Philippa Kaur

RESEARCH BRIEFINGS
01 March 2023

Tropical deforestation affects local and regional precipitation, but the effects are uncertain and have not been determined using observations. Satellite data sets were used to show reductions in precipitation over areas of tropical forest loss, with stronger reductions seen as the deforested area expands. More

Data sourcesMODIS on the Aqua satellite provides a global coverage within 1–2 days. All images acquired by this satellite mission from January 2003 to December 2020 were used in our study to detect global coastal phytoplankton blooms, with a total of 0.76 million images. MODIS Level-1A images were downloaded from the Ocean Biology Distributed Active Archive Center (OB.DAAC) at NASA Goddard Space Flight Center (GSFC), and were subsequently processed with SeaDAS software (version 7.5) to obtain Rayleigh-corrected reflectance (Rrc (dimensionless), which was converted using the rhos (in sr−1) product (rhos × π) from SeaDAS)41, remote sensing reflectance (Rrs (sr−1)) and quality control flags (l2_flags). If a pixel was flagged by any of the following, it was then removed from phytoplankton bloom detection: straylight, cloud, land, high sunglint, high solar zenith angle and high sensor zenith angle (https://oceancolor.gsfc.nasa.gov/atbd/ocl2flags/). MODIS level-3 product for aerosol optical thicknesses (AOT) at 869 nm was also obtained from OB.DAAC NASA GSFC (version R2018.0), which was used to examine the impacts of aerosols on bloom trends.We examined the algal blooms in the EEZs of 153 ocean-bordering countries (excluding the EEZs in the Caspian Sea or around the Antarctic), 126 of which were found with at least one bloom in the past two decades. The EEZ dataset is available at https://www.marineregions.org/download_file.php?name=World_EEZ_v11_20191118.zip. The EEZs are up to 200 nautical miles (or 370 km) away from coastlines, which include all continental shelf areas and offer the majority of marine resources available for human use. Regional statistics of algal blooms were also performed for LMEs. LMEs encompass global coastal oceans and outer edges of coastal currents areas, which are defined by various distinct features of the oceans, including hydrology, productivity, bathymetry and trophically dependent populations42. Of the 66 LMEs identified globally, we excluded the Arctic and Antarctic regions and examined 54 LMEs. The boundaries of LMEs were obtained from https://www.sciencebase.gov/catalog/item/55c77722e4b08400b1fd8244.We used HAEDAT to validate our satellite-detected phytoplankton blooms in terms of presence or absence. The HAEDAT dataset (http://haedat.iode.org) is a collection of records of HAB events, maintained under the UNESCO Intergovernmental Oceanographic Commission and with data archives since 1985. For each HAB event, the HAEDAT records its bloom period (ranging from days to months) and geolocation. We merged duplicate entries when both the recorded locations and times of the HAEDAT events were very similar to one another, and a total number of 2,609 HAEDAT events were ultimately selected between 2003 and 2020.We used the ¼° resolution National Oceanic and Atmospheric Administration Optimum Interpolated SST (v. 2.1) data to examine the potential simulating effects of warming on the global phytoplankton trends. We also estimated the SST gradients following the method of Martínez-Moreno33. As detailed in ref. 33, the SST gradient can be used as a proxy for the magnitude of oceanic mesoscale currents (EKE). We used the SST gradient to explore the effects of ocean circulation dynamics on algal blooms.Fertilizer uses and aquaculture production for different countries was used to examine the potential effects of nutrient enrichment from humans on global phytoplankton bloom trends. Annual data between 2003 and 2019 on synthetic fertilizer use, including nitrogen and phosphorus, are available from https://ourworldindata.org/fertilizers. Annual aquaculture production includes cultivated fish and crustaceans in marine and inland waters, and sea tanks, and the data between 2003 and 2018 are available from https://ourworldindata.org/grapher/aquaculture-farmed-fish-production.The MEI, which combines various oceanic and atmospheric variables36, was used to examine the connections between El Niño–Southern Oscillation activities and marine phytoplankton blooms. The dataset is available from https://psl.noaa.gov/enso/mei/.Development of an automated bloom detection methodA recent study by the UNESCO Intergovernmental Oceanographic Commission revealed that globally reported HAB events have increased6. However, such an overall increasing trend was found to be highly correlated with recently intensified sampling efforts6. Once this potential bias was accounted for by examining the ratio between HAB events to the number of samplings5, there was no significant global trend in HAB incidence, though there were increases in certain regions. With synoptic, frequent, and large-scale observations, satellite remote sensing has been extensively used to monitor algal blooms in oceanic environments17,18,19. For example, chlorophyll a (Chla) concentrations, a proxy for phytoplankton biomass, has been provided as a standard product by NASA since the proof-of-concept Coastal Zone Color Scanner (1978–1986) era43,44. The current default algorithm used to retrieve Chla products is based on the high absorption of Chla at the blue band45,46, which often shows high accuracy in the clear open oceans but high uncertainties in coastal waters. This is because, in productive and dynamic coastal oceans, the absorption of Chla in the blue band can be obscured by the presence of suspended sediments and/or coloured dissolved organic matter (CDOM)47. To address this problem, various regionalized Chla algorithms have been developed48. Unfortunately, the concentrations of the water constituents (CDOM, sediment and Chla) can vary substantially across different coastal oceans. As a result, a universal Chla algorithm that can accurately estimate Chla concentrations in global coastal oceans is not currently available.Alternatively, many spectral indices have been developed to identify phytoplankton blooms instead of quantifying their bloom biomass, including the normalized fluorescence line height21 (nFLH), red tide index49 (RI), algal bloom index47 (ABI), red–blue difference (RBD)50, Karenia brevis bloom index50 (KBBI) and red tide detection index51 (RDI). In practice, the most important task for these index-based algorithms is to determine their optimal thresholds for bloom classification. However, such optimal thresholds can be regional-or image-specific20, due to the complexity of optical features in coastal waters and/or the contamination of unfavourable observational conditions (such as thick aerosols, thin clouds, and so on), making it difficult to apply spectral-index-based algorithms at a global scale.To circumvent the difficulty in determining unified thresholds for various spectral indices across global coastal oceans, an approach from a recent study to classify algal blooms in freshwater lakes52 was adopted and modified here. In that study, the remotely sensed reflectance data in three visible bands (red, green and blue) were converted into two-dimensional colour space created by the Commission Internationale del’éclairage (CIE), in which the position on the CIE chromaticity diagram represented the colour perceived by human eyes (Extended Data Fig. 1a). As the algal blooms in freshwater lakes were manifested as greenish colours, the reflectance of bloom-containing pixels was expected to be distributed in the green gamut of the CIE chromaticity diagram; the stronger the bloom, the closer the distance to the upper border of the diagram (the greener the water).Here, the colour of phytoplankton blooms in the coastal oceans can be greenish, yellowish, brownish, or even reddish53, owing to the compositions of bloom species (diatoms or dinoflagellates) and the concentrations of different water constituents. Furthermore, the Chla concentrations of the coastal blooms are typically lower than those in inland waters, thus demanding more accurate classification algorithms. Thus, the algorithm proposed by Hou et al.52 was modified when using the CIE chromaticity space for bloom detection in marine environments. Specifically, we used the following coordinate conversion formulas to obtain the xy coordinate values in the CIE colour space:$$begin{array}{c}x=X/(X+Y+Z)\ y=Y/(X+Y+Z)\ X=2.7689R+1.7517G+1.1302B\ Y=1.0000R+4.5907G+0.0601B\ Z=0.0000R+0.0565G+5.5943Bend{array}$$
(1)
where R, G and B represent the Rrc at 748 nm, 678 nm (fluorescence band) and 667 nm in the MODIS Aqua data, respectively. By contrast, the R, G and B channels used in Hou et al.52 were the red, green and blue bands. We used the fluorescence band for the G channel because, for a given region, the 678 nm signal increases monotonically with the Chla concentration for blooms of moderate intensity21, which is similar to the response of greenness to freshwater algal blooms. Thus, the converted y value in the CIE coordinate system represents the strength of the fluorescence. In practice, for pixels with phytoplankton blooms, the converted colours in the chromaticity diagram will be located within the green, yellow or orange–red gamut (see Extended Data Fig. 1a); the stronger the fluorescence signal is, the closer the distance to the upper border of the CIE diagram (larger y value). By contrast, for bloom-free pixels without a fluorescence signal, their converted xy coordinates will be located in the blue or purple gamut. Therefore, we can determine a lower boundary in the CIE two-dimensional coordinate system to separate bloom and non-bloom pixels, similar to the method proposed by Hou et al.52.We selected 53,820 bloom-containing pixels from the MODIS Rrc data as training samples to determine the boundary of the CIE colour space. These sample points were selected from nearshore waters worldwide where frequent phytoplankton blooms have been reported (Extended Data Fig. 2); the algal species included various species of dinoflagellates and diatoms20. A total of 80 images was used, which were acquired from different seasons and across various bloom magnitudes, to ensure that the samples used could almost exhaustively represent the different bloom conditions in the coastal oceans.We combined the MODIS FLHRrc (fluorescence line height based on Rrc) and enhanced red–green–blue composite (ERGB) to delineate bloom pixels manually. The FLHRrc image was calculated as:$$begin{array}{c}{{rm{FLH}}}_{{rm{Rrc}}}={R}_{{rm{rc}}678}times {F}_{678}-[{R}_{{rm{rc}}667}times {F}_{667}+({R}_{{rm{rc}}748}times {F}_{748}\ ,,-,{R}_{{rm{rc}}667}times {F}_{667})times (678-667)/(748-667)]end{array}$$
(2)
where Rrc667, Rrc678 and Rrc748 are the Rrc at 667, 678 and 748 nm, respectively, and F667, F678 and F748 are the corresponding extraterrestrial solar irradiance. ERGB composite images were generated using Rrc of three bands at 555 (R), 488 (G) and 443 nm (B). Although phytoplankton-rich and sediment-rich waters have high FLHRrc values, they appear as darkish and bright features in the ERGB images (Extended Data Fig. 3), respectively21. In fact, visual examination with fluorescence signals and ERGB has been widely accepted as a practical way to delineate coastal algal blooms on a limited number of images21,54,55. Note that the FLHRrc here was slightly different from the NASA standard nFLH product56, as the latter is generated using Rrs (corrected for both Rayleigh and aerosol scattering) instead of Rrc (with residual effects of aerosols). However, when using the NASA standard algorithm to further perform aerosol scattering correction over Rrc, 20.7% of our selected bloom-containing pixels failed to obtain valid Rrs (without retrievals or flagged as low quality), especially for those with strong blooms (see examples in Extended Data Fig. 4). Likewise, we also found various nearshore regions with invalid Rrs retrievals. By contrast, Rrc had valid data for all selected samples and showed more coverage in nearshore coastal waters. The differences between Rrs and Rrc were because the assumptions for the standard atmospheric correction algorithm do not hold for bloom pixels or nearshore waters with complex optical properties57. In fact, Rrc has been used as an alternative to Rrs in various applications in complex waters58,59.We converted the Rrc data of 53,820 selected sample pixels into the xy coordinates in the CIE colour space (Extended Data Fig. 1a). As expected, these samples of bloom-containing pixels were located in the upper half of the chromaticity diagram (the green, yellow and orange–red gamut) (Extended Data Fig. 1a). We determined the lower boundary of these sample points in the chromaticity diagram, which represents the lightest colour and thus the weakest phytoplankton blooms; any point that falls above this boundary represents stronger blooms. The method to determine the boundary was similar to Hou et al.52: we first binned the sample points according to the x value in the chromaticity diagram and estimated the 1st percentile (Q1%) of the corresponding Y for each bin; then, we fit the Q1% using two-order polynomial regression. Sensitivity analysis with Q0.3% (the three-sigma value) resulted in minor changes ( 1/3 AND y  > y2), it is classified as a ‘bloom’ pixel.Depending on the local region and application purpose, the meaning of ‘phytoplankton bloom’ may differ. Here, for a global application, the pixelwise bloom classification is based on the relationship (represented using the CIE colour space) between Rrc in the 667-, 678- and 754-nm bands derived from visual interpretation of the 80 pairs of FLHRrc and ERGB imagery. Instead of a simple threshold, we used a lower boundary of the sample points in the chromaticity diagram to define a bloom. In simple words, a pixel is classified as a bloom if its fluorescence signal is detectable (the associated xy coordinate in the CIE colour space located above the lower boundary). Histogram of the nFLH values from the 53,820 training pixels demonstrated the minimum value of ~0.02 mW cm−2 μm−1 (Extended Data Fig. 1a), which is in line with the lower-bound signal of K. brevis blooms on the West Florida shelf21,47. Note that, such a minimum nFLH is determined from the global training pixels, and it does not necessarily represent a unified lower bound for phytoplankton blooms across the entire globe, especially considering that fluorescence efficiency may be a large variable across different regions. Different regions may have different lower bounds of nFLH to define a bloom, and such variability is represented by the predefined boundary in the CIE chromaticity diagram in our study. Correspondingly, although the accuracy of Chla retrievals may have large uncertainties in coastal waters, the histogram of the 53,820 training pixels shows a lower bound of ~1 mg m−3 (Extended Data Fig. 1a). Similarly to nFLH, such a lower bound may not be applicable to all coastal regions, as different regions may have different lower bounds of Chla for bloom definition.Although the MODIS cloud (generated by SeaDAS with Rrc869 0.12) and Index2 ( More

Balashov, S. P. et al. Xanthorhodopsin: a proton pump with a light-harvesting carotenoid antenna. Science 309, 2061–2064 (2005).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Imasheva, E. S., Balashov, S. P., Choi, A. R., Jung, K.-H. & Lanyi, J. K. Reconstitution of Gloeobacter violaceus rhodopsin with a light-harvesting carotenoid antenna. Biochemistry 48, 10948–10955 (2009).Article 
CAS 
PubMed 

Google Scholar 
Fuhrman, J. A., Schwalbach, M. S. & Stingl, U. Proteorhodopsins: an array of physiological roles? Nat. Rev. Microbiol. 6, 488–494 (2008).Article 
CAS 
PubMed 

Google Scholar 
Vollmers, J. et al. Poles apart: Arctic and Antarctic Octadecabacter strains share high genome plasticity and a new type of xanthorhodopsin. PLoS ONE 8, e63422 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bertsova, Y. V., Arutyunyan, A. M. & Bogachev, A. V. Na+-translocating rhodopsin from Dokdonia sp. PRO95 does not contain carotenoid antenna. Biochem. Mosc. 81, 414–419 (2016).Article 
CAS 

Google Scholar 
Misra, R., Eliash, T., Sudo, Y. & Sheves, M. Retinal–salinixanthin interactions in a thermophilic rhodopsin. J. Phys. Chem. B 123, 10–20 (2019).Article 
CAS 
PubMed 

Google Scholar 
Béjà, O. et al. Bacterial rhodopsin: evidence for a new type of phototrophy in the sea. Science 289, 1902–1906 (2000).Article 
ADS 
PubMed 

Google Scholar 
Béjà, O., Spudich, E. N., Spudich, J. L., Leclerc, M. & DeLong, E. F. Proteorhodopsin phototrophy in the ocean. Nature 411, 786–789 (2001).Article 
ADS 
PubMed 

Google Scholar 
Atamna-Ismaeel, N. et al. Widespread distribution of proteorhodopsins in freshwater and brackish ecosystems. ISME J. 2, 656–662 (2008).Article 
CAS 
PubMed 

Google Scholar 
Frigaard, N.-U., Martinez, A., Mincer, T. J. & DeLong, E. F. Proteorhodopsin lateral gene transfer between marine planktonic Bacteria and Archaea. Nature 439, 847–850 (2006).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Finkel, O. M., Béjà, O. & Belkin, S. Global abundance of microbial rhodopsins. ISME J. 7, 448–451 (2013).Article 
CAS 
PubMed 

Google Scholar 
Gómez-Consarnau, L. et al. Microbial rhodopsins are major contributors to the solar energy captured in the sea. Sci. Adv. 5, eaaw8855 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
DeLong, E. F. & Béjà, O. The light-driven proton pump proteorhodopsin enhances bacterial survival during tough times. PLoS Biol. 8, e1000359 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Munson-McGee, J. H. et al. Decoupling of respiration rates and abundance in marine prokaryoplankton. Nature 612, 764–770 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, W.-W., Sineshchekov, O. A., Spudich, E. N. & Spudich, J. L. Spectroscopic and photochemical characterization of a deep ocean proteorhodopsin. J. Biol. Chem. 278, 33985–33991 (2003).Article 
CAS 
PubMed 

Google Scholar 
Man, D. Diversification and spectral tuning in marine proteorhodopsins. EMBO J. 22, 1725–1731 (2003).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lanyi, J. K. & Balashov, S. P. in Halophiles and Hypersaline Environments (eds. Ventosa, A., Oren, A. & Ma, Y.) 319–340 (Springer, 2011).Balashov, S. P. et al. Reconstitution of Gloeobacter rhodopsin with echinenone: role of the 4-keto group. Biochemistry 49, 9792–9799 (2010).Article 
CAS 
PubMed 

Google Scholar 
Kopejtka, K. et al. A bacterium from a mountain lake harvests light using both proton-pumping xanthorhodopsins and bacteriochlorophyll-based photosystems. Proc. Natl Acad. Sci. USA 119, e2211018119 (2022).Article 
CAS 
PubMed 

Google Scholar 
Pushkarev, A. & Béjà, O. Functional metagenomic screen reveals new and diverse microbial rhodopsins. ISME J. 10, 2331–2335 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pushkarev, A. et al. A distinct abundant group of microbial rhodopsins discovered using functional metagenomics. Nature 558, 595–599 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Chazan, A. et al. Diverse heliorhodopsins detected via functional metagenomics in freshwater Actinobacteria, Chloroflexi and Archaea. Environ. Microbiol. 24, 110–121 (2022).Article 
CAS 
PubMed 

Google Scholar 
Inoue, K. et al. A light-driven sodium ion pump in marine bacteria. Nat. Commun. 4, 1678 (2013).Article 
ADS 
PubMed 

Google Scholar 
Bhosale, P. & Bernstein, P. S. Microbial xanthophylls. Appl. Microbiol. Biotechnol. 68, 445–455 (2005).Article 
CAS 
PubMed 

Google Scholar 
Demmig-Adams, B., Polutchko, S. K. & Adams, W. W. Structure–function–environment relationship of the isomers zeaxanthin and lutein. Photochem 2, 308–325 (2022).Article 

Google Scholar 
Barreiro C. & Barredo J. L. Microbial Carotenoids: Methods and Protocols (Humana Press, 2018).Ram, S., Mitra, M., Shah, F., Tirkey, S. R. & Mishra, S. Bacteria as an alternate biofactory for carotenoid production: a review of its applications, opportunities and challenges. J. Funct. Foods 67, 103867 (2020).Article 
CAS 

Google Scholar 
Shibata, M. et al. Oligomeric states of microbial rhodopsins determined by high-speed atomic force microscopy and circular dichroic spectroscopy. Sci. Rep. 8, 8262 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Luecke, H. et al. Crystallographic structure of xanthorhodopsin, the light-driven proton pump with a dual chromophore. Proc. Natl Acad. Sci. USA 105, 16561–16565 (2008).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chuon, K. et al. Assembly of natively synthesized dual chromophores into functional actinorhodopsin. Front. Microbiol. 12, 652328 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Yoshizawa, S., Kawanabe, A., Ito, H., Kandori, H. & Kogure, K. Diversity and functional analysis of proteorhodopsin in marine Flavobacteria. Environ. Microbiol. 14, 1240–1248 (2012).Article 
CAS 
PubMed 

Google Scholar 
Ahmed, F. et al. Profiling of carotenoids and antioxidant capacity of microalgae from subtropical coastal and brackish waters. Food Chem. 165, 300–306 (2014).Article 
CAS 
PubMed 

Google Scholar 
Shihoya, W. et al. Crystal structure of heliorhodopsin. Nature 574, 132–136 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kishi, K. E. et al. Structural basis for channel conduction in the pump-like channelrhodopsin ChRmine. Cell 185, 672–689.e23 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Balashov, S. P., Imasheva, E. S., Wang, J. M. & Lanyi, J. K. Excitation energy-transfer and the relative orientation of retinal and carotenoid in xanthorhodopsin. Biophys. J. 95, 2402–2414 (2008).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lakowicz, J. R. (ed.) in Principles of Fluorescence Spectroscopy 27–61 (Springer, 2006).Dana, J. et al. Testing the fate of nascent holes in CdSe nanocrystals with sub-10 fs pump–probe spectroscopy. Nanoscale 13, 1982–1987 (2021).Article 
CAS 
PubMed 

Google Scholar 
Polívka, T. et al. Femtosecond carotenoid to retinal energy transfer in xanthorhodopsin. Biophys. J. 96, 2268–2277 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Iyer, E. S. S., Gdor, I., Eliash, T., Sheves, M. & Ruhman, S. Efficient femtosecond energy transfer from carotenoid to retinal in Gloeobacter rhodopsin–salinixanthin complex. J. Phys. Chem. B 119, 2345–2349 (2015).Article 
CAS 
PubMed 

Google Scholar 
Doi, S., Tsukamoto, T., Yoshizawa, S. & Sudo, Y. An inhibitory role of Arg-84 in anion channelrhodopsin-2 expressed in Escherichia coli. Sci. Rep. 7, 41879 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nagiri, C. et al. Crystal structure of human endothelin ETB receptor in complex with peptide inverse agonist IRL2500. Commun. Biol. 2, 236 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Yamashita, K., Hirata, K. & Yamamoto, M. KAMO: towards automated data processing for microcrystals. Acta Crystallogr. D Struct. Biol. 74, 441–449 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kabsch, W. XDS. Acta Crystallogr. D Biol. Crystallogr. 66, 125–132 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine.Acta Crystallogr. D Biol. Crystallogr. 68, 352–367 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3.eLife 7, e42166 (2018).Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).Punjani, A., Zhang, H. & Fleet, D. J. Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat. Methods 17, 1214–1221 (2020).Article 
CAS 
PubMed 

Google Scholar 
Rosenthal, P. B. & Henderson, R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333, 721–745 (2003).Article 
CAS 
PubMed 

Google Scholar 
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004).Article 
PubMed 

Google Scholar 
Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yamashita, K., Palmer, C. M., Burnley, T. & Murshudov, G. N. Cryo-EM single-particle structure refinement and map calculation using Servalcat. Acta Crystallogr. D Struct. Biol. 77, 1282–1291 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).Article 
CAS 
PubMed 

Google Scholar 
Inoue, K. et al. Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design. Commun. Biol. 4, 362 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083.e21 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chen, I.-M. A. et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 49, D751–D763 (2021).Article 
CAS 
PubMed 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).Article 
CAS 
PubMed 

Google Scholar 
Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat. Methods 10, 1196–1199 (2013).Article 
CAS 
PubMed 

Google Scholar 
Wickham, H. in ggplot2 (eds Gentleman, R., Hornik, K. & Parmigiani, G.) 189–201 (Springer, 2016).Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).Article 
CAS 
PubMed 

Google Scholar 
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).Article 
CAS 
PubMed 

Google Scholar  More

RESEARCH BRIEFINGS
01 March 2023

Global spatial and temporal patterns of coastal phytoplankton blooms were characterized using daily satellite imaging between 2003 and 2020. These blooms were identified on the coast of 126 of the 153 ocean-bordering countries examined. The extent and frequency of blooms have increased globally over the past two decades. More

Global biodiversity has been dramatically declining over the last decades1,2,3,4. The current biodiversity crisis is primarily driven by human-induced factors, the most serious of which are land-use change, habitat fragmentation, and climate change5. While global public awareness of climate change matters is high6,7, public recognition of biodiversity loss has, historically, been low8. The understanding of biodiversity concepts highly varies among countries and social groups9,10,11: in Nigeria, the biodiversity concept was known of 20.5% of non-professional Nigerians (with basic education or no formal training) while among 88.8% of professionals with tertiary education, it reached 88.8%; 60% of participants in a study in Switzerland had never heard the term biodiversity and Chinese farmers in another pilot study have never heard about biodiversity. In the European Union, the global leader of the environmental movement on both the political and discursive levels12,13, in 2018, 71% of EU citizens had heard of biodiversity, but only around 41% of these knew what biodiversity meant14. This illiteracy is a significant constraint for conservation strategies because the development and success of actions to halt and reverse biodiversity loss strongly rely on public support15.If general awareness of biodiversity loss is low, knowledge about plant diversity is even lower16. Plants have traditionally been overlooked, and expressions such as “plant blindness”, defined as a human tendency to ignore plant species17, perfectly illustrate the situation in terms of plant conservation. And yet, current estimates suggest that two out of five plant species are threatened with extinction18. Moreover, plants play a crucial role in the world ecosystems by providing habitat, shelter, oxygen, and food, including for humans19. Local community support boosts the effectiveness of biodiversity conservation actions20,21,22. However, how biodiversity is perceived and the benefits it provides to local populations have a significant influence on this support23. Therefore, stopping the loss of plant biodiversity and the impact it has on ecosystem health and human well-being must also strive to raise public awareness on the importance of plant conservation24.A big challenge, however, is to engage people with conservation. Nowadays, in a world where a large part of the human population lives in urban areas, the contact of people with nature is declining. This is a trend that will be even more accentuated in the future25. Perhaps society’s interest in plants is decreasing because of limited exposure to plants in daily lives, schools, and work. However, by critically examining our roles as plant scientists and educators, we realize that there are probably things we could, and should, do differently. New strategies to connect people to nature are required to spark people’s interest in and knowledge of plants. Citizen science programs and mobile applications (apps) are noteworthy initiatives that are helping to achieve this goal.Citizen science is defined as the general public involvement in scientific research activities and currently is a mainstream approach to collect information and data on a wide range of scientific subjects26,27. The development of mobile technologies and the widespread use of smartphones have boosted citizen science and enabled the development of mobile apps, which are digital tools that integrate, in real-time, data from multiple sources28.The goal of this article is to show how citizen science and mobile apps can be used as educational tools to raise awareness about plant biodiversity and conservation among the general public. We focused on formal education activities, at the Bachelor of Science (BSc) level, that were designed to collect data on various aspects of plant community and functional ecology. We also present the outcomes of two informal education initiatives that used citizen science to gather data on the distribution of plant diversity. We discuss these activities and results in light of their potential to engage the public into biodiversity conservation, and as educational and outreach tools.Formal education: UniversityDuring the COVID-19 pandemic (2021), Ecology practical classes of the Bologna Bachelor Degree in Biology (Faculty of Sciences of the University of Lisbon) had to be adapted to remote learning. Fortunately, during the States of Emergency imposed by the Portuguese Government, citizens were allowed to take brief walks. Taking advantage of citizen’s ability to briefly travel outdoors, we created three activities for students, as alternatives to those typically carried out in the classroom/campus, which we describe below.Activity 1—Analysis of the impact of disturbance on plant diversity in grasslandsThe objective of this activity was for students to explore the impact of disturbance and site attributes (such as soil type) on the diversity of the herbaceous plant community and its associated pollinators. This was undertaken in grasslands located near their homes, within walking distance (due to COVID lockdown movement restrictions). To achieve this goal, we developed a comprehensive sampling protocol that included methods for (i) selecting and characterizing sampling sites based on the level of human perturbation, (ii) soil characterization, (iii) sampling, identifying, and registering plants using the iNaturalist/Biodiversity4All platform and Flora-on web (Box 1), and (iv) pollinator sampling (Supplementary Data 1). To ensure accurate plant and pollinators identification, all observations were verified by professors responsible for each topic.First, each student chose one sampling site and teachers, using photographs, classified all sites regarding their perturbation level (low, medium, and high). Then, using the sampling protocol, students were invited to study different aspects of their sampling site, in loco or at their homes. Soil samples were analysed using simple methods and available household instruments (such as plastic cups, kitchen scale, and oven). Students were introduced to soil biodiversity as well as soil parameters (humidity, texture, structure, infiltration and draining) during the remote classes. Plants were sampled using a home-made 1 m2 quadrat. All species within were counted and identified to the lowest taxonomic level possible, using the mentioned apps and website. Before plant sampling, students were also asked to count and identify pollinators within their quadrats (broad taxonomic groups, bees, butterflies, flies, beetles) for 5 min, again using the apps to aid identification.Following field sampling, students were asked to calculate two taxonomic indices of plant communities. These included species richness, which measures the number of different species that occur in a sample, and the Simpson Diversity Index, which evaluates the probability that two individuals randomly selected from a sample will belong to the same species. Students also calculated functional diversity indices such as Functional Richness and Functional Dissimilarity, since functional diversity explores functional differences between species and how these differences reflect and affect the interactions with the environment and with other species29. Then, students assessed the relation between these indices and perturbation level. They analysed several functional traits of plants that are likely to respond to local perturbation (e.g., height, leaf size). Finally, they attempted to relate plant indices with the occurrence of pollinators.Overall, students sampled 147 grasslands that were affected by low (n = 17); medium (n = 86) and high (n = 40) levels of perturbation, scattered across mainland Portugal (Fig. 1a). In total, 3015 observations corresponding to 543 species of plant and 88 of insects (Fig. 1b) were registered in the iNaturalist/Biodiversity4All project Ecologia2_FCUL, created specifically to record all of the diversity data associated with this activity. Other registered taxa included six species of molluscs and 13 of arachnids, and other occasional soil macrofauna.Fig. 1: Analysis of the impact of disturbance on plant diversity in grasslands.a Location of grasslands sampled; b Banner and overview of main results of the project created in the platform iNaturalist/Biodiversity4All to register the sampled species; c Boxplots include data of the taxonomic diversity indices (plant species richness and Simpson Diversity Index) of sampled grasslands at three different perturbation levels: low, medium and high. Central lines represent median values, box limits indicate the upper and lower quartiles, whiskers correspond to 1.5 × the interquartile range above and below the upper and lower quartiles and points are the outliers. Boxplots with different letters indicate statistically significant differences among perturbation levels based on multiple pairwise comparisons.Full size imageThe results showed that the number of species (richness) decreased consistently with the level of perturbation. Simpson Diversity Index values increased, indicating low diversity values in highly perturbed herbaceous plant communities (Fig. 1c). Results revealed a trend towards an increase in the proportion of species with lower stature as perturbation increased. However, with no clear relationship with either biodiversity or perturbation. Finally, results indicated no clear relation of pollinator abundance or richness with plant richness and diversity, although field records relate a lower number of pollinators as wind intensity increased. In fact, pollinator sampling is extremely weather sensitive, which may have contributed to the lack of consistent relationships between pollinator diversity and perturbation.Box 1 Citizen science platforms and apps used for formal and informal educational activitiesiNaturalist (https://www.inaturalist.org/home): is a social network of naturalists, citizen scientists, and biologists that is based on mapping and sharing biodiversity observations. They describe themselves as “an online social network of people sharing biodiversity information in order to help each other learn about nature”. iNaturalist may be accessed via website or mobile app. Records are validated by the iNaturalist community. Observations reached approximately 110 million as of July 2022. This app allows the development of both open-access and registration-restricted projects. BioDiversity4All (https://www.biodiversity4all.org/) is a Portuguese biodiversity citizen science platform created by the Biodiversity for All Association. This platform was founded in 2010 and is currently linked to the “iNaturalist” network43. All the projects presented in this article were developed on the Biodiversity4All platform.Flora-on (https://flora-on.pt/): this portal contains occurrence data of vascular plants from the Portuguese flora collected by project collaborators (over 575,000 records as of July 2022). Flora-on was created by the Botanical Society of Portugal (SPBotânica), a Portuguese association devoted to the promotion and study of botany in Portugal. Botanists and naturalists provide most of the data, but occasional contributors are welcomed. Records are supervised by the portal editors, ensuring the dataset’s quality level. The portal includes stunning images of leaves, flowers, fruits, and other plant parts for 2299 of the 3300 taxa occurring in Portugal44. Additionally, the portal includes a powerful search engine that allows geographical, morphological, and taxonomical searches.LeafBite (https://zoegp.science/leafbyte): is a free, open-source iPhone app that measures total leaf area as well as consumed leaf area when herbivory is present45.Leaf-IT is a free and simple Android app created for scientific purposes. It was designed to measure leaf area under challenging field conditions. It has simple features for area calculation and data output, and can be used for ecological research and education46.Activity 2—Leaf trait assessment of shrub and tree speciesStudents were asked to assess three leaf traits Specific leaf area (SLA), Specific leaf mass (LMA), and Leaf Water Content (LWC) of two or three shrub or tree species. Each species should ideally fall into one of three functional groups known for their water adaptations, namely Hydrophytes, Mesophytes and Xerophytes. Students were challenged to choose charismatic Mediterranean species that grew nearby, such as Olea europaea, Nerium oleander or Phillyrea angustifolia. Alternatively, they could take the “Quercus challenge”, which involved ranking the Portuguese oak species based on their drought tolerance. A detailed protocol was developed to assist students for this purpose (Supplementary Data 2). In this protocol was demonstrated how to calculate the leaf area using the LeafBite and Leaf-IT apps (Box 1).The students calculated the SLA, LMA, and LWC of a total of 104 species (Supplementary Data 3) belonging to the main functional groups under study. Regarding the “Quercus challenge”, they were able to classify the six most representative oak species in Portugal and confirm the relationship among these indices and their tolerance to drought (Fig. 2).Fig. 2: Leaf trait assessment of shrub and tree species: Quercus challenge.Classification of Portuguese oak species regarding their drought tolerance (higher tolerance, left-up, lower tolerance right-down).Full size imageOne of the students, accomplished to present his own learning experience related to these activities at the XXIII Conference of the Environmental Research Network of Portuguese-speaking Nations – REALP, under the title “Plant Ecology during Confinement – A Digital Approach”.Activity 3—Evaluating the impact on the biodiversity of lawn management at the University of Lisbon campusAlthough, after the lockdown, practical classes returned to the laboratories and the field in 2021/22, we continued to use the iNaturalist/Biodiversity4All platform and the Flora-on website for biodiversity registering and identification, because of the success of the activities, as evidenced by the positive comments we received from students.The goal of this activity was to study the impact of lawn management on plant diversity and pollination on the University of Lisbon campus. To accomplish this, the students described the herbaceous communities and pollinators on four lawns (named C8, RL, RR, and TT) that had different management practices (mowing and irrigation). A comprehensive document with sampling guidelines was developed (Supplementary Data 4).The project Ecologia 2 Relvados 2022 registered 100 plant and 17 pollinator species (Fig. 3a). Given that the sampling took place during a cold and rainy week, which limited pollinator activity, the low number of pollinators registered was expectable (Lawson and Rands 2019). Following these analyses, the TT lawn (Fig. 3b), which had low levels of mowing and no watering, showed a significantly higher value of diversity, indicating it had the best management strategy for these systems (Fig. 3c), if the goal is to increase biodiversity.Fig. 3: Evaluating the impact on the biodiversity of lawn management at the University of Lisbon campus.a Banner and overview of main results of the project Ecologia 2 Relvados created in the platform iNaturalist/BioDiversity4All to register the sampled species; b Location of the lawns sampled in the Campus of the University of Lisbon; c Boxplots include data of the taxonomic diversity indices (plant species richness and Simpson Diversity Index) of sampled grasslands. Central lines represent median values, box limits indicate the upper and lower quartiles, whiskers correspond to 1.5 × the interquartile range above and below the upper and lower quartiles and points are the outliers. Boxplots with different letters indicate statistically significant differences among lawns based on multiple pairwise comparisons.Full size imageInformal education: BioBlitzesIntense biological surveys known as “BioBlitz” are carried out to record all organisms found in certain locations, such as cities, protected areas, or even entire countries. They are being used all over the world to collect and share georeferenced biodiversity data30. We developed two Plant Bioblitzes based on the BioDiversity4All/iNaturalist and Flora-on platforms. Social media, such as Facebook, Instagram, and Twitter, were used to promote these events and engage citizens (Fig. 4). The BioBlitzes were developed by SPBotânica in collaboration with BioDiversity4All.Fig. 4: Bioblitz I & II – Flora of Portugal.Posters created for the promotion of the two Flora of Portugal Bioblitzes.Full size imageBioblitz I & II – Flora of PortugalThe celebration of Fascination of Plants Day (18th of May) served as the backdrop for the organization of two-weekend Bioblitzes: Bioblitz Flora of Portugal I and Bioblitz Flora of Portugal II.In 2021, the Bioblitz was solely focused on project members, which meant that only those who had voluntarily joined the initiative could participate. In total, the 119 project members registered 4234 observations of 890 plant species. In contrast, the 2022 Bioblitz was an open project (no registration required). In total, the 323 observers made 6547 records of 1198 species. To evaluate the impact of the Bioblitz events, we compared the data registered in BioDiverstiy4All during the weekends of both events (2021 and 2022) with (i) the data registered in the platform during the equivalent weekends of 2019 and 2020 and (ii) also during the weekends before both Bioblitzes. The number of species, observations, and observers increased significantly from 2019 to 2020, 2021, and 2022, but, when comparing values from 2020 with 2021 and 2022, this rise was only verified during the Bioblitz weekends, proving the importance of Bioblitzes in this increase (Fig. 5).Fig. 5: Number of observations, species and observers registered on the BioDiversity4All/iNaturalist platform over equivalent weekends in 2019, 2020, 2021, and 2022.Numbers for 2021 and 2022 correspond to the weekends in which Bioblitzes I & II – Flora of Portugal were conducted, as well as previous ones.Full size image More

Tucker, C. et al. Nature 615, 80–86 (2023).Article 

Google Scholar 
Bayala, J. et al. Agric. Ecosyst. Environ. 205, 25–35 (2015).Article 

Google Scholar 
Keesstra, S. D. et al. Soil 2, 111–128 (2016).Article 

Google Scholar 
Dewi, S. et al. Int. J. Biodivers. Sci. Ecosyst. Serv. Mgmt 13, 312–329 (2017).Article 

Google Scholar 
Ahlström, A. et al. Science 348, 895–899 (2015).Article 
PubMed 

Google Scholar 
Poulter, B. et al. Nature 509, 600–603 (2014).Article 
PubMed 

Google Scholar 
Prăvălie, R. et al. Environ. Res. 201, 111580 (2021).Article 
PubMed 

Google Scholar 
Reij, C. P. & Smaling, E. M. A. Land Use Policy 25, 410–420 (2008).Article 

Google Scholar 
Zomer, R. J., Bossio, D. A., Trabucco, A., van Noordwijk, M. & Xu, J. Circ. Agric. Syst. 2, 3 (2022).Article 

Google Scholar 
Chomba, S., Sinclair, F., Savadogo, P., Bourne, M. & Lohbeck, M. Front. For. Glob. Change 3, 571679 (2020).Article 

Google Scholar 
Dakpogan, A., Bayala, J., Ouattara, I. & Ellington, E. in United for Lands: From National Coalitions to a Pipeline of Bankable Projects for the Great Green Wall 54–56 (United Nations, 2022).
Google Scholar 
Garrity, D. P. & Bayala, J. in Sustainable Development Through Trees on Farms: Agroforestry in its Fifth Decade (ed. van Noordwijk, M.) 153–175 (World Agroforestry, 2019).
Google Scholar 
Schnell, S., Kleinn, C. & Ståhl, G. Environ. Monit. Assess. 187, 600 (2015).Article 
PubMed 

Google Scholar  More

Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
IKI. The Bonn Challenge. https://www.bonnchallenge.org/ (2022).UNCCD. Land Degradation Neutrality. https://www.unccd.int/land-and-life/land-degradation-neutrality/overview (2022).Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).Article 
CAS 
PubMed 

Google Scholar 
Brancalion, P. H. S. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).Article 
CAS 
PubMed 

Google Scholar 
Erbaugh, J. T. et al. Global forest restoration and the importance of prioritizing local communities. Nat. Ecol. Evol. 4, 1472–1476 (2020).Article 
CAS 
PubMed 

Google Scholar 
Fleischman, F. et al. Restoration prioritization must be informed by marginalized people. Nature 607, E5–E6 (2022).Article 
CAS 
PubMed 

Google Scholar 
Chaturvedi, R. et al. Restoration Opportunities Atlas of India. www.india.restorationatlas.org/methodology (2022).McLain, R., Lawry, S., Guariguata, M. R. & Reed, J. Toward a tenure-responsive approach to forest landscape restoration: a proposed tenure diagnostic for assessing restoration opportunities. Land Use Policy 104, 103748 (2021).Article 

Google Scholar 
Binod, B., Bhattarcharjee, A. & Ishwar, N. M. Bonn Challenge and India: Progress on Restoration Efforts Across States and Landscapes (IUCN, 2018).Government of India. Aspirational Districts Phase 1 (vikaspedia, 2018).Government of India. Census of India. https://censusindia.gov.in/2011census/dchb/DCHB.html (2011).DeFries, R. et al. Land management can contribute to net zero. Science 376, 1163–1165 (2022).Article 
CAS 
PubMed 

Google Scholar 
Borah, B., Bhattacharya, A. & Ishwar, N. M. Bonn Challenge and India. Progress On Restoration Efforts Across States and Landscapes. https://www.bonnchallenge.org/pledges/india (2018).Gopalakrishna, T. et al. Existing land uses constrain climate change mitigation potential of forest restoration in India. Conserv. Lett. https://doi.org/10.1111/conl.12867 (2022).Dhyani, S. et al. Agroforestry to achieve global climate adaptation and mitigation targets: are South Asian countries sufficiently prepared? Forests 12, 303 (2021).Article 

Google Scholar 
Nerlekar, A. N. et al. Removal or utilization? Testing alternative approaches to the management of an invasive woody legume in an arid Indian grassland. Restor. Ecol. https://doi.org/10.1111/rec.13477 (2022).Coleman, E. A. et al. Limited effects of tree planting on forest canopy cover and rural livelihoods in Northern India. Nat Sustain 4, 997–1004 (2021).Article 

Google Scholar 
Ramprasad, V., Joglekar, A. & Fleischman, F. Plantations and pastoralists: afforestation activities make pastoralists in the Indian Himalaya vulnerable. Ecol. Soci. https://doi.org/10.5751/ES-11810-250401 (2020).DeFries, R. et al. Improved household living standards can restore dry tropical forests. Biotropica https://doi.org/10.1111/btp.12978 (2021).Lele, S., Khare, A. & Mokashi, S. Estimating and Mapping CFR Potential (ATREE, 2020).Agarwala, M. et al. Impact of biogas interventions on forest biomass and regeneration in southern India. Global Ecol. Conservation 11, 213–223 (2017).Article 

Google Scholar 
Menon, A. & Schmidt-Vogt, D. Effects of the COVID-19 pandemic on farmers and their responses: a study of three farming systems in Kerala. South India. Land 11, 144 (2022).
Google Scholar 
Fremout, T. et al. Diversity for Restoration (D4R): Guiding the selection of tree species and seed sources for climate‐resilient restoration of tropical forest landscapes. J. Appl. Ecol. 59, 664–679 (2022).Article 

Google Scholar 
Hughes, K. A. et al. Can restoration of the commons reduce rural vulnerability? A Quasi-experimental comparison of COVID-19 livelihood-based coping strategies among rural households in three Indian States. Int. J. Common. 16, 189 (2022).Article 

Google Scholar 
Madhusudan, M. D. & Vanak, A. Mapping the Distribution and Extent of India’s Semi-arid Open Natural Ecosystems. https://doi.org/10.1002/essoar.10507612.1 (2021).Vanak, A. T., Hiremath, A. J., Ganesh, T. & Rai, N. D. Filling in the (Forest) Blanks: the Past, Present and Future of India’s Savanna Grasslands (ATREE, 2017).Oxford Poverty & Human Development Initiative. Global Multidimensional Poverty Index 2018. The Most Detailed Picture to Date of the World’s Poorest People. https://ophi.org.uk/wp-content/uploads/G-MPI_2018_2ed_web.pdf (2018).Hijmans, R. J. et al. raster: Geographic Data Analysis and Modeling. https://rspatial.org/raster (2023).Bivand, R. et al. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. https://cran.r-project.org/web/packages/rgdal/index.html (2023).QGIS Development Team. QGIS Geographic Information System (Open Source Geospatial Foundation Project, 2022). More

Campbell, J. L. & Laudon, H. Carbon response to changing winter conditions in northern regions: current understanding and emerging research needs. Environ. Rev. 27, 545–566 (2019).Article 

Google Scholar 
Groffman, P. M. et al. Effects of mild winter freezing on soil nitrogen and carbon dynamics in a northern hardwood forest. Biogeochemistry 56, 191–213 (2001).Article 
CAS 

Google Scholar 
Song, C. et al. Large methane emission upon spring thaw from natural wetlands in the northern permafrost region. Environ. Res. Lett. 7, 034009 (2012).Article 

Google Scholar 
Chen, H. et al. Methane emissions during different freezing-thawing periods from a fen on the Qinghai-Tibetan Plateau: Four years of measurements. Agric. Ecosyst. Environ. 297, 108279 (2021).
Google Scholar 
Bao, T., Xu, X., Jia, G., Billesbach, D. P. & Sullivan, R. C. Much stronger tundra methane emissions during autumn freeze than spring thaw. Glob. Chang. Biol. 27, 376–387 (2021).Article 
CAS 

Google Scholar 
Yu, J. et al. Enhanced net formations of nitrous oxide and methane underneath the frozen soil in Sanjiang wetland, northeastern China. J. Geophys. Res 112, D07111 (2007).Article 

Google Scholar 
Kreyling, J., Peršoh, D., Werner, S., Benzenberg, M. & Wöllecke, J. Short-term impacts of soil freeze-thaw cycles on roots and root-associated fungi of Holcus lanatus and Calluna vulgaris. Plant Soil 353, 19–31 (2012).Article 
CAS 

Google Scholar 
Min, K., Chen, K. & Arora, R. Effect of short-term versus prolonged freezing on freeze–thaw injury and post-thaw recovery in spinach: Importance in laboratory freeze–thaw protocols. Environ. Exp. Bot. 106, 124–131 (2014).Article 
CAS 

Google Scholar 
Kennedy, A. Photosynthetic response of the Antarctic moss Polytrichum alpestre Hoppe to low temperatures and freeze-thaw stress. Polar Biol. 13, 271–279 (1993).Article 

Google Scholar 
Sanders-DeMott, R., Sorensen, P. O., Reinmann, A. B. & Templer, P. H. Growing season warming and winter freeze–thaw cycles reduce root nitrogen uptake capacity and increase soil solution nitrogen in a northern forest ecosystem. Biogeochemistry 137, 337–349 (2018).Article 
CAS 

Google Scholar 
Vankoughnett, M. R. & Henry, H. A. L. Soil freezing and N deposition: transient vs. multi-year effects on extractable C and N, potential trace gas losses and microbial biomass. Soil Biol. Biochem. 77, 170–178 (2014).Article 
CAS 

Google Scholar 
Kreyling, J., Beierkuhnlein, C., Pritsch, K., Schloter, M. & Jentsch, A. Recurrent soil freeze-thaw cycles enhance grassland productivity. New Phytol. 177, 938–945 (2008).Article 

Google Scholar 
Song, Y., Zou, Y., Wang, G. & Yu, X. Altered soil carbon and nitrogen cycles due to the freeze-thaw effect: a meta-analysis. Soil Biol. Biochem. 109, 35–49 (2017).Article 
CAS 

Google Scholar 
Vankoughnett, M. R. & Henry, H. A. L. Soil freezing and N deposition: transient vs multi-year effects on plant productivity and relative species abundance. New Phytol. 202, 1277–1285 (2014).Article 
CAS 

Google Scholar 
Luan, Z. & Cao, H. Response of fine root growth and nitrogen and phosphorus contents to soil freezing in Calamagrostis angustifolia wetland, Sanjiang Plain, Northeast China. J. Food Agric. Environ. 10, 1495–1499 (2012).
Google Scholar 
Garcia, M. O. et al. Soil microbes trade-off biogeochemical cycling for stress tolerance traits in response to year-round climate change. Front. Microbiol. 11, 616 (2020).Article 

Google Scholar 
Tang, H., Bai, J., Chen, F., Liu, Y. & Lou, Y. Effects of salinity and temperature on tuber sprouting and growth of Schoenoplectus nipponicus. Ecosphere 12, e03448 (2021).Article 

Google Scholar 
Satyanti, A., Guja, L. K. & Nicotra, A. B. Temperature variability drives within-species variation in germination strategy and establishment characteristics of an alpine herb. Oecologia 189, 407–419 (2019).Article 

Google Scholar 
Harrison, J. L., Schultz, K., Blagden, M., Sanders-DeMott, R. & Templer, P. H. Growing season soil warming may counteract trend of nitrogen oligotrophication in a northern hardwood forest. Biogeochemistry 151, 139–152 (2020).Article 
CAS 

Google Scholar 
Semenchuk, P. R. et al. Deeper snow alters soil nutrient availability and leaf nutrient status in high Arctic tundra. Biogeochemistry 124, 81–94 (2015).Article 

Google Scholar 
Song, Y., Zou, Y., Wang, G. & Yu, X. Stimulation of nitrogen turnover due to nutrients release from aggregates affected by freeze-thaw in wetland soils. Phys. Chem. Earth 97, 3–11 (2017).Article 

Google Scholar 
Keith, D. A., Rodoreda, S. & Bedward, M. Decadal change in wetland-woodland boundaries during the late 20th century reflects climatic trends. Glob. Chang. Biol. 16, 2300–2306 (2010).Article 

Google Scholar 
Wang, J., Song, C., Hou, A. & Xi, F. Methane emission potential from freshwater marsh soils of Northeast China: response to simulated freezing-thawing cycles. Wetlands 37, 437–445 (2017).Article 

Google Scholar 
Yu, X. et al. Wetland plant litter decomposition occurring during the freeze season under disparate flooded conditions. Sci. Total Environ. 706, 136091 (2020).Article 
CAS 

Google Scholar 
Dong, X. et al. Variations in active layer soil hydrothermal dynamics of typical wetlands in permafrost region in the Great Hing’an Mountains, northeast China. Ecol. Indic. 129, 107880 (2021).Article 

Google Scholar 
Li, Y. et al. Freeze-thaw cycles increase the mobility of phosphorus fractions based on soil aggregate in restored wetlands. CATENA 209, 105846 (2022).Article 
CAS 

Google Scholar 
Song, C., Zhang, J., Wang, Y., Wang, Y. & Zhao, Z. Emission of CO2, CH4 and N2O from freshwater marsh in northeast of China. J. Environ. Manage. 88, 428–436 (2008).Article 
CAS 

Google Scholar 
Wang, G., Liu, J., Zhao, H., Wang, J. & Yu, J. Phosphorus sorption by freeze–thaw treated wetland soils derived from a winter-cold zone (Sanjiang Plain, Northeast China). Geoderma 138, 153–161 (2007).Article 
CAS 

Google Scholar 
Ji, X., Liu, M., Yang, J. & Feng, F. Meta-analysis of the impact of freeze–thaw cycles on soil microbial diversity and C and N dynamics. Soil Biol. Biochem. 168, 108608 (2022).Article 
CAS 

Google Scholar 
Ren, J. et al. Shifts in soil bacterial and archaeal communities during freeze-thaw cycles in a seasonal frozen marsh, Northeast China. Sci. Total. Environ. 625, 782–791 (2018).Article 
CAS 

Google Scholar 
Mitsch, W. J. & Gosselink, J. G. Wetlands. 5th edn (Wiley, Hoboken, New Jersey, 2015).Yu, X., Zou, Y., Jiang, M., Lu, X. & Wang, G. Response of soil constituents to freeze–thaw cycles in wetland soil solution. Soil Biol. Biochem. 43, 1308–1320 (2011).Article 
CAS 

Google Scholar 
Sawicka, J. E., Robador, A., Hubert, C., Jørgensen, B. B. & Bruchert, V. Effects of freeze-thaw cycles on anaerobic microbial processes in an Arctic intertidal mud flat. ISME J 4, 585–594 (2010).Article 
CAS 

Google Scholar 
Song, Y. The Freeze-thaw Effect On Soil Mineralization Between Various Moisture States Of Wetlands. Master of Natural Science thesis (Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 2017).Mason, R. E. et al. Evidence, causes, and consequences of declining nitrogen availability in terrestrial ecosystems. Science 376, eabh3767 (2022).Article 
CAS 

Google Scholar 
Koerselman, W. & Meuleman, A. F. M. The vegetation N:P ratio: a new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 33, 1441–1450 (1996).Article 

Google Scholar 
Yang, K. et al. Immediate and carry-over effects of increased soil frost on soil respiration and microbial activity in a spruce forest. Soil Biol. Biochem. 135, 51–59 (2019).Article 
CAS 

Google Scholar 
Lambers, H., Chapin, F. S. I. & Pons, T. L. Plant Physiological Ecology. 2nd edn (Springer, 2008).Ott, J. P., Klimešová, J. & Hartnett, D. C. The ecology and significance of below-ground bud banks in plants. Ann. Bot. 123, 1099–1118 (2019).Article 

Google Scholar 
Pedersen, E. P., Elberling, B. & Michelsen, A. Foraging deeply: depth‐specific plant nitrogen uptake in response to climate‐induced N‐release and permafrost thaw in the High Arctic. Glob. Chang. Biol. 26, 6523–6536 (2020).Article 

Google Scholar 
Dyer, A.R. Maternal and sibling factors induce dormancy in dimorphic seed pairs of Aegilops triuncialis. Plant Ecol. 172, 211–218 (2004).Article 

Google Scholar 
Renne, I. J. et al. Eavesdropping in plants: delayed germination via biochemical recognition. J. Ecol. 102, 86–94 (2014).Article 

Google Scholar 
Li, H. Eco-physiological Responding Characteristics of Scirpus Planiculmis on Coupling of Water Table Depths and Salinity in Momoge Wetland. Master Dissertation thesis, University of Chinese Academy of Sciences (Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 2013).Yu, D. Responses of Sprouting and Growth to Environmental Factors in Bolboschoenus Planiculmis. Master Dissertation thesis (Harbin Normal University, 2022).Zhang, C., Willis, C. G., Donohue, K., Ma, Z. & Du, G. Effects of environment, life-history and phylogeny on germination strategy of 789 angiosperms species on the eastern Tibetan Plateau. Ecol. Indic. 129, 107974 (2021).Article 

Google Scholar 
Hoyle, G. L. et al. Seed germination strategies: an evolutionary trajectory independent of vegetative functional traits. Front. Plant Sci. 6, 731 (2015).Article 

Google Scholar 
Mercer, K. L., Alexander, H. M. & Snow, A. A. Selection on seedling emergence timing and size in an annual plant, Helianthus annuus (common sunflower, Asteraceae). Am. J. Bot. 98, 975–985 (2011).Article 

Google Scholar 
Cui, Y. et al. Ecoenzymatic stoichiometry reveals microbial phosphorus limitation decreases the nitrogen cycling potential of soils in semi-arid agricultural ecosystems. Soil Tillage. Res. 197, 104463 (2020).Article 

Google Scholar 
Ye, Z. et al. Ecoenzymatic stoichiometry reflects the regulation of microbial carbon and nitrogen limitation on soil nitrogen cycling potential in arid agriculture ecosystems. J. Soils Sediments 22, 1228–1241 (2022).Article 
CAS 

Google Scholar 
Pan, Y. et al. Drivers of plant traits that allow survival in wetlands. Funct. Ecol. 34, 956–967 (2020).Article 

Google Scholar 
Pezeshki, S. R. Wetland plant responses to soil flooding. Environ. Exp. Bot. 46, 299–312 (2001).Article 

Google Scholar 
Zheng, S. Soil Water-heat Process and Nitrogen Transformation During Freezing and Thawing Period in Wetland of Momoge. Master Dissertation thesis (Jilin Agricultural University, 2019).An, Y., Gao, Y., Zhang, Y., Tong, S. & Liu, X. Early establishment of Suaeda salsa population as affected by soil moisture and salinity: implications for pioneer species introduction in saline-sodic wetlands in Songnen Plain, China. Ecol. Indic. 107, 105654 (2019).Article 
CAS 

Google Scholar 
FAO/IIASA/ISRIC/ISS-CAS/JRC. Harmonized World Soil Database (version 1.2). (FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012).Jiang, M., Lu, X., Xu, L. & Yang, Q. Estimation on benefit of latent soil nutrient in melmeg reserve wetlands. J. Nat. Resour 20, 279–285 (2005).
Google Scholar 
Wang, Y. & Zhang, S. The pH distribution and soil nutrient characteristic at different habitats-a case study of Momoge Wetland. J. Anhui Agric. Sci. 50, 135–139 (2022).
Google Scholar 
Hao, M. The Ecological Restoration Research on Momoge Scripus Planiculmis Wetland. Master Dissertation thesis, University of Chinese Academy of Sciences (Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 2016).Ma, H. et al. Effect of nitrate supply on the facilitation between two salt-marsh plants (Suaeda salsa and Scirpus planiculmis). J. Plant. Ecol. 13, 204–212 (2020).Article 

Google Scholar 
Liu, B. et al. Effects of burial depth and water depth on seedling emergence and early growth of Scirpus planiculmis Fr. Schmidt. Ecol. Eng. 87, 30–33 (2016).Article 

Google Scholar 
Zhang, L., Zhang, G., Li, H. & Sun, G. Eco-physiological responses of Scirpus planiculmis to different water-salt conditions in Momoge wetland. Pol. J. Environ. Stud. 23, 1813–1820 (2014).
Google Scholar 
Sosnová, M., van Diggelen, R. & Klimešová, J. Distribution of clonal growth forms in wetlands. Aquat. Bot. 92, 33–39 (2010).Article 

Google Scholar 
Lu, R. Analytical Methods of Soil Agrochemistry (China Agricultural Science and Technology Press, 2000).Bao, S. Soil and Agricultural Chemistry Analysis. 3 edn. (China Agriculture Press, 2000).Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).Article 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 

Google Scholar 
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).Article 
CAS 

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

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
CAS 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).Article 
CAS 

Google Scholar 
Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 
CAS 

Google Scholar 
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).Article 
CAS 

Google Scholar 
Zhu, W., Lomsadze, A. & Borodovsky, M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 38, e132 (2010).Article 

Google Scholar 
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).Article 
CAS 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).Article 
CAS 

Google Scholar 
Lauro, F. M. et al. An integrative study of a meromictic lake ecosystem in Antarctica. ISME J 5, 879–895 (2011).Article 
CAS 

Google Scholar 
Shen, M. et al. Trophic status is associated with community structure and metabolic potential of planktonic microbiota in Plateau lakes. Front. Microbiol. 10, 2560 (2019).Article 

Google Scholar 
Kieft, B. et al. Microbial community structure-function relationships in Yaquina Bay estuary reveal spatially distinct carbon and nitrogen cycling capacities. Front. Microbiol. 9, 1282 (2018).Article 

Google Scholar 
Kay, M. Effect Sizes with ART (2021).Mangiafico, S. S. Summary and Analysis of Extension Program Evaluation in R, version 1.18.8 https://rcompanion.org/handbook/ (2016).R Core Team R: A Language and Environment for Statistical Computing (2020).Fox, J. & Weisberg, S. An R Companion to Applied Regression. 3rd edn (Thousand Oaks, Sage, CA, 2019).Kay, M., Elkin, L. A., Higgins, J. J. & Wobbrock, J. O. ARTool: Aligned Rank Transform for Nonparametric Factorial ANOVAs. R package version 0.11.1. https://doi.org/10.5281/zenodo.594511 (2021).Wobbrock, J. O., Findlate, L., Gergle, D. & Higgins, J. J. The aligned rank transform for nonparametric factorial analyses using only anova procedures. 29th Annual Chi Conference on Human Factors in Computing Systems (CHI 2011), p. 143-146. https://doi.org/10.1145/1978942.1978963 (2011).Elkin, L. A., Kay, M., Higgins, J. J. & Wobbrock, J. O. An aligned rank transform procedure for multifactor contrast Tests. Proceedings of the ACM Symposium on User Interface Software and Technology (UIST 2021), p. 754-768. https://doi.org/10.1145/3472749.3474784 (2021).Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.6.3. (2021).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (2016).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).Revelle, W. psych: Procedures for Psychological, Psychometric, and Personality Research, Northwestern University, Evanston, Illinois, USA, R package version 2.2.9 (2022).Wei, T. & Simko, V. R package ‘corrplot’: Visualization of a Correlation Matrix (Version 0.90) (2021). More