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

IPBES. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Zenodo https://doi.org/10.5281/zenodo.5657041 (2019).Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–232 (2014).ADS 
CAS 
Article 

Google Scholar 
Ibisch, P. L. et al. A global map of roadless areas and their conservation status. Science 354, 1423–1427 (2016).ADS 
CAS 
Article 

Google Scholar 
Forman, R. T. T., Sperling, D. & Bissonette, J. A. Road Ecology: Science and Solutions. (Island Pr, 2003).van der Ree, R., Smith, D. J. & Grilo, C. Handbook of Road Ecology. (John Wiley & Sons, 2015).Laender. Hunting Statistics. Game casualties 2017/2018: furred game (red deer, roe deer, chamois, moufflon) https://www.statistik.at/web_en/statistics/Economy/agriculture_and_forestry/livestock_animal_production/hunting/index.html (2018).Steiner, W., Leisch, F. & Hacklander, K. A review on the temporal pattern of deer-vehicle accidents: Impact of seasonal, diurnal and lunar effects in cervids. Accident; analysis and prevention 66, (2014).Kioko, J. et al. Driver knowledge and attitudes on animal vehicle collisions in Northern Tanzania. TROPICAL CONSERVATION SCIENCE 8, 352–366 (2015).Article 

Google Scholar 
Bíl, M., Andrášik, R. & Janoška, Z. Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation. Accident Analysis & Prevention 55, 265–273 (2013).Article 

Google Scholar 
Page, Y. A statistical model to compare road mortality in OECD countries. Accident Analysis and Prevention 33, 371–385 (2001).CAS 
Article 

Google Scholar 
Teixeira, F. Z. et al. Are Road-kill Hotspots Coincident among Different Vertebrate Groups? Oecologia Australis 17, 36–47 (2017).Article 

Google Scholar 
Canova, L. & Balestrieri, A. Long-term monitoring by roadkill counts of mammal populations living in intensively cultivated landscapes. Biodivers Conserv https://doi.org/10.1007/s10531-018-1638-3 (2018).Brehme, C. S., Hathaway, S. A. & Fisher, R. N. An objective road risk assessment method for multiple species: ranking 166 reptiles and amphibians in California. Landscape Ecol 33, 911–935 (2018).Article 

Google Scholar 
Heigl, F. et al. Comparing Road-Kill Datasets from Hunters and Citizen Scientists in a Landscape Context. Remote Sensing 8, (2016).Heigl, F., Horvath, K., Laaha, G. & Zaller, J. G. Amphibian and reptile road-kills on tertiary roads in relation to landscape structure: using a citizen science approach with open-access land cover data. BMC Ecol 17, 24 (2017).Article 

Google Scholar 
Dörler, D. & Heigl, F. A decrease in reports on road-killed animals based on citizen science during COVID-19 lockdown. PeerJ 9, e12464 (2021).Article 

Google Scholar 
Peer, M. et al. Predicting spring migration of two European amphibian species with plant phenology using citizen science data. Sci Rep 11, 21611 (2021).ADS 
CAS 
Article 

Google Scholar 
Schwartz, A. L. W. UK Roadkill Records. The Global Biodiversity Information Facility https://doi.org/10.15468/r3xakd (2018).Lin, T. The Taiwan Roadkill Observation Network Data Set. Version 1.3. The Global Biodiversity Information Facility https://doi.org/10.15468/cidkqi (2018).Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biological Conservation 213, 280–294 (2017).Article 

Google Scholar 
Périquet, S., Roxburgh, L., le Roux, A. & Collinson, W. J. Testing the Value of Citizen Science for Roadkill Studies: A Case Study from South Africa. Front. Ecol. Evol. 6, (2018).Abra, F. D., Huijser, M. P., Pereira, C. S. & Ferraz, K. M. P. M. B. How reliable are your data? Verifying species identification of road-killed mammals recorded by road maintenance personnel in São Paulo State, Brazil. Biological Conservation 225, 42–52 (2018).Article 

Google Scholar 
Bíl, M., Kubeček, J., Sedoník, J. & Andrášik, R. Srazenazver.cz: A system for evidence of animal-vehicle collisions along transportation networks. Biological Conservation 213, 167–174 (2017). Part A.Article 

Google Scholar 
Vercayie, D. & Herremans, M. Citizen science and smartphones take roadkill monitoring to the next level. Nature Conservation 11, 29–40 (2015).Article 

Google Scholar 
Waetjen, D. P. & Shilling, F. M. Large Extent Volunteer Roadkill and Wildlife Observation Systems as Sources of Reliable Data. Front. Ecol. Evol. 5, (2017).Shilling, F. M., Perkins, S. E. & Collinson, W. Wildlife/Roadkill Observation and Reporting Systems. in Handbook of Road Ecology 492–501 (John Wiley & Sons, 2015).Eitzel, M. V. et al. Citizen Science Terminology Matters: Exploring Key Terms. Citizen Science: Theory and Practice 2, 1–20 (2017).
Google Scholar 
Haklay, M. et al. Contours of citizen science: a vignette study. Royal Society Open Science 8, 202108 (2021).ADS 
Article 

Google Scholar 
Heigl, F., Kieslinger, B., Paul, K. T., Uhlik, J. & Dörler, D. Opinion: Toward an international definition of citizen science. PNAS 116, 8089–8092 (2019).CAS 
Article 

Google Scholar 
Heigl, F. et al. Quality Criteria for Citizen Science Projects on Österreich forscht | Version 1.1. Open Science Framework https://doi.org/10.17605/OSF.IO/48J27 (2018).Heigl, F. et al. Co-Creating and Implementing Quality Criteria for Citizen Science. Citizen Science: Theory and Practice 5, 23 (2020).
Google Scholar 
Heigl, F. & Zaller, J. G. Using a Citizen Science Approach in Higher Education: a Case Study reporting Roadkills in Austria. Human Computation 1, (2014).University of Natural Resources and Life Sciences, Vienna. Roadkill, The Global Biodiversity Information Facility, https://doi.org/10.15468/ejb47y (2021).Heigl, F. & Roadkill Community. Roadkill Dataset 2014-2020 Quality level 2, Zenodo, https://doi.org/10.5281/zenodo.5878813 (2022).August, T. A. et al. Citizen meets social science: predicting volunteer involvement in a global freshwater monitoring experiment. Freshwater Science 38, 321–331 (2019).Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2021-3. IUCN Red List of Threatened Species https://www.iucnredlist.org/en (2021). More

Statistical characteristics of rill cross-sectional asymmetry (RCA)The rill cross-sectional asymmetry (RCA) is a key parameter in describing rill morphology and includes the asymmetry ratio of the width (Aw) and the asymmetry ratio of the area (Aa). It reflects the differences in certain aspects of natural conditions resulting in inconsistent development speeds on both sides of a rill cross-section. The cross-section was classified as left-biased if Aw, Aa < 0, quasi-symmetrical if Aw, Aa = 0, and right skewed if Aw, Aa > 0. The left/right deflection reflects that erosion on the right happened faster than on the left, so the slope on the left is not as steep as on the right. The results of this study show that asymmetry is a common phenomenon in the cross-section of a rill. The Aw ranged from − 1.77 to 1.97, with an average value of − 0.034. There were 374 cross-sections whose RCA was less than or equal to 0, meaning that 53% of the cross-sections were right-biased. The Aa ranged from − 1.81 to 1.71, with an average of − 0.046. There were 374 cross-sections with an RCA of less than or equal to 0, meaning that 53% of the cross-sections were right-biased (Fig. 1).Figure 1Statistical characteristics of the rill cross-sectional asymmetry (RCA).Full size imageFigure 2 shows that there are four Aw groups in the interval (− 1.7, − 1.5), 53 groups in the interval (− 1.5, − 1.0), 144 groups in the interval (− 1.0, − 0.5), 173 groups in the interval (− 0.5, 0), 174 groups in the interval (0, 0.5), 120 groups in the interval (0.5, 1.0), 39 groups in the interval (1.0, 1.5), and five groups in the interval (1.5, 2). The Aa has 15 groups in the interval (− 1.8, − 1.5), 63 groups in the interval (− 1.5, − 1.0), 130 groups in the interval (− 1.0, − 0.5), 166 groups in the interval (− 0.5, 0), 161 groups in the interval (0, 0.5), 110 groups in the interval (0.5, 1.0), 53 groups in the interval (1.0, 1.5), and 14 groups in the interval (1.5, 2). The RCA of most cross-sections is concentrated in the interval (− 0.5, 0.5). This interval of Aw contains 491 cross-sections, accounting for 68.96% of the total. There are 470 cross-sections in this interval of Aa, accounting for 66.01% of the total. This indicates that, although the rill cross-section exhibits some asymmetry, the difference between both sides of the section is small (Fig. 2).Figure 2Distribution characteristics of the RCA.Full size imageThe influence of a single topographic factor on the RCACorrelation analyses of the Aw, Aa, and the slope difference on both sides (B), rill length (L), rill slope length (I), rill head catchment area (A), difference between the catchment areas of both sides (R), rill bending coefficient (K), and location of the section angle of turning of the rill (J) were carried out. The results show that the main factors that have a significant linear correlation with the Aw and the Aa are B (p < 0.01), with correlation coefficients of 0.32 and 0.22, respectively (Fig. 3). That is, the greater the difference in slope between the two sides, the more asymmetric the rill cross-section. R also has a significant linear correlation with the Aw (p < 0.05), with a correlation coefficient of 0.07. This means that the greater the difference in the catchments between the left and right sides of the rill, the greater the asymmetry of the rill cross-section. However, other topographic factors have no significant correlation with the RCA.Figure 3Correlation between rill cross-sectional asymmetry (RCA) and topographic factors.Full size imageB is the difference in slope between the left and right sides of the rill cross-section catchment area. The closer B gets to 0, the smaller the difference in slope between the left and right sides of the rill cross-section catchment area. When the catchment area slope on the right side of the cross-section is greater than that on the left side, B < 0; and when the catchment area slope on the left side of the cross-section is greater than that on the right side, B > 0. Grouping B reveals that the average RCA increases as B increases (Fig. 4). When B is (− 30, − 20), Aw is − 0.48 and Aa is − 0.38; when B is (− 20, − 10), Aw is − 0.36 and Aa is − 0.31; when B is (− 10, 0), Aw is − 0.23 and Aa is − 0.22; when B is (0, 10), Aw is 0.21 and Aa is 0.16; when B is (10, 20), Aw is 0.47 and Aa is 0.40; and when B is (20, 40), Aw is 0.31 and Aa is 0.13. These are relatively low values because this group only has two sets of cross-sections which cannot represent the characteristics of interval B. The sign of the RCA is the same as the sign of B. The directionality of the RCA is significantly affected by B. When the slope of the left catchment area is large, RCA > 0, and the rill cross-section appears to be left-biased; when the slope of the right catchment area is large, RCA < 0, and the cross-section appears to the righ-biased.Figure 4The asymmetry of different B values.Full size imageThe influence of multiple topographic factors on the RCAIn order to explore the influence of multiple topographic factors on the RCA, principal component analysis (PCA) was used to extract the main feature components of the topographic data. The PCA results show that the nine topographic factors can be reflected by two principal components at 61.84% (characteristic value: 3.117+1.211=4.328 variables) (Table 1). Therefore, the analysis of the first two principal components could reflect most of the information from all the data.Table 1 Calculation results of topographic factor principal component analysis (PCA).Full size tableThe contribution rate of the first principal component is 44.534%. The characteristic is that the factor variables have high positive loads for the four factors L, I, A, and K. L has the largest contribution rate at 88.5%, followed by A, I, and K, at 87.5%, 81.1%, and 60.2%, respectively. Therefore, the first component represents the rill slope and rill shape.The contribution rate of the second principal component is 17.303%. The characteristic is that the factor variables have high positive loads for the three factors B, J, and R. B has the largest contribution rate at 83.5%, followed by J and R, at 57.4% and 55.7%, respectively. Therefore, the second component represents the effect of the difference between the two sides of the rill.Based on the correlation between the topographic factors and the RCA of a rill cross-section in the Yuanmou dry-hot valley area, the following was observed: asymmetry in rill cross-sections is ubiquitous. The distribution range of Aw is − 1.77 to 1.97, the average value is − 0.034, and the cross-section that is right-biased accounts for 53%. A correlation analysis of the RCA and seven topographic factors shows that B has a significant positive correlation with the Aw and Aa (p < 0.01), the average RCA increases as B increases, and the directionality of the RCA is affected by B. When B > 0, RCA > 0, and the rill cross-section appears to the left; when B < 0, RCA < 0, and the cross-section appears to the right. The difference in catchment area between the sides has a significant linear correlation with the Aw (p < 0.05). Other single topographic factors have no significant correlation with the RCA. Principal component analysis and calculations show that the first principal component represents the influence of the rill slope surface and rill shape on the rill cross-sectional asymmetry. The contribution rate is 44.534%, which is characterized by a high positive load on the L, I, A, and K factors. The second principal component represents the effect of the difference between the two sides of the rill. The contribution rate is 44.534%, which is characterized by a high positive load on the B, J, and R factors. More

Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
CAS 
Article 

Google Scholar 
Díaz, S. et al. Pervasive human-driven decline of life on earth points to the need for transformative change. Science 366, eaax3100 (2019).Article 

Google Scholar 
United Nations Food and Agriculture Organization (FAO). State of the World’s Forests 2022: Forest Pathways for Green Recovery and Building Inclusive, Resilient and Sustainable Economies (FAO, 2022).Williams, M. Deforesting the Earth: From Prehistory to Global Crisis (University of Chicago Press, 2003).Laurence, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–294 (2012).ADS 
Article 

Google Scholar 
UNEP-WCMC and IUCN. Protected Planet: The World Database on Protected Areas (WDPA) http://www.protectedplanet.net (2020).Convention on Biological Diversity. First Draft of the Post-2020 Global Biodiversity Framework https://www.cbd.int/meetings/WG2020-03 (2021).Wilson, E. O. Half-Earth: Our Planet’s Fight for Life (Liveright Publishing Company, 2016).Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS One 4, e8273 (2009).ADS 
Article 

Google Scholar 
Sarkar, S. et al. Biodiversity conservation planning tools: present status and challenges for the future. Annu. Rev. Environ. Resour. 31, 123–159 (2006).Article 

Google Scholar 
Naidoo, R. et al. Integrating economic costs into conservation planning. Trends Ecol. Evol. 21, 681–687 (2006).Article 

Google Scholar 
Ando, A., Camm, J., Polasky, S. & Solow, A. Species distributions, land values, and efficient conservation. Science 279, 2126–2128 (1998).ADS 
CAS 
Article 

Google Scholar 
Meir, E., Andelman, S. & Possingham, H. P. Does conservation planning matter in a dynamic and uncertain world? Ecol. Lett. 7, 615–622 (2004).Article 

Google Scholar 
Costello, C. & Polasky, S. Dynamic reserve site selection. Resour. Energy Econ. 26, 157–174 (2004).Article 

Google Scholar 
The Nature Conservancy. Terrestrial Ecoregions https://geospatial.tnc.org/datasets/7b7fb9d945544d41b3e7a91494c42930_0 (2008).Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).ADS 
CAS 
Article 

Google Scholar 
Kummu, M., Taka, M. & Guillaume, J. Gridded global datasets for gross domestic product and human development index over 1990–2015. Sci. Data 5, 180004 (2018).Article 

Google Scholar 
Kier, G. et al. Global patterns of plant diversity and floristic knowledge. J. Biogeogr. 32, 1107–1116 (2005).Article 

Google Scholar 
Tilman, D., Reich, P. B. & Isbell, F. Biodiversity impacts ecosystem productivity as much as resources, disturbance or herbivory. Proc. Natl Acad. Sci. USA 109, 10394–10397 (2012).ADS 
CAS 
Article 

Google Scholar 
Thompson, I., Mackey, B., McNulty, S. & Mosseler, A. Forest Resilience, Biodiversity, and Climate Change CBD Technical Series No. 43 (Secretariat of the Convention on Biological Diversity, 2009).Butsic, V., Lewis, D. J. & Radeloff, V. C. Reserve selection with land market feedbacks. J. Environ. Manag. 114, 276–284 (2013).Article 

Google Scholar 
Wilson, K. A., McBride, M. F., Bode, M. & Possingham, H. P. Prioritizing global conservation efforts. Nature 440, 337–340 (2006).ADS 
CAS 
Article 

Google Scholar 
McBride, M. F., Wilson, K. A., Bode, M. & Possingham, H. P. Incorporating the effects of socioeconomic uncertainty into priority setting for conservation investment. Conserv. Biol. 21, 1463–1474 (2007).Article 

Google Scholar 
Bode, M., Wilson, K., McBride, M. & Possingham, H. P. Optimal dynamic allocation of conservation funding among priority regions. Bull. Math. Biol. 70, 2039–2054 (2008).MathSciNet 
Article 

Google Scholar 
Pouzols, F. M. et al. Global protected area expansion is compromised by projected land-use and parochialism. Nature 516, 383–386 (2014).ADS 
Article 

Google Scholar 
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141–144 (2017).ADS 
CAS 
Article 

Google Scholar 
Dinerstein, E. et al. A ‘Global Safety Net’ to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, eabb2824 (2020).ADS 
Article 

Google Scholar 
Jung, M. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat. Ecol. Evol. 5, 1499–1509 (2021).Article 

Google Scholar 
Polasky, S. et al. Where to put things? Spatial land management to sustain biodiversity and economic returns. Biol. Conserv. 141, 1505–1524 (2008).Article 

Google Scholar 
Lennox, G. D., Fargione, J., Spector, S., Williams, G. & Armsworth, P. The value of flexibility in conservation financing. Conserv. Biol. 31, 666–674 (2016).Article 

Google Scholar 
Drechsler, M. & Wätzold, F. Biodiversity conservation in a dynamic world may lead to inefficiencies due to lock-in effects and path dependence. Ecol. Econ. 173, 106652 (2020).Article 

Google Scholar 
Boulton, C. A., Lenton, T. M. & Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Chang. 12, 271–278 (2022).ADS 
Article 

Google Scholar 
Lomolino, M. V. Ecology’s most general, yet protean pattern: the species‐area relationship. J. Biogeogr. 27, 17–26 (2000).Article 

Google Scholar 
Drake, J. & Griffen, B. Early warning signals of extinction in deteriorating environments. Nature 467, 456–459 (2010).ADS 
CAS 
Article 

Google Scholar 
Grantham, H. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 1, 5978 (2020).ADS 
Article 

Google Scholar 
Naidoo, R. & Iwamura, T. Global-scale mapping of economic benefits from agricultural land: Implications for conservation priorities. Biol. Conserv. 140, 40–49 (2007).Article 

Google Scholar 
UNEP-FAO. United Nations Decade on Ecosystem Restoration 2021–2030 https://www.decadeonrestoration.org/ (2021).UN Climate Change Conference. Glasgow Leaders’ Declaration on Forests and Land Use https://ukcop26.org/glasgow-leaders-declaration-on-forests-and-land-use/ (2021)UN Climate Change Conference. The Global Forest Finance Pledge https://ukcop26.org/the-global-forest-finance-pledge/ (2021).Convention on Biological Diversity. Preparations for the Post-2020 Biodiversity Framework https://www.cbd.int/conferences/post2020 (2021)Deutz, A. et al. Financing Nature: Closing the Global Biodiversity Financing Gap (The Paulson Institute, The Nature Conservancy, and the Cornell Atkinson Center for Sustainability, 2020).Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).ADS 
CAS 
Article 

Google Scholar 
Guerry, A. D. et al. Natural capital and ecosystem services informing decisions: from promise to practice. Proc. Natl Acad. Sci. USA 112, 7348–7355 (2015).CAS 

Google Scholar 
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).ADS 
Article 

Google Scholar 
Wintle, B. A. et al. Spending to save: what will it cost to halt Australia’s extinction crisis? Conserv. Lett. 12, e12682 (2019).Article 

Google Scholar 
Ostrom, E. Governing the Commons: The Evolution of Institutions for Collective Action (Cambridge University Press, 1990)WWF et al. The State Of Indigenous Peoples’ and Local Communities’ Lands and Territories https://wwflac.awsassets.panda.org/downloads/report_the_state_of_the_indigenous_peoples_and_local_communities_lands_and_territories_1.pdf (2021) .Chaplin-Kramer, R. et al. Conservation needs to integrate knowledge across scales. Nat. Ecol. Evol. 6, 118–119 (2022).Article 

Google Scholar 
IUCN. Spatial Data Download: Terrestrial Mammals. IUCN Red List https://www.iucnredlist.org/resources/spatial-data-download (2021).Abay, K. A., Chamberlin, J. & Berhane, G. Are land rental markets responding to rising population pressures and land scarcity in sub-Saharan Africa? Land Use Policy 101, 105139 (2021).Article 

Google Scholar  More

Reaka-Kudla, M. L. The global biodiversity of coral reefs: a comparison with rain forests. Biodivers. II. Underst. Prot. Our Biol. Resour. 2, 551 (1997).
Google Scholar 
Connell, J. H. Population ecology of reef-building corals. in Biology and Geology of Coral Reefs (eds. Jones, O. A. & Endean, R.) 205–245 (Academic Press, 1973). doi:https://doi.org/10.1016/B978-0-12-395526-5.50015-8.Berumen, M. L. et al. The status of coral reef ecology research in the Red Sea. Coral Reefs 32, 737–748 (2013).ADS 
Article 

Google Scholar 
Hughes, T. P., Graham, N. A., Jackson, J. B., Mumby, P. J. & Steneck, R. S. Rising to the challenge of sustaining coral reef resilience. Trends Ecol. Evol. 25, 633–642 (2010).Article 
PubMed 

Google Scholar 
Edmunds, P. J. & Riegl, B. Urgent need for coral demography in a world where corals are disappearing. Mar. Ecol. Prog. Ser. 635, 233–242 (2020).ADS 
Article 

Google Scholar 
Pisapia, C. et al. Projected shifts in coral size structure in the Anthropocene. Adv Mar Biol 87, 31–60 (2020).Article 
PubMed 

Google Scholar 
Meesters, E. et al. Colony size-frequency distributions of scleractinian coral populations: spatial and interspecific variation. Mar. Ecol. Prog. Ser. 209, 43–54 (2001).ADS 
Article 

Google Scholar 
Riegl, B. et al. Demographic mechanisms of reef coral species winnowing from communities under increased environmental stress. Front. Mar. Sci. 4, 344 (2017).Article 

Google Scholar 
Pisapia, C., Burn, D. & Pratchett, M. Changes in the population and community structure of corals during recent disturbances (February 2016-October 2017) on Maldivian coral reefs. Sci. Rep. 9, 1–12 (2019).CAS 
Article 

Google Scholar 
Dietzel, A., Bode, M., Connolly, S. R. & Hughes, T. P. Long-term shifts in the colony size structure of coral populations along the Great Barrier Reef. Proc. R. Soc. B 287, 20201432 (2020).PubMed Central 
Article 
PubMed 

Google Scholar 
Lachs, L. et al. Linking population size structure, heat stress and bleaching responses in a subtropical endemic coral. Coral Reefs 40, 777–790 (2021).Article 

Google Scholar 
McClanahan, T., Ateweberhan, M. & Omukoto, J. Long-term changes in coral colony size distributions on Kenyan reefs under different management regimes and across the 1998 bleaching event. Mar. Biol. 153, 755–768 (2008).Article 

Google Scholar 
Grimsditch, G. et al. Variation in size frequency distribution of coral populations under different fishing pressures in two contrasting locations in the Indian Ocean. Mar. Environ. Res. 131, 146–155 (2017).CAS 
Article 
PubMed 

Google Scholar 
Bak, R. P. & Meesters, E. H. Coral population structure: the hidden information of colony size-frequency distributions. Mar. Ecol. Prog. Ser. 162, 301–306 (1998).ADS 
Article 

Google Scholar 
Hughes, T. & Jackson, J. Do corals lie about their age? Some demographic consequences of partial mortality, fission, and fusion. Science 209, 713–715 (1980).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hughes, T. P. & Jackson, J. Population dynamics and life histories of foliaceous corals. Ecol. Monogr. 55, 141–166 (1985).Article 

Google Scholar 
Soong, K. Colony size as a species character in massive reef corals. Coral Reefs 12, 77–83 (1993).ADS 
Article 

Google Scholar 
Bak, R. P. & Meesters, E. H. Population structure as a response of coral communities to global change. Am. Zool. 39, 56–65 (1999).Article 

Google Scholar 
Adjeroud, M., Pratchett, M. S., Kospartov, M. C., Lejeusne, C. & Penin, L. Small-scale variability in the size structure of scleractinian corals around Moorea, French Polynesia: patterns across depths and locations. Hydrobiologia 589, 117–126 (2007).Article 

Google Scholar 
Adjeroud, M., Mauguit, Q. & Penin, L. The size-structure of corals with contrasting life-histories: A multi-scale analysis across environmental conditions. Mar. Environ. Res. 112, 131–139 (2015).CAS 
Article 
PubMed 

Google Scholar 
Bauman, A. G. et al. Variation in the size structure of corals is related to environmental extremes in the Persian Gulf. Mar. Environ. Res. 84, 43–50 (2013).CAS 
Article 
PubMed 

Google Scholar 
Smith, L., Devlin, M., Haynes, D. & Gilmour, J. A demographic approach to monitoring the health of coral reefs. Mar. Pollut. Bull. 51, 399–407 (2005).CAS 
Article 
PubMed 

Google Scholar 
Lowe, R. J. & Falter, J. L. Oceanic forcing of coral reefs. Annu. Rev. Mar. Sci. 7, 43–66 (2015).ADS 
Article 

Google Scholar 
Thornborough, K., Davies, P. Reef flats. Encycl. Mod. Coral Reefs 869–876 (2011).Camp, E. F. et al. The future of coral reefs subject to rapid climate change: lessons from natural extreme environments. Front. Mar. Sci. 5, 4 (2018).Article 

Google Scholar 
Bellwood, D. R. et al. The role of the reef flat in coral reef trophodynamics: Past, present, and future. Ecol. Evol. 8, 4108–4119 (2018).PubMed Central 
Article 
PubMed 

Google Scholar 
Pineda, J. et al. Two spatial scales in a bleaching event: Corals from the mildest and the most extreme thermal environments escape mortality. Limnol. Oceanogr. https://doi.org/10.4319/lo.2013.58.5.1531 (2013).Article 

Google Scholar 
Riegl, B. M., Bruckner, A. W., Rowlands, G. P., Purkis, S. J. & Renaud, P. Red Sea coral reef trajectories over 2 decades suggest increasing community homogenization and decline in coral size. PLoS ONE 7, e38396 (2012).ADS 
CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
Riegl, B., Berumen, M. & Bruckner, A. Coral population trajectories, increased disturbance and management intervention: A sensitivity analysis. Ecol. Evol. https://doi.org/10.1002/ece3.519 (2013).Article 
PubMed Central 
PubMed 

Google Scholar 
Loya, Y. The red sea coral Stylophora pistillata is an r strategist. Nature https://doi.org/10.1038/259478a0 (1976).Article 
PubMed 

Google Scholar 
Lozano-Cortés, D. F. & Berumen, M. L. Colony size-frequency distribution of pocilloporid juvenile corals along a natural environmental gradient in the Red Sea. Mar. Pollut. Bull. 105, 546–552 (2016).Article 
CAS 
PubMed 

Google Scholar 
Ellis, J. et al. Cross shelf benthic biodiversity patterns in the Southern Red Sea. Sci. Rep. 7, 1–14 (2017).Article 
CAS 

Google Scholar 
Furby, K. A., Bouwmeester, J. & Berumen, M. L. Susceptibility of central Red Sea corals during a major bleaching event. Coral Reefs 32, 505–513 (2013).ADS 
Article 

Google Scholar 
Monroe, A. A. et al. In situ observations of coral bleaching in the central Saudi Arabian Red Sea during the 2015/2016 global coral bleaching event. PLoS ONE 13, e0195814 (2018).PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
Davis, K. et al. Observations of the thermal environment on Red Sea platform reefs: A heat budget analysis. Coral Reefs 30, 25–36 (2011).ADS 
Article 

Google Scholar 
Liu, G. et al. Reef-scale thermal stress monitoring of coral ecosystems: new 5-km global products from NOAA Coral Reef Watch. Remote Sens. 6, 11579–11606 (2014).ADS 
Article 

Google Scholar 
Voolstra, C. R. et al. Standardized short-term acute heat stress assays resolve historical differences in coral thermotolerance across microhabitat reef sites. Glob. Change Biol. https://doi.org/10.1111/gcb.15148 (2020).Article 

Google Scholar 
Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004).
Google Scholar 
Morais, J., Morais, R. A., Tebbett, S. B., Pratchett, M. S. & Bellwood, D. R. Dangerous demographics in post-bleach corals reveal boom-bust versus protracted declines. Sci. Rep. 11, 1–7 (2021).Article 
CAS 

Google Scholar 
Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: Comparative studies of reef-building corals. Ecology https://doi.org/10.2307/2265514 (1996).Article 

Google Scholar 
Rinkevich, B. & Loya, Y. Reproduction of the Red Sea coral Stylophora pistillata. 2. Synchronization in breeding and seasonality of planulae shedding. Mar. Ecol. Prog. Ser. 1, 145–152 (1979).ADS 
Article 

Google Scholar 
Komsta, L. & Novomestky, F. Moments, cumulants, skewness, kurtosis and related tests. R Package Version 14, (2015).Anderson, M., Gorley, R. & Clarke, K. PERMANOVA+ for PRIMER: guide to software and statistical methods. Primer-E Plymouth UK (2008).Meziere, Z. et al. Stylophora under stress: A review of research trends and impacts of stressors on a model coral species. Sci. Total Environ. 816, 151639 (2022).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Rinkevich, B. & Loya, Y. Reproduction of the Red Sea coral Stylophora pistillata 1. Gonads and planulae. Mar. Ecol. Prog. Ser. 1, 133–144 (1979).ADS 
Article 

Google Scholar 
Nishikawa, A., Katoh, M. & Sakai, K. Larval settlement rates and gene flow of broadcast-spawning (Acropora tenuis) and planula-brooding (Stylophora pistillata) corals. Mar. Ecol. Progress Ser. https://doi.org/10.3354/meps256087 (2003).Article 

Google Scholar 
Monroe, A. Genetic differentiation across multiple spatial scales of the Red Sea of the corals Stylophora pistillata and Pocillopora verrucosa. M.S. thesis, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia (2015).Gouezo, M. et al. Relative roles of biological and physical processes influencing coral recruitment during the lag phase of reef community recovery. Sci. Rep. 10, 1–12 (2020).ADS 
Article 
CAS 

Google Scholar 
Boco, S. R., Cabansag, J. B. P., Jamodiong, E. A. & Ticzon, V. S. Size-frequency distributions of scleractinian coral (Porites spp.) colonies inside and outside a marine reserve in Leyte Gulf, central Philippines. Reg. Stud. Mar. Sci. 35, 101147 (2020).
Google Scholar 
River, G. F. & Edmunds, P. J. Mechanisms of interaction between macroalgae and scleractinians on a coral reef in Jamaica. J. Exp. Mar. Biol. Ecol. 261, 159–172 (2001).Article 
PubMed 

Google Scholar 
Kuffner, I. B. et al. Inhibition of coral recruitment by macroalgae and cyanobacteria. Mar. Ecol. Prog. Ser. 323, 107–117 (2006).ADS 
Article 

Google Scholar 
Hughes, T. & Jackson, J. Do corals lie about their age? Some demographic consequences of partial mortality, fission, and fusion. Science 209, 713–715 (1980).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Lewis, J. B. Abundance, distribution and partial mortality of the massive coral Siderastrea siderea on degrading coral reefs at Barbados West Indies. Mar. Pollut. Bull. 34, 622–627 (1997).CAS 
Article 

Google Scholar 
Meesters, E. H., Wesseling, I. & Bak, R. P. Coral colony tissue damage in six species of reef-building corals: partial mortality in relation with depth and surface area. J. Sea Res. 37, 131–144 (1997).ADS 
Article 

Google Scholar 
Meesters, E. H., Wesseling, I. & Bak, R. P. Partial mortality in three species of reef-building corals and the relation with colony morphology. Bull. Mar. Sci. 58, 838–852 (1996).
Google Scholar 
Graham, J. & Van Woesik, R. The effects of partial mortality on the fecundity of three common Caribbean corals. Mar. Biol. 160, 2561–2565 (2013).Article 

Google Scholar 
Rinkevich, B. & Loya, Y. Intraspecific competitive networks in the Red Sea coral Stylophora pistillata. Coral Reefs https://doi.org/10.1007/BF00571193 (1983).Article 

Google Scholar 
Takabayashi, M. & Hoegh-Guldberg, O. Ecological and physiological differences between two colour morphs of the coral Pocillopora damicornis. Mar. Biol. 123, 705–714 (1995).Article 

Google Scholar 
Innis, T., Cunning, R., Ritson-Williams, R., Wall, C. & Gates, R. Coral color and depth drive symbiosis ecology of Montipora capitata in Kāne ‘ohe Bay, O ‘ahu, Hawai ‘i. Coral Reefs 37, 423–430 (2018).ADS 
Article 

Google Scholar 
Gochfeld, D., Ansley, M., Ankisetty, S. & Aeby, G. Antibacterial chemical resistance to disease in the Hawaiian coral Montipora capitata. Planta Med. 80, CL31 (2014).Article 

Google Scholar 
Shore-Maggio, A., Callahan, S. M. & Aeby, G. S. Trade-offs in disease and bleaching susceptibility among two color morphs of the Hawaiian reef coral Montipora capitata. Coral Reefs 37, 507–517 (2018).ADS 
Article 

Google Scholar 
Dove, S. G., Takabayashi, M. & Hoegh-Guldberg, O. Isolation and partial characterization of the pink and blue pigments of pocilloporid and acroporid corals. Biol. Bull. 189, 288–297 (1995).CAS 
Article 
PubMed 

Google Scholar 
Hume, B. C. C., Mejia-Restrepo, A., Voolstra, C. R. & Berumen, M. L. Fine-scale delineation of Symbiodiniaceae genotypes on a previously bleached central Red Sea reef system demonstrates a prevalence of coral host-specific associations. Coral Reefs https://doi.org/10.1007/s00338-020-01917-7 (2020).Article 

Google Scholar  More

Clapham, P. J. Encyclopedia of Marine Mammals 489–492 (Elsevier, 2018).Book 

Google Scholar 
Calambokidis, J. et al. Movements and population structure of humpback whales in the North Pacific. Mar. Mamm. Sci. 17, 769–794. https://doi.org/10.1111/j.1748-7692.2001.tb01298.x (2001).Article 

Google Scholar 
Rosenbaum, H. C. et al. First circumglobal assessment of Southern Hemisphere humpback whale mitochondrial genetic variation and implications for management. Endangered Species Res. 32, 551–567. https://doi.org/10.3354/esr00822 (2017).Article 

Google Scholar 
Darling, J. D. & Sousa-Lima, R. S. Songs indicate interaction between humpback whale (Megaptera novaeangliae) populations in the western and eastern South Atlantic Ocean. Mar. Mamm. Sci. 21, 557–566 (2005).Article 

Google Scholar 
Marcondes, M. C. C. et al. The Southern Ocean Exchange: Porous boundaries between humpback whale breeding populations in southern polar waters. Sci. Rep. 11, 23618. https://doi.org/10.1038/s41598-021-02612-5 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Witteveen, B. H., Foy, R. J., Wynne, K. M. & Tremblay, Y. Investigation of foraging habits and prey selection by humpback whales (Megaptera novaeangliae) using acoustic tags and concurrent fish surveys. Mar. Mamm. Sci. 24, 516–534. https://doi.org/10.1111/j.1748-7692.2008.00193.x (2008).Article 

Google Scholar 
Barendse, J. et al. Migration redefined? Seasonality, movements and group composition of humpback whales Megaptera novaeangliae off the west coast of South Africa. Afr. J. Mar. Sci. 32, 1–22 (2010).Article 

Google Scholar 
Findlay, K. P. et al. Humpback whale “super-groups” – A novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLoS One 12, e0172002. https://doi.org/10.1371/journal.pone.0172002 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Barendse, J. et al. Transit station or destination? Attendance patterns, movements and abundance estimate of humpback whales off west South Africa from photographic and genotypic matching. Afr. J. Mar. Sci. 33, 353–373 (2011).Article 

Google Scholar 
Schall, E. et al. Multi-year presence of humpback whales in the Atlantic sector of the Southern Ocean but not during El Niño. Commun. Biol. 4, 1–7. https://doi.org/10.1038/s42003-021-02332-6 (2021).Article 

Google Scholar 
Amaral, A. R. et al. Population genetic structure among feeding aggregations of humpback whales in the Southern Ocean. Mar. Biol. 163, 1–13. https://doi.org/10.1007/s00227-016-2904-0 (2016).Article 

Google Scholar 
Schall, E. et al. Humpback whale song recordings suggest common feeding ground occupation by multiple populations. Sci. Rep. 11, 1–13. https://doi.org/10.1038/s41598-021-98295-z (2021).ADS 
CAS 
Article 

Google Scholar 
International Whaling Commission. Annex H: Report of the Sub-Committee on Other Southern Hemisphere Whale Stocks. (2016).Payne, R. & Guinee, L. N. Humpback whale (Megaptera novaeangliae) songs as an indicator of “stocks”. Commun. Behav. Whales 20, 333–358 (1983).
Google Scholar 
Riekkola, L. et al. Application of a multi-disciplinary approach to reveal population structure and Southern Ocean feeding grounds of humpback whales. Ecol. Indic. 89, 455–465. https://doi.org/10.1016/j.ecolind.2018.02.030 (2018).Article 

Google Scholar 
Herman, L. M. The multiple functions of male song within the humpback whale (Megaptera novaeangliae) mating system: Review, evaluation, and synthesis. Biol. Rev. 92, 1795–1818. https://doi.org/10.1111/brv.12309 (2017).Article 
PubMed 

Google Scholar 
Garland, E. C. et al. Humpback Whale song on the Southern Ocean feeding grounds: Implications for cultural transmission. PLoS One 8, e79422. https://doi.org/10.1371/journal.pone.0079422 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McSweeney, D., Chu, K., Dolphin, W. & Guinee, L. North Pacific humpback whale songs: A comparison of southeast Alaskan feeding ground songs with Hawaiian wintering ground songs. Mar. Mamm. Sci. 5, 139–148. https://doi.org/10.1111/j.1748-7692.1989.tb00328.x (1989).Article 

Google Scholar 
Van Opzeeland, I. C. et al. Towards collective circum-antarctic passive acoustic monitoring: The southern ocean hydrophone network (SOHN). Polarforschung 83, 47–61 (2013).
Google Scholar 
Gridley, T., Silva, M., Wilkinson, C., Seakamela, S. & Elwen, S. H. Song recorded near a super-group of humpback whales on a mid-latitude feeding ground off South Africa. J. Acoust. Soc. Am. 143, 298–304 (2018).ADS 
Article 

Google Scholar 
Ross-Marsh, E., Elwen, S. H., Prinsloo, A., James, B. & Gridley, T. Singing in South Africa: Monitoring the occurrence of humpback whale (Megaptera novaeangliae) song near the Western Cape. Bioacoustics 30, 163–179 (2021).Article 

Google Scholar 
Garland, E. C. et al. Population structure of humpback whales in the western and central South Pacific Ocean as determined by vocal exchange among populations. Conserv. Biol. 29, 1198–1207. https://doi.org/10.1111/cobi.12492 (2015).Article 
PubMed 

Google Scholar 
Bombosch, A. et al. Predictive habitat modelling of humpback (Megaptera novaeangliae) and Antarctic minke (Balaenoptera bonaerensis) whales in the Southern Ocean as a planning tool for seismic surveys. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 91, 101–114 (2014).Article 

Google Scholar 
El-Gabbas, A., Van Opzeeland, I., Burkhardt, E. & Boebel, O. Static species distribution models in the marine realm: The case of baleen whales in the Southern Ocean. Divers. Distrib. 27, 1536–1552. https://doi.org/10.1111/ddi.13300 (2021).Article 

Google Scholar 
Schall, E. et al. Large-scale spatial variabilities in the humpback whale acoustic presence in the Atlantic sector of the Southern Ocean. R. Soc. Open Sci. 7, 201347. https://doi.org/10.1098/rsos.201347 (2020).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
International Whaling Commission. Report of the scientific committee. Annex G. Report of the sub-committee on comprehensive assessment of southern hemisphere humpback whales. Appenix4. Initial alternative hypotheses for the distribution of humpack breeding stocks on the feeding grounds. Report of the International Whlaing Commission 48, 181 (1998).International Whaling Commission. Report on the workshop on the comprehensive assessment of Southern Hemisphere humpback whales. J. Cetacean Res. Manage. Spec. Issue 3, 1–50 (2011).
Google Scholar 
Winn, H. E. & Winn, L. K. Song of Humpback Whale Megaptera-Novaeangliae in West-Indies. Mar. Biol. 47, 97–114. https://doi.org/10.1007/Bf00395631 (1978).Article 

Google Scholar 
Payne, K. & Payne, R. Large-scale changes over 19 years in songs of Humpback Whales in Bermuda. Z. Tierpsychol. 68, 89–114 (1985).Article 

Google Scholar 
Thomisch, K. et al. Temporal patterns in the acoustic presence of baleen whale species in a presumed breeding area off Namibia. Mar. Ecol. Prog. Ser. 620, 201–214 (2019).ADS 
Article 

Google Scholar 
Buchan, S. J., Stafford, K. M. & Hucke-Gaete, R. Seasonal occurrence of southeast Pacific blue whale songs in southern Chile and the eastern tropical Pacific. Mar. Mamm. Sci. 31, 440–458. https://doi.org/10.1111/mms.12173 (2015).Article 

Google Scholar 
Ross-Marsh, E., Elwen, S., Prinsloo, A., James, B. & Gridley, T. Singing in South Africa: Monitoring the occurrence of humpback whale (Megaptera novaeangliae) song near the Western Cape. Bioacoustics https://doi.org/10.1080/09524622.2019.1710254 (2020).Article 

Google Scholar 
Cholewiak, D. M., Sousa-Lima, R. S. & Cerchio, S. Humpback whale song hierarchical structure: Historical context and discussion of current classification issues. Mar. Mamm. Sci. 29, E312–E332. https://doi.org/10.1111/mms.12005 (2013).Article 

Google Scholar 
M_Map: A Mapping Package for MATLAB v. 1.4m. (2020).Raven Pro: Interactive sound analysis software. Version 1.6 ([Ithaca (NY)]: The Cornell Lab of Ornithology. Accessed 1 Mar 2018 (2022).Schall, E., Roca, I. & Van Opzeeland, I. Acoustic metrics to assess humpback whale song unit structure from the Atlantic sector of the Southern ocean. J. Acoust. Soc. Am. 149, 4649–4658. https://doi.org/10.1121/10.0005315 (2021).ADS 
Article 
PubMed 

Google Scholar 
Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297–302. https://doi.org/10.2307/1932409 (1945).Article 

Google Scholar 
R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2018).Suzuki, R., Terada, Y. & Shimodaira, H. pvclust: Hierarchical clustering with P-values via multiscale bootstrap resampling. R package version 2.2-0 (2019).Kohonen, T. Median strings. Pattern Recogn. Lett. 3, 309–313. https://doi.org/10.1016/0167-8655(85)90061-3 (1985).ADS 
Article 

Google Scholar 
Garland, E. C. et al. Improved versions of the Levenshtein distance method for comparing sequence information in animals’ vocalisations: Tests using humpback whale song. Behaviour 149, 1413–1441. https://doi.org/10.1163/1568539x-00003032 (2012).Article 

Google Scholar 
Van der Loo, M. P. The stringdist package for approximate string matching. R J. 6, 111–122 (2014).Article 

Google Scholar 
Zerbini, A. et al. Migration and summer destinations of humpback whales (Megaptera novaeangliae) in the western South Atlantic Ocean. J. Cetacean Res. Manage. Spec. Issue 3, 113–118. https://doi.org/10.3354/meps313295 (2011).Article 

Google Scholar 
Rosenbaum, H. C., Maxwell, S. M., Kershaw, F. & Mate, B. Long-range movement of Humpback Whales and their overlap with anthropogenic activity in the South Atlantic Ocean. Conserv. Biol. 28, 604–615. https://doi.org/10.1111/cobi.12225 (2014).Article 
PubMed 

Google Scholar 
Reisinger, R. R. et al. Combining regional habitat selection models for large-scale prediction: Circumpolar habitat selection of Southern Ocean humpback whales. Remote Sens. 13, 2074. https://doi.org/10.3390/rs13112074 (2021).ADS 
Article 

Google Scholar 
Garland, E. C. & McGregor, P. K. Cultural transmission, evolution, and revolution in vocal displays: Insights from bird and whale song. Front. Psychol. 11, 2387. https://doi.org/10.3389/fpsyg.2020.544929 (2020).Article 

Google Scholar 
Findlay, K. P. et al. Humpback whale “super-groups”—a novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLoS One https://doi.org/10.1371/journal.pone.0172002 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Owen, K. et al. Effect of prey type on the fine-scale feeding behaviour of migrating east Australian humpback whales. Mar. Ecol. Prog. Ser. 541, 231–244. https://doi.org/10.3354/meps11551 (2015).ADS 
Article 

Google Scholar 
Riekkola, L., Andrews-Goff, V., Friedlaender, A., Zerbini, A. N. & Constantine, R. Longer migration not necessarily the costliest strategy for migrating humpback whales. Aquat. Conserv. Mar. Freshwat. Ecosyst. 30, 937–948. https://doi.org/10.1002/aqc.3295 (2020).Article 

Google Scholar 
Torres, L. G. A sense of scale: Foraging cetaceans’ use of scale-dependent multimodal sensory systems. Mar. Mamm. Sci. 33, 1170–1193. https://doi.org/10.1111/mms.12426 (2017).Article 

Google Scholar 
Horton, T. W. et al. Straight as an arrow: Humpback whales swim constant course tracks during long-distance migration. Biol. Lett. 7, 674–679. https://doi.org/10.1098/rsbl.2011.0279 (2001).Article 

Google Scholar 
Au, W. W. L. et al. Acoustic properties of humpback whale songs. J. Acoust. Soc. Am. 120, 1103–1110. https://doi.org/10.1121/1.2211547 (2006).ADS 
Article 
PubMed 

Google Scholar 
Dunlop, R. A., Cato, D. H., Noad, M. J. & Stokes, D. M. Source levels of social sounds in migrating humpback whales (Megaptera novaeangliae). J. Acoust. Soc. Am. 134, 706–714. https://doi.org/10.1121/1.4807828 (2013).ADS 
Article 
PubMed 

Google Scholar 
Cheeseman, T. et al. Advanced image recognition: A fully automated, high-accuracy photo-identification matching system for humpback whales. Mamm. Biol. https://doi.org/10.1007/s42991-021-00180-9 (2021).Article 

Google Scholar 
Felix, F. et al. A new case of interoceanic movement of a humpback whale in the Southern Hemisphere: The El Nino Link. Aquat. Mamm. 46, 578–584. https://doi.org/10.1578/AM.46.6.2020.578 (2020).Article 

Google Scholar 
Pomilla, C. & Rosenbaum, H. C. Against the current: An inter-oceanic whale migration event. Biol. Lett. 1, 476–479 (2005).Article 

Google Scholar 
Stevick, P. T. et al. A quarter of a world away: Female humpback whale moves 10,000 km between breeding areas. Biol. Lett. 7, 299–302. https://doi.org/10.1098/rsbl.2010.0717 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Nicol, S. Krill, currents, and sea ice: Euphausia superba and its changing environment. Bioscience 56, 111–120. https://doi.org/10.1641/0006-3568(2006)056[0111:Kcasie]2.0.Co;2 (2006).Article 

Google Scholar 
Atkinson, A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103. https://doi.org/10.1038/nature02996 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147. https://doi.org/10.1038/s41558-018-0370-z (2019).ADS 
Article 

Google Scholar 
Loeb, V. J. & Santora, J. A. Climate variability and spatiotemporal dynamics of five Southern Ocean krill species. Prog. Oceanogr. 134, 93–122 (2015).ADS 
Article 

Google Scholar 
Marrari, M., Daly, K. L. & Hu, C. Spatial and temporal variability of SeaWiFS chlorophyll a distributions west of the Antarctic Peninsula: Implications for krill production. Deep Sea Res. Part II 55, 377–392. https://doi.org/10.1016/j.dsr2.2007.11.011 (2008).ADS 
Article 

Google Scholar 
Sremba, A. L., Hancock-Hanser, B., Branch, T. A., LeDuc, R. L. & Baker, C. S. Circumpolar diversity and geographic differentiation of mtDNA in the critically endangered Antarctic Blue Whale (Balaenoptera musculus intermedia). PLoS One 7, e32579. https://doi.org/10.1371/journal.pone.0032579 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bortolotto, G. A., Danilewicz, D., Andriolo, A., Secchi, E. R. & Zerbini, A. N. Whale, whale, everywhere: Increasing abundance of Western South Atlantic Humpback Whales (Megaptera novaeangliae) in their wintering grounds. PLoS One 11, e0164596. https://doi.org/10.1371/journal.pone.0164596 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Félix, F., Castro, C. & Laake, J. L. Abundance and survival estimates of the southeastern Pacific humpback whale stock from 1991–2006 photo-identification surveys in Ecuador. J. Cetacean Res. Manage. https://doi.org/10.47536/jcrm.vi.303 (2020).Article 

Google Scholar 
Ward, E., Zerbini, A. N., Kinas, P. G., Engel, M. H. & Andriolo, A. Estimates of population growth rates of humpback whales (Megaptera novaeangliae) in the wintering grounds off the coast of Brazil (Breeding Stock A). J. Cetacean Res. Manage. https://doi.org/10.47536/jcrm.vi3.323 (2020).Article 

Google Scholar 
Seyboth, E. et al. Influence of krill (Euphausia superba) availability on humpback whale (Megaptera novaeangliae) reproductive rate. Mar. Mammal Sci. https://doi.org/10.1111/mms.12805 (2021).Article 

Google Scholar 
Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111–116 (2014).ADS 
Article 

Google Scholar 
Santora, J. A., Reiss, C. S., Loeb, V. J. & Veit, R. R. Spatial association between hotspots of baleen whales and demographic patterns of Antarctic krill Eupahusia superba suggests size-dependent predation. Mar. Ecol. Prog. Ser. 405, 255–269 (2010).ADS 
Article 

Google Scholar 
Friedlaender, A. S., Lawson, G. L. & Halpin, P. N. Evidence of resource partitioning between humpback and minke whales around the western Antarctic Peninsula. Mar. Mamm. Sci. 25, 402–415 (2009).Article 

Google Scholar 
Reid, K., Brierley, A. S. & Nevitt, G. A. An initial examination of relationships between the distribution of whales and antarctic krill Euphausia superba at South Georgia. J. Cetacean Res. Manage. 2, 143–149 (2000).
Google Scholar 
Nicol, S. et al. Southern Ocean iron fertilization by baleen whales and Antarctic krill. Fish Fish. 11, 203–209. https://doi.org/10.1111/j.1467-2979.2010.00356.x (2010).Article 

Google Scholar 
Teschke, K., Pehlke, H., Deininger, M., Jerosch, K. & Brey, T. Scientific background document in support of the development of a CCAMLR MPA in the Weddell Sea (Antarctica)-Version 2016. (2016).Teschke, K. et al. Planning marine protected areas under the CCAMLR regime—the case of the Weddell Sea (Antarctica). Mar. Policy 124, 104370 (2021).Article 

Google Scholar  More

Tracing the symptomology of predation through macroscopic plaquesA culture bioassay (Expanded Microcoleus Mortality Assay, or EMMA) (Fig. 1 and see Materials and Methods) based on the capacity of a soil to induce complete mortality in the foundational biocrust cyanobacterium Microcoleus vaginatus helped us trace the pathogen detected in biocrust production facilities to the development of cm-sized plaques, or zones of cyanobacterial clearing, in natural biocrusts. These plaques were revealed to the naked eye (Fig. 2) when the soil was wet (i.e., after a rain event), as impacted areas would fail to green up by the migration of cyanobacteria to the surface21, enabling us to detect and quantify them with relative ease. Soil samples obtained from such plaques (n = 30) from different sites (n = 6; Table S1) in the US Southwest were invariably EMMA + , and the pathogens always filterable with pore sizes 0.45–1 µm but not larger, and always insensitive to the eukaryotic inhibitor cycloheximide, indicating the agent’s prokaryotic nature and small size, while paired samples from asymptomatic areas just outside the plaques were always EMMA- (Table S2). These end-point EMMA solutions never gave rise to cyanobacterial re-growth upon further incubation and maintained its infectivity of fresh cyanobacterial cultures for up to 6 months. A one-time, small-scale sampling across a plaque at intervals of 2 mm using microcoring22 showed that the boundary of the visible plaque demarcated exactly the end of infectivity, samples 0–2 mm outside the plaque proving non-infective. Further, inoculation of healthy, natural biocrusts with EMMA + suspensions resulted in the local development of biocrust plaques, and soil from these plaques was itself EMMA + , in partial fulfillment of Koch’s postulates. Yet, standard microbiological plating failed to yield any isolates that were EMMA + (we tested 30 unique isolates), even though standard plating with similar isolation efforts can successfully cultivate a large portion of heterotrophs from biocrusts23.Fig. 1: EMMA bioassay (Expanded Microcoleus Mortality Assay), used to study biocrust pathogens.a Typical visual progression of a positive EMMA inoculated with soil or culture to be tested, as used to test for pathogenicity to Microcoleus vaginatus PPC 9802 in the field and in enrichments. b Typical degradation of cyanobacterial biomass during an EMMA displayed through electron microscopy: healthy Microcoleus vaginatus PPC 9802 filaments (top) display abundant photosynthetic membranes (white arrows), peptidoglycan cross-walls (yellow arrows) and carboxysomes (green arrow). As infection proceeds (downwards), patent degradation of intracellular structures follows, leaving only cellular ghosts in the form of peptidoglycan wall remnants (yellow arrows), including the characteristically enlarged peptidoglycan “bumper” of terminal cells (red arrow). Intracellular bacilloid bacteria can sometimes be observed (blue arrow). Cyanobacterial cultures lose all viability. Scale bars = 1 µm. n = 250 images from 4 independent experiments. c Assay modification used in flow cytometry/cell sorting, showing enrichments positive for predation in the top two rows and those negative for predation below. d Test and controls in EMMA to ensure prokaryotic nature of the disease agent.Full size imageFig. 2: Symptomology in nature: biocrust plaques.Main: Macroscopic view of a soil surface colonized by cyanobacterial biocrusts and impacted by multiple plaques as taken after a rain in a quadrat used for field surveys. Insert: Close-up of a single plaque, showing well-demarcated boundaries and a typical central area of new cyanobacterial colonization.Full size imageCultivation, identification, and salient genomic traits of the cyanobacterial pathogenTo study these organisms, we turned to enrichment of pathogen/prey co-cultures based on repeated passages through EMMA and differential size filtration combined with dilution-to-extinction approaches, followed by purification with flow cytometry/cell sorting. The process was monitored by 16S rRNA gene amplicon sequencing, and eventually yielded a highly enriched co-culture of the cyanobacterium with a genetically homogenous (one single Amplicon Sequence Variant) population that made up more than 80% of reads (Fig. 3 a, b). We name the organism represented by this ASV Candidatus Cyanoraptor togatus. That it corresponds indeed to the predator is supported by the fact that of the 17 ASV’s detected in the final enrichment, only 10 were consistently detected at all infectious stages in the process and, among these, only our candidate ASV steadily increased in relative abundance through the enrichment process (Fig. 3 a, b). This final enrichment of C. togatus, LGM-1, constitutes the basis for downstream biological and molecular analyses. Its ASV was most similar to little-known members of the family Chitinophagaceae in the phylum Bacteroidetes. LGM-1’s genome was sequenced and assembled into a single 3.3 Mb contig with 1,781 putative and 1,328 hypothetical genes (Table S3), though most proteins had low identity (Fig. 4: Compiled paired ratios of functional parameters and compositional (relative) abundance in biocrusts across plaque boundaries (circles), red bars denoting the medians for each group of ratios, and bar background color denoting the p-values that the median is significantly different from unity (Wilcoxon paired ratio two-sided tests), where gray is non-significant (p  >  0.1), light orange is 0.05   > p   p  More

Darwin, C. On the Origin of Species. Facsimile of the First Edition (Harvard University Press, 1859).
Google Scholar 
Grafen, A. The phylogenetic regression. Philos. Trans. R. Soc. Lond. B Biol. Sci. 326, 119–157 (1989).ADS 
CAS 
PubMed 

Google Scholar 
Sillero, N., Reis, M., Vieira, C. P., Vieira, J. & Morales-Hojas, R. Niche evolution and thermal adaptation in the temperate species Drosophila americana. J. Evol. Biol. 27, 1549–1561 (2014).CAS 
PubMed 

Google Scholar 
Ramos, R. et al. Global spatial ecology of three closely-related gadfly petrels. Sci. Rep. 6, 23447 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kumar, B., Cheng, J., Ge, D., Xia, L. & Yang, Q. Phylogeography and ecological niche modeling unravel the evolutionary history of the Yarkand hare, Lepus yarkandensis (Mammalia: Leporidae), through the Quaternary. BMC Evol. Biol. 19, 113 (2019).PubMed 
PubMed Central 

Google Scholar 
Wiens, J. J. & Graham, C. H. Niche conservatism: Integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. 36, 519–539 (2005).
Google Scholar 
Losos, J. B. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11, 995–1003 (2008).PubMed 

Google Scholar 
Crisp, M. D. & Cook, L. G. Phylogenetic niche conservatism: What are the underlying evolutionary and ecological causes?. New Phytol. 196, 681–694 (2012).PubMed 

Google Scholar 
Qian, H. & Ricklefs, R. E. Geographical distribution and ecological conservatism of disjunct genera of vascular plants in eastern Asia and eastern North America. J. Ecol. 92, 253–265 (2004).
Google Scholar 
Vitt, L. J., Zani, P. A. & Espósito, M. C. Historical ecology of Amazonian lizards: Implications for community ecology. Oikos 87, 286–294 (1999).
Google Scholar 
Rice, N. H., Martínez-Meyer, E. & Peterson, A. T. Ecological niche differentiation in the Aphelocoma jays: A phylogenetic perspective. Biol. J. Linn. Soc. 80, 369–383 (2003).
Google Scholar 
Jost, L. Explosive local radiation of the genus Teagueia (Orchidaceae) in the Upper Pastaza Watershed of Ecuador. Lyonia 7, 42–47 (2004).
Google Scholar 
Antonelli, A., Verola, C. F., Parisod, C. & Gustafsson, A. L. S. Climate cooling promoted the expansion and radiation of a threatened group of South American orchids (Epidendroideae: Laeliinae). Biol. J. Linn. Soc. 100, 597–607 (2010).
Google Scholar 
Johnson, S. D., Linder, H. P. & Steiner, K. E. Phylogeny and radiation of pollination systems in Disa (Orchidaceae). Am. J. Bot. 85, 402–411 (1998).CAS 
PubMed 

Google Scholar 
Kolanowska, M., Grochocka, E. & Konowalik, K. Phylogenetic climatic niche conservatism and evolution of climatic suitability in Neotropical Angraecinae (Vandeae, Orchidaceae) and their closest African relatives. PeerJ 5, e3328 (2017).PubMed 
PubMed Central 

Google Scholar 
Dressler, R. L., Blanco, M. A., Pupulin, F. & Neubig, K. M. Proposal to conserve the name Sobralia (Orchidaceae) with a conserved type. Taxon 60, 907–908 (2011).
Google Scholar 
Baranow, P., Dudek, M. & Szlachetko, D. L. Brasolia, a new genus highlighted from Sobralia (Orchidaceae). Plant Syst. Evol. 303, 853–871 (2017).CAS 

Google Scholar 
Dressler, R. L. The major sections or groups within Sobralia, with four new species from Panama and Costa Rica, S. crispissima, S. gloriana, S. mariannae and S. nutans. Lankesteriana 5, 9–15 (2002).
Google Scholar 
Pridgeon, A. M., Cribb, P. J., Chase, M. W. & Rasmussen, F. N. Genera Orchidacearum Vol. 4: Epidendroideae Part 1 (Oxford University Press, 2005).
Google Scholar 
Van der Cingel, N. A. An Atlas of Orchid Pollination: America, Africa, Asia and Australia (Balkema, 2001).
Google Scholar 
Dodson, C. H. Why are there so many orchid species. Lankesteriana 7, 99–103 (2003).
Google Scholar 
Van Der Pijl, L. & Dodson, C. H. Orchid Flowers: Their Pollination and Evolution (University of Miami Press, 1966).
Google Scholar 
Neubig, K. M. Systematics of Tribe Sobralieae (Orchidaceae): Phylogenetics, Pollination, Anatomy, and Biogeography of a Group of Neotropical Orchids (University of Florida, 2012).
Google Scholar 
Neubig, K. M. et al. Preliminary molecular phylogenetics of Sobralia and relatives (Orchidaceae; Sobralieae). Lankesteriana 11, 307–317 (2011).
Google Scholar 
Ramírez, S. R., Roubik, D. W., Skov, C. & Pierce, N. E. Phylogeny, diversification patterns and historical biogeography of euglossine orchid bees (Hymenoptera: Apidae). Biol. J. Linn. Soc. 100, 552–572 (2010).
Google Scholar 
Gregory-Wodzicki, K. M. Uplift history of the Central and Northern Andes: A review. Geol. Soc. Am. Bull. 112, 1091–1105 (2000).ADS 

Google Scholar 
Sundell, K. E., Saylor, J. E., Lapen, T. J. & Horton, B. K. Implications of variable late Cenozoic surface uplift across the Peruvian central Andes. Sci. Rep. 9, 4877 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Mescua, J. F. et al. Middle to late miocene contractional deformation in Costa Rica triggered by plate geodynamics. Tectonics 36, 2936–2949 (2017).ADS 

Google Scholar 
Kolanowska, M., Mystkowska, K., Kras, M., Dudek, M. & Konowalik, K. Evolution of the climatic tolerance and postglacial ranges of the most primitive orchids (Apostasioideae) within Sunduland, Wallacea and Sahul. PeerJ 4, e2384 (2016).PubMed 
PubMed Central 

Google Scholar 
Arnal, P. et al. The evolution of climate tolerance in conifer-feeding aphids in relation to their host’s climatic niche. Ecol. Evol. 9, 11657–11671 (2019).PubMed 
PubMed Central 

Google Scholar 
Zangiabadi, S., Zaremaivan, H., Brotons, L., Mostafavi, H. & Ranjbar, H. Using climatic variables alone overestimate climate change impacts on predicting distribution of an endemic species. PLoS ONE 16, e0256918. https://doi.org/10.1371/journal.pone.0256918 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Soberón, J. & Peterson, A. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform. https://doi.org/10.17161/bi.v2i0.4 (2005).Article 

Google Scholar 
Jiménez-Valverde, A., Lobo, J. & Hortal, J. Not as good as they seem: The importance of concepts in species distribution modelling. Divers. Distrib. 14, 885–890. https://doi.org/10.1111/j.1472-4642.2008.00496.x (2008).Article 

Google Scholar 
Bonetti, M. F. & Wiens, J. J. Evolution of climatic niche specialization: a phylogenetic analysis in amphibians. Proc. Biol. Sci. 281, 20133229. https://doi.org/10.1098/rspb.2013.3229 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
George, P. M., Walter, E. W. & Yeuh-Lih, Y. Realized versus fundamental niche functions in a model of chaparral response to climatic change. Ecol. Modell. 7, 261–277 (1992).
Google Scholar 
Hijmans, R. J., Schreuder, M., Cruz, J. & Guarino, L. Using GIS to check co-ordinates of genebank accessions. Genet. Resour. Crop Evol. 46, 291–296 (1999).
Google Scholar 
Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. In ICML ’04. Proceedings of the Twenty-First International Conference on MACHINE LEARNing, 655–662 (ACM, New York, 2004).Phillips, S. J., Anderson, R. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).
Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Google Scholar 
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Modell. 222, 1810–1819 (2011).
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).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Brown, J. L. SDMtoolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700 (2014).
Google Scholar 
Feng, X., Park, D. S., Liang, Y., Pandey, R. & Papeş, M. Collinearity in ecological niche modeling: Confusions and challenges. Ecol. Evol. https://doi.org/10.1002/ece3.5555 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hosmer, D. W. & Lemeshow, S. Applied Logistic Regression (Wiley, 2000).MATH 

Google Scholar 
Mason, S. J. & Graham, N. E. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves statistical significance and interpretation. Q. J. R. Meteorol. Soc. 128, 2145–2166 (2002).ADS 

Google Scholar 
Evangelista, P. H. et al. Modelling invasion for a habitat generalist and a specialist plant species. Divers. Distrib. 14, 808–817 (2008).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Warren, D. L. et al. ENMTools 1.0: An R package for comparative ecological biogeography. Ecography 44, 504–511 (2021).
Google Scholar 
Schoener, T. W. The Anolis lizards of Bimini: Resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).
Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).PubMed 

Google Scholar 
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).
Google Scholar 
Heibl, C. & Calenge, C. Phyloclim: integrating phylogenetics and climatic niche modeling. R package version 0.9-4. http://CRAN.R-project.org/package=phyloclim (2013).Evans, M. E., Smith, S. A., Flynn, R. S. & Donoghue, M. J. Climate, niche evolution, and diversification of the ‘“bird-cage”’ evening primroses (Oenothera, sections Anogra and Kleinia). Am. Nat. 173, 225–240 (2009).PubMed 

Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 

Google Scholar 
Galtier, N., Gouy, M. & Gautier, C. SeaView and Phylo_win, two graphic tools for sequence alignment and molecular phylogeny. Comput. Appl. Biosci. 12, 543–548 (1996).CAS 
PubMed 

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

Google Scholar 
Nylander, J. A. A. MrModeltest v2 (Uppsala University, 2004).
Google Scholar 
Ronquist, F. & Huelsenbeck, J. P. MRBAYES: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
PubMed 

Google Scholar 
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Givnish, T. et al. Orchid phylogenomics and multiple drivers of their extraordinary diversification. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2015.1553 (2015).Article 
PubMed 
PubMed Central 

Google Scholar  More