Ecology

Electrohydrogenesis effluent as a potential biofertilizerTo characterize the electrohydrogenesis left-over residues as potential biofertilizers, the sample from the operating reactors was performed a 16S rRNA sequencing test, and interestingly, the results revealed that the bio-electrohydrogenesis effluent was enriched with various microorganisms including plant growth-promoting microbes that display biofertilizer-like features. Among the well-known plant-promoting bacterial genera observed in DF-MEC residues included Azospirillum, Mycobacterium, Chryseobacterium, Paenibacillus, Rhizobacter, Pseudomonas, Achromobacter, Bradyrhizobium, Actinomyces, Sphingomonas, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, Gordonia, Rhodococcus, Bacillus, Methylobacterium-Methylorubrum, Microbacterium, Flavobacterium, Devosia, Acinetobacter, Mesorhizobium, Enterobacter, Aeromonas, Beijerinckia, etc.24,25,26 (Fig. 2). Lots of investigations working on the feasibility of using biofertilizers other than chemical fertilizers have revealed that those aforementioned microbes play a major role in providing the required nutrients for enhanced crop yield.Figure 2The abundance of the Plant growth-promoting bacteria (genus level) detected from the DF-MEC digestate (%).Full size imageNitrogen-fixing microorganismsThe detected nitrogen-fixing microorganisms from the electrohydrogenesis effluent include Azospirillum sp. (0.11 ± 0.02%), rhizobia (Rhizobium (0.058 ± 0.02%), Bradyrhizobium (0.11 ± 0.04%), and Mesorhizobium (0.1 ± 0.03%)), and Beijerinckia (0.08 ± 0.03%) (Fig. 2) and were repeatedly reported for their superior contribution to the plants’ nitrogen requirements through biological nitrogen fixation, which is an important component of sustainable agriculture25. Although the atmosphere counts about 78% N2, it couldn’t be used by plants in its natural state. Prior to getting used by plants, it needs to be converted to ammonia, which is the readily assimilable form of nitrogen by plants/or crops via a biological nitrogen fixation mechanism25. The biological Nitrogen fixation mechanism is summarized in Fig. 3.Figure 3Mechanism of nitrogen fixation bio-catalyzed by nitrogenase enzyme. The plant growth-promoting bacteria produce nitrogenase which is a complex enzyme consisting of dinitrogenase reductase and dinitrogenase. This complex enzyme plays a major role in molecular N2 fixation. Dinitrogenase reductase provides electrons and dinitrogenase uses those electrons to reduce N2 to NH3. However, oxygen is a potential threat to this process since it has the ability to get bound to the enzyme complex and make it inactive and consequently inhibit the process. Interestingly, bacterial leghemoglobin has a strong affinity for O2 and thus gets bound to free oxygen more strongly and effectively to suppress the available oxygen effects on the whole process of nitrogen fixation.Full size imagePhosphate-solubilizing microorganismsFurthermore, various phosphate-solubilizing and mineralizing strains were also found in bioelectrohydrogenesis residues collected from our DF-MEC integrated reactors. Among those microorganisms with the ability to solubilize/metabolize the insoluble inorganic phosphorus, the dominant bacterial genera included Pseudomonas (0.65 ± 0.15%), Bacillus (0.44 ± 0.11%), Rhodococcus (0.04 ± 0.009%), Rhizobium (0.05 ± 0.02%), Microbacterium sp. (0.04 ± 0.01%), Achromobacter (0.16 ± 0.07%), and Flavobacterium (0.058 ± 0.014%) (Fig. 2). Though enormous amounts of phosphorus are available in the soil, its high portion never contributes to plant growth in its primitive state, unless it is bio-transformed into absorbable forms including monobasic and dibasic. Microbial phosphate solubilizing mechanisms are well described in Fig. 4.Figure 4Inorganic phosphorus solubilization by phosphate-solubilizing rhizobacteria. A bacterium solubilizes inorganic phosphorus through the action of low molecular weight organic acids such as gluconic and citric acids. The hydroxyl (OH) and carboxyl (COOH) groups of these acids chelate the cations bound to phosphate and thus convert insoluble phosphorus into a soluble organic form. The mineralization of soluble phosphorus occurs by synthesizing different phosphatases which catalyze the hydrolysis process. When plants incorporate these solubilized and mineralized phosphorus molecules, eventually, overall plant growth and crop yield significantly increase.Full size imagePhytohormone-producing microorganismsIn this current work, the electrohydrogenesis effluent also contained bacterial genera such as Mycobacterium (0.77 ± 0.18%), Allorhizobium (0.05 ± 0.02%), Pararhizobium (0.05 ± 0.02%), Paenibacillus (1.18 ± 0.24%), Bradyrhizobium (0.11 ± 0.04%), Rhizobium (0.05 ± 0.02%), Acinetobacter (0.14 ± 0.02%), and Azospirillum (0.11 ± 0.025%) (Fig. 2) that have the ability to synthesize indole-3-acetic acid/indole acetic acid (IAA) through indole-3-pyruvic acid and indole-3-acetic aldehyde25. IAA is a well-known type of phytohormone that enhances plant/crop growth. Particularly, Azospirillum sp., also produce various phytohormones namely cytokinins, gibberellins, ethylene, abscisic acid and salicylic acid, auxins, vitamins such as niacin, pantothenic acid, and thiamine. The conceptional model delineating the positive effects of inoculation with Azospirillum sp. a phytohormones-producer plant growth-promoting rhizobacteria and its detailed functions on plant growth are summarized and illustrated in Fig. S1. Therefore, the existence of those rhizobacteria in the bioelectrohydrogenesis residues further implies the suitability of considering the DF-MEC left-over residues as potential biofertilizers.Heavy metals-bioremediating microorganismsSome other bacterial genera with the ability to bioremediate the heavy metal toxicity were also found within the bioelectrohydrogenesis left-over residues as well. Among the detected plant growth-promoting bacterial genera; Rhizobium (0.058 ± 0.023%), Mesorhizobium (0.1 ± 0.026%), Bradyrhizobium (0.11 ± 0.04%), Pseudomonas (0.65 ± 0.15%), and Achromobacter (0.16 ± 0.077%) were reported for their key contribution to alleviate the toxicity of the heavy metals via bioremediation process and improve the soil quality for a relief plant development26 (Fig. 2). Other detected heavy metals-bioremediating microorganisms’ species were Chryseobacterium sp. (0.08 ± 0.007%), Azospirillum (0.11 ± 0.02%), Bacillus (0.44 ± 0.11%), Enterobacter (8.57 ± 0.9%), Gordonia (0.06 ± 0.02%), Paenibacillus (1.18 ± 0.24%), Pseudomonas (0.65 ± 0.15%), and Actinomycetes (0.36 ± 0.05%) that either use microbial siderophores or enzymatic biodegradation process.Electrohydrogenesis left-over residues as a potential source of essential elements for plant growthAs aforementioned in “Materials and methods” section, the electrohydrogenesis left-over residues contained diverse microbial communities that degraded the MEC substrate and generate biogas and inorganic compounds. Moreover, it has been reported that those inorganic nutrients are generally available in fermentation effluent in readily plant-utilizable formats owing to substrate mineralization27. Beside detecting various plant growth-promoting microorganisms in the electrohydrogenesis effluent, a larger number of mineral elements essential for promoted growth and development of crop plants were also investigated and analyzed from the residues. The detected primary and secondary macro-elements’ concentrations in the residues were arranged in decreasing order as follows P  > S  > Na  > K  > N  > Ca  > Mg. Interestingly the findings show that the residues abundantly contained Phosphorus (2.766 × 103 mg/L), Nitrogen (274 mg/L), Potassium (282 mg/L), Calcium (17.66 mg/L), Magnesium (16.3 mg/L), Sulfur (1.225 × 103 mg/L), and Sodium (294.3 mg/L) which are well known as macro-nutrients needed in larger amounts for enhanced plant/ crop growth (Fig. 5).Figure 5Macro-, and micronutrients detected from the bio-electrohydrogenesis left-over residues (mg/L).Full size imageMoreover, small amounts of the microelements including Ni, Pb, Zn, Cu, Cr, Hg, Cd were also found in the electrohydrogenesis residues, and consistently these elements are generally required in small quantities for the development of plants (Fig. 5), otherwise, their high concentrations are toxic for the plant cells thus suppress or inhibit plant growth. The detected concentrations for the main microelements in this current research ranged only from 0.36 to 9.6 × 10–5 mg/L and were all reported to play fundamental roles in plant metabolic reactions.Cultivation of the leguminous crops using electrohydrogenesis left-over residues as fertilizerAfter evaluating the plant-growth promoting bacterial communities and the macro- and micronutrients required for plant/crop growth in the electrohydrogenesis left-over residues, the latter was directly used as fertilizer to grow three different plant species including tomato, chili, and brinjal as afore-described in the “Materials and methods” section. To access the potentials of the electrohydrogenesis effluent as fertilizer, the plants grown in the soil amended with the effluent (Soil + Effluent), were directly compared with their corresponding control plants (Soil + water). The results indicated that at the end of 1st month, the plants with effluent grew faster and generated a good amount of branching than the control plants (see Fig. 6), possibly due to the availability of both microbial species with bio-fertilizing aspects and micro-and macronutrients in the effluent.Figure 6Analysis of the plant growth at the end of the 1st month of cultivation. (a) Tomato in soil with effluent, and its control without effluent (b); (c) Chilli grown in soil with effluent, and its control without effluent (d); and (e) brinjal grown in soil with effluent, and its corresponding control grown without effluent (f) (after 2 months).Full size imageFor instance, tomato (Solanum lycopersicum L.) and chilli (Capsicum annuum L.) height in soil + electrohydrogenesis effluent was ~ 36.9 ± 2.1 cm and ~ 32.6 ± 0.8 cm respectively which was ~ 2.03 and ~ 1.2 times the height of their corresponding plant species in the control protocol, respectively (see Fig. 7). However, the brinjal species (Solanum melongena L.) didn’t show any remarkable height differences in both protocols after a month of cultivation (data not shown), probably due to their low adaptative characteristics to the new environment. However, after the 2nd month, the brinjal height in soil + effluent became 2.7 times that of the brinjal control cultivated without effluent (see Fig. 6e,f). Moreover, both the number of the plants’ leaves and their length in plants cultivated in soil + effluent, were remarkably higher than in plants grown without the supply of the effluent.Figure 7Daily plant growth analysis within one month of cultivation. (a) Tomato growth monitoring, (b) Chili growth analysis.Full size imageAt the end of the 3rd month, the plants in soil + electrohydrogenesis effluent generated more fruit with big size than the control plants (see Fig. 8), but the tomato (Solanum lycopersicum L.) didn’t generate fruits in both protocols at that time probably due to the high weather temperature that inhibitory affected its continuous growth, as previously reported that tomato species are generally so sensitive to temperature change28,29. The final yield was evaluated in terms of the size and number of fruits per cultivated plant. Chili cultivated in soil with MEC effluent generated 3 fruits/plant and its corresponding control without effluent produced only 1 fruit/plant. The chili fruit size in soil + effluent was 16 cm, approximately 18.7% higher than its corresponding control. Moreover, at the time of collecting data, the brinjal plant cultivated in soil with MEC effluent generated brinjal fruits whereas its corresponding cultivated without electrohydrogenesis effluent started flowering (see Fig. 8). These further indicate the significant contribution of the electrohydrogenesis effluent in speeding up the plant growth. Herein, the electrohydrogenesis left-over residues have notably improved the soil quality and significantly promoted the plants’ phenology characterized by plant growth, the generation of new leaves, flowering, and the production of fruits.Figure 8Analysis of plant growth characterized by the flowering and fruiting process at the end of the 3 months. (a) Chili grown in soil with effluent, and its control without effluent (b); (c) brinjal grown in soil with effluent and its corresponding control grown without effluent (d).Full size image More

Our findings indicated that mine water had a certain inhibitory effect on ryegrass seed germination, and the intensity of this inhibitory effect increased with increased mine water proportion. These effects were mainly reflected as changes in germination potential. Concretely, irrigation with mine water prolonged the germination of ryegrass seeds but had no significant effect on germination rate. Min Zhu et al. found that recycled water inhibited the seed germination of turfgrass, and this effect became more notorious when the concentration of reclaimed water increased. This was likely because the water contained salt ions, heavy metal ions, and E. coli, all of which are known to affect seed germination22. The mine water was taken from the Laohutai mining area, where the water composition and quality are good. Therefore, mine water did not significantly affect seed germination and the seeds maybe germinate normally if given sufficient time.The physiological and photosynthetic characteristics of ryegrass were impacted by the mine water, and the intensity of inhibition increased with higher mine water proportions. When the ratio of mine water to clean water reached a certain proportion (1:2, A1 and B1), the physiological and growth characteristics of ryegrass were improved to a certain extent. When only mine water was used for irrigation, the indices were significantly suppressed. In contrast, mixing clear water with mine water for irrigation promoted the physiological characteristics of the plants, as well as photosynthesis. This was likely because the mineral content of mine water is higher. However, mine water not only contains elements needed for plant growth but also some elements and ions that have inhibitory effects on plant growth. Therefore, the quality of ryegrass growth were suppressed when irrigating only with mine water. In contrast, after mixing the mine water with clean water, the concentrations of certain substances that produce adverse effects are diluted, and the mixed irrigation water promoted ryegrass growth in appropriate proportion.A certain concentration of heavy metal elements will affect the absorption of essential elements by plants and produce antagonism, and high concentration can directly lead to plant death. Heavy metal stress affects chlorophyll content through two aspects: Heavy metal destroys enzymes needed for chlorophyll synthesis, affects plant chlorophyll synthesis, and then inhibits plant photosynthesis23. The second is the destruction of chloroplast structure and cell membrane24,25,26. In the treatment of high concentrations of heavy metals, the chlorophyll content of plants is significantly reduced due to the inhibition of chlorophylase or aminolevulinic acid dehydrase, thus inhibiting plant photosynthesis27. The heavy metal threat forcing stimulates the formation of reactive oxygen species that convert fatty acids into toxic lipid peroxides, which damage to plant cells28,29,30. Heavy metal stress can induce a lot of activity in plants sexual oxygen and inhibit the normal metabolism of plants, causing membrane lipid peroxidation and increased plasma membrane permeability31, 32. Low concentration of heavy metal stress will stimulate the protective mechanism of plants, so low concentration of stimulation will not damage plants, on the contrary, may help plant growth. Heavy metal stress causes water loss in plants, and a certain amount of proline can be produced to regulate the water balance of plant cells and reduce the damage degree of plant cells33. SOD, POD and CATT are important antioxidant enzymes in plants, which can scavenge excessive free radicals. The synergistic action of three enzymes can protect plants from free radical damage. When the concentration of heavy metals was low, the activity of protective enzymes increased under the induction of reactive oxygen radicals. However, with the increase of stress degree, the activities of SOD, POD and CAT decreased, which eventually led to the persecution of plant cells34. These conclusions are consistent with the results of this paper. When mine water was mixed with clear water at a ratio of 1:2, heavy metal stress stimulated the protective mechanism of ryegrass most appropriately, and improved plant quality and resistance. On the contrary, when the proportion of mine water increased, the physiological characteristics and quality of ryegrass plants were inhibited to different degrees.Precious Nneka Amori et al. studied physiological traits of leaves and Silverbeet using treated wastewater, the results show that the biomass of plants watered with only the treated wastewater were more than 50% higher than the yield in tap water control and plants exhibited high degree of root foraging1. Libutti et al. irrigated tomato and broccoli with purified agro-industrial effluent and reported that yield and quality traits of agricultural products were not affected35. Radish was grown using a reclaimed synthetic textile wastewater treated in an anoxic-aerobic photobioreactor, and the dry weight, leaf number and leaf area of plant harvest were 49, 19.2 and 62% higher than the growth performance in freshwater irrigation36. FU et al. studied four native Chenopodiaceae plants of Halogeton glomeratus, Kochia scoparia, Suaeda glauca and Chenopodium glaucum in Jinchang area northwest China, from their changes of net photosynthetic rate (Pn), Stomatal conductance (Gs), transpiration rate (Tr). chlorophyll content (Chl), malondialdehyde (MDA), soluble protein (SP), proline (Pro) and antioxidant enzymes activity under the treatment of farmland soil (T1) and sedendary soil mixed with tailing (1:1, T2), they concluded that under T2 treatment, Pn, Gs, and Tr of Halogeton glomeratus and Kochia scoparia were decreased , the other six indexes were increased significantly. Gs, Tr, MDA, Pro, and SOD increased, yet CAT, Chl and Pn of Suaeda glauca decreased significantly, respectively. Pn, Gs, and Tr of Chenopodium glaucum decreased significantly, while SP, POD increased significantly37. Our results also indicated that mine water irrigation had significant effects on soil characteristics. At higher mine water ratios, the soil conductivity increased exponentially, the pH decreased gradually, the content of K+, Na+, Ca2+ and Mg2+ increased, and the content of N, P and K also increased gradually. In contrast, the clean water and mine water mixture had little effect on the soil properties. This was because the salt and metal ions in mine water migrate to the soil during the irrigation process, which significantly changes the soil properties. As a result, the concentration of salt in the soil increased and soil acidity also increased. After mixing with clean water, the concentration of salt decreases, and the influence on the soil matrix weakened. These results also indicated that the growth, physiological, and photosynthetic effects of ryegrass in the pot experiments were better than those in soilless culture, because there were many other organic materials and inorganic ions in soil that could promote growth, whereas the plants in the hydroponic system lacked other nutrients that benefit plant growth. Many existing studies have shown that mine drainage or other wastewater can improve the quality and yield of one or more kinds of plants to different degrees after certain treatment, but some studies also show that the reclaimed water used for irrigation will cause harm to plants, soil and even human health.Jinfang Yang et al. reported that long-term irrigation with mine water significantly reduced the soil respiration rate and soil enzyme activity. Mine water irrigation also significantly inhibited wheat plant height, leaf area, chlorophyll content, and photosynthetic rate, and wheat production was also markedly reduced38. Jianjun Cha found that acidic mining waste water can reduce the pH of the soil profile and increase its electrical conductivity39. Junhao Qin et al. found that if treated mine water is used as an irrigation water source, acidic substances may still be introduced into the soil. This inhibits plant growth and may also enhance leaching of some trace elements in the soil to shallow aquifers, resulting in groundwater pollution40, 41. The results of this study are consistent with the above conclusions, that is, directly irrigating with mine water can significantly inhibit plant growth and photosynthesis, thus affecting the quality of ryegrass plants. MA et al. studied the effects of irrigation with mine wastewater on the physiological characters and heavy metals accumulation of winter wheat. It shows that irrigation with mine wastewater had negative effects on the winter wheat growth and grain yield. At anthesis stage, the leaf area, dry mass per stem, root activity and net photosynthetic rate of winter wheat in treatments were significantly lower and the plant height and leaf chlorophyll content was decreased. In addition, the heavy metals (Cr, Pb, Cu and Zn) contents in the grain of winter wheat under mine wastewater irrigation were significantly higher than those in control, it suggested that the irrigation with mine water could result in the heavy metals accumulation in wheat grain42. A large number of studies have shown that direct use of mine water for irrigation will have a negative impact on soil and plants, but this study found that after a certain processing of mine water (mine water was mixed with clear water in a ratio of 1:2) used for irrigation does not significantly alter soil properties, but can increase plant yield and quality, it will be meaningful to mine water reuse, soil utilization around the mining area and the agriculture.The conclusions of this study are based on mine water from Fushun mining area in Northeast China, but the effects of mine water on plants from other mining areas are uncertain. At the same time, ryegrass, a cold-season turfgrass, is only selected in this study. If it is other kinds of plants, how they respond to mine water irrigation needs further study. What are the effects of mine water irrigation on plants other than ryegrass that need further study. Moreover, this study was only a short-term experiment, and the effects of mine water on the properties of the soil matrix cannot be generalized. Indoor experiments can be regularly watered to maintain moisture, indoor temperature is relatively fixed, while the natural environment is a lot of uncertainty and uncontrollable. Would the results of a small-scale pot experiment in a controlled environment be different if it were applied to a field where there are many uncertainties about soil properties and atmospheric conditions? Long-term field experiments must also be conducted to confirm our findings in more realistic conditions. The use of mine water resources not only has environmental and social benefits but could also bring economic benefits43. This study demonstrated that mine water can be used in ecological restoration and agricultural irrigation in mining areas, and is therefore of great significance to environmental restoration. More

Ceballos, G. et al. Accelerated modern human–induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, e1400253. https://doi.org/10.1126/sciadv.1400253 (2015).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Bradshaw, C. J. A. et al. Underestimating the challenges of avoiding a ghastly future. Front. Environ. Sci. 1, 615469. https://doi.org/10.3389/fcosc.2020.615419 (2021).Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. IUCN, Gland, Switzerland. http://www.iucnredlist.org (2020).Murray, K. A., Verde Arregoitia, L. D., Davidson, A., Di Marco, M. & Di Fonzo, M. M. I. Threat to the point: Improving the value of comparative extinction risk analysis for conservation action. Glob. Chang. Biol. 20, 483–494 (2014).Article 
ADS 

Google Scholar 
Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).Article 
ADS 

Google Scholar 
Chichorro, F., Juslén, A. & Cardoso, P. A review of the relation between species traits and extinction risk. Biol. Conserv. 237, 220–229 (2019).Article 

Google Scholar 
Ripple, W. J. et al. Bushmeat hunting and extinction risk to the world’s mammals. R. Soc. Open. Sci. 3, 160498. https://doi.org/10.1098/rsos.160498 (2016).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann, M. et al. The changing fates of the world’s mammals. Phil. Trans. R. Soc. B. 366, 2598–2610 (2011).Article 

Google Scholar 
Verde Arregoitia, L. D. Biases, gaps, and opportunities in mammalian extinction risk research. Mammal. Rev. 46, 17–29 (2016).Article 

Google Scholar 
Di Marco, M. et al. Drivers of extinction risk in African mammals: The interplay of distribution state, human pressure, conservation response and species biology. Philos. Trans. R. Soc. Lond. B. 369, 1–12 (2014).Article 

Google Scholar 
Di Marco, M., Collen, B., Rondinini, C. & Mace, G. M. Historical drivers of extinction risk: Using past evidence to direct future monitoring. Proc. R. Soc. B. 282, 20150928. https://doi.org/10.1098/rspb.2015.0928 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Bogoni, J. A., Ferraz, K. M. & Peres, C. A. Continental-scale local extinctions in mammal assemblages are synergistically induced by habitat loss and hunting pressure. Biol. Conserv. 272, 109635. https://doi.org/10.1016/j.biocon.2022.109635 (2022).Article 

Google Scholar 
Yusefi, G. H., Faizolahi, K., Darvish, J., Safi, K. & Brito, J. C. The species diversity, distribution and conservation status of the terrestrial mammals of Iran. J. Mammal. 100, 55–71 (2019).Article 

Google Scholar 
Keil, P. et al. Spatial scaling of extinction rates: Theory and data reveal nonlinearity and a major upscaling and downscaling challenge. Glob. Ecol. Biogeogr. 27, 2–13 (2018).Article 

Google Scholar 
Howard, C., Flather, C. H. & Stephens, P. A. A global assessment of the drivers of threatened terrestrial species richness. Nat. Commun. 11, 993. https://doi.org/10.1038/s41467-020-14771-6 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rodríguez, J. P. The difference conservation can make: integrating knowledge to reduce extinction risk. Oryx 51, 1–2 (2017).Article 

Google Scholar 
Cardillo, M. & Meijaard, E. Are comparative studies of extinction risk useful for conservation?. Trends Ecol. Evol. 27, 167–171 (2012).Article 

Google Scholar 
Davidson, A. D. et al. Geography of current and future global mammal extinction risk. PLoS ONE 12, e0186934. https://doi.org/10.1371/journal.pone.0186934 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Collen, B., Bykova, E., Ling, S., Milner-Gulland, E. J. & Purvis, A. Extinction risk: A comparative analysis of central Asian vertebrates. Biodivers. Conserv. 15, 1859–1871 (2006).Article 

Google Scholar 
Peñaranda, D. A. & Simonetti, J. A. Predicting and setting conservation priorities for Bolivian mammals based on biological correlates of the risk of decline. Conserv. Biol. 29, 834–843 (2015).Article 

Google Scholar 
Fritz, S. A., Bininda-Emonds, O. R. & Purvis, A. Geographical variation in predictors of mammalian extinction risk: Big is bad, but only in the tropics. Ecol. Lett. 12, 538–549 (2009).Article 

Google Scholar 
Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2, 909–914 (2004).Article 
CAS 

Google Scholar 
Cardillo, M. et al. The predictability of extinction: Biological and external correlates of decline in mammals. Proc. R. Soc. B. 275, 1441–1448 (2008).Article 

Google Scholar 
Yackulic, C. B., Sanderson, E. W. & Uriat, M. Anthropogenic and environmental drivers of modern range loss in large mammals. Proc. Natl. Acad. Sci. USA 108, 4024–4029 (2011).Article 
ADS 
CAS 

Google Scholar 
Ripple, W. J. et al. Are we eating the world’s megafauna to extinction?. Conserv Lett 12, e12627. https://doi.org/10.1111/conl.12627 (2019).Article 

Google Scholar 
Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. PNAS https://doi.org/10.1073/pnas.1702078114 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Bodmer, R. E., Eisenberg, J. E. & Redford, K. H. Hunting and the likelihood of extinction of Amazonian mammals. Conserv. Biol. 11, 460–466 (1997).Article 

Google Scholar 
Lee, T. M. & Jetz, W. Unravelling the structure of species extinction risk for predictive conservation science. Proc. R. Soc. B. 278, 1329–1338 (2011).Article 

Google Scholar 
Wolf, C. & Ripple, W. J. Prey depletion as a threat to the world’s large carnivores. Roy. Soc Open Sci 3, 160252. https://doi.org/10.1098/rsos.160252 (2016).Article 
ADS 

Google Scholar 
Firouz, E. The complete fauna of Iran. I. B. (Tauris and Co Ltd, London, 2005).Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).Article 
ADS 
CAS 

Google Scholar 
Davidson, A. D., Hamilton, M. J., Boyer, A. G., Brown, J. H. & Ceballos, G. Multiple ecological pathways to extinction in mammals. Proc. Natl. Acad. Sci. USA 106, 10702–10705 (2009).Article 
ADS 
CAS 

Google Scholar 
Hill, J., DeVault, T. & Belant, J. Comparative influence of anthropogenic landscape pressures on cause-specific mortality of mammals. Perspect. Ecol. Conserv. 20, 38–44 (2022).
Google Scholar 
DOE-GIS. Areas under protection by the Department of Environment of Iran. Department of the Environment of Iran: GIS and Remote Sensing Section (2016).Kolahi, M., Sakai, T., Moriya, K. & Makhdoum, M. F. Challenges to the future development of Iran’s protected areas system. Environ. Manage. 50, 750–765 (2012).Article 
ADS 

Google Scholar 
Morrison, J. M., Sechrest, W., Dinerstein, E., Wilcove, D. S. & Lamoreux, J. L. Persistence of large mammal faunas as indicators of human impact. J. Mammal. 88, 1363–1380 (2007).Article 

Google Scholar 
Ghoddousi, A. et al. The decline of ungulate populations in Iranian protected areas calls for urgent action against poaching. Oryx 53, 151–158 (2017).Article 

Google Scholar 
Soofi, M. et al. Assessing the relationship between illegal hunting of ungulates, wild prey occurrence and livestock depredation rate by large carnivores. J. Appl. Ecol. 56, 365–374 (2019).Article 

Google Scholar 
Khalatbari, L., Yusefi, G. H., Martinez-Freiria, F., Jowkar, H. & Brito, J. C. Availability of prey and natural habitats are related with temporal dynamics in range and habitat suitability for Asiatic cheetah. Hystrix Ital. J. Mammal. 29, 145–151 (2018).
Google Scholar 
Ripple, W. J. et al. Collapse of the world’s largest herbivores. Sci. Adv. 1, e1400103 (2015).Article 
ADS 

Google Scholar 
Hoffmann, M. et al. The difference conservation makes to extinction risk of the world’s ungulates. Conserv. Biol. 29, 1303–1313 (2015).Article 

Google Scholar 
Yusefi, G. H. Conservation biogeography of terrestrial mammals in Iran diversity distribution and vulnerability to extinction. Front Biogeogr 13(2), 49765. https://doi.org/10.21425/F5FBG49765 (2021).Article 

Google Scholar 
Faurby, S. & Svenning, J.-C. A species-level phylogeny of all extant and late Quaternary extinct mammals using a novel heuristic-hierarchical Bayesian approach. Mol. Phylogenet. Evol. 84, 14–26 (2015).Article 

Google Scholar 
R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing (2021).Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).Article 

Google Scholar 
Jones, K. et al. PanTHERIA: A species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648. https://doi.org/10.1890/08-1494.1 (2009).Article 

Google Scholar 
González-Suárez, M., Lucas, P. M. & Revilla, E. Biases in comparative analyses of extinction risk: Mind the gap. J. Anim. Ecol. 81, 1211–1222 (2012).Article 

Google Scholar 
Wang, Y. et al. Ecological correlates of extinction risk in Chinese birds. Ecography 41, 782–794 (2018).Article 

Google Scholar 
Wildlife Conservation Society-WCS, and Center for International Earth Science Information Network-CIESIN, Columbia University. Last of the wild project, Version 2, 2005 (LWP-2): Global human influence index (HII) Dataset. https://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-influence-index-geographic (2005).ESRI ArcGIS Desktop10.6. Redlands, CA: Environmental Systems Research Institute (2017).Hijmans, R. J. et al. raster: geographic data analysis and modeling. https://cran.r-project.org/web/packages/raster/index.html (2018).Pebesma, E. et al. rgdal: bindings for the geospatial data abstraction library. https://cran.r-project.org/web/packages/rgdal/index.html (2018).Bivand, R. et al. maptools: tools for reading and handling spatial objects. https://cran.r-project.org/web/packages/maptools/ index.html (2018).Purvis, A. Phylogenetic approaches to the study of extinction. Annu. Rev. Ecol. Evol. Syst. 39, 301–319 (2008).Article 

Google Scholar 
Venables, W. N. & Ripley, B. D. Modern applied statistics with S (Springer, 2002).Book 

Google Scholar 
Gittleman, J. L. & Kot, M. Adaptation: Statistics and a null model for estimating phylogenetic effects. Syst. Zool. 39, 227–241 (1990).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodal inference in ecology and solution: Challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).Article 
CAS 

Google Scholar 
Bartón, K. MuMIn: multi-model inference R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020). More

Peñuelas, J. et al. Assessment of the impacts of climate change on mediterranean terrestrial ecosystems based on data from field experiments and long-term monitored field gradients in Catalonia. Environ. Exp. Bot. 152, 49–59 (2018).
Google Scholar 
Pugnaire, F. I. et al. Climate change effects on plant-soil feedbacks and consequences for biodiversity and functioning of terrestrial ecosystems. Sci. Adv. 5, eaaz1834 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baho, D. L., Bundschuh, M. & Futter, M. N. Microplastics in terrestrial ecosystems: Moving beyond the state of the art to minimize the risk of ecological surprise. Glob. Change Biol. 27, 3969–3986 (2021).CAS 

Google Scholar 
Rillig, M. C., Kim, S. W., Kim, T.-Y. & Waldman, W. R. The global plastic toxicity debt. Environ. Sci. Technol. 55, 2717–2719 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nizzetto, L., Futter, M. & Langaas, S. Are agricultural soils dumps for microplastics of urban origin?. (2016).Geyer, R., Jambeck, J. R. & Law, K. L. Production, use, and fate of all plastics ever made. Sci. Adv. 3, e1700782 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Barnes, D. K., Galgani, F., Thompson, R. C. & Barlaz, M. Accumulation and fragmentation of plastic debris in global environments. Philos. Trans. R. Soc. B Biol. Sci. 364, 1985–1998 (2009).CAS 

Google Scholar 
Rillig, M. C. Microplastic in terrestrial ecosystems and the soil?. (2012).de Souza Machado, A. A. et al. Microplastics can change soil properties and affect plant performance. Environ. Sci. Technol. 53, 6044–6052 (2019).ADS 
PubMed 

Google Scholar 
Büks, F. & Kaupenjohann, M. Global concentrations of microplastics in soils–A review. Soil 6, 649–662 (2020).ADS 

Google Scholar 
Evangeliou, N. et al. Atmospheric transport is a major pathway of microplastics to remote regions. Nat. Commun. 11, 1–11 (2020).
Google Scholar 
Steinmetz, Z. et al. Plastic mulching in agriculture. Trading short-term agronomic benefits for long-term soil degradation?. Sci. Total Environ. 550, 690–705 (2016).ADS 
CAS 
PubMed 

Google Scholar 
de Souza Machado, A. A., Kloas, W., Zarfl, C., Hempel, S. & Rillig, M. C. Microplastics as an emerging threat to terrestrial ecosystems. Glob. Change Biol. 24, 1405–1416 (2018).ADS 

Google Scholar 
Qi, Y. et al. Macro-and micro-plastics in soil-plant system: Effects of plastic mulch film residues on wheat (Triticum aestivum) growth. Sci. Total Environ. 645, 1048–1056 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Weithmann, N. et al. Organic fertilizer as a vehicle for the entry of microplastic into the environment. Sci. Adv. 4, eaap8060 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Allen, S. et al. Atmospheric transport and deposition of microplastics in a remote mountain catchment. Nat. Geosci. 12, 339–344 (2019).ADS 
CAS 

Google Scholar 
Kiyama, Y., Miyahara, K. & Ohshima, Y. Active uptake of artificial particles in the nematode Caenorhabditis elegans. J. Exp. Biol. 215, 1178–1183 (2012).PubMed 

Google Scholar 
Helmberger, M. S., Tiemann, L. K. & Grieshop, M. J. Towards an ecology of soil microplastics. Funct. Ecol. 34, 550–560 (2020).
Google Scholar 
Liu, M. et al. Microplastic and mesoplastic pollution in farmland soils in suburbs of Shanghai, China. Environ. Pollut. 242, 855–862 (2018).CAS 
PubMed 

Google Scholar 
Lehmann, A., Fitschen, K. & Rillig, M. C. Abiotic and biotic factors influencing the effect of microplastic on soil aggregation. Soil Syst. 3, 21 (2019).CAS 

Google Scholar 
de Souza Machado, A. A. et al. Impacts of microplastics on the soil biophysical environment. Environ. Sci. Technol. 52, 9656–9665 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kim, S. W. & Rillig, M. C. Research trends of microplastics in the soil environment: Comprehensive screening of effects. Soil Ecol. Lett. 4, 109–118 (2022).CAS 

Google Scholar 
Lehmann, A., Leifheit, E. F., Gerdawischke, M. & Rillig, M. C. Microplastics have shape-and polymer-dependent effects on soil aggregation and organic matter loss–An experimental and meta-analytical approach. Microplast. Nanoplast. 1, 1–14 (2021).
Google Scholar 
Wan, Y., Wu, C., Xue, Q. & Hui, X. Effects of plastic contamination on water evaporation and desiccation cracking in soil. Sci. Total Environ. 654, 576–582 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Liang, Y., Lehmann, A., Yang, G., Leifheit, E. F. & Rillig, M. C. Effects of microplastic fibers on soil aggregation and enzyme activities are organic matter dependent. Front. Environ. Sci. 9, 97 (2021).
Google Scholar 
Lozano, Y. M., Lehnert, T., Linck, L. T., Lehmann, A. & Rillig, M. C. Microplastic shape, polymer type, and concentration affect soil properties and plant biomass. Front. Plant Sci. 12, 169 (2021).
Google Scholar 
Kemper, W. Aggregate stability. Methods Soil Anal. Part 1 Phys. Mineral. Prop. Incl. Stat. Meas. Sampl. 9, 511–519 (1965).
Google Scholar 
Rose, C. W. & Rose, C. W. An Introduction to the Environmental Physics of Soil, Water and Watersheds (Cambridge University Press, 2004).
Google Scholar 
Horn, R., Taubner, H., Wuttke, M. & Baumgartl, T. Soil physical properties related to soil structure. Soil Tillage Res. 30, 187–216 (1994).
Google Scholar 
Beven, K. & Germann, P. Macropores and water flow in soils. Water Resour. Res. 18, 1311–1325 (1982).ADS 

Google Scholar 
De Vries, F. T. et al. Abiotic drivers and plant traits explain landscape-scale patterns in soil microbial communities. Ecol. Lett. 15, 1230–1239 (2012).PubMed 

Google Scholar 
Kaisermann, A., de Vries, F. T., Griffiths, R. I. & Bardgett, R. D. Legacy effects of drought on plant–soil feedbacks and plant–plant interactions. New Phytol. 215, 1413–1424 (2017).CAS 
PubMed 

Google Scholar 
Martorell, C., MartÍnez-Blancas, A. & GarcíaMeza, D. Plant–soil feedbacks depend on drought stress, functional group, and evolutionary relatedness in a semiarid grassland. Ecology 102, e03499 (2021).PubMed 

Google Scholar 
Rillig, M. C., Lehmann, A., de Souza Machado, A. A. & Yang, G. Microplastic effects on plants. New Phytol. 223, 1066–1070 (2019).PubMed 

Google Scholar 
Lozano, Y. M. & Rillig, M. C. Effects of microplastic fibers and drought on plant communities. Environ. Sci. Technol. 54, 6166–6173 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Denef, K. et al. Influence of dry–wet cycles on the interrelationship between aggregate, particulate organic matter, and microbial community dynamics. Soil Biol. Biochem. 33, 1599–1611 (2001).CAS 

Google Scholar 
Ochoa-Hueso, R. et al. Drought consistently alters the composition of soil fungal and bacterial communities in grasslands from two continents. Glob. Change Biol. 24, 2818–2827 (2018).ADS 

Google Scholar 
Naylor, D. & Coleman-Derr, D. Drought stress and root-associated bacterial communities. Front. Plant Sci. 8, 2223 (2018).PubMed 
PubMed Central 

Google Scholar 
Fu, W. et al. Community response of arbuscular mycorrhizal fungi to extreme drought in a cold-temperate grassland. New Phytol. 234, 2003–2017 (2022).PubMed 

Google Scholar 
Lin, D. et al. Microplastics negatively affect soil fauna but stimulate microbial activity: Insights from a field-based microplastic addition experiment. Proc. R. Soc. B 287, 20201268 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Boots, B., Russell, C. W. & Green, D. S. Effects of microplastics in soil ecosystems: Above and below ground. Environ. Sci. Technol. 53, 11496–11506 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Bosker, T., Bouwman, L. J., Brun, N. R., Behrens, P. & Vijver, M. G. Microplastics accumulate on pores in seed capsule and delay germination and root growth of the terrestrial vascular plant Lepidium sativum. Chemosphere 226, 774–781 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Zimmerman, R. & Kardos, L. Effect of bulk density on root growth. Soil Sci. 91, 280–288 (1961).ADS 

Google Scholar 
Ruser, R., Sehy, U., Weber, A., Gutser, R. & Munch, J. Main driving variables and effect of soil management on climate or ecosystem-relevant trace gas fluxes from fields of the FAM. In Perspectives for agroecosystem
management Chp 2.2, 79–120. ISBN 9780444519054 Elsevier, (2008).Huang, Y. et al. LDPE microplastic films alter microbial community composition and enzymatic activities in soil. Environ. Pollut. 254, 112983 (2019).CAS 
PubMed 

Google Scholar 
Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).PubMed 

Google Scholar 
Hortal, S. et al. Soil microbial community under a nurse-plant species changes in composition, biomass and activity as the nurse grows. Soil Biol. Biochem. 64, 139–146 (2013).CAS 

Google Scholar 
Scheurer, M. & Bigalke, M. Microplastics in Swiss floodplain soils. Environ. Sci. Technol. 52, 3591–3598 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Jambeck, J. R. et al. Plastic waste inputs from land into the ocean. Science 347, 768–771 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Fuller, S. & Gautam, A. A procedure for measuring microplastics using pressurized fluid extraction. Environ. Sci. Technol. 50, 5774–5780 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Schloerke, B. et al. GGally: Extension to ‘ggplot2’. R package version 2.1. 2. (2021).Venables, W. & Ripley, B. Modern applied statistics with S fourth edition. Publisher Springer-Verlag, New York. (2002).Lenth, R. V. Emmeans: Estimated marginal means, aka least-squares means. R package version 1.6.3 (2021).R Core Team et al. R: A language and environment for statistical computing. (2013).Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 

Google Scholar 
Pedersen, T. L. Patchwork: The composer of plots. R package version 1, 182 (2020).Neuwirth, E. RColorBrewer: ColorBrewer palettes. R package version 1.1-2. (2014). More

Fuhrman JA, Campbell L. Marine ecology – microbial microdiversity. Nature. 1998;393:410–1.CAS 

Google Scholar 
Thompson JR, Pacocha S, Pharino C, Klepac-Ceraj V, Hunt DE, Benoit J, et al. Genotypic diversity within a natural coastal bacterioplankton population. Science. 2005;307:1311–3.CAS 
PubMed 

Google Scholar 
Oh S, Buddenborg S, Yoder-Himes DR, Tiedje JM, Konstantinidis KT. Genomic diversity of Escherichia isolates from diverse habitats. PLoS ONE. 2012;7:e47005.CAS 
PubMed 
PubMed Central 

Google Scholar 
Fuhrman JA, Steele JA, Hewson I, Schwalbach MS, Brown MV, Green JL, et al. A latitudinal diversity gradient in planktonic marine bacteria. Proc Natl Acad Sci USA. 2008;105:7774–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.PubMed 

Google Scholar 
Raes EJ, Bodrossy L, van de Kamp J, Bissett A, Ostrowski M, Brown MV, et al. Oceanographic boundaries constrain microbial diversity gradients in the South Pacific Ocean. Proc Natl Acad Sci USA. 2018;115:EB266–EB75.
Google Scholar 
Brown MV, Fuhrman JA. Marine bacterial microdiversity as revealed by internal transcribed spacer analysis. Aquat Microb Ecol. 2005;41:15–23.
Google Scholar 
Johnson ZI, Zinser ER, Coe A, McNulty NP, Woodward EMS, Chisholm SW. Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science. 2006;311:1737–40.CAS 
PubMed 

Google Scholar 
Larkin AA, Blinebry SK, Howes C, Chandler J, Zinser ER, Johnson ZI. Niche partitioning and biogeography of high light adapted Prochlorococcus across taxonomic ranks in the North Pacific. ISME J. 2016;10:1555–67.PubMed 
PubMed Central 

Google Scholar 
Zinser ER, Johnson ZI, Coe A, Karaca E, Veneziano D, Chisholm SW. Influence of light and temperature on Prochlorococcus ecotype distributions in the Atlantic Ocean. Limnol Oceanogr. 2007;52:2205–20.
Google Scholar 
Carlson M, Ribalet F, Maidanik I, Durham BP, Hulata Y, Ferrón S, et al. Viruses affect picocyanobacterial abundance and biogeography in the North Pacific Ocean. Nat Microbiol. 2022;7:570–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Follett CL, Dutkiewicz S, Ribalet F, Zakem E, Caron D, Armbrust EV, et al. Trophic interactions with heterotrophic bacteria limit the range of Prochlorococcus. Proc Natl Acad Sci USA. 2022;119:e2110993118.CAS 
PubMed 
PubMed Central 

Google Scholar 
Moore LR, Rocap G, Chisholm SW. Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature. 1998;393:464–7.CAS 
PubMed 

Google Scholar 
Zwirglmaier K, Jardillier L, Ostrowski M, Mazard S, Garczarek L, Vaulot D, et al. Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes. Environ Microbiol. 2008;10:147–61.PubMed 

Google Scholar 
Carlson CA, Morris R, Parsons R, Treusch AH, Giovannoni SJ, Vergin K. Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea. ISME J. 2009;3:283–95.CAS 
PubMed 

Google Scholar 
Ribalet F, Swalwell J, Clayton S, Jimenez V, Sudek S, Lin YJ, et al. Light-driven synchrony of Prochlorococcus growth and mortality in the subtropical Pacific gyre. Proc Natl Acad Sci USA. 2015;112:8008–12.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pernthaler A, Pernthaler J, Schattenhofer M, Amann R. Identification of DNA-synthesizing bacterial cells in coastal North Sea plankton. Appl Environ Microbiol. 2002;68:5728–36.CAS 
PubMed 
PubMed Central 

Google Scholar 
Del Giorgio PA, Gasol JM. Physiological structure and single-cell activity in marine bacterioplankton. In: Kirchman DL, editor. Microbial ecology of the oceans. 2nd ed. New Jersey:John Wiley & Sons, Inc.; 2008. p. 243–298.Lin Y, Gazsi K, Lance VP, Larkin A, Chandler J, Zinser ER, et al. In situ activity of a dominant Prochlorococcus ecotype (eHL-II) from rRNA content and cell size. Environ Microbiol. 2013;15:2736–47.CAS 
PubMed 

Google Scholar 
Kirchman DL. Growth rates of microbes in the oceans. Annu Rev Mar Sci. 2016;8:285–309.
Google Scholar 
Landry MR, Selph KE, Yang EJ. Decoupled phytoplankton growth and microzooplankton grazing in the deep euphotic zone of the eastern equatorial Pacific. Mar Ecol Prog Ser. 2011;421:13–24.
Google Scholar 
Selph KE, Landry MR, Taylor AG, Yang E-J, Measures CI, Yang J, et al. Spatially-resolved taxon-specific phytoplankton production and grazing dynamics in relation to iron distributions in the Equatorial Pacific between 110 and 140 degrees W. Deep Sea Res Part II Top Stud Oceanogr. 2011;58:358–77.CAS 

Google Scholar 
Hunt DE, Lin Y, Church MJ, Karl DM, Tringe SG, Izzo LK, et al. Relationship between abundance and specific activity of bacterioplankton in open ocean surface waters. Appl Environ Microbiol. 2013;79:177–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sintes E, Herndl GJ. Quantifying substrate uptake by individual cells of marine bacterioplankton by catalyzed reporter deposition fluorescence in situ hybridization combined with microautoradiography. Appl Environ Microbiol. 2006;72:7022–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hall E, Maixner F, Franklin O, Daims H, Richter A, Battin T. Linking microbial and ecosystem ecology using ecological stoichiometry: a synthesis of conceptual and empirical approaches. Ecosystems. 2011;14:261–73.
Google Scholar 
Musat N, Foster R, Vagner T, Adam B, Kuypers MM. Detecting metabolic activities in single cells, with emphasis on nanoSIMS. FEMS Microbiol Rev. 2012;36:486–511.CAS 
PubMed 

Google Scholar 
Hatzenpichler R, Scheller S, Tavormina PL, Babin BM, Tirrell DA, Orphan VJ. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ Microbiol. 2014;16:2568–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sebastián M, Gasol JM. Visualization is crucial for understanding microbial processes in the ocean. Philos Trans R Soc Lond B. 2019;374:20190083.
Google Scholar 
Korem T, Zeevi D, Suez J, Weinberger A, Avnit-Sagi T, Pompan-Lotan M, et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science. 2015;349:1101–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Brown CT, Olm MR, Thomas BC, Banfield JF. Measurement of bacterial replication rates in microbial communities. Nat Biotech. 2016;34:1256–63.CAS 

Google Scholar 
Gao Y, Li H. Quantifying and comparing bacterial growth dynamics in multiple metagenomic samples. Nat Methods. 2018;15:1041–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Emiola A, Zhou W, Oh J. Metagenomic growth rate inferences of strains in situ. Sci Adv. 2020;6:eaaz2299.CAS 
PubMed 
PubMed Central 

Google Scholar 
Long ANM, Hou SW, Ignacio-Espinoza JC, Fuhrman JA. Benchmarking microbial growth rate predictions from metagenomes. ISME J. 2020;15:183–195.PubMed 
PubMed Central 

Google Scholar 
Carroll J, Van Oostende N, Ward BB. Evaluation of genomic sequence-based growth rate methods for synchronized Synechococcus cultures. Appl Environ Microbiol. 2021;01743-21.Vaulot D, Marie D. Diel variability of photosynthetic picoplankton in the equatorial Pacific. J Geophys Res Oceans. 1999;104:3297–310.CAS 

Google Scholar 
Hunter-Cevera KR, Neubert MG, Olson RJ, Shalapyonok A, Solow AR, Sosik HM. Seasons of Syn. Limnol Oceanogr. 2020;65:1085–102.PubMed 

Google Scholar 
Baer SE, Rauschenberg S, Garcia CA, Garcia NS, Martiny AC, Twining BS, et al. Carbon and nitrogen productivity during spring in the oligotrophic Indian Ocean along the GO-SHIP IO9N transect. Deep Sea Res Part II Top Stud Oceanogr. 2019;161:81–91.CAS 

Google Scholar 
Larkin AA, Garcia CA, Ingoglia KA, Garcia NS, Baer SE, Twining BS, et al. Subtle biogeochemical regimes in the Indian Ocean revealed by spatial and diel frequency of Prochlorococcus haplotypes. Limnol Oceanogr. 2020;65:S220–S32.CAS 

Google Scholar 
Larkin AA, Garcia CA, Garcia N, Brock ML, Lee JA, Ustick LJ, et al. High spatial resolution global ocean metagenomes from Bio-GO-SHIP repeat hydrography transects. Sci Data. 2021;8:1–6.
Google Scholar 
Baym M, Kryazhimskiy S, Lieberman TD, Chung H, Desai MM, Kishony R. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS ONE. 2015;10:e0128036.PubMed 
PubMed Central 

Google Scholar 
Wandro S, Oliver A, Gallagher T, Weihe C, England W, Martiny JBH, et al. Predictable molecular adaptation of coevolving Enterococcus faecium and lytic phage EfV12-phi1. Front Microbiol. 2019;9:3192.PubMed 
PubMed Central 

Google Scholar 
Oliver A, LaMere B, Weihe C, Wandro S, Lindsay KL, Wadhwa PD, et al. Cervicovaginal microbiome composition is associated with metabolic profiles in healthy pregnancy. mBio. 2020;11:e01851–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Eren AM, Esen OC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.PubMed 
PubMed Central 

Google Scholar 
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
PubMed 

Google Scholar 
van Dongen S, Abreu-Goodger C. Using MCL to extract clusters from networks. In: van Helden J, Toussaint A, Thieffry D, editors. Bacterial molecular networks: methods and protocol. New Jersey:Humana Totowa; 2012. p. 281–295.Bergeron A, Mixtacki J, Stoye J. A unifying view of genome rearrangements. In: Proceeding of the International Workshop on Algorithms in Bioinformatics. Berlin, Heidelberg: Springer; 2006. p. 163–73.Hilker R, Sickinger C, Pedersen CNS, Stoye J. UniMoG-a unifying framework for genomic distance calculation and sorting based on DCJ. Bioinformatics. 2012;28:2509–11.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gelman A, Carlin JB, Stern HS, Dunson D, Vehtari A, Rubin DB. Bayesian data analysis: 3rd ed. Boca Raton:Chapman and Hall/CRC; 2013.Martiny AC, Ustick L, Garcia CA, Lomas MW. Genomic adaptation of marine phytoplankton populations regulates phosphate uptake. Limnol Oceanogr. 2020;65:S340–S50.CAS 

Google Scholar 
Garcia CA, Hagstrom GI, Larkin AA, Ustick LJ, Levin SA, Lomas MW, et al. Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry. Philos Trans R Soc Lond, B. 2020;375:20190254.CAS 

Google Scholar 
Ustick LJ, Larkin AA, Garcia CA, Garcia NS, Brock ML, Lee JA, et al. Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation. Science. 2021;372:287–291.CAS 
PubMed 

Google Scholar 
Delmont TO, Eren AM. Linking pangenomes and metagenomes: the Prochlorococcus metapangenome. PeerJ 2018;6:e4320.PubMed 
PubMed Central 

Google Scholar 
Worden AZ, Binder BJ. Application of dilution experiments for measuring growth and mortality rates among Prochlorococcus and Synechococcus populations in oligotrophic environments. Aquat Microb Ecol. 2003;30:159–74.
Google Scholar 
Casey JR, Boiteau RM, Engqvist MKM, Finkel ZV, Li G, Liefer J, et al. Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology. Sci Adv. 2022;8:eabl4930.CAS 
PubMed 
PubMed Central 

Google Scholar 
Berube PM, Rasmussen A, Braakman R, Stepanauskas R, Chisholm SW. Emergence of trait variability through the lens of nitrogen assimilation in Prochlorococcus. Elife. 2019;8:e41043.PubMed 
PubMed Central 

Google Scholar 
Luo HW, Benner R, Long RA, Hu JJ. Subcellular localization of marine bacterial alkaline phosphatases. Proc Natl Acad Sci USA. 2009;106:21219–23.CAS 
PubMed 
PubMed Central 

Google Scholar 
Schapiro JM, Libby SJ, Fang FC. Inhibition of bacterial DNA replication by zinc mobilization during nitrosative stress. Proc Natl Acad Sci USA. 2003;100:8496–501.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hantke K. Bacterial zinc uptake and regulators. Curr Opin Microbiol. 2005;8:196–202.CAS 
PubMed 

Google Scholar 
Glass JB, Axler RP, Chandra S, Goldman CR. Molybdenum limitation of microbial nitrogen assimilation in aquatic ecosystems and pure cultures. Front Microbiol. 2012;3:331.CAS 
PubMed 
PubMed Central 

Google Scholar 
Rusch DB, Halpern AL, Sutton G, Heidelberg KB, Williamson S, Yooseph S, et al. The Sorcerer II Global Ocean Sampling expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol. 2007;5:e77.PubMed 
PubMed Central 

Google Scholar 
Noell SE, Barrell GE, Suffridge C, Morré J, Gable KP, Graff JR, et al. SAR11 cells rely on enzyme multifunctionality to transport and metabolize a range of polyamine compounds. 2021. https://www.biorxiv.org/content/10.1101/2021.05.13.444117v1.Binder BJ, Chisholm SW. Cell-cycle regulation in marine Synechcococcus sp strains. Appl Environ Microbiol. 1995;61:708–17.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zinser ER, Lindell D, Johnson ZI, Futschik ME, Steglich C, Coleman ML, et al. Choreography of the transcriptome, photophysiology, and cell cycle of a minimal photoautotroph, Prochlorococcus. PLoS ONE. 2009;4:e5135.PubMed 
PubMed Central 

Google Scholar 
Hynes AM, Rhodes KL, Binder BJ. Assessing cell cycle-based methods of measuring Prochlorococcus division rates using an individual-based model. Limnol Oceanogr-Meth. 2015;13:640–50.
Google Scholar 
Vieira-Silva S, Rocha EP. The systemic imprint of growth and its uses in ecological (meta) genomics. PLoS Genet. 2010;6:e1000808.PubMed 
PubMed Central 

Google Scholar 
Weissman JL, Hou SW, Fuhrman JA. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc Natl Acad Sci USA. 2021;118:e2016810118.CAS 
PubMed 
PubMed Central 

Google Scholar 
Chase AB, Karaoz U, Brodie EL, Gomez-Lunar Z, Martiny AC, Martiny JB. Microdiversity of an abundant terrestrial bacterium encompasses extensive variation in ecologically relevant traits. mBio. 2017;8:e01809–17.CAS 
PubMed 
PubMed Central 

Google Scholar 
Olm MR, Crits-Christoph A, Bouma-Gregson K, Firek BA, Morowitz MJ, Banfield JF. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat Biotechnol. 2021;39:727–36.CAS 
PubMed 
PubMed Central 

Google Scholar 
Flombaum P, Gallegos JL, Gordillo RA, Rincon J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci USA. 2013;110:9824–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Brown MV, Lauro FM, DeMaere MZ, Muir L, Wilkins D, Thomas T, et al. Global biogeography of SAR11 marine bacteria. Mol Sys Biol. 2012;8:595.
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The current study is the first to directly compare the differences in eukaryotic community diversity by metabarcoding eRNA and eDNA from an estuarine benthic ecosystem. This is also the first study to compare the diversity of environmental RNA and DNA using two of the most common loci examined in metabarcoding applications: COI and 18S. Only a handful of studies have used eRNA to assess diversity through metabarcoding and have demonstrated some differences in detected diversity between eRNA and eDNA in metabarcoding19,29,38. Environmental DNA has many useful applications, but one of the downfalls of DNA is that it can be detected long after an organism has inhabited an area, such as from dead organisms and even significant distances away from the source1. While the persistence of eDNA may be useful in detecting the presence of endangered, rare, or invasive species in marine systems, the persistence of eDNA, such as legacy DNA, may skew the accurate representation of community structure immediately after a disturbance event or during the exact moment of sampling21. Environmental RNA proves to be a useful tool because it is far less persistent in marine environments2,27. In the current study, RNA provides a snapshot of living organisms present in the mesocosms at the time of sampling, compared to DNA which detects past and present organisms in the sample.Benthic ecosystems often have high biodiversity because they are dynamic environments with various substrates favorable for hosting communities39. To date, there is no one loci and associated primer pair that will effectively detect all eukaryotic organisms, and in particular metazoans, so it is beneficial to use multiple loci when looking for a variety of taxa6. Previous marine eDNA studies have demonstrated the preferred method of using two loci to achieve comprehensive metabarcoding for taxonomically diverse environments5,30,40. The current study further demonstrates the need to use at least two different loci for targeted PCR and sequencing to truly capture the wide diversity in rich systems, such as the top oxygenated layer of the marine benthos. Our results demonstrate the types of organisms detected using the 18S loci vary widely from the organisms detected by COI. The 18S loci also detected a higher number of organisms than using the COI loci. The 18S allowed greater detection of metazoans whereas COI was useful in the detection of Oomycota (i.e., a eukaryotic microorganism that resembles fungi) protozoa. Protozoa are often food sources for meiofauna41; therefore, using COI for protozoan detection and 18S for metazoan detection showcases multiple trophic levels present in the mesocosms of the current study. Utilizing multiple loci not only broadens taxonomic diversity detection but can also highlight multiple trophic levels for understanding food webs originating in benthic environments15.A study evaluating arctic benthic diversity similarly found that COI detected fewer taxa than 18S using eDNA metabarcoding, and there was only ~ 40% taxa overlap between markers at the class level42. In the current study, the COI marker using both nucleic acid templates yielded a higher percentage of unassigned taxa after filtering for presence in the majority of mesocosms compared to 18S. A possible explanation for the low metazoan detection by COI may be that the unassigned taxa are metazoans rather than more SAR organisms. Additionally, singly detected metazoans were filtered out of the analysis if they were not detected in at least 4 mesocosms. For example, one type of amphipod was detected in only 3 mesocosm by RNA using the COI marker, but was not detected in any mesocosms using DNA. Therefore, the amphipod observation was excluded from the COI results in Fig. 3 and Table S3 because it was not found in at least 4 mesocosms.Overall, RNA provides a broader assessment of benthic community structure than DNA, particularly when using two loci/ markers for sequencing. In the current study, we used nuclear 18S ribosomal RNA and DNA and mitochondrial COI RNA and DNA sequences as markers for metabarcoding. The number of copies of ribosomal RNA per cell is higher than the copies of ribosomal DNA, and the ratio of RNA: DNA is higher in single-cell organisms, such as protists43. The higher number of unique ASVs detected using eRNA is likely attributed to the higher number of RNA copies of each marker in small, single-cell organisms successfully amplified during PCR, therefore making rare organisms easier to detect. The DNA of highly abundant or higher biomass organisms may “drown out” (i.e., mask) the sequences from lower abundance and biomass organisms during PCR amplification, thus resulting in lower detected α-diversity. The increased detection of ciliates and protozoa using eRNA are consistent with other recent eRNA metabarcoding results that found higher ciliate and protozoan diversity compared to eDNA using the same Uni18S primers19. Positive correlations between organism biomass and sequence copy numbers have been demonstrated for DNA metabarcoding conducted on invertebrate species44. In the current study, RNA allowed for the detected of both larger meiofauna and smaller microfauna, which is optimal for assessing true biodiversity with molecular assays. Chaetonotida, a type of gastrotrich, was the only taxa detected in 4 of the mesocosms using DNA that was not found with RNA. It is possible the chaetonotida may have died during the experiment due to sensitivities to new environmental conditions, and therefore were not detected with RNA. The temperature of the flowing seawater in the mesocosms was approximately 18 °C, and many marine chaetonotida prefer 23–28 °C and high organic matter substrate45.Previous studies have compared the accuracy of conventional morphological identification to molecular metabarcoding methods for assessing biodiversity. In estuarine-specific studies, metabarcoding methods are able to detect the majority of taxa identified with traditional methods and often detected higher species richness, or higher numbers of unique organisms, that was not found conventionally10,46,47. The limiting factor for higher resolution of taxonomic identification is the availability of species-specific sequences in barcoding databases48. However, barcoding databases are becoming more robust as an increasing number of researchers contribute high quality sequencing data to databases7. Therefore, eRNA metabarcoding techniques perform similarly to conventional morphological methods, and may even uncover higher biodiversity in systems like estuaries where meiofauna have been historically understudied and identified.Although eRNA α-diversity is higher compared to eDNA, there is some overlap between ASVs detected with eRNA and eDNA. The higher percentage of overlap between eDNA and eRNA ASVs is predominantly seen in the ASVs detected in all 7 of the mesocosms. The increased overlap in ASVs detected in all 7 mesocosms compared to the relatively lower overlap of ASVs found in only 1 of the 7 mesocosms is due to the filtering of random organisms found in only 1 mesocosm. Detection of a unique organism in a single mesocosm is likely not representative of the sample community and filters potential artifacts that may be introduced during cDNA synthesis from eRNA. However, all uniquely identified ASVs are used in the β-diversity analysis, so the higher number of unique ASVs detected from eRNA are likely driving the significant differences observed between eRNA and eDNA β-diversity. It is possible that using eRNA could increase the statistical power of a study design compared to eDNA because eRNA detects a higher number of ASVs. It is unlikely that a higher number of RNA ASVs could be due to splice variants contributing to unique sequences because the amplified regions of both markers do not contain introns. Therefore, no splicing of transcripts would be expected. Thus, the detected DNA ASVs are from living organisms in the mesocosms and the higher diversity of ASVs detected from RNA demonstrate that RNA is a more suitable option for assessing diversity of living organisms.It is evident that the mesocosms in the current study were rich with meio- and microfauna due to the number of unique organisms and broad diversity of different taxa. It is likely that collecting samples for nucleic acid extraction directly from the field site may result in higher diversity because there is no mechanical disturbance during the laboratory acclimation period and the presence of other organisms in the system, such as fish, macroinvertebrates, or birds. eDNA molecular abundance in samples has been shown to correlate to actual organismal abundance in laboratory environments, but the same correlation is not as apparent in field samples, which is likely due to a variety of collection and processing methods49. eRNA may be the better nucleic acid template for field collection once flash frozen, especially for the detection of protozoans. Future studies will compare the diversity of eukaryotic communities detected using eDNA and eRNA collected directly from the field rather than from sediment core mesocosms. Repeated sampling from a field site may help reduce transient eRNA detection when establishing accurate baseline community composition in field-based biomonitoring studies.Recently, meiofaunal organisms and communities are being explored as bioindicators, which are organisms whose presence are indicators for environmental stress and pollution17. For example, lower meiofaunal diversity and abundance is associated with higher pollution in harbors, and the presence of some genera of nematodes are correlated with higher concentrations of polycyclic aromatic hydrocarbons because they are more tolerant to pollution50. A previous study that used field-collected mesocosms from the same location as our current study found similar phyla detected with a metabarcoding approach; the majority of the sequences detected in benthic communities were from nematodes, arthropods, and the microfaunal SAR clade10. Similarly, the majority of the ASVs detected from the current study also corresponded with nematodes, arthropods, and the SAR clade, as well as other commonly detected meiobenthic organisms, such as polychaetes and Homalorhagida (i.e., mud dragons). A recent study exposed a benthic foraminiferal community to chromium, and found that eRNA metabarcoding was more robust for detecting changes in diversity at lower chromium concentrations compared to eDNA51. The eRNA metabarcoding method used in the current study detected meiofaunal taxa typical of marine or estuarine environments. Therefore, eRNA metabarcoding may be useful for efficiently identifying bioindicator species or taxa impacted by exposures to different contaminants and environmental stressors to aid with management of aquatic systems. Another advantage of eRNA is that RNA provides functional information about how organisms response to stress through altered transcription of activated pathways. eRNA will likely be a more powerful tool than eDNA because it allows for the detection of both bioindicator species and, in the future with increase development of genetic databases, environmental detection of biomarkers of stress through increased transcription of response genes.Many academic researchers are adopting molecular methods using High Throughput Sequencing as the future of biomonitoring surveys; however, few regulatory and environmental management organizations/ agencies have adopted metabarcoding into their routine biomonitoring practices for regulatory purposes. In marine benthic communities, metabarcoding provides a comprehensive assessment of diversity and is useful for detecting a broader array of organisms in biomonitoring surveys especially when using two markers47. eDNA is also useful for monitoring discrete communities (i.e., benthic versus pelagic)52, so it is possible that using eRNA could provide vital information about living organisms in specific environments compared to eDNA. Metabarcoding sequencing and bioinformatic approaches for benthic environments vary among studies, thus requiring some standardization between methods to further advance the use of metabarcoding in conservation and regulatory applications30,53.Like eDNA, some advancements must be made with eRNA to be used as a quantitative tool in molecular ecology. Validation of eDNA metabarcoding for assessing relative abundance of species is rapidly progressing by correlating laboratory studies of DNA shedding with field experiments1,54. Similar validation techniques can be used to develop eRNA metabarcoding as a quantitative or semi-quantitative method. There is growing interest in optimizing eRNA extraction protocols from different types of environmental media to standardize the use of eRNA in downstream molecular applications55. Thus, standardizing eRNA protocols will help with integration into environmental management toolkits for regulatory purposes.RNA poses unique barcoding challenges compared to DNA because the number of RNA transcripts from a gene are not always present in the same proportion compared to the gene copy number per genome (i.e., one DNA copy per cell), especially for differentially expressed genes. However, one possible way to work around this issue is to utilize constitutively expressed marker loci where transcription is generally stable and unaffected by environmental stressors, such as those used as reference genes for quantitative real-time PCR49. Fortunately, many loci chosen for metabarcoding purposes fit this criterium; the transcription of 18S and COI remain steady within the cell regardless of environmental stress.Collecting sediment cores from the environment and bringing them into controlled laboratory settings for community analysis through eRNA metabarcoding is a powerful tool that opens opportunities for this method to be used in a broad range of fields. For instance, field-collected mesocosms could be used in controlled settings to investigate the effects of individual or mixtures of toxicants on entire community and population-level outcomes. Marine sediments are often the ultimate sink for environmental contaminants, such as organic pollutants , heavy metals56, and plastic particles57, yet few studies investigate actual community-level changes in contaminant exposures. Marine benthic environments have high biodiversity, but the breadth of diversity in micro- and meiofaunal organisms is often understudied because traditional morphological methods are immensely time consuming. Sediment core mesocosms could also be used to understand the effects of global climate change stressors, such as fluctuating temperatures, surface water salinity and pH, on communities as well as conventional and emerging contaminants in combination with climate change stressors. Additionally, this method could also be used to understand how communities respond to a significant disturbance event or smaller series of stressors, which are often difficult to measure in environmental settings32,58. In these applications, eRNA is favorable for constructing community composition at a specific moment of the experiment to better regulate anthropogenic causes of environmental stress. More

At least 11 coastal ecosystems have been considered based on a minimum of actionably defined criteria to be “blue carbon ecosystems”. These include mangrove wetlands, tidal marshes (salt, brackish, fresh), seagrasses, salt flats, freshwater (upper estuarine) tidal forests, macroalgae, phytoplankton, coral reef, marine fauna (fish), oyster reefs, and mud flats5; all but three of these would be considered wetlands, with salt flats and mud flats being examples of non-emergent (plant) blue carbon wetlands. Herein, we focus on mangroves.Adjusting intrinsic leaf-level photosynthetic water use efficiency (({WUE}_{int})) in response to environmental gradients (Introduction)We used data provided by B.F. Clough & R.G. Sims20, which presented leaf-scale net photosynthesis (({P}_{n}) [sic]; μmol CO2 m−2 s−1), stomatal conductance (({g}_{w}): mol m−2 s−1), leaf-intercellular CO2 concentrations (({c}_{i}): μl l−1), and intrinsic photosynthetic water use efficiency (({WUE}_{int}): (frac{{P}_{n}}{{g}_{w}})) for 19 mangrove species occupying 9 different sites in Papua New Guinea and northern Australia. These field data were collected using an infrared gas analyzer (model Li-6000, Li-Cor Biosciences, Inc., Lincoln, NE, USA) attached to leaves at saturation light levels (reported as  > 800 μmol PPFD m−2 s−1). Soil salinity at the time of data collection ranged from 10 to 49 psu, and median long-term atmospheric temperature and relative humidity among sites ranged from 19.9 to 27.4 °C and 35.1 to 92.2%, respectively (Fig. S1)20. These data were among the first to offer insight from field study into the plasticity of mangroves across a range of natural salinity and aridity gradients to adjust leaf-level ({WUE}_{int}) as needed for local environmental condition. While it is not new for trees to adjust their ({WUE}_{int}) when they develop in arid, semi-arid, or even some humid and tropical environments60, what is distinctive is that mangroves may be further driven to water savings by salinity gradients as a condition of development.
({{varvec{W}}{varvec{U}}{varvec{E}}}_{{varvec{i}}{varvec{n}}{varvec{t}}}) and individual tree water use of mangrove wetlands versus terrestrial ecosystemsFor Fig. 1a, we compare leaf-level ({WUE}_{int}) data collected from 17 published papers (using maximum and minimum values), providing 67 independent measurements of ({WUE}_{int}) for mangroves (Table S3). While we mention in the main text that as many as 214 independent measurements of water use efficiency are available, not all of these present raw ({P}_{n}) or ({g}_{w}) data, with some reporting leaf transpiration (({T}_{r})) which do not enable reporting of intrinsic water use efficiencies. Also, we strategically included studies from reproducible experimental designs and readily available papers. Mangrove species included in this review represented a global distribution of greenhouse and field observations, and encompassed species in the following mangrove genera: Rhizophora, Avicennia, Laguncularia, Bruguiera, Aegialitis, Aegiceras, Ceriops, Sonneratia, Kandelia, Excoecaria, Heritiera, Xylocarpus, and Conocarpus.We then accessed an existing database (n = 11,328 observations) that reported raw ({P}_{n}) and ({g}_{w}) data from 210 upland deciduous and evergreen shrubs and trees of savannah, boreal, temperate, and tropical habitats60. From these data, we evaluated a range of upland tree and shrub species that occurred and developed naturally in environments along a global gradient of vapor pressure deficit (i.e., atmospheric moisture and temperature), including arid, semi-arid, dry semi-humid, and humid locations.For Fig. 1b, we started with a review by Wullschleger et al.61 that provides maximum individual tree water use data (L H2O day−1) from 52 published studies representing 67 species of upland trees from around the world. Of those studies, dbh values (8 to 134 cm) were provided alongside 47 individual tree water use values. Maximum individual tree water use and dbh (4.1 to 45.3 cm) were available from the original source for 8 mangrove studies representing 7 species from French Guiana, Mayotte Island (Indian Ocean), China, Florida (USA), and Louisiana (USA) (Table S4). These represent the extent of published sap flow data that provided both individual tree water use and dbh from mangroves (numerically); e.g., we could not extract specific individual tree water use versus dbh from a Moreton Bay (Australia) study site62, south Florida study site63, or from five additional study sites in China51,52. However, regressions for two of the Chinese study sites provided over two years51 indicated that mangrove trees from a suite of species ranging in dbh from 8 to 24 cm used approximately 0.76 and 9.31 L H2O day−1, or 0.53 L H2O day−1 cm−1 of dbh. These apparent rates were even lower than what was reported as average for mangroves in Fig. 1b of 1.4 L H2O day−1 cm−1. The mangrove species included in this analysis were Avicennia germinans (L.) L., Laguncularia racemosa (L.) C.F. Gaertn., Rhizophora mangle L., Ceriops tagal (Perr.) C.B. Rob., Rhizophora mucronata Lam., Sonneratia apetala Buch.-Ham, and Sonneratia caseolaris (L.) Engl.. Additional comparative mangrove species reported by B. Leng & K.-F. Cao51 included Bruguiera sexangula (Lour.) Poir., Bruguiera sexangula var. rhynchopetala W.C. Ko, Excoecaria agallocha L., Rhizophora apiculata Blume, Sonneratia alba Sm., and Xylocarpus granatum J. Koenig.Estimation of canopy transpiration (({{varvec{E}}}_{{varvec{c}}})) from net primary productivity dataEstimates of carbon uptake from CO2 can provide insight into the water use requirement for that uptake of carbon64. We used leaf-level instantaneous water use efficiency (({WUE}_{ins}): (frac{{P}_{N}}{{T}_{r}})), which relates to net CO2 uptake from leaves of the dominant mangrove forest canopy relative to the specific amount of water used, and developed a predictive relationship (predicted) for determining mangrove net primary productivity (NPP) values from ({E}_{c}) using ({WUE}_{ins}). For A. germinans, L. racemosa, and R. mangle forest components, we used light-saturated, leaf-level ({WUE}_{ins}) values of 3.82 ± 0.3, 4.57 ± 0.3, and 5.15 ± 0.4 mmol CO2 (mol H2O)−1 [± 1 SE], respectively, from mangrove saplings and small trees of south Florida65. ({WUE}_{ins}) values were stratified by species relative to basal area distributions on each south Florida study plot, converted from molar fractions of H20 (from ({E}_{c}) determination) and CO2 to molecular weights, and multiplied by ({WUE}_{ins}) with applicable unit conversions to attain kg CO2 m−2 year−1. This value was multiplied by 0.273 to yield kg C m−2 year−1.This predictive relationship was validated in two independent ways. First, for one of the calibration sites (lower Shark River, Everglades National Park, Florida, USA), we modeled ({E}_{c}) from sap flow data50, determined NPP from ({WUE}_{ins}) calculations relative to the amount of water the stand used, and had independent measurements of net ecosystem exchange (NEE) of CO2 between the mangrove ecosystem and atmosphere from an eddy flux tower66. For this site, NPP estimation and NEE were closely aligned once soil CO2 effluxes were accounted; respiratory CO2 effluxes from soil and pneumatophores were determined to be 1.2 kg C m−2 year−1 from previous study67. Using our NPP estimations from ({WUE}_{ins}) calculations and subtracting soil and pneumatophore CO2 effluxes of 1.2 kg C m−2 for 2004 and 0.8 kg C m−2 for 2005 (partial year), NPP becomes 0.96 kg C m−2 for 2004 and 0.85 kg C m−2 for January to August of 2005 (see Observed 1, Florida on Fig. S2). Our approach underestimated NPP from ({E}_{c}) relative to measurements from eddy covariance by 0.21 kg C m-2 for 2004 (within 17.5% of predicted) and was nearly identical for 2005 (within 0.02 kg C m−2, or 2% of predicted).Second, we wanted to determine whether ({E}_{c})-to-NPP predictions developed on a few sites in south Florida, USA, represented other global sites, so we included an analysis from several mangrove sites in Guangdong Province, China, to represent an entirely different location. Similar to south Florida analyses, we combined data for NPP from co-located sites of ({E}_{c}) determination using sap flow techniques. NPP of the mangrove forests were measured using multiple procedures (including eddy flux) for improved accuracy68,69. The relationships of predicted NPP versus ({E}_{c}) and observed NPP versus ({E}_{c}) did not differ between Florida and China (t = 0.48, p = 0.643).Projecting mangrove ({{varvec{E}}}_{{varvec{c}}}) to other locationsWe reviewed data from 26 published records that report mangrove NPP, or enough data to estimate NPP, from 71 study sites located in the Florida-Caribbean Region (25 sites) and Asia–Pacific Region (46 sites) (Table S5). Table S1 reveals mangrove literature sources used, as well as how NPP was estimated from values provided in the original source; itemizes assumptions for determinations of aboveground NPP, wood production, litter production, and root production from various ratios70; and reveals unit conversions.We then convert NPP to ({E}_{c}) for all 71 sites using the predicted curve in Fig. S2 (Eq. 1, main text), and provide summary results by location in Table S1. Regional (ET) data were extracted from the MODIS Global Evapotranspiration Project (MOD16-A3), which are provided at a resolution of 1-km. The locations of mangrove NPP study sites were identified, assigned to a single 1-km2 grid in MOD16, and (ET) was extracted from that grid and used for ({E}_{c})-to-(ET) comparison. Average (ET) from single cells (1 km2) was combined with the average of up to 8 additional neighboring cells to provide comparative (ET) projections over up to 9 km2 for each location from 2000 to 2013 to compare sensitivity among suites of the specific MODIS16-A3 cells selected over land. When neighboring cells were completely over water, they were excluded since component mangrove forest ({E}_{c}) estimation was not possible from the cells. Estimates of (ET) by individual cells used to compare with mangrove ({E}_{c}) versus up to 9 cells differed by an average of only 43 mm H2O year−1 (± 16 mm H2O year−1, S.E.). Therefore, we use (ET) from individual, overlapping ({E}_{c}) cells in Table S1.The average ({E}_{c})-to-(ET) ratio from mangroves was subtracted from ({E}_{c})-to-(ET) ratio for specific ecoregions48, and this ratio difference was assumed to represent net water use strategy affecting differences by the dominant vegetation between ecosystem types. We were also mindful that salinity reductions can affect ({E}_{c}). We used scaled (0–1) mean and standard deviations from ({WUE}_{int}) data previously reported for mangroves (Fig. S1)20. Standard deviation was 32% of mean ({WUE}_{int}) related to salinity gradients, and if we re-scale this deviation to ({E}_{c}) data and add it to the mean ({E}_{c}) to assume low salinity, average ({E}_{c})-to-(ET) ratio becomes 57.4%. This is theoretical and assumes a relatively linear relationship between ({WUE}_{int}) and ({E}_{c}).Comparative water use scaling among ecoregionsTable 1 presents the projected reduction in water used through ({E}_{c}) if a mangrove ({E}_{c})-to-(ET) ratio was applied to tropical rainforest (290.52 mm H2O year−1), temperate deciduous forest (131.76 mm H2O year−1), tropical grassland (110.77 mm H2O year−1), temperate grassland (46.48 mm H2O year−1), temperate coniferous forest (54.96 mm H2O year−1), desert (22.99 mm H2O year−1), and Mediterranean shrubland (12.08 mm H2O year−1). To convert potential water use differences to kL H2O ha−1 year−1 (as presented in the abstract), the following calculation is used (using the example of tropical rainforest):$$frac{290.52 L {H}_{2}O {year}^{-1}}{1 {m}^{2}} times frac{mathrm{10,000 }{m}^{2}}{1 ha} times frac{1 kL {H}_{2}O}{mathrm{1000 }L {H}_{2}O} =mathrm{2905 } kL {H}_{2}O {ha}^{-1}{year}^{-1}$$
(2)
For comparisons made to mature ( > 12 years) oil palm (Elaeis guineensis Jacq.) plantations, ({E}_{c})-to-(ET) ratio was assumed to range from 5332 to 70%33, for a water use difference of 1170 and 3160 kL H2O ha−1 year−1, respectively, relative to annual global mangrove (ET) (of 1172 mm). We multiply these values by the 18,467 ha of land area that was converted from mangroves to oil palm31 to attain potential water use differences of 21.6 to 58.4 GL H2O year−1 from avoided conversion of mangrove to oil palm in this region.Global water use scalingIn order to determine how much global mangrove area is adjacent to each ecoregion, we conducted a cross-walk between terrestrial ecoregions71 and those used by Global Mangrove Watch in the 2010 classification of global mangrove area72. Terrestrial ecoregions used by Schlesinger & Jasechko48 were then able to be associated with specific mangrove areas (Table S6). In other words, given a specific ecoregion, we determined how much mangrove area would be occurring within that same ecoregional geography. Global mangrove area assignment to those ecoregions mapped within 0.1% of the total mangrove area of 13,760,000 ha reported in Bunting et al.72. To convert kL H2O ha−1 year−1 to GL H2O year−1 among ecoregions, the following calculation was used (continuing with the example of tropical rainforest, which has an area of adjacent mangroves of 112,331.9 km2):$$frac{mathrm{2905} kL {H}_{2}O {year}^{-1}}{1 ha} times frac{100 ha}{1 {km}^{2}} times frac{mathrm{112,331.90} {km}^{2 }mangroves}{1.0 times {10}^{6} kL {H}_{2}0} times frac{1 GL {H}_{2}O}{1} = mathrm{32,632.42} GL {H}_{2}O {year}^{-1}$$
(3)
Agent-based modelling of individual tree water use (Discussion)The BETTINA model simulates the growth of mangrove trees as a response to above- and below-ground resources, i.e. light and water41. In the model, an individual tree is described by four geometric measures, including stem radius, stem height, crown radius and root radius; attributing functional relevance in terms of resource uptake. Aiming to maximize resource uptake, new biomass is allocated to increase these measures in an optimal but not constant proportion. Water uptake of the tree is driven by the water potential gradient between the soil and the leaves. Thus, porewater salinity is part of what determines the water availability for plants.With the BETTINA model, we simulated the growth of nine individual mangrove trees under different salinity conditions, ranging between 0 and 80 psu, while all other environmental and tree-specific conditions were kept constant. Simulation time was 200 years so that trees could achieve very close to their maximum possible size, and the hydrological parameters were similar to that reported previously42. We can show that the ratio of the actual transpiration to the potential transpiration decreases with increasing salinity; plants use less water. Potential transpiration was the transpiration of a given tree without a simulated reduction in water availability due to porewater salinity. These parameter details are presented graphically for mangroves (Fig. S3), comparing porewater salinity along a gradient against the ratio of actual-to-potential individual tree transpiration.Further, BETTINA simulation results include morphological plasticity adjustments to allometry. To highlight this, we also displayed results assuming a constant allometry as for 40 psu. Naturally, for this arbitrary benchmark the solid and the dashed line coincide (Fig. 3a). Adaptation to higher salinities improves water uptake (primarily girth and root growth), thus the adapted trees (solid lines) have a higher water uptake than the average allometry (dashed lines) for salinities below 40 psu. Lower salinities promote increase of height and crown radius to improve light availability. That is why the adapted trees have a lower water uptake than an average tree would for salinities above 40 psu. Tree water use decreases with increasing salinity (Fig. 3b), as ({WUE}_{int}) coincidently increases (Fig. S1).Virtual water use explained (Discussion)Water is required to produce products or acquire services from natural ecosystems; e.g., forest products, fisheries biomass, nutrient processing (nitrification, denitrification), food production. If a net kilogram of a food is grown on a hectare of land where water is abundant and that kilogram of food requires 400 mm of water to be produced, the export of that food to an area of low water availability provides an ecosystem service in the amount of 1 kg of food, plus 400 mm of “virtual” water not actually needed at the destination but used at the source. This water is defined as the product’s “virtual water content”56. There is a rich body of literature exploring the concept of virtual water73,74, but we expand on this concept here as a comparison among 7 ecoregions48 and mangroves. Raw data used for calculations are presented in Table S2.Statistical analysisData for leaf-level ({WUE}_{int}) comparisons between terrestrial woody plants and mangroves, as well as individual tree water use by dbh for both terrestrial and mangrove trees, were not normally distributed. We used a Kruskal–Wallis ANOVA based on ranks, and the Dunn’s Method for difference tests. Individual tree water use by dbh for both terrestrial and mangrove trees were determined using linear regression, mostly applied to mean values. For a couple of mangrove studies, only median values were extractable from minimum and maximum values. Likewise, all other data relationships were best fit with linear models, including the calibration curves between ({E}_{c}) and NPP. All data were analyzed using SigmaPlot (v. 14.0, Systat, Inc., Palo Alto, California, USA). More

Hoham, R. W. & Remias, D. Snow and glacial algae: A review. J. Phycol. 56, 264–282 (2020).PubMed 
PubMed Central 

Google Scholar 
Williamson, C. J. et al. Glacier algae: A dark past and a darker future. Front. Microbiol. 10, 524 (2019).
PubMed 
PubMed Central 

Google Scholar 
Hodson, A. J., Mumford, P. N., Kohler, J. & Wynn, P. M. The High Arctic glacial ecosystem: New insights from nutrient budgets. Biogeochemistry 72, 233–256 (2005).CAS 

Google Scholar 
Stibal, M., Tranter, M., Telling, J. & Benning, L. G. Speciation, phase association and potential bioavailability of phosphorus on a Svalbard glacier. Biogeochemistry 90, 1–13 (2008).CAS 

Google Scholar 
Telling, J. et al. Microbial nitrogen cycling on the Greenland ice sheet. Biogeosciences 9, 2431–2442 (2012).ADS 
CAS 

Google Scholar 
Cook, J. M. et al. Glacier algae accelerate melt rates on the south-western Greenland ice sheet. Cryosphere 14, 309–330 (2020).ADS 

Google Scholar 
Yallop, M. L. et al. Photophysiology and albedo-changing potential of the ice algal community on the surface of the Greenland ice sheet. ISME J. 6, 2302–2313 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stibal, M. et al. Algae drive enhanced darkening of bare ice on the Greenland ice sheet. Geophys. Res. Lett. 44, 11463–11471 (2017).ADS 

Google Scholar 
Di Mauro, B. et al. Glacier algae foster ice-albedo feedback in the European Alps. Sci. Rep. 10, 1–9 (2020).ADS 

Google Scholar 
Lutz, S. et al. The biogeography of red snow microbiomes and their role in melting arctic glaciers. Nat. Commun. 7, 1–9 (2016).ADS 

Google Scholar 
Ganey, G. Q., Loso, M. G., Burgess, A. B. & Dial, R. J. The role of microbes in snowmelt and radiative forcing on an Alaskan icefield. Nat. Geosci. 10, 754–759 (2017).ADS 
CAS 

Google Scholar 
Khan, A. L., Dierssen, H. M., Scambos, T. A., Höfer, J. & Cordero, R. R. Spectral characterization, radiative forcing and pigment content of coastal Antarctic snow algae: Approaches to spectrally discriminate red and green communities and their impact on snowmelt. Cryosphere 15(1), 133–148 (2021).ADS 

Google Scholar 
Huovinen, P., Ramírez, J. & Gómez, I. Remote sensing of albedo-reducing snow algae and impurities in the Maritime Antarctica. ISPRS J. Photogramm. Remote Sens. 146, 507–517 (2018).ADS 

Google Scholar 
Stibal, M., Šabacká, M. & Žárský, J. Biological processes on glacier and ice sheet surfaces. Nat. Geosci. 5, 771–774 (2012).ADS 
CAS 

Google Scholar 
Williamson, C. J. et al. Algal photophysiology drives darkening and melt of the Greenland ice sheet. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1918412117 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Chevrollier, L.-A. et al. Light absorption and albedo reduction by pigmented microalgae on snow and ice. J. Glaciol. https://doi.org/10.1017/jog.2022.64 (2022).Article 

Google Scholar 
Procházková, L., Řezanka, T., Nedbalová, L. & Remias, D. Unicellular versus filamentous: The glacial alga ancylonema alaskana comb. et. stat. nov. and its ecophysiological relatedness to ancylonema nordenskioeldii (zygnematophyceae, streptophyta). Microorganisms 9(5), 1103 (2021).PubMed 
PubMed Central 

Google Scholar 
Remias, D. et al. Characterization of an UV- and VIS-absorbing, purpurogallin-derived secondary pigment new to algae and highly abundant in Mesotaenium berggrenii (Zygnematophyceae, Chlorophyta), an extremophyte living on glaciers. FEMS Microbiol. Ecol. 79, 638–648 (2012).CAS 
PubMed 

Google Scholar 
Williamson, C. J. et al. Ice algal bloom development on the surface of the Greenland ice sheet. FEMS Microbiol. Ecol. 94, 1–10 (2018).
Google Scholar 
Remias, D., Lütz-Meindl, U. & Lütz, C. Photosynthesis, pigments and ultrastructure of the alpine snow alga Chlamydomonas nivalis. Eur. J. Phycol. 40, 259–268 (2005).CAS 

Google Scholar 
Leya, T., Rahn, A., Lütz, C. & Remias, D. Response of arctic snow and permafrost algae to high light and nitrogen stress by changes in pigment composition and applied aspects for biotechnology. FEMS Microbiol. Ecol. 67, 432–443 (2009).CAS 
PubMed 

Google Scholar 
Müller, T., Bleiß, W., Martin, C. D., Rogaschewski, S. & Fuhr, G. Snow algae from northwest Svalbard: Their identification, distribution, pigment and nutrient content. Polar Biol. 20, 14–32 (1998).
Google Scholar 
Remias, D., Karsten, U., Lütz, C. & Leya, T. Physiological and morphological processes in the Alpine snow alga Chloromonas nivalis (Chlorophyceae) during cyst formation. Protoplasma 243, 73–86 (2010).PubMed 

Google Scholar 
Bidigare, R. R. et al. Evidence for a photoprotective function for secondary carotenoids of snow algae taxonomy, life histories, ecology and geographical habitats and polar regions. J. Phycol. 434, 427–434 (1993).CAS 

Google Scholar 
Remias, D. & Lütz, C. Characterisation of esterified secondary carotenoids and of their isomers in green algae: A HPLC approach. Arch. Hydrobiol. Suppl. Algol. Stud. 124, 85–94 (2007).CAS 

Google Scholar 
Dial, R. J., Ganey, G. Q. & Skiles, S. M. What color should glacier algae be ? An ecological role for red carbon in the cryosphere. FEMS Microbiol. Ecol. 94(3), 1–9. https://doi.org/10.1093/femsec/fiy007 (2018).Article 
CAS 

Google Scholar 
Uusikivi, J., Vähätalo, A. V., Granskog, M. A. & Sommaruga, R. Contribution of mycosporine-like amino acids and colored dissolved and particulate matter to sea ice optical properties and ultraviolet attenuation. Limnol. Oceanogr. 55, 703–713 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Xie, H., Aubry, C., Zhang, Y. & Song, G. Chromophoric dissolved organic matter (CDOM) in first-year sea ice in the western Canadian Arctic. Mar. Chem. 165, 25–35 (2014).CAS 

Google Scholar 
Holzinger, A. & Lütz, C. Algae and UV irradiation: Effects on ultrastructure and related metabolic functions. Micron 37, 190–207 (2006).PubMed 

Google Scholar 
Piiparinen, J. et al. The contribution of mycosporine-like amino acids, chromophoric dissolved organic matter and particles to the UV protection of sea-ice organisms in the Baltic Sea. Photochem. Photobiol. Sci. 14, 1025–1038 (2015).CAS 
PubMed 

Google Scholar 
Cook, J. M. et al. Quantifying bioalbedo: A new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo. Cryosphere 11, 2611–2632 (2017).ADS 

Google Scholar 
Lutz, S., Anesio, A. M., Jorge Villar, S. E. & Benning, L. G. Variations of algal communities cause darkening of a Greenland glacier. FEMS Microbiol. Ecol. 89, 402–414 (2014).CAS 
PubMed 

Google Scholar 
Hoham, R. W. & Ling, H. U. Snow algae: Tile effects of chemical and physical factors on their life cycles and populations. In Journey to Diverse Microbial Worlds (ed. Seckback, J.) 131–145 (Springer, Netherlands, 2000).
Google Scholar 
Procházková, L., Leya, T., Krížková, H. & Nedbalová, L. Sanguina nivaloides and Sanguina aurantia gen. Et spp. Nov. (Chlorophyta): The taxonomy, phylogeny, biogeography and ecology of two newly recognised algae causing red and orange snow. FEMS Microbiol. Ecol. 95, 1–21 (2019).
Google Scholar 
Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).
Google Scholar 
Montagnes, D. J. S., Berges, J. A., Harrison, P. J. & Taylor, F. J. R. Estimating carbon, nitrogen, protein and chlorophyll a from volume in marine phytoplankton. Limnol. Oceanogr. 39, 1044–1060 (1994).ADS 
CAS 

Google Scholar 
Schreiber, U., Gademann, R., Ralph, P. J. & Larkum, A. W. D. Assessment of photosynthetic performance of Prochloron in Lissoclinum patella in hospite by chlorophyll fluorescence measurements. Plant Cell Physiol. 38, 945–951 (1997).CAS 

Google Scholar 
Jassby, A. D. & Platt, T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547 (1976).ADS 
CAS 

Google Scholar 
Silsbe, G. M. & Malkin, S. Y. Package ‘phytotools’: Phytoplankton Production Tools. (2015).Aigner, S., Remias, D., Karsten, U. & Holzinger, A. Unusual phenolic compounds contribute to ecophysiological performance in the purple-colored green alga Zygogonium ericetorum (Zygnematophyceae, Streptophyta) from a high-alpine habitat. J. Phycol. 49, 648–660 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Holzinger, A. et al. Arctic, Antarctic, and temperate green algae Zygnema spp. under UV-B stress: Vegetative cells perform better than pre-akinetes. Protoplasma 255, 1239–1252 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bidigare, R. R., Ondrusek, M. E., Morrow, J. H. & Kiefer, D. A. In-vivo absorption properties of algal pigments. Ocean Opt. X 1302, 290 (1990).ADS 

Google Scholar 
Clementson, L. A. & Wojtasiewicz, B. Dataset on the absorption characteristics of extracted phytoplankton pigments. Data Br. 24, 103875 (2019).
Google Scholar 
Bidigare, R. R., Ondrusek, M. E., Morrow, J. H. & Kiefer, D. A. In vivo absorption properties of algal pigments. Ocean Opt. X 1302, 290–302 (1990).ADS 

Google Scholar 
Bricaud, A. & Stramski, D. Spectral absorption coefficients of living phytoplankton and nonalgal biogenous matter: A comparison between the Peru upwelling areaand the Sargasso Sea. Limnol. Oceanogr. 35, 562–582 (1990).ADS 
CAS 

Google Scholar 
Duval, B., Shetty, K. & Thomas, W. H. Phenolic compounds and antioxidant properties in the snow alga Chlamydomonas nivalis after exposure to UV light. J. Appl. Phycol. 11, 559–566 (2000).
Google Scholar 
Onuma, Y. et al. Observations and modelling of algal growth on a snowpack in north-western Greenland. Cryosphere 12, 2147–2158 (2018).ADS 

Google Scholar 
Christner, B. C. et al. Microbial processes in the weathering crust aquifer of a temperate glacier. Cryosphere 12, 3653–3669 (2018).ADS 

Google Scholar 
Cook, J. M., Hodson, A. J. & Irvine-Fynn, T. D. L. Supraglacial weathering crust dynamics inferred from cryoconite hole hydrology. Hydrol. Process. 30, 433–446 (2016).ADS 

Google Scholar 
Tedstone, A. J. et al. Algal growth and weathering crust state drive variability in western Greenland ice sheet ice albedo. Cryosphere 14, 521–538 (2020).ADS 

Google Scholar 
Smith, L. C. et al. Efficient meltwater drainage through supraglacial streams and rivers on the southwest Greenland ice sheet. Proc. Natl. Acad. Sci. U. S. A. 112, 1001–1006 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Irvine-Fynn, T. D. L. et al. Storage and export of microbial biomass across the western Greenland ice sheet. Nat. Commun. 12, 1–11 (2021).
Google Scholar 
Cameron, K. A. et al. Meltwater export of prokaryotic cells from the Greenland ice sheet. Environ. Microbiol. 19, 524–534 (2017).PubMed 

Google Scholar 
Stibal, M. et al. Environmental controls on microbial abundance and activity on the Greenland ice sheet: A multivariate analysis approach. Microb. Ecol. 63, 74–84 (2012).PubMed 

Google Scholar 
Mernild, S. H., Liston, G. E., Hasholt, B. & Knudsen, N. T. Snow distribution and melt modeling for Mittivakkat Glacier, Ammassalik Island, southeast Greenland. J. Hydrometeorol. 7, 808–824 (2006).ADS 

Google Scholar 
Stibal, M., Elster, J., Šabacká, M. & Kaštovská, K. Seasonal and diel changes in photosynthetic activity of the snow alga Chlamydomonas nivalis (Chlorophyceae) from Svalbard determined by pulse amplitude modulation fluorometry. FEMS Microbiol. Ecol. 59, 265–273 (2007).CAS 
PubMed 

Google Scholar 
Remias, D., Holzinger, A., Aigner, S. & Lu, C. Ecophysiology and ultrastructure of Ancylonema nordenskioldii (Zygnematales, Streptophyta), causing brown ice on glaciers in Svalbard (high arctic). 899–908 (2012). doi:https://doi.org/10.1007/s00300-011-1135-6Remias, D., Holzinger, A. & Lütz, C. Physiology, ultrastructure and habitat of the ice Alga Mesotaenium berggrenii (Zygnemaphyceae, Chlorophyta) from Glaciers in the European Alps. Phycologia 48, 302–312 (2009).
Google Scholar 
Nakashima, T. et al. Spatial and temporal variations in pigment and species compositions of snow algae on Mt. Tateyama in Toyama prefecture, Japan.. Front. Plant Sci. 12, 1–16 (2021).
Google Scholar 
Remias, D., Albert, A. & Lütz, C. Effects of realistically simulated, elevated UV irradiation on photosynthesis and pigment composition of the alpine snow alga Chlamydomonas nivalis and the arctic soil alga Tetracystis sp. (Chlorophyceae). Photosynthetica 48, 269–277 (2010).CAS 

Google Scholar 
Procházková, L., Remias, D., Holzinger, A., Řezanka, T. & Nedbalová, L. Ecophysiological and ultrastructural characterisation of the circumpolar orange snow alga Sanguina aurantia compared to the cosmopolitan red snow alga Sanguina nivaloides (Chlorophyta). Polar Biol. 44, 105–117 (2021).PubMed 

Google Scholar 
Niyogi, K. K. Photoprotection revisited: Genetic and molecular approaches. Annu. Rev. Plant Biol. 50, 333–359 (1999).CAS 

Google Scholar 
Sakshaug, E. & Holm-hansen, O. Photoadaptation in Antarctic phytopfankton: Variations in growth rate, chemical composition and P versus I curves. J. Plankton Res. 8, 459–473 (1986).
Google Scholar 
Malerba, M. E., Palacios, M. M., Palacios Delgado, Y. M., Beardall, J. & Marshall, D. J. Cell size, photosynthesis and the package effect: An artificial selection approach. New Phytol. 219, 449–461 (2018).CAS 
PubMed 

Google Scholar 
Wagner, B., Ochs, D. & Bieler, K. Derivatives as antimicrobial agents. Engineering 8, 240–244 (2011).
Google Scholar 
Perini, L. et al. Darkening of the Greenland ice sheet: Fungal abundance and diversity are associated with algal bloom. Front. Microbiol. 10, 557 (2019).PubMed 
PubMed Central 

Google Scholar 
Perini, L. et al. Interactions of fungi and algae from the Greenland ice sheet. Microb. Ecol. https://doi.org/10.1007/s00248-022-02033-5 (2022).Article 
PubMed 

Google Scholar 
Taskjelle, T. et al. Spectral albedo and transmittance of thin young Arctic sea ice. J. Geophys. Res. Ocean. 121, 540–553 (2015).ADS 

Google Scholar 
Lutz, S., Anesio, A. M., Edwards, A. & Benning, L. G. Linking microbial diversity and functionality of arctic glacial surface habitats. Environ. Microbiol. 19, 551–565 (2017).CAS 
PubMed 

Google Scholar 
Smith, H., Dieser, M., McKnight, D., SanClements, M. & Foreman, C. Relationship between dissolved organic matter quality and microbial community composition across polar glacial environments. FEMS Microbiol. Ecol. 94(7), fiy090 (2018).CAS 

Google Scholar 
Kirk, J. T. O. A theoretical analysis of the contributino of algal cells to the attenuation of light within natural waters. New Phytol. 77, 341–358 (1976).
Google Scholar 
Morel, A. & Bricaud, A. Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton. Deep Sea Res. Part A. Oceanogr. Res. Pap. 28, 1375–1393 (1981).ADS 

Google Scholar 
Stuart, V., Sathyendranath, S., Platt, T., Maass, H. & Irwin, B. D. Pigments and species composition of natural phytoplankton populations: Effect on the absorption spectra. J. Plankton Res. 20, 187–217 (1998).CAS 

Google Scholar 
Kirk, J. Light and Photosynthesis in Aquatic Environment (University Press, 1983).
Google Scholar 
Nelson, N. B., Prezelin, B. B. & Bidigare, R. R. Phytoplankton light absorption and the package effect in California coastal waters. Mar. Ecol. Prog. Ser. 94, 217–227 (1993).ADS 

Google Scholar 
Holzinger, A., Allen, M. C. & Deheyn, D. D. Hyperspectral imaging of snow algae and green algae from aeroterrestrial habitats. J. Photochem. Photobiol. B Biol. https://doi.org/10.1016/j.jphotobiol.2016.07.001 (2016).Article 

Google Scholar 
Anesio, A. M. et al. Monitoring glacial algae and impurities on the Greenland Ice Sheet. Aarhus Univ. DCE – Danish Cent. Environ. Energy, Sci. Rep. No. 489 26 (2022).QGIS.org, %Y. QGIS Geographic Information System. QGIS Association. http://www.qgis.org. More