Ecology

Global map of forest cover change and its validationWe use a high-resolution map of global forest cover change15 (annual intervals at 30 m spatial resolution; version 1.7) to quantify forest loss across the tropics. The GFC dataset maps where and when forests were converted (naturally and anthropogenically) from 2001 to 2019. Trees are defined as all vegetation taller than 5 m in height, and forests are defined with a tree canopy threshold of at least 30%. The definition of forests includes plantations and tree crops such as oil palm. Forest loss is the mortality or removal of all tree cover within a pixel.Our study uses the v.1.7 product that spans the period 2001–2019 for the analysis. Different methods are used for detecting forest cover loss in two periods (2001–2010 and 2011–2019). This change in detection method as well as in satellite data (Landsat 7 and Landsat 8) might result in inconsistencies of data during the two periods. Therefore, we perform an independent assessment of the v.1.7 product throughout the study period (2001–2019) using stratified random-sample reference data. We randomly sample 18,000 pixels, a much larger sample population than the assessment of the original product (v.1.0, 628 pixels across the tropics; ref. 15), and visually interpret forest loss using Landsat imagery. Specifically, we randomly select 50 path/row locations (World Reference System II) of Landsat imagery in each tropical continent (150 path/row locations in total in the three tropical continents; Supplementary Fig. 6). For each path/row location, we randomly select 20 loss pixels and 10 non-loss pixels in each period of 2001–2005, 2006–2010, 2011–2014 and 2015–2019, with total sampling pixels of ~18,000 ((20 loss + 10 non-loss) × 50 path/row locations × 4 periods × 3 continents). Our study compares the increase in forest carbon loss from the start (~2001) to the end (~2019) of the study period. Thus, we divided the whole 19 yr study period into four subperiods (2001–2005, 2006–2010, 2011–2014 and 2015–2019), with the first five years considered as the start period and the last five years considered as the end period for the comparison. Some path/row locations do not have 20 loss pixels in a specific period; for example, there is no loss detected in some locations of the Sahara. Therefore, we sample 11,198 loss pixels and 6,000 non-loss pixels (Supplementary Data). Finally, we download time-series Landsat imagery covering 1999–2020 to visually interpret these pixels as reference data.Following the suggestion of Global Forest Watch19 and per best practice guidance of ref. 18, we use a stratified random-sample approach for area estimation, which is independent of the method and satellite changes in the GFC data. The sampling reference data (Supplementary Data) are used to estimate loss area:$${it{p}}_{hi} = w_hfrac{{mathop {sum}limits_{j = 1}^{150} {n_{hij} times A_{hi}} }}{{mathop {sum}limits_{j = 1}^{150} {n_{hj} times A_i} }}$$
(1)
where phi is the stratified random-sample estimated area for GFC map class h that is classified as reference class i; wh is the proportion of the total area GFC map class h; nhij is the number of pixels in GFC map class h that is classified as reference class i in jth Landsat path/row location; nhj is the total number of pixels in GFC map class h in jth Landsat path/row location; Ahj is the total area in GFC map class h in jth Landsat path/row location; and Aj is the total land area of jth Landsat path/row location.We then calculate the error matrix, which includes overall accuracy (OA), user’s accuracy (UA) and producer’s accuracy (PA), as follows:$${mathrm{OA}} = mathop {sum}limits_{h = 1}^H {p_{hh}}$$
(2)
$${mathrm{UA}}_h = frac{{p_{hh}}}{{p_h}}$$
(3)
$${mathrm{PA}}_j = frac{{p_{jj}}}{{p_j}}$$
(4)
where UAh is the UA for stratum h; ph and pj are the total area in stratum h and j, respectively; and PAj is the PA in stratum j.Using the preceding equations, we estimate OA, UA and PA in the four periods (Supplementary Table 2). In general, OAs are >99%, UAs are >88% and PAs are >72% in each period. The stratified random-sample approach for area estimation is considered the most robust method to investigate loss trends in GFC product and can avoid inconsistencies of the dataset due to changes in detection model and satellite sensors19. Our results show that the forest loss from the stratified random-sample approach is similar to GFC mapped loss, both of which show a consistent increase during the four periods of 2001–2019 (Fig. 1a), confirming the increasing forest carbon loss across the tropics during the early twenty-first century.Forest carbon stocksWe estimate forest (aboveground and belowground) biomass carbon losses by co-locating GFC loss data with corresponding biomass data. Forest biomass maps are not universally reliable, owing to uncertainties and some degree of bias. We use four biomass maps to quantify forest carbon stocks, which helps reduce the uncertainties and bias44. The four maps were developed by refs. 9,48,49,20 and are hereafter referred to as ‘Baccini’, ‘Saatchi’, ‘Avitabile’ and ‘Zarin’ maps, respectively. The Baccini map, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, presents aboveground live woody biomass (AGB) across the tropics at 500 m spatial resolution. The Saatchi map, also derived from MODIS data, presents total forest carbon stocks at 1 km spatial resolution across the tropics. The Avitabile map, integrated from the Baccini and Saatchi maps, shows AGB at 1 km resolution across the tropics. The Zarin map, derived from Landsat data, presents AGB across the globe at 30 m resolution.Belowground root biomass (BGB) data are sparse because measurements of BGB are time consuming, laborious and technically challenging50. Thus, we calculate BGB (in Mg ha−1 biomass) from AGB maps (Baccini, Avitabile and Zarin) using an empirical model at the pixel level51:$${mathrm{BGB}} = 0.489 times {mathrm{AGB}}^{0.89}$$
(5)
Total forest biomass is calculated as the sum of AGB and BGB. Finally, total forest carbon stocks (MgC ha−1) in live woody forest are estimated as 50% of total biomass20,50. The Saatchi map provides total forest carbon stocks rather than AGB. The total forest carbon stocks are calculated from AGB using the same method mentioned in the preceding50. Thus, we estimate AGB and BGB from total forest carbon stocks in the Saatchi map using the preceding method to separate aboveground and belowground parts of forest carbon stocks.There are inconsistencies in the MODIS-derived biomass maps (Baccini, Saatchi and Avitabile) and Landsat-derived GFC data41, which may underestimate forest carbon loss by the three coarse forest biomass maps (Supplementary Fig. 7). The Zarin map is derived from Landsat data and considers tree cover using GFC data, and tree loss can be co-located with the corresponding biomass20, indicating the consistencies of the Zarin map and GFC. Therefore, to correct the three biomass maps with coarse resolution and reduce the inconsistencies, we resample the Zarin map from 30 m to 500 m (the resolution of the Baccini map) and 1 km (the resolutions of the Avitabile and Baccini maps) and calculate forest carbon loss using the two resampled biomass maps. We then estimate the ratios of forest carbon loss derived from the 30 m biomass map to the forest carbon loss derived from resampled biomass maps in each GFC tile (10° × 10°). The ratios are then used as a scale factor to correct the three biomass maps (Baccini, Saatchi and Avitabile). For the resampling, forest carbon density at 500 m or 1 km is averaged from all 30 m pixels in the corresponding locations.Soil organic carbon stocksDeforestation not only causes forest biomass carbon loss, but also results in loss of SOC10. We calculate SOC loss at 0–30 cm soil depth as:$${mathrm{SOC}}_{{{{mathrm{loss}}}}} = {mathrm{OCS}} times theta$$
(6)
where SOCloss is SOC loss resulting from forest loss measured in MgC ha−1, OCS is SOC stocks at 0–30 cm depth measured in MgC ha−1 and θ is the SOC loss rate.We obtain SOC stocks at 0–30 cm depth from SoilGrids (version 2.0), created by the International Soil Reference and Information Centre52. SOC stocks are calculated using a calibrated quantile random forest model at a spatial resolution of 250 m. We further resample the data from 250 m to 30 m using the nearest-neighbour method to match the scale of forest cover loss data.SOC loss rate is affected predominantly by land-use types following forest loss and tree species53. The loss rate data are compiled from a previous meta-analysis, which summarizes the rate of SOC loss resulting from forest loss over the tropics50. SOC losses resulting from primary and secondary forest loss differ (Supplementary Table 3). We use a map of primary humid tropical forests in 2001 developed by ref. 54 to classify primary and secondary forests. The land covers following forest loss are determined according to the driver of forest loss (see Drivers of forest carbon loss). The spatial resolution of the SOC data is coarse compared with GFC data, which may result in inconsistencies in the calculation. However, as SOC loss resulting from forest loss accounts for a small proportion of total forest carbon loss (8%), we ignore the potential inconsistencies.Drivers of forest carbon lossWe determine drivers of tree-cover loss using the dataset generated by ref. 27. This dataset shows the dominant driver of tree-cover loss at each 10 km grid cell for 2001–2019. There are five categories of drivers of tree-cover loss: commodity-driven deforestation, defined as permanent and/or long-term clearing of trees to other land uses (for example, commodity croplands), shifting agriculture, forestry, wildfire and urbanization. Commodity-driven deforestation, shifting agriculture and forestry dominate tropical forest loss27. Thus, wildfire and urbanization are combined and categorized as ‘others’. In tropical Africa, spatial patterns of commodity-driven deforestation are almost similar to that of shifting agriculture, as pointed out by ref. 27, resulting in large uncertainties in separating commodity agriculture from shifting agriculture. Since commodity-driven deforestation is usually for large-scale agricultural plantations, we treat commodity-driven deforestation as loss for large-scale agriculture. Because shifting agriculture is usually smallholder and/or patchy farming systems, we term shifting agriculture as small-scale agriculture, which may include commodity agriculture with similar spatial patterns to shifting agriculture in some regions such as tropical Africa. The driver data are created using decision-tree models trained by ~5,000 high-resolution Google Earth imagery cells, showing overall accuracy of 89 ± 3% from a separate validation of more than 1,500 randomly selected cells. We resample the data from 10 km resolution to 30 m using the nearest-neighbour method to match the scale of forest cover loss data.Interpretation of post-forest-loss land covers in 2020We collect cloudless and very-high-resolution satellite imagery in 2020 from Planet to determine the fate of the agriculture-driven forest loss during 2001–2019. Planet provides two products, RapidEye (at a spatial resolution of 5 m) and Doves (at a spatial resolution of 3 m; 4-band PlanetScope Scene). We randomly sample 500 pixels that show forest loss owing to agriculture expansion during 2001–2005 and 500 pixels that show forest loss owing to agriculture expansion during 2015–2019, then check cloudless satellite imagery in 2020 to visually interpret the land cover of each sampled pixel in 2020. We classify three types of land cover: agricultural land, forest/shrubland and others (Supplementary Fig. 4). Rubber and oil-palm plantations are classified as agricultural lands.Uncertainty and methods for analysisWe use committed emissions of forest carbon, even though some of this carbon will be lost only in later years or transited to other carbon pools or stored as wood products12. Forest carbon loss is defined as gross carbon loss due to forest removal (as indicated by GFC product), including (aboveground and belowground) forest biomass carbon and SOC losses. We calculate only the gross loss of forest carbon stocks while gain of carbon via reforestation and afforestation is not considered.We first estimate reference sample-based forest loss area using sample data:$${mathrm{AS}}_j = {mathrm{AM}}_jmathop {sum}limits_{h = 1}^H {p_{hj}}$$
(7)
where ASj and AMj are reference sample-based forest loss areas and mapped forest loss areas in stratum j, respectively.To estimate forest carbon loss (aboveground, belowground and soil carbon) using sample-based forest loss area, we apply a ‘stratify and multiply’ approach21,22 by assigning mean forest carbon density for each stratum. Aboveground and belowground forest carbon losses are estimated using four biomass maps (Baccini, Saatchi, Avitabile and Zarin), and we report ensemble mean ± s.d. from the four maps as our best estimate.Mountain forest carbon loss at different elevations is calculated by overlaying mountain polygon from the Global Mountain Biodiversity Assessment inventory55 (version 1.2) and the 30 m ASTER Global Digital Elevation Model56 (version 3).Although our validation shows that the GFC (v.1.7) product could accurately map forest loss, we cannot reduce the omission and commission errors. In addition, the accuracy of the disturbance year is 75.2%, with 96.7% of the disturbance occurring within one year before or after the estimated disturbance year in GFC product15. Therefore, we calculate 3 yr moving averages of annual forest and related carbon losses for time-series analysis, following the suggestion of the Global Forest Watch19 and refs. 28,38. We use a non-parametric Theil–Sen estimator regression method53 to detect trends in time-series results and test the significance of the trend by Mann–Kendall test57.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Kim, I. R. et al. Genetic diversity and population structure of nutria (Myocastor coypus) in South Korea. Animals 9, 1164. https://doi.org/10.3390/ani9121164 (2019).Article 
PubMed Central 

Google Scholar 
GISD. Of the World’s Worst Invasive Alien Species. Global Invasive Species Database. http://www.iucngisd.org/gisd/100_worst.php. 100, (2021).Hong, S., Do, Y., Kim, J. Y., Kim, D. & Joo, G. Distribution, spread and habitat preferences of nutria (Myocastor coypus) invading the lower Nakdong River, South Korea. Biol. Invas. 17, 1485–1496. https://doi.org/10.1007/s10530-014-0809-8 (2015).Article 

Google Scholar 
Ojeda, R., Bidau, C. & Emmons, L. Myocastor coypus (errata version published in 2017). The IUCN Red List Threat. Species (2016): e.T14085A121734257.Tsiamis, K. et al. Baseline Distribution of Invasive Alien Species of Union Concern (Publications Office of the European Union, 2017).
Google Scholar 
Carter, J. & Leonard, B. P. A review of the literature on the worldwide distribution, spread of, and efforts to eradicate the coypu (Myocastor coypus). Wildl. Soc. Bull. 30, 162–175 (2002).
Google Scholar 
Kim, Y. C. et al. Distribution and management of nutria (Myocastor coypus) populations in South Korea. Sustainability 11, 4169. https://doi.org/10.3390/su11154169 (2019).Article 

Google Scholar 
Park, J. H. et al. The first case of Capillaria hepatica infection in a nutria (Myocastor coypus) in Korea. Korean J. Parasitol. 52, 527–529. https://doi.org/10.3347/kjp.2014.52.5.527 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Fratini, F., Turchi, B. E., Ebani, V. V. & Bertelloni, F. The presence of Leptospira in coypus (Myocastor coypus) and rats (Rattus norvegicus) living in a protected wetland in Tuscany (Italy). Vet. Arh. 85, 407–414 (2015).
Google Scholar 
Lee, D. H., Kil, J. H. & Kim, D. E. The study on the distribution and inhabiting status of nutria (Myocastor coypus) in Korea. Korean J. Environ. Ecol. 27, 316–326 (2013).CAS 

Google Scholar 
Guichón, M. L., Doncaster, C. P. & Cassini, M. H. Population structure of coypus (Myocastor coypus) in their region of origin and comparison with introduced populations. J. Zool. 261, 265–272. https://doi.org/10.1017/S0952836903004187 (2003).Article 

Google Scholar 
Bertolino, S., Perrone, A. & Gola, L. Effectiveness of coypu control in small Italian Wetland areas. Wildl. Soc. Bull. 33, 714–720. https://doi.org/10.2193/0091-7648(2005)33[714:EOCCIS]2.0.CO;2 (2005).Article 

Google Scholar 
Schertler, A. et al. The potential current distribution of the coypu (Myocastor coypus) in Europe and climate change induced shifts in the near future. NeoBiota 58, 129–160. https://doi.org/10.3897/neobiota.58.33118 (2020).Article 

Google Scholar 
Hilts, D. J., Belitz, M. W., Gehring, T. M., Pangle, K. L. & Uzarski, D. G. Climate change and nutria range expansion in the Eastern United States. J. Wild. Manaag. 83, 591–598. https://doi.org/10.1002/jwmg.21629’ (2019).Article 

Google Scholar 
Jarnevich, C. et al. Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: An example with coypu (Myocastor coypus). NeoBiota 32, 107–125. https://doi.org/10.3897/neobiota.32.8884 (2017).Article 

Google Scholar 
Korean Metrological Administration, (2020). Korean Climate Change Assessment Report 2020.Guillera-Arroita, G. et al. Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292. https://doi.org/10.1111/geb.12268 (2015).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar 
Hong, S., Cowan, P., Do, Y. & Gim, J. S. Seasonal feeding habits of coypu (Myocastor coypus) in South Korea. Hystrix 27, 123–128 (2016).
Google Scholar 
Kim, H. S., Kong, J. Y., Kim, J. H., Yeon, S. C. & Hong, I. H. A Case of Fascioliasis in A Wild Nutria, Myocastor coypus Republic of Korea. Korean J. Parasitol. 56, 375–378. https://doi.org/10.3347/kjp.2018.56.4.375 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Do, Y., Kim, J. Y., Im, R. Y. & Kim, S. B. Spatial distribution and social characteristics for wetlands in Gyeongsangnam-do Province. Korean J. Limnol. 45, 252–260 (2012).
Google Scholar 
IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (2013).Sheffels, T. R. Status of Nutria (Myocastor coypus) Populations in the Pacific Northwest and Development of Associated Control and Management Strategies, with an Emphasis on Metropolitan Habitats, PhD Thesis (Portland State Univ., 2013).Doncaster, C. P. & MlCOL, T. Annual cycle of a coypu (Myocastor coypus) population: Male and female strategies. J. Zool. 217, 227–240. https://doi.org/10.1111/j.1469-7998.1989.tb02484.x (1989).Article 

Google Scholar 
Reggiani, G., Boitani, L. & Stefano, R. Population dynamics and regulation in the coypu Myocastor coypus in Central Italy. Ecography 18, 138–146. https://doi.org/10.1111/j.1600-0587.1995.tb00334.x (1995).Article 

Google Scholar 
Cha, Y., Cho, K. H., Lee, H., Kang, T. & Kim, J. H. The relative importance of water temperature and residence time in predicting cyanobacteria abundance in regulated rivers. Water Res. 124, 11–19. https://doi.org/10.1016/j.watres.2017.07.040 (2017).CAS 
Article 
PubMed 

Google Scholar 
Hellmann, J. J., Byers, J. E., Bierwagen, B. G. & Dukes, J. S. Five potential consequences of climate change for invasive species. Conserv. Biol. 22, 534–543. https://doi.org/10.1111/j.1523-1739.2008.00951.x (2008).Article 
PubMed 

Google Scholar 
Pereira, A. D. et al. Modeling the geographic distribution of Myocastor coypus (Mammalia, Rodentia) in Brazil: Establishing priority areas for monitoring and an alert about the risk of invasion. Stud. Neotrop. Fauna Environ. 55, 139–148. https://doi.org/10.1080/01650521.2019.1707419 (2020).Article 

Google Scholar 
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Glob. Ecol. Biogeogr. 12, 361–371. https://doi.org/10.1046/j.1466-822X.2003.00042.x (2003).Article 

Google Scholar 
Rogers, C. E. & McCarty, J. P. Climate change and ecosystems of the mid-atlantic region. Clim. Res. 14, 235–244. https://doi.org/10.3354/cr014235 (2000).Article 

Google Scholar 
Adhikari, P. et al. Potential impact of climate change on plant invasion in the Republic of Korea. J. Ecol. Environ. 43, 36. https://doi.org/10.1186/s41610-019-0134-3 (2019).Article 

Google Scholar 
Welsch, D. J., Smart, D. L., Boyer, J. N. & Minkin, P. Forested Wetlands: Functions, Benefits and the Use of Best Management Practices (US Dept of the Interior Fish and Wildlife Service, 2021).
Google Scholar 
Borgnia, M., Galante, M. L. & Cassini, M. H. Diet of the coypu (nutria, Myocastor coypus) in agro-systems of Argentinean pampas. J. Wildl. Manag. 64, 354–361. https://doi.org/10.2307/3803233 (2000).Article 

Google Scholar 
Colares, I. G., Oliveira, R. N. V., Liveira, R. M. & Colares, E. P. Feeding habits of coypu (Myocastor coypus Molina 1978) in the wetlands of the Southern region of Brazil. An. Acad. Bras. Cienc. 82, 671–678. https://doi.org/10.1590/s0001-37652010000300015 (2010).Article 
PubMed 

Google Scholar 
Corriale, M. J., Arias, S. M., Bó, R. F. & Porini, G. Habitat-use patterns of the coypu (Myocastor coypus) in an urban wetland of its original distribution. Acta Theriol. 51, 295–302. https://doi.org/10.1007/BF03192681 (2006).Article 

Google Scholar 
Linscombe, G., Kinler, N. & Wright, V. Nutria population density and vegetative changes in brackish marsh in coastal Louisiana. In Worldwide Furbearer Conference Proceedings (eds Chapman, J. A. & Pursley, D.) 129–141 (Worlwide Furbearer Conference Inc, 1981).
Google Scholar 
Aliev, F. Contribution to the study of nutria migrations (Myocastor coypus). Saugetierkd. Mitt. 16, 301–303 (1968).
Google Scholar 
Farashi, A. & Najafabadi, M. S. A model to predict dispersion of the alien nutria, Myocastor coypus Molina, 1782 (Rodentia) Northern Iran. Acta Zool. Bulg. 69, 65–70 (2017).
Google Scholar 
Vilà, M. et al. How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Front. Ecol. Environ. 8, 135–144. https://doi.org/10.1890/080083 (2010).Article 

Google Scholar 
Adhikari, P. et al. Seasonal and altitudinal variation in roe deer (Capreolus pygargus tianschanicus) diet on Jeju Island, South Korea. J. Asia Pac. Biodivers. 9, 422–428. https://doi.org/10.1016/j.japb.2016.09.001 (2016).Article 

Google Scholar 
Koo, K. A., Kong, W. S., Nibbelink, N. P., Hopkinson, C. S. & Lee, J. H. Potential effects of climate change on the distribution of cold-tolerant evergreen broadleaved woody plants in the Korean Peninsula. PLoS ONE 10, e0134043. https://doi.org/10.1371/journal.pone.0134043 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
National Institute of Biological Research. Korean Red List of Threatened Species 2nd edn. (Ministry of Environement of Korea, 2014).
Google Scholar 
Kil, J. et al. Monitoring of Invasive Alien Species Designated by the Wildlife Protection Act (VII) (Natl Inst. of Environmental Research, 2013).
Google Scholar 
Busby, J. R. In Bioclim, a Bioclimatic Analysis and Prediction System in Nature Conservation: Cost Effective Biological Surveys and Data Analysis (eds Margules, C. R. & Austin, M. P.) 64–68 (CSIRO, 1991).
Google Scholar 
Lee I. H., Park S. H., Kang, H. S. & Cho C. H. Regional climate projections using the HadGEM3-RA in Proceedings of the 3rd International Conference on Earth System Modelling; Hamburg, Germany. 17–21 September 2012. (2012).Robert, J. H., Phillips, S., Leathwick, J. & Elith, J. Package ‘dismo’ version 1.3. , https://cran.rproject.org/web/packages/dismo.pdf (2021).Jeon, J. Y., Adhikari, P. & Seo, C. Impact of climate change on potential dispersal of Paeonia obovata (Paeoniaceae), a critically endangered medicinal plant of South Korea. Ecol. Environ. Conserv. 26, S145–S155 (2020).
Google Scholar 
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x (2013).Article 

Google Scholar 
Shin, M. S., Seo, C., Lee, M. & Kim, J. Y. Prediction of potential species richness of plants adaptable to climate change in the Korean Peninsula. J. Environ. Impact Assess. 27, 562–581 (2018).
Google Scholar 
Adhikari, P. et al. Northward range expansion of southern butterflies according to climate change in South Korea. KSCCR 11, 643–656. https://doi.org/10.15531/KSCCR.2020.11.6.643 (2020).Article 

Google Scholar 
Song, C. et al. Estimation of future land cover considering shared socioeconomic pathways using scenario generators. KSCCR 9, 223–234. https://doi.org/10.15531/KSCCR.2018.9.3.223 (2018).Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315. https://doi.org/10.1002/joc.5086 (2017).Article 

Google Scholar 
Dukes, J. S. & Mooney, H. A. Does global change increase the success of biological invaders?. Trends Ecol. Evol. 14, 135–139. https://doi.org/10.1016/s0169-5347(98)01554-7 (1999).CAS 
Article 
PubMed 

Google Scholar 
Thuiller, W., Georges, D., Gueguen, M., Engler, R. & Breiner, F. Package ‘biomod2’: Ensemble Platform for Species Distribution Modeling, version 3.5.1. https://cran.r-project.org/web/packages/biomod2/biomod2.pdf (2021).Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x (2006).Article 

Google Scholar 
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?. Methods Ecol. Evol. 3, 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x (2012).Article 

Google Scholar 
Brown, J. L. SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700. https://doi.org/10.1111/2041-210X.12200 (2014).Article 

Google Scholar 
Veloz, S. D. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence–only niche models. J. Biogeogr. 36, 2290–2299. https://doi.org/10.1111/j.1365-2699.2009.02174.x (2009).Article 

Google Scholar 
Adhikari, P., Lee, Y. H., Park, Y.-S. & Hong, S. H. Assessment of the spatial invasion risk of intentionally introduced alien plant species (IIAPS) under environmental change in South Korea. Biology 10, 1169 (2021).Article 

Google Scholar 
Hong, S. H., Lee, Y. H., Lee, G., Lee, D. H. & Adhikari, P. Predicting impacts of climate change on northward range expansion of invasive weeds in South Korea. Plants 10, 1604. https://doi.org/10.3390/plants10081604 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Pearsons, R. G. Species distribution modeling for conservation educators and practitioners. Lessons Conserv. 3, 54–58 (2010).
Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x (2006).Article 

Google Scholar 
Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357. https://doi.org/10.1111/j.1466-822X.2005.00162.x (2005).Article 

Google Scholar 
Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: A misleading measure of the performance of predictive distribution models. Global. Ecol. Biogeography. 17, 145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x (2008).Article 

Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).MathSciNet 
CAS 
Article 
ADS 

Google Scholar 
Baldwin, R. Use of maximum entropy modeling in wildlife research. Entropy 11, 854–866. https://doi.org/10.3390/e11040854 (2009).Article 
ADS 

Google Scholar 
Adhikari, P. et al. Potential impact of climate change on the species richness of subalpine plant species in the mountain national parks of South Korea. J. Ecol. Environ. 42, 36. https://doi.org/10.1186/s41610-018-0095-y (2018).Article 

Google Scholar 
Hijmans, R. J. et al. Package ‘raster’ v 3.5: geographical data analysis and modeling. https://cran.r-project.org/web/packages/raster/raster.pdf, (2021). More

We used publicly available, daily, county-level COVID-19 cases and deaths from the Pennsylvania Department of Health (PA DOH) (https://www.health.pa.gov/topics/disease/coronavirus/pages/cases.aspx)13,14 for Centre County and the six neighboring counties with which it shares borders: Blair, Clearfield, Clinton, Huntingdon, Mifflin, and Union (Table 1, Fig. 1). Official COVID-19 reporting for these counties began on March 1, 2020 and is ongoing.Table 1 Summary statistics. COVID-19 reporting, census data, SafeGraph mobile-device derived data.Full size tableFigure 1(a) The cumulative COVID-19 case trajectory for Centre County minus the student cases (red line) has the same shape as the outbreak for the neighboring counties. When looking at student cases only (blue line), the curve leads other counties. Centre County cumulative cases including the university (purple line) take on the shape of an early increase because of the student cases. (b) When aggregating cases from students and non-students, Centre County (purple dot) reported about the number of cases expected for its population size, relative to the neighboring counties (black dots). When the university-reported student cases are separated from the non-student residents of the county, cases reported in Centre County non-students (red dots show possible range of total cases) fall below the number of cases we would expect for the population size. Student cases only (blue dot) are high for the student population size.Full size imageWithin Centre County, PSU provided COVID-19 testing for UP students from August 7, 2020 onward and reported anonymized weekly (2020) and daily (2021) confirmed cases, negative test results, and total tests completed for each campus in a public dashboard (Figs. 1a, S1) (https://virusinfo.psu.edu/covid-19-dashboard/)8. Two types of testing were conducted: students who were enrolled in on-campus classes were randomly selected for surveillance testing and all students could use on-demand testing. Through March 23, 2021, a total of 45,092 random tests were conducted for surveillance, of which 462, or 1.0%, were infected. Surveillance testing efforts ranged from 2440 to 4020 weekly tests through the Fall 2020 semester and were designed to consistently test approximately 1% of students throughout the school year.During the same time period, 75,436 on-demand tests were conducted, of which 6093, or 8.1%, were infected. Students living in both on-campus dorms and off-campus apartments had equal access to university-provided testing. Both on-campus and off-campus residences are within Centre County so positive and negative tests results were also included in the overall Centre County reports of COVID-19 cases.Pre-arrival testing was required for students returning to campus from transmission hotspots. Students with positive tests from pre-arrival testing were required to isolate for 10–14 days after their positive test before arriving on campus. Results from pre-arrival testing for students returning to campus in the Fall of 2020 are not included in these data.At the county level, PA DOH reports the total positive, probable, and negative tests for each county. Because PSU is within Centre County, we estimated the number of total positive and negative tests for non-student Centre County residents by subtracting the PSU estimates (from the PSU dashboard) from the Centre County estimates provided by PA DOH. However, not all student tests were reported to DOH. A portion of the on-demand tests conducted for PSU UP students were completed by a third-party vendor, which required student registration. At the time of student registration, an estimated 0–25% of students registered with an address for a family home that did not reflect their residence in Centre County. Their test results were reported to the county of their registered address. This impacts a maximum of 1,166 positive student test results and 10,760 negative student tests.We conducted a sensitivity analysis to assess the uncertainty in reporting around the negative and positive students tests that may have been misallocated due to the reported residence of student tests. We have calculated the minimum and maximum number of affected positive and negative student tests. This uncertainty from student tests impacts non-student values, which are calculated by subtracting student values from county level reports. The calculations are based on a range of a possible 0–1166 positive student tests misallocated to other counties and up to 10,760 misallocated negative student tests. We have used the ranges of misallocated student tests to calculate, for non-student Centre County residents, the full possible range of (1) total cases, (2) reported cases per capita, and (3) tests per capita (Table 1, Fig. 1b). As a result, our estimates of cases and per capita testing among non-student residents in Centre County are imprecise (Table 1).We also used publicly available data from PA DOH data and PSU to calculate COVID-19 deaths per 100,000 for Centre County, the six neighboring counties, and PSU UP.We acquired county-level data on median household income, population size, and college enrollment status from the 2019 United States Census Bureau’s American Community Survey (ACS) 5-year data (https://www.census.gov/data/developers/data-sets/acs-5year.html) for all seven previously mentioned counties in central PA15.We divide the census block groups (CBG) of Centre County into two categories. We first designated ‘student-dominated CBGs’ as CBGs where  > 50% of ACS responses report enrollment as undergraduate students. We consider data from the 19 student-dominated CBGs in Centre County to be representative of the student population in Centre County. In addition to off-campus locations, the 19 student-dominated CBGs include all on-campus dorms. These 19 CBGs are either on or adjacent to PSU’s UP campus and occupy exactly 6 census tracts. The remaining 25 county census tracts were designated as non-student dominated areas.SafeGraph16 receives geolocation data from anonymized mobile devices collected from numerous applications. We analyzed SafeGraph’s mobile device-derived daily visit counts to points of interest (POI), which are fixed locations, such as businesses or attractions. SafeGraph data provide daily counts for total numbers of visits by mobile devices while using at least one application that provides geolocation data to SafeGraph. A “visit” indicates that the device entered the building or spatial perimeter designated as a POI. We acquired daily visit counts for POIs in the seven previously mentioned counties in central PA from January 1, 2019 forward (Table 1) and within Centre County grouped counts into student-dominated CBGs and non-student dominated CBGs. From January 1, 2020 forward, we used SafeGraph data on the median daily minutes that devices spent outside of their home in each county and the student- and non-student dominated CBG divisions in Centre County. The “home location” of each device is defined by its location overnight. Finally, we used SafeGraph’s weekly calculated number of devices residing in each county and the CBGs of Centre County for 2019 to measure SafeGraph’s data representation across the seven counties and the CBGs of Centre County.No administrative permissions were required to obtain these data. Academic researchers can register to receive access to SafeGraph data at no charge for non-commercial purposes only. See Data Availability statement below for details. More

Knapp, A. K. & Smith, M. D. Variation among biomes in temporal dynamics of aboveground primary production. Science 291, 481–484 (2001).CAS 
PubMed 
ADS 

Google Scholar 
Collins, S. L. et al. Stability of tallgrass prairie during a 19-year increase in growing season precipitation. Funct. Ecol. 26, 1450–1459 (2012).
Google Scholar 
Maurer, G. E., Hallmark, A. J., Brown, R. F., Sala, O. E. & Collins, S. L. Sensitivity of primary production to precipitation across the United States. Ecol. Lett. 23, 527–536 (2020).PubMed 

Google Scholar 
IPCC. IPCC. (Cambridge University Press, 2013) https://doi.org/10.1017/cbo9781107415324.Knapp, A. K. et al. Differential sensitivity to regional-scale drought in six central US grasslands. Oecologia 177, 949–957 (2015).PubMed 
ADS 

Google Scholar 
Smith, M. D. An ecological perspective on extreme climatic events: A synthetic definition and framework to guide future research. J. Ecol. 99, 656–663 (2011).
Google Scholar 
Zeppel, M. J. B., Wilks, J. V. & Lewis, J. D. Impacts of extreme precipitation and seasonal changes in precipitation on plants. Biogeosciences 11, 3083–3093 (2014).ADS 

Google Scholar 
Frank, D. A. Drought effects on above- and belowground production of a grazed temperate grassland ecosystem. Oecologia 152, 131–139 (2007).PubMed 
ADS 

Google Scholar 
Skinner, R. H., Hanson, J. D., Hutchinson, G. L. & Schuman, G. E. Response of C3 and C4 grasses to supplemental summer precipitation. J. Range Manag. 55, 517–522 (2002).
Google Scholar 
Shi, Z. et al. Dual mechanisms regulate ecosystem stability under decade-long warming and hay harvest. Nat. Commun. 7, 1–6 (2016).ADS 

Google Scholar 
Zavaleta, E. S. et al. Grassland responses to three years of elevated temperature, CO2, precipitation, and N deposition. Ecol. Monogr. 73, 585–604 (2003).
Google Scholar 
Prather, R. M., Castillioni, K., Welti, E. A. R., Kaspari, M. & Souza, L. Abiotic factors and plant biomass, not plant diversity, strongly shape grassland arthropods under drought conditions. Ecology 101, 1–7 (2020).
Google Scholar 
Nippert, J. B., Knapp, A. K. & Briggs, J. M. Intra-annual rainfall variability and grassland productivity: Can the past predict the future?. Plant Ecol. 184, 65–74 (2006).
Google Scholar 
La Pierre, K. J. et al. Explaining temporal variation in above-ground productivity in a mesic grassland: The role of climate and flowering. J. Ecol. 99, 1250–1262 (2011).
Google Scholar 
Cleland, E. E. et al. Sensitivity of grassland plant community composition to spatial vs. temporal variation in precipitation. Ecology 94, 1687–1696 (2013).PubMed 

Google Scholar 
Grant, K., Kreyling, J., Heilmeier, H., Beierkuhnlein, C. & Jentsch, A. Extreme weather events and plant–plant interactions: Shifts between competition and facilitation among grassland species in the face of drought and heavy rainfall. Ecol. Res. 29, 991–1001 (2014).
Google Scholar 
Brooker, R. W. et al. Facilitation in plant communities: The past, the present, and the future. J. Ecol. 96, 18–34 (2008).MathSciNet 

Google Scholar 
Schöb, C., Armas, C. & Pugnaire, F. I. Direct and indirect interactions co-determine species composition in nurse plant systems. Oikos 122, 1371–1379 (2013).
Google Scholar 
Gross, N., Börger, L., Duncan, R. P. & Hulme, P. E. Functional differences between alien and native species: Do biotic interactions determine the functional structure of highly invaded grasslands?. Funct. Ecol. 27, 1262–1272 (2013).
Google Scholar 
van der Merwe, S., Greve, M., Olivier, B. & le Roux, P. C. Testing the role of functional trait expression in plant–plant facilitation. Funct. Ecol. https://doi.org/10.1111/1365-2435.13681 (2020).Article 

Google Scholar 
Tremmel, D. C. & Bazzaz, F. A. How neighbor canopy architecture affects target plant performance. Ecology 74, 2114–2124 (1993).
Google Scholar 
Weiher, E. & Keddy, P. A. In Ecological Assembly Rules: Perspective, Advances, Retreats. (eds. Weiher, E. & Keddy, P. A.) (Cambridge University Press, 2001).Anten, N. P. R. & Hirose, T. Interspecific differences in above-ground growth patterns result in spatial and temporal partitioning of light among species in a tall-grass meadow. J. Ecol. 87, 583–597 (1999).
Google Scholar 
Yann, H., Pascal, A. & Niklaus, A. H. Competition for light causes plant. Science 324, 636–638 (2009).
Google Scholar 
Walker, B., Kinzig, A. & Langridge, J. Plant attribute diversity, resilience, and ecosystem function: The nature and significance of dominant and minor species. Ecosystems 2, 95–113 (1999).
Google Scholar 
Brooker, R. W. Plant–plant interactions and environmental change. New Phytol. 171, 271–284 (2006).PubMed 

Google Scholar 
Michalet, R. & Pugnaire, F. I. Facilitation in communities: Underlying mechanisms, community and ecosystem implications. Funct. Ecol. 30, 3–9 (2016).
Google Scholar 
Maestre, F. T., Callaway, R. M., Valladares, F. & Lortie, C. J. Refining the stress-gradient hypothesis for competition and facilitation in plant communities. J. Ecol. 97, 199–205 (2009).
Google Scholar 
Saccone, P., Delzon, S., Jean-Philippe, P., Brun, J. J. & Michalet, R. The role of biotic interactions in altering tree seedling responses to an extreme climatic event. J. Veg. Sci. 20, 403–414 (2009).
Google Scholar 
Smith, M. D., Knapp, A. K. & Collins, S. L. A framework for assessing ecosystem dynamics in response to chronic resource alterations induced by global change. Ecology 90, 3279–3289 (2009).PubMed 

Google Scholar 
Borer, E. T., Seabloom, E. W., Gruner, D. S., Harpole, W. S. & Hillebrand, H. Herbivores and nutrients control grassland plant diversity via light limitation. Nature 508, 517–520 (2014).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
de Sassi, C. & Tylianakis, J. M. Climate change disproportionately increases herbivore over plant or parasitoid biomass. PLoS One 7, e40557 (2012).PubMed 
PubMed Central 

Google Scholar 
Strauss, S. Y. & Ivalú Cacho, N. Nowhere to run, nowhere to hide: The importance of enemies and apparency in adaptation to harsh soil environments. Am. Nat. 182, E1 (2013).PubMed 

Google Scholar 
Brady, K. U., Kruckeberg, A. R. & Bradshaw, H. D. Evolutionary ecology of plant adaptation to serpentine soils. Annu. Rev. Ecol. Evol. Syst. 36, 243–266 (2005).
Google Scholar 
Moran, M. S. et al. Soil evaporation response to Lehmann lovegrass (Eragrostis lehmanniana) invasion in a semiarid watershed. Agric. For. Meteorol. 149, 2133–2142 (2009).ADS 

Google Scholar 
Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).
Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).PubMed 
ADS 

Google Scholar 
Gross, N., Suding, K. N. & Lavorel, S. Leaf dry matter content and lateral spread predict response to land use change for six subalpine grassland species. J. Veg. Sci. 18, 289–300 (2007).
Google Scholar 
Quiroga, R., Golluscio, R., Blanco, L. & Fernandez, R. Aridity and grazing as convergent selective forces: An experiment with an Arid Chaco bunchgrass. Ecol. Appl. https://doi.org/10.1890/09-0641 (2010).Article 
PubMed 

Google Scholar 
Blumenthal, D. M. et al. Traits link drought resistance with herbivore defence and plant economics in semi-arid grasslands: The central roles of phenology and leaf dry matter content. J. Ecol. 108, 2336–2351 (2020).
Google Scholar 
Taylor, S. H. et al. Ecophysiological traits in C3 and C4 grasses: A phylogenetically controlled screening experiment. New Phytol. 185, 780–791 (2010).CAS 
PubMed 

Google Scholar 
N’Guessan, M. & Hartnett, D. C. Differential responses to defoliation frequency in little bluestem (Schizachyrium scoparium) in tallgrass prairie: Implications for herbivory tolerance and avoidance. Plant Ecol. 212, 1275–1285 (2011).
Google Scholar 
Castillioni, K. et al. Drought mildly reduces plant dominance in a temperate prairie ecosystem across years. Ecol. Evol. 10, 6702–6713 (2020).PubMed 
PubMed Central 

Google Scholar 
Ivalú Cacho, N. & Strauss, S. Y. Occupation of bare habitats, an evolutionary precursor to soil specialization in plants. Proc. Natl. Acad. Sci. U. S. A. 111, 15132–15137 (2014).PubMed 
PubMed Central 
ADS 

Google Scholar 
Cottingham, K. L., Lennon, J. T. & Brown, B. L. Knowing when to draw the line: Designing more informative ecological experiments. Front. Ecol. Environ. 3, 145–152 (2005).
Google Scholar 
Xu, X., Sherry, R. A., Niu, S., Li, D. & Luo, Y. Net primary productivity and rain-use efficiency as affected by warming, altered precipitation, and clipping in a mixed-grass prairie. Glob. Change Biol. 19, 2753–2764 (2013).ADS 

Google Scholar 
Braun-Blanquet, J. Plant Sociology: The Study of Plant Communities. (1932).Shipley, B. The AIC model selection method applied to path analytic models compared using ad-separation test. Ecology 94, 560–564 (2013).PubMed 

Google Scholar 
Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
Google Scholar 
Grace, J. B. In Structural Equation Modeling and Natural Systems. (Cambridge University Press, 2006). https://doi.org/10.1017/CBO9780511617799.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R.C. nlme: Linear and nonlinear mixed effects models. R package version 3.1 111 (2013).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. Ecol. Austral 67, 1–48 (2015).
Google Scholar 
Pearson, D. E., Ortega, Y. K. & Maron, J. L. The tortoise and the hare: reducing resource availability shifts competitive balance between plant species. J. Ecol. 105, 999–1009 (2017).CAS 

Google Scholar 
Maron, J. L. & Crone, E. Herbivory: Effects on plant abundance, distribution and population growth. Proc. R. Soc. B Biol. Sci. 273, 2575–2584 (2006).
Google Scholar 
Bertness, M. & Callaway, R. M. Positive interactions in communities. Trends Ecol. Evol. 9, 191–193 (1994).CAS 
PubMed 

Google Scholar 
Ploughe, L. W. et al. Community Response to Extreme Drought (CRED): A framework for drought-induced shifts in plant–plant interactions. New Phytol. 222, 52–69 (2019).PubMed 

Google Scholar 
Klanderud, K., Vandvik, V. & Goldberg, D. The importance of Biotic vs. Abiotic drivers of local plant community composition along regional bioclimatic gradients. PLoS One 10, 1–15 (2015).
Google Scholar 
Maricle, B. R., Caudle, K. L. & Adler, P. B. Influence of water availability on photosynthesis, water potential, leaf δ 13 C, and phenology in dominant C 4 grasses in Kansas, USA. Trans. Kans. Acad. Sci. 118, 173–193 (2015).
Google Scholar 
Collins, S. L., Knapp, A. K., Briggs, J. M., Blair, J. M. & Steinauer, E. M. Modulation of diversity by grazing and mowing in native tallgrass prairie. Science 280, 745–747 (1998).CAS 
PubMed 
ADS 

Google Scholar 
Gornish, E. S. & Tylianakis, J. Community shifts under climate change: Mechanisms at multiple scales. Am. J. Bot. 100, 1422–1434 (2013).PubMed 

Google Scholar  More

Download PDF

I study the Chinese giant salamander (Andrias davidianus), which is native to the Yangtze River Basin of central China. This particular species is critically endangered in the wild owing to habitat loss and overcatching — a particular problem is their use in traditional Chinese medicine. My research focuses on the salamander’s conservation biology and evolutionary ecology.In this photo, I am releasing a Chinese giant salamander at the Golden Whip River in Zhangjiajie National Forest Park on an early morning in September 2021. My team and I caught the salamander the night before, to measure its size and collect tissue samples for genetic analyses.My interest in aquatic animals started as a child. I grew up in a rural village in Hunan province, and I remember spending most of my childhood playing and fishing near my home. Because of this, I knew where each fish species lived in nearby rivers and lakes, and it sparked my interest in river ecology.I’m employed as an associate professor at Jishou University, where I lead a team dedicated to researching this species of salamander. Wild salamanders are quiet, nocturnal animals that live in remote areas. This makes studying them challenging. My team tried many creative ways to track down the animals, including walking along riverbanks with torches and photographing salamanders under water — but these techniques didn’t work as well as we needed them to. We eventually found that the best way to trap wild salamanders is to use small live fish and chicken livers as bait. The research is challenging, but we’ve learnt to be patient and celebrate every small success we have.Studying Chinese giant salamanders has also taught me an important life lesson: adapt to thrive. When food is abundant, the salamanders grow rapidly; when food is scarce, they can go up to 11 months without feeding. In my personal life and work, I have experienced successes and failures, and taking on that lesson has been useful.

Nature 603, 194 (2022)
doi: https://doi.org/10.1038/d41586-022-00564-y

Related Articles

Close-up with a parasite that can blind

Handling snakes for science

Broaden your scientific audience with video animation

Managing up: how to communicate effectively with your PhD adviser

Subjects

Careers

Conservation biology

Ecology

Latest on:

Careers

Smaller science company? Tailor your CV for a manager, not HR
Career Column 25 FEB 22

Female scientists in Africa are changing the face of their continent
Editorial 22 FEB 22

African scientists engage with the public to tackle local challenges
Career Feature 15 FEB 22

Ecology

How colonialism fed the flames of Australia’s catastrophic wildfires
Research Highlight 24 FEB 22

Apply Singapore Index on Cities’ Biodiversity at scale
Correspondence 22 FEB 22

Marching in the streets for climate-crisis action
Career Q&A 22 FEB 22

Jobs

POST-DOC POSITION IN ELECTROPHYSIOLOGY OF FUNGAL NETWORKS

VU University Amsterdam
Amsterdam, Netherlands

Director, Division of Receipt and Referral Center for Scientific Review National Institutes of Health (NIH) Department of Health and Human Services (DHHS)

National Institutes of Health (NIH)
Bethesda, MD, United States

Postdoctoral Fellow

NIH National Heart, Lung, and Blood Institute (NHLBI)
Bethesda, MD, United States

Two postdoctorial researchers in structural biology, with focus on artificial intelligence

University of Gothenburg (GU)
Uppsala, Sweden More

The full-length sequences of PacBio SMRT sequencingBased on PacBio SMRT sequencing, 3,751,089, 3,434,452, 3,900,180, 8,535,019, and 4,435,846 subreads were generated for root, stem, leaf, flower, and seed, with a N50 of 3040, 3367, 2611, 2198, and 4584 bp, respectively (Table S1; Fig. S1). Subreads were processed to generate circular consensus sequences (CCSs). By detecting the primers and poly(A) tail, 238,196, 232,290, 211,535, 257,905, and 231,877 full-length non-chimeric (FLNC) reads were identified for root, stem, leaf, flower, and seed, with a mean length of 2633, 3070, 2561, 1746, and 3762 bp, respectively (Table S2; Fig. S2). After Iterative Clustering for Error Correction (ICE) clustering, polishing, base correction, de-redundancy, and non-plant sequences filtering, 37,789, 34,034, 38,100, 54,937, and 53,906 unigenes were retained for root, stem, leaf, flower, and seed, respectively, with an average unigene length of 1802–3786 bp and N50 of 2238–4707 bp (Table S2). The length of most unigenes from five organs exceeded 2000 bp, accounting for 68.88% of the total number (Table S3; Fig. 1A). Based on Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment, about 88.1% (single-copy: 353; duplicated: 916) of the 1440 core embryophyte genes were found to be complete (90.6% were present when counting fragmented genes), suggesting the high integrity of the M. micrantha transcriptome (Fig. S3).Figure 1Length distribution of unigenes from PacBio SMRT sequencing (A) and Illumina RNA-Seq (B) across five organs.Full size imageDe novo assembly of Illumina RNA-Seq dataBased on Illumina RNA-Seq, 43.23, 40.27, 41.01, 65.85, and 41.09 million clean reads were obtained for root, stem, leaf, flower, and seed, respectively, with Q20 exceeding 96.72%. Using Trinity software, clean reads were de novo assembled into 124,238, 60,232, 63,370, 93,229, and 66,411 unigenes for root, stem, leaf, flower, and seed. After filtering non-plant sequences, 124,233, 60,232, 63,370, 93,228, and 66,410 unigenes were finally retained for the five organs, respectively (Table S4). The length of most unigenes (84.70%) was shorter than 2000 bp (Table S3). In addition, the average length and N50 of unigenes generated by Illumina RNA-Seq were 1067–1312 bp and 1336–1685 bp, respectively, which were shorter than that from PacBio SMRT sequencing (Table S4; Fig. 1B).Functional annotationTo obtain a comprehensive functional annotation of M. micrantha transcriptome, unigenes generated by PacBio SMRT sequencing were annotated in seven public databases, including NCBI non-redundant nucleotide sequences (NT), NCBI non-redundant protein sequences (NR), Gene Ontology (GO), Eukaryotic Orthologous Groups (KOG), Kyoto Encyclopedia of Genes and Genomes (KEGG), Swiss-Prot, and Pfam protein families. For root, stem, leaf, flower, and seed, 35,714 (94.51%), 32,614 (95.83%), 36,134 (94.84%), 49,197 (89.55%), and 50,962 (94.54%) unigenes were annotated to at least one database, respectively, suggesting that our transcriptome is well annotated and that most of unigenes may be functional (Table 1).Table 1 Statistics of annotation of full-length transcripts from five M. micrantha organs in seven databases.Full size tableBased on NR database annotation, the top three homologous species for the five organs were Cynara cardunculus, Vitis vinifera, and Daucus carota (Fig. S4). The top homologous species was a plant of the Asteraceae family. For the GO function annotation, “binding”, “catalytic activities”, “metabolic process”, “cellular process”, “cell”, and “cell part” were functional categories with the most abundant unigenes (Fig. S5). In addition, numerous unigenes were assigned to “response to stimulus”, “response to biotic stimulus”, and “response to oxidative stress” category (Table S5). Positive response to stress stimuli is an important strategy for invasive plants to adapt to the environment. In the KEGG annotation, the top two pathways with the most abundant unigenes were “carbohydrate metabolism” and “translation”. Furthermore, “energy metabolism” and “environmental adaptation” were also worthy of attention, which are important pathways responsible for energy supply and stress responses (Fig. S6).TFs identification and AS analysisUsing the iTAK pipeline, 1776 (root), 1293 (stem), 1627 (leaf), 2529 (flower), and 1733 (seed) unigenes were identified as TFs, which were classified into 68 families (Table S6). C3H (884), C2H2 (525), and bHLH (501) were the most abundant TF families (Fig. S7A). In addition, MYB (333) TFs were also found in the five organs. The differential expression levels of the top 15 TF families were further characterized. We found that the top 15 TF families had a certain amount of expression in the five organs of M. micrantha (Fig. S7B).For root, stem, leaf, flower, and seed, 3300, 2324, 3219, 4730, and 3740 unique transcript models (UniTransModels) were constructed, among which the UniTransModels containing two isoforms were the most common (Fig. S8A). There were 329, 270, 358, 336, and 537 AS events identified in root, stem, leaf, flower, and seed, respectively. Retained introns (RIs) were detected as the most abundant AS event in all five organs, followed by alternative 3′ splice sites (A3) and alternative 5′ splice sites (A5). Mutually exclusive exons (MX) were the least frequent event (Fig. S8B).Gene expression analysisThe number of unigenes in different expression level intervals was similar across the five organs (Fig. 2A). Using FPKM  > 0.3 as the threshold for unigene expression, the total number of unigenes expressed in the five organs was 102,464 (Fig. 2B). Among them, 39,227 unigenes were co-expressed in all five organs. The information of differentially expressed genes (DEGs) identified in pairwise comparisons among the five organs is listed in Table S7. In total, 21,161 DEGs were identified among the five organs (Fig. S9). The number of DEGs between the five organs varied from 3469 (root vs stem) to 10,716 (leaf vs seed) (Fig. 2C). Notably, 933, 428, 1410, 1018, and 1292 DEGs showed significant higher expression in root, stem, leaf, flower, and seed, respectively (Figs. S10 and S11).Figure 2Gene expression patterns in five M. micrantha organs. (A) The FPKM interval distribution in the five organs. (B) Venn diagram of the number of unigenes expressed in five organs. (C) Number of differentially expressed genes in each pairwise comparison of five organs.Full size imageKEGG enrichment of unigenes with higher expression in each organAccording to the KEGG enrichment analysis results, there were obvious differences in enriched pathways in the five organs (Table S8; Fig. 3). The unigenes with higher expression in root were mainly enriched to defense response and protein processing pathways, such as “plant-pathogen interaction” and “protein processing in endoplasmic reticulum”. In stem, unigenes with higher expression were predominantly enriched to pathways related to the secondary metabolite, sugar, and terpenoid biosynthesis, such as “phenylpropanoid biosynthesis”, “starch and sucrose metabolism”, and “diterpenoid biosynthesis”. In flower, unigenes with higher expression were mainly related to “starch and sucrose metabolism”, “phenylpropanoid biosynthesis”, and “cutin, suberine, and wax biosynthesis”. The unigenes with higher expression in seed were mainly enriched in three fatty acid and sugar metabolism pathways, namely “biosynthesis of unsaturated fatty acids”, “galactose metabolism”, and “amino sugar and nucleotide sugar metabolism”. The unigenes with higher expression in leaf were significantly enriched in photosynthesis pathways, including “photosynthesis-antenna proteins”, “photosynthesis”, “porphyrin and chlorophyll metabolism”, and “carbon fixation in photosynthetic organisms”, which are important for the photosynthesis of M. micrantha.Figure 3The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of unigenes with higher expression in each organ. The significantly enriched pathways with corrected p-value (q value)  More

Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).CAS 
PubMed 

Google Scholar 
Orgiazzi, A. et al. Global Soil Biodiversity Atlas (European Commission, Publications Office of the European Union, 2016).Tecon, R. & Or, D. Biophysical processes supporting the diversity of microbial life in soil. FEMS Microbiol. Rev. 41, 599–623 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williamson, K. E., Fuhrmann, J. J., Wommack, K. E. & Radosevich, M. Viruses in soil ecosystems: an unknown quantity within an unexplored territory. Annu. Rev. Virol. 4, 201–219 (2017). This Review provides a comprehensive overview of methods and technologies used to study soil viruses alongside a guide of metrics describing soil viruses across diverse soil ecosystems.CAS 
PubMed 

Google Scholar 
Stefan, G., Cornelia, B., Jörg, R. & Michael, B. Soil water availability strongly alters the community composition of soil protists. Pedobiologia 57, 205–213 (2014).
Google Scholar 
Leake, J. et al. Networks of power and influence: the role of mycorrhizal mycelium in controlling plant communities and agroecosystem functioning. Can. J. Bot. 82, 1016–1045 (2004).
Google Scholar 
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018). This study compiled metagenomic and metabarcoding data from 189 sites to demonstrate global patterns in the structure and function of soil microbial communities as well as the widespread prevalence of bacterial–fungal antagonism as an important structuring force of microbial communities.CAS 
PubMed 

Google Scholar 
He, L. et al. Global biogeography of fungal and bacterial biomass carbon in topsoil. Soil Biol. Biochem. 151, 108024 (2020).CAS 

Google Scholar 
Bach, E. M., Williams, R. J., Hargreaves, S. K., Yang, F. & Hofmockel, K. S. Greatest soil microbial diversity found in micro-habitats. Soil Biol. Biochem. 118, 217–226 (2018).CAS 

Google Scholar 
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).CAS 
PubMed 

Google Scholar 
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).CAS 
PubMed 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 

Google Scholar 
Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).CAS 
PubMed 

Google Scholar 
Liang, C., Amelung, W., Lehmann, J. & Kästner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob. Change Biol. 25, 3578–3590 (2019). This article estimates that more than 50% of SOM may be derived from microbial necromass in grassland and agricultural ecosystems based on extrapolations from amino sugar biomarker data.
Google Scholar 
Angst, G., Mueller, K. E., Nierop, K. G. J. & Simpson, M. J. Plant- or microbial-derived? A review on the molecular composition of stabilized soil organic matter. Soil Biol. Biochem. 156, 108189 (2021).CAS 

Google Scholar 
Ludwig, M. et al. Microbial contribution to SOM quantity and quality in density fractions of temperate arable soils. Soil Biol. Biochem. 81, 311–322 (2015). This study uses lipid biomarkers to estimate that at least 50% of SOM may be derived from microbial necromass.CAS 

Google Scholar 
Simpson, A. J., Simpson, M. J., Smith, E. & Kelleher, B. P. Microbially derived inputs to soil organic matter: are current estimates too low? Environ. Sci. Technol. 41, 8070–8076 (2007).CAS 
PubMed 

Google Scholar 
Blazewicz, S. J. et al. Taxon-specific microbial growth and mortality patterns reveal distinct temporal population responses to rewetting in a California grassland soil. ISME J. 14, 1520–1532 (2020). This study used quantitative stable isotope probing to calculate growth and mortality rates of bacteria following the rewetting of a dry Mediterranean soil, and demonstrated that bacterial growth was density independent whereas bacterial mortality was density dependent.PubMed 
PubMed Central 

Google Scholar 
Vieira, S. et al. Drivers of the composition of active rhizosphere bacterial communities in temperate grasslands. ISME J. 14, 463–475 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nuccio, E. E. et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J. 14, 999–1014 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, S. et al. Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio 6, e00746 (2015).PubMed 
PubMed Central 

Google Scholar 
Bastian, F., Bouziri, L., Nicolardot, B. & Ranjard, L. Impact of wheat straw decomposition on successional patterns of soil microbial community structure. Soil Biol. Biochem. 41, 262–275 (2009).CAS 

Google Scholar 
Whitman, T. et al. Microbial community assembly differs across minerals in a rhizosphere microcosm. Environ. Microbiol. 20, 4444–4460 (2018).CAS 
PubMed 

Google Scholar 
Maynard, D. S., Crowther, T. W. & Bradford, M. A. Fungal interactions reduce carbon use efficiency. Ecol. Lett. 20, 1034–1042 (2017). This study demonstrated that antagonistic interactions between wood-decay fungi can reduce CUE of the fungal community.PubMed 

Google Scholar 
Crowther, T. W. et al. Environmental stress response limits microbial necromass contributions to soil organic carbon. Soil Biol. Biochem. 85, 153–161 (2015).CAS 

Google Scholar 
Hu, Y., Zheng, Q., Noll, L., Zhang, S. & Wanek, W. Direct measurement of the in situ decomposition of microbial-derived soil organic matter. Soil Biol. Biochem. 141, 107660 (2020).CAS 

Google Scholar 
Fernandez, C. W., Langley, J. A., Chapman, S., McCormack, M. L. & Koide, R. T. The decomposition of ectomycorrhizal fungal necromass. Soil Biol. Biochem. 93, 38–49 (2016). This review article summarizes how the stoichiometry, morphology and chemistry of microbial necromass affects its decomposition rate in soil.CAS 

Google Scholar 
Buckeridge, K. M. et al. Sticky dead microbes: rapid abiotic retention of microbial necromass in soil. Soil Biol. Biochem. 149, 107929 (2020).CAS 

Google Scholar 
Creamer, C. A. et al. Mineralogy dictates the initial mechanism of microbial necromass association. Geochim. Cosmochim. Acta 260, 161–176 (2019). This study used Raman microspectroscopy and 13C-labelled necromass to demonstrate that different mineral types retained microbial necromass through different mechanisms and with different strengths.CAS 

Google Scholar 
Schurig, C. et al. Microbial cell-envelope fragments and the formation of soil organic matter: a case study from a glacier forefield. Biogeochemistry 113, 595–612 (2013).CAS 

Google Scholar 
Kopittke, P. M. et al. Nitrogen-rich microbial products provide new organo-mineral associations for the stabilization of soil organic matter. Glob. Change Biol. 24, 1762–1770 (2018).
Google Scholar 
Miltner, A., Bombach, P., Schmidt-Brücken, B. & Kästner, M. SOM genesis: microbial biomass as a significant source. Biogeochemistry 111, 41–55 (2012).CAS 

Google Scholar 
Kleber, M. et al. Dynamic interactions at the mineral–organic matter interface. Nat. Rev. Earth Environ. 2, 402–421 (2021).
Google Scholar 
Blagodatskaya, E. & Kuzyakov, Y. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol. Biochem. 67, 192–211 (2013).CAS 

Google Scholar 
Or, D., Smets, B. F., Wraith, J. M., Dechesne, A. & Friedman, S. P. Physical constraints affecting bacterial habitats and activity in unsaturated porous media–a review. Adv. Water Resour. 30, 1505–1527 (2007).
Google Scholar 
Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 

Google Scholar 
Finzi, A. C. et al. Rhizosphere processes are quantitatively important components of terrestrial carbon and nutrient cycles. Glob. Change Biol. 21, 2082–2094 (2015).
Google Scholar 
Yuan, M. M. et al. Fungal-bacterial cooccurrence patterns differ between arbuscular mycorrhizal fungi and nonmycorrhizal fungi across soil niches. mBio 12, e03509-20 (2015).
Google Scholar 
Zhang, L. & Lueders, T. Micropredator niche differentiation between bulk soil and rhizosphere of an agricultural soil depends on bacterial prey. FEMS Microbiol. Ecol. 93, fix103 (2017).
Google Scholar 
Sokol, N. W. & Bradford, M. A. Microbial formation of stable soil carbon is more efficient from belowground than aboveground input. Nat. Geosci. 12, 46–53 (2019).CAS 

Google Scholar 
Kallenbach, C. M., Frey, S. D. & Grandy, A. S. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat. Commun. 7, 13630 (2016). This study used artificial soils to provide empirical evidence that SOM can be entirely microbially derived, and also demonstrated a positive relationship between CUE and SOM formation.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wood, J. L., Tang, C. & Franks, A. E. Competitive traits are more important than stress-tolerance traits in a cadmium-contaminated rhizosphere: a role for trait theory in microbial ecology. Front. Microbiol. 9, 121 (2018).PubMed 
PubMed Central 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
Google Scholar 
Madin, J. S. et al. A synthesis of bacterial and archaeal phenotypic trait data. Sci. Data 7, 170 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Brown, C. T., Olm, M. R., Thomas, B. C. & Banfield, J. F. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34, 1256–1263 (2016). This study developed an algorithm, iRep, that uses draft-quality genome sequences and single time-point metagenome sequencing to infer microbial population replication rates.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nayfach, S. & Pollard, K. S. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 16, 51 (2015).PubMed 
PubMed Central 

Google Scholar 
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. USA 112, 10967–10972 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vieira-Silva, S. & Rocha, E. P. C. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 6, e1000808 (2010).PubMed 
PubMed Central 

Google Scholar 
Hasby, F. A., Barbi, F., Manzoni, S. & Lindahl, B. D. Transcriptomic markers of fungal growth, respiration and carbon-use efficiency. FEMS Microbiol. Lett. 368, fnab100 (2021).PubMed 
PubMed Central 

Google Scholar 
Maillard, F., Schilling, J., Andrews, E., Schreiner, K. M. & Kennedy, P. Functional convergence in the decomposition of fungal necromass in soil and wood. FEMS Microbiol. Ecol. 96, fiz209 (2020).CAS 
PubMed 

Google Scholar 
Clemmensen, K. E. et al. Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. N. Phytol. 205, 1525–1536 (2015).CAS 

Google Scholar 
Olivelli, M. S. et al. Unraveling mechanisms behind biomass–clay interactions using comprehensive multiphase nuclear magnetic resonance (NMR) Spectroscopy. ACS Earth Space Chem. 4, 2061–2072 (2020).CAS 

Google Scholar 
Achtenhagen, J., Goebel, M.-O., Miltner, A., Woche, S. K. & Kästner, M. Bacterial impact on the wetting properties of soil minerals. Biogeochemistry 122, 269–280 (2015).CAS 

Google Scholar 
Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534 (2020).CAS 

Google Scholar 
Ahmed, E. & Holmström, S. J. M. Microbe–mineral interactions: The impact of surface attachment on mineral weathering and element selectivity by microorganisms. Chem. Geol. 403, 13–23 (2015).CAS 

Google Scholar 
Chenu, C. Clay- or sand-polysaccharide associations as models for the interface between micro-organisms and soil: water related properties and microstructure. Geoderma 56, 143–156 (1993).CAS 

Google Scholar 
Sher, Y. et al. Microbial extracellular polysaccharide production and aggregate stability controlled by switchgrass (Panicum virgatum) root biomass and soil water potential. Soil Biol. Biochem. 143, 107742 (2020).CAS 

Google Scholar 
Lybrand, R. A. et al. A coupled microscopy approach to assess the nano-landscape of weathering. Sci. Rep. 9, 5377 (2019).PubMed 
PubMed Central 

Google Scholar 
Prommer, J. et al. Increased microbial growth, biomass, and turnover drive soil organic carbon accumulation at higher plant diversity. Glob. Change Biol. 26, 669–681 (2020).
Google Scholar 
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).
Google Scholar 
Liang, C., Schimel, J. P. & Jastrow, J. D. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2, 17105 (2017).CAS 
PubMed 

Google Scholar 
Geyer, K. M., Kyker-Snowman, E., Grandy, A. S. & Frey, S. D. Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter. Biogeochemistry 127, 173–188 (2016).CAS 

Google Scholar 
Kallenbach, C. M., Grandy, A. S., Frey, S. D. & Diefendorf, A. F. Microbial physiology and necromass regulate agricultural soil carbon accumulation. Soil Biol. Biochem. 91, 279–290 (2015).CAS 

Google Scholar 
Buckeridge, K. M. et al. Environmental and microbial controls on microbial necromass recycling, an important precursor for soil carbon stabilization. Commun. Earth Env. 1, 36 (2020).
Google Scholar 
Saifuddin, M., Bhatnagar, J. M., Segrè, D. & Finzi, A. C. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat. Commun. 10, 3568 (2019).PubMed 
PubMed Central 

Google Scholar 
Schimel, J., Balser, T. C. & Wallenstein, M. Microbial stress-response physiology and its implications for ecosystem function. Ecology 88, 1386–1394 (2007).PubMed 

Google Scholar 
Mason‐Jones, K., Banfield, C. C. & Dippold, M. A. Compound-specific 13C stable isotope probing confirms synthesis of polyhydroxybutyrate by soil bacteria. Rapid Commun. Mass. Spectrom. 33, 795–802 (2019).PubMed 

Google Scholar 
Bååth, E. The use of neutral lipid fatty acids to indicate the physiological conditions of soil fungi. Microb. Ecol. 45, 373–383 (2003).PubMed 

Google Scholar 
Slessarev, E. W. et al. Cellular and extracellular C contributions to respiration after wetting dry soil. Biogeochemistry 147, 307–324 (2020).CAS 

Google Scholar 
Slessarev, E. W. & Schimel, J. P. Partitioning sources of CO2 emission after soil wetting using high-resolution observations and minimal models. Soil Biol. Biochem. 143, 107753 (2020).CAS 

Google Scholar 
Lennon, J. T. & Jones, S. E. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol. 9, 119–130 (2011).CAS 
PubMed 

Google Scholar 
Brangarí, A. C., Manzoni, S. & Rousk, J. A soil microbial model to analyze decoupled microbial growth and respiration during soil drying and rewetting. Soil Biol. Biochem. 148, 107871 (2020).
Google Scholar 
Zha, J. & Zhuang, Q. Microbial dormancy and its impacts on northern temperate and boreal terrestrial ecosystem carbon budget. Biogeosciences 17, 4591–4610 (2020).CAS 

Google Scholar 
Anderson, T.-H. Microbial eco-physiological indicators to asses soil quality. Agric. Ecosyst. Environ. 98, 285–293 (2003).
Google Scholar 
Geyer, K., Schnecker, J., Grandy, A. S., Richter, A. & Frey, S. Assessing microbial residues in soil as a potential carbon sink and moderator of carbon use efficiency. Biogeochemistry 151, 237–249 (2020).CAS 

Google Scholar 
Sepehrnia, N. et al. Transport, retention, and release of Escherichia coli and Rhodococcus erythropolis through dry natural soils as affected by water repellency. Sci. Total Environ. 694, 133666 (2019).CAS 
PubMed 

Google Scholar 
Boeddinghaus, R. S. et al. The mineralosphere — interactive zone of microbial colonization and carbon use in grassland soils. Biol. Fertil. Soils 57, 587–601 (2021).CAS 

Google Scholar 
Vieira, S. et al. Bacterial colonization of minerals in grassland soils is selective and highly dynamic. Environ. Microbiol. 22, 917–933 (2020).CAS 
PubMed 

Google Scholar 
Ma, T. et al. Divergent accumulation of microbial necromass and plant lignin components in grassland soils. Nat. Commun. 9, 3480 (2018).PubMed 
PubMed Central 

Google Scholar 
Blazewicz, S. J., Schwartz, E. & Firestone, M. K. Growth and death of bacteria and fungi underlie rainfall-induced carbon dioxide pulses from seasonally dried soil. Ecology 95, 1162–1172 (2014).PubMed 

Google Scholar 
Ceja-Navarro, J. A. et al. Protist diversity and community complexity in the rhizosphere of switchgrass are dynamic as plants develop. Microbiome 9, 96 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Starr, E. P., Nuccio, E. E., Pett-Ridge, J., Banfield, J. F. & Firestone, M. K. Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil. Proc. Natl Acad. Sci. USA 116, 25900–25908 (2019). This comprehensive study of RNA viruses detectable in a grassland soil showed how these viruses are shaped by the presence of plant roots and litter.CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, S. et al. The interconnected rhizosphere: high network complexity dominates rhizosphere assemblages. Ecol. Lett. 19, 926–936 (2016).PubMed 

Google Scholar 
Yan, Y., Kuramae, E. E., de Hollander, M., Klinkhamer, P. G. L. & van Veen, J. A. Functional traits dominate the diversity-related selection of bacterial communities in the rhizosphere. ISME J. 11, 56–66 (2017).PubMed 

Google Scholar 
Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470 (2018).CAS 
PubMed 

Google Scholar 
Pett-Ridge, J. et al. in Rhizosphere Biology: Interactions Between Microbes and Plants (eds Gupta, V. V. S. R. & Sharma, A. K.) 51–73 (Springer, 2021).Poll, C., Marhan, S., Ingwersen, J. & Kandeler, E. Dynamics of litter carbon turnover and microbial abundance in a rye detritusphere. Soil Biol. Biochem. 40, 1306–1321 (2008).CAS 

Google Scholar 
Buchkowski, R. W., Bradford, M. A., Grandy, A. S., Schmitz, O. J. & Wieder, W. R. Applying population and community ecology theory to advance understanding of belowground biogeochemistry. Ecol. Lett. 20, 231–245 (2017).PubMed 

Google Scholar 
Erktan, A., Or, D. & Scheu, S. The physical structure of soil: determinant and consequence of trophic interactions. Soil Biol. Biochem. 148, 107876 (2020).CAS 

Google Scholar 
Roesch, L. F. W. et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1, 283–290 (2007).CAS 
PubMed 

Google Scholar 
Carson, J. K. et al. Low pore connectivity increases bacterial diversity in soil. Appl. Environ. Microbiol. 76, 3936–3942 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Raynaud, X. & Nunan, N. Spatial ecology of bacteria at the microscale in soil. PLoS ONE 9, e87217 (2014).PubMed 
PubMed Central 

Google Scholar 
Ekelund, F., Rønn, R. & Christensen, S. Distribution with depth of protozoa, bacteria and fungi in soil profiles from three Danish forest sites. Soil Biol. Biochem. 33, 475–481 (2001).CAS 

Google Scholar 
Sharrar, A. M. et al. Bacterial secondary metabolite biosynthetic potential in soil varies with phylum, depth, and vegetation type. mBio 11, e00416-20 (2020).PubMed 
PubMed Central 

Google Scholar 
Georgiou, K., Abramoff, R. Z., Harte, J., Riley, W. J. & Torn, M. S. Microbial community-level regulation explains soil carbon responses to long-term litter manipulations. Nat. Commun. 8, 1223 (2017). This modelling study demonstrated that including a density-dependent microbial mortality term can reduce the oscillatory behaviour of soil carbon models.PubMed 
PubMed Central 

Google Scholar 
Thakur, M. P. & Geisen, S. Trophic regulations of the soil microbiome. Trends Microbiol. 27, 771–780 (2019).CAS 
PubMed 

Google Scholar 
Fanin, N. et al. The ratio of Gram-positive to Gram-negative bacterial PLFA markers as an indicator of carbon availability in organic soils. Soil Biol. Biochem. 128, 111–114 (2019).CAS 

Google Scholar 
Wang, W. et al. Predatory Myxococcales are widely distributed in and closely correlated with the bacterial community structure of agricultural land. Appl. Soil Ecol. 146, 103365 (2020).
Google Scholar 
Hungate, B. A. et al. The functional significance of bacterial predators. mBio 12, e00466-21 (2021).PubMed 
PubMed Central 

Google Scholar 
Jover, L. F., Effler, T. C., Buchan, A., Wilhelm, S. W. & Weitz, J. S. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat. Rev. Microbiol. 12, 519–528 (2014).CAS 
PubMed 

Google Scholar 
Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018). This study identified novel viral genomes from metagenomes and linked many of these viruses in silico to bacterial hosts and carbon metabolisms across the spatial gradient of permafrost thaw.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ren, D., Madsen, J. S., Sørensen, S. J. & Burmølle, M. High prevalence of biofilm synergy among bacterial soil isolates in cocultures indicates bacterial interspecific cooperation. ISME J. 9, 81–89 (2015).CAS 
PubMed 

Google Scholar 
Lee, K. W. K. et al. Biofilm development and enhanced stress resistance of a model, mixed-species community biofilm. ISME J. 8, 894–907 (2014).CAS 
PubMed 

Google Scholar 
Witzgall, K. et al. Particulate organic matter as a functional soil component for persistent soil organic carbon. Nat. Commun. 12, 4115 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Frey, S. D. Mycorrhizal fungi as mediators of soil organic matter dynamics. Annu. Rev. Ecol. Evol. Syst. 50, 237–259 (2019).
Google Scholar 
Drigo, B. et al. Shifting carbon flow from roots into associated microbial communities in response to elevated atmospheric CO2. Proc. Natl Acad. Sci. USA 107, 10938–10942 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kaiser, C. et al. Exploring the transfer of recent plant photosynthates to soil microbes: mycorrhizal pathway vs direct root exudation. N. Phytol. 205, 1537–1551 (2015).CAS 

Google Scholar 
Shah, F. et al. Ectomycorrhizal fungi decompose soil organic matter using oxidative mechanisms adapted from saprotrophic ancestors. N. Phytol. 209, 1705–1719 (2016).CAS 

Google Scholar 
Tisserant, E. et al. Genome of an arbuscular mycorrhizal fungus provides insight into the oldest plant symbiosis. Proc. Natl Acad. Sci. USA 110, 20117–20122 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hestrin, R., Hammer, E. C., Mueller, C. W. & Lehmann, J. Synergies between mycorrhizal fungi and soil microbial communities increase plant nitrogen acquisition. Commun. Biol. 2, 233 (2019).PubMed 
PubMed Central 

Google Scholar 
Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543–545 (2014).CAS 
PubMed 

Google Scholar 
Averill, C. & Hawkes, C. V. Ectomycorrhizal fungi slow soil carbon cycling. Ecol. Lett. 19, 937–947 (2016).PubMed 

Google Scholar 
Craig, M. E. et al. Tree mycorrhizal type predicts within-site variability in the storage and distribution of soil organic matter. Glob. Change Biol. 24, 3317–3330 (2018).
Google Scholar 
See, C. R. et al. Hyphae move matter and microbes to mineral microsites: Integrating the hyphosphere into conceptual models of soil organic matter stabilization. Glob. Change Biol. https://doi.org/10.1111/gcb.16073 (2022).Article 

Google Scholar 
Adamczyk, B., Sietiö, O.-M., Biasi, C. & Heinonsalo, J. Interaction between tannins and fungal necromass stabilizes fungal residues in boreal forest soils. N. Phytol. 223, 16–21 (2019).
Google Scholar 
Vidal, A. et al. Visualizing the transfer of organic matter from decaying plant residues to soil mineral surfaces controlled by microorganisms. Soil Biol. Biochem. 160, 108347 (2021).CAS 

Google Scholar 
Kallenbach, C. M., Wallenstein, M. D., Schipanksi, M. E. & Grandy, A. S. Managing agroecosystems for soil microbial carbon use efficiency: ecological unknowns, potential outcomes, and a path forward. Front. Microbiol. 10, 1146 (2019).PubMed 
PubMed Central 

Google Scholar 
Blagodatskaya, E., Blagodatsky, S., Anderson, T.-H. & Kuzyakov, Y. microbial growth and carbon use efficiency in the rhizosphere and root-free soil. PLoS ONE 9, e93282 (2014).PubMed 
PubMed Central 

Google Scholar 
Domeignoz-Horta, L. A. et al. Microbial diversity drives carbon use efficiency in a model soil. Nat. Commun. 11, 3684 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fernandez, C. W. & Kennedy, P. G. Revisiting the ‘Gadgil effect’: do interguild fungal interactions control carbon cycling in forest soils? N. Phytol. 209, 1382–1394 (2016).CAS 

Google Scholar 
Nicolas, A. M. et al. Soil candidate phyla radiation bacteria encode components of aerobic metabolism and co-occur with nanoarchaea in the rare biosphere of rhizosphere grassland communities. mSystems 6, e0120520 (2021).PubMed 

Google Scholar 
Starr, E. P. et al. Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon. Microbiome 6, 122 (2018).PubMed 
PubMed Central 

Google Scholar 
Pace, M. L. Bacterial mortality and the fate of bacterial production. Hydrobiologia 159, 41–49 (1988).
Google Scholar 
Cram, J. A., Parada, A. E. & Fuhrman, J. A. Dilution reveals how viral lysis and grazing shape microbial communities. Limnol. Oceanogr. 61, 889–905 (2016).
Google Scholar 
Ankrah, N. Y. D. et al. Phage infection of an environmentally relevant marine bacterium alters host metabolism and lysate composition. ISME J. 8, 1089–1100 (2014). This study demonstrated that in a marine environment, the mechanism of death (that is, phage infection) altered the biochemistry of microbial necromass relative to uninfected cells.CAS 
PubMed 

Google Scholar 
Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).
Google Scholar 
Clarholm, M. Interactions of bacteria, protozoa and plants leading to mineralization of soil nitrogen. Soil Biol. Biochem. 17, 181–187 (1985).CAS 

Google Scholar 
Pasternak, Z. et al. In and out: an analysis of epibiotic vs periplasmic bacterial predators. ISME J. 8, 625–635 (2014).CAS 
PubMed 

Google Scholar 
Lee, X., Wu, H.-J., Sigler, J., Oishi, C. & Siccama, T. Rapid and transient response of soil respiration to rain. Glob. Change Biol. 10, 1017–1026 (2004).
Google Scholar 
Schimel, J. P. Life in dry soils: effects of drought on soil microbial communities and processes. Annu. Rev. Ecol. Evol. Syst. 49, 409–432 (2018).
Google Scholar 
Granato, E. T., Meiller-Legrand, T. A. & Foster, K. R. The evolution and ecology of bacterial warfare. Curr. Biol. 29, R521–R537 (2019).CAS 
PubMed 

Google Scholar 
Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Change 6, 751–758 (2016).
Google Scholar 
Sierra, C. A. & Müller, M. A general mathematical framework for representing soil organic matter dynamics. Ecol. Monogr. 85, 505–524 (2015).
Google Scholar 
Wang, G. et al. Microbial dormancy improves development and experimental validation of ecosystem model. ISME J. 9, 226–237 (2015).CAS 
PubMed 

Google Scholar 
Wieder, W., Grandy, S., Kallenbach, M. & Bonan, B. Integrating microbial physiology and physio-chemical principles in soils with the MIcrobial-MIneral Carbon Stabilization (MIMICS) model. Biogeosciences 11, 3899–3917 (2014).
Google Scholar 
Allison, S. D. A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012). This paper described one of the first trait-based modelling approaches to link microbial community composition with physiological and enzymatic traits to predict litter decomposition in soil.CAS 
PubMed 

Google Scholar 
Kaiser, C., Franklin, O., Dieckmann, U. & Richter, A. Microbial community dynamics alleviate stoichiometric constraints during litter decay. Ecol. Lett. 17, 680–690 (2014).PubMed 
PubMed Central 

Google Scholar 
Ebrahimi, A. & Or, D. Microbial community dynamics in soil aggregates shape biogeochemical gas fluxes from soil profiles – upscaling an aggregate biophysical model. Glob. Change Biol. 22, 3141–3156 (2016). This paper presented a demonstration of how to upscale results from a mechanistic model of microbial activity in soil aggregates to scales of practical interest for hydrological and climate models.
Google Scholar 
Lajoie, G. & Kembel, S. W. Making the most of trait-based approaches for microbial ecology. Trends Microbiol. 27, 814–823 (2019). This opinion article discussed trait-based approaches in microbial ecology with a focus on utilization of large-scale datasets for improved ecological understanding.CAS 
PubMed 

Google Scholar 
Wang, G., Post, W. M. & Mayes, M. A. Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses. Ecol. Appl. 23, 255–272 (2013).PubMed 

Google Scholar 
Moorhead, D. L. & Sinsabaugh, R. L. A theoretical model of litter decay and microbial interaction. Ecol. Monogr. 76, 151–174 (2006).
Google Scholar 
Kooijman, S. A. L. M., Muller, E. B. & Stouthamer, A. H. Microbial growth dynamics on the basis of individual budgets. Antonie Van Leeuwenhoek 60, 159–174 (1991).CAS 
PubMed 

Google Scholar 
Evans, S., Dieckmann, U., Franklin, O. & Kaiser, C. Synergistic effects of diffusion and microbial physiology reproduce the Birch effect in a micro-scale model. Soil Biol. Biochem. 93, 28–37 (2016).CAS 

Google Scholar 
Allison, S. D. Modeling adaptation of carbon use efficiency in microbial communities. Front. Microbiol. 5, 571 (2014).PubMed 
PubMed Central 

Google Scholar 
Hawkes, C. V. & Keitt, T. H. Resilience vs. historical contingency in microbial responses to environmental change. Ecol. Lett. 18, 612–625 (2015).PubMed 

Google Scholar 
Tang, J. & Riley, W. J. Weaker soil carbon–climate feedbacks resulting from microbial and abiotic interactions. Nat. Clim. Change 5, 56–60 (2015).CAS 

Google Scholar 
Zhang, Y. et al. Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically-defined MEMS 2.0 model. Biogeosciences 18, 3147–3171 (2021).CAS 

Google Scholar 
Blankinship, J. C. et al. Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models. Biogeochemistry 140, 1–13 (2018).CAS 

Google Scholar 
Ebrahimi, A. N. & Or, D. Microbial dispersal in unsaturated porous media: Characteristics of motile bacterial cell motions in unsaturated angular pore networks. Water Resour. Res. 50, 7406–7429 (2014).
Google Scholar 
Tang, J. & Riley, W. J. A theory of effective microbial substrate affinity parameters in variably saturated soils and an example application to aerobic soil heterotrophic respiration. J. Geophys. Res. Biogeosci. 124, 918–940 (2019).
Google Scholar 
Manzoni, S., Schaeffer, S. M., Katul, G., Porporato, A. & Schimel, J. P. A theoretical analysis of microbial eco-physiological and diffusion limitations to carbon cycling in drying soils. Soil Biol. Biochem. 73, 69–83 (2014).CAS 

Google Scholar 
Brangarí, A. C., Fernàndez-Garcia, D., Sanchez-Vila, X. & Manzoni, S. Ecological and soil hydraulic implications of microbial responses to stress – a modeling analysis. Adv. Water Resour. 116, 178–194 (2018).
Google Scholar 
Alster, C. J., Weller, Z. D. & von Fischer, J. C. A meta-analysis of temperature sensitivity as a microbial trait. Glob. Change Biol. 24, 4211–4224 (2018).
Google Scholar 
Wang, G., Li, W., Wang, K. & Huang, W. Uncertainty quantification of the soil moisture response functions for microbial dormancy and resuscitation. Soil Biol. Biochem. 160, 108337 (2021).CAS 

Google Scholar 
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S. & Janssens, I. Sensitivity of decomposition rates of soil organic matter with respect to simultaneous changes in temperature and moisture. J. Adv. Model. Earth Syst. 7, 335–356 (2015).
Google Scholar 
Nunan, N., Schmidt, H. & Raynaud, X. The ecology of heterogeneity: soil bacterial communities and C dynamics. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190249 (2020).CAS 

Google Scholar 
Kaiser, C., Franklin, O., Richter, A. & Dieckmann, U. Social dynamics within decomposer communities lead to nitrogen retention and organic matter build-up in soils. Nat. Commun. 6, 8960 (2015).CAS 
PubMed 

Google Scholar 
Craig, M. E., Mayes, M. A., Sulman, B. N. & Walker, A. P. Biological mechanisms may contribute to soil carbon saturation patterns. Glob. Change Biol. 27, 2633–2644 (2021).
Google Scholar 
Fan, X. et al. Improved model simulation of soil carbon cycling by representing the microbially derived organic carbon pool. ISME J. 15, 2248–2263 (2021).CAS 
PubMed 

Google Scholar 
Sulman, B. N. et al. Multiple models and experiments underscore large uncertainty in soil carbon dynamics. Biogeochemistry 141, 109–123 (2018). This paper addressed key uncertainties in the representation of microbial degradation and mineral stabilization in five microbially explicit soil carbon models.CAS 

Google Scholar 
Marschmann, G. L., Pagel, H., Kügler, P. & Streck, T. Equifinality, sloppiness, and emergent structures of mechanistic soil biogeochemical models. Environ. Model. Softw. 122, 104518 (2019).
Google Scholar 
Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, aac9323 (2015).PubMed 

Google Scholar 
Malik, A. A., Thomson, B. C., Whiteley, A. S., Bailey, M. & Griffiths, R. I. Bacterial physiological adaptations to contrasting edaphic conditions identified using landscape scale metagenomics. mBio 8, e00799-17 (2017).PubMed 
PubMed Central 

Google Scholar 
Westoby, M. et al. Trait dimensions in bacteria and archaea compared to vascular plants. Ecol. Lett. 24, 1487–1504 (2021).PubMed 

Google Scholar 
Jung, M.-Y. et al. Ammonia-oxidizing archaea possess a wide range of cellular ammonia affinities. ISME J. 16, 272–283 (2022).CAS 
PubMed 

Google Scholar 
Kempes, C. P., Wang, L., Amend, J. P., Doyle, J. & Hoehler, T. Evolutionary tradeoffs in cellular composition across diverse bacteria. ISME J. 10, 2145–2157 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dethlefsen, L. & Schmidt, T. M. Performance of the translational apparatus varies with the ecological strategies of bacteria. J. Bacteriol. 189, 3237–3245 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Andersen, K. H. et al. Characteristic sizes of life in the oceans, from bacteria to whales. Annu. Rev. Mar. Sci. 8, 217–241 (2016).CAS 

Google Scholar 
Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).CAS 
PubMed 

Google Scholar 
Weissman, J. L., Hou, S. & Fuhrman, J. A. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc. Natl Acad. Sci. USA 118, e2016810118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, G., Rabe, K. S., Nielsen, J. & Engqvist, M. K. M. Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. ACS Synth. Biol. 8, 1411–1420 (2019).CAS 
PubMed 

Google Scholar 
Hungate, B. A. et al. Quantitative microbial ecology through stable isotope probing. Appl. Environ. Microbiol. 81, 7570–7581 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Couradeau, E. et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat. Commun. 10, 2770 (2019).PubMed 
PubMed Central 

Google Scholar 
Starr, E. P. et al. Stable-isotope-informed, genome-resolved metagenomics uncovers potential cross-kingdom interactions in rhizosphere soil. mSphere 6, e0008521 (2021).PubMed 

Google Scholar 
Rousk, J. & Bååth, E. Fungal and bacterial growth in soil with plant materials of different C/N ratios. FEMS Microbiol. Ecol. 62, 258–267 (2007).CAS 
PubMed 

Google Scholar 
Koechli, C., Campbell, A. N., Pepe-Ranney, C. & Buckley, D. H. Assessing fungal contributions to cellulose degradation in soil by using high-throughput stable isotope probing. Soil Biol. Biochem. 130, 150–158 (2019).CAS 

Google Scholar 
Wilhelm, R. C., Singh, R., Eltis, L. D. & Mohn, W. W. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 13, 413–429 (2019).CAS 
PubMed 

Google Scholar 
Neurath, R. A. et al. Root carbon interaction with soil minerals is dynamic, leaving a legacy of microbially derived residues. Environ. Sci. Technol. 55, 13345–13355 (2021).CAS 
PubMed 

Google Scholar 
Luo, Y. et al. Rice rhizodeposition promotes the build-up of organic carbon in soil via fungal necromass. Soil Biol. Biochem. 160, 108345 (2021).CAS 

Google Scholar 
Carini, P. et al. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2, 16242 (2016).PubMed 

Google Scholar 
Sharma, K., Palatinszky, M., Nikolov, G., Berry, D. & Shank, E. A. Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms. eLife 9, e56275 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Arellano-Caicedo, C., Ohlsson, P., Bengtsson, M., Beech, J. P. & Hammer, E. C. Habitat geometry in artificial microstructure affects bacterial and fungal growth, interactions, and substrate degradation. Commun. Biol. 4, 1226 (2021).PubMed 
PubMed Central 

Google Scholar 
Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).CAS 
PubMed 

Google Scholar 
García-Palacios, P. et al. Evidence for large microbial-mediated losses of soil carbon under anthropogenic warming. Nat. Rev. Earth Env. 2, 507–517 (2021).
Google Scholar 
Schulz, F. et al. Hidden diversity of soil giant viruses. Nat. Commun. 9, 4881 (2018).PubMed 
PubMed Central 

Google Scholar 
Trubl, G. et al. Towards optimized viral metagenomes for double-stranded and single-stranded DNA viruses from challenging soils. PeerJ 7, e7265 (2019).PubMed 
PubMed Central 

Google Scholar 
Guo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9, 37 (2021).PubMed 
PubMed Central 

Google Scholar 
Sommers, P., Chatterjee, A., Varsani, A. & Trubl, G. Integrating viral metagenomics into an ecological framework. Annu. Rev. Virol. 8, 133–158 (2021).PubMed 

Google Scholar 
Pratama, A. A. & van Elsas, J. D. The ‘neglected’ soil virome–potential role and impact. Trends Microbiol. 26, 649–662 (2018).CAS 
PubMed 

Google Scholar 
Ghosh, D. et al. Prevalence of lysogeny among soil bacteria and presence of 16S rRNA and trzN genes in viral-community DNA. Appl. Environ. Microbiol. 74, 495–502 (2008).CAS 
PubMed 

Google Scholar 
Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).CAS 
PubMed 

Google Scholar 
Howard-Varona, C. et al. Phage-specific metabolic reprogramming of virocells. ISME J. 14, 881–895 (2020).PubMed 
PubMed Central 

Google Scholar 
Howard-Varona, C. et al. Multiple mechanisms drive phage infection efficiency in nearly identical hosts. ISME J. 12, 1605–1618 (2018).PubMed 
PubMed Central 

Google Scholar 
Van Goethem, M. Characteristics of wetting-induced bacteriophage blooms in biological soil crust. mBio 10, e02287-19 (2019).PubMed 
PubMed Central 

Google Scholar 
Trubl, G. et al. Active virus-host interactions at sub-freezing temperatures in Arctic peat soil. Microbiome 9, 208 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, S. et al. Methane-derived carbon flows into host–virus networks at different trophic levels in soil. Proc. Natl Acad. Sci. USA 118, e2105124118 (2021). This study used stable isotope probing metagenomics to connect, in situ, active virus–host infections with the biogeochemical process of methane oxidation in soil.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolduc, B., Youens-Clark, K., Roux, S., Hurwitz, B. L. & Sullivan, M. B. iVirus: facilitating new insights in viral ecology with software and community data sets imbedded in a cyberinfrastructure. ISME J. 11, 7–14 (2017).PubMed 

Google Scholar  More

Castelle CJ, Banfield JF. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell. 2018;172:1181–97.CAS 
PubMed 

Google Scholar 
Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:16048.CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
PubMed 

Google Scholar 
Imachi H, Nobu MK, Nakahara N, Morono Y, Ogawara M, Takaki Y. et al. Isolation of an archaeon at the prokaryote-eukaryote interface. Nature. 2020;577:519–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
Spang A, Saw JH, Jorgensen SL, Zaremba-Niedzwiedzka K, Martijn J, Lind AE. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature. 2015;521:173–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu Y, Makarova KS, Huang W-C, Wolf YI, Nikolskaya AN, Zhang X. et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature. 2021;593:553–7.CAS 
PubMed 

Google Scholar 
Huber H, Stetter KO Thermoplasmatales. In: M Dworkin, S Falkow, E Rosenberg, KH Schleifer, E Stackebrandt (eds). The Prokaryotes, 3rd edn. (Springer, New York, 2006), pp 101–12.Inagaki F, Suzuki M, Takai K, Oida H, Sakamoto T, Aoki K, et al. Microbial communities associated with geological horizons in coastal subseafloor sediments from the Sea of Okhotsk. Appl Environ Microbiol. 2003;69:7224–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
Vetriani C, Jannasch HW, MacGregor AJ, Stahl DA, Reysenbach AR. Population structure and phylogenetic characterization of marine benthic archaea in deep-sea sediments. Appl Environ Microbiol. 1999;65:4375–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
Orsi WD, Vuillemin A, Rodriguez P, Coskun OK, Gomez-Saez GV, Lavik G, et al. Metabolic activity analyses demonstrate that Lokiarchaeon exhibits homoacetogenesis in sulfidic marine sediments. Nat Microbiol. 2019;5:248–55.PubMed 

Google Scholar 
Yu T, Wu W, Liang W, Lever MA, Hinrichs KU, Wang F. Growth of sedimentary Bathyarchaeota on lignin as an energy source. Proc Natl Acad Sci USA. 2018;115:6022–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin X, Cai M, Liu Y, Zhou G, Richter-Heitmann T, Aromokeye DA, et al. Subgroup level differences of physiological activities in marine Lokiarchaeota. ISME J. 2020;15:848–61.PubMed 
PubMed Central 

Google Scholar 
Lloyd KG, Schreiber L, Petersen DG, Kjeldsen KU, Lever MA, Steen AD. et al. Predominant archaea in marine sediments degrade detrital proteins. Nature. 2013;496:215–8.CAS 
PubMed 

Google Scholar 
Lin X, Handley KM, Gilbert JA, Kostka JE. Metabolic potential of fatty acid oxidation and anaerobic respiration by abundant members of Thaumarchaeota and Thermoplasmata in deep anoxic peat. ISME J. 2015;9:2740–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
He Y, Li M, Perumal V, Feng X, Fang J, Xie J, et al. Genomic and enzymatic evidence for acetogenesis among multiple lineages of the archaeal phylum Bathyarchaeota widespread in marine sediments. Nat Microbiol. 2016;1:16035.CAS 
PubMed 

Google Scholar 
Zhou Z, Liu Y, Lloyd KG, Pan J, Yang Y, Gu J-D, et al. Genomic and transcriptomic insights into the ecology and metabolism of benthic archaeal cosmopolitan, Thermoprofundales (MBG-D archaea). ISME J. 2019;13:885–901.CAS 
PubMed 

Google Scholar 
Lazar CS, Baker BJ, Seitz K, Hyde AS, Dick GJ, Hinrichs KU, et al. Genomic evidence for distinct carbon substrate preferences and ecological niches of Bathyarchaeota in estuarine sediments. Environ Microbiol. 2016;18:1200–11.CAS 
PubMed 

Google Scholar 
Cai M, Liu Y, Yin X, Zhou Z, Friedrich MW, Richter-Heitmann T, et al. Diverse Asgard archaea including the novel phylum Gerdarchaeota participate in organic matter degradation. Sci China Life Sci. 2020;63:886–97.CAS 
PubMed 

Google Scholar 
Spang A, Stairs CW, Dombrowski N, Eme L, Lombard J, Caceres EF, et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat Microbiol. 2019;4:1138–48.CAS 
PubMed 

Google Scholar 
Baker BJ, Appler KE, Gong X. New microbial biodiversity in marine sediments. Ann Rev Mar Sci. 2020;13:161–75.PubMed 

Google Scholar 
Gorke B, Stulke J. Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat Rev Microbiol. 2008;6:613–24.PubMed 

Google Scholar 
Siliakus MF, van der Oost J, Kengen SWM. Adaptations of archaeal and bacterial membranes to variations in temperature, pH and pressure. Extremophiles. 2017;21:651–70.CAS 
PubMed 
PubMed Central 

Google Scholar 
Takano Y, Chikaraishi Y, Ogawa NO, Nomaki H, Morono Y, Inagaki F. et al. Sedimentary membrane lipids recycled by deep-sea benthic archaea. Nat Geosci. 2010;3:858–61.CAS 

Google Scholar 
Li M, Baker BJ, Anantharaman K, Jain S, Breier JA, Dick GJ. Genomic and transcriptomic evidence for scavenging of diverse organic compounds by widespread deep-sea archaea. Nat Commun. 2015;6:8933.CAS 
PubMed 

Google Scholar 
Dekas AE, Parada AE, Mayali X, Fuhrman JA, Wollard J, Weber PK, et al. Characterizing chemoautotrophy and heterotrophy in marine Archaea and Bacteria with single-cell multi-isotope NanoSIP. Front Microbiol. 2019;10:2682.PubMed 
PubMed Central 

Google Scholar 
Vuillemin A, Wankel SD, Coskun ÖK, Magritsch T, Vargas S, Estes ER. et al. Archaea dominate oxic subseafloor communities over multimillion-year time scales. Sci Adv. 2019;5:eaaw4108PubMed 
PubMed Central 

Google Scholar 
Könneke M, Bernhard AE, de la Torre JR, Walker CB, Waterbury JB, Stahl DA. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature. 2005;437:543–6.PubMed 

Google Scholar 
Qin W, Amin SA, Martens-Habbena W, Walker CB, Urakawa H, Devol AH, et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc Natl Acad Sci USA. 2014;111:12504–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aoyagi T, Hanada S, Itoh H, Sato Y, Ogata A, Friedrich MW, et al. Ultra-high-sensitivity stable-isotope probing of rRNA by high-throughput sequencing of isopycnic centrifugation gradients. Environ Microbiol Rep. 2015;7:282–7.CAS 
PubMed 

Google Scholar 
Yin X, Wu W, Maeke M, Richter-Heitmann T, Kulkarni AC, Oni OE, et al. CO2 conversion to methane and biomass in obligate methylotrophic methanogens in marine sediments. ISME J. 2019;13:2107–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oni O, Miyatake T, Kasten S, Richter-Heitmann T, Fischer D, Wagenknecht L, et al. Distinct microbial populations are tightly linked to the profile of dissolved iron in the methanic sediments of the Helgoland mud area, North Sea. Front Microbiol. 2015;6:365.PubMed 
PubMed Central 

Google Scholar 
Bohrmann G, Aromokeye AD, Bihler V, Dehning K, Dohrmann I, Gentz T, et al. R/V METEOR Cruise Report M134, Emissions of free gas from cross-shelf troughs of South Georgia: distribution, quantification, and sources for methane ebullition sites in sub-Antarctic waters, Port Stanley (Falkland Islands) – Punta Arenas (Chile). Ber aus dem MARUM und dem Fachbereich Geowissenschaften der Univät Brem. 2017;317:1–220.
Google Scholar 
Yin X, Kulkarni AC, Friedrich MW DNA and RNA stable isotope probing of methylotrophic methanogenic archaea. In: Dumont M, Hernández García M (eds), Stable Isotope Probing, Methods in Molecular Biology, (Humana Press New York, 2019) pp 189–206.Danovaro R, Dell¹Anno A, Fabiano M. Bioavailability of organic matter in the sediments of the Porcupine Abyssal Plain, northeastern Atlantic. Mar Ecol Prog Ser. 2001;220:25–32.CAS 

Google Scholar 
Yang T, Jiang S-Y, Yang J-H, Lu G, Wu N-Y, Liu J, et al. Dissolved inorganic carbon (DIC) and its carbon isotopic composition in sediment pore waters from the Shenhu area, northern South China Sea. J Oceanogr. 2008;64:303–10.CAS 

Google Scholar 
Lueders T, Manefield M, Friedrich MW. Enhanced sensitivity of DNA- and rRNA-based stable isotope probing by fractionation and quantitative analysis of isopycnic centrifugation gradients. Environ Microbiol. 2003;6:73–8.
Google Scholar 
Ovreas L, Forney L, Daae FL, Torsvik V. Distribution of bacterioplankton in meromictic Lake Saelenvannet, as determined by denaturing gradient gel electrophoresis of PCR-amplified gene fragments coding for 16S rRNA. Appl Environ Microbiol. 1997;63:3367–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
Takai K, Horikoshi K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl Environ Microbiol. 2000;66:5066–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aromokeye DA, Richter-Heitmann T, Oni OE, Kulkarni A, Yin X, Kasten S, et al. Temperature controls crystalline iron oxide utilization by microbial communities in methanic ferruginous marine sediment incubations. Front Microbiol. 2018;9:2574.PubMed 
PubMed Central 

Google Scholar 
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
Wegener G, Kellermann MY, Elvert M. Tracking activity and function of microorganisms by stable isotope probing of membrane lipids. Curr Opin Biotechnol. 2016;41:43–52.CAS 
PubMed 

Google Scholar 
Boschker HTS, Nold SC, Wellsbury P, Bos D, de Graaf W, Pel R. et al. Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers. Nature. 1998;392:801–5.CAS 

Google Scholar 
Sturt HF, Summons RE, Smith K, Elvert M, Hinrichs KU. Intact polar membrane lipids in prokaryotes and sediments deciphered by high-performance liquid chromatography/electrospray ionization multistage mass spectrometry-new biomarkers for biogeochemistry and microbial ecology. Rapid Commun Mass Spectrom. 2004;18:617–28.CAS 
PubMed 

Google Scholar 
Liu XL, Lipp JS, Simpson JH, Lin YS, Summons RE, Hinrichs KU. Mono- and dihydroxyl glycerol dibiphytanyl glycerol tetraethers in marine sediments: Identification of both core and intact polar lipid forms. Geochim Cosmochim Acta. 2012;89:102–15.CAS 

Google Scholar 
Ertefai TF, Heuer VB, Prieto-Mollar X, Vogt C, Sylva SP, Seewald J, et al. The biogeochemistry of sorbed methane in marine sediments. Geochim Cosmochim Acta. 2010;74:6033–48.CAS 

Google Scholar 
Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.CAS 
PubMed 

Google Scholar 
Hu W, Pan J, Wang B, Guo J, Li M, Xu M. Metagenomic insights into the metabolism and evolution of a new Thermoplasmata order (Candidatus Gimiplasmatales). Environ Microbiol. 2020;23:3695–709.PubMed 

Google Scholar 
Spang A, Stairs CW, Dombrowski N, Eme L, Lombard J, Caceres EF, et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat Microbiol. 2019;4:1138–48.CAS 
PubMed 

Google Scholar 
Almagro Armenteros JJ, Tsirigos KD, Sonderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol. 2019;37:420–3.CAS 
PubMed 

Google Scholar 
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158PubMed 
PubMed Central 

Google Scholar 
Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K. et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.CAS 
PubMed 

Google Scholar 
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2015;32:605–7.PubMed 

Google Scholar 
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
PubMed 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359–e.PubMed 
PubMed Central 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
PubMed 
PubMed Central 

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

Google Scholar 
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 

Google Scholar 
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. J Bioinform. 2019;36:1925–7.
Google Scholar 
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.
Google Scholar 
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.CAS 
PubMed 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2006;23:127–8.PubMed 

Google Scholar 
Zhou Z, Pan J, Wang F, Gu JD, Li M. Bathyarchaeota: globally distributed metabolic generalists in anoxic environments. FEMS Microbiol Rev. 2018;42:639–55.CAS 
PubMed 

Google Scholar 
Lee MD. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics. 2019;35:4162–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Eren AM, Esen OC, Quince C, Vineis JH, Morrison HG, Sogin ML. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319PubMed 
PubMed Central 

Google Scholar 
Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 

Google Scholar 
Manefield M, Whiteley AS, Ostle N, Ineson P, Bailey MJ. Technical considerations for RNA- based stable isotope probing an approach to associating microbial diversity with microbial community function. Rapid Commun Mass Spectrom. 2002;16:2179–83.CAS 
PubMed 

Google Scholar 
Lazar CS, Baker BJ, Seitz KW, Teske AP. Genomic reconstruction of multiple lineages of uncultured benthic archaea suggests distinct biogeochemical roles and ecological niches. ISME J. 2017;11:1118–29.CAS 
PubMed 
PubMed Central 

Google Scholar 
Villanueva L, Damste JS, Schouten S. A re-evaluation of the archaeal membrane lipid biosynthetic pathway. Nat Rev Microbiol. 2014;12:438–48.CAS 
PubMed 

Google Scholar 
Konstantinidis KT, Rossello-Mora R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.PubMed 
PubMed Central 

Google Scholar 
Hedges JI, Keil RG. Sedimentary organic matter preservation: an assessment and speculative synthesis. Mar Chem. 1995;49:81–115.CAS 

Google Scholar 
Arndt S, Jørgensen BB, LaRowe DE, Middelburg JJ, Pancost RD, Regnier P. Quantifying the degradation of organic matter in marine sediments: a review and synthesis. Earth-Sci Rev. 2013;123:53–86.CAS 

Google Scholar 
LaRowe DE, Arndt S, Bradley JA, Estes ER, Hoarfrost A, Lang SQ, et al. The fate of organic carbon in marine sediments – New insights from recent data and analysis. Earth-Sci Rev. 2020;204:103146.CAS 

Google Scholar 
Zhu Q-Z, Elvert M, Meador TB, Becker KW, Heuer VB, Hinrichs KU. Stable carbon isotopic compositions of archaeal lipids constrain terrestrial, planktonic, and benthic sources in marine sediments. Geochim Cosmochim Acta. 2021;307:319–37.CAS 

Google Scholar 
Jain S, Caforio A, Driessen AJ. Biosynthesis of archaeal membrane ether lipids. Front Microbiol. 2014;5:641.PubMed 
PubMed Central 

Google Scholar 
Yang S, Lv Y, Liu X, Wang Y, Fan Q, Yang Z, et al. Genomic and enzymatic evidence of acetogenesis by anaerobic methanotrophic archaea. Nat Commun. 2020;11:3941.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zinke LA, Evans PN, Santos-Medellín C, Schroeder AL, Parks DH, Varner RK, et al. Evidence for non-methanogenic metabolisms in globally distributed archaeal clades basal to the Methanomassiliicoccales. Environ Microbiol. 2021;23:340–57.CAS 
PubMed 

Google Scholar 
Bhatnagar L, Jain MK, Aubert JP, Zeikus JG. Comparison of assimilatory organic nitrogen, sulfur, and carbon sources for growth of methanobacterium species. Appl Environ Microbiol. 1984;48:785–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
Maupin-Furlow JA. Proteolytic systems of archaea: slicing, dicing, and mincing in the extreme. Emerg Top Life Sci. 2018;2:561–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pohlschroder M, Pfeiffer F, Schulze S, Abdul Halim MF. Archaeal cell surface biogenesis. FEMS Microbiol Rev. 2018;42:694–717.CAS 
PubMed 
PubMed Central 

Google Scholar 
Yancey P, Clark M, Hand S, Bowlus R, Somero G. Living with water stress: evolution of osmolyte systems. Science. 1982;217:1214–22.CAS 
PubMed 

Google Scholar 
Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016;10:2158–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oni OE, Schmidt F, Miyatake T, Kasten S, Witt M, Hinrichs KU, et al. Microbial communities and organic matter composition in surface and subsurface sediments of the Helgoland Mud Area, North Sea. Front Microbiol. 2015;6:1290.PubMed 
PubMed Central 

Google Scholar 
Pelikan C, Wasmund K, Glombitza C, Hausmann B, Herbold CW, Flieder M, et al. Anaerobic bacterial degradation of protein and lipid macromolecules in subarctic marine sediment. ISME J. 2021;15:833–47.CAS 
PubMed 

Google Scholar 
Orsi WD, Schink B, Buckel W, Martin WF. Physiological limits to life in anoxic subseafloor sediment. FEMS Microbiol Rev. 2020;44:219–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
Heijnen JJ, Van, Dijken JP. In search of a thermodynamic description of biomass yields for the chemotrophic growth of microorganisms. Biotechnol Bioeng. 1992;39:833–58.CAS 
PubMed 

Google Scholar 
Braun S, Mhatre SS, Jaussi M, Røy H, Kjeldsen KU, Pearce C, et al. Microbial turnover times in the deep seabed studied by amino acid racemization modelling. Sci Rep. 2017;7:5680.PubMed 
PubMed Central 

Google Scholar  More