).
Google Scholar 

25.
Hughes, M., Aung, M. M. & Armstrong, K. An updated checklist and new species of Begonia (B. rheophytica) from Myanmar. Edinb. J. Bot. 76, 285–295 (2019).
Google Scholar 

26.
Rodda, M., Aung, M. M. & Armstrong, K. A new species, a new subspecies, and new records of Hoya (Apocynaceae, Asclepiadoideae) from Myanmar and China. Brittonia 71, 424–434 (2019).
Google Scholar 

27.
Yang, B., Zhou, S.-S., Maung, W. & Tan, Y.-H. Two new species of Impatiens (Balsaminaceae) from Putao, Kachin State, northern Myanmar. Phytotaxa 321, 103–113 (2017).
Google Scholar 

28.
Tong, Y. H. & Xia, N. H. New taxa of Agapetes (Ericaceae) from Myanmar. Phytotaxa 184, 39–45 (2014).
Google Scholar 

29.
Rabinowitz, A., Amato, G. & Saw, T. K. Discovery of the black muntjac, Muntiacus crinifrons (Artiodactyla, Cervidae), in north Myanmar. Mammalia 62, 105–107 (1998).
Google Scholar 

30.
Amato, G., Egan, M. G. & Rabinowitz, A. A new species of muntjac, Muntiacus putaoensis (Artiodactyla: Cervidae) from northern Myanmar. Anim. Conserv. 2, 1–7 (1999).
Google Scholar 

31.
Soisook, P. et al. A new species of Murina (Chiroptera: Vespertilionidae) from sub-Himalayan forests of northern Myanmar. Zootaxa 4320, 159–172 (2017).
Google Scholar 

32.
Rappole, J. H., Renner, S. C., Shwe, N. M. & Sweet, P. R. A new species of Scimitar-Babbler (Timaliidae: Jabouilleia) from the sub-Himalayan region of Myanmar. Auk 122, 1064–1069 (2005).
Google Scholar 

33.
Rappole, J. H., Rasmussen, P. C., Aung, T., Milensky, C. M. & Renner, S. C. Observations on a new species: The Naung Mung Scimitar-Babbler Jabouilleia naungmungensis. Ibis 150, 623–627 (2008).
Google Scholar 

34.
Renner, S. C., Rappole, J. H., Kyaw, M., Milensky, C. M. & Päckert, M. Genetic confirmation of the species status of Jabouilleia naungmungensis. J. Ornithol. 159, 63–71 (2018).
Google Scholar 

35.
Päckert, M. et al. Pilot biodiversity assessment of the Hkakabo Razi passerine avifauna in northern Myanmar—implications for conservation from molecular genetics. Bird Conserv. Int. 30, 267–288 (2020).
Google Scholar 

36.
Bates, P. et al. Intact forests of Hkakabo Razi Landscape are a hotspot of bat diversity in Southeast Asia. Oryx (In Press).

37.
Oo, S. S. L., Kyaw, M., Hlaing, N. M. & Renner, S. C. New to Myanmar: the Rosy Starling Pastor roseus (Aves: Passeriformes: Sturnidae) in the Hkakabo Razi Landscape. JoTT 12, 15493–15494 (2020).
Google Scholar 

38.
Oo, S. S. L., Kyaw, M., Meyers, K. & Renner, S. C. Confirmation of the White-winged Duck from the Hkakabo Razi Landscape, Myanmar. BirdingASIA 30, 86–87 (2018).
Google Scholar 

39.
Renner, S. C. et al. Land cover in the Northern forest complex of Myanmar: New insights for conservation. Oryx 41, 27–37 (2007).
Google Scholar 

40.
Rao, M. et al. Biodiversity conservation in a changing climate: A review of threats and implications for conservation planning in Myanmar. Ambio 42, 789–804 (2013).
PubMed  PubMed Central  Google Scholar 

41.
Webb, E. L., Phelps, J., Friess, D. A., Rao, M. & Ziegler, A. D. Environment-friendly reform in Myanmar. Science 336, 295–295 (2012).
ADS  CAS  PubMed  Google Scholar 

42.
Prescott, G. W. et al. Political transition and emergent forest-conservation issues in Myanmar. Conserv. Biol. 31, 1257–1270 (2017).
PubMed  Google Scholar 

43.
De Alban, D. J. et al. Integrating analytical frameworks to investigate land-cover regime shifts in dynamic landscapes. Sustainability 11, 1139 (2019).
Google Scholar 

44.
Clifton, J., Hampton, M. P. & Jeyacheya, J. Opening the box? Tourism planning and development in Myanmar: Capitalism, communities and change. Asia Pac. Viewpoint 59, 323–337 (2018).
Google Scholar 

45.
Belle, E., Shi, Y. & Bertzky, B. Comparative analysis methodology for World Heritage nominations under biodiversity criteria: A contribution to the IUCN evaluation of natural World Heritage nominations. 21 (UNEP-WCMC and IUCN, Cambridge, UK and Gland, Switzerland, 2014).

46.
Renner, S. C. et al. Avifauna of the Southeastern Himalayan mountains and neighboring Myanmar hill country. Bonn Zoological Bulletin—Supplementum 62, 1–75 (2015).
Google Scholar 

47.
BirdLife International. Endemic Bird Area factsheet: Eastern Himalayas (130), (2015).

48.
BirdLife International. Endemic Bird Area factsheet: Yunnan mountains (139), (2015).

49.
BirdLife International. Endemic Bird Area factsheet: Northern Myanmar lowlands (s079), (2015).

50.
Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data. 4, 170122 (2017).
PubMed  PubMed Central  Google Scholar 

51.
Renner, S. C. & Rappole, J. H. Bird diversity, biogeographic patterns, and endemism of the eastern Himalayas and southeastern Sub-Himalayan mountains in Ornithological Monographs Vol. 70 (ed M. L. Morrison) Ch. 8, 153–166 (American Ornithologists’ Union, 2011).

52.
Dumbacher, J. P., Miller, J. R., Flannery, M. E. & Yang Xiaojun. Avifauna of the Gaoligong Shan mountains of western China: A hotspot of avian species diversity in Ornithological Monographs Vol. 70 (eds S.C. Renner & J.H. Rappole) Ch. 3, 30–63 (American Ornithologists’ Union, 2011).

53.
Rappole, J. H., Thein Aung, Rasmussen, P. C. & Renner, S. C. Ornithological exploration in the southeastern sub-Himalayan region of Myanmar in Ornithological Monographs Vol. 70 (ed M. L. Morrison) Ch. 2, 10–29 (American Ornithologists’ Union, 2011).

54.
Zhu, Z., Wang, S. & Woodcock, C. E. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sens. Environ. 159, 269–277 (2015).
ADS  Google Scholar 

55.
Riano, D., Chuvieco, E., Salas, J. & Aguado, I. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003). IEEE T Geosci. Remote 41, 1056–1061 (2003).
ADS  Google Scholar 

56.
Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, 2 (2007).
Google Scholar 

57.
Deng, Y., Chen, X., Chuvieco, E., Warner, T. & Wilson, J. P. Multi-scale linkages between topographic attributes and vegetation indices in a mountainous landscape. Remote Sens. Environ. 111, 122–134 (2007).
ADS  Google Scholar 

58.
Guisan, A., Weiss, S. B. & Weiss, A. D. GLM versus CCA spatial modeling of plant species distribution. Plant Ecol. 143, 107–122 (1999).
Google Scholar 

59.
Running, S. W. Estimating primary productivity by combining remote sensing with ecosystem simulation in Remote Sensing of Biosphere Functioning (eds R.J. Hobbs & H.A Mooney) 65–86 (Springer-Verlag, Berlin, 1990).

60.
Myneni, R. B., Hall, F., Sellers, P. & Marshak, A. The interpretation of spectral vegetation indexes. IEEE Trans. Geosci. Rem. Sens. 33, 481–486 (1995).
ADS  Google Scholar 

61.
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
ADS  Google Scholar 

62.
Liaw, A. & Wiener, M. Classification and regression by random. Forest 2, 18–22 (2002).
Google Scholar 

63.
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. 125 (Vienna).

64.
R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2014).

65.
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M. & Rigol-Sanchez, J. P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 67, 93–104 (2012).
ADS  Google Scholar 

66.
Connette, G., Oswald, P., Songer, M. & Leimgruber, P. Mapping distinct forest types improves overall forest identification based on multi-spectral landsat imagery for Myanmar’s Tanintharyi region. Remote Sens. 8, 2 (2016).
Google Scholar 

67.
De Alban, J. D., Connette, G., Oswald, P. & Webb, E. Combined Landsat and L-Band SAR data improves land cover classification and change detection in dynamic tropical landscapes. Remote Sens. 10, 306 (2018).
ADS  Google Scholar 

68.
Belgiu, M. & Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogram. Sens. 114, 24–31 (2016).
Google Scholar 

69.
Horning, N. Random Forests: An algorithm for image classification and generation of continuous fields data sets. (2010).

70.
SNAP – ESA Sentinel Application Platform v2.0 (2015).

71.
Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).
ADS  Google Scholar 

72.
Colditz, R. R. et al. Potential effects in multi-resolution post-classification change detection. Int. J. Remote Sens. 33, 6426–6445 (2012).
ADS  Google Scholar 

73.
Cuba, N. Research note: Sankey diagrams for visualizing land cover dynamics. Landsc. Urban Plan. 139, 163–167 (2015).
Google Scholar 

74.
Riitters, K. H. et al. Fragmentation of continental United States forests. Ecosystem 5, 815–822 (2002).
Google Scholar 

75.
Riitters, K. H. & Wickham, J. D. Decline of forest interior conditions in the conterminous United States. Sci. Rep. 2, 653 (2012).
ADS  PubMed  PubMed Central  Google Scholar 

76.
Riitters, K. H., O’Neill, R. V. & Jones, K. B. Assessing habitat suitability at multiple scales: A landscape-level approach. Biol. Conserv. 81, 191–202 (1997).
Google Scholar 

77.
McIntyre, S. & Hobbs, R. A framework for conceptualizing human effects on landscapes and its relevance to management and research models. Conserv. Biol. 13, 1282–1292 (1999).
Google Scholar 

78.
Vogt, P. & Riitters, K. GuidosToolbox: universal digital image object analysis. Eur. J. Remote Sens. 50, 352–361 (2017).
Google Scholar 

79.
Gillanders, S. N., Coops, N. C., Wulder, M. A., Gergel, S. E. & Nelson, T. Multitemporal remote sensing of landscape dynamics and pattern change: Describing natural and anthropogenic trends. Prog. Phys. Geogr. 32, 503–528 (2008).
Google Scholar 

80.
Rubiano, K., Clerici, N., Norden, N. & Etter, A. Secondary forest and shrubland dynamics in a highly transformed landscape in the northern Andes of Colombia (1985–2015). Forest 8, 216 (2017).
Google Scholar 

81.
IUSS, W. G. W. World Reference Base for Soil Resources 2014, update 2015. International soil classification system for naming soils and creating legends for soil maps, (2015).

82.
Oldeman, L., Hakkeling, R. & Sombroek, W. World map of the status of human-induced soil degradation: An explanatory note rev. (UNEP and ISRIC, Wageningen, 1991).
Google Scholar 

83.
Fick, S. E. & Hijmans, R. J. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 

84.
Venables, W. N. & Ripley, B. D. Modern applied statistics with S 4th edn. (Springer, Berlin, 2002).
Google Scholar 

85.
Greene, W. H. Econometric analysis (Prentice Hall, Pearson, 2000).
Google Scholar 

86.
Songer, M., Aung Myint, S. B., DeFries, R. & Leimgruber, P. Spatial and temporal deforestation dynamics in protected and unprotected dry forests: A case study from Myanmar (Burma). Metrics 18, 1001–1018 (2008).
Google Scholar 

87.
Reddy, C. S. et al. Quantifying and predicting multi-decadal forest cover changes in Myanmar: A biodiversity hotspot under threat. Metrics 28, 1129–1149 (2019).
Google Scholar 

88.
Hall, C. A. S., Tian, H., Qi, Y., Pontius, G. & Cornell, J. Modelling spatial and temporal patterns of tropical land use change. J. Biogrph. 22, 753–757 (1995).
Google Scholar 

89.
Di Lallo, G., Mundhenk, P., Zamora López, S., Marchetti, M. & Köhl, M. REDD+: Quick assessment of deforestation risk based on available data. Forests 8, 29 (2017).
Google Scholar 

90.
Bax, V. & Francesconi, W. Environmental predictors of forest change: An analysis of natural predisposition to deforestation in the tropical Andes region, Peru. Appl. Geogr. 91, 99–110 (2018).
Google Scholar 

91.
Pacheco, P. et al. Landscape transformation in tropical Latin America: Assessing trends and policy implications for REDD+. Forest 2, 1–29 (2010).
Google Scholar  More

1.
Goodale CL, Apps MJ, Birdsey RA, Field CB, Heath LS, Houghton RA, et al. Forest carbon sinks in the Northern Hemisphere. Ecol Appl. 2002;12:891–9.
Google Scholar 
2.
Anderson J. The effects of climate change on decomposition processes in grassland and coniferous forests. Ecol Appl. 1991;1:326–47.
CAS  PubMed  Google Scholar 

3.
Turetsky MR, Bond‐Lamberty B, Euskirchen E, Talbot J, Frolking S, McGuire AD, et al. The resilience and functional role of moss in boreal and arctic ecosystems. N Phytol. 2012;196:49–67.
CAS  Google Scholar 

4.
DeLuca TH, Zackrisson O, Gundale MJ, Nilsson M-C. Ecosystem feedbacks and nitrogen fixation in boreal forests. Science. 2008;320:1181.
CAS  PubMed  Google Scholar 

5.
Nilsson M-C, Wardle DA. Understory vegetation as a forest ecosystem driver: evidence from the northern Swedish boreal forest. Front Ecol Environ. 2005;3:421–8.
Google Scholar 

6.
Rousk K, Jones D, DeLuca T. Moss-cyanobacteria associations as biogenic sources of nitrogen in boreal forest ecosystems. Front Microbiol. 2013;4:150.
CAS  PubMed  PubMed Central  Google Scholar 

7.
Carleton T, Read D. Ectomycorrhizas and nutrient transfer in conifer–feather moss ecosystems. Can J Bot. 1991;69:778–85.
Google Scholar 

8.
Gundale MJ, Nilsson M, Bansal S, Jäderlund A. The interactive effects of temperature and light on biological nitrogen fixation in boreal forests. N Phytol. 2012;194:453–63.
CAS  Google Scholar 

9.
Gundale MJ, Wardle DA, Nilsson M-C. The effect of altered macroclimate on N-fixation by boreal feather mosses. Biol Lett. 2012;8:805–8.
PubMed  PubMed Central  Google Scholar 

10.
Jackson BG, Martin P, Nilsson M-C, Wardle DA. Response of feather moss associated N2 fixation and litter decomposition to variations in simulated rainfall intensity and frequency. Oikos. 2011;120:570–81.
Google Scholar 

11.
Jean M-E, Cassar N, Setzer C, Bellenger J-P. Short-term N2 fixation kinetics in a moss-associated cyanobacteria. Environ Sci Technol. 2012;46:8667–71.
CAS  PubMed  Google Scholar 

12.
Sorensen PL, Lett S, Michelsen A. Moss-specific changes in nitrogen fixation following two decades of warming, shading, and fertilizer addition. Plant Ecol. 2012;213:695–706.
Google Scholar 

13.
Warshan D, Bay G, Nahar N, Wardle DA, Nilsson MC, Rasmussen U. Seasonal variation in nifH abundance and expression of cyanobacterial communities associated with boreal feather mosses. ISME J. 2016;10:2198–208.
CAS  PubMed  PubMed Central  Google Scholar 

14.
Rai AN, Soderback E, Bergman B. Tansley review No. 116 cyanobacterium–plant symbioses. N Phytol. 2000;147:449–81.
CAS  Google Scholar 

15.
Meeks JC. Physiological adaptations in nitrogen-fixing Nostoc–plant symbiotic associations. In: Pawlowski K, editor. Prokaryotic symbionts in plants. Berlin, Heidelberg: Springer Berlin Heidelberg; 2009. p. 181–205.
Google Scholar 

16.
Steinberg NA, Meeks JC. Physiological sources of reductant for nitrogen fixation activity in Nostoc sp. strain UCD 7801 in symbiotic association with Anthoceros punctatus. J Bacteriol. 1991;173:7324–9.
CAS  PubMed  PubMed Central  Google Scholar 

17.
Khamar HJ, Breathwaite EK, Prasse CE, Fraley ER, Secor CR, Chibane FL, et al. Multiple roles of soluble sugars in the establishment of Gunnera-Nostoc endosymbiosis. Plant Physiol. 2010;154:1381–9.
CAS  PubMed  PubMed Central  Google Scholar 

18.
Bay G, Nahar N, Oubre M, Whitehouse MJ, Wardle DA, Zackrisson O, et al. Boreal feather mosses secrete chemical signals to gain nitrogen. N Phytol. 2013;200:54–60.
CAS  Google Scholar 

19.
Warshan D, Liaimer A, Pederson E, Kim S-Y, Shapiro N, Woyke T, et al. Genomic changes associated with the evolutionary transitions of Nostoc to a plant symbiont. Mol Biol Evol. 2018;35:1160–75.
CAS  PubMed  PubMed Central  Google Scholar 

20.
Warshan D, Espinoza JL, Stuart RK, Richter RA, Kim S-Y, Shapiro N, et al. Feathermoss and epiphytic Nostoc cooperate differently: expanding the spectrum of plant-cyanobacteria symbiosis. ISME J. 2017;11:2821–33.
PubMed  PubMed Central  Google Scholar 

21.
Douglas AE. The symbiotic habit. Princeton, NJ: Princeton University Press; 2010.

22.
Bronstein JL. Mutualism. USA: Oxford, UK: Oxford University Press; 2015.
Google Scholar 

23.
Holland JN, Ness JH, Boyle A, Bronstein JL. Mutualisms as consumer-resource interactions. Ecology of predator–prey interactions. Oxford, UK: Oxford University Press; 2005. p. 17–33.

24.
van der Ploeg JR, Eichhorn E, Leisinger T. Sulfonate-sulfur metabolism and its regulation in Escherichia coli. Arch Microbiol. 2001;176:1–8.
PubMed  Google Scholar 

25.
Sugawara M, Shah GR, Sadowsky MJ, Paliy O, Speck J, Vail AW, et al. Expression and functional roles of Bradyrhizobium japonicum genes involved in the utilization of inorganic and organic sulfur compounds in free-living and symbiotic conditions. Mol Plant Microbe Interact. 2011;24:451–7.
CAS  PubMed  Google Scholar 

26.
Musat N, Foster R, Vagner T, Adam B, Kuypers MMM. Detecting metabolic activities in single cells, with emphasis on nanoSIMS. FEMS Microbiol Rev. 2012;36:486–511.
CAS  PubMed  Google Scholar 

27.
Pederson ERA, Warshan D, Rasmussen U. Genome sequencing of Pleurozium schreberi: the assembled and annotated draft genome of a pleurocarpous feather moss. G3: Genes, Genomes, Genetics. 2019;9:2791–7.
CAS  Google Scholar 

28.
Hardy RW, Holsten R, Jackson E, Burns R. The acetylene-ethylene assay for N2 fixation: laboratory and field evaluation. Plant Physiol. 1968;43:1185–207.
CAS  PubMed  PubMed Central  Google Scholar 

29.
Khayatan B, Bains DK, Cheng MH, Cho YW, Huynh J, Kim R, et al. A putative O-linked β-N-acetylglucosamine transferase is essential for hormogonium development and motility in the filamentous cyanobacterium Nostoc punctiforme. J Bacteriol. 2017;199:e00075-17.
PubMed  PubMed Central  Google Scholar 

30.
Falkowski PG, Raven JA. Aquatic photosynthesis. Princeton, NJ: Princeton University Press; 2013.

31.
Dabundo R, Lehmann MF, Treibergs L, Tobias CR, Altabet MA, Moisander PH, et al. The contamination of commercial 15N2 gas stocks with 15N–labeled nitrate and ammonium and consequences for nitrogen fixation measurements. PloS ONE. 2014;9:e110335.
PubMed  PubMed Central  Google Scholar 

32.
Ndegwa PM, Vaddella VK, Hristov AN, Joo HS. Measuring concentrations of ammonia in ambient air or exhaust air stream using acid traps. J Environ Qual. 2009;38:647–53.
CAS  PubMed  Google Scholar 

33.
Pett-Ridge J, Weber PK. NanoSIP: NanoSIMS applications for microbial biology. Microbial systems biology. Totowa, NJ: Humana Press; 2012. p. 375–408.

34.
Popa R, Weber PK, Pett-Ridge J, Finzi JA, Fallon SJ, Hutcheon ID, et al. Carbon and nitrogen fixation and metabolite exchange in and between individual cells of Anabaena oscillarioides. ISME J. 2007;1:354–60.
CAS  PubMed  Google Scholar 

35.
Liaimer A, Helfrich EJN, Hinrichs K, Guljamow A, Ishida K, Hertweck C, et al. Nostopeptolide plays a governing role during cellular differentiation of the symbiotic cyanobacterium Nostoc punctiforme. Proc Natl Acad Sci USA. 2015;112:1862–7.
CAS  PubMed  Google Scholar 

36.
Koch M, Delmotte N, Rehrauer H, Vorholt JA, Pessi G, Hennecke H. Rhizobial adaptation to hosts, a new facet in the legume root-nodule symbiosis. Mol Plant Microbe Interact. 2010;23:784–90.
CAS  PubMed  Google Scholar 

37.
Meeks JC, Elhai J. Regulation of cellular differentiation in filamentous cyanobacteria in free-living and plant-associated symbiotic growth states. Microbiol Mol Biol Rev. 2002;66:94–121.
CAS  PubMed  PubMed Central  Google Scholar 

38.
Wong FC, Meeks JC. Establishment of a functional symbiosis between the cyanobacterium Nostoc punctiforme and the bryophyte Anthoceros punctatus requires genes involved in nitrogen control and initiation of heterocyst differentiation. Microbiology. 2002;148:315–23.
CAS  PubMed  Google Scholar 

39.
Hill DJ. The control of the cell cycle in microbial symbionts. N Phytol. 1989;112:175–84.
Google Scholar 

40.
Adams DG, Duggan PS. Signalling in cyanobacteria–plant symbioses. Signaling and communication in plant symbiosis. New York City, NY: Springer; 2012. p. 93–121.

41.
Hashidoko Y, Nishizuka H, Tanaka M, Murata K, Murai Y, Hashimoto M. Isolation and characterization of 1-palmitoyl-2-linoleoyl-sn-glycerol as a hormogonium-inducing factor (HIF) from the coralloid roots of Cycas revoluta (Cycadaceae). Sci Rep. 2019;9:4751.
PubMed  PubMed Central  Google Scholar 

42.
Calderwood A, Kopriva S. Hydrogen sulfide in plants: from dissipation of excess sulfur to signaling molecule. Nitric Oxide. 2014;41:72–8.
CAS  PubMed  Google Scholar 

43.
Koppenol WH, Bounds PL. Signaling by sulfur-containing molecules. Quantitative aspects. Arch Biochem Biophys. 2017;617:3–8.
CAS  PubMed  Google Scholar 

44.
Miller JB, Oldroyd GE. The role of diffusible signals in the establishment of rhizobial and mycorrhizal symbioses. Signaling and communication in plant symbiosis. New York City, NY: Springer; 2012. p. 1–30.

45.
Duhamel S, Van Wambeke F, Lefevre D, Benavides M, Bonnet S. Mixotrophic metabolism by natural communities of unicellular cyanobacteria in the western tropical South Pacific Ocean. Environ Microbiol. 2018;20:2743–56.
CAS  PubMed  Google Scholar 

46.
Stuart RK, Mayali X, Lee JZ, Everroad RC, Hwang M, Bebout BM, et al. Cyanobacterial reuse of extracellular organic carbon in microbial mats. ISME J. 2016;10:1240–51.
CAS  PubMed  Google Scholar 

47.
Kaplan D, Peters GA. Interaction of carbon metabolism in the Azolla-Anabaena symbiosis. Symbiosis. 1988;6:53–68.
CAS  Google Scholar 

48.
Ray TB, Mayne BC, Toia RE, Peters GA. Azolla-Anabaena relationship: VIII. Photosynthetic characterization of the association and individual partners. Plant Physiol. 1979;64:791–5.
CAS  PubMed  PubMed Central  Google Scholar 

49.
Kiers ET, Duhamel M, Beesetty Y, Mensah JA, Franken O, Verbruggen E, et al. Reciprocal rewards stabilize cooperation in the mycorrhizal symbiosis. Science. 2011;333:880–82.
CAS  PubMed  Google Scholar 

50.
Nürnberg DJ, Mariscal V, Bornikoel J, Nieves-Morión M, Krauß N, Herrero A, et al. Intercellular diffusion of a fluorescent sucrose analog via the septal junctions in a filamentous cyanobacterium. mBio. 2015;6:e02109-14.
PubMed  PubMed Central  Google Scholar 

51.
Mullineaux CW, Mariscal V, Nenninger A, Khanum H, Herrero A, Flores E, et al. Mechanism of intercellular molecular exchange in heterocyst-forming cyanobacteria. EMBO J. 2008;27:1299–308.
CAS  PubMed  PubMed Central  Google Scholar 

52.
Rousk K, Degboe J, Michelsen A, Bradley R, Bellenger JP. Molybdenum and phosphorus limitation of moss‐associated nitrogen fixation in boreal ecosystems. N Phytol. 2017;214:97–107.
CAS  Google Scholar 

53.
Solheim B, Zielke M. Associations between cyanobacteria and mosses. In: Rai AN, Bergman B, Rasmussen U, editors. Cyanobacteria in symbiosis. Dordrecht: Springer Netherlands; 2002. p. 137–52.
Google Scholar  More

1.
Prgzelin BB, Alldredge AL. Primary production of marine snow during and after an upwelling event. Limnol Oceanogr. 1983;28:1156–67.
Google Scholar 
2.
Shanks AL, Trent JD. Marine snow: microscale nutrient patches. Limnol Oceanogr. 1979;24:850–4.
CAS  Google Scholar 

3.
Azam F, Malfatti F. Microbial structuring of marine ecosystems. Nat Rev Microbiol. 2007;5:782–91.
CAS  PubMed  Google Scholar 

4.
Moran MA. The global ocean microbiome. Science. 2015;350:aac8455.
PubMed  Google Scholar 

5.
Stocker R. Marine microbes see a sea of gradients. Science. 2012;338:628–33.
CAS  PubMed  Google Scholar 

6.
Stocker R, Seymour JR, Samadani A, Hunt DE, Polz MF. Rapid chemotactic response enables marine bacteria to exploit ephemeral microscale nutrient patches. Proc Natl Acad Sci USA. 2008;105:4209–14.
CAS  PubMed  Google Scholar 

7.
Stocker R, Seymour JR. Ecology and physics of bacterial chemotaxis in the ocean. Microbiol Mol Biol Rev. 2012;76:792–812.
CAS  PubMed  PubMed Central  Google Scholar 

8.
Rosenwasser S, Ziv C, Creveld SGvan, Vardi A. Virocell metabolism: metabolic innovations during host–virus interactions in the ocean. Trends Microbiol. 2016;24:821–32.
CAS  PubMed  Google Scholar 

9.
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.
CAS  PubMed  Google Scholar 

10.
Seymour JR, Amin SA, Raina J-B, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat Microbiol. 2017;2:1–12.
Google Scholar 

11.
Smriga S, Fernandez VI, Mitchell JG, Stocker R. Chemotaxis toward phytoplankton drives organic matter partitioning among marine bacteria. Proc Natl Acad Sci USA. 2016;113:1576–81.
CAS  PubMed  Google Scholar 

12.
Moran MA, Belas R, Schell MA, Gonzalez JM, Sun F, Sun S, et al. Ecological genomics of marine Roseobacters. Appl Environ Microbiol. 2007;73:4559–69.
CAS  PubMed  PubMed Central  Google Scholar 

13.
Bischoff V, Bunk B, Meier-Kolthoff JP, Spröer C, Poehlein A, Dogs M, et al. Cobaviruses—a new globally distributed phage group infecting Rhodobacteraceae in marine ecosystems. ISME J. 2019;13:1404–21.
CAS  PubMed  PubMed Central  Google Scholar 

14.
Zhan Y, Chen F. Bacteriophages that infect marine roseobacters: genomics and ecology. Environ Microbiol. 2019;21:1885–95.
PubMed  Google Scholar 

15.
Ankrah NYD, May AL, Middleton JL, Jones DR, Hadden MK, Gooding JR, et al. Phage infection of an environmentally relevant marine bacterium alters host metabolism and lysate composition. ISME J. 2014;8:1089–100.
CAS  PubMed  Google Scholar 

16.
Sonnenschein EC, Nielsen KF, D’Alvise P, Porsby CH, Melchiorsen J, Heilmann J, et al. Global occurrence and heterogeneity of the Roseobacter-clade species Ruegeria mobilis. ISME J. 2017;11:569–83.
CAS  PubMed  Google Scholar 

17.
Luo H, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol Rev. 2014;78:573–87.
PubMed  PubMed Central  Google Scholar 

18.
Buchan A, LeCleir GR, Gulvik CA, González JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.
CAS  PubMed  Google Scholar 

19.
Ramanan R, Kim B-H, Cho D-H, Oh H-M, Kim H-S. Algae–bacteria interactions: evolution, ecology and emerging applications. Biotechnol Adv. 2016;34:14–29.
CAS  PubMed  Google Scholar 

20.
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.
CAS  PubMed  Google Scholar 

21.
Amin SA, Parker MS, Armbrust EV. Interactions between diatoms and bacteria. Microbiol Mol Biol Rev. 2012;76:667–84.
CAS  PubMed  PubMed Central  Google Scholar 

22.
Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522:98–101.
CAS  PubMed  Google Scholar 

23.
Green DH, Echavarri-Bravo V, Brennan D, Hart MC. Bacterial diversity associated with the coccolithophorid algae Emiliania huxleyi and Coccolithus pelagicus f. braarudii. BioMed Res Int. https://www.hindawi.com/journals/bmri/2015/194540/. Accessed 28 May 2020.

24.
González JM, Simó R, Massana R, Covert JS, Casamayor EO, Pedrós-Alió C, et al. Bacterial cmmunity structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom. Appl Environ Microbiol. 2000;66:4237–46.
PubMed  PubMed Central  Google Scholar 

25.
Park BS, Guo R, Lim W-A, Ki J-S. Pyrosequencing reveals specific associations of bacterial clades Roseobacter and Flavobacterium with the harmful dinoflagellate Cochlodinium polykrikoides growing in culture. Mar Ecol. 2017;38:maec.12474.
Google Scholar 

26.
Li S, Chen M, Chen Y, Tong J, Wang L, Xu Y, et al. Epibiotic bacterial community composition in red-tide dinoflagellate Akashiwo sanguinea culture under various growth conditions. FEMS Microbiol Ecol. 2019;95:fiz057.
CAS  PubMed  Google Scholar 

27.
Bell W, Mitchell R. Chemotactic and growth responses of marine bacteria to algal extracellular products. Biol Bull. 1972;143:265–77.
Google Scholar 

28.
Cole JJ. Interactions between bacteria and algae in aquatic ecosystems. Annu Rev Ecol Syst. 1982;13:291–314.
Google Scholar 

29.
Moran MA, Buchan A, González JM, Heidelberg JF, Whitman WB, Kiene RP, et al. Genome sequence of Silicibacter pomeroyi reveals adaptations to the marine environment. Nature. 2004;432:910–3.
CAS  PubMed  Google Scholar 

30.
Durham BP, Dearth SP, Sharma S, Amin SA, Smith CB, Campagna SR, et al. Recognition cascade and metabolite transfer in a marine bacteria-phytoplankton model system. Environ Microbiol. 2017;19:3500–13.
CAS  PubMed  Google Scholar 

31.
Barak-Gavish N, Frada MJ, Ku C, Lee PA, DiTullio GR, Malitsky S, et al. Bacterial virulence against an oceanic bloom-forming phytoplankter is mediated by algal DMSP. Sci Adv. 2018;4:eaau5716.
CAS  PubMed  PubMed Central  Google Scholar 

32.
Segev E, Wyche TP, Kim KH, Petersen J, Ellebrandt C, Vlamakis H, et al. Dynamic metabolic exchange governs a marine algal-bacterial interaction. Elife. 2016;5:e17473.
PubMed  PubMed Central  Google Scholar 

33.
Darling AE, Mau B, Perna NT. Progressivemauve: multiple genome alignment with gene gain, loss and rearrangement. PLoS ONE. 2010;5:e11147.
PubMed  PubMed Central  Google Scholar 

34.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
CAS  PubMed  PubMed Central  Google Scholar 

35.
Didelot X, Wilson DJ. ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLoS Comput Biol. 2015;11:e1004041.
PubMed  PubMed Central  Google Scholar 

36.
Lawson DJ, Hellenthal G, Myers S, Falush D. Inference of population structure using dense haplotype data. PLoS Genet. 2012;8:e1002453.
CAS  PubMed  PubMed Central  Google Scholar 

37.
Sun Y, Luo H. Homologous recombination in core genomes facilitates marine bacterial adaptation. Appl Environ Microbiol. 2018;84:e02545–17.
CAS  PubMed  PubMed Central  Google Scholar 

38.
Shapiro BJ, Friedman J, Cordero OX, Preheim SP, Timberlake SC, Szabó G, et al. Population genomics of early events in the ecological differentiation of bacteria. Science. 2012;336:48–51.
CAS  PubMed  PubMed Central  Google Scholar 

39.
Achtman M. Evolution, population structure, and phylogeography of genetically monomorphic bacterial pathogens. Annu Rev Microbiol. 2008;62:53–70.
CAS  PubMed  Google Scholar 

40.
Holt KE, Parkhill J, Mazzoni CJ, Roumagnac P, Weill FX, Goodhead I, et al. High-throughput sequencing provides insights into genome variation and evolution in Salmonella Typhi. Nat Genet. 2008;40:987–93.
CAS  PubMed  PubMed Central  Google Scholar 

41.
Morelli G, Song Y, Mazzoni CJ, Eppinger M, Roumagnac P, Wagner DM, et al. Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity. Nat Genet. 2010;42:1140–3.
CAS  PubMed  PubMed Central  Google Scholar 

42.
Achtman M. Insights from genomic comparisons of genetically monomorphic bacterial pathogens. Philos Trans R Soc Lond B Biol Sci. 2012;367:860–7.
PubMed  PubMed Central  Google Scholar 

43.
Didelot X, Maiden MCJ. Impact of recombination on bacterial evolution. Trends Microbiol. 2010;18:315–22.
CAS  PubMed  PubMed Central  Google Scholar 

44.
Vos M, Didelot X. A comparison of homologous recombination rates in bacteria and archaea. ISME J. 2009;3:199–208.
CAS  PubMed  Google Scholar 

45.
Hanage WP. Not so simple after all: bacteria, their population genetics, and recombination. Cold Spring Harb Perspect Biol. 2016;8:a018069.
PubMed  PubMed Central  Google Scholar 

46.
Fraser C, Hanage WP, Spratt BG. Recombination and the nature of bacterial speciation. Science. 2007;315:476–80.
CAS  PubMed  PubMed Central  Google Scholar 

47.
Hershberg R, Lipatov M, Small PM, Sheffer H, Niemann S, Homolka S, et al. High functional diversity in Mycobacterium tuberculosis driven by genetic drift and human demography. PLoS Biol. 2008;6:e311.
PubMed  PubMed Central  Google Scholar 

48.
Holt KE, Baker S, Weill F-X, Holmes EC, Kitchen A, Yu J, et al. Shigella sonnei genome sequencing and phylogenetic analysis indicate recent global dissemination from Europe. Nat Genet. 2012;44:1056–9.
CAS  PubMed  PubMed Central  Google Scholar 

49.
Okoro CK, Kingsley RA, Connor TR, Harris SR, Parry CM, Al-Mashhadani MN, et al. Intracontinental spread of human invasive Salmonella Typhimurium pathovariants in sub-Saharan Africa. Nat Genet. 2012;44:1215–21.
CAS  PubMed  PubMed Central  Google Scholar 

50.
Zhi X-Y, Zhao W, Li W-J, Zhao G-P. Prokaryotic systematics in the genomics era. Antonie Van Leeuwenhoek. 2012;101:21–34.
PubMed  Google Scholar 

51.
Yahara K, Furuta Y, Oshima K, Yoshida M, Azuma T, Hattori M, et al. Chromosome painting in silico in a bacterial species reveals fine population structure. Mol Biol Evol. 2013;30:1454–64.
CAS  PubMed  PubMed Central  Google Scholar 

52.
Cadillo-Quiroz H, Didelot X, Held NL, Herrera A, Darling A, Reno ML, et al. Patterns of gene flow define species of thermophilic Archaea. PLoS Biol. 2012;10:e1001265.

53.
Ellegaard KM, Klasson L, Näslund K, Bourtzis K, Andersson SGE. Comparative genomics of Wolbachia and the bacterial species concept. PLoS Genet. 2013;9:e1003381.
CAS  PubMed  PubMed Central  Google Scholar 

54.
Wielgoss S, Didelot X, Chaudhuri RR, Liu X, Weedall GD, Velicer GJ, et al. A barrier to homologous recombination between sympatric strains of the cooperative soil bacterium Myxococcus xanthus. ISME J. 2016;10:2468–77.
CAS  PubMed  PubMed Central  Google Scholar 

55.
Hoetzinger M, Hahn MW. Genomic divergence and cohesion in a species of pelagic freshwater bacteria. BMC Genom. 2017;18:794.
Google Scholar 

56.
Arevalo P, VanInsberghe D, Elsherbini J, Gore J, Polz MF. A reverse ecology approach based on a biological definition of microbial populations. Cell. 2019;178:820–34.
CAS  PubMed  Google Scholar 

57.
Bobay L-M, Ochman H. Biological species are universal across life’s domains. Genome Biol Evol. 2017;9:491–501.
PubMed Central  Google Scholar 

58.
Engel P, Stepanauskas R, Moran NA. Hidden diversity in honey bee gut symbionts detected by single-cell genomics. PLoS Genet. 2014;10:e1004596.
PubMed  PubMed Central  Google Scholar 

59.
Hughes AL, French JO. Homologous recombination and the pattern of nucleotide substitution in Ehrlichia ruminantium. Gene. 2007;387:31–7.
CAS  PubMed  Google Scholar 

60.
Hughes AL, Friedman R. Nucleotide substitution and recombination at orthologous loci in Staphylococcus aureus. J Bacteriol. 2005;187:2698–704.
CAS  PubMed  PubMed Central  Google Scholar 

61.
Charrad M, Ghazzali N, Boiteau V, Niknafs A. NbClust: an R package for determining the relevant number of clusters in a data set. J Stat Softw. 2014;61:1–36.
Google Scholar 

62.
Raina J-B, Fernandez V, Lambert B, Stocker R, Seymour JR. The role of microbial motility and chemotaxis in symbiosis. Nat Rev Microbiol. 2019;17:284–94.
CAS  PubMed  Google Scholar 

63.
Hünken M, Harder J, Kirst GO. Epiphytic bacteria on the Antarctic ice diatom Amphiprora kufferathii Manguin cleave hydrogen peroxide produced during algal photosynthesis. Plant Biol. 2008;10:519–26.
PubMed  Google Scholar 

64.
Morris JJ, Kirkegaard R, Szul MJ, Johnson ZI, Zinser ER. Facilitation of robust growth of Prochlorococcus colonies and dilute liquid cultures by “helper” heterotrophic bacteria. Appl Environ Microbiol. 2008;74:4530–4.
CAS  PubMed  PubMed Central  Google Scholar 

65.
Durham BP, Sharma S, Luo H, Smith CB, Amin SA, Bender SJ, et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Natl Acad Sci USA. 2015;112:453–7.
CAS  PubMed  Google Scholar 

66.
Cooper MB, Kazamia E, Helliwell KE, Kudahl UJ, Sayer A, Wheeler GL, et al. Cross-exchange of B-vitamins underpins a mutualistic interaction between Ostreococcus tauri and Dinoroseobacter shibae. ISME J. 2019;13:334–45.
CAS  PubMed  Google Scholar 

67.
Tang YZ, Koch F, Gobler CJ. Most harmful algal bloom species are vitamin B1 and B12 auxotrophs. Proc Natl Acad Sci USA. 2010;107:20756–61.
CAS  PubMed  Google Scholar 

68.
Helliwell KE. The roles of B vitamins in phytoplankton nutrition: new perspectives and prospects. New Phytol. 2017;216:62–8.
CAS  PubMed  Google Scholar 

69.
Gao R, Krysciak D, Petersen K, Utpatel C, Knapp A, Schmeisser C, et al. Genome-wide RNA sequencing analysis of quorum sensing-controlled regulons in the plant-associated Burkholderia glumae PG1 strain. Appl Environ Microbiol. 2015;81:7993–8007.
PubMed  PubMed Central  Google Scholar 

70.
Ng VH, Cox JS, Sousa AO, MacMicking JD, McKinney JD. Role of KatG catalase-peroxidase in mycobacterial pathogenesis: countering the phagocyte oxidative burst. Mol Microbiol. 2004;52:1291–302.
CAS  PubMed  Google Scholar 

71.
Ivanova A, Miller C, Glinsky G, Eisenstark A. Role of rpoS (katF) in oxyR-independent regulation of hydroperoxidase I in Escherichia coli. Mol Microbiol. 1994;12:571–8.
CAS  PubMed  Google Scholar 

72.
Amábile-Cuevas CF, Demple B. Molecular characterization of the soxRS genes of Escherichia coli: two genes control a superoxide stress regulon. Nucleic Acids Res. 1991;19:4479–84.
PubMed  PubMed Central  Google Scholar 

73.
Landfald B, Strøm AR. Choline-glycine betaine pathway confers a high level of osmotic tolerance in Escherichia coli. J Bacteriol. 1986;165:849–55.
CAS  PubMed  PubMed Central  Google Scholar 

74.
Lidbury I, Kimberley G, Scanlan DJ, Murrell JC, Chen Y. Comparative genomics and mutagenesis analyses of choline metabolism in the marine Roseobacter clade. Mol Microbiol. 2015;17:5048–62.
CAS  Google Scholar 

75.
Bochner BR, Gadzinski P, Panomitros E. Phenotype microArrays for high-throughput phenotypic testing and assay of gene function. Genome Res. 2001;11:1246–55.
CAS  PubMed  PubMed Central  Google Scholar 

76.
Vaas LAI, Sikorski J, Hofner B, Fiebig A, Buddruhs N, Klenk H-P, et al. opm: an R package for analysing OmniLog(R) phenotype microarray data. Bioinformatics. 2013;29:1823–4.
CAS  PubMed  Google Scholar 

77.
Mou X, Vila-Costa M, Sun S, Zhao W, Sharma S, Moran MA. Metatranscriptomic signature of exogenous polyamine utilization by coastal bacterioplankton. Environ Microbiol Rep. 2011;3:798–806.
CAS  PubMed  Google Scholar 

78.
Porter SS, Chang PL, Conow CA, Dunham JP, Friesen ML. Association mapping reveals novel serpentine adaptation gene clusters in a population of symbiotic. Mesorhizobium ISME J. 2017;11:248–62.
CAS  PubMed  Google Scholar 

79.
Andam CP, Gogarten JP. Biased gene transfer in microbial evolution. Nat Rev Microbiol. 2011;9:543–55.
CAS  PubMed  Google Scholar 

80.
Boucher Y, Cordero OX, Takemura A. Endemicity within global Vibrio cholerae populations. mBio. 2011;2:1–8.
Google Scholar 

81.
Coleman ML, Chisholm SW. Ecosystem-specific selection pressures revealed through comparative population genomics. Proc Natl Acad Sci USA. 2010;107:18634–9.
CAS  PubMed  Google Scholar 

82.
Polz MF, Alm EJ, Hanage WP. Horizontal gene transfer and the evolution of bacterial and archaeal population structure. Trends Genet. 2013;29:170–5.
CAS  PubMed  PubMed Central  Google Scholar 

83.
Cordero OX, Polz MF. Explaining microbial genomic diversity in light of evolutionary ecology. Nat Rev Microbiol. 2014;12:263–73.
CAS  PubMed  Google Scholar 

84.
Hoetzinger M, Schmidt J, Jezberová J, Koll U, Hahn MW. Microdiversification of a pelagic Polynucleobacter species Is mainly driven by acquisition of genomic islands from a partially interspecific gene pool. Appl Environ Microbiol. 2017;83:e02266–16.
PubMed  PubMed Central  Google Scholar 

85.
Moran MA, Kujawinski EB, Stubbins A, Fatland R, Aluwihare LI, Buchan A, et al. Deciphering ocean carbon in a changing world. Proc Natl Acad Sci USA. 2016;113:3143–51.
CAS  PubMed  Google Scholar 

86.
Christie-Oleza JA, Sousoni D, Lloyd M, Armengaud J, Scanlan DJ. Nutrient recycling facilitates long-term stability of marine microbial phototroph-heterotroph interactions. Nat Microbiol. 2017;2:17100.
CAS  PubMed  PubMed Central  Google Scholar  More