Ecology

Physicochemical properties of water
Physicochemical properties of water and associated environmental factors according to the flow directions of surface flow wetlands are shown in Table 1. Analysis of variance for indexes with p values of less than 0.05 showed that all indexes exhibited large variations during different months.
Table 1 Physicochemical characteristics of the surface-flow constructed wetlands in each unit.
Full size table

Some indexes exhibited large variations during different months according to the flow directions of surface flow wetlands. For example, DO was first reduced and then increased in May, but increased in August and showed differences compared with that in May and October. The salinity was first reduced and then increased in May but then remained stable from August to October. Some indexes showed similar changes according to the flow directions of surface flow wetlands. For example, ORP showed an initial decrease followed by an increase. At the same time, pH, SpCond, TDSs, and TN showed reduced variability over time. The changes in temperature were minimal, although the temperature was higher in the summer and autumn than in the spring.
Surface flow wetlands are in direct contact with the environment and are greatly influenced by outside environmental factors17. Thus, most physicochemical factors of the water samples showed large variability.
Denitrifying bacteria diversity and abundance
For 10 samples from different seasons showing 97% similarity in clustering analysis, the numbers of OTUs differed in May, August, and October (575, 869, and 741, respectively), and the Fig. 2 showed that the denitrifying bacterial abundance indexes (ACEs) were 686.8, 996.2, and 887.3 in May, August, and October, respectively. Additionally, the Shannon-Weiner indexes (H′) were 3.718, 4.303, and 4.432, respectively, indicating that the abundance tended to increase initially, followed by a decrease, and diversity tended to increase. The different seasons affected both the denitrifying bacteria abundance and diversity. Abundance was the largest in August, but its diversity was lower than that in October. These data suggested that the main species became dominant during August, affecting the structure of the denitrifying bacteria.
Figure 2

Biodiversity and abundance of the surface-flow constructed wetland in each unit.

Full size image

For different processing units, the abundance and diversity of denitrifying bacteria varied slightly; both the ACE and H′ index showed low variability. The units F, H, and J showed greater declines than the initial values. In May, the ACE index peaked, with a value of 841 at location I. In August, the ACE index peaked at location E (1251), and that in October peaked at location G (1042). In different months, denitrifying bacteria abundances showed similar changes. Because bacterial diversity in the flowing water and static water were affected by different factors, the surface flow wetlands will be susceptible to various factors, and the bacterial community interactions with internal and external environmental factors will be important for bacterial survival18. Additionally, the number of constructed wetland bacteria decreases as the depth and distance increases19,20, suggesting that denitrifying bacteria may be affected by physical and chemical indicators of changes in water.
Community structure of denitrifying bacteria
Similar OTUs (97% similarity) were identified by sequencing. Database analysis of sequence alignment results revealed that there were many bacteria in the environmental samples that could not be cultivated but that showed high similarity; thus, the denitrifying bacteria were mostly present in the surface flow wetlands and were not cultured. Figure 3 shows statistical analysis of the denitrifying bacterial categories in a histogram format. During the different months, OTUs mainly belonged to seven genera: unclassified bacteria (37.12%), unclassified Proteobacteria (18.16%), Dechloromonas (16.21%), unranked environmental samples (12.51%), unclassified Betaproteobacteria (9.73%), unclassified Rhodocyclaceae (2.14%), Rhodanobacter (1.51%), and other genera (2.62%, representing less than 1% each). Several genera have also been found in surface flow wetlands21 and other types of constructed wetlands22,23,24, albeit with different proportions.
Figure 3

Community structure of the surface-flow constructed wetland in each unit.

Full size image

The same processing units showed different denitrifying bacterial community structures during different seasons and were always changing. Unclassified bacteria showed a greater weight during May for the A processing unit, although its weight was lower than that of Dechloromonas in August. In October, unclassified bacteria had become the most dominant group, and the proportion of Dechloromonas was extremely low. For the B processing unit, from May to October, the proportion of Dechloromonas was decreased, and the proportion of unclassified Proteobacteria was increased, overtaking Dechloromonas. For the C and D processing units, unclassified Proteobacteria were dominant in May, and unclassified bacteria were dominant in August and October. For the E, G, H, I, and J processing units, unclassified bacteria were dominant at all time points. For the F processing unit, the bacterial groups were similar to those of the B processing unit, with proportion of Dechloromonas decreasing and the proportion of unclassified bacteria increasing in October.
Figure 4 shows the means and variances of denitrifying bacteria genus proportions among different processing units and seasons. The means and variances of the dominant genus were large at the same time during different seasons. Thus, the dominant genus often determined the changes in denitrifying bacteria community structures during different seasons in the same unit. However, the greatest variance was observed in the genus Dechloromonas, which was the second most dominant genus in May. This suggested that this genus showed greater changes in different processing units than others. In August, the largest variances were observed in the genus Dechloromonas and in unclassified Proteobacteria, which had lower means than unclassified bacteria. Similar results were observed in October. The largest variance was observed in unclassified Proteobacteria, indicating that the denitrifying bacterial community structures were affected by the second dominant genus over time in the different processing units.
Figure 4

Mean and variance of different denitrifying bacterial taxa in the surface-flow constructed wetland (May, August, and October).

Full size image

For the denitrifying bacteria community structures in different months, we used nonmetric multidimensional scaling to determine the similarities between different processing units during the same months. As shown in Fig. 5, in May, A, B, C, and E showed high similarity, whereas other samples were more dispersed. The distances between D and H and between G and I were shorter than the other distances. F and J were alone in a group. Sample distributions were concentrated in August; C, D, E, F, G, H, and I were relatively similar, and D and E showed maximum similarity. A and B showed some similarity. In contrast, J was distinct. In October, distributions were more dispersed, and the distances between two points were not relatively similar, whereas the differences between the various processing units were higher.
Figure 5

Nonmetric multidimensional scaling map (May, August, and October).

Full size image

Relationships between denitrifying bacteria and environmental factors
Next, we carried out PCA analysis to determine the main factors affecting denitrifying bacteria. After maximum variance orthogonal rotating (p = 0.05), there were two principal component eigenvalues that were greater than the average. The two top principal components contributed to 53.9% and 28.7% of the variance. The first principal component mainly reflected SpCond, TDSs, ORP, and salinity (factor loading was 0.409, 0.403, 0.398, and 0.403, respectively), and the second principal component reflected DO, TN, pH, and temperature (factor loading was 0.449, 0.449, 0.465, and 0.446, respectively). The load distribution characteristics of different environmental factors showed that the surface flow wetlands were affected by the main environmental factors, including temperature, SpCond, DO, pH, ORP, and TN (Fig. 6).
Figure 6

PCA of various environmental factors in the surface-flow constructed wetlands.

Full size image

Table 2 shows bacterial abundance indexes and H′ index for different external environmental factors, as analyzed by Pearson correlation analysis. The results showed that the bacterial abundance was strongly correlated with temperature, DO, and pH, and H′ was strongly correlated with all parameters except TN.
Table 2 Relationships between biodiversity and environment factors.
Full size table

RDA was performed (Fig. 7) for analysis of community distributions and the relationships among environmental factors. For screening of the physicochemical factors of water and the proportions of denitrifying bacterial genera, standardization to center (Monte Carlo permutation) tests were used, and refinement of the information extracted from the first and second axes showed that the total explained variance rate was 80.94%. The results showed that all denitrifying bacterial genera were greatly affected by environmental factors, including temperature and pH, and that the effects of SpCond and ORP were similar. The predominance of unclassified bacteria and unclassified Proteobacter could be explained by positive correlations with temperature, pH, ORP, and SpCond and negative correlations with TN and DO. Dechloromonas showed the opposite trends. In contrast, unranked environmental samples were similar to unclassified Betaproteobacteria, with positive correlations for temperature and pH but negative correlations for TN and DO.
Figure 7

Relationships between denitrifying bacterial community structures and environment factors.

Full size image

Denitrifying bacterial diversity is affected by water nutrient elements and other environmental factors. Most denitrifying bacteria were heterotrophic bacteria. In this study, the autotrophic denitrifying bacteria Dechloromonas accounted for a large proportion in each processing unit25,26,these bacteria can accumulate phosphate and exhibit denitrification activity, partly explaining the lack of TOC removal in association with the observed TN removal. The SpCond of the water reflected its salinity and could be explained by positive correlations with a high proportion of unclassified Proteobacteria. However, SpCond was not generally correlated with denitrifying bacterial abundance. Our results showed that the water SpCond in surface-flow constructed wetlands affected salinity-related denitrifying bacteria but did not affect other denitrifying bacteria. The ORP was positively correlated with denitrifying bacterial genera that were suitable for survival in a strong oxidizing environment, such as unclassified Proteobacteria.
Different physical and chemical properties can influence the structure of the bacterial community owing to the influence of different species on the living environment27,28. In this study, we assessed environmental factors that differed according to season and showed that denitrifying bacteria varied according to some environmental parameters. A comprehensive analysis of the trend of physical and chemical properties of water showed that all parameters except DO and salinity were not highly affected by season and that the trend of the abundances of denitrifying bacteria communities did not change with season along the flow direction of different processing units. However, environmental indicators have a more significant impact on different denitrifying bacteria, which also changes the diversity of denitrifying bacteria community. Accordingly, these results, combined with prediction models of the effects of environmental factors on nitrogen and phosphorus29,30,31, could be used to predict changes in the denitrifying bacterial community structure. More

1.
Sanchez-Gonzalez, A. & Lopez-Mata, L. Plant species richness and diversity along an altitudinal gradient in the Sierra Nevada, Mexico. Divers. Distrib. 11, 567–575 (2005).
Article  Google Scholar 
2.
Dai, L., Feng, Y., Luo, G., Li, Y. & Xu, W. The relationship between soil, climate and forest development in the mid-mountain zone of the Sangong River watershed in the northern Tianshan Mountains, China. J. Arid Land 7, 63–72 (2014).
Article  Google Scholar 

3.
Baiping, Z., Ya, T. & Senguo, M. O. Digital spectrum and analysis of altitudinal belts in the Tianshan Mountains. J. Mt. Res. 1, 18–28 (2004).
Google Scholar 

4.
Pretzsch, H., Biber, P., Schutze, G., Uhl, E. & Rotzer, T. Forest stand growth dynamics in Central Europe have accelerated since 1870. Nat. Commun. 5, 4967 (2014).
ADS  CAS  Article  Google Scholar 

5.
Li, W. J. et al. Effects of climate change on potential habitats of the cold temperate coniferous forest in Yunnan province, southwestern China. J. Mt. Sci. Engl. 13, 1411–1422 (2016).
Article  Google Scholar 

6.
Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A. & Maher, N. Addendum: More extreme precipitation in the world’s dry and wet regions. Nat. Clim. Change 7, 154–158 (2017).
ADS  Article  Google Scholar 

7.
Sun, J., Qin, X. J. & Yang, J. The response of vegetation dynamics of the different alpine grassland types to temperature and precipitation on the Tibetan Plateau. Environ. Monit. Assess. 188, 20 (2016).
Article  Google Scholar 

8.
Windmaisser, T. & Reisch, C. Long-term study of an alpine grassland: Local constancy in times of global change. Alpine Bot. 123, 1–6 (2013).
Article  Google Scholar 

9.
Mahdavi, P., Akhani, H. & Van der Maarel, E. Species diversity and life-form patterns in steppe vegetation along a 3000 m altitudinal gradient in the Alborz Mountains, Iran. Folia Geobot. 48, 7–22 (2013).
Article  Google Scholar 

10.
Rumpf, S. B. et al. Extinction debts and colonization credits of non-forest plants in the European Alps. Nat. Commun. 10, 4293 (2019).
ADS  Article  Google Scholar 

11.
Lamprecht, A., Semenchuk, P. R., Steinbauer, K., Winkler, M. & Pauli, H. Climate change leads to accelerated transformation of high-elevation vegetation in the central Alps. New Phytol. 220, 447–459 (2018).
Article  Google Scholar 

12.
Lenoir, J., Gegout, J. C., Marquet, P. A., de Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).
ADS  CAS  Article  Google Scholar 

13.
Kueppers, L. M. et al. Warming and provenance limit tree recruitment across and beyond the elevation range of subalpine forest. Glob. Change Biol. 23, 2383–2395 (2017).
ADS  Article  Google Scholar 

14.
Sedmakova, D. et al. Growth-climate responses indicate shifts in the competitive ability of European beech and Norway spruce under recent climate warming in East-Central Europe. Dendrochronologia 54, 37–48 (2019).
Article  Google Scholar 

15.
Fadrique, B. & Feeley, K. J. Commentary: Novel competitors shape species’ responses to climate change. Front. Ecol. Evol. 4, 33 (2016).
Article  Google Scholar 

16.
Cavieres, L. A. et al. Facilitative plant interactions and climate simultaneously drive alpine plant diversity. Ecol. Lett. 17, 193–202 (2014).
ADS  Article  Google Scholar 

17.
Li, B. F., Chen, Y. N., Chen, Z. S., Xiong, H. G. & Lian, L. S. Why does precipitation in northwest China show a significant increasing trend from 1960 to 2010?. Atmos. Res. 167, 275–284 (2016).
Article  Google Scholar 

18.
Peng, D. D. & Zhou, T. J. Why was the arid and semiarid northwest China getting wetter in the recent decades?. J. Geophys. Res. Atmos. 122, 9060–9075 (2017).
ADS  Article  Google Scholar 

19.
Hong, C. P. et al. Impacts of climate change on future air quality and human health in China. Proc. Natl. Acad. Sci. U.S.A. 116, 17193–17200 (2019).
ADS  CAS  Article  Google Scholar 

20.
Xu, C. C., Chen, Y. N., Chen, Y. P., Zhao, R. F. & Ding, H. Responses of surface runoff to climate change and human activities in the arid region of Central Asia: A case study in the Tarim River Basin, China. Environ Manag. 51, 926–938 (2013).
ADS  Article  Google Scholar 

21.
Deng, H. J., Chen, Y. N., Wang, H. J. & Zhang, S. H. Climate change with elevation and its potential impact on water resources in the Tianshan Mountains, Central Asia. Glob. Planet. Change 135, 28–37 (2015).
ADS  Article  Google Scholar 

22.
Luo, M. et al. Identifying climate change impacts on water resources in Xinjiang, China. Sci. Total Environ. 676, 613–626 (2019).
ADS  CAS  Article  Google Scholar 

23.
Yue, X. Y., Liu, G., Chen, J. M. & Zhou, C. Y. Synergistic regulation of the interdecadal variability in summer precipitation over the Tianshan mountains by sea surface temperature anomalies in the high-latitude Northwest Atlantic Ocean and the Mediterranean Sea. Atmos. Res. 233, UNSP 104717 (2020).
Article  Google Scholar 

24.
Zhang, H. K. K. & Roy, D. P. Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification. Remote Sens. Environ. 197, 15–34 (2017).
ADS  Article  Google Scholar 

25.
Hu, Z. Y., Dietz, A. & Kuenzer, C. The potential of retrieving snow line dynamics from Landsat during the end of the ablation seasons between 1982 and 2017 in European mountains. Int. J. Appl. Earth Obs. 78, 138–148 (2019).
Article  Google Scholar 

26.
Geng, L. Y., Che, T., Wang, X. F. & Wang, H. B. Detecting spatiotemporal changes in vegetation with the BFAST model in the Qilian Mountain region during 2000–2017. Remote Sens. Basel 11, 103 (2019).
ADS  Article  Google Scholar 

27.
Pham, H. T., Marshall, L., Johnson, F. & Sharma, A. A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data. Remote Sens. Environ. 210, 229–241 (2018).
ADS  Article  Google Scholar 

28.
Piloyan, A. & Milan, K. Semi-automated classification of landform elements in Armenia based on SRTM DEM using K-means unsupervised classification. Quaest. Geogr. 36, 93–103 (2017).
Article  Google Scholar 

29.
Gonzalez-Moradas, M. D. R. & Viveen, W. Evaluation of ASTER GDEM2, SRTMv3.0, ALOS AW3D30 and TanDEM-X DEMs for the Peruvian Andes against highly accurate GNSS ground control points and geomorphological-hydrological metrics. Remote Sens. Environ. 237, 111509 (2020).
ADS  Article  Google Scholar 

30.
Florinsky, I., Skrypitsyna, T. & Luschikova, O. Comparative accuracy of the AW3D30DSM, ASTER GDEM, and SRTM1 DEM: A case study on the Zaoksky testing ground, Central European Russia. Remote Sens. Lett. 9, 706–714 (2018).
Article  Google Scholar 

31.
Xu, M., Kang, S. C., Wu, H. & Yuan, X. Detection of spatio-temporal variability of air temperature and precipitation based on long-term meteorological station observations over Tianshan Mountains, Central Asia. Atmos. Res. 203, 141–163 (2018).
Article  Google Scholar 

32.
Wu, P., Ding, Y. H., Liu, Y. J. & Li, X. C. The characteristics of moisture recycling and its impact on regional precipitation against the background of climate warming over Northwest China. Int. J. Climatol. 39, 5241–5255 (2019).
Article  Google Scholar 

33.
Lutz, A. F., Immerzeel, W. W., Shrestha, A. B. & Bierkens, M. F. P. Consistent increase in High Asia’s runoff due to increasing glacier melt and precipitation. Nat. Clim. Change. 4, 587–592 (2014).
ADS  Article  Google Scholar 

34.
Chen, Y. N., Li, W. H., Deng, H. J., Fang, G. H. & Li, Z. Changes in Central Asia’s water tower: Past, present and future. Sci. Rep. 6, 35458 (2016).
ADS  CAS  Article  Google Scholar  More

1.
Hutchinson, G. E. Cold spring harbor symposium on quantitative biology. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).
Article  Google Scholar 
2.
Chown, S., Gaston, K. & Robinson, D. Macrophysiology: large‐scale patterns in physiological traits and their ecological implications. Funct. Ecol. 18, 159–167 (2004).
Article  Google Scholar 

3.
Spicer, J. I., Morley, S. A. & Bozinovic, F. Physiological diversity, biodiversity patterns and global climate change: testing key hypotheses involving temperature and oxygen. Philos. Trans. R. Soc. B 374 (2019).

4.
Chown, S. L. & Gaston, K. J. Macrophysiology—progress and prospects. Funct. Ecol. 30, 330–344 (2016).
Article  Google Scholar 

5.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
Article  Google Scholar 

6.
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. PNAS 105, 6668–6672 (2008).
CAS  Article  Google Scholar 

7.
Waldock, C., Stuart‐Smith, R. D., Edgar, G. J., Bird, T. J. & Bates, A. E. The shape of abundance distributions across temperature gradients in reef fishes. Ecol. Lett. 22, 685–696 (2019).
Article  Google Scholar 

8.
Payne, N. L. et al. Temperature dependence of fish performance in the wild: links with species biogeography and physiological thermal tolerance. Funct. Ecol. 30, 903–912 (2016).
Article  Google Scholar 

9.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B. 278, 1823–1830 (2011).
Article  Google Scholar 

10.
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. PNAS 111, 5610–5615 (2014).
CAS  Article  Google Scholar 

11.
Araujo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
Article  Google Scholar 

12.
Payne, N. L. & Smith, J. A. An alternative explanation for global trends in thermal tolerance. Ecol. Lett. 20, 70–77 (2017).
Article  Google Scholar 

13.
Sinclair, B. J. et al. Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures? Ecol. Lett. 19, 1372–1385 (2016).
Article  Google Scholar 

14.
Rezende, E. L. & Bozinovic, F. Thermal performance across levels of biological organization. Philos. Trans. R. Soc. B 374, 20180549 (2019).
CAS  Article  Google Scholar 

15.
Barnes, D. K., Peck, L. S. & Morley, S. A. Ecological relevance of laboratory determined temperature limits: colonization potential, biogeography and resilience of Antarctic invertebrates to environmental change. Glob. Change Biol. 16, 3164–3169 (2010).
Article  Google Scholar 

16.
Clark, T. D., Sandblom, E. & Jutfelt, F. Aerobic scope measurements of fishes in an era of climate change: respirometry, relevance and recommendations. J. Exp. Biol. 216, 2771–2782 (2013).
Article  Google Scholar 

17.
Norin, T., Malte, H. & Clark, T. D. Aerobic scope does not predict the performance of a tropical eurythermal fish at elevated temperatures. J. Exp. Biol. 217, 244–251 (2014).
Article  Google Scholar 

18.
Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12, 334–350 (2009).
Article  Google Scholar 

19.
Buckley, L. B. et al. Can mechanism inform species’ distribution models? Ecol. Lett. 13, 1041–1054 (2010).
Article  Google Scholar 

20.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Article  Google Scholar 

21.
Peck, L. S., Clark, M. S., Morley, S. A., Massey, A. & Rossetti, H. Animal temperature limits and ecological relevance: effects of size, activity and rates of change. Funct. Ecol. 23, 248–256 (2009).
Article  Google Scholar 

22.
Richard, J., Morley, S. A., Thorne, M. A. & Peck, L. S. Estimating long-term survival temperatures at the assemblage level in the marine environment: towards macrophysiology. PLoS ONE 7, e34655 (2012).
CAS  Article  Google Scholar 

23.
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).
CAS  Article  Google Scholar 

24.
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).
CAS  Article  Google Scholar 

25.
Stuart-Smith, R. D., Edgar, G. J. & Bates, A. E. Thermal limits to the geographic distributions of shallow-water marine species. Nat. Ecol. Evol. 1, 1846–1852 (2017).
Article  Google Scholar 

26.
Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).
Article  Google Scholar 

27.
Martin, T. L. & Huey, R. B. Why “Suboptimal” is optimal: Jensen’s inequality and ectotherm thermal preferences. Am. Nat. 171, E102–E118 (2008).
Article  Google Scholar 

28.
Pörtner, H.-O. et al. Cod and climate in a latitudinal cline: physiological analyses of climate effects in marine fishes. Clim. Res. 37, 253–270 (2008).
Article  Google Scholar 

29.
Sylvestre, E.-L., Lapointe, D., Dutil, J.-D. & Guderley, H. Thermal sensitivity of metabolic rates and swimming performance in two latitudinally separated populations of cod, Gadus morhua L. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 177, 447–460 (2007).
Article  Google Scholar 

30.
Claireaux, G., Webber, D., Lagardère, J.-P. & Kerr, S. Influence of water temperature and oxygenation on the aerobic metabolic scope of Atlantic cod (Gadus morhua). J. Sea Res. 44, 257–265 (2000).
Article  Google Scholar 

31.
Björnsson, B. & Steinarsson, A. The food-unlimited growth rate of Atlantic cod (Gadus morhua). Can. J. Fish. Aquat. Sci. 59, 494–502 (2002).
Article  Google Scholar 

32.
Morley, S., Peck, L., Sunday, J., Heiser, S. & Bates, A. Physiological acclimation and persistence of ectothermic species under extreme heat events. Glob. Ecol. Biogeogr. 28, 1018–1037 (2019).
Article  Google Scholar 

33.
Edgar, G. J. & Stuart-Smith, R. D. Systematic global assessment of reef fish communities by the Reef Life Survey program. Sci. Data 1, 140007 (2014).
Article  Google Scholar 

34.
Grafen, A. The phylogenetic regression. Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci. 326, 119–157 (1989).
CAS  Google Scholar 

35.
Orme, D., Freckleton, R., Thomas, G., Petzoldt, T. & Fritz, S. The caper package: comparative analysis of phylogenetics and evolution in R. R. Package Version 5, 1–36 (2013).
Google Scholar 

36.
Halsey, L. G., Butler, P. J. & Blackburn, T. M. A phylogenetic analysis of the allometry of diving. Am. Nat. 167, 276–287 (2006).
Article  Google Scholar 

37.
Michonneau, F., Brown, J. W. & Winter, D. J. rotl: an R package to interact with the Open Tree of Life data. Methods Ecol. Evol. 7, 1476–1481 (2016).
Article  Google Scholar 

38.
Freckleton, R. The seven deadly sins of comparative analysis. J. Evol. Biol. 22, 1367–1375 (2009).
CAS  Article  Google Scholar 

39.
Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: a test and review of evidence. Am. Nat. 160, 712–726 (2002).
CAS  Article  Google Scholar  More

1.
Roberts, C. M. et al. Marine biodiversity hotspots and conservation priorities for tropical reefs. Science 295, 1280–1284 (2002).
CAS  Article  Google Scholar 
2.
Kenchington, R. & Hutchings, P. Some implications of high biodiversity for management of tropical marine ecosystems—an Australian perspective. Diversity 10, 1 (2017).
Article  Google Scholar 

3.
Field, C. et al. Mangrove biodiversity and ecosystem function. Glob. Ecol. Biogeogr. Lett. 7, 3–14 (1998).
Article  Google Scholar 

4.
Honda, K., Nakamura, Y., Nakaoka, M., Uy, W. H. & Fortes, M. D. Habitat use by fishes in coral reefs, seagrass beds and mangrove habitats in the Philippines. PLoS ONE 8, e65735 (2013).
CAS  Article  Google Scholar 

5.
Nagelkerken, I. et al. Importance of mangroves, seagrass beds and the shallow coral reef as a nursery for important coral reef fishes, using a visual census technique. Estuar. Coast. Shelf Sci. 51, 31–44 (2000).
Article  Google Scholar 

6.
Unsworth, R. K. et al. High connectivity of Indo-Pacific seagrass fish assemblages with mangrove and coral reef habitats. Mar. Ecol. Prog. Ser. 353, 213–224 (2008).
Article  Google Scholar 

7.
Hoeksema, B. W. in Biogeography, Time, and Place: Distributions, Barriers, and Islands. Topics in Geobiology (ed. Renema, W.) 117–178 (Springer, 2007).

8.
Pitcher, C. R. et al. Seabed Biodiversity on the Continental Shelf of the Great Barrier Reef World Heritage Area AIMS/CSIRO/QM/QDPI Final Report to CRC Reef Research (CSIRO Marine and Atmospheric Research, 2007); http://www.frdc.com.au/Archived-Reports/FRDC%20Projects/2003-021-DLD.pdf

9.
Richards, Z. T. & Day, J. C. Biodiversity of the Great Barrier Reef—how adequately is it protected? PeerJ 6, e4747 (2018).
Article  Google Scholar 

10.
Harris, P. T. et al. Submerged banks in the Great Barrier Reef, Australia, greatly increase available coral reef habitat. ICES J. Mar. Sci. 70, 284–293 (2013).
Article  Google Scholar 

11.
Chin, A. in State of the Great Barrier Reef Report 2003 (ed. Chin, A.) 1–16 (Great Barrier Reef Marine Park Authority, 2003); https://hdl.handle.net/11017/669

12.
Whiteway, T., Smithers, S., Potter, A. & Brooke, B. Geological and Geomorphological Features of Outstanding Universal Value in the Great Barrier Reef World Heritage Area. Report prepared for SEWPaC (Coastal Marine and Climate Change Group, Geoscience Australia and School of Earth and Environmental Sciences, James Cook Univ., 2013).

13.
Mathews, E., Heap, A. & Woods, M. Inter-Reefal Seabed Sediments and Geomorphology of the Great Barrier Reef: A Spatial Analysis (Geoscience Australia, 2007).

14.
Huang, Z. et al. A conceptual surrogacy framework to evaluate the habitat potential of submarine canyons. Prog. Oceanogr. 169, 199–213 (2018).
Article  Google Scholar 

15.
McNeil, M. A., Webster, J. M., Beaman, R. J. & Graham, T. L. New constraints on the spatial distribution and morphology of the Halimeda bioherms of the Great Barrier Reef, Australia. Coral Reefs 35, 1343–1355 (2016).
Article  Google Scholar 

16.
Cumings, E. R. Reefs or bioherms? Geol. Soc. Am. Bull. 43, 331–352 (1932).
Article  Google Scholar 

17.
Klement, K. W. Practical classification of reefs and banks, bioherms and biostromes. Am. Assoc. Pet. Geol. Bull. 51, 167–168 (1967).

18.
Beaman, R. J. High-Resolution Depth Model for the Great Barrier Reef—30 m (Geoscience Australia 2017); https://doi.org/10.4225/25/5a207b36022d2

19.
Orme, G. The sedimentological importance of Halimeda in the development of back reef lithofacies, northern Great Barrier Reef (Australia). In Proc. 5th International Coral Reef Symposium 31–37 (1985).

20.
Orme, G. R. & Salama, M. S. Form and seismic stratigraphy of Halimeda banks in part of the northern Great Barrier Reef Province. Coral Reefs 6, 131–137 (1988).
Article  Google Scholar 

21.
Davies, P. in Encyclopaedia of Modern Coral Reefs—Structure, Form and Process (ed. Hopley, D.) 539–549 (Springer, 2011).

22.
Marshall, J. F. & Davies, P. J. Halimeda bioherms of the northern Great Barrier Reef. Coral Reefs 6, 139–148 (1988).
Article  Google Scholar 

23.
McNeil, M. A., Nothdurft, L. D., Dyriw, N. J., Webster, J. M. & Beaman, R. J. Morphotype differentiation in the Great Barrier Reef Halimeda bioherm carbonate factory: internal architecture and surface geomorphometrics. Depos. Rec. https://doi.org/10.1002/dep2.122 (2020).

24.
Great Barrier Reef Outlook Report 2009 (Great Barrier Reef Marine Park Authority, 2009).

25.
Great Barrier Reef Outlook Report 2014 (Great Barrier Reef Marine Park Authority, 2014).

26.
Ferrari, R. et al. Habitat structural complexity metrics improve predictions of fish abundance and distribution. Ecography 41, 1077–1091 (2017).
Article  Google Scholar 

27.
Ferrari, R. et al. Quantifying the response of structural complexity and community composition to environmental change in marine communities. Glob. Change Biol. 22, 1965–1975 (2016).
Article  Google Scholar 

28.
Dustan, P., Doherty, O. & Pardede, S. Digital reef rugosity estimates coral reef habitat complexity. PLoS ONE 8, e57386 (2013).
CAS  Article  Google Scholar 

29.
Pyle, R. L. & Copus, J. M. in Mesophotic Coral Ecosystems (eds Loya, Y. et al.) 3–27 (Springer International Publishing, 2019).

30.
Hopley, D., Smithers, S. G. & Parnell, K. E. The Geomorphology of the Great Barrier Reef: Development, Diversity, and Change (Cambridge Univ. Press, 2007).

31.
Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Philos. Trans. R. Soc. Lond. B Biol. Sci 345, 101–118 (1994).
CAS  Article  Google Scholar 

32.
Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391 (2001).
Article  Google Scholar 

33.
Chao, A. Estimating the population size for capture–recapture data with unequal catchability. Biometrics 43, 783–791 (1987).
CAS  Article  Google Scholar 

34.
IUCN Red List of Threatened Species. Version 2020-2 (IUCN, 2020); https://www.iucnredlist.org

35.
Obura, D., Fenner, D., Hoeksema, B., Devantier, L. & Sheppard, C. Tubipora musica. IUCN Red List of Threatened Species 2008: e.T133065A3589084 (IUCN, 2008); https://doi.org/10.2305/IUCN.UK.2008.RLTS.T133065A3589084.en

36.
Turak, E., Sheppard, C. & Wood, E. Catalaphyllia jardinei. IUCN Red List of Threatened Species 2008: e.T132890A3479919 (IUCN, 2008); https://doi.org/10.2305/IUCN.UK.2008.RLTS.T132890A3479919.en

37.
Cappo, M. & Kelley, R. in Oceanographic Processes of Coral Reefs: Physical and Biological Links in the Great Barrier Reef (ed. Wolanski, E.) 161–187(CRC Press, 2000).

38.
Cappo, M., De’ath, G. & Speare, P. Inter-reef vertebrate communities of the Great Barrier Reef Marine Park determined by baited remote underwater video stations. Mar. Ecol. Prog. Ser. 350, 209–221 (2007).
Article  Google Scholar 

39.
Sambrook, K. et al. Beyond the reef: the widespread use of non-reef habitats by coral reef fishes. Fish Fish. (Oxf.) 20, 903–920 (2019).
Article  Google Scholar 

40.
Hurrey, L. P., Pitcher, C. R., Lovelock, C. E. & Schmidt, S. Macroalgal species richness and assemblage composition of the Great Barrier Reef seabed. Mar. Ecol. Prog. Ser. 492, 69–83 (2013).
Article  Google Scholar 

41.
Kämpf, J. & Chapman, P. Upwelling Systems of the World: A Scientific Journey to the Most Productive Marine Ecosystems (Springer International Publishing, 2016).

42.
Wolanski, E., Drew, E., Abel, K. M. & O’Brien, J. Tidal jets, nutrient upwelling and their influence on the productivity of the alga Halimeda in the Ribbon Reefs, Great Barrier Reef. Estuar. Coast. Shelf Sci. 26, 169–201 (1988).
CAS  Article  Google Scholar 

43.
Andrews, J. C. & Gentien, P. Upwelling as a source of nutrients for the Great Barrier Reef ecosystems: a solution to Darwin’s question?. Mar. Ecol. Prog. Ser. 8, 257–269 (1982).
Article  Google Scholar 

44.
Benthuysen, J. A., Tonin, H., Brinkman, R., Herzfeld, M. & Steinberg, C.Intrusive upwelling in the Central Great Barrier Reef. J. Geophys. Res. Oceans 121, 8395–8416 (2016).
Article  Google Scholar 

45.
Berkelmans, R., Weeks, S. J. & Steinberg, C. R. Upwelling linked to warm summers and bleaching on the Great Barrier Reef. Limnol. Oceanogr. 55, 2634–2644 (2010).
Article  Google Scholar 

46.
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).
CAS  Article  Google Scholar 

47.
Bridge, T. C. L., Hughes, T. P., Guinotte, J. M. & Bongaerts, P. Call to protect all coral reefs. Nat. Clim. Change 3, 528–530 (2013).
Article  Google Scholar 

48.
Slattery, M., Lesser, M. P., Brazeau, D., Stokes, M. D. & Leichter, J. J.Connectivity and stability of mesophotic coral reefs. J. Exp. Mar. Biol. Ecol. 408, 32–41(2011).
Article  Google Scholar 

49.
Campbell, J. E., Fisch, J., Langdon, C. & Paul, V. J. Increased temperature mitigates the effects of ocean acidification in calcified green algae (Halimeda spp.). Coral Reefs 35, 357–368 (2016).
Article  Google Scholar 

50.
Mongin, M. et al. The exposure of the Great Barrier Reef to ocean acidification. Nat. Commun. 7, 10732 (2016).
CAS  Article  Google Scholar 

51.
Price, N. N., Hamilton, S. L., Tootell, J. S. & Smith, J. E. Species-specific consequences of ocean acidification for the calcareous tropical green algae Halimeda. Mar. Ecol. Prog. Ser. 440, 67–78 (2011).
CAS  Article  Google Scholar 

52.
Sinutok, S., Hill, R., Doblin, M. A., Kühl, M. & Ralph, P. J. Microenvironmental changes support evidence of photosynthesis and calcification inhibition in Halimeda under ocean acidification and warming. Coral Reefs 31, 1201–1213 (2012).
Article  Google Scholar 

53.
Wizemann, A., Meyer, F. W., Hofmann, L. C., Wild, C. & Westphal, H. Ocean acidification alters the calcareous microstructure of the green macro-alga Halimeda opuntia. Coral Reefs 34, 941–954 (2015).
Article  Google Scholar 

54.
Smithers, S., Harvey, N., Hopley, D. & Woodroffe, C. D. in Climate Change and the Great Barrier Reef: A Vulnerability Assessment (eds Johnson, J. E. & Marshall, P. A.) 667–716 (Great Barrier Reef Marine Park Authority, 2007).

55.
Cappo, M., Speare, P. & De’ath, G. Comparison of baited remote underwater video stations (BRUVS) and prawn (shrimp) trawls for assessments of fish biodiversity in inter-reefal areas of the Great Barrier Reef Marine Park. J. Exp. Mar. Biol. Ecol. 302, 123–152 (2004).
Article  Google Scholar 

56.
Pitcher, C. R. GBR Seabed Biodiversity Mapping Project: Phase 1. Draft Report to CRC-Reef (Australian Institute of Marine Science, 2002).

57.
Ugland, K. I., Gray, J. S. & Ellingsen, K. E. The species–accumulation curve and estimation of species richness. J. Anim. Ecol. 72, 888–897 (2003).
Article  Google Scholar 

58.
Clarke, K. & Gorley, R. PRIMER v7: User Manual/Tutorial (PRIMER-e, 2015).

59.
Clarke, K. R., Gorley, R. N., Somerfield, P. J. & Warwick, R. M. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation 3rd edn (PRIMER-e, 2014).

60.
Ridgway, K. R., Dunn, J. R. & Wilkin, J. L. Ocean interpolation by four-dimensional weighted least squares—application to the waters around Australasia. J. Atmos. Ocean. Technol. 19, 1357–1375 (2002).
Article  Google Scholar  More

1.
Merlino G, Barozzi A, Michoud G, Ngugi DK, Daffonchio D. Microbial ecology of deep-sea hypersaline anoxic basins. FEMS Microbiol Ecol. 2018;94:1–15.
Article  CAS  Google Scholar 
2.
Antunes A, Ngugi DK, Stingl U. Microbiology of the Red Sea (and other) deep-sea anoxic brine lakes. Environ Microbiol Rep. 2011;3:416–33.
PubMed  Article  PubMed Central  Google Scholar 

3.
La Cono V, Smedile F, Bortoluzzi G, Arcadi E, Maimone G, Messina E, et al. Unveiling microbial life in new deep-sea hypersaline Lake Thetis. Part I: prokaryotes and environmental settings. Environ Microbiol. 2011;13:2250–68.
PubMed  Article  PubMed Central  Google Scholar 

4.
Daffonchio D, Borin S, Brusa T, Brusetti L, van der Wielen PWJJ, Bolhuis H, et al. Stratified prokaryote network in the oxic-anoxic transition of a deep-sea halocline. Nature. 2006;440:203–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
van der Wielen PWJJ, Bolhuis H, Borin S, Daffonchio D, Corselli C, Giuliano L, et al. The enigma of prokaryotic life in deep hypersaline anoxic basins. Science. 2005;307:121–3.
PubMed  Article  CAS  PubMed Central  Google Scholar 

6.
Borin S, Brusetti L, Mapelli F, D’Auria G, Brusa T, Marzorati M, et al. Sulfur cycling and methanogenesis primarily drive microbial colonization of the highly sulfidic Urania deep hypersaline basin. Proc Natl Acad Sci USA. 2009;106:9151–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Joye SB, Samarkin VA, Orcutt BN, MacDonald IR, Hinrichs K-U, Elvert M, et al. Metabolic variability in seafloor brines revealed by carbon and sulphur dynamics. Nat Geosci. 2009;2:349–54.
CAS  Article  Google Scholar 

8.
Guan Y, Hikmawan T, Antunes A, Ngugi DK, Stingl U. Diversity of methanogens and sulfate-reducing bacteria in the interfaces of five deep-sea anoxic brines of the Red Sea. Res Microbiol. 2015;166:688–99.
PubMed  Article  PubMed Central  Google Scholar 

9.
Pachiadaki MG, Yakimov M, LaCono V, Leadbetter E, Edgcomb V. Unveiling microbial activities along the halocline of Thetis, a deep-sea hypersaline anoxic basin. ISME J. 2014;8:1–12.
Article  CAS  Google Scholar 

10.
Borin S, Mapelli F, Rolli E, Song B, Tobias C, Schmid MC, et al. Anammox bacterial populations in deep marine hypersaline gradient systems. Extremophiles. 2013;17:289–99.
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Yakimov MM, La Cono V, Spada GL, Bortoluzzi G, Messina E, Smedile F, et al. Microbial community of the deep-sea brine Lake Kryos seawater-brine interface is active below the chaotropicity limit of life as revealed by recovery of mRNA. Environ Microbiol. 2015;17:364–82.
CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Ngugi DK, Blom J, Alam I, Rashid M, Ba-Alawi W, Zhang G, et al. Comparative genomics reveals adaptations of a halotolerant thaumarchaeon in the interfaces of brine pools in the Red Sea. ISME J. 2015;9:396–411.
Article  CAS  Google Scholar 

13.
Ngugi DK, Blom J, Stepanauskas R, Stingl U. Diversification and niche adaptations of Nitrospina-like bacteria in the polyextreme interfaces of Red Sea brines. ISME J. 2016;10:1383–99.
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Zhang W, Ding W, Yang B, Tian RM, Gu S, Luo H, et al. Genomic and transcriptomic evidence for carbohydrate consumption among microorganisms in a cold seep brine pool. Front Microbiol. 2016;7:1825.
PubMed  PubMed Central  Google Scholar 

15.
Bougouffa S, Yang JK, Lee OO, Wang Y, Batang Z, Al-Suwailem A, et al. Distinctive microbial community structure in highly stratified deep-sea brine water columns. Appl Environ Microbiol. 2013;79:3425–37.
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Abdallah RZ, Adel M, Ouf A, Sayed A, Ghazy MA, Alam I, et al. Aerobic methanotrophic communities at the Red Sea brine-seawater interface. Front Microbiol. 2014;5:1–16.
Article  Google Scholar 

17.
Erguder TH, Boon N, Wittebolle L, Marzorati M, Verstraete W. Environmental factors shaping the ecological niches of ammonia-oxidizing archaea. FEMS Microbiol Rev. 2009;33:855–69.
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Gunde-Cimerman N, Plemenitaš A, Oren A. Strategies of adaptation of microorganisms of the three domains of life to high salt concentrations. FEMS Microbiol Rev. 2018;42:353–75.
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Yakimov M, La Cono V, Denaro R, D’Auria G, Decembrini F, Timmis KN, et al. Primary producing prokaryotic communities of brine, interface and seawater above the halocline of deep anoxic lake L’Atalante, Eastern Mediterranean Sea. ISME J. 2007;1:743–55.
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Brune A, Frenzel P, Cypionka H. Life at the oxic–anoxic interface: microbial activities and adaptations. FEMS Microbiol Rev. 2000;24:691–710.
CAS  PubMed  Article  Google Scholar 

21.
Oren A. Thermodynamic limits to microbial life at high salt concentrations. Environ Microbiol. 2011;13:1908–23.
CAS  PubMed  Article  Google Scholar 

22.
Baumann A, Richter H, Schoell M. Suakin deep: brines and hydrothermal sediments in the deepest part of the Red Sea. Geol Rundsch. 1973;62:684–97.
CAS  Article  Google Scholar 

23.
Backer H, Schoell M. New deeps with brines and metalliferous sediments in the red sea. Nat Phys Sci. 1972;240:153–8.
Article  Google Scholar 

24.
Schmidt, M, Al-Farawati R, Botz R. Geochemical classification of brine-filled Red Sea Deeps. In: Rasul NMA, Stewart ICF, editors. The Red Sea. Springer; 2015. p. 219–233.

25.
Calleja ML, Al-Otaibi N, Morán XAG. Dissolved organic carbon contribution to oxygen respiration in the central Red Sea. Sci Rep. 2019;9:4690.
PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Duarte CM, Røstad A, Michoud G, Barozzi A, Merlino G, Delgado-Huertas A, et al. Discovery of Afifi, the shallowest and southernmost brine pool reported in the Red Sea. Sci Rep. 2020;10:910.
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B Stat Methodol. 2011;73:3–36.
Article  Google Scholar 

28.
Salata GG, Roelke LA, Cifuentes LA. A rapid and precise method for measuring stable carbon isotope ratios of dissolved inorganic carbon. Mar Chem. 2000;69:153–61.
CAS  Article  Google Scholar 

29.
McIlvin MR, Altabet MA. Chemical conversion of nitrate and nitrite to nitrous oxide for nitrogen and oxygen isotopic analysis in freshwater and seawater. Anal Chem. 2005;77:5589–95.
CAS  PubMed  Article  Google Scholar 

30.
Green MR, Sambrook J. Isolation of high-molecular-weight DNA using organic solvents. Cold Spring Harb Protoc. 2017;2017:pdb.prot093450.

31.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Bushnell B. BBMap short read aligner. https://sourceforge.net/projects/bbmap/. 2016. Accessed 03 Feb 2021.

33.
Andrews S. FastQC A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. 2010. Accessed 30 Jan 2021.

34.
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Parks DH, Beiko RG. Measures of phylogenetic differentiation provide robust and complementary insights into microbial communities. ISME J. 2013;7:173–83.
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.
PubMed  PubMed Central  Article  CAS  Google Scholar 

39.
Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics. 2010;26:680–2.
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Nayfach S, Pollard KS. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 2015;16:51.
PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Alam I, Antunes A, Kamau AA, Alawi WB, Kalkatawi M, Stingl U, et al. INDIGO – Integrated data warehouse of microbial genomes with examples from the red sea extremophiles. PLoS ONE. 2013;8:e82210.
PubMed  PubMed Central  Article  CAS  Google Scholar 

43.
Llorens-Marès T, Yooseph S, Goll J, Hoffman J, Vila-Costa M, Borrego CM, et al. Connecting biodiversity and potential functional role in modern euxinic environments by microbial metagenomics. ISME J. 2015;9:1648–61.
PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
Lüke C, Speth DR, Kox MAR, Villanueva L, Jetten MSM. Metagenomic analysis of nitrogen and methane cycling in the Arabian Sea oxygen minimum zone. PeerJ. 2016;4:e1924.
PubMed  PubMed Central  Article  CAS  Google Scholar 

45.
Wickham H. ggplot2: elegant graphics for data analysis. Springer New York; 2016.

46.
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. https://github.com/vegandevs/vegan, https://cran.r-project.org/package=vegan. 2017. Accessed 28 November 2020.

47.
Blanchet FG, Legendre P, Borcard D. Forward selection of explanatory variables. Ecology. 2008;89:2623–32.
PubMed  Article  PubMed Central  Google Scholar 

48.
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.
PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.
PubMed  Article  CAS  PubMed Central  Google Scholar 

50.
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
CAS  Article  Google Scholar 

54.
Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics. 2010;11:538.
PubMed  PubMed Central  Article  Google Scholar 

55.
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–27.
CAS  Google Scholar 

56.
Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol. 2000;17:540–52.
CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
Darriba D, Taboada GL, Doallo R, Posada D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics. 2011;27:1164–5.
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Rambaut A. FigTree. http://tree.bio.ed.ac.uk/software/figtree/. 2009. Accessed 04 Jan 2011.

60.
Contreras-Moreira B, Vinuesa P. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis. Appl Environ Microbiol. 2013;79:7696–701.
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
Li L, Stoeckert CJ, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13:2178–89.
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, et al. gplots: various r programming tools for plotting data. 2019. https://cran.r-project.org/package=gplots. Accessed 28 Nov 2020.

63.
Long A, Heitman J, Tobias C, Philips R, Song B. Co-occurring anammox, denitrification, and codenitrification in agricultural soils. Appl Environ Microbiol. 2013;79:168–76.
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Shu D, He Y, Yue H, Wang Q. Metagenomic and quantitative insights into microbial communities and functional genes of nitrogen and iron cycling in twelve wastewater treatment systems. Chem Eng J. 2016;290:21–30.
CAS  Article  Google Scholar 

65.
Augustin N, Devey CW, van der Zwan FM. A Modern view on the Red Sea Rift: tectonics, volcanism and salt blankets. In: Rasul NMA, Stewart ICF, editors. Geological setting, palaeoenvironment and archaeology of the Red Sea. Springer International Publishing, 2019. p. 37–52.

66.
Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.
CAS  PubMed  Article  Google Scholar 

67.
Ward BB, Kilpatrick KA. Relationship between substrate concentration and oxidation of ammonium and methane in a stratified water column. Cont Shelf Res. 1990;10:1193–208.
Article  Google Scholar 

68.
Stedmon CA, Thomas DN, Papadimitriou S, Granskog MA, Dieckmann GS. Using fluorescence to characterize dissolved organic matter in Antarctic sea ice brines. J Geophys Res. 2011;116:G03027.
Google Scholar 

69.
Taylor PG, Townsend AR. Stoichiometric control of organic carbon–nitrate relationships from soils to the sea. Nature. 2010;464:1178–81.
CAS  PubMed  Article  Google Scholar 

70.
Granger J, Sigman DM, Lehmann MF, Tortell PD. Nitrogen and oxygen isotope fractionation during dissimilatory nitrate reduction by denitrifying bacteria. Limnol Oceanogr. 2008;53:2533–45.
CAS  Article  Google Scholar 

71.
Nigro LM, Hyde AS, MacGregor BJ. Teske A. Phylogeography, salinity adaptations and metabolic potential of the candidate division kb1 bacteria based on a partial single cell genome. Front Microbiol. 2016;7:1266.

72.
Mwirichia R, Alam I, Rashid M, Vinu M, Ba-Alawi W, Anthony Kamau A, et al. Metabolic traits of an uncultured archaeal lineage -MSBL1- from brine pools of the Red Sea. Sci Rep. 2016;6:19181.
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Adam PS, Borrel G, Brochier-Armanet C, Gribaldo S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 2017;11:2407–25.
PubMed  PubMed Central  Article  Google Scholar 

74.
Pereira AD, Leal CD, Dias MF, Etchebehere C, Chernicharo CAL, de Araújo JC. Effect of phenol on the nitrogen removal performance and microbial community structure and composition of an anammox reactor. Bioresour Technol. 2014;166:103–11.
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Yamada T, Sekiguchi Y. Anaerolineaceae. In: Trujillo ME, Dedysh S, DeVos P, Hedlund B, Kämpfer P, Rainey FA et al, editors. Bergey’s manual of systematics of archaea and bacteria. Wiley, 2018. p. 1–5.

76.
Browne P, Tamaki H, Kyrpides N, Woyke T, Goodwin L, Imachi H, et al. Genomic composition and dynamics among Methanomicrobiales predict adaptation to contrasting environments. ISME J. 2017;11:87–99.
CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Youssef NH, Ashlock-Savage KN, Elshahed MS. Phylogenetic diversities and community structure of members of the extremely halophilic archaea (order Halobacteriales) in multiple saline sediment habitats. Appl Environ Microbiol. 2012;78:1332–44.
CAS  PubMed  PubMed Central  Article  Google Scholar 

78.
Sorokin DY, Merkel AY, Abbas B, Makarova KS, Rijpstra WIC, Koenen M, et al. Methanonatronarchaeum thermophilum gen. nov., sp. nov. and ‘Candidatus Methanohalarchaeum thermophilum’, extremely halo(natrono)philic methyl-reducing methanogens from hypersaline lakes comprising a new euryarchaeal class Methanonatronarchaeia classis nov. Int J Syst Evol Microbiol. 2018;68:2199–208.
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
van Niftrik L, Geerts WJC, van Donselaar EG, Humbel BM, Webb RI, Fuerst JA, et al. Linking ultrastructure and function in four genera of anaerobic ammonium-oxidizing bacteria: cell plan, glycogen storage, and localization of cytochrome c proteins. J Bacteriol. 2008;190:708–17.
PubMed  Article  CAS  PubMed Central  Google Scholar 

80.
Muck S, De Corte D, Clifford EL, Bayer B, Herndl GJ, Sintes E. Niche differentiation of aerobic and anaerobic ammonia oxidizers in a high latitude deep oxygen minimum zone. Front Microbiol. 2019;10:2141.
PubMed  PubMed Central  Article  Google Scholar 

81.
Jensen MM, Lam P, Revsbech NP, Nagel B, Gaye B, Jetten MS, et al. Intensive nitrogen loss over the Omani Shelf due to anammox coupled with dissimilatory nitrite reduction to ammonium. ISME J. 2011;5:1660–70.
CAS  PubMed  PubMed Central  Article  Google Scholar 

82.
Tian R, Ning D, He Z, Zhang P, Spencer SJ, Gao S, et al. Small and mighty: adaptation of superphylum Patescibacteria to groundwater environment drives their genome simplicity. Microbiome. 2020;8:51.
PubMed  PubMed Central  Article  Google Scholar 

83.
Yakimov MM, La Cono V, Slepak VZ, La Spada G, Arcadi E, Messina E, et al. Microbial life in the Lake Medee, the largest deep-sea salt-saturated formation. Sci Rep. 2013;3:3554.
PubMed  PubMed Central  Article  Google Scholar 

84.
Jayakumar A, Chang BX, Widner B, Bernhardt P, Mulholland MR, Ward BB. Biological nitrogen fixation in the oxygen-minimum region of the eastern tropical North Pacific ocean. ISME J. 2017;11:2356–67.
CAS  PubMed  PubMed Central  Article  Google Scholar 

85.
Woebken D, Lam P, Kuypers MMM, Naqvi SWA, Kartal B, Strous M, et al. A microdiversity study of anammox bacteria reveals a novel Candidatus scalindua phylotype in marine oxygen minimum zones. Environ Microbiol. 2008;10:3106–19.
CAS  PubMed  Article  PubMed Central  Google Scholar 

86.
Speth DR, Lagkouvardos I, Wang Y, Qian P-Y, Dutilh BE, Jetten MSM. Draft genome of Scalindua rubra, obtained from the interface above the discovery deep brine in the red sea, sheds light on potential salt adaptation strategies in anammox bacteria. Micro Ecol. 2017;74:1–5.
CAS  Article  Google Scholar 

87.
Ali M, Shaw DR, Saikaly PE. Application of an enrichment culture of the marine anammox bacterium “Ca. Scalindua” for nitrogen removal under moderate salinity and in the presence of organic carbon. Water Res. 2020;170:115345.
CAS  PubMed  Article  PubMed Central  Google Scholar 

88.
Ren M, Feng X, Huang Y, Wang H, Hu Z, Clingenpeel S, et al. Phylogenomics suggests oxygen availability as a driving force in Thaumarchaeota evolution. ISME J. 2019;13:2150–61.
PubMed  PubMed Central  Article  Google Scholar 

89.
Awata T, Goto Y, Kindaichi T, Ozaki N, Ohashi A. Nitrogen removal using an anammox membrane bioreactor at low temperature. Water Sci Technol. 2015;72:2148–53.
CAS  PubMed  Article  PubMed Central  Google Scholar 

90.
Pappalardo RT. Seeking Europa’s ocean. Proc Int Astron Union. 2010;6:101–14.
Article  Google Scholar 

91.
Martínez GM, Renno NO. Water and brines on mars: current evidence and implications for MSL. Space Sci Rev. 2013;175:29–51.
Article  CAS  Google Scholar 

92.
Jokinen SA, Virtasalo JJ, Jilbert T, Kaiser J, Dellwig O, Arz HW, et al. A 1500-year multiproxy record of coastal hypoxia from the northern Baltic Sea indicates unprecedented deoxygenation over the 20th century. Biogeosciences. 2018;15:3975–4001.
CAS  Article  Google Scholar  More

1.
Nowak-Chmura, M. Fauna of ticks (Ixodida) of Central Europe (Wydawnictwo Naukowe Uniwersytetu Pedagogicznego, Kraków, 2013).
Google Scholar 
2.
Balmelli, T. & Piffaretti, J. C. Association between different clinical manifestations of Lyme disease and different species of Borrelia burgdorferi sensu lato. Res. Microbiol. 146, 329–340 (1995).
CAS  Article  Google Scholar 

3.
Strzelczyk, J. K. et al. Prevalence of Borrelia burgdorferi sensu lato in Ixodes ricinus ticks collected from southern Poland. Acta Parasitol. 60, 666–674 (2015).
CAS  Article  Google Scholar 

4.
Blanco, J. R. & Oteo, J. A. Human granulocytic ehrlichiosis in Europe. Clin. Microbiol. Infect. 8, 763–772 (2002).
CAS  Article  Google Scholar 

5.
Boustani, M. R. & Gelfand, J. A. Babesiosis. Clin. Infect. Dis. 22, 611–614 (1996).
CAS  Article  Google Scholar 

6.
Siuda, K. Kleszcze (Acari: Ixodida) Polski Część II Systematyka i Rozmieszczenie (Polskie Towarzystwo Parazytologiczne, Warsaw, 1993).
Google Scholar 

7.
Guy, E. & Stanek, G. Detection of Borrelia burgdorferi in patients with Lyme disease by the polymerase chain reaction. J. Clin. Pathol. 29, 610–611 (1991).
Article  Google Scholar 

8.
Wójcik-Fatla, A., Szymańska, J., Wdowiak, L., Buczek, A. & Dutkiewicz, J. Coincidence of three pathogens (Borrelia burgdorferi sensu lato, Anaplasma phagocytophilum and Babesia microti) in Ixodes ricinus ticks in the Lublin macroregion. Ann. Agric. Environ. Med. 16, 151–158 (2009).
PubMed  Google Scholar 

9.
Wodecka, B., Rymaszewska, A., Sawczuk, M. & Skotarczak, B. Detectability of tick-borne agents DNA in the blood of dogs, undergoing treatment for borreliosis. Ann. Agric. Environ. Med. 16, 9–14 (2009).
CAS  PubMed  Google Scholar 

10.
Wodecka, B. FlaB gene as a molecular marker for distinct identification of Borrelia species in environmental samples by the PCR-restriction fragment length polymorphism method. Appl. Environ. Microbiol. 77, 7088–7092 (2011).
CAS  Article  Google Scholar 

11.
Massung, R. F. et al. Nested PCR assay for detection of granulocytic ehrlichiae. J. Clin. Microbiol. 36, 1090–1095 (1998).
CAS  Article  Google Scholar 

12.
Persing, D. H. et al. Detection of Babesia microti by polymerase chain reaction. J. Clin. Microbiol. 30, 2097–2103 (1992).
CAS  Article  Google Scholar 

13.
Stańczak, J., Kubica-Biernat, B., Racewicz, M., Kruminis-Łozowska, W. & Kur, J. Detection of three genospecies of Borrelia burgdorferi sensu lato in Ixodes ricinus ticks collected in different regions of Poland. Int. J. Med. Microbiol. 290, 559–566 (2000).
Article  Google Scholar 

14.
Wodecka, B. & Skotarczak, B. First isolation of Borrelia lusitaniae DNA from Ixodes ricinus ticks in Poland. Scand. J. Infect. Dis. 37, 27–34 (2005).
CAS  Article  Google Scholar 

15.
Kiewra, D., Stańczak, J. & Richter, M. Ixodes ricinus ticks (Acari, Ixodidae) as a vector of Borrelia burgdorferi sensu lato and Borrelia miyamotoi in Lower Silesia. Tick Tick-borne Dis. 5, 892–897 (2014).
Article  Google Scholar 

16.
Asman, M. et al. Occupational risk of infections with Borrelia burgdorferi sensu lato, B. burgdorferi sensu stricto, B. garinii and B. afzelii in agricultural workers on the territory of Beskid Żywiecki. in Arthropods: Medical and Economical Significance (ed. Buczek, A. & Błaszak, Cz.) 163–170 (Akapit, 2012).

17.
Asman, M., Witecka, J., Solarz, K., Zwonik, A. & Szilman, P. Occurrence of Borrelia burgdorferi sensu lato, Anaplasma phagocytophilum and Babesia microti in Ixodes ricinus ticks collected from selected areas of Opolskie Province in south-west Poland. Ann. Agric. Environ. Med. 26, 544–547 (2019).
CAS  Article  Google Scholar 

18.
Wodecka, B. & Skotarczak, B. Genetic diversity of Borrelia burgdorferi sensu lato in Ixodes ricinus ticks collected in north-west Poland. Wiad Parazytol. 46, 475–485 (2000).
CAS  PubMed  Google Scholar 

19.
Bartosik, K., Lachowska-Kotowska, P., Szymańska, J., Pabis, A. & Buczek, A. Lyme borreliosis in south-eastern Poland: Relationships with environmental factors and medical attention standards. Ann. Agric. Environ. Med. 18, 131–137 (2011).
PubMed  Google Scholar 

20.
Hubálek, Z., Halouzka, J., Juricová, Z., Sikutová, S. & Rudolf, I. Effect of forest clearing on the abundance of Ixodes ricinus ticks and the prevalence of Borrelia burgdorferi s.l. Med. Vet. Entomol. 20, 166–172 (2006).
Article  Google Scholar 

21.
Hanincova, K. et al. Association of Borrelia afzelii with rodents in Europe. Parasitology 126, 11–20 (2003).
CAS  Article  Google Scholar 

22.
Hanincova, K. et al. Association of Borrelia garinii and B. valaisiana with songbirds in Slovakia. Appl. Environ. Microbiol. 69, 2825–2830 (2003).
CAS  Article  Google Scholar 

23.
Kurtenbach, K. et al. Differential transmission of the genospecies of Borrelia burgdorferi sensu lato by game birds and small rodents in England. Appl. Environ. Microbiol. 64, 1169–1174 (1998).
CAS  Article  Google Scholar 

24.
Rauter, C. & Hartung, T. Prevalence of Borrelia burgdorferi sensu lato genospecies in Ixodes ricinus ticks in Europe: A metaanalysis. Appl. Environ. Microbiol. 71, 7203–7216 (2005).
CAS  Article  Google Scholar 

25.
Wodecka, B. Significance of red deer (Cervus elaphus) in the ecology of Borrelia burgdorferi sensu lato. Wiad Parazytol. 53, 231–237 (2007).
PubMed  Google Scholar 

26.
Chen, S.-M., Dumler, J. S., Bakken, J. S. & Walker, D. H. Identification of a granulocytotropic Ehrlichia species as the etiologic agent of human disease. J. Clin. Microbiol. 32, 589–595 (1994).
CAS  Article  Google Scholar 

27.
Zhang, Y., Cui, Y., Sun, Y., Jing, H. & Ning, Ch. Novel Anaplasma variants in small ruminants from central China. Front. Vet. Sci. 7, 1–7 (2020).
ADS  Article  Google Scholar 

28.
Petrovec, M. et al. Human disease in Europe caused by a granulocytic Ehrlichia species. J. Clin. Microbiol. 35, 1556–1559 (1997).
CAS  Article  Google Scholar 

29.
Siński, E. Enzoonotic reservoir for new Ixodes ricinus—Transmitted infections. Wiad Parazytol. 45, 135–142 (1999).
PubMed  Google Scholar 

30.
Kiewra, D., Zaleśny, G. & Czułowska, A. The risk of infection with Anaplasma phagocytophilum and Babesia microti in Lower Silesia, SW Poland. in Arthropods: Threat to Human and Animals Health (ed. Buczek, A. & Błaszak, Cz.) 103–110 (Koliber, 2014).

31.
Asman, M. et al. The risk of exposure to Anaplasma phagocytophilum, Borrelia burgdorferi sensu lato, Babesia sp. and coinfections in Ixodes ricinus ticks on the territory of Niepołomice Forest (southern Poland). Ann. Parasitol. 59, 13–19 (2013).
PubMed  Google Scholar 

32.
Asman, M. et al. Anaplasma phagocytophilum, Babesia microti, Borrelia burgdorferi sensu lato, and Toxoplasma gondii in Ixodes ricinus (Acari, Ixodida) ticks collected from Slowinski National Park (Northern Poland). J. Vector Ecol. 42, 200–202 (2017).
Article  Google Scholar 

33.
Stańczak, J., Gabre, M. R., Kruminis-Łozowska, W., Racewicz, M. & Kubica-Biernat, B. Ixodes riciuns as a vector of Borrelia burgdorferi sensu lato, Anaplasma phagocytophilum and Babesia microti in urban and suburban forests. Ann. Agric. Environ. Med. 11, 109–114 (2004).
PubMed  Google Scholar 

34.
Asman, M. et al. Detection of protozoans Babesia microti and Toxoplasma gondii and their co-existence in ticks (Acari: Ixodida) collected in Tarnogórski district (Upper Silesia, Poland). Ann. Agric. Environ. Med. 22, 80–83 (2015).
Article  Google Scholar 

35.
Yabsley, M. J. & Shock, B. C. Natural history of Zoonotic Babesia: Role of wildlife reservoirs. Int. J. Parasitol. Parasites Wildl. 2, 18–31 (2013).
Article  Google Scholar 

36.
Sytykiewicz, H. et al. Molecular evidence of Anaplasma phagocytophilum and Babesia microti co-infections in Ixodes ricinus ticks in central-eastern region of Poland. Ann. Agric. Environ. Med. 19, 45–49 (2012).
CAS  PubMed  Google Scholar 

37.
Asman, M. et al. The occurrence of three tick-borne pathogens in Ixodes ricinus ticks collected from the area of the Kraków—Czestochowa Upland (Southern Poland). Acarologia 58, 967–975 (2018).
Google Scholar  More

1.
Behrenfeld MJ, Randerson JT, McClain CR, Feldman GC, Los SO, Tucker CJ, et al. Biospheric primary production during an ENSO transition. Science. 2001;291:2594–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Buchan A, LeCleir GR, Gulvik CA, González JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40.
CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Needham DM, Fuhrman JA. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat Microbiol. 2016;1:16005.
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Teeling H, Fuchs BM, Bennke CM, Krüger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. eLife. 2016;5:e11888.
PubMed  PubMed Central  Article  Google Scholar 

6.
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Williams TJ, Wilkins D, Long E, Evans F, DeMaere MZ, Raftery MJ, et al. The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics. Environ Microbiol. 2013;15:1302–17.
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Chafee M, Fernàndez-Guerra A, Buttigieg PL, Gerdts G, Eren AM, Teeling H, et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 2018;12:237–52.
PubMed  Article  PubMed Central  Google Scholar 

9.
Francis TB, Krüger K, Fuchs BM, Teeling H, Amann RI. CandidatusProsiliicoccus vernus, a spring phytoplankton bloom associated member of the Flavobacteriaceae. Syst Appl Microbiol. 2019;42:41–53.
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Krüger K, Chafee M, Francis TB, Glavina del Rio T, Becher D, Schweder T, et al. In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes. ISME J. 2019;13:2800–16.
PubMed  PubMed Central  Article  CAS  Google Scholar 

11.
Needham DM, Fichot EB, Wang E, Berdjeb L, Cram JA, Fichot CG, et al. Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling. ISME J. 2018;12:2417–32.
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Cottrell MT, Kirchman DL. Natural assemblages of marine Proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl Environ Microbiol. 2000;66:1692–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Fernández-Gomez B, Richter M, Schüler M, Pinhassi J, Acinas SG, González JM, et al. Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J. 2013;7:1026–37.
PubMed  PubMed Central  Article  CAS  Google Scholar 

14.
Grondin JM, Tamura K, Déjean G, Abbott DW, Brumer H. Polysaccharide utilization loci: fueling microbial communities. J Bacteriol. 2017;199:e00860–16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Kappelmann L, Krüger K, Hehemann J-H, Harder J, Markert S, Unfried F, et al. Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/D-protein expression for predicting major phytoplankton glycans. ISME J. 2019;13:76–91.
CAS  PubMed  Article  Google Scholar 

16.
Kirchman DL. The ecology of Cytophaga–Flavobacteria in aquatic environments. FEMS Microbiol Ecol. 2002;39:91–100.
CAS  PubMed  Google Scholar 

17.
Thomas F, Hehemann J-H, Rebuffet E, Czjzek M, Michel G. Environmental and gut Bacteroidetes: the food connection. Front Microbiol. 2011;2:93–93.
PubMed  PubMed Central  Article  Google Scholar 

18.
Glenwright AJ, Pothula KR, Bhamidimarri SP, Chorev DS, Baslé A, Firbank SJ, et al. Structural basis for nutrient acquisition by dominant members of the human gut microbiota. Nature. 2017;541:407–11.
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
Joglekar P, Sonnenburg ED, Higginbottom SK, Earle KA, Morland C, Shapiro-Ward S, et al. Genetic variation of the SusC/SusD homologs from a polysaccharide utilization locus underlies divergent fructan specificities and functional adaptation in Bacteroides thetaiotaomicron strains. mSphere. 2018;3:e00185–18.
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Cuskin F, Lowe EC, Temple MJ, Zhu Y, Cameron EA, Pudlo NA, et al. Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism. Nature. 2015;517:165–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Reintjes G, Arnosti C, Fuchs BM, Amann R. An alternative polysaccharide uptake mechanism of marine bacteria. ISME J. 2017;11:1640–50.
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Hehemann J-H, Truong LV, Unfried F, Welsch N, Kabisch J, Heiden SE, et al. Aquatic adaptation of a laterally acquired pectin degradation pathway in marine Gammaproteobacteria. Environ Microbiol. 2017;19:2320–33.
CAS  PubMed  Article  Google Scholar 

23.
Neumann AM, Balmonte JP, Berger M, Giebel H-A, Arnosti C, Voget S, et al. Different utilization of alginate and other algal polysaccharides by marine Alteromonas macleodii ecotypes. Environ Microbiol. 2015;17:3857–68.
CAS  PubMed  Article  Google Scholar 

24.
Mirus O, Strauss S, Nicolaisen K, von Haeseler A, Schleiff E. TonB-dependent transporters and their occurrence in Cyanobacteria. BMC Biol. 2009;7:68.
PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Gudmundsdottir A, Bell PE, Lundrigan MD, Bradbeer C, Kadner RJ. Point mutations in a conserved region (TonB box) of Escherichia coli outer membrane protein BtuB affect vitamin B12 transport. J Bacteriol. 1989;171:6526–33.
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Köster W, Braun V. Iron (III) hydroxamate transport into Escherichia coli. Substrate binding to the periplasmic FhuD protein. J Biol Chem. 1990;265:21407–10.
PubMed  Article  Google Scholar 

27.
Schauer K, Gouget B, Carrière M, Labigne A, Reuse HD. Novel nickel transport mechanism across the bacterial outer membrane energized by the TonB/ExbB/ExbD machinery. Mol Microbiol. 2007;63:1054–68.
CAS  PubMed  Article  Google Scholar 

28.
Reeves AR, D’Elia JN, Frias J, Salyers AA. A Bacteroides thetaiotaomicron outer membrane protein that is essential for utilization of maltooligosaccharides and starch. J Bacteriol. 1996;178:823–30.
CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Cheng Q, Yu MC, Reeves AR, Salyers AA. Identification and characterization of a Bacteroides gene, csuF, which encodes an outer membrane protein that is essential for growth on chondroitin sulfate. J Bacteriol. 1995;177:3721–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Neugebauer H, Herrmann C, Kammer W, Schwarz G, Nordheim A, Braun V. ExbBD-dependent transport of maltodextrins through the novel MalA protein across the outer membrane of Caulobacter crescentus. J Bacteriol. 2005;187:8300–11.
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
Noinaj N, Guillier M, Barnard TJ, Buchanan SK. TonB-dependent transporters: regulation, structure, and function. Annu Rev Microbiol. 2010;64:43–60.
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Schauer K, Rodionov DA, de Reuse H. New substrates for TonB-dependent transport: do we only see the ‘tip of the iceberg’? Trends Biochem Sci. 2008;33:330–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Lapébie P, Lombard V, Drula E, Terrapon N, Henrissat B. Bacteroidetes use thousands of enzyme combinations to break down glycans. Nat Commun. 2019;10:2043.
PubMed  PubMed Central  Article  CAS  Google Scholar 

34.
Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res. 2009;37:D233–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Foley MH, Cockburn DW, Koropatkin NM. The Sus operon: a model system for starch uptake by the human gut Bacteroidetes. Cell Mol Life Sci. 2016;73:2603–17.
CAS  PubMed  PubMed Central  Article  Google Scholar 

36.
Terrapon N, Lombard V, Gilbert HJ, Henrissat B. Automatic prediction of polysaccharide utilization loci in Bacteroidetes species. Bioinformatics. 2015;31:647–55.
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Terrapon N, Lombard V, Drula E, Lapébie P, Al-Masaudi S, Gilbert HJ, et al. PULDB: the expanded database of polysaccharide utilization loci. Nucleic Acids Res. 2017;46:D677–83.
PubMed Central  Article  CAS  Google Scholar 

38.
Bergauer K, Fernandez-Guerra A, Garcia JA, Sprenger RR, Stepanauskas R, Pachiadaki MG, et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc Natl Acad Sci USA. 2018;115:E400–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Dong H-P, Hong Y-G, Lu S, Xie L-Y. Metaproteomics reveals the major microbial players and their biogeochemical functions in a productive coastal system in the northern South China Sea. Environ Microbiol Rep. 2014;6:683–95.
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
McCarren J, Becker JW, Repeta DJ, Shi Y, Young CR, Malmstrom RR, et al. Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea. Proc Natl Acad Sci USA. 2010;107:16420–7.
CAS  PubMed  Article  Google Scholar 

41.
Morris RM, Nunn BL, Frazar C, Goodlett DR, Ting YS, Rocap G. Comparative metaproteomics reveals ocean-scale shifts in microbial nutrient utilization and energy transduction. ISME J. 2010;4:673–85.
CAS  PubMed  Article  Google Scholar 

42.
Williams TJ, Long E, Evans F, DeMaere MZ, Lauro FM, Raftery MJ, et al. A metaproteomic assessment of winter and summer bacterioplankton from Antarctic Peninsula coastal surface waters. ISME J. 2012;6:1883–900.
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Article  CAS  Google Scholar 

45.
Besemer J, Lomsadze A, Borodovsky M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001;29:2607–18.
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Orellana LH, Francis TB, Krüger K, Teeling H, Müller M-C, Fuchs BM, et al. Niche differentiation among annually recurrent coastal Marine Group II Euryarchaeota. ISME J. 2019;13:3024–36.
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Eren AM, Esen ÖC, 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  Article  Google Scholar 

48.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Deusch S, Seifert J. Catching the tip of the iceberg—evaluation of sample preparation protocols for metaproteomic studies of the rumen microbiota. Proteomics. 2015;15:3590–5.
CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.
CAS  PubMed  Article  Google Scholar 

51.
Nesvizhskii AI, Keller A, Kolker E, Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem. 2003;75:4646–58.
CAS  PubMed  Article  Google Scholar 

52.
Florens L, Carozza MJ, Swanson SK, Fournier M, Coleman MK, Workman JL, et al. Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors. Methods. 2006;40:303–11.
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47:D442–50.
CAS  PubMed  Article  Google Scholar 

54.
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–402.
CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.
PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
CAS  PubMed  Article  Google Scholar 

57.
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:538.
Article  Google Scholar 

59.
El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47:D427–32.
CAS  Article  Google Scholar 

60.
Saier MH, Reddy VS, Tsu BV, Ahmed MS, Li C, Moreno-Hagelsieb G. The Transporter Classification Database (TCDB): recent advances. Nucleic Acids Res. 2016;44:D372–9.
CAS  PubMed  Article  Google Scholar 

61.
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2012;40:W445–51.
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.
CAS  PubMed  Article  Google Scholar 

64.
Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2013;42:D490–5.
PubMed  PubMed Central  Article  CAS  Google Scholar 

65.
Tang K, Jiao N, Liu K, Zhang Y, Li S. Distribution and functions of TonB-dependent transporters in marine bacteria and environments: implications for dissolved organic matter utilization. PLoS ONE. 2012;7:e41204.
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Gómez-Santos N, Glatter T, Koebnik R, Świątek-Połatyńska MA, Søgaard-Andersen L. A TonB-dependent transporter is required for secretion of protease PopC across the bacterial outer membrane. Nat Commun. 2019;10:1360.
PubMed  PubMed Central  Article  CAS  Google Scholar 

67.
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.
PubMed  PubMed Central  Article  CAS  Google Scholar 

69.
Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.
CAS  PubMed  PubMed Central  Article  Google Scholar 

70.
Engel A, Händel N. A novel protocol for determining the concentration and composition of sugars in particulate and in high molecular weight dissolved organic matter (HMW-DOM) in seawater. Mar Chem. 2011;127:180–91.
CAS  Article  Google Scholar 

71.
Reintjes G, Fuchs BM, Scharfe M, Wiltshire KH, Amann R, Arnosti C. Short-term changes in polysaccharide utilization mechanisms of marine bacterioplankton during a spring phytoplankton bloom. Environ Microbiol. 2020;22:1884–900.
CAS  PubMed  Article  PubMed Central  Google Scholar 

72.
Avcı B, Krüger K, Fuchs BM, Teeling H, Amann RI. Polysaccharide niche partitioning of distinct Polaribacter clades during North Sea spring algal blooms. ISME J. 2020;14:1369–83.
PubMed  PubMed Central  Article  CAS  Google Scholar 

73.
Sperling M, Piontek J, Engel A, Wiltshire KH, Niggemann J, Gerdts G, et al. Combined carbohydrates support rich communities of particle-associated marine bacterioplankton. Front Microbiol. 2017;8:65.
PubMed  PubMed Central  Article  Google Scholar 

74.
Koch H, Dürwald A, Schweder T, Noriega-Ortega B, Vidal-Melgosa S, Hehemann J-H, et al. Biphasic cellular adaptations and ecological implications of Alteromonas macleodii degrading a mixture of algal polysaccharides. ISME J. 2019;13:92–103.
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Görke B, Stülke J. Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat Rev Microbiol. 2008;6:613–24.
PubMed  Article  CAS  PubMed Central  Google Scholar 

76.
Becker S, Scheffel A, Polz MF, Hehemann J-H. Accurate quantification of laminarin in marine organic matter with enzymes from marine microbes. Appl Environ Microbiol. 2017;83:e03389–16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

77.
Becker S, Tebben J, Coffinet S, Wiltshire K, Iversen MH, Harder T, et al. Laminarin is a major molecule in the marine carbon cycle. Proc Natl Acad Sci USA. 2020;117:6599–607.
CAS  PubMed  Article  PubMed Central  Google Scholar  More

Taxonomic and morphological results
Four species new to science (Mesobiotus emiliae sp. nov., Xerobiotus gretae sp. nov., Itaquascon magnussoni sp. nov., and Thulinius gustavi sp. nov.) were found in the Kristianstad Vattenrike Biosphere Reserve, and their formal descriptions follow.
Morphometric data for the animals and the egg of these species are reported in the Tables 1 and 2 respectively (Supplementary Table S1 for the raw and the Thorpe’s normalized data). The morphometric data used in differential diagnoses of the species were not Thorpe’s normalized. After Thorpe’s normalization, only the lengths of very few characters in the new species resulted not isometric with respect to the length of the buccal tube (Supplementary Table S1). For these characters, the range (min–max) of pt indexes calculated on the Thorpe’s normalized data and the range (min–max) of pt indexes calculated on the non-normalized data did not change significantly (Supplementary Table S1).
Table 1 Summary of the morphometric data of the animals of the species new to science.
Full size table

Table 2 Summary of the morphometric data of the eggs of two of the species new to science.
Full size table

Mesobiotus emiliae sp. nov.
ZooBank: lsid:zoobank.org:act:3DA2F1C0-BEC8-4D9A-B111-5D5F8F1FCCA0.
Type locality
Sånnarna, west of the nature reserve (Kristianstad, Skåne, Sweden). Sandy soil with grass (55.928931 N, 14.246299 E), collected on June 10th, 2014; sample SVC22 (C4342 in Bertolani’s Collection). The species was also found in two other localities (SVC34, 35) and it is probably present in further four localities in which only animals were found (SVC2, 8, 27, 30; Supplementary Table S2).
Type repositories
The holotype (SVC22 s11o), 34 paratypes, and an egg (SVC22 s1, s5, s7, s8, s11) are at Kristianstad University [HKR], 19 paratypes and an egg (SVC22 s4, s10) are in the collection of the Swedish Museum of Natural History [SMNH], and 3 paratypes and two eggs (C4342 s3, s9) are in the Bertolani’s Collection of University of Modena and Reggio Emilia [Unimore].
Description
Body whitish, 96.8–342.0 µm in length (Fig. 1a). Eye-spots absent in mounted specimens. Cuticle smooth, with sparse granules on the posterior side of the legs IV (visible with Light Microscopy [LM]; Fig. 1g,i), and with granules covered with 1–5 dots on the external side of the legs I-III (Fig. 1h; visible with Scanning Electron Microscopy [SEM]).
Figure 1

Mesobiotus emiliae sp. nov. (a) In toto (ventro-dorsal view) (b) Bucco-pharyngeal apparatus (dorso-ventral view from multiplanar images stack), (c) Buccal armature (ventral view), (d) Bucco-pharyngeal apparatus (lateral view) (e) Claw III (lateral view from multiplanar images stack), (f,g) Claw IV (lateral view from multiplanar images stack), (h) Claw III (fronto-lateral view), (i) Claw IV (frontal view). Empty indented arrows: crests of the buccal armature; indented white arrows: placoids constrictions; white arrowhead: cuticular ring of the buccal tube pharynx ending; empty arrows: accessory points of the main claw branch; empty indented arrowheads: lunules; asterisks: granulation on the legs IV. (a–c,e) Holotype. (a–g) LM, PhC; (h,i) SEM. Scale bars (a) 100 µm; (b–d) 10 µm; (e–i) 5 µm.

Full size image

Bucco-pharyngeal apparatus with antero-ventral mouth (Fig. 1b, d). Buccal ring with ten lamellae on its external margin. Buccal armature composed of: an anterior band of small teeth; a posterior line of conical teeth; three dorsal and three ventral transversal crests, the medio-ventral crest reduced to two or four mucrones in some smaller specimens (Fig. 1c), the latero-ventral crests shorter than the latero-dorsal ones. Short and straight stylet supports with distal flat enlargement, inserted at the 73.8–79.4% of the buccal tube. Typically-shaped stylet furca, with spherical condyles supported by short branches provided with small apophyses. Buccal tube ending with a thick cuticular ring within the pharynx (Fig. 1d). In the pharynx: pear-shaped pharyngeal apophyses; three grain-shaped (in lateral view) macroplacoids and an evident drop-shaped microplacoid. In frontal view, first macroplacoid triangular, second and third rectangular with rounded corners, and third with a deep distal constriction (Fig. 1b); length sequence 3  > 2  > 1.
Double-claws of Mesobiotus type (Fig. 1e–i) with evident accessory points on the main branch. All claws similar in shape, external claws slightly larger than internal. Claws increasing in size from the first to the fourth, claws of hind legs clearly the largest. Smooth lunules under all claws, larger under claws of the hind legs (the anterior lunules clearly larger with respect to the posterior).
Spherical eggs free laid, ornamented with processes in shape of large and short cones or mammillated with tips of different lengths (generally short), sometimes terminating in a tuft of filaments (Fig. 2a–c). Process wall formed by two sides (an internal and an external), interspersed with trabecular structures forming irregular meshes (Fig. 2b), in shape of bubble-like structures in the longer process tips (Fig. 2c). Base of the processes with a crown of irregular and small thickenings: smaller thickenings in shape of large dots, the larger ones triangular-shaped (Fig. 2b). Filaments of the process tips mostly short, elongated in abnormal processes. Processes in numbers of 11–14 on the circumference. Egg surface between the processes smooth or sparsely dotted. Egg with embryos found.
Figure 2

Eggs of Mesobiotus emiliae sp. nov. and Xerobiotus gretae sp. nov. (a–c) M. emiliae sp. nov. (a) iIn toto, (b) Processes surface detail, (c) Abnormal processes (lateral view). (d–j) X. gretae sp. nov. (d) In toto, (e,f) Processes (lateral view), (g) Processes (frontal view), (h) In toto, (i) Processes and surface detail, (j) Process (fronto-lateral view). Arrowhead: tuft of filaments on the tip of the egg process; arrow: septum dividing trunk and terminal disk of egg process; empty arrowhead: crown of dots at process base; indented arrowheads: pore on egg surface; empty indented arrowheads: indentation-like structures (provided with granular ornamentation) occurring in the upper surfaces of the disk. (a–g) LM, PhC; (h–j) SEM. Scale bars (a,d,h) 10 µm; (b,c,e–g,i) 5 µm; (j) 1 µm.

Full size image

Differential diagnosis
According to the taxonomic key of Mesobiotus species32,33, M. emiliae sp. nov. is different from any other described species of this genus. Mesobiotus emiliae sp. nov. belongs to the harmsworthi group of species, and within this group Mesobiotus insuetus (Pilato, Sabella & Lisi, 2014)34, Mesobiotus lusitanicus (Maucci & Durante Pasa, 1984)35, Mesobiotus occultatus Kaczmarek, Zawierucha, Buda, Stec, Gawlak, Michalczyk & Roszkowska, 201836, Mesobiotus patiens (Pilato, Binda, Napolitano & Moncada, 2000)37, Mesobiotus pseudoblocki Roszkowska, Stec, Ciobanu & Kaczmarek, 201638, and Mesobiotus snaresensis (Horning, Schuster & Grigarick, 1978)39 share with Mesobiotus emiliae sp. nov. the following characteristics: smooth cuticle; buccal armature with both anterior and posterior rows of teeth visible with LM, and without accessory teeth between posterior row and transversal crests; smooth lunules in the claws of the hind legs; eggs with processes in shape of cone or hemisphere with elongated tip or inverted funnel surrounded by a basal crown of dots or digitations, and with smooth or wrinkled surface between them.
Mesobiotus emiliae sp. nov. differs from:
M. insuetus by: the presence of granulation on legs IV, the shorter first macroplacoid (pt 15.4–16.8 in M. insuetus; pt 7.9–12.7 in M. emiliae), the shorter macroplacoids row (pt 46.2–48.9 in M. insuetus; pt 35.2–46.0 in M. emiliae), the different morphology of the hind claws (i.e. not with secondary branches diverging distally and forming a right angle with the primary branches as in M. insuetus), the shorter primary branch of all claws (e.g., pt of the primary branch of the claws II, 31.9–36.0 in M. insuetus; 20.4–25.4 in M. emiliae), and the processes of the egg with larger meshes on the surface (with LM, as small separated dots in M. insuetus and bubbles in contact to each other in M. emiliae);
M. lusitanicus by: the presence of granulation on legs IV, the length sequence of macroplacoids (3  > 1  > 2 in M. lusitanicus), the more evident microplacoid (pt 4.2 in M. lusitanicus; pt 7.3–12.3 in M. emiliae), and the shape of egg processes (i.e. not in shape of hemispheres terminating with a cap-like structure or with a fringed cones as in M. lusitanicus);
M. occultatus by: the absence of granulation on the legs I-III (with LM), the length sequence of macroplacoids (1 ≥ 3  > 2 in M. occultatus), the smaller eggs (full diameter 97.4–126.6 µm in M. occultatus and 62.4–76.5 µm in M. emiliae), the shape of the egg processes with a base/height ratio (74–106% in M. occultatus and 144–164% in M. emiliae), the distance between the egg processes (mean 2.6 µm, 0.6 SD in M. occultatus and mean 1.1 µm, 0.4 SD in M. emiliae);
M. patiens by: the absence of granulation on the legs I-III (with LM), the length sequence of macroplacoids (1  > 3  > 2 in M. patiens), the smaller eggs (full diameter 90.5–100.0 µm in M. patiens and 62.4–76.5 µm in M. emiliae), absence of slender tips in the egg processes;
M. pseudoblocki by: the presence of granulation on legs IV, the length sequence of macroplacoids (1  > 3  > 2 in M. pseudoblocki), the smaller anterior claw of the hind legs (pt 27.5–33.5 in the primary branch and 19.8–27.4 in the secondary branch in M. pseudoblocki and pt 27.5–33.5 in the primary branch and 19.8–27.4 in the secondary branch in M. emiliae), the closer processes on the egg surface (mean 2.8 µm, 0.6 SD in M. pseudoblocki and mean 1.1 µm, 0.4 SD in M. emiliae), the processes of the egg not in shape of sharpened narrow cones, the processes base/height ratio (47–70% in M. pseudoblocki and 144–164% in M. emiliae);
M. snaresensis by: the presence of granulation on hind legs, the more evident microplacoid (pt 4.2–7.3 in M. snaresensis; pt 7.3–12.3 in M. emiliae), the processes of the egg not terminating with a sharp or bifid tips, and the absence of pseudoareolation on the egg surface between the processes.
Molecular characterization
The analyses of the molecular markers were not possible due to the lack of alive specimens: the genomic material extracted from dead specimens gave no amplicons.
Etymology
We dedicate this species to Emilia Lonis, the beloved hundred-years-old grandmother of the coauthor Massa E., one of the last living workers that with their hand-on work for the reclamation of the “Piana di Terralba” (Sardinia, Italy; now site of the “Natura 2000” network) have contributed to the eradication of malaria in the island saving thousands of life.
Xerobiotus gretae sp. nov.
ZooBank: lsid:zoobank.org:act:E5265E82-86E9-4069-9B3B-B0F11D43DE79.
Type locality
Sånnarna, (Kristianstad, Skane, Sweden). Moss on ground (55.928056 N, 14.252694 E), collected on June 10th, 2014. Sample SVC15 (C4341 in the Bertolani’s Collection). An animal of this species was also found within a Saxifraga sp. (SVC19; Supplementary Table S2).
Type repositories
The holotype (SVC15 s2m), 51 paratypes, and an egg (SVC15 s2, s3) are at HKR, 50 paratypes (SVC15 s5) are in the collection of the SMNH, and 29 paratypes and an egg (C4341 s1, s4) are in the Bertolani’s Collection of Unimore. Five paratypes recovered from the old slide C4341 s1 (for the extraction and mounting protocol, see: Methods section) together with four paratypes and an egg freshly extracted were mounted on stubs for SEM observation.
Description
Body whitish or pale green, 177.5–438.3 µm in length (Figs. 3a, 4a,f). Orange eye-spots present in mounted specimens. Very small scattered pores (about 0.5 µm in diameter) in the dorso-lateral cuticle (Figs. 3b, 4b,c). Very small single granules, distributed almost regularly, present on the entire cuticle (only visible with SEM; Fig. 4c). Legs of the first pair smaller than those of the second and third pairs. The area of the leg cuticle surrounding the claws with a swelling (forming a garter-like structure; Fig. 4a, d–f). These swellings appearing covered with microdigitations and few minute scattered granules (with SEM; Fig. 4d).
Figure 3

Xerobiotus gretae sp. nov. (a) In toto (ventro-dorsal view), (b) Cuticular ornamentation (dorsal view), (c) Bucco-pharyngeal apparatus (dorso-ventral view from multiplanar images stack), (d) Bucco-pharyngeal apparatus (lateral view from multiplanar images stack), (e) Buccal armature (ventral view), (f) Stylet furca (frontal view), (g) Macroplacoids (frontal view from multiplanar images stack), (h) Claw III (frontal view), (i) Claw VI (lateral view). Arrow: cuticular pores; empty indented arrows: crests on the buccal armature; empty arrowheads: cuticular ring of the buccal tube pharynx ending; indented white arrows: placoid constrictions; empty arrow: accessory points of the main claw branch; arrowheads: basal rounded cuticular thickening of the claws; black arrowhead: lunules; indented white arrowheads: cuticular bars. (a,c) Holotype; (a–i) LM, PhC. Scale bars (a) 100 µm; (b–h) 10 µm.

Full size image

Figure 4

Xerobiotus gretae sp. nov. (a–e) Specimens mounted on stub from alive tardigrades. (f–g) Specimens mounted on stub from old permanent slide. (a) In toto (lateral view), (b,c) Cuticle (dorsal view), (d) Claw I (fronto-lateral view), (e) Claw IV (lateral view), (f) In toto (ventral view), (g) Buccal opening. White arrows: garter-like structure covered with microdigitations; indented white arrows: pores; arrowheads: very small single dots on the cuticle; empty arrows: accessory points; black arrows: basal rounded cuticular thickening of the claws; black arrowhead: granules on the garter-like structure; empty indented arrowhead: anterior band of small teeth at the proximal end of the peribuccal lamellae; empty arrowhead: posterior line of small teeth; indented white arrowhead: dorsal transversal crests; empty indented arrow: cribrose area in buccal tube. SEM. Scale bars (a,f) 100 µm; (b,c) 1 µm; (d,e,g) 5 µm.

Full size image

Bucco-pharyngeal apparatus with antero-ventral mouth (Fig. 3c,d). Buccal ring with ten peribuccal lamellae (Fig. 4g). Buccal tube of Macrobiotus type, curved in the first half, and ending with a thick cuticular ring within the pharynx (Fig. 3c,d). Ventral lamina with an antero-ventral thickening (Fig. 3d). Buccal armature (Figs. 3c,e, 4g) composed of: an anterior band of small teeth at the base of the peribuccal lamellae (with SEM); a thin posterior band of small teeth not always visible with LM, but clearly visible with SEM; three dorsal and three ventral transversal crests, medio-ventral crest appearing split in two or three mucrones in some specimens (with LM). Lateral cribrose areas posterior to the transversal crests visible with SEM (Fig. 4g). Stylet support, inserted at 77.0–81.2% of buccal tube, in shape of an elongated sigma with a distal flat enlargement (Fig. 3c). Typically-shaped stylet furcae, with oval condyles supported by short branches provided with rounded apophyses (Fig. 3f). In the pharynx: large and triangular pharyngeal apophyses overlapping the first macroplacoid; two rod-shaped macroplacoids (in lateral view; Fig. 3d), and evident drop-shaped microplacoid. In frontal view (Fig. 3g), the first macroplacoid in shape of a drop with a medial slight constriction longer than the second, the second rectangular with rounded corners and with a small terminal slight constriction.
Double-claws I-III different from claws IV (Figs. 3h,i, 4d,e): claws I–III of Xerobiotus type (without lunules), claws IV with a longer common tract, small and short claw branches, and pale lunules (more sclerified proximally than distally) sometimes visible (Fig. 3i). Internal and external claws of the same leg similar in shape, external (or posterior, in claw IV) claw slightly larger than the internal (or anterior, in claw IV). Proximal portion of the basal part of all claws with a small enlargement, larger in claw IV (Figs. 3h,i, 4d). Claws increasing in length from the first to the third pair. Primary branch of all claws with short accessory points (larger in claws IV). Cuticular bars under the base of the claw IV thick and with ragged margin (Fig. 3i): cuticular bar under the posterior claw wider and stretched toward the anterior claw, cuticular bar under anterior claw developed toward the front of the body (Fig. 3i).
Spherical eggs laid free (Fig. 2d,h), ornamented with processes in shape of inverted goblets with straight or concave cross section (according to Kaczmarek et al.35; Fig. 2e–g,i,j). Processes base surrounded by a crown of dots (Fig. 2g); terminal disc slightly concave and divided by a septum from the trunk (Fig. 2e,f). The edge of the terminal disks indented, the indentations appearing like tapered tip (with LM; Fig. 2g) and like elongated processes ornamented with granules (with SEM; Fig. 2i); in several processes indentation-like structures (provided with granular ornamentation) occurring also in the upper surfaces of the disk (Fig. 2j). Wrinkled egg surface between the processes (Fig. 2g, i) and scattered with dot-like pores (with SEM; Fig. 2i). Egg with an embryo found.
Differential diagnosis
Xerobiotus gretae sp. nov. differs from all other Xerobiotus species by having pores on the cuticle visible with LM, an enlargement in the basal part of the claws, and cuticular bars under the claws IV.
Moreover, Xerobiotus gretae sp. nov. differs from:
Xerobiotus xerophilus (Dastych, 1978)41,42 by: the presence of a posterior band of teeth and the dorsal transversal crests not fused in the buccal armature, the shape of the egg processes (flattened hemispherical processes in X. xerophilus), and the egg surface lacking reticulation;
Xerobiotus euxinus Pilato, Kiosya, Lisi, Inshina & Biserov, 201143 by: the dorsal transversal crests not fused in the buccal armature, and the presence of cuticular bars under the claws of the hind legs;
Xerobiotus pseudohufelandi (Iharos, 1966)44 by: the presence of a posterior band of teeth, shorter common tract in the claws I-III (pt 11.07–11.99 in X. pseudohufelandi; pt 9.19–9.91 in X. gretae), and the egg surface lacking reticulation.
Molecular characterization
It was not possible to extract genetic material from the specimens recollected from the permanent slides (C4341 A–E). The analyses of the molecular markers amplified from four specimens (C4341 G–L; GenBank accession number: MW581665-8, cox1; MW588431-3, ITS2; MW588438-41, 28S; MW588434-7, 18S; Supplementary Table S5) revealed single haplotypes for ITS2, 18S, and 28S genes, and three haplotypes for cox1 gene (highest p-distance = 0.4%; Supplementary Table S3).
Xerobiotus gretae sp. nov., in comparison to the more similar GenBank sequences which belong to Xerobiotus sp. collected in South Africa (Cape of Good Hope, Western Cape)45, differs for p-distances of 1.6–2.6% for cox1 (796 bp), 1.0% for ITS2 (452 bp), and 0.0% for 18S (870 bp). Xerobiotus pseudohufelandi, collected in Italy (Monte Calvario)46, differs from X. gretae sp. nov. for p-distance of 16.9–17.8% for cox1, and 0.0–0.1% for 18S, Xerobiotus sp., collected in Poland (Błedowska Desert)45, differs from X. gretae sp. nov. for p-distances of 17.8–18.1% for cox1, 5.1% for ITS2, and 0.1–0.2% for 18S (Supplementary Table S3).
Etymology
We dedicate this species to the climate activist Greta Thunberg, for her brave and insightful efforts to open the eyes of the world leaders about the need for action against climate change. The achievements of Greta Thunberg give us hope that the challenges of changing the unsustainable path of human societies may still be possible, just like the tiny tardigrades are able to overcome seemingly impossible environmental challenges. But we have to act now!
Itaquascon magnussoni sp. nov.
ZooBank: lsid:zoobank.org:act:254843BC-60F7-4B8E-A94D-8783214F3399.
Type locality
Näsby Fält (Kristianstad, Skåne, Sweden), along a trail to Araslövssjön Lake. Moss on bark of Alnus sp. (56.059328 N, 14.136678 E), 2 m up on the tree, collected on June 10th, 2014. Sample SVC32 (C4344 in the Bertolani’s Collection). The species was also found in two other localities (SVC3, 27; Supplementary Table S2).
Type repositories
The holotype (SVC32 s4c) and 13 paratypes are at HKR, four paratypes (SVC32 s8) are in the collection of the SMNH, and eight paratypes (C4344 s12) in the Bertolani’s Collection of Unimore.
Description
Body whitish, 135.9–509.3 µm in length (Fig. 5a). Eye-spots absent in mounted specimens. Cuticle smooth. Bucco-pharyngeal apparatus of Itaquascon type (Fig. 5b). Rigid and straight buccal tube, clearly longer than the apophyses for the insertion of the stylet muscles [AISM]. AISM symmetrical and flat ridge-shaped. Buccal tube followed by a pharyngeal tube almost of the same length (pharyngeal tube pt 91.6–113.8). Flexible pharyngeal tube formed by a rope-shaped thickening organized in a geometrical repeated pattern resembling an alternating hexagonal “wire meshes” (Fig. 5b,e,f); the “wire meshes” pattern begins in a more anterior position dorsally and ventrally than laterally (Fig. 5e,f). Very thin stylet supports present but hardly detectable and inserted on the pharyngeal tube in its anterior portion (Fig. 5b,e,f). Small stylet furca with short branches ending in drop-shaped condyles. Stylet coat more sclerotized in its proximal and distal portions than in its middle part. Pharyngeal tube ending within the pharynx with three small triangular apophyses (Fig. 5b). In the pharynx, only a single long, straight, and weakly thickened bar present (Fig. 5b).
Figure 5

Itaquascon magnussoni sp. nov. (a) In toto (ventro-lateral view), (b) Bucco-pharyngeal apparatus (dorso-ventral view from multiplanar images stack), (c) Claw II (lateral view from multiplanar images stack), (d) Claw VI (frontal view), (e) First section of the pharyngeal tube (3D dorsal reconstruction), (f) First section of the pharyngeal tube (drawn of dorsal view). White arrows: stylet supports; indented arrow: thickened bar in the pharynx; empty arrows: accessory points of the main claw branch; empty indented arrows: pseudolunules; arrowhead: cuticular bar under the claw. (a–c) Holotype. (a–d) LM, PhC; (e) CLSM; (f) schematic drawing. Scale bars (a) 100 µm; (b–d) 10 µm, (e,f) 5 µm.

Full size image

Double-claws of Hypsibius type (Fig. 5c,d), internal (anterior, in the claw IV) and external (posterior, in the claw IV) claws of the same legs different both in shape and size. Claws increasing in length from the first to the fourth legs. Basal part of all claws long, with enlarged base. Main branch of external claw (posterior, in the claw IV) long, quite straight, and poorly sclerotized throughout its length, with evident accessory points; its proximal part placed on a cuticular digit and connected with the secondary branch with a pair of filaments spanning from the tip of the branch (Fig. 5c). Main branch of internal (anterior, in the claw IV) claw shorter and more curved than the external, with evident accessory points. Thin and hardly detectable pseudolunules present under all claws (Fig. 5c,d). Straight cuticular bars (Fig. 5c), similar in size, with ragged margins on the internal side of the legs I-III, extending from the internal claw base to the anterior side of the leg, weakly visible only on the first pair of legs.
Eggs unknown.
Differential diagnosis
Itaquascon magnussoni sp. nov. differs from all other Itaquascon species by having the stylet support inserted on the flexible pharyngeal tube. Considering the presence of the thickening within the pharynx, the most similar species of I. magnussoni sp. nov. are Itaquascon placophorum Maucci, 197347 and Itaquascon simplex (Mihelčič, 1971)48 (considered nomen dubium by Ramazzotti et al.49, thus not considered in this diagnosis).
Itaquascon magnussoni sp. nov. differs from I. placophorum by: the longer buccal tube with respect to the bucco-pharyngeal tube (buccal tube 16–17% of the bucco-pharyngeal tube in the holotype of I. placophorum and 48.0–48.4% in I. magnussoni), the longer thickening in the pharynx (calculated pt 31.3 in the holotype of I. placophorum; pt 44.8–63.5 in I. magnussoni), the claws with pseudolunules and evident accessory points on the main branch.
Molecular characterization
The analyses of the molecular markers were not possible due to the lack of alive specimens: the genomic material extracted from dead specimens gave no amplicons.
Etymology
The species name is to honor Sven-Erik Magnusson, a sustainability visionary and leading person behind the development of Kristianstads Vattenrike Biosphere Reserve, and the first Coordinator of the Biosphere Reserve.
Thulinius gustavi sp. nov.
ZooBank: lsid:zoobank.org:act:149B5B73-580F-4BBE-BCB0-BCFD53E54EB5.
Type locality
Araslövssjön Lake, Näsby Fält (Skåne, Sweden). Upper layer of freshwater sediments in the bottom of the shore of the lake (56.059050 N, 14.135425 E), sample SVC31 (C4343 in Bertolani’s Collection).
Type repositories
The holotype (SVC31 s3b) and nine paratypes (SVC31 s2, s4, s5; SVC31b s2, s6, s8) are at HKR, one paratype (SVC31 s4) is in the collection of the SMNH, and one paratype (C4343 s1) in the Bertolani’s Collection of the Unimore. Two paratypes were mounted on stubs for SEM observation.
Description
Body whitish, 231.0–346.0 µm in length (Fig. 6a,j). Eye-spots present. Dorsal cuticle sculptured with large tubercles, with polygonal base, that gradually increase in size from the head to the posterior side of the body (Fig. 6b,k).
Figure 6

Thulinius gustavi sp. nov. (a) In toto (dorso-lateral view), (b) Cuticular ornamentation (dorso-lateral view), (c) Bucco-pharyngeal apparatus (dorso-ventral view from multiplanar images stack), (d) Buccal armature (dorsal view), (e) Buccal armature (ventral view), (f) Stylet furca (frontal view), (g) Macroplacoids (frontal view from multiplanar images stack), (h) Claw I (lateral view), (i) Claw IV (frontal view), (j) In toto (lateral view), (k) Cuticular ornamentation (dorso-lateral), (l) Claw II (frontal-lateral view), (m) Claw IV (frontal view), (n) Bucco-pharyngeal apparatus (3D lateral reconstruction). Indented arrowheads: cuticular ornamentation; white arrowhead: peribuccal lamellae; empty indented arrowhead: posterior line of small round teeth; white arrow: second macroplacoid constriction; white indented arrows: accessory points of the main claw branch; empty arrow: pseudolunules; empty arrowheads: apophyses for the insertion of the stylet muscles. (a–c) Holotype. (a–f), (h,i). LM, PhC; (g) LM, DIC; (j–m) SEM; (n,o) CLSM; Scale bars (a) 100 µm; (b–j,n). 10 µm; (k) 2 µm; (l,m) 5 µm.

Full size image

Bucco-pharyngeal apparatus with antero-ventral mouth opening (Fig. 6c,n). Buccal tube straight. Twelve peribuccal lamellae present. Buccal armature formed by a posterior line of small round teeth, followed by a line of large rounded teeth in the position of the transversal crests (Fig. 6d,e). Two bigger rounded teeth present ventrally within the second line in correspondence of the stylet sheaths (Fig. 6d). Ventral and dorsal AISM crest-shaped and symmetrical with respect to the frontal plane (Fig. 6n). Long and straight stylet supports with a distal flat enlargement. Typically-shaped stylet furca, with long branches provided with large apophyses (Fig. 6f). In the pharynx, large pharyngeal apophyses overlapping the first macroplacoid (Fig. 6n); three rod-shaped (in lateral view) macroplacoids arranged in a curved line; first and second almost fuse together, third spaced from the second (Fig. 6g). In frontal view, the first macroplacoid in shape of a triangle, second in shape of a rectangle with rounded corners, and the third almond-shaped and slightly constricted in the middle; length sequence 3  > 1  > 2.
Double-claws of Isohypsibius type (Fig. 6h,i,l,m) increasing in length from the first to the fourth pair of legs, external claw slightly longer than the internal. Basal portions of all claws short and slender, enlarged in their proximal portion. Primary branch of all claws with thin and short accessory points (never reaching the end of the branch and not always visible) and divided from the rest of the claw by a basal septum, with a dorsal knob-like thickening (Fig. 6h,i). Pseudolunules hardly detectable and small on external claws of legs I–III (Fig. 6h,l), larger on posterior claw of legs IV (Fig. 6i,m).
Smooth oval eggs laid in exuvium (an exuvium with four eggs was found).
Differential diagnosis
Thulinius gustavi sp. nov. differs from all other Thulinius species by having a dorso-lateral ornamented cuticle with tubercles. Considering the presence of pseudolunules only under the external (posterior, in claws IV) claws, the most similar species are Thulinius romanoi bertolani, bartels, Guidetti, Cesari & Nelson, 201450 and Thulinius saltarsus (Schuster, Toftner & Grigarick, 1978)51.
T. gustavi sp. nov. differs from:
T. romanoi by: the absence of ornamented cuticle in ventral side, the presence of eye-spots, the more narrow buccal tube (pt 21.9 and 13.4 in the holotype and a paratype of T. romanoi; pt 9.1–10.7 in T. gustavi), the stylet support inserted more posteriorly (pt 62.9 in the holotype of T. romanoi; pt 70.2–73.1 in T. gustavi), the macroplacoid length sequence (1  > 3  > 2 in T. romanoi), the shorter accessory points that never reach the end of the branch, and the presence of evident pseudolunules in claw IV;
T. saltarsus by: the dorso-lateral ornamented cuticle, presence of eye-spots, the macroplacoid length sequence (1  > 3  > 2 in T. saltarsus).
Molecular characterization
The analyses of the molecular markers were not possible due to the lack of alive specimens: the genomic material extracted from dead specimens gave no amplicons.
Etymology
The species name has been chosen in honor of Gustav Thulin (1889–1945), the first internationally recognized Swedish tardigradologist, who made important contributions to the knowledge of the Swedish tardigrade fauna and to modern taxonomy and phylogenetics of tardigrades.
Faunistic results
The analysis of the 34 samples collected (33 terrestrial and one freshwater; see “Methods”) in the five sampled areas within the KVBR revealed the presence of 33 morphospecies belonging to 20 genera (Table 3) of Eutardigrada (18 genera and 29 species of Parachela, and one genus and two species of Apochela), and Heterotardigrada (one genus, two species). The identification of the morphospecies was carried out with morphological and morphometric approaches.
Table 3 Morphospecies identified with a morphological approach in the samples collected from five areas within Kristianstads Vattenrike.
Full size table

The highest densities of tardigrades (ind/g) were found in a lichen and a moss (Table 3, Supplementary Table S4). However, tardigrade densities in both lichens (five samples) and mosses (13 samples) were highly variable, ranging from 2.7 to 75.4 ind/g (mean: 27.6 ind/g, SD: 29.5, N = 5) in lichens, and from 0.2 to 66.4 ind/g (mean: 11.1 ind/g, SD: 17.9, N = 13) in mosses. In contrast, leaf litter (two samples) and soil with grass (three samples) were less abundant in animals and had a more homogeneous density: 0.1–1.9 ind/g (mean: 0.9 ind/g, SD: 0.68, N = 5; Table 3, Supplementary Table S4).
The species belonging to the family Macrobiotidae were the most represented, found in 23 samples. The 11 macrobiotid species belonged to five genera (Macrobiotus, Mesobiotus, Minibiotus, Paramacrobiotus, and Xerobiotus) and were found in 69.7% of the terrestrial substrates, with variable diversity (1–4 species per substrate) and variable density (0.1–22.4 ind/g; Table 3) within each sample. The genus Macrobiotus was the most represented among the Macrobiotidae and among all the genera identified in all samples (7 species distributed among 17 samples).
The sample SVC11 (C4340 in Bertolani’s Collection) was the richer in terms of overall density (66.4 ind/g; Table 3). Within this sample Macrobiotus polonicus Pilato, Kaczmarek, Michalczyk & Lisi, 200352 and Macrobiotus wandae Kayastha, Berdi, Mioduchowska, Gawlak, Łukasiewicz, Gołdyn, & Kaczmarek, 202053 were initially morphologically identified, but the evidence of intraspecific variability for some characters led us to suspect the presence of cryptic species. The analyses were performed by genotyping the markers ITS2 and cox1. The analyses of the cox1 were unsuccessful, but the ITS2 sequences amplified from nine specimens (C4340 C–D, J–P; GenBank accession numbers: XXXX) were sufficient to reveal the presence of three species: Macrobiotus polonicus, Macrobiotus cf. polonicus, and Macrobiotus aff. wandae. Macrobiotus polonicus, already identified via morphology, was confirmed also by a very low p-distance of its sequences (0.00–0.01%; 587 bp) with respect to those already attributed to this species (Supplementary Table S3). In the population, eight males with spermatozoans within the gonad were found. One specimen previously identified as M. polonicus was revealed to belong to a cryptic taxon that we named M. cf. polonicus (p-distance 0.04–0.05% with respect to M. polonicus sequences; Supplementary Table S3). Macrobiotus cf. polonicus differs morphologically from M. polonicus by the presence of fine granules on the external side of all legs, for this species the egg morphology is unknown. Macrobiotus aff. wandae is probably a species new to science both for the ITS2 differences (p-distance 0.17–0.18% from the three available sequences of Macrobiotus wandae; Supplementary Table S3) and for the different shape of the egg having a more expanded distal disk on the processes. Since only one egg and few animals of this species were collected and the cox1 sequencing gave no result, further collection and analyses will needed before a possible new species description.
The most common morphospecies in the samples was Ramazzottius oberhaeuseri (Doyére, 1840)54. It was retrieved from 39.4% (13 terrestrial samples) of the samples and from all the sampled areas except Balsberget, with a highly variable density: e.g., 70.8 ind/g in a lichen, 0.3 ind/g in a soil with grass, and 0.1 ind/g in a moss or in a leaf litter. Milnesium asiaticum Tumanov, 200655 was found in 33.3% (11 samples) of the terrestrial samples from all the sampled areas, but with low density (0.1–1.4 ind/g). Hypsibius convergens (Urbanowicz, 1925)56 was found in 32.4% (11 samples) of both terrestrial and freshwater samples, and in all the sampled areas except the HKR campus, with a low density (0.1–6.2 ind/g). Macrobiotus persimilis Binda & Pilato, 197257 had a wide distribution, found in 30.3% (10 samples) of terrestrial samples, with low density (0.3–2.3 ind/g). All the other morphospecies have a more restricted distribution within the samples (Table 3).
Considering all samples, the species diversity within individual samples and between sampling areas was variable (number of morphospecies: 0–7; 12–19, respectively), but most of the samples (67.6%) had three to six morphospecies, and only within two samples (SVC5, 6; 0.5%) there were no tardigrades (Table 3). More