Ecology

Hwang, H. Y. et al. Effect of cover cropping on the net global warming potential of rice paddy soil. Geoderma 292, 49–58 (2017).ADS 
CAS 
Article 

Google Scholar 
IPCC. Climate change 2013: The physical science basis. The Working Group I contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013).
Google Scholar 
Cardoso, R. M., Soares, P. M. M., Lima, D. C. A. & Miranda, P. M. A. Mean and extreme temperatures in warming climate: EURO CORDEX and WRF regional climate high-resolution projection for Portugal. Clim. Dyn. 52, 129–157 (2019).Article 

Google Scholar 
Ding, T., Gao, H. & Li, W. J. Extreme high-temperature event in southern China in 2016 and the possible role of cross-equatorial flows. Int. J. Climatol. 38, 3579–3594 (2018).Article 

Google Scholar 
Escalas, A. et al. Functional diversity and redundancy across fish gut, sediment, and water bacterial communities. Environ. Microbiol. 19, 3268–3282 (2017).Article 

Google Scholar 
Philippot, L. et al. Loss in microbial diversity affects nitrogen cycling in soil. ISME J. 7, 1609–1619 (2013).CAS 
Article 

Google Scholar 
Li, Y. B. et al. Serratia spp. Are responsible for nitrogen fixation fueled by As(III) oxidation, a novel biogeochemical process identified in mine tailings. Environ. Sci. Technol 56, 2033–2043 (2022).ADS 
Article 

Google Scholar 
Jia, M., Gao, Z. W., Gu, H. J., Zhao, C. Y. & Han, G. D. Effects of precipitation change and nitrogen addition on the composition, diversity, and molecular ecological network of soil bacterial communities in a desert steppe. PLoS ONE 16, e0248194. https://doi.org/10.1371/journal.pone.0248194 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Waghmode, T. R. et al. Response of nitrifier and denitrifier abundance and microbial community structure to experimental warming in an agricultural ecosystem. Front. Microbiol. 9, 474. https://doi.org/10.3389/fmicb.2018.00474 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Hu, Y. L., Wang, S., Niu, B., Chen, Q. & Zhang, G. Effect of increasing precipitation and warming on microbial community in Tibetan alpine steppe. Environ. Res. 189, 109917. https://doi.org/10.1016/j.envres.2020.109917 (2020).CAS 
Article 
PubMed 

Google Scholar 
Li, H. et al. Responses of soil bacterial communities to nitrogen deposition and precipitation increment are closely linked with aboveground community variation. Microb. Ecol. 71, 974–989 (2016).CAS 
Article 

Google Scholar 
Wang, H. et al. Experimental warming reduced topsoil carbon content and increased soil bacterial diversity in a subtropical planted forest. Soil Biol. Biochem. 133, 155–164 (2019).CAS 
Article 

Google Scholar 
Haumann, F. A., Gruber, N. & Münnich, M. Sea-Ice Induced Southern Ocean Subsurface Warming and Surface Cooling in a Warming Climate. AGU Advances 1, e2019AV000132. https://doi.org/10.1029/2019AV000132 (2020).ADS 
Article 

Google Scholar 
Ji, F., Wu, Z. H., Huang, J. P. & Chassignet, E. P. Evolution of land surface air temperature trend. Nat. Clim. Chang. 4, 462–466 (2014).ADS 
Article 

Google Scholar 
Sabri, N. S. A., Zakaria, Z., Mohamad, S. E., Jaafar, A. B. & Hara, H. Importance of soil temperature for the growth of temperate crops under a tropical climate and functional role of soil microbial diversity. Microbes Environ. 33, 144–150 (2018).Article 

Google Scholar 
McGrady-Steed, J. & Morin, P. T. Biodiversity, density compensation, and the dynamics of populations and functional groups. Ecology 81, 361–373 (2000).Article 

Google Scholar 
Jiang, L. Density compensation can cause no effect of biodiversity on ecosystem function. Oikos 116, 324–334 (2007).Article 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538. https://doi.org/10.1038/nrmicro283 (2012).CAS 
Article 
PubMed 

Google Scholar 
Gao, X. X. et al. Revegetation significantly increased the bacterial-fungal interactions in different successional stages of alpine grasslands on the Qinghai-Tibetan Plateau. CATENA 205, 105385. https://doi.org/10.1016/j.catena.2021.105385 (2021).CAS 
Article 

Google Scholar 
Morriën, E. et al. Soil networks become more connected and take up more carbon as nature restoration progresses. Nat. Commun. 8, 14349. https://doi.org/10.1038/ncomms14349 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).Article 

Google Scholar 
Pržulj, N. & Malod-Dognin, N. Network analytics in the age of big data. Science 353, 123–124 (2016).ADS 
Article 

Google Scholar 
Ratzke, C., Barrere, J. M. R. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).Article 

Google Scholar 
Fuhrman, J. A. Microbial community structure and its functional implications. Nature 45, 193–199 (2009).ADS 
Article 

Google Scholar 
Zhao, M. X., Cong, J., Cheng, J. M., Qi, Q. & Zhang, Y. G. Soil microbial community assembly and interactions are constrained by nitrogen and phosphorus in broadleaf forests of southern China. Forest 11, 285. https://doi.org/10.3390/f11030285 (2020).Article 

Google Scholar 
Wan, X. L. et al. Biogeographic patterns of microbial association networks in paddy soil within Eastern China. Soil Biol. Biochem. 142, 07696. https://doi.org/10.1016/j.soilbio.2019.107696 (2020).CAS 
Article 

Google Scholar 
Yuan, M. M., Guo, X., Wu, L., Zhang, Y. & Zhou, J. Climate warming enhances microbial network complexity and stability. Nat. Clim. Change 11, 343–348 (2021).ADS 
Article 

Google Scholar 
Lassaletta, L. et al. Food and feed trade as a driver in the global nitrogen cycle: 50-year trends. Biogeochemistry 11, 225–241 (2014).Article 

Google Scholar 
Phoenix, G. K. et al. Impacts of atmospheric nitrogen deposition: Responses of multiple plant and soil parameters across contrasting ecosystems in long-term field experiments. Glob. Change Biol. 18, 1197–1215 (2012).ADS 
Article 

Google Scholar 
Nakaji, T., Fukami, M., Dokiya, Y. & Izuta, T. Effects of high nitrogen load on growth, photosynthesis and nutrient status of Cryptomeria japonica and Pinus densiflora seedlings. Trees-Struct. Funct. 15, 453–461 (2001).CAS 
Article 

Google Scholar 
Wang, H. Y. et al. Reduction in nitrogen fertilizer use results in increased rice yields and improved environmental protection. Int. J. Agric. Sustain. 15, 681–692 (2017).Article 

Google Scholar 
Zhou, X. G. & Wu, F. Z. Land-use conversion from open field to greenhouse cultivation differently affected the diversities and assembly processes of soil abundant and rare fungal communities. Sci. Total Environ. 788, 147751. https://doi.org/10.1016/j.scitotenv.2021.147751 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Guo, H. et al. Long-term nitrogen & phosphorus additions reduce soil microbial respiration but increase its temperature sensitivity in a Tibetan alpine meadow. Soil Biol. Biochem. 113, 26–34 (2017).CAS 
Article 

Google Scholar 
Zhang, C. et al. Effects of simulated nitrogen deposition on soil respiration components and their temperature sensitivities in a semiarid grassland. Soil Biol. Biochem. 75, 113–123 (2014).CAS 
Article 

Google Scholar 
Zhang, J. J. et al. Different responses of soil respiration and its components to nitrogen and phosphorus addition in a subtropical secondary forest. For. Ecosyst. 8, 37. https://doi.org/10.1186/s40663-021-00313-z (2021).Article 

Google Scholar 
Norse, D. & Ju, X. T. Environmental costs of China’s food security. Agric. Ecosyst. Environ. 209, 5–14 (2015).Article 

Google Scholar 
Xu, H. F., Du, H., Zeng, F. P., Song, T. Q. & Peng, W. X. Diminished rhizosphere and bulk soil microbial abundance and diversity across succession stages in Karst area, southwest China. Appl. Soil Ecol. 158, 103799. https://doi.org/10.1016/j.apsoil.2020.103799 (2020).Article 

Google Scholar 
Li, Y. B. et al. Arsenic and antimony co-contamination influences on soil microbial community composition and functions: Relevance to arsenic resistance and carbon, nitrogen, and sulfur cycling. Environ. Int. 153, 106522. https://doi.org/10.1016/j.envint.2021.106522 (2021).CAS 
Article 
PubMed 

Google Scholar 
Zhou, J. & Fong, J. J. Strong agricultural management effects on soil microbial community in a non-experimental agroecosystem. Appl. Soil Ecol. 165, 103970. https://doi.org/10.1016/j.apsoil.2021.103970 (2021).Article 

Google Scholar 
Bárcenas-Moreno, G., Gómez-Brandón, M., Rousk, J. & Bååth, E. Adaptation of soil microbial communities to temperature: Comparison of fungi and bacteria in a laboratory experiment. Glob. Chang. Biol. 15, 2950–2957 (2009).ADS 
Article 

Google Scholar 
Tan, E. H., Zou, W., Zheng, Z., Yan, X. & Kao, S. J. Warming stimulates sediment denitrification at the expense of anaerobic ammonium oxidation. Nat. Clim. Change 10, 349–355 (2020).ADS 
CAS 
Article 

Google Scholar 
Supramaniam, Y., Chong, C. W., Silvaraj, S. & Tan, K. P. Effect of short term variation in temperature and water content on the bacterial community in a tropical soil. Appl Soil Ecol. 107, 279–289 (2016).Article 

Google Scholar 
Zhu, Y. Z., Li, Y. Y., Zheng, N. G., Chapman, S. J. & Yao, H. Y. Similar but not identical resuscitation trajectories of the soil microbial community based on either DNA or RNA after flooding. Agronomy 10, 502. https://doi.org/10.3390/agronomy10040502 (2020).CAS 
Article 

Google Scholar 
Donhauser, J., Qi, W., Bergk-Pinto, B. & Frey, B. High temperatures enhance the microbial genetic potential to recycle C and N from necromass in high-mountain soils. Glob. Chang. Biol. 27, 1365–1386 (2021).ADS 
Article 

Google Scholar 
Santoyo, G., Hernandez-Pacheco, C., Hernandez-Salmeron, J. & Hernandez-Leon, R. The role of abiotic factors modulating the plant-microbe-soil interactions: Toward sustainable agriculture. A review. Span. J. Agric. Res. 15, e03R01-e11. https://doi.org/10.5424/sjar/2017151-9990 (2017).Article 

Google Scholar 
Lefcheck, J. S. et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat. Commun. 6, 6936. https://doi.org/10.1038/ncomms7936 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cardinale, B. J. et al. Corrigendum: Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
Article 

Google Scholar 
Ma, B., Wang, H., Dsouza, M., Lou, J. & Xu, J. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J. 10, 1891–1901 (2016).CAS 
Article 

Google Scholar 
Trivedi, C. et al. Losses in microbial functional diversity reduce the rate of key soil processes. Soil Biol. Biochem. 135, 267–274 (2019).CAS 
Article 

Google Scholar 
Melanie, K. et al. Effects of season and experimental warming on the bacterial community in a temperate mountain forest soil assessed by 16S rRNA gene pyrosequencing. FEMS Microbiol. Ecol. 82, 551–562 (2012).Article 

Google Scholar 
Zheng, H. F., Liu, Y., Chen, Y., Zhang, J. & Chen, Q. Short-term warming shifts microbial nutrient limitation without changing the bacterial community structure in an alpine timberline of the eastern Tibetan Plateau. Geoderma 360, 113985. https://doi.org/10.1016/j.geoderma.2019.113985 (2020).ADS 
CAS 
Article 

Google Scholar 
Finlay, B. J. & Cooper, J. L. Microbial diversity and ecosystem function. CEH Integrating Fund second progress report to the Director, Centre for Ecology and Hydrology Nov 1996–Sept (1997).Xing, X. Y. et al. Warming shapes nirS- and nosZ-type denitrifier communities and stimulates N2O emission in acidic paddy soil. Appl. Environ. Microbiol. 87, e02965-e3020. https://doi.org/10.1128/AEM.0296520 (2021).CAS 
Article 
PubMed Central 

Google Scholar 
Lin, Y. T., Whitman, W. B., Coleman, D. C., Jien, S. H. & Chiu, C. Y. Soil bacterial communities at the treeline in subtropical alpine areas. CATENA 201, 105205. https://doi.org/10.1016/j.catena.2021.105205 (2021).CAS 
Article 

Google Scholar 
Wang, J. C. et al. Impacts of inorganic and organic fertilization treatments on bacterial and fungal communities in a paddy soil. Appl. Soil Ecol. 112, 42–50 (2017).Article 

Google Scholar 
Chacón, J. M., Shaw, A. K. & Harcombe, W. R. Increasing growth rate slows adaptation when genotypes compete for diffusing resources. PLoS Comput. Biol. 16, e1007585. https://doi.org/10.1371/journal.pcbi.1007585 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hartley, I. P., Hopkins, D. W., Garnett, M. H., Sommerkorn, M. & Wookey, P. A. Soil microbial respiration in arctic soil does not acclimate to temperature. Ecol. Lett. 11, 1092–1100 (2008).Article 

Google Scholar 
Baath, E. Growth rates of bacterial communities in soils at varying pH: A comparison of the thymidine and leucine incorporation techniques. Microb. Ecol. 36, 316–327 (1998).CAS 
Article 

Google Scholar 
Qin, H. L. et al. Soil moisture and activity of nitrite- and nitrous oxide-reducing microbes enhanced nitrous oxide emissions in fallow paddy soils. Biol. Fertil. Soils 56, 53–67 (2020).CAS 
Article 

Google Scholar 
Chen, Z. et al. Impact of long term fertilization on the composition of denitrifier communities based on nitrite reductase analyses in a paddy soil. Microb. Ecol. 60, 850–861 (2010).CAS 
Article 

Google Scholar 
Wei, G. S. et al. Similar drivers but different effects lead to distinct ecological patterns of soil bacterial and archaeal communities. Soil Biol. Biochem. 144, 107759. https://doi.org/10.1016/j.soilbio.2020.107759 (2020).CAS 
Article 

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

Google Scholar 
Levins, R. Evolution in Changing Environments: Some Theoretical Explorations (Princeton University Press, 1968).Book 

Google Scholar  More

Healthy foods are green foodsAn analysis of 57,000 foods reveals which have the best and worst environmental impacts. A team of researchers used an algorithm to estimate how much of each ingredient was in thousands of products sold in major UK supermarket chains. The scientists then gave food items an environmental-impact score out of 100 — with 100 being the worst — by combining the impacts of the ingredients in 100 grams of each product. They considered several factors, including greenhouse-gas emissions and land use.Healthier foods tended to have low environmental impacts, the team found. Products containing lamb and beef — such as ready-made meat pies — had the most serious environmental impact. The lowest-impact foods tended to be made with plants and included bread products, fruits, vegetables, grains and sugar-rich drinks. There were some notable exceptions: both nuts and seafood had a good nutrition score but relatively high environmental impacts.

Source: M. Clark et al. Proc. Natl Acad. Sci. USA 119, e2120584119 (2022).

COVID-19 reboundAfter the COVID-19 antiviral Paxlovid began to be used in late 2021, researchers noticed that some people experience a rebound in the virus and symptoms after taking the drug. Two recent studies suggest that it is surprisingly common for SARS-CoV-2 to return in untreated cases of COVID-19 too. To determine the frequency of rebound, Jonathan Li, a physician-scientist at Brigham and Women’s Hospital in Boston, Massachusetts, and his team analysed data from hundreds of people who were randomized to receive a placebo in a large-scale trial of COVID-19 antibody drugs. More than one-quarter of participants who were infected with SARS-CoV-2 reported a rebound in their symptoms, according to the study, which has not yet been peer reviewed. “The main take-home message is that recovery from COVID-19 is not going to be a linear process,” Li says.

Source: Deo, R. et al. Preprint at medRxiv https://doi.org/10.1101/2022.08.01.22278278 (2022).

How trees grow in a warmer worldThis chart shows how the early arrival of spring, due to climate change, affects the growth of trees and the amount of carbon they sequester. In a paper in Nature, researchers investigated the consequences of an early start to the growing season in deciduous forests. Leaf emergence is followed by carbon uptake through the process of photosynthesis. Over time, carbon can be captured for long-term sequestration if it contributes to the radial growth of stems or to wood formation. The areas under the curves represent annual growth in terms of: the amount of carbon captured by leaves (top curves, brown); annual radial growth (lower left curve, blue); and increase in woody biomass (lower right curve, red).The authors report that the early arrival of spring, which shifts the margins of the growing season (lighter curves), had little impact on the final annual tree-ring width or the amount of woody biomass produced, whereas high temperatures in summer had a negative effect on radial growth (dotted curve). Other studies (plotted here as dotted curves) indicate that high temperatures and related drought can suppress carbon capture and woody-biomass production.The study provides evidence that warmer springs have advanced the leaf emergence of temperate deciduous forests but have not substantially increased their wood production. This suggests that the extra uptake of carbon dioxide does not contribute to sustainable carbon sequestration in the trunks of long-lived trees, as our News & Views article explains. More

Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).PubMed 
Article 
CAS 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wingfield, J. C. et al. Organism-environment interactions in a changing world: a mechanistic approach. J. Ornithol. 152, 279–288 (2011).Article 

Google Scholar 
Mendoza-Gonzalez, G., Martinez, M. L., Rojas-Soto, O. R., Vazquez, G. & Gallego-Fernandez, J. B. Ecological niche modeling of coastal dune plants and future potential distribution in response to climate change and sea level rise. Glob. Change Biol. 19, 2524–2535 (2013).ADS 
Article 

Google Scholar 
Saunders, S. P. et al. Community science validates climate suitability projections from ecological niche modeling. Ecol. Appl. 30, 17 (2020).Article 

Google Scholar 
Peterson, A. T., Cobos, M. E. & Jimenez-Garcia, D. Major challenges for correlational ecological niche model projections to future climate conditions. Ann. N. Y. Acad. Sci. 1429, 66–77 (2018).ADS 
PubMed 
Article 

Google Scholar 
Mays, H. L. et al. Genomic analysis of demographic history and Ecological niche modeling in the endangered Sumatran Rhinoceros Dicerorhinus sumatrensis. Curr. Biol. 28, 70–76 (2018).CAS 
PubMed 
Article 

Google Scholar 
Malcolm, R. J., Liu, C., Neilson, P. R., Hansen, L. & Hannah, L. A. Global warming and extinctions of endemic species from biodiversity hotspots. Conserv. Biol. 20, 538–548 (2005).Article 

Google Scholar 
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).PubMed 
Article 

Google Scholar 
Gotelli, J. N. & Stanton-Geddes, J. Climate change, genetic markers and species distribution modelling. J. Biogeogr. 42, 1577–1585 (2015).Article 

Google Scholar 
Ruegg, K. et al. Ecological genomics predicts climate vulnerability in an endangered southwestern songbird. Ecol. Lett. 21, 1085–1096 (2018).PubMed 
Article 

Google Scholar 
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).PubMed 
Article 

Google Scholar 
Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rhone, B. et al. Pearl millet genomic vulnerability to climate change in West Africa highlights the need for regional collaboration. Nat. Commun. 11, 5274 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science 365, 1114–1119 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fjeldså, J., Bowie, R. C. K. & Rahbek, C. The role of mountain ranges in the diversification of birds. Annu. Rev. Ecol. Evol. Syst. 43, 249–265 (2012).Article 

Google Scholar 
Freeman, B. G., Scholer, M. N., Ruiz-Gutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl Acad. Sci. USA 115, 11982–11987 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
He, J. K., Lin, S. L., Li, J. T., Yu, J. H. & Jiang, H. S. Evolutionary history of zoogeographical regions surrounding the Tibetan Plateau. Commun. Biol. 3, 9 (2020).Article 
CAS 

Google Scholar 
Wu, Y. J. et al. Explaining the species richness of birds along a subtropical elevational gradient in the Hengduan Mountains. J. Biogeogr. 40, 2310–2323 (2013).Article 

Google Scholar 
del Hoyo, J., Elliott, A., Sargatal, J. & Christie, D. A. Handbook of the Birds of the World (Lynx Edicions, 2013).Qu, Y. et al. Lineage diversification and historical demography of a montane bird Garrulax elliotii – implications for the Pleistocene evolutionary history of the eastern Himalayas. BMC Evolut. Biol. 11, 174 (2011).Article 

Google Scholar 
Qu, Y. et al. Long-term isolation and stability explain high genetic diversity in the Eastern Himalaya. Mol. Ecol. 23, 705–720 (2014).PubMed 
Article 

Google Scholar 
Wang, W. J. et al. Glacial expansion and diversification of an East Asian montane bird, the green-backed tit (Parus monticolus). J. Biogeogr. 40, 1156–1169 (2013).Article 

Google Scholar 
Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).PubMed 
Article 
CAS 

Google Scholar 
Laine, V. N. et al. Evolutionary signals of selection on cognition from the great tit genome and methylome. Nat. Commun. 7, 9 (2016).Article 
CAS 

Google Scholar 
Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).PubMed 
Article 

Google Scholar 
Giorgetta, M. A. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013).ADS 
Article 

Google Scholar 
Gent, P. R. et al. The community climate system model version 4. J. Clim. 24, 4973–4991 (2011).ADS 
Article 

Google Scholar 
Watanabe, M. et al. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010).ADS 
Article 

Google Scholar 
Voldoire, A. et al. The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dyn. 40, 2091–2121 (2013).Article 

Google Scholar 
Frichot, E., Schoville, S. D., Bouchard, G. & Francois, O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol. 30, 1687–1699 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L. & Lasky, J. R. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol. Ecololgy 25, 104–120 (2016).CAS 
Article 

Google Scholar 
Forester, B. R., Lasky, J. R., Wagner, H. H. & Urban, D. L. Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Mol. Ecol. 27, 2215–2233 (2018).CAS 
PubMed 
Article 

Google Scholar 
Li, C. et al. Two Antarctic penguin genomes reveal insights into their evolutionary history and molecular changes related to the Antarctic environment. Gigascience 3, 27 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Pirri, F. et al. Selection-driven adaptation to the extreme Antarctic environment in Emperor penguin. Preprint at bioRxiv https://doi.org/10.1101/2021.12.14.471946 (2021).Wang, L. C. et al. Involvement of the Arabidopsis HIT1/AtVPS53 tethering protein homologuein the acclimation of the plasma membrane to heat stess.J. Exp. Bot. 62, 3609–3620 (2011).CAS 
PubMed 
Article 

Google Scholar 
Piñol, R. A. et al. Preoptic BRS3 neurons increase body temperature and heart rate via multiple pathways. Cell Metab. 33, 1389–1403 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Guilherme, A. et al. Neuronal modulation of brown adipose activity through perturbation of white adipocyte lipogenesis. Mol. Metab. 16, 116–125 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, Y., Guo, W., zhang, Y., Zhang, H. & Wu, C. Insights into hypoxic adaptation in Tibetan chicken embryos from comparative proteomics. Comp. Biochem. Physiol. Part D. 31, 100602 (2019).CAS 

Google Scholar 
Pizzagalli, M. D., Bensimon, A. & Superti-Furga, G. A guide to plasma membrane solute carrier proteins. FEBS J. 288, 2784–2835 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Qu, Y. et al. Rapid phenotypic evolution with shallow genomic differentiation during early stages of high elevation adaptation in Eurasian Tree Sparrows. Natl Sci. Rev. 7, 113–127 (2020).PubMed 
Article 

Google Scholar 
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity Distrib. 13, 252–264 (2007).Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD – a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).Article 

Google Scholar 
Chen, Y. et al. Large-scale genome-wide reveals climate adaptive variability in a cosmopolitan pest. Nat. Commun. 12, 7206 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clarke, R. T., Rothery, P. & Raybould, A. F. Confidence limits for regression relationships between distance matrices: Estimating gene flow with distance. J. Agric. Biol. Environ. Stat. 7, 361–372 (2002).Article 

Google Scholar 
Excoffier, L., Dupanloup, I., Huerta-Sanchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Foden, W. B. et al. Climate change vulnerability assessment of species. WIREs Clim. Change 10, e551 (2019).Article 

Google Scholar 
Smith, T. B. et al. Genomic vulnerablity and soci-economic threats under climate change in an African rainforest bird. Evolut. Appl. 14, 1239–1247 (2021).Article 

Google Scholar 
Liu, B., Liang, E. Y., Liu, K. & Camarero, J. J. Species- and elevation-dependent growth responses to climate warming of mountain forests in the Qinling Mountains, central China. Forests 9, 11 (2018).
Google Scholar 
Dang, H. S., Zhang, Y. J., Zhang, K. R., Jiang, M. X. & Zhang, Q. F. Climate-growth relationships of subalpine fir (Abies fargesii) across the altitudinal range in the Shennongjia Mountains, central China. Clim. Change 117, 903–917 (2013).ADS 
Article 

Google Scholar 
Lingua, E., Cherubini, P., Motta, R. & Nola, P. Spatial structure along an altitudinal gradient in the Italian central Alps suggests competition and facilitation among coniferous species. J. Veg. Sci. 19, 425–436 (2008).Article 

Google Scholar 
Zhang, D. C., Zhang, Y. H., Boufford, D. E. & Sun, H. Elevational patterns of species richness and endemism for some important taxa in the Hengduan Mountains, southwestern China. Biodivers. Conserv. 18, 699–716 (2009).Article 

Google Scholar 
Zhang, R. Z., Zheng, D., Yang, Q. Y. & Liu, Y. H. Physical Geography of Hengduan Mountains (Science Press, 1997).Liu, Y. et al. Sino-Himalayan mountains act as cradles of diversity and immigration centres in the diversification of parrotbills (Paradoxornithidae). J. Biogeogr. 43, 1488–1501 (2016).Bush, A. et al. Incorporating evolutionary adaptation in species distribution modeling reduces projected vulnerability to climate change. Ecol. Lett. 17, 1468–148 (2016).Article 

Google Scholar 
Sparks, M. M., Westley, A. A. H., Falke, J. A. & Quinn, T. P. Thermal adaptation and phenotypic plasticity in a warming world: insights from common garden experiments on Alaskan sockeye salmon. Glob. Change Biol. 23, 5203–5217 (2017).ADS 
Article 

Google Scholar 
Merow, C., Wilson, A. M. & Jetz, W. Integrating occurrence data and expert maps for improved species range predictions. Glob. Ecol. Biogeogr. 26, 243–258 (2017).Article 

Google Scholar 
Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
She, R., Chu, J. S. C., Wang, K., Pei, J. & Chen, N. GenBlastA: enabling BLAST to identify homologous gene sequences. Genome Res. 19, 143–149 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res. 14, 988–995 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McKenna, A. et al. The genome analysis toolkit: a mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
Robinson, J. D., Bunnefeld, L., Hearn, J., Stone, G. N. & Hickerson, M. J. ABC inference of multi-population divergence with admixture from unphased population genomic data. Mol. Ecol. 23, 4458–4471 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nazareno, A. G., Bemmels, J. B., Dick, C. W. & Lohmann, L. G. Minimum sample sizes for population genomics: an empirical study from an Amazonian plant species. Mol. Ecol. Resour. 17, 1136–1147 (2017).CAS 
PubMed 
Article 

Google Scholar 
Willing, E. M., Dreyer, C. & van Oosterhout, C. Estimates of genetic differentiation measured by FST do not necessary require large sample size when using many SNP markers. PLoS One 7, e2649 (2012).Article 
CAS 

Google Scholar 
Keenan, K., Mcginnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. diveRsity: an Rpackage for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).Article 

Google Scholar 
Rellstab, C., Gugerli, F., Eckert, I. A., Hancock, M. A. & Holderegger, R. A practical guide to environmental assocaition analysis in landscape genomics. Mol. Ecol. 24, 4348–4370 (2015).PubMed 
Article 

Google Scholar 
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xie, C. et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 39, W316–W322 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).PubMed 
Article 

Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 275, 73–77 (2014).Article 

Google Scholar 
Anderson, R. P. & Raza, A. The effect of the extent of the study region on GISmodels of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. J. Biogeogr. 37, 1378–1393 (2010).Article 

Google Scholar 
Pearson, R. G., Raxworthy, C., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: a test case using crypticgeckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).Article 

Google Scholar 
Heming, N. M., Dambros, C. & Gutiérrez, E. E. ENMwizard: advanced techniques for Ecological Niche Modeling made easy. https://github.com/HemingNM/ENMwizard (2018).Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling. Ecography 37, 191–203 (2014).Article 

Google Scholar 
Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for MAXENT ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).Article 

Google Scholar 
Owens, H. L. et al. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecol. Model. 263, 10–18 (2013).Article 

Google Scholar 
Akaike, H. New look at statistical-model identification. IEEE Trans. Autom. Control AC19, 716–723 (1974).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).PubMed 
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 (2012).Article 

Google Scholar 
Bellard, C. et al. Will climate change promote future invasions? Glob. Change Biol. 19, 3740–3748 (2013).ADS 
Article 

Google Scholar 
Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).Article 

Google Scholar 
Anantharaman, R., Hall, K., Shah, V. B. & Edelman, A. Circuitscape in Julia: high performance connectivity modelling to support conservation decisions. Proc. JuliaCon Conf. 1, 58 (2020).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Anderson, D. R. & Burnham, K. P. Avoiding pitfalls when using information-theoretic methods. J. Wildl. Manag. 66, 912–918 (2002).Article 

Google Scholar 
Van Strien, M. J., Keller, D. & Holderegger, R. A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models. Mol. Ecol. 21, 4010–4023 (2012).Article 

Google Scholar 
Bartoń, K. MuMIn: multi-model inference, R package version 1.9.13 (2013).Zhang, G. et al. Comparative genomics reveal insights into avian genome evolution and adaptation. Science 346, 1311–1320 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Roesti, M., Kueng, B., Moser, D. & Berner, D. The genomics of ecological vicariance in threespine stickleback fish. Nat. Commun. 6, 8767 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar  More

Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
PubMed 
Article 

Google Scholar 
Martiny, J. B. H. et al. Microbial biogeography: Putting microorganisms on the map. Nat. Rev. Microbiol. 4, 102–112 (2006).CAS 
PubMed 
Article 

Google Scholar 
Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. H. Beyond biogeographic patterns: Processes shaping the microbial landscape. Nat. Rev. Microbiol. 10, 497–506 (2012).CAS 
PubMed 
Article 

Google Scholar 
Grossart, H. P. Ecological consequences of bacterioplankton lifestyles: Changes in concepts are needed. Environ. Microbiol. Rep. 2, 706–714 (2010).PubMed 
Article 

Google Scholar 
Simon, M., Grossart, H. P., Schweitzer, B. & Ploug, H. Microbial ecology of organic aggregates in aquatic ecosystems. Aquat. Microb. Ecol. 28, 175–211 (2002).Article 

Google Scholar 
Smith, D. C., Simon, M., Alldredge, A. L. & Azam, F. Intense hydrolytic enzyme activity on marine aggregates and implication for rapid particle dissolution. Nature 359, 139–141 (1992).ADS 
CAS 
Article 

Google Scholar 
Grossart, H. P., Tang, K. W., Kiørboe, T. & Ploug, H. Comparison of cell-specific activity between free-living and attached bacteria using isolates and natural assemblages. FEMS Microbiol. Lett. 206, 194–200 (2007).Article 
CAS 

Google Scholar 
Rieck, A., Herlemann, D. P. R., Jürgens, K. & Grossart, H. Particle-associated differ from free-living bacteria in surface waters of the Baltic Sea. Front. Microbiol. 6, 1297 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Karner, M. & Herndl, G. J. Extracellular enzymatic activity and secondary production in free-living and marine-snow-associated bacteria. Mar. Biol. 113, 341–347 (1992).CAS 
Article 

Google Scholar 
Lyons, M. M. & Dobbs, F. C. Differential utilization of carbon substrates by aggregate-associated and water-associated heterotrophic bacterial communities. Hydrobiologia 686, 181–193 (2012).CAS 
Article 

Google Scholar 
Simon, H. M., Smith, M. W. & Herfort, L. Metagenomic insights into particles and their associated microbiota in a coastal margin ecosystem. Front. Microbiol. 5, 466 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Smith, M. W., Allen, L. Z., Allen, A. E., Herfort, L. & Simon, H. M. Contrasting genomic properties of free-living and particle-attached microbial assemblages within a coastal ecosystem. Front. Microbiol. 4, 120 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mestre, M. et al. Spatial variability of marine bacterial and archaeal communities along the particulate matter continuum. Mol. Ecol. 26, 6827–6840 (2017).CAS 
PubMed 
Article 

Google Scholar 
Bižic-Ionescu, M. et al. Comparison of bacterial communities on limnic versus coastal marine particles reveals profound differences in colonization. Environ. Microbiol. 17, 3500–3514 (2015).PubMed 
Article 
CAS 

Google Scholar 
Hollibaugh, J. T., Wong, P. S. & Murrell, M. C. Similarity of particle-associated and free-living bacterial communities in northern San Francisco Bay, California. Aquat. Microb. Ecol. 21, 103–114 (2000).Article 

Google Scholar 
Ortega-Retuerta, E., Joux, F., Jeffrey, W. H. & Ghiglione, J. F. Spatial variability of particle-attached and free-living bacterial diversity in surface waters from the Mackenzie River to the Beaufort Sea (Canadian Arctic). Biogeosciences 10, 2747–2759 (2013).ADS 
Article 

Google Scholar 
Noble, P. A., Bidle, K. D. & Fletcher, M. Natural microbial community compositions compared by a back-propagating neural network and cluster analysis of 5S rRNA. Appl. Environ. Microbiol. 63, 1762–1770 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou, J. & Ning, D. Stochastic community assembly: Does it matter in microbial ecology?. Microbiol. Mol. Biol. Rev. 81, e00002-17 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Jain, A., Balmonte, J. P., Singh, R., Bhaskar, P. V. & Krishnan, K. P. Spatially resolved assembly, connectivity and structure of particle-associated and free-living bacterial communities in a high Arctic fjord. FEMS Microbiol. Ecol. 97, 1–12 (2021).Article 
CAS 

Google Scholar 
Yao, Z. et al. Bacterial community assembly in a typical estuarine marsh. Appl. Environ. Microbiol. 85, e02602-18 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, J. et al. Assembly processes and source tracking of planktonic and benthic bacterial communities in the Yellow River estuary. Environ. Microbiol. 23, 2578–2591 (2021).PubMed 
Article 

Google Scholar 
Balmonte, J. P. et al. Sharp contrasts between freshwater and marine microbial enzymatic capabilities, community composition, and DOM pools in a NE Greenland fjord. Limnol. Oceanogr. 65, 77–95 (2019).ADS 
Article 
CAS 

Google Scholar 
Fortunato, C. S., Herfort, L., Zuber, P., Baptista, A. M. & Crump, B. C. Spatial variability overwhelms seasonal patterns in bacterioplankton communities across a river to ocean gradient. ISME J. 6, 554–563 (2012).CAS 
PubMed 
Article 

Google Scholar 
Yawata, Y., Carrara, F., Menolascina, F. & Stocker, R. Constrained optimal foraging by marine bacterioplankton on particulate organic matter. Proc. Natl. Acad. Sci. USA 117, 25571–25579 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hu, Y. et al. The relationships between the free-living and particle-attached bacterial communities in response to elevated eutrophication. Front. Microbiol. 11, 423 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Lima-Mendez, G. et al. Determinants of community structure in the grobal plankton interactome. Science (80-) 348, 1262073-1–10 (2015).Article 
CAS 

Google Scholar 
Milici, M. et al. Co-occurrence analysis of microbial taxa in the Atlantic ocean reveals high connectivity in the free-living bacterioplankton. Front. Microbiol. 7, 649 (2016).PubMed 
PubMed Central 

Google Scholar 
Herren, C. M. & McMahon, K. D. Cohesion: A method for quantifying the connectivity of microbial communities. ISME J. 11, 2426–2438 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Deng, Y. et al. Molecular ecological network analyses. BMC Bioinform. 13, 113 (2012).Article 

Google Scholar 
Labry, C. et al. High alkaline phosphatase activity in phosphate replete waters: The case of two macrotidal estuaries. Limnol. Oceanogr. 61, 1513–1529 (2016).ADS 
Article 

Google Scholar 
Crump, B. C. et al. Quantity and quality of particulate organic matter controls bacterial production in the Columbia River estuary. Limnol. Oceanogr. 62, 2713–2731 (2017).ADS 
CAS 
Article 

Google Scholar 
Canuel, E. A. & Hardison, A. K. Sources, ages, and alteration of organic matter in Estuaries. Ann. Rev. Mar. Sci. 8, 409–434 (2016).PubMed 
Article 

Google Scholar 
He, W., Chen, M., Schlautman, M. A. & Hur, J. Dynamic exchanges between DOM and POM pools in coastal and inland aquatic ecosystems: A review. Sci. Total Environ. 551–552, 415–428 (2016).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Bianchi, T. S. The role of terrestrially derived organic carbon in the coastal ocean: A changing paradigm and the priming effect. Proc. Natl. Acad. Sci. 108, 19473–19481 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Auffret, G. A. Dynamique sédimentaire de la marge continentale celtique-Evolution Cénozoïque-Spécificité du Pleistocène supérieur et de l’Holocène (Université de Bordeaux I, 1983).
Google Scholar 
Delmas, R. & Tréguer, P. Évolution saisonnière des nutriments dans un écosystème eutrophe d’Europe occidentale (la rade de Brest). Interactions marines et terrestres. Oceanol. Acta 6, 345–356 (1983).CAS 

Google Scholar 
Bassoullet, P. Etude de la dynamique des sédiments en suspension dans l’estuaire de l’Aulne (rade de Brest) (Université de Bretagne Occidentale, 1979).
Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
PubMed 
Article 

Google Scholar 
Bolyen, E. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Olesen, S. W., Duvallet, C. & Alm, E. J. dbOTU3: A new implementation of distribution-based OTU calling. PLoS ONE 12, 1–13 (2017).Article 
CAS 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).Article 
CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (2013).Whickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Book 

Google Scholar 
Lê, S., Josse, J. & Husson, F. FactoMineR: An R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).Article 

Google Scholar 
Wei, T. & Simko, V. R package ‘corrplot’: Visualization of a Correlation Matrix (2011).McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oksanen, J. et al. Vegan: Community Ecology Package (2022).Liu, C., Cui, Y., Li, X. & Yao, M. Microeco: An R package for data mining in microbial community ecology. FEMS Microbiol. Ecol. 97, fiaa255 (2021).CAS 
PubMed 
Article 

Google Scholar 
Kandlikar, G. ranacapa: Utility Functions and ‘shiny’ App for Simple Environmental DNA Visualizations and Analyses (2021).Cao, Y. microbiomeMarker: microbiome biomarker analysis toolkit (2021).Tsirogiannis, C. & Brody, S. PhyloMeasures: Fast and Exact Algorithms for Computing Phylogenetic Biodiversity Measures (2017).McKnight, D. T. et al. Methods for normalizing microbiome data: An ecological perspective. Methods Ecol. Evol. 10, 389–400 (2019).Article 

Google Scholar 
Paradis, E. & Schliep, K. Ape 50: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics https://doi.org/10.1093/bioinformatics/bty633 (2019).Article 
PubMed 

Google Scholar 
Legendre, P. & Legendre, L. Numerical Ecology (Third English Edition) (Elsevier, 2012).MATH 

Google Scholar 
Stegen, J. C., Lin, X., Fredrickson, J. K. & Konopka, A. E. Estimating and mapping ecological processes influencing microbial community assembly. Front. Microbiol. 6, 370 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Stegen, J. C., Lin, X., Konopka, A. E. & Fredrickson, J. K. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 6, 1653–1664 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Naimi, B. usdm: Uncertainty Analysis for Species Distribution Models (2017).Wu, W., Xu, Z., Dai, M., Gan, J. & Liu, H. Homogeneous selection shapes free-living and particle-associated bacterial communities in subtropical coastal waters. Divers. Distrib. 00, 1–14 (2020).
Google Scholar 
Wang, Y. et al. Patterns and processes of free-living and particle-associated bacterioplankton and archaeaplankton communities in a subtropical river-bay system in South China. Limnol. Oceanogr. 65, 161–179 (2020).
Google Scholar 
Zhou, L. et al. Environmental filtering dominates bacterioplankton community assembly in a highly urbanized estuarine ecosystem. Environ. Res. 196, 110934 (2021).CAS 
PubMed 
Article 

Google Scholar 
Graham, E. B. & Stegen, J. C. Dispersal-based microbial community assembly decreases biogeochemical function. Processes 5, 65 (2017).Article 

Google Scholar 
Campbell, B. J. & Kirchman, D. L. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 7, 210–220 (2013).CAS 
PubMed 
Article 

Google Scholar 
Herlemann, D. P. R. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 
Article 

Google Scholar 
Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science (80-) 348, 1261359 (2015).Article 
CAS 

Google Scholar 
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 
Article 

Google Scholar 
Martinez-Garcia, M. et al. Capturing single cell genomes of active polysaccharide degraders: An unexpected contribution of verrucomicrobia. PLoS ONE 7, e35314 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reintjes, G., Arnosti, C., Fuchs, B. M. & Amann, R. An alternative polysaccharide uptake mechanism of marine bacteria. ISME J. 11, 1640–1650 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).CAS 
PubMed 
Article 

Google Scholar 
Liu, J., Meng, Z., Liu, X. & Zhang, X. H. Microbial assembly, interaction, functioning, activity and diversification: a review derived from community compositional data. Mar. Life Sci. Technol. 1, 112–128 (2019).ADS 
Article 

Google Scholar 
Hernandez, D. J., David, A. S., Menges, E. S., Searcy, C. A. & Afkhami, M. E. Environmental stress destabilizes microbial networks. ISME J. 15, 1722–1734 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Herren, C. M. & McMahon, K. D. Keystone taxa predict compositional change in microbial communities. Environ. Microbiol. 20, 2207–2217 (2018).PubMed 
Article 

Google Scholar 
Liénart, C. et al. Dynamics of particulate organic matter composition in coastal systems: A spatio-temporal study at multi-systems scale. Prog. Oceanogr. 156, 221–239 (2017).Article 

Google Scholar 
Fraisse, S., Bormans, M. & Lagadeuc, Y. Morphofunctional traits reflect differences in phytoplankton community between rivers of contrasting flow regime. Aquat. Ecol. 47, 315–327 (2013).Article 

Google Scholar 
Treguer, P. & Queguiner, B. Seasonal variations in conservative and nonconservative mixing of nitrogen compounds in a West European macrotidal estuary. Oceanol. Acta 12, 371–380 (1989).CAS 

Google Scholar 
Grossart, H. P. & Tang, K. W. Communicative & integrative biology. Commun. Integr. Biol. 3, 491–494 (2010).PubMed 
PubMed Central 
Article 

Google Scholar  More

Taxonomy, variation, and biochronologyThe fossils described herein represent one of the most valuable and best-preserved samples of “Dama-like” deer from the European Early Pleistocene. The systematics of these forms has been essentially based on the morphology of the antlers and teeth, with less attention paid to the skull (due to the rarity of well-preserved finds) and postcranial bones.The Pantalla sample shows a combination of characters allowing an unambiguous attribution to ‘P.’ nestii, a species reported confidently so far in the early Late Villafranchian of Italy (several sites) and in the Georgian Homo-bearing locality of Dmanisi (Supplementary Table S1). Based on the literature6,8,12,38, these characters include: four-pointed antler with elongated, slender, and tubular beam; basal tine branching off at a certain distance from the burr forming an acute angle; well-developed middle tine; terminal bifurcation oriented normal to the sagittal plane; cranium with large orbits, preorbital fossae, and ethmoidal vacuities; relatively elongated neurocranium with flat parietals; caudally-oriented pedicles; molarized P2-P3; presence of cingula in upper molars; enlarged i1; un-molarized p4. However, some characters observed in the Pantalla specimens (e.g., rostral edge of the orbit reaching the level of M2; elongated metapodials) do not fit the revised diagnosis of ‘P.’ nestii by Croitor12. The latter author considers nestii as the earliest species of the genus Cervus based on similarities with the extant red deer especially in cranial morphology12,22,23. However, in our opinion, his conclusions are biased by relying mostly on the skull IGF 243 of ‘P.’ nestii from Upper Valdarno6,8, which is heavily deformed and belongs to a juvenile individual (see below for details on ontogenetic variation in ‘Pseudodama’).A broader look at the entire record of ‘P.’ nestii reveals that this species displays a mosaic of characters between Dama and Cervus, but also that the shared characters with Dama are prevalent (as already pointed out by Azzaroli8). The Pantalla sample allows to substantiate these conclusions very well. Our CT-based comparisons between the crania from Pantalla and those of extant red deer and fallow deer (Fig. 3) highlight some morphological similarities with the former, including a relatively longish neurocranium with steep forehead and deep preorbital fossa. On the other hand, ‘P.’ nestii from Pantalla clearly shows Dama-like cranial characters, such as a marked interfrontal crest, horizontal zygomatic arch, high maxilla below the orbit, muzzle more inclined ventrally and less cylindrical in overall shape, sub-horizontal upper cheek tooth row (i.e., the occlusal margin of the row is approximately straight in buccal view), apical surface of the pedicle more inclined dorsocaudally, and overall morphology of the antlers, which in rostral view diverge, rather than converge as in the red deer (Fig. 2).Likewise, the teeth from Pantalla, have a mixture of Dama and Cervus characters although the former are prevalent. All the premolar characters (the complete absence of a lingual grove on P4, the presence of a cingulum on the distolingual wall of P4, the presence of a small paraconid in p2, the entoconid more aligned with the mesiodistal axis in p3-p4, and a weak mesial cingulum on p4) and most of the lower molar characters are Dama-like. The upper molar features are instead more reminiscent of Cervus being either intermediate between the morphology of the latter and that of extant Dama or even matching Cervus (see Supplementary Table S6 and below).The postcranial remains from Pantalla appear more similar to Dama than to Cervus. Of the 23 morphological characters by Lister39 which are present in the preserved bones (axis, metacarpal, tibia, astragalus, calcaneum, cubo-navicular, metatarsal, phalanx I, and phalanx II), 21 scores as fallow deer and only two as red deer (details in Supplementary Table S7).A mixed character suite between Dama and Cervus are revealed also by our palaeoneurological analysis. The brain of ‘P.’ nestii shows Dama-like size and Cervus-like morphology with a prominent cerebellum and a dorsoventrally flattened cerebrum. The latter character is clearly noticeable in ‘Pseudodama’ and Eucladoceros, is less evident in extant Cervus, and is missing in Dama. The hypothesis that depressed and longish cerebra represent a primitive character in Cervini (at least in Pleistocene European forms) is supported by our preliminary data and agree with Azzaroli8.Most interestingly, the two crania from Pantalla actually show some remarkable morphological differences. The neurocranium of 337643 is more lengthened (i.e., more Cervus-like), albeit this shape might be taphonomically modified by the lateral compression of the specimen. This morphology fits that observed in some other ‘P.’ nestii specimens such as IGF 1403 from Olivola (Italy), while the relatively shorter and more rounded neurocranium of 337655 resembles that of other specimens such as IGF 1404 also from Olivola. Moreover, 337643 shows a stronger nuchal crest than 337655. These differences may be related to ontogenesis (see the advanced age of 337643 based on tooth wear). In several cervid species including fallow deer, aging leads to morphological changes in the neurocranium, which tends to elongate and flatten and shows a more developed nuchal crest, probably as a response to the support of larger and heavier antlers18,38. Similarly, in 337643, the pedicles are apparently closer to one another due to their thickening—an expected condition for an old individual as the distance between the pedicles tends to decrease with age8—and markedly shorter than wide. Our comparative data on European Dama-like deer show that the pedicle section can be highly variable both within and between species, although a general trend of laterolateral flattening (i.e., oval shape with major axis oriented anteroposteriorly) can be traced through time (Supplementary Fig. S3), probably as a result of the development of wide, laterally-projecting palmated antlers (in extant deer, D. dama is among those with the heaviest antlers relative to body size40,41). Therefore, the Pantalla sample on the one hand confirms the variation in cranial morphology already observed for ‘P.’ nestii6,8, on the other hand it supports the affinities between this species and the fallow deer. The presence of Cervus-like features especially in cranial morphology may be interpreted as plesiomorphic characters which, associated with some characters of the dentition and of the brain, suggest a basal position of ‘Pseudodama’ in the evolutionary history of the Cervini. This hypothesis may be tested in the future through phylogenetic analyses, currently made difficult by the lack of sufficiently well-preserved material of some species of ‘Pseudodama’ (e.g., ‘P.’ lyra, ‘P.’ perolensis).Compared with other specimens of ‘P.’ nestii6,8, the sample from Pantalla shows some plesiomorphic characters including a high ratio between the premolar and molar lengths, i.e., 0.77–0.82 (n = 2) for upper teeth (LP/LM) and 0.68–0.69 (n = 3) for lower teeth (Lp/Lm). These values are closer to the basal forms of ‘Pseudodama’, such as ‘P.’ lyra from Montopoli (LP/LM = 0.73, n = 1; Lp/Lm = 0.64, n = 2) and ‘P.’ rhenana from Saint Vallier (LP/LM = 0.75, n = 9; Lp/Lm = 0.68, n = 18; data from Valli42), than to ‘P.’ nestii from Olivola and Upper Valdarno (LP/LM = 0.72, n = 10; Lp/Lm = 0.63, n = 17). Other putatively plesiomorphic features of the sample from Pantalla are all those that approach it morphologically to Cervus (see Supplementary Table S6), i.e., the strong development of lingual conids and stylids in lower molars (Char. 439) and of buccal cones and styles in upper molars (Char. 139), the lack of a clear step between 2nd and 3rd lobe of m3 (Char. 1139), the strong lingual cingulum on upper molars (Char. 339), and the lack of the horizontal turning of the buccal columns of upper molars (Char. 439—the so-called buccal “cingulum”43). The strong lingual cingulum on upper molars is constantly present in the earliest species of the ‘Pseudodama’ group, ‘P.’ pardinensis9, and still present, although extremely rare, in ‘P.’ lyra from Montopoli, ‘P.’ rhenana from Saint Vallier and Senèze, ‘P.’ perolensis from Peyrolles, and ‘P.’ nestii from Olivola. However, this feature is back less rare in ‘P.’ nestii from Upper Valdarno and ‘P.’ farnetensis from Selvella, suggesting a certain polymorphism at this stage. The lack of buccal “cingulum” is a constant in the earliest ‘Pseudodama’ populations (Lower Valdarno, Saint Vallier, Senèze), the buccal “cingulum” appearing, although rare, in ‘P.’ perolensis from Peyrolles and ‘P.’ nestii from Olivola and Upper Valdarno but becoming more common only in later ‘P.’ farnetensis, ‘P.’ vallonnetensis, and constant in Dama.The above affinities between the Pantalla deer and the early representatives of ‘Pseudodama’ support the idea that the age of the assemblage may be close to the beginning of the Late Villafranchian (ca. 2.1–2.0 Ma), as already suggested based on the occurrence of Leptobos merlai44 and a primitive form of Equus stenonis35. Thus, the ‘P.’ nestii sample described herein may represent one of the earliest occurrences of the species in Europe.Palaeoecological and palaeoethological inferencesThe Pantalla sample is also noteworthy as it allows opening a window into the behaviour of these extinct deer. The anomalies found on the two male crania are probably the result of different traumas during their life.Deer are well known for the intense fights they engage in during the rutting season using their antlers, as a result of an escalation of a broad repertoire of threats and displays45. Mineralized antlers are solid structures able to withstand the vehemence of the fight46, whereas growing antlers are extremely fragile and any contact with a solid object may result in a serious injury47,48 that may jeopardize the bearer’s ability to compete with conspecifics and, consequently, its dominance status49. Accidents are inevitable in the life of a deer and, in case of the suffered damage not leading to the breakage of the growing beam and consequent loss of its distal part, the antler may continue its growth although, in case of a severe lesion, at a crooked angle45. Thus, if the antler was just cracked and the broken part was held together by the velvet and periosteum, with the blood supply still being guaranteed, the damaged beam would just present a conspicuous swelling around the area of fracture (i.e., a fracture callus)45,50 and a change in the axis of orientation. These features match those seen in the left beam of 337655, which shows a fracture callus between the basal and middle tines corresponding to a change in the orientation of the beam.The supernumerary tine of the right antler of the same individual can be interpreted as the result of a trauma, too. Considering the delicate nature of the growing antlers and the non-negligible risks of occurrence of an injury, it is safe to believe that the right antler has undergone a light traumatic event (most likely concerning the pedicle) at some early stage of its growth. In fact, it is known that limited injuries could result in the growth of supernumerary tines, even in atypical positions51, as it has been documented in other deer species (e.g., reindeer52, sambar53). It is therefore reasonable to hypothesize that both antler anomalies of 337655 derive from traumas suffered by the deer during the antler growth, when the velvet was still present. It is not known whether the two injuries happened at the same time or in two different events. In fact, it cannot even be said that the two events took place during the same season. While the breakage of the left beam must have occurred in the year of the animal’s death (i.e., during the velvet period preceding the period of hard antler in which the individual died), the development of the supernumerary tine on the right may be the result of a trauma suffered in a previous year. This is due to the fact that when unilateral trauma affects the generative region of the antler (i.e., the pedicle area), abnormalities such as supernumerary tines can reappear in next antler cycles even in more intensified forms54, as in the case of 337655 in which the extra-tine is extremely long.The bone anomaly on the right squamosal of 337643 is also likely the outcome of an injury. Although the external portion was artificially smoothed during the preparation of the specimen, the outer and inner morphology matches that of a callus related to the healing of a major lesion and probable intracranial abscessation. Post-traumatic inflammatory processes are known to cause erosion or pitting of cranial bones in deer55 and can be triggered by many factors (e.g., wounds and abrasions of the pedicle56), among which violent sexual competition among males with hard antlers is considered one of the most common55,57. The advanced healing of the injury shown by 337643 suggests that it was not the cause of death, but rather that the individual survived a long time after the trauma albeit with the brain partially compressed by the callus.The six mandibles recovered at Pantalla, all coming from the same bone accumulation hence reasonably referable to a single deer population, represent several age classes, from calves as young as a few months up to very old individuals (i.e., over 15 years; Supplementary Table S4). Unfortunately, no mandible can be safely associated with the two male crania, although 337631 may belong to the same individual as 337643 based on advanced wear and size. Interestingly, the three most significant cranial remains (crania 337655 and 337643 and frontal bone fragment with basal antler base 337625) belong to adult males, which probably died during the hard antler period (i.e., rutting season: 337655 and 337625) or shortly after (i.e., 337643). The absence of females (at least among the remains with certain sex attribution) contrasts with the population structure in the extant fallow deer, in which females represent on average 75% of the herd58. However, the relative abundance of males may increase up to 50% in the rutting season59,60. Therefore, in spite of the relatively low number of fossils available, based on the age and sex structure of the palaeopopulation and by analogy with the extant fallow deer, the most plausible hypothesis is that the Pantalla deer died during or immediately after the rutting season (Fig. 5).Figure 5Life appearance of ‘Pseudodama’ nestii represented during the rutting season. The reconstruction is based on the cranial and postcranial material from the Early Pleistocene of Pantalla (Italy) and on literature data. Artwork by D.A. Iurino.Full size image More

Experimental set-upFour vegetation species were selected: Spartina maritima, Salicornia europaea, Halimione portulacoides and Juncus maritimus. These species were chosen for a broad representation of the biomechanical properties and morphological characteristics of saltmarsh species42,43. Plants were collected in Cantabrian estuaries in late summer and early autumn (from early September to late October) during low tide (please refer to the “Methods” section). A total of 105 boxes were collected, of which 94 boxes were used to build a 9.05 m long and 0.58 m wide meadow in a flume (Fig. 1). Five boxes were used to directly estimate the meadow standing biomass in the field (Sample 1 in Table 1), leaving 6 extra boxes for possible contingencies.Figure 1(A) Shows a sketch of the experimental flume, where the vegetation box distribution in the 100% and 50% density cases is displayed in the two upper panels and a lateral view in the bottom panel. The green boxes indicate the vegetated area in each case. Free surface sensors are displayed by blue lines and numbers. (B) Shows the four species within the flume. From left to right: view of the Spartina sp. frontal edge, aerial view of Salicornia sp., frontal view of Juncus sp. and top view of the Halimione sp. rear edge.Full size imageTable 1 Standing biomass (g/m2) and plant height (m) for the four species.Full size tableExperiments were conducted in a flume 20.71 m long and 0.58 m wide at the University of Cantabria. The flume is equipped with a piston wave maker at its left end and a dissipation beach at the rear end. The 94 vegetation boxes used to create a meadow were introduced into the flume following the pattern shown in panel A of Fig. 1 to minimize any edge effects along the edges of the boxes. To ensure a smooth transition from the bottom of the channel to the vegetated area, two false bottoms were constructed with wood, and a thin sediment layer was glued to the wood to mimic the field roughness.Three meadow densities per species were considered. The meadow density directly determined in the field was chosen under the 100% density scenario. To consider a second meadow density, and therefore a second standing biomass value, plants were removed from half of the boxes following the pattern shown in Panel A of Fig. 1 to prevent creating preferential flow channels along the meadow. This case was considered the 50% density scenario. The study of these two biomass scenarios for each vegetation species is carried out with the aim of covering a wide range of standing biomass values, including low values that may be more representative of meadow winter conditions, thus facilitating the applicability of obtained results. Finally, a second cut was made, in which all plants were removed, resulting in the final scenario with a zero density. Plants were cut from above to avoid any damage along the meadow surface (as shown in Supplementary Fig. S2). In each cut, plants in 5 boxes along the leading edge and in 5 boxes at the center of the meadow were collected to quantify the standing biomass (Samples 2 and 3 for the first cut and Sample 4 and 5 for the second cut in Table 1). Therefore, the standing biomass could be monitored throughout the entire duration of the experiments, from the field until the second cut, when all plants were removed.Once located in the flume, the meadow was evaluated under regular and random wave conditions considering three water depths, i.e., h = 0.20, 0.30 and 0.40 m. Regular waves were generated using Stokes II-, III- and V-order and Cnoidal theories when applicable. Wave heights ranging from 0.05 to 0.15 m and wave periods varying between 1.5 and 4 s were considered. Random waves were generated using a Jonswap spectrum with a peak enhancement factor of 3.3, a significant wave height varying between 0.05 and 0.15 m and a peak wave period ranging from 1.8 to 4.8 s (please refer to Supplementary Table S1). Additionally, all wave conditions were considered under the zero-density scenario with bare soil for each species. The wave height evolution along the flume was recorded using 15 capacitive free surface gauges, as shown in Fig. 1 (please refer to Supplementary Table S2 for detailed coordinates).Meadow characteristics analysisThe characteristics of the vegetation meadows were analyzed by measuring the standing biomass throughout the full duration of the experiments and by measuring the individual plant height (please refer to the “Methods” section). The mean standing biomass value obtained for each species was considered the value associated with the 100% density scenario. Then, half of the standing biomass value was considered under the 50% density scenarios since half of the boxes was randomly cut, and the standing biomass values obtained after the second cut agreed with those obtained after the first cut and in the field, as indicated in Table 1. The plant height for each species was also measured (please refer to the “Methods” section), and the resultant mean value detailed in Table 1 was considered.Wave height attenuation analysisWave height attenuation analysis was performed following previous studies reported in the literature assessing the capacity by fitting a damping coefficient6,7,35,44. The18 formulation was used for regular waves, and that of19 was used for random waves (please refer to the “Methods” section). Cases with a zero density were also considered in this analysis to quantify the influence of bare soil friction by determining the corresponding damping coefficient, ({beta }_{B}). Consequently, β was obtained in the 100% and 50% density cases and the cases without vegetation (please refer to Supplementary Tables S3, S4 and S5 to find the obtained coefficients for all cases). This allowed the determination of a new damping coefficient isolating the effect of the standing biomass, ({beta }_{SB}), following24 (please refer to the “Methods” section). Figure 2 shows an example of wave height attenuation analysis for the four species and the different densities under wave condition JS07 (Supplementary Table S1).Figure 2Analysis of wave attenuation under wave condition JS07 for Spartina sp. 100% (S100), 50% (S050) and zero density (S000); Salicornia sp. 100% (L100), 50% (L050) and zero density (L000); Juncus sp. 100% (J100), 50% (J050) and zero density (J000); and Halimione sp. 100% (H100), 50% (H050) and zero density (H000). The damping coefficients for the bare soil cases, ({beta }_{B}), are displayed in blue. The damping coefficients for the 100% and 50% density cases, (beta ), are displayed in dark and light green, respectively. The damping coefficients obtained after subtracting the dissipation obtained in the bare soil cases, ({beta }_{SB}), are displayed in black and dark gray. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageThe damping coefficients for the bare soil cases shown in Fig. 2, ({beta }_{B}), are consistent with the soil properties observed in the field. Spartina sp. was collected in a muddy area, whereas the other three species were collected in areas with coarser sediments and exhibited a mixture of sand and mud. For all species, wave dissipation was significantly higher under the 100% density scenario than that under the 50% density cases, as expected, highlighting the importance of the standing biomass in wave energy dissipation. It was also observed that bottom friction-induced dissipation plays a more important role for the pioneer species, i.e., Spartina sp. and Salicornia sp., than for the upper marsh species, i.e., Juncus sp. and Halimione sp., which can dissipate wave energy to a greater extent.The importance of wave parameters in the resultant wave attenuation has been highlighted by several works in the literature. Therefore, not only vegetation characteristics but also incident wave conditions determine the coastal protection capacity. Figure 3 shows a comparison of the obtained wave height attenuation due to Halimione sp. under the different wave conditions.Figure 3Analysis of wave attenuation under the different irregular wave conditions for the Halimione sp. 100% (H100) and zero-density (H000) cases. The top panel shows two cases with different h but equal Hs and Tp values (JS01 and JS08), the middle panel shows two cases with different Tp but equal h and Hs values (JS10 and JS11), and the bottom panel shows two cases with different Hs but equal h and Tp values (JS09 and JS12). 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageThe top panel in Fig. 3 shows two cases where Hs and Tp are equal, i.e., JS01 and JS08 in Supplementary Table S1, and two water depths are considered, namely, h = 0.2 and 0.3 m. As can be observed, wave damping is higher for the smallest water depth, where most of the water column is covered by vegetation since the mean vegetation height for Halimione sp. reaches 0.187 m (Table 1). The importance of the water depth with respect to the plant height in terms of wave height attenuation has been reported by several authors44,45,46 who have highlighted this aspect based on the submergence ratio, i.e., the plant height divided by the water depth, revealing higher attenuation at lower submergence ratios on a consistent basis. Bottom friction attenuation is also higher for the smallest water depth, as expected.The middle panel of Fig. 3 shows two cases with equal h and Hs but different Tp values, namely, JS10 and JS11 in Supplementary Table S1. Wave height attenuation is higher for the shortest wave period, as well as the damping produced by bottom friction. This is in line with previous studies, such as35 and44, who conducted experiments involving simulated and real saltmarshes, respectively. Finally, the bottom panel of Fig. 3 shows two cases with different Hs but equal h and Tp values, i.e., JS09 and JS12 in Supplementary Table S1. As widely reported in the literature, e.g.,7,47,48, wave height attenuation increases with the wave height, as shown in the bottom panel of Fig. 3. Bottom friction also increases with the wave height, as expected.A set of damping coefficients was obtained via the 288 tests conducted in the laboratory, 144 tests involving regular waves and 144 tests involving random waves. Additionally, in all cases, the damping coefficient considering the isolated effect of the standing biomass, ({beta }_{SB}), was determined. The relationship of these damping coefficients to the measured standing biomass is explored in the next section with the aim of establishing a new relationship to estimate the wave damping effect of the different saltmarsh species based on the standing biomass, without the need for data fitting.Wave damping coefficient as a function of the standing biomassThe mean standing biomass obtained for the different species, Table 1, is considered here to analyze the relationship with the wave damping coefficients obtained by fitting18 formulation to wave heights measured along the meadow for regular waves and19 formulation for random waves. The plant height was highly variable among the different species (Table 1), ranging from 0.170 m for Spartina sp. to 0.714 m for Juncus sp. Then, some species were submerged at all tested water depths, while other species remained above water in all tests. In the latter cases, there remained a portion of each plant above the water level, thus not contributing to wave attenuation. To consider the actual interaction between the standing biomass and flow conditions and assuming a uniform vertical distribution, the effective standing biomass, (ESB), can be defined as follows:$$ESB=DryWeight*frac{minleft{{h}_{v},hright}}{{h}_{v}}$$
(1)
where (DryWeight) denotes the measured dry weight for each species (g/m2), ({h}_{v}) is the mean plant height and (h) is the water depth. Additionally, in the submerged cases, the same (ESB) value will impact flow differently depending on the submergence ratio, (SR), as defined in Eq. (2). To consider this effect, the standing biomass ratio, (SBR) in Eq. (3), can be defined as follows:$$SR=frac{{h}_{v}}{h}, ;;where ;; SR=1 ;;for ;;{h}_{v} >h$$
(2)
$$SBR=ESB*SR$$
(3)
Figure 4 shows the relationship between (SBR) and the measured wave damping coefficient, (beta ). The results for regular and random waves are displayed for each water depth, and a linear fit was found under each condition.Figure 4Wave damping coefficient, (beta ), as a function of the standing biomass ratio, (SBR), under all regular (left panels) and random (right panels) wave conditions. Each panel shows the wave trains assessed at each water depth, h = 0.20, 0.30 and 0.40 m. The results for the 100% density case are marked with circles and those for the 50% density case are marked with squares. The linear fitting results obtained under each wave condition are also displayed.Full size imageUnder each wave condition, a linear fitting relationship between (beta ) and (SBR) was obtained for the eight (SBR) values, as shown in Fig. 4. For similar (SBR) values, the highest (beta ) values were consistently obtained at the smallest water depth, highlighting the notable influence of this parameter on the obtained wave attenuation. Following previous works, such as those of24 and25, who considered the vegetation submerged solid volume fraction to estimate the resulting wave attenuation and established a common relationship for different water depths, the volumetric standing biomass, (VSB), can be defined as follows:$$VSB= SBR*frac{1}{h}$$
(4)
(VSB) is expressed in units of g/m3, which is the weight per unit volume. Exploring the relationship of (beta ) with this new parameter, it was found that the results for the three water depths could be fitted with a single linear relationship, as shown in Fig. 5. However, despite the linear trend observed in Fig. 5, notable data scatter was observed for each (VSB) value. Each of these groups corresponds to a certain water depth and (SBR) value, which were determined under different wave heights and wave periods.Figure 5Wave damping coefficient, (beta ), as a function of the volumetric standing biomass, (VSB), under all regular (top panel) and random (bottom panel) wave conditions. The obtained linear fitting results are displayed in both panels. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageFinally, to account for the characteristics of the incident wave conditions, including the wave height and period, two nondimensional parameters were considered. The first parameter, considering the wave height, is the relative wave height, defined as the ratio of the incident wave height to the water depth, (H/h). Previous studies have highlighted the importance of this parameter in the resultant wave attenuation (e.g.24,44). Under random wave conditions, the considered wave height is ({H}_{rms}), according to wave attenuation analysis. The second parameter, considering the effect of the different wave periods and the importance of the number of wave lengths inside the vegetation length49, is the relative meadow length, defined as the ratio of the meadow length to the wave length, ({L}_{v}/L). To ensure consistency with the above wave attenuation analysis, in which the wave damping amount per unit length was obtained, the unit meadow length was considered here. Thus, the hydraulic standing biomass, (HSB), can be defined as:$$HSB=VSB*frac{H}{h}*frac{{L}_{v}}{L}$$
(5)
Figure 6 shows the relationship obtained between (beta ) and this new variable under all regular and random conditions following the linear fitting relationship of (beta =A*HSB+B), where (A) and (B) are fitting constants with units of (g/m2)−1 and m−1, respectively.Figure 6Wave damping coefficient, (beta ), as a function of the hydraulic standing biomass, (HSB), under all regular (top panel) and random (bottom panel) wave conditions. Both panels show linear fitting results obtained without considering the saturation point, indicated by the black solid line, and those obtained considering the saturation point, indicated by the gray solid line. The black dashed line indicates the saturation point. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageThe linear fitting results obtained between (beta ) and (HSB) under regular and random wave conditions are shown in Fig. 6 as solid black lines and expressed as Eqs. (6) and (7), respectively, where values between brackets are the 95% confidence interval for each coefficient.$$beta =9.206cdot {10}^{-4} left(9.006cdot {10}^{-5}right)*HSB+0.103 (0.021)$$
(6)
$$beta =1.192 cdot {10}^{-3} left(9.124 cdot {10}^{-5}right)*HSB+0.071 (0.016)$$
(7)
The inclusion of incident wave condition characteristics reduces the resulting data scatter, highlighting the role of the wave height and period in the obtained wave attenuation, as described in the previous section. An interesting aspect observed in Fig. 6 is that the four cases with the highest wave damping coefficients yielded similar values for the different (HSB) values. Under regular wave conditions, the mean (beta ) value for these four cases is 0.76, and under random wave conditions, the value reaches 0.68. This may indicate that the damping coefficient has reached its maximum value and no longer increases with increasing (HSB) value. To analyze this aspect in more detail, the wave height evolution measured for the four tests in which (beta ) reaches its maximum value are plotted (as shown in Supplementary Fig. S3). These tests correspond to Halimione sp. with a density of 100% and the shallowest water depth, h = 0.20 m. This species achieved the highest standing biomass value among the species considered in these experiments, and for h = 0.20 m, almost the entire water column was covered by vegetation. For these tests, a notable wave height attenuation was observed, where the wave height strongly decayed along the first 5 m of vegetation, and the wave height entirely dissipated along the last 4 m (as shown in Supplementary Fig. S3). The wave damping equation cannot suitably reproduce the strong wave decay within this few meters. Then, an almost constant wave damping coefficient value is reached under the different considered wave conditions, and a saturation regime is observed, in which the wave height beyond the meadow can be assumed to be negligible. To consider this phenomenon, a two-section fitting relationship is proposed, as shown in Fig. 6. The value of the saturation damping coefficient, chosen as the mean value of the four cases analyzed, is plotted as a dashed gray line, and a linear fit is obtained for the remaining data. The two-section fitting relationship is expressed in Eqs. (8) and (9) for both regular and random waves, respectively, where values between brackets are the 95% confidence interval for each coefficient.$$beta =left{begin{array}{ll}1.020 cdot {10}^{-3}left(1.112 cdot {10}^{-4}right)*HSB+0.088 ; (0.020) \ 0.758; (0.027)end{array}right. begin{array}{l} ;;0 < HSB < 659\ ;; HSB > 659end{array}$$
(8)
$$beta =left{begin{array}{l}1.310cdot {10}^{-3}left(1.232cdot {10}^{-4}right)*HSB+0.059; (0.017) \ 0.684 ;(0.066)end{array}right. begin{array}{l};;0474end{array}$$
(9)
All damping coefficients considered in the previous analysis were obtained without subtracting any additional source of dissipation such as bottom and wall friction. Previous works, such as24, highlighted the high importance of considering any other sources of wave dissipation besides the effect of vegetation elements when quantifying the wave height attenuation capacity. In this case, the flume walls were made of glass, and the friction induced by these walls could be considered negligible. However, bottom friction could be significant, as observed in tests run after removing all vegetation stems. Then, the wave damping coefficient obtained after subtracting the bottom friction contribution, ({beta }_{SB}), is studied here. Figure 7 shows the relationship obtained between this damping coefficient, ({beta }_{SB}), and hydraulic standing biomass, (HSB).Figure 7Wave damping coefficient, ({beta }_{SB}), as a function of the hydraulic standing biomass, (HSB), under all regular (top panel) and random (bottom panel) wave conditions. Both panels show linear fitting results obtained without considering the saturation point, indicated by the black solid line, and those obtained considering the saturation point, indicated by the gray solid line. The black dashed line indicates the saturation point. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageA linear relationship was also obtained for ({beta }_{SB}), revealing correlation coefficients similar to those obtained when analyzing (beta ). The obtained linear relationships under regular and random wave conditions are expressed as Eqs. (10) and (11), respectively, where values between brackets are the 95% confidence interval for each coefficient. A two-section fitting relationship, Eqs. (12) and (13), was also included considering the saturation regime obtained in the Halimione sp. 100% density and h = 0.20 m cases with a ({beta }_{SB}=) 0.69 and 0.63 under regular and random wave conditions, respectively.$${beta }_{SB}=1.051*{10}^{-3} left(7.063cdot {10}^{-5}right)*HSB$$
(10)
$${beta }_{SB}=1.296*{10}^{-3} left(6.894cdot {10}^{-5}right)*HSB$$
(11)
$${beta }_{SB}=left{begin{array}{l}1.151cdot {10}^{-3} left(7.445cdot {10}^{-5}right)*HSB \ 0.685 ;(0.047)end{array}right. begin{array}{l} ;; 0599end{array}$$
(12)
$${beta }_{SB}=left{begin{array}{l}1.396cdot {10}^{-3}left(7.919cdot {10}^{-5}right)*HSB \ 0.631 ;left(0.055right)end{array}right. begin{array}{l};; 0451end{array}$$
(13)
As can be noted, the ({beta }_{SB}) values are significantly lower than those obtained for (beta ), especially in the shallowest water depth cases where bottom friction is the highest, as discussed above. The estimation of (beta ) and ({beta }_{SB}) allows two possible approaches to determine the wave damping effect of a saltmarsh. The first approach, based on (beta ), includes wave damping induced by the combined effect of vegetation and bottom friction. Therefore, the consideration of (beta ) in analytical or numerical analysis could provide the total dissipation induced by the species under study, and sediment characteristics are not necessary for analysis. Considering that saltmarsh species grow in muddy to sandy environments and that the major contribution to the obtained wave attenuation is associated with vegetation, this approach may be the best option if soil properties are not thoroughly characterized.The second approach relies on the definition of ({beta }_{SB}). In this case, the wave damping contributions of vegetation drag and bottom friction are separated. Then, ({beta }_{SB}) can be used in cases where the effect of both momentum sinks can be separately evaluated. To quantify the wave damping contribution of vegetation drag only, ({beta }_{SB}) can be used, and then, the additional friction due to the bottom effect can be added considering the soil properties in each case. This second approach assumes a linear sum of both momentum sinks and could be applicable when soil properties are thoroughly characterized. More

Strandburg-Peshkin, A., Papageorgiou, D., Crofoot, M. C. & Farine, D. R. Inferring influence and leadership in moving animal groups. Philos. Trans. R. Soc. Lond. B Biol. Sci. 373(1746), 20170006 (2018).PubMed 
PubMed Central 

Google Scholar 
Garland, J., Berdahl, A. M., Sun, J. & Bollt, E. M. Anatomy of leadership in collective behaviour. Chaos 28(7), 075308 (2018).ADS 
MathSciNet 
PubMed 

Google Scholar 
King, A. J., Douglas, C. M. S., Huchard, E., Isaac, N. J. B. & Cowlishaw, G. Dominance and affiliation mediate despotism in a social primate. Curr. Biol. 18(23), 1833–1838 (2008).CAS 
PubMed 

Google Scholar 
Lewis, J. S., Wartzok, D. & Heithaus, M. R. Highly dynamic fission–fusion species can exhibit leadership when traveling. Behav. Ecol. Sociobiol. 65(5), 1061–1069 (2011).
Google Scholar 
Van Belle, S., Estrada, A. & Garber, P. A. Collective group movement and leadership in wild black howler monkeys (Alouatta pigra). Behav. Ecol. Sociobiol. 67(1), 31–41 (2013).
Google Scholar 
Smith, J. E. et al. Collective movements, leadership and consensus costs at reunions in spotted hyaenas. Anim. Behav. 105, 187–200 (2015).
Google Scholar 
Kerth, G., Ebert, C. & Schmidtke, C. Group decision making in fission–fusion societies: Evidence from two-field experiments in Bechstein’s bats. Proc. R. Soc. B Biol. Sci. 273(1602), 2785–2790 (2006).
Google Scholar 
Nagy, M., Ákos, Z., Biro, D. & Vicsek, T. Hierarchical group dynamics in pigeon flocks. Nature 464(7290), 890–893 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Giuggioli, L., McKetterick, T. J. & Holderied, M. Delayed response and biosonar perception explain movement coordination in trawling bats. PLoS Comput. Biol. 11(3), e1004089 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Pettit, B., Ákos, Z., Vicsek, T. & Biro, D. Speed determines leadership and leadership determines learning during pigeon flocking. Curr. Biol. 25(23), 3132–3137 (2015).CAS 
PubMed 

Google Scholar 
Strandburg-Peshkin, A., Farine, D. R., Couzin, I. D. & Crofoot, M. C. Group decisions. Shared decision-making drives collective movement in wild baboons. Science 348(6241), 1358–1361 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tokuyama, N. & Furuichi, T. Leadership of old females in collective departures in wild bonobos (Pan paniscus) at Wamba. Behav. Ecol. Sociobiol. 71(3), 55 (2017).
Google Scholar 
Montanari, D., O’Hearn, W. J., Hambuckers, J., Fischer, J. & Zinner, D. Coordination during group departures and progressions in the tolerant multi-level society of wild Guinea baboons (Papio papio). Sci. Rep. 11(1), 21938 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Papageorgiou, D. & Farine, D. R. Shared decision-making allows subordinates to lead when dominants monopolize resources. Sci. Adv. 6(48), 5881 (2020).ADS 

Google Scholar 
Bousquet, C. A. H., Sumpter, D. J. T. & Manser, M. B. Moving calls: A vocal mechanism underlying quorum decisions in cohesive groups. Proc. R. Soc. Lond. B Biol. Sci. 278(1711), 1482–1488 (2011).
Google Scholar 
Stahl, J., Tolsma, P. H., Loonen, M. J. J. E. & Drent, R. H. Subordinates explore but dominants profit: Resource competition in high Arctic barnacle goose flocks. Anim. Behav. 61(1), 257–264 (2001).PubMed 

Google Scholar 
Boinski, S. Social manipulation within and between troops mediate primate group movement. In On the Move: How and Why Animals Travel in Groups (ed. Boinski, S.) (University of Chicago Press, 2000).
Google Scholar 
Conradt, L. & Roper, T. J. Consensus decision making in animals. Trends Ecol. Evol. 20(8), 449–456 (2005).PubMed 

Google Scholar 
Conradt, L. & Roper, T. J. Conflicts of interest and the evolution of decision sharing. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364(1518), 807–819 (2009).PubMed 

Google Scholar 
Byrne, R. W. How monkeys find their way: Leadership, coordination, and cognitive maps of African baboons. In On the Move: How and Why Animals Travel in Groups (eds Boinski, S. & Garber, P. A.) (University of Chicago Press, 2000).
Google Scholar 
Conradt, L. & Roper, T. J. Deciding group movements: Where and when to go. Behav. Proc. 84, 675–677 (2010).
Google Scholar 
Herbert-Read, J. E. et al. Inferring the rules of interaction of shoaling fish. PNAS 108(46), 18726–18731 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Katz, Y., Tunstrøm, K., Ioannou, C. C., Huepe, C. & Couzin, I. D. Inferring the structure and dynamics of interactions in schooling fish. PNAS 108(46), 18720–18725 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jolles, J. W., Boogert, N. J., Sridhar, V. H., Couzin, I. D. & Manica, A. Consistent individual differences drive collective behavior and group functioning of schooling fish. Curr. Biol. 27(18), 2862–2868 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Doolan, S. P. & Macdonald, D. W. Breeding and juvenile survival among slender-tailed meerkats (Suricatu suricatta) in the south-western Kalahari: Ecological and social influences. J. Zool. 242(2), 309–327 (1997).
Google Scholar 
Clutton-Brock, T. H. & Manser, M. B. Meerkats: Cooperative breeding in the Kalahari. In Cooperative Breeding in Vertebrates (eds Koenig, W. D. & Dickinson, J. L.) (Cambridge University Press, 2016).
Google Scholar 
Doolan, S. & Macdonald, D. Diet and foraging behaviour of group living meerkats, Suricata suricatta, in the southern Kalahari. J. Zool. 239, 697–716 (1996).
Google Scholar 
Engesser, S. Function of ‘Close’ Calls in a Group Foraging Carnivore, Suricata suricatta (2011).Kranstauber, B., Gall, G. E. C., Vink, T., Clutton-Brock, T. & Manser, M. B. Long-term movements and home-range changes: Rapid territory shifts in meerkats. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13129 (2019).Article 
PubMed 

Google Scholar 
Manser, M. B. et al. Vocal Complexity in Meerkats and Other Mongoose Species Vol. 46, 281 (Elsevier, 2014).
Google Scholar 
Gall, G. E. C. & Manser, M. B. Group cohesion in foraging meerkats: Follow the moving ‘vocal hot spot’. R. Soc. Open Sci. 4, 170004 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Engesser, S. & Manser, M. B. Collective close calling mediates group cohesion in foraging meerkats via spatially determined differences in call rates. Anim. Behav. 185, 73–82 (2022).
Google Scholar 
Gall, G. E. C., Strandburg-Peshkin, A., Clutton-brock, T. & Manser, M. B. As dusk falls: Collective decisions about the return to sleeping sites in meerkats. Anim. Behav. 132, 91–99 (2017).
Google Scholar 
Townsend, S. W., Rasmussen, M., Clutton-Brock, T. & Manser, M. B. Flexible alarm calling in meerkats: The role of the social environment and predation urgency. Behav. Ecol. 23(6), 1360–1364 (2012).
Google Scholar 
Clutton-Brock, T. H. et al. Contributions to cooperative rearing in meerkats. Anim. Behav. 61(4), 705–710 (2001).
Google Scholar 
Griffin, A. S. et al. A genetic analysis of breeding success in the cooperative meerkat (Suricata suricatta). Behav. Ecol. 14(4), 472–480 (2003).
Google Scholar 
Thavarajah, N. K., Fenkes, M. & Clutton-Brock, T. H. The determinants of dominance relationships among subordinate females in the cooperatively breeding meerkat. Behaviour 151(1), 89–102 (2014).
Google Scholar 
Young, A. J. et al. Stress and the suppression of subordinate reproduction in cooperatively breeding meerkats. Proc. Natl. Acad. Sci. 103(32), 12005–12010 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hodge, S. J., Manica, A., Flower, T. P. & Clutton-Brock, T. H. Determinants of reproductive success in dominant female meerkats. J. Anim. Ecol. 77(1), 92–102 (2008).PubMed 

Google Scholar 
Bell, M. B. V. et al. Suppressing subordinate reproduction provides benefits to dominants in cooperative societies of meerkats. Nat. Commun. 22(5), 4499 (2014).ADS 

Google Scholar 
Bousquet, C. A. H. & Manser, M. B. Resolution of experimentally induced symmetrical conflicts of interest in meerkats. Anim. Behav. 81(6), 1101–1107 (2011).
Google Scholar 
Strandburg-Peshkin, A., Clutton-Brock, T. & Manser, M. B. Burrow usage patterns and decision-making in meerkat groups. Behav. Ecol. 31(2), 292–302 (2020).
Google Scholar 
Turbé, A. Foraging Decisions and Space Use in a Social Mammal, The Meerkat—Chapter 6: Leadership pby Lactating Female in Meerkats (University of Cambridge, 2006).
Google Scholar 
Barelli, C., Reichard, U., Boesch, C. & Heistermann, M. Female white-handed gibbons (Hylobates lar) lead group movements and have priority of access to food resources. Behaviour 145(7), 965–981 (2008).
Google Scholar 
Clutton-Brock, T. H. et al. Reproduction and survival of suricates (Suricata suricatta) in the southern Kalahari. Afr. J. Ecol. 37(1), 69–80 (1999).
Google Scholar 
Kutsukake, N. & Clutton-Brock, T. H. Do meerkats engage in conflict management following aggression? Reconciliation, submission and avoidance. Anim. Behav. 75(4), 1441–1453 (2008).
Google Scholar 
Spong, G. F., Hodge, S. J., Young, A. J. & Clutton-Brock, T. H. Factors affecting the reproductive success of dominant male meerkats: Reproductive success in male meerkats. Mol. Ecol. 17(9), 2287–2299 (2008).PubMed 

Google Scholar 
Russell, A. F., Carlson, A. A., McIlrath, G. M., Jordan, N. R. & Clutton-Brock, T. Adaptive size modification in dominant female meerkats. Evolution 58(7), 1600–1607 (2004).PubMed 

Google Scholar 
R. Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/ (2008).Pinheiro, J. & Bates, D. M. Mixed-Effects Models in S and S-PLUS (Springer-Verlag, 2000).MATH 

Google Scholar 
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometr. J. 50(3), 346–363 (2008).MathSciNet 
MATH 

Google Scholar 
Makowski, D., Ben-Shachar, M. S., Patil, I. & Lüdecke, D. Methods and algorithms for correlation analysis in R. J. Open Source Softw. 5(51), 2306 (2020).ADS 

Google Scholar 
Farine, D. R., Strandburg-Peshkin, A., Couzin, I. D., Berger-Wolf, T. Y. & Crofoot, M. C. Individual variation in local interaction rules can explain emergent patterns of spatial organization in wild baboons. Proc. R. Soc. B 284(1853), 20162243 (2017).PubMed 
PubMed Central 

Google Scholar 
Holekamp, K. E., Boydston, E. E. & Smale, L. Group tarvel in social carnivores. In On the Move (eds Boinski, S. & Garber, P. A.) (University of Chicago Press, 2000).
Google Scholar 
Fischhoff, I. R. et al. Social relationships and reproductive state influence leadership roles in movements of plains zebra, Equus burchellii. Anim. Behav. 73(5), 825–831 (2007).
Google Scholar 
Furrer, R. D., Kunc, H. P. & Manser, M. B. Variable initiators of group departure in a cooperative breeder: The influence of sex, age, state and foraging success. Anim. Behav. 84(1), 205–212 (2012).
Google Scholar 
Clutton-Brock, T. H. et al. Costs of cooperative behaviour in suricates (Suricata suricatta). Proc. R. Soc. B Biol. Sci. 265(1392), 185–190 (1998).CAS 

Google Scholar 
MacLeod, K. J. & Clutton-Brock, T. H. Low costs of allonursing in meerkats: Mitigation by behavioral change? Behav. Ecol. 26(3), 697–705 (2015).
Google Scholar 
Boinski, S. The coordination of spatial position: A field study of the vocal behaviour of adult female squirrel monkeys. Anim. Behav. 41(1), 89–102 (1991).
Google Scholar 
Bode, N. W. F., Franks, D. W. & Wood, A. J. Leading from the front? Social networks in navigating groups. Behav. Ecol. Sociobiol. 66(6), 835–843 (2012).
Google Scholar 
Reber, S. A., Townsend, S. W. & Manser, M. B. Social monitoring via close calls in meerkats. Proc. R. Soc. B Biol. Sci. 280(1765), 20131013 (2013).
Google Scholar 
Bracken, A. M., Christensen, C., O’Riain, M. J., Fürtbauer, I. & King, A. J. Flexible group cohesion and coordination, but robust leader–follower roles, in a wild social primate using urban space. Proc. R. Soc. B Biol. Sci. 289(1967), 20212141 (2022).
Google Scholar  More

Climate models need to capture a full spectrum of data from forests such as the Brazilian Amazon.Credit: Florence Goisnard/AFP/Getty

Humanity’s understanding of how forests are responding to climate change is disconcertingly fragile. Take carbon fertilization, for example — the phenomenon by which plants absorb more carbon dioxide as its concentration in the atmosphere increases. This is one of the principal mechanisms by which nature has so far saved us from the worst of climate change, but there’s little understanding of its future trajectory. In fact, researchers don’t fully understand how climate change interacts with a multitude of forest processes. Complex, unsolved questions include how climate warming affects forest health; how it affects the performance of forests as a carbon sink; and whether it alters the ecosystem services that forests provide. Forests are our life-support system, and we should be more serious about taking their pulse.Six papers in this week’s Nature provide important insights into those questions. They also underline some of the challenges that must be overcome if we are to fully understand forests’ potential in the fight against climate change. These challenges are not only in the science itself, but also relate to how forest scientists collaborate, how they are funded (especially where data collection is concerned) and how they are trained.Forest science is an amalgam of disciplines. Ecologists and plant scientists measure tree growth, soil nutrients and other parameters in thousands of forest plots around the world. Physical scientists monitor factors such as forest height and above-ground forest biomass using remote-sensing data from drones or satellites. Experimental scientists investigate how forests might behave in a warming world by artificially altering factors such as temperature or carbon dioxide levels in experimental plots. Some of the data they generate are absorbed by yet another community: the modellers, who have created dynamic global vegetation models (DGVMs). These simulate how carbon and water cycles change with climate and, in turn, inform broader earth-system and climate models of the type that feed into policymaking.Different DGVMs make different predictions about how long forests will continue to absorb anthropogenic CO2. One reason for these differences is that models are sensitive to assumptions made about the processes in forests. There are many influences — including temperature, moisture, fire and nutrients — that are generally studied in isolation. Yet they interact with each other.Not all DGVMs account for the dampening effect that a lack of soil phosphorus can have on carbon fertilization, for example. Much of central and eastern Amazonia is poor in phosphorus, and research has shown that introducing phosphorus limitation into DGVMs can cut the carbon-fertilization effect1. This week, Hellen Fernanda Viana Cunha at the National Institute for Amazonian Research in Manaus, Brazil, and her colleagues report2 a powerful experimental demonstration of how the soil’s poor phosphorus content limits carbon absorption in an old-growth Amazonian forest.Models simulating the northward spread of boreal forest as temperatures rise are also missing key drivers3, according to Roman Dial at Alaska Pacific University in Anchorage and his colleagues. They report today that a white-spruce population has migrated surprisingly far north into the Arctic tundra. To explain this, it is necessary to take into account winter winds (which facilitate long-distance dispersal) along with the availability of deep snow and soil nutrients (which promote plant growth).Models are often based on a small number of ‘functional tree types’ — for example, ‘evergreen broadleaf’ or ‘evergreen needle leaf’. These are chosen as a proxy for the behaviour of the planet’s more than 60,000 known tree species. Yet ecologists are discovering that the biology of individual species matters when it comes to a tree’s response to climate change.David Bauman at the Environmental Change Institute at the University of Oxford, UK, and his co-workers reported in May that tree mortality on 24 moist tropical plots in northern Australia has doubled in the past 35 years (and life expectancy has halved), apparently owing to the increasing dryness of the air4. But that was an average of the 81 dominant tree species: mortality rates varied substantially between species, a variation that seemed to be related to the density of their wood.Peter Reich at the Institute for Global Change Biology at the University of Michigan in Ann Arbor and his colleagues now report that modest alterations in temperature and rainfall led to varying rates of growth and survival5 for different species in southern boreal-forest trees. The species that prospered were rare.Failure to examine multiple factors simultaneously means that scientists are making findings that challenge the assumptions in models. Spring is coming earlier for temperate forests and most models assume that, by prolonging the growing season, this increases woody-stem biomass. However, observational work carried out in temperate deciduous forests by Kristina Anderson-Teixeira at the Smithsonian Conservation Biology Institute in Front Royal, Virginia, and her colleagues found no sign of this happening6.Modellers are all too aware of the need to incorporate more complexity into their models, and of the potential that increasing amounts of computing power have to assist them in this endeavour. But they need more data.Continuity problemTo obtain comprehensive, valuable data for the models, continuous, long-term observations need to be made, and that depends on the availability of long-term funding. Achieving such continuity is a problem for both remote-sensing and ground-based operations. The former can cost hundreds of millions of dollars, but the value of its long-term data sets is immense, as demonstrated by a team led by Giovanni Forzieri at the University of Florence in Italy. The authors used 20 years of satellite data to show that nearly one-quarter of the world’s intact forests have already reached their critical threshold for abrupt decline7. But even field-based data collection, which costs a pittance by comparison, struggles to achieve financial security.Important ground-based operations include the Forest Global Earth Observatory (ForestGEO), part of the Smithsonian Tropical Research Institute, which is headquartered in Washington DC. This monitors 7.5 million individual trees in plots around the world. The amount of work that goes into this monitoring is formidable. For example, at present, ForestGEO is conducting the eighth five-yearly census of a plot in Peninsular Malaysia. This involves determining the species for each of the 350,000 trees (there are some 800 species growing there) and measuring the circumference of each trunk. It will take 16 skilled people a year to measure all the trees. Delays in the provision of funding to ForestGEO have held up similar censuses at plots in countries including Papua New Guinea, Vietnam, Brunei and Ecuador.

A ForestGEO researcher making tree measurements at a forest plot in Barro Colorado Island, Panama.Credit: Jorge Aleman, STRI

The future of the plots in North Queensland, which supplied Bauman with a rare 49 years’ worth of continuous data, is uncertain. They have been monitored since the mid-1970s by the Australian public research-funding agency CSIRO — initially every two years, then, more recently, every five years. In 2019, monitoring of the plots was switched to every 50 years because of funding shortages at CSIRO, leaving scientists searching for new sources of funding.Without continuity of funding, organizations such as ForestGEO can’t equip researchers with the requisite skills or collect data over periods longer than an individual’s time in a specific post or a funder’s cycle. “We have trained people and then lost them due to job insecurity,” says Stuart Davies, who leads ForestGEO.Different groups of forest researchers are trying to address these problems. ForestGEO is coordinating the Alliance for Tropical Forest Science in an effort to make it easier to share data, and to bolster the morale and careers of the skilled technicians and scientists — many of whom live in low- and middle-income countries — who do the bulk of the data collection.But we also need more-imaginative funding mechanisms that lift long-term observational plots out of three- to five-year funding cycles. Space agencies that fund remote-sensing satellites could collaborate with other funding agencies, for example, so that earth-observation missions include a fully funded component for ground-based data collection — which is, after all, crucial for calibrating their results. Journals, too, could do more to value and incentivise the production of long-term data sets.And there is a need for more interdisciplinarity. The US Department of Energy is funding a project called NGEE–Tropics (Next-Generation Ecosystem Experiments–Tropics) in which modellers will work with empirical researchers, both observational and experimental, who study tropical forests to create a full, process-rich model of such forests. This is encouraging, and the idea could be pushed further. What is needed is an initiative that pulls the disciplines together towards a goal of building a better understanding of forest processes. Among other things, such an initiative would encourage researchers in different disciplines to take each other’s data needs into account when planning their projects.For this to work, we need to remember that the edifice of forest science relies on the long-term data that scientists wring from forests over decades. Our chances of overcoming climate change are small, but they will diminish further if we forget the basics of monitoring our home planet. More