Ecology

Concentrations and sources of organic compoundsMid- and long-chain n-alkanesThroughout most sections, the biomarker composition of the Hala Hu record is dominated by long-chain n-alkanes (Fig. 2a). In most samples, concentrations were highest for nC31 (ca 5–35 µg/g d.w.) and decreasing with chain-length (Supplementary Fig. S3–1). Generally, the concentrations were low in the glacial period, and increased between ca. 10 and 8 ka cal BP, while gradually decreasing after ca. 5 ka cal BP. These alkanes can be attributed to vascular higher plants from the lake catchment27, which is characterized by alpine meadows. Mid-chain nC23 and nC25-alkanes are frequently attributed to aquatic macrophytes28. However, n-alkane patterns of aquatic and terrestrial plants overlap. Moreover, we consider the contribution from macrophyte to the sedimentary n-alkane pool at the coring location to be minor, because of the specific n-alkane pattern of the samples, the overall low concentrations of mid-chain n-alkanes, and the deep water depth of the core site. An exception is a section in the core dated to ca. 15–14 ka cal BP, that has concentrations from ca. 20 up to 50 µg/g d.w. for individual mid-chain n-alkanes (Fig. 2a). This can be explained by enhanced contribution from submerged aquatic plants29 when the lake level was much lower than present9,23. Alternatively, a contribution from other mid-chain producers, such as Sphagnum species30,31, is possible during phases of lake regression and the potential formation of peatlands around the lake shore.Fig. 2: Summary of measured proxy parameters in cores H7 and H11 (overlap 7.4−9.1 kyrs BP).a δD values and concentrations of mid- and long-chain n-alkanes. Arrows (ASM) indicate the maximum strength of the summer monsoon over Asia. b Concentrations of aquatic biomarkers (C20-HBI, alkenones, n-alkenes) and of microbial derived PMI; alkenone index (Uk´3738) and C37-alkenone δD values. c Concentrations of firemarkers (ΣM, L, G). d Titanium, sulfur, strontium, and calcium contents from XRF-scanning22,23. e Lake level reconstructed from ostracod assemblages9. Dark dashed interval 8.4−8.0 kyrs BP indicates mass flow layer. Light and medium grey shaded areas mark episodes of late glacial and mid-Holocene regime shifts within the aquatic ecosystem.Full size imageAlgal biomarkersAside from n-alkanes, a range of compounds of mostly aquatic origin can be identified in the aliphatic and ketone fraction of the sediment extracts. Unsaturated mid-chain alkenes nC21:1, nC23:1, nC25:1, and nC27:1 were abundant in traces in large parts of the core, but exhibit very high concentrations ( >100 µg/g d.w.) in the glacial period in a single sample at 17.5 ka cal BP and between 15 and 14 ka cal BP. Relatively high concentrations up to 25 µg/d.w. were also observed in the mid-Holocene sequence from ca. 9 to 5 ka cal BP (Fig. 2b). The origin of those compounds is not fully resolved and so far n-alkenes have not been detected in samples from aquatic and terrestrial vascular plants on the TP. However, algae such as eustigmatophytes and chlorophytes have been suggested as possible precursors in the open freshwater Lake Lugu, southeastern TP32, and in Lake Challa, eastern Africa33,34. Therefore, we consider phytoplankton as the most likely source of those compounds in Hala Hu.It is notable that different n-alkene distribution patterns are visible throughout the core, with a predominance of nC27:1 and nC25:1 in the mid-Holocene section and nC23:1 and nC25:1 in other core sections (Supplementary Fig. S3–1). This indicates either different source organisms for at least nC23:1 compared to nC27:1 (supported by more depleted δD-values, see below and Supplementary Fig. S3–3) or a change towards the synthesis of longer chain-lengths by the same source organisms, triggered by changing environmental conditions.The C20-highly branched isoprenoid (HBI) compound is another observed phytoplankton biomarker, which is widely absent in the glacial sequences but shows traces throughout the Holocene with peak abundances (up to 30 µg/g d.w) in the mid-Holocene (8−5 ka cal BP; Fig. 2b). As often been found in cyanobacterial and algal mats (e.g.,35,36,37), it has recently been assigned as a trophic indicator derived from diatoms in lake systems38.Other observed biomarkers of algal origin are alkenones, which are primarily produced by haptophytes, even though a variety of species are possible precursors39,40 The summed concentrations of C37-, C38-, and C39-compounds were low (300 ng/g d.w.; ca. 7.8–6.3 ka cal BP) (Fig. 2b). Here, the di- and tri-unsaturated homologues of all chain lengths appear to elute as peaks undisturbed by contamination, while the tetra-unsaturated alkenones show co-elution with a fatty acid ethyl ester (FAEE) at least for the C37-compound.Pentamethylicosane (PMI) is a compound that was detected in most samples in minor concentrations (400 ng/g d.w.) during an episode in the glacial period (ca 16.6−14 ka cal BP) and during the late Holocene (4.5 ka cal BP to present). This compound has been assigned to microorganisms, i.e bacteria and archaea, often related to the methane cycle41,42,43,44.Fire markersAnother analyzed biomarker group, anhydrous sugars levoglucosan (L), galactosan (G), and mannosan (M) are generated by combustion and pyrolysis of cellulose and hemicellulose, thus, are often referenced as “pyromarkers” or “firemarkers”45,46,47. They occur in low concentrations ( More

1.Science Plan and Implementation Strategy IGBP Report No. 53/IHDP Report No. 19 (Global Land Project, 2005).2.Millennium Ecosystem Assessment—Ecosystems and Human Well-Being: Desertification Synthesis Encyclopedia of the Anthropocene vols 1–5 (MEA, 2017).3.Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2015).Article 

Google Scholar 
4.Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).Article 

Google Scholar 
5.Tilman, D. & Downing, J. A. Biodiversity and stability in grasslands. Nature 367, 363–365 (1994).Article 

Google Scholar 
6.Belnap, J. Surface disturbances: their role in acceleration desertification. Environ. Monit. Assess. 37, 38–57 (1995).Article 

Google Scholar 
7.Zhao, Y., Jia, R. L. & Wang, J. Towards stopping land degradation in drylands: water-saving techniques for cultivating biocrusts in situ. Land Degrad. Dev. 30, 2336–2346 (2019).Article 

Google Scholar 
8.Rodriguez-Caballero, E. et al. Dryland photoautotrophic soil surface communities endangered by global change. Nat. Geosci. 11, 185–189 (2018).CAS 
Article 

Google Scholar 
9.Coe, K. K. & Sparks, J. P. Physiology-based prognostic modeling of the influence of changes in precipitation on a keystone dryland plant species. Oecologia 176, 933–942 (2014).Article 

Google Scholar 
10.Ferrenberg, S., Tucker, C. L. & Reed, S. C. Biological soil crusts: diminutive communities of potential global importance. Front. Ecol. Environ. 15, 160–167 (2017).Article 

Google Scholar 
11.Belnap, J. & Gillette, D. A. Soil surface disturbance: impacts on potential wind erodibility of sand desert soils in SE Utah, USA. Land Degrad. Dev. 8, 355–362 (1997).Article 

Google Scholar 
12.Rutherford, W. A. et al. Albedo feedbacks to future climate via climate change impacts on dryland biocrusts. Sci. Rep. 7, 44188 (2017).13.Duniway, M. C. et al. Wind erosion and dust from US drylands: a review of causes, consequences, and solutions in a changing world. Ecosphere 10, e02650 (2019).14.Ferrenberg, S., Faist, A. M., Howell, A. & Reed, S. C. Biocrusts enhance soil fertility and Bromus tectorum growth, and interact with warming to influence germination. Plant Soil 429, 77–90 (2018).CAS 
Article 

Google Scholar 
15.Eldridge, D. J. et al. The pervasive and multifaceted influence of biocrusts on water in the world’s drylands. Glob. Change Biol. 26, 6003–6014 (2020).16.Ferrenberg, S., Reed, S. C. & Belnap, J. Climate change and physical disturbance cause similar community shifts in biological soil crusts. Proc. Natl Acad. Sci. USA 112, 12116–12121 (2015).CAS 
Article 

Google Scholar 
17.Reed, S. C. et al. Changes to dryland rainfall result in rapid moss mortality and altered soil fertility. Nat. Clim. Change 2, 752–755 (2012).CAS 
Article 

Google Scholar 
18.Concostrina-Zubiri, L. et al. Biological soil crusts across disturbance-recovery scenarios: effect of grazing regime on community dynamics. Ecol. Appl. 24, 1863–1877 (2014).CAS 
Article 

Google Scholar 
19.Weber, B., Bowker, M., Zhang, Y. & Belnap, J. in Biological Soil Crusts: An Organizing Principle in Drylands (eds Weber, B., Büdel, B. & Belnap, J.) 479–498 (Springer, 2016).20.Reynolds, J. F. et al. Global desertification: building a science for dryland development. Science 316, 847–851 (2007).CAS 
Article 

Google Scholar 
21.Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).CAS 
Article 

Google Scholar 
22.Bestelmeyer, B. T. et al. Analysis of abrupt transitions in ecological systems. Ecosphere 2, e03360 (2011).Article 

Google Scholar 
23.Bestelmeyer, B. T. et al. Desertification, land use, and the transformation of global drylands. Front. Ecol. Environ. 13, 28–36 (2015).Article 

Google Scholar 
24.Beisner, B., Haydon, D. & Cuddington, K. Alternative stable states in ecology. Front. Ecol. Environ. 1, 376–382 (2003).25.Fukami, T. & Nakajima, M. Community assembly: alternative stable states or alternative transient states? Ecol. Lett. 14, 973–984 (2011).Article 

Google Scholar 
26.Belnap, J. & Büdel, B. in Biological Soil Crusts: An Organizing Principle in Drylands (eds Weber, B., Büdel, B. & Belnap, J.) 305–320 (Springer, 2016).27.Belnap, J. & Warren, S. D. Measuring restoration success: a lesson from Patton’s tank tracks. Ecol. Bull. 79, 33 (1998).28.Belnap, J. & Elderidge, D. in Biological Soil Crusts: Structure, Function and Management (eds Belnap, J. & Lange, O. L.) 363–383 (Springer, 2001).29.Turner, M. G. Disturbance and landscape dynamics in a changing world. Ecology 91, 2833–2849 (2010).Article 

Google Scholar 
30.Scheffer, M. & Carpenter, S. R. Catastrophic regime shifts in ecosystems: linking theory to observation. Trends Ecol. Evol. 18, 648–656 (2003).Article 

Google Scholar 
31.Sala, O. E. & Lauenroth, W. K. Small rainfall events: an ecological role in semiarid regions. Oecologia 53, 301–304 (1982).32.Cayan, D. R. et al. Future dryness in the Southwest US and the hydrology of the early 21st century drought. Proc. Natl Acad. Sci. USA 107, 21271–21276 (2010).CAS 
Article 

Google Scholar 
33.Christensen, N. S., Wood, A. W., Nathalie, V., Lettenmaier, D. P. & Palmer, R. N. The effects of climate change on the hydrology and water resources of the Colorado river basin. Clim. Change 62, 337 (2004).Article 

Google Scholar 
34.Herrick, J. et al. Field soil aggregate stability kit for soil quality and rangeland health evaluations. Catena 44, 27–35 (2001).Article 

Google Scholar 
35.Escolar, C., Martínez, I., Bowker, M. A. & Maestre, F. T. Warming reduces the growth and diversity of biological soil crusts in a semi-arid environment: implications for ecosystem structure and functioning. Phil. Trans. R. Soc. B 367, 3087–3099 (2012).Article 

Google Scholar 
36.Scheffer, M. et al. Creating a safe operating space for iconic ecosystems: manage local stressors to promote resilience to global change. Science 347, 1317–1319 (2015).CAS 
Article 

Google Scholar 
37.Collins, S. L., Micheli, F. & Hartt, L. A method to determine rates and patterns of variability in ecological communities. Oikos 91, 285–293 (2000).Article 

Google Scholar 
38.Rillig, M. C. et al. The role of multiple global change factors in driving soil functions and microbial biodiversity. Science 366, 886–890 (2019).CAS 
Article 

Google Scholar 
39.IPCC. Climate Change 2014: Impacts, Adaptations, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).40.Mirzabaev, A. et al. in IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) Ch. 3 (WMO, 2018).41.Torres-Cruz, T. J. et al. Species-specific nitrogenase activity in lichen-dominated biological soil crusts from the Colorado Plateau, USA. Plant Soil 429, 113–125 (2018).CAS 
Article 

Google Scholar 
42.Omernik, J. M. & Griffith, G. E. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environ. Manag. 54, 1249–1266 (2014).Article 

Google Scholar 
43.Kuske, C. R., Yeager, C. M., Johnson, S., Ticknor, L. O. & Belnap, J. Response and resilience of soil biocrust bacterial communities to chronic physical disturbance in arid shrublands. ISME J. 6, 886–897 (2011).Article 

Google Scholar 
44.Tucker, C. L., Ferrenberg, S. & Reed, S. C. Climatic sensitivity of dryland soil CO2 fluxes differs dramatically with biological soil crust successional state. Ecosystems 22, 15–32 (2018). https://doi.org/10.1007/s10021-018-0250-445.Cable, J. M. & Huxman, T. E. Precipitation pulse size effects on Sonoran Desert soil microbial crusts. Oecologia 141, 317–324 (2004).Article 

Google Scholar 
46.Karl, T. R., Knight, R. W. & Plummer, N. Trends in high-frequency climate variability in the twentieth century. Nature 377, 217–220 (1995).CAS 
Article 

Google Scholar 
47.Kunkel, K. E., Easterling, D. R., Redmond, K. & Hubbard, K. Temporal variations of extreme precipitation events in the United States: 1895–2000. Geophys. Res. Lett. 30, 1895–2000 (2003).Article 

Google Scholar 
48.Kim, J. A projection of the effects of the climate change induced by increased CO2 on extreme hydrologic events in the Western US. Clim. Change 68, 153–168 (2005).CAS 
Article 

Google Scholar 
49.Smith, S. J. et al. Climate change impacts for the conterminous USA: an integrated assessment part 1. Scenarios and context. Clim. Change 69, 7–25 (2005). https://doi.org/10.1007/1-4020-3876-3_250.Schwinning, S., Belnap, J., Bowling, D. R. & Ehleringer, J. R. Sensitivity of the Colorado Plateau to change: climate, ecosystems, and society. Ecol. Soc. 13, 28 (2008).51.Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064 (2008).Article 

Google Scholar 
52.Jonasson, S. The point intercept method for non-destructive estimation of biomass. Phytocoenologia 11, 385–388 (1983).Article 

Google Scholar 
53.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).54.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).55.Oksanen, A. J. et al. Vegan: Community Ecology Package. Rpackage version 2.5-7 https://CRAN.R-project.org/package=vegan (2020).56.Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2008).Article 

Google Scholar 
57.Venables, W. & Ripley, B. Modern Applied Statistics with S. (Springer, 2002).58.Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. Package ‘emmeans’ https://github.com/rvlenth/emmeans (2018).59.Signorell, A. DescTools: Tools for Descriptive Statistics (2021).60.Hallett, L. M. et al. codyn: an R package of community dynamics metrics. Methods Ecol. Evol. 7, 1146–1151 (2016).61.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn 1–476 (CRC/Taylor & Francis, 2017).62.Bürkner, P. C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).63.Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 547–511 (1992).
Google Scholar 
64.Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).Article 

Google Scholar 
65.Modrák, M., Barrett, M., Weber, F. & Coronado, E. bayesplot: Plotting for Bayesian Models. R package version 1.8.0 https://mc-stan.org/bayesplot/ (2021).66.Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).Article 

Google Scholar 
67.Phillips, M. L., Howell, A., Lauria, C. M., Belnap, J. & Reed, S. C. Data and software code from two long-term experiments (1996–2011 and 2005–2018) at three sites on the Colorado Plateau of North America (US Geological Survey, 2021); https://doi.org/10.5066/P9RUN1TP More

Study areaThe North Sea is a continental sea connected to the Atlantic Ocean through the English Channel in the southwest and between northern Shetland along the 61° latitude parallel to Norway in the north (Fig. 1). It is bordered by Norway, Denmark, Germany, the Netherlands, Belgium, France and the UK, and has a surface of 570,000 km2. The North Sea has an average depth of 95 m, yet maximum depths of ca. 700 m are found in the Norwegian Trench. The maximum tidal amplitude of the North Sea can reach up to 8 m, average winter sea surface temperatures are ca. 6 °C and average summer temperatures reach ca. 17°C33. The English Channel encompasses the marine strait between the UK and France. It covers 75,000 km2, has an average depth of 63 m, a maximum depth of 174 m and can reach a maximum tidal amplitude up to 12 m. The average winter and summer sea surface temperatures in the English Channel are ca. 5 and 20 °C, respectively54.TaggingIn total, 320 silver eels were tagged with pop-off archival tags (Table 1; Supplementary Table S2). In Belgium, 238 eels were caught and tagged at a drainage system upstream of the Yser Estuary (hereafter referred to as the Belgian eels) in 2018–2020 via nets that were attached to gravitational discharge sluice gates (coordinates: 51.127 N, 2.761 E) in October, November and December (n2018 = 102, n2019 = 60 and n2020 = 76). In Germany, 82 eels were tagged in 2011 and 2012. In early December 2011, seven eels were caught at Lake Plön (coordinates: 54.137 N, 10.334 E) with fyke nets. During September, October and November 2012, eels were caught in the Rivers Eider (n = 30; coordinates: 54.190 N, 9.093 E) and Havel (n = 45; coordinates: 52.419 N, 12.571 E) with fyke and stow nets, respectively.Upon capture, the eels were anaesthetized with 0.3 ml/L clove oil (Belgium), 0.4 ml/L ethylene glycol monophenyl ether (Germany 2011) or 120 mg/L MS-222 (Germany 2012), and various morphometric characteristics were measured to identify the life stage55: total length (to the nearest mm), weight (to the nearest g), horizontal and vertical eye diameter (to the nearest 0.01 mm in Belgium and to the nearest 0.1 mm in Germany) and pectoral fin length (to the nearest 0.01 mm and 0.1 mm in Belgium and Germany, respectively). Given that their total body length was  > 450 mm, all eels were considered female55. According to the morphometrics, five Belgian eels could be considered in the premigratory stage (FIII); however, based on visual inspection, they were considered silver eels (i.e. silver-coloured abdomen, dark grey on the dorsal side, jaw hinge not proceeding beyond the eye, enlarged eyes and dark coloured pectoral fins). The other 315 eels identified as silver eels based on both morphometry and visual inspection (201 FIV stage and 114 FV stage).Eels weighing ≥ 550 g were externally fitted with a G5 PDST (CEFAS Technology Ltd, UK), which log temperature and pressure (providing information on depth). They were attached applying the three-point Westerberg attachment method56. Two tag types were used: one with a separate tag and pop-off mechanism (Germany) and one where both mechanisms were integrated (Belgium). The flotation collar of the PDSTs was painted bright red, contained contact information and a cash reward to stimulate retrieval by the general public (e.g. beach combers and fishermen). The seven eels caught in 2011 in Germany (minimum 1220 g) were fitted with PSATs (X-Tag, Microwave Telemetry Inc., USA), also using the Westerberg-method56. Like the PDSTs, the PSATs record temperature and pressure. After release, they drift to the surface and transmit the data to the user via the ARGOS satellite system (www.argos-system.org). For the specifications of the different tags, we refer to Supplementary Table S3.Upon recovery from the anaesthetic, eels tagged with PDSTs were released close to their capture locations in the rivers Eider (coordinates 2011: 54.381 N, 9.009 E; coordinates 2012: 54.379 N, 9.013 E), Elbe (coordinates 1: 53.793 N, 9.402 E; coordinates 2: 53.569 N, 9.700 E; coordinates 3: 53.396 N, 10.171 E) and Yser (coordinates: 51.135 N, 2.757 E) (Table 1). The seven eels captured for PSAT tagging in 2011 were held for several weeks in the Thünen Institute of Fisheries Ecology, then tagged and released the same day; others were tagged in the field.PreprocessingOnce downloaded, the temperature and pressure data obtained from the PDSTs was subsampled to 1-min (Belgian eels) or 2-min (German eels) intervals to reduce the datasets and improve geolocation calculation time; this discrepancy is due to the minimum logging rate of the tags (Supplementary Table S3). Linear regression was applied to correct for pressure sensor drift over time. Indeed, pressure values increased over time even if the tag was kept at atmospheric pressure level. The regression was applied between 15 min before release and the moment the tag popped off and reached the surface, since the tag was then considered at sea level and hence to be under zero pressure.The PSAT data were retrieved through the ARGOS satellite system as a subset with 15-min intervals and converted to values of pressure and temperature. Contemporaneous values of temperature and depth were not always transmitted due to the transmission method. As a consequence of the tag release programming, the transmission of the first position for one of the tags was only received five days after the tag reached the sea surface.GeolocationThe daily movements of each electronically tagged European eel were reconstructed using an adapted version of the tidal geolocation model of Pedersen et al.57. The geolocation model uses a novel Fokker–Planck based method to combine the tidal location method of Metcalfe and Arnold58 with a hidden Markov model (HMM), such that an individual’s daily location d is modelled conditionally on its previous location (d − 1), its inferred behavioural state ds, where behaviour is defined by a single diffusivity parameter (i.e. the maximum amount of movement permitted in a given day), and the observations made between d and d − 1. In this case, observations consisted of the recorded depth (m; D1, …, Dn) and temperature (°C; T1, …, Tn), where n is the number of measurements made per day (the HMM down-samples to 10-min intervals, hence 144 measurements per day), and any hydrostatic (tidal) data which are derived from the sinusoidal pressure cycle recorded in the depth data when a fish is at rest on the seafloor. In addition to bathymetry and tidal amplitude with phase, the model was developed to include sea surface temperature (SST), which can provide additional validation when fish are swimming at or near the surface (i.e. depth ≤ 20 m)59,60, and temperature at depth, which can provide additional validation when fish remain at depths well below the sea surface61,62.The model was run in three different configurations for each recovered dataset: (i) using the tidal location model only (as for Pedersen et al.57), hereafter termed TLM geolocation; (ii) using the TLM plus sea surface temperature (as for Wright et al.60), hereafter termed SST geolocation, and (iii) using temperature at the surface and sub-surface, hereafter termed 3D geolocation (Supplemental Fig. S3). The final trajectory output for the PDST Belgian eels and PSAT German eels was obtained via 3D geolocation, while SST geolocation was used for the PDST German eels. The reason for this discrepancy is that the German PDST eels stayed closer to the coast and in shallower water. Consequently, the 3D geolocation results were more prone to error due to coastal influences on water temperature. As a result, we used the SST geolocation method for these datasets to obtain more reliable results.Data for the model were derived from publicly available resources. Gridded global bathymetry data were obtained from the general bathymetric chart of the oceans (Gebco; British Oceanographic Data Centre, Liverpool, United Kingdom, 2009). Tidal constituents were obtained from the Oregon State University Tidal Prediction model, as described in Egbert and Erofeeva63. Sea surface temperature data were sourced from OSTIA64, while temperature at depth data were sourced from the operational Mercator global ocean analysis and forecast system65. These datasets were downloaded from the Copernicus Marine Environmental Model Service (CMEMS: documented here http://resources.marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-024.pdf). Data were sourced so as to fit the spatial scale of the model (30°N to 80°N and from 110°W to 60°E) and coarsened to reduce model run-time by modifying the spatial grid to a 1/10th of a degree resolution. The output of the model is a nonparametric probability distribution of the geographical position from which a most probable location, for each day at liberty, and a most probable movement path can be estimated.Prior to running the model, a number of constraints and input parameters were defined to ensure that the model ran effectively. The recapture information was either set as (a) the latitude and longitude where the tag was recaptured, with a high confidence ( 200 m) and hence did not exhibit diel vertical migrations, the input estimates of longitude were based on a simple linear interpolation from release to estimated pop-up. However, for eels that did reach oceanic depths, the time of local noon was estimated (based on the timing of significant diel vertical migrations, as for Righton et al.16), and used to estimate longitude. Geolocation was conducted with MATLAB software66.Migration routesOnly datasets containing ≥ 100 km of net tracking distance were included for further analysis, leading to 54 datasets from the 96 retrieved tags and 320 tagged eels. The net tracking distance was identified as the distance along the reconstructed trajectory between the release of the tagged eel and the pop-off event. When an eel was ingested by a predator, leading to the tag tracking the predator rather than the eel, the data were excluded from the day the eel was predated. The 100 km cut-off point was arbitrarily chosen to select migration paths of sufficient length for further analysis (e.g. migration direction); tracks had a minimum deployment duration of 4 days.Migration speedTo exclude a size-effect, we first applied an independent two-sample t-test to confirm eel sizes (i.e. weight) did not differ between Belgian and German eels. The assumptions of normality (Shapiro–Wilk test), homogeneity of variances (F-test) and independence were met (weight measurements are individual-specific and therefore independent).Next, an independent two-sample t-test was conducted to test if the total migration speeds (i.e. the ground speed along the reconstructed trajectory between the release of the tagged eel and the pop-off or predation event) differed between Belgian and German eels. The assumptions were tested and met as described above.Finally, we tested if the daily migration speed (i.e. the ground speed along the reconstructed trajectory per day) differed according to the eel’s position (i.e. modelled latitude and longitude) via a linear mixed effects model. The tag IDs were implemented as a random effect to account for autocorrelation. Since the two-sample t-test showed a significant difference between Belgian and German total migration speeds, we performed a separate analysis on eels from both countries. Assumptions of normality, homogeneity of variances and independence were tested and met.The migration speed analyses were conducted in R (version 3.6.3)67. The packages ‘lme4’ and ‘nlme’ were used to conduct the linear mixed effects model.Ethical statementEels were tagged using approved protocols by trained and individually licensed scientists working under national project authority in accordance with institutional and national guides for the care and use of laboratory animals. These guidelines are consistent with Institutional Review Board/Institutional Animal Care and Use Committee guidelines. Tagging in Belgium was carried out in accordance with the Belgian national and regional regulations for animal welfare and treatment (Permit ID: EC INBO-011). Tagging in Germany followed German legislation concerning care and use of laboratory animals, and ethical permission for the experiments was given by the Ministry of Energy, Agriculture, the Environment, and Rural Areas of the federal state Schleswig–Holstein (reference numbers V312-72241.123-34 (90-8/11) and V311-7224.123.3 (93-6/12) for tagging in 2011 and 2012 respectively). More

Plant defence traitsWe compiled species level data for five plant traits: wood density (WD), leaf and stem spinescence, latex production, and leaf size, for tropical and extra-tropical South and Central American woody species (i.e., the Neotropical biogeographic realm). WD was obtained for 2577 species from ref. 44. We only used wood density data from Zanne et al.44, because this study used WD measured in stems, whereas most other studies with available data used WD measured in branches. Leaf size data were obtained for 2660 woody species from Wright et al.37. We did not include leaf size from herbaceous species because herbaceous and woody species are influence by different megafauna guilds, suggesting distinct mechanisms, and because this dataset37 only included data for 253 Neotropical herbaceous species. The presence or absence of stem (and/or branch) spines (mostly thorns, but also prickles) were obtained from Dantas and Pausas45 for Neotropical savanna and forest species (1004 species) and complemented with other literature sources for other ecoregions (listed in the supplementary materials) using the names of the species for which we had WD and Leaf Size data. Our final stem spines dataset included 2843 woody species. We also compiled data on the presence of latex in plant stems and leaves for all the species for which we had data on other traits (3160 species; references in the supplementary materials). Finally, we also compiled data on leaf spines. While we managed to find leaf spine data for a total of 2173 woody species, we found spinescence in leaves to be especially concentrated in the palm Family (Arecaceae; 198 out of 221 species with leaf spines). Moreover, out of the non-palm species, all but three species also presented stem spines, indicating that, for other taxa, leaf spines might be dependent on the presence of stem spines at the region (in palms, 51% have stem spines). Thus, we only used leaf spinescence data of palm species (694 species) from the global Palm Traits Database 1.046.For wood density and leaf size, we often had more than one trait value per species (1005 and 831 species with more than one trait value, respectively). Thus, we computed the species mean trait value. This rarely occurred for binary traits (spinescence and latex) and, when occurred, the maximum value was used (0 for absence and 1 for presence). This later decision was based in the assumption that omitting the presence of spines or latex is more likely than incorrectly reporting the presence when it is absent. Moreover, some of these traits can be plastic18.From species to ecoregionsWe searched for geographical distribution data (coordinates) from the Global Biodiversity Information Facility (GBIF) for all of the species in each species-trait dataset (Data available from GBIF using the following doi: WD: https://doi.org/10.15468/dl.3vua3x; Stem spines: https://doi.org/10.15468/dl.ar5ddj; Latex: https://doi.org/10.15468/dl.m8dzjd; Leaf spines: https://doi.org/10.15468/dl.vv8gw4; Leaf size: https://doi.org/10.15468/dl.k98nxc). For this search, we used tools provided by the “rgbif” package for R in which species names are updated to the most recent classification and the returned occurrences also include those associated with synonyms (i.e., the “backbone” method). We labelled the obtained geographical coordinates according to their ecoregion and biogeographical realm (following Dinerstein et al.47) and cropped out occurrences falling outside of the Neotropical realm. Since occurrence data was not available to all the species in our initial trait dataset, the number of species used to calculate ecoregion level means was reduced to 2110 species, for wood density, 2133, for leaf size, 2629, for stem spines, 2714, for latex, and 657, for leaf spines. A detailed evaluation of the representativity of this data in relation to ecoregion- and Neotropical- level patterns can be found in the Supplementary Methods. Based on the occurrence data and their ecoregion label, we built a species abundance (columns) by ecoregion (rows) matrix for each trait.We obtained ecoregion scale abundance-weighted means for continuous traits (WD and Leaf Size) by: (1) Multiplying species abundance in each grid cell of the ecoregion by the mean species value; (2) Summing up the row values; (3) dividing the resulting row sum by the total species abundance (row sum prior to trait multiplication), and (4) calculating the ecoregions’ means (across all of the grid cells). For Stem Spines and Latex (binary traits), we used a similar procedure, but the maximum (0 for absence and 1 for presence) value was used instead of the mean in step (1), and step (2) was directly used to calculate the number of presences (i.e., 1 s). Moreover, instead of the steps (3) and (4), we calculated the number of absences as the difference between the total abundance (row sums before trait multiplication) and the values obtained in step (2). This process resulted in weighted means for WD and stem spinescence for 173 ecoregions, and Leaf Size and Latex for 174, out of the 179 Neotropical ecoregions. For leaf spinescence, we used a similar approach, although, because of the fewer species, the abundance estimate from GBIF was less reliable. Thus, we transformed the ecoregion species abundance to presence/absence before multiplying the trait values (0/1 for absence/presence). We obtained leaf spinescence data for 159 out of the 179 Neotropical ecoregions. The species- and ecoregion- level data is provided in the Supplementary Data and in ref. 47.Historical megafauna distributionWe obtained data on historical distribution of megafauna species from the MegaPast2Future/PHYLACINE_1.2 dataset24, a dataset containing distribution maps (96.5 km of spatial resolution) and functional traits for mammal species of the last 130,000 years. From this dataset, we obtained the probable past distribution of extinct large mammal herbivore (hereafter, “megafauna”) species, if these species were still alive today (“Present Natural” scenario; see details below). The “Present Natural” distribution of extinct species in this database is based on the estimated historical distribution (i.e., preceding anthropogenic range modifications) of extant species that are known (from the fossil record) to have coexisted with the extinct species. In this approach, an extinct species is considered to have been present in a given grid cell if at least 50% of the extant species that were found coexisting with the extinct species in the fossil (and subfossil) record was predicted to have occurred in the same cell prior to anthropogenic range modifications24,48. This approach assumes that, since extant and extinct species coexisted in the same locations, they must have had similar ecological requirements. It also assumes that megafauna extinction had anthropogenic causes, instead of causes related to climate change49, which is largely accepted in the literature50.We extracted the “Present Natural” distribution of extinct mammal (coded “EP” for IUCN status; i.e., “extinct in prehistory”, meaning before 1500 CE) whose body mass was higher than 50 kg (megafauna), and for which at least 90% of their diet consisted of plants (i.e., strict herbivores). For each Ecoregion, we began by calculating two megafauna-related metrics: extinct megafauna species richness (Mrich) and their mean body mass (Mbm). For this, we cropped the distribution maps of the megafauna species (containing 1 for presence and 0 for absence of each species) to the Neotropical realm. To calculate Mrich, we (1) counted species presences within each of the grid cells in the global grid (i.e., calculated the cell’s megafauna richness); (2) assigned the corresponding ecoregion label to the resulting richness grid cells, subset the richness cell values corresponding to the Neotropical region; and (3) calculated the mean for each Neotropical ecoregion. For Mbm, we replaced the presences of the megafauna species in the initial raster object (grid cell map of each megafauna species) by their body masses and calculated the grid cell-level mean body mass, before calculating the ecoregion-level means. We also calculated megafauna density and secondary productivity based on allometric equations that relate these metrics to megafauna body mass. However, we did not used megafauna density and secondary productivity because they were strongly correlated to megafauna richness (Supplementary Fig. 3). More details on how these metrics were calculated can be found in the Supplementary Methods.We also obtained diet preference information from the literature for most megafauna species that occurred in the Neotropical region (details and references in the Supplementary Material). Based on these information, we calculated the richness of large browser (MBrich for megabrowser richness), grazer (MGrich for megagrazer richness), and mixed-feeder (MMfrich for mega mixed-feeder richness) species by sub setting the megafauna species by grid cell array before the richness calculation in order to select only species that were classified within the correspondent subgroup.Extant herbivore mammal distributionWe also compiled data on the distribution, body mass and diet of extant and recently extinct (i.e., extinct after 1500 CE) herbivore mammal species (for simplicity, called ‘extant’ species in this study). As with megafauna maps, the distributions used represented reconstructions for periods preceding anthropogenic reduction of extant herbivores ranges (“Present Natural” scenario), based on abiotic, biotic and geographic variables48, rather than the currently observed distribution. This scenario was used because modern anthropogenic range reductions are too recent to produce substantial geographic effects at this spatial scale. These data were obtained by sub setting the MegaPast2Future/PHYLACINE_1.2 dataset to exclude species that were coded “EP” for IUCN status and that were not strict herbivores (at least 90% of the diet constituting of plants). We subsequently associated diet information to these species using data from ref. 51 and excluded all species that did not feed mainly on aboveground vegetative plant tissues (i.e., species that fed mostly on fruits, seed, roots were excluded). This later filtering was because the number of herbivores that feed mostly on seed and fruit increase with decreasing size (and this dataset included small mammals). We subsequently calculated the same metrics as for the extinct megafauna species (except for the richness of mixed-feeders as our source for diets50 labelled species according to dominant feeding pattern). For this, we used the same approach described for extinct megafauna species. We did not use a size threshold for extant species because there were only 13 extant mammal herbivore species with over 50 kg in the Neotropical region, most of which were grazers (9 species; 4 species were mixed-feeders and none were browsers). Therefore, we relied on the mean body mass metric calculated for extant mammals to detect potential size-related effects.Climate, soil, fire, insularity, and hurricanesFor each Ecoregion, we obtained data on climate (mean annual precipitation and temperature, and rainfall seasonality) and soil (sand content, pH, and cation exchange capacity) variables. Climate data was obtained from WorldClim 2.1 (10 min spatial resolution) and was based on climate data from 1970 to 200052. Soil data were obtained from SoilGrids (5 km of spatial resolution)53, and consisted of mean values for two depths, 0.05 and 2 m. We calculated Ecoregion level means for all of the soil and climate variables after intersecting the climate and soil grid maps with the ecoregion map.We obtained the number (a proxy for frequency) and intensity of wildfires per ecoregion area using the MODIS active fire location product (MCD14ML)54. We only considered fires (i.e., hotspots) with detection confidence of 95% or higher occurring from November 2000 to December 2019 (both included). To ensure that only wildfires were considered, we associated each fire pixel with a land cover type (300 m of spatial resolution) from ref. 55 for a buffer area of 1000 m surrounding the fire pixel centroid. We excluded all of the fires occurring in areas in which more than 10% of the surrounding land cover pixels corresponded to agricultural, urban and water classes. We calculated the number of wildfires per ecoregion area by dividing the fire count of each Ecoregion by the ecoregion area, and multiplying the resulting value by the proportion of vegetated land cover pixels (same classes used to exclude fires in anthropogenic areas and water bodies above). Fire intensity was estimated as the average fire radiative power across all detected MODIS hotspots in the ecoregion. Ecoregions lacking large preserved vegetated areas (criteria above) were excluded from subsequent analyses.Using the ecoregion map, we also classified ecoregions into insular (1), when most of the ecoregion area was located in islands, vs. continental (0), otherwise. This was performed because island biogeography theory predicts that, in island, species richness should be low due to low colonization and high extinction rates. Insularity has also been shown to reduce megafauna body size (i.e., the island rule), even though the mechanisms are not fully understood56. We also compiled data on hurricane activity, as woody density was suggested to confer resistance against this disturbance57. We used data from 1990 to 2019 from the HURDAT2 dataset58, containing six-hourly information about the location of all of the known tropical and subtropical cyclones (0.1° latitude/longitude). We used the sum of hurricane occurrences per ecoregions divided by ecoregion area as an indicator of hurricane activity.Statistical analysesTo understand megafauna patterns, we began by fitting (multiple) regression models with habitat-related (fire, climate, soil) and geographical (insularity) variables as predictors. We expected that megafauna richness in general was higher under savanna conditions (arid nutrient-rich or mesic nutrient-poor environments with frequent fires)1,22. We also expected that megafauna richness and body mass were affected negatively by insularity (i.e., following the island biogeography theory and island rule). Before the analyses, we tested the correlations among all of the variables that would eventually be entered as predictors in the same model for both the megafauna and trait models (Supplementary Table 1), in order to avoid multicollinearity associated with highly correlated variables (here, r ≥ 0.60). Since mean annual precipitation and soil pH were strongly positively correlated (r = −0.78), for all of the analyses (including the analyses with functional traits, described below), model selection was performed separately for these two variables (i.e., two different model selection procedures, one containing each of the two variables among the initial set of predictors). We selected the best among the two resulting models as that with the lowest AIC (differences higher than two points in all of the cases). To make sure that no multicollinearity remained we also calculated the Variation Inflation Factor (VIF) for all of the predictor variables as 1/tolerance, where tolerance is calculated as 1 minus the R2 of all of the model regressing a predictive variable against all of the other predictors. In all of the models, VIF was 3.33 or smaller (i.e., a tolerance of 0.30 or higher), indicating absence of multicollinearity.Model simplification was carried interactively using stepwise (both forward and backward) searching for the model with the lowest AIC (using R’s “step” function) and subsequently retaining only the significant variables (p ≤ 0.05). We calculated the Pearson r statistics as a measure of effect size for the selected variables as well as the associated confidence intervals, using the packages “parameters” and “effectsize” for R. The average contribution of each predictor variable was also calculated, using the package “dominanceanalysis”, as the mean difference in R2 before and after removing the target variable from models containing all of the possible subset combinations of the selected predictor variables, including the full selected model.For testing whether the studied plant functional traits were related to our megafauna indicators, we fit linear models to WD and leaf size, and generalized linear models (GLM; binomial family) for spinescence and latescence, using ecoregion as the unit. For spinescence and latescence, we used the matrix containing the count of spiny/latex and non-spiny/non-latex plants (species abundance; for stem spines and latex) or number of species with or without spines (for leaf spines; see above) as response variables. The predictor variables included the animal indicators for extinct megafauna and extant herbivores, as well as climate, soil, and fire predictors (and, for WD, hurricane counts). Because total, as well as megagrazer, megabrowser, and mega mixed-feeder species richness were strongly positively correlated (Supplementary Table 1), we used the richness difference between grazers and browsers to evaluate the effect of diet (Supplementary Fig. 1). For consistency, we used the same diet variable for extant and extinct species. Since we did not identify strong correlations among extinct megafauna and extant herbivore indicators (Supplementary Table 1), these variables were all entered simultaneously in the same initial models. As with the analyses of the megafauna indices, we also used r as effect size and calculated the average predictor contribution in terms of R2 for these models. For the later, we used the MacFadden Pseudo-R2 in the GLM models as implemented in the “pscl” and “dominanceanalysis” packages for R, as this statistic is the most comparable with R2 from linear multiple regression (Maximum Likelihood and Cragg and Uhler’s Pseudo-R2 were also calculated for the logistic models), and adjusted R2 for continuous traits. Islands were not included in these models, as island plants were expected to respond differently due to the effects of insularity on animal species richness, precluding megafauna and extant mammal richness from being accurate proxies for consumer abundance. For stem spines, we always included a quadratic term to both megafauna and extant mammal herbivore body mass, as evidence suggest that medium-size herbivores (i.e., approximately 250 kg) are important selective drivers of this trait12. If a significant relationship with our herbivory indicators (both extant and extinct) were significant but not indicative of a selective effect by herbivores (for more defended plants), this relationship was discarded (along with related variables, such as diet); this happened only once, for leaf size, which increased with extant herbivore richness (Supplementary Table 8).For all of the general linear regression models, assumptions of normality, homoscesticity and lack of spatial autocorrelation in the residuals were checked using the Kolmogorov–Smirnov, Breusch–Pagan and Moran’s I tests, respectively. For the later, ecoregions were considered neighbours when they were adjacent and non-neighbour otherwise. In some cases, heteroscesticity was detected and, thus, the significance of the coefficients was tested using heteroskedasticity-consistent covariance matrix estimation. If one or more variable lost their significance they were stepwise removed from the final model, beginning by the least significant, until all remaining variables had a significant effect. Overdispersion in the generalized linear model was also detected and dealt with using overdispersed binomial logit models, as implemented in the “dispmod” package for R, in which weights are interactively calculated and used to maintain the residual deviance lower than the degrees of freedom. To confirm that the detected associations between megafauna indices and plant traits were robust, we also tested the coefficient significance using randomization of the plant species by ecoregion matrices (see Supplementary Methods for details).To test the prediction that Neotropical ecoregions could be broadly classified into the three hypothesised antiherbiomes, we used hierarchical clustering on principal component axes of the ecoregion by trait matrix (five plant traits, standardized to zero mean and unit variance). We selected the number of clusters associated with the highest loss of inertia (within group variability) when progressively increasing the number of clusters, using the R package “FactoMineR”. This procedure allowed the recognition of large regions characterised by specific patterns of defence strategies (‘antiherbiomes’). We subsequently tested for axes score, megafauna and environmental differences among the resulting antiherbiomes to verify whether and how trait, climate and soil patterns matched those described for African ecosystems, and to understand the megafaunal differences among the antiherbiomes. For these comparisons, we used Kruskal-Wallis and post-hoc pairwise Dunn tests, using the Benjamini & Hochberg59 (1995) correction of P-values for multiple comparisons in both cases, and exclusively included continental ecoregions. For spines, we used the proportion of spinescent plants/species (rather than the number of “yes” and “no” used on previous analyses) in the principal component analysis. Because palms were missing from 20 ecoregions, we completed the values for these ecoregions using predicted model probabilities. To better understand these associations between traits and the environmental and megafauna variables, we also regressed the PCA axes against the same predictors used for traits.We also developed a framework to identify forest ecoregions most likely to have experienced a biome shift after megafauna extinction using antiherbiome, biome and megafauna distribution data. Ecoregions likely to have experienced a savanna-to-forest shift since the Pleistocene are those that: (1) are currently forest-dominated; (2) are classified in antiherbiomes analogous to African arid nutrient-rich or mesic nutrient-poor savannas; and (3) were megafauna- and, especially, megagrazer- rich during the Pleistocene (richness equal or greater than the 0.75 quantile: 14 species for Mrich, and 3 for exclusively grazing species; MGrich). We validated the distribution of these areas with fossil evidence (22 sites) from the Last Glacial Maximum and mid-Holocene (see Supplementary Methods and Supplementary Table 9). For this, we also used information about the present dominant vegetation type in the fossil sites, extracted from the reference sources (see Supplementary Table 9), to segregate savanna-forest shifts from data coming from stable savanna patches within forest or long-term savanna regions. We also contrasted the predicted patterns with the present location of savanna patches within the Amazon Forest region from ref. 60.All statistical analyses and data handling were carried out in the R (v.4.0.2) environment, using the previously mentioned packages, in addition to FSA, gridExtra, grid, lattice, lmtest, latticeExtra, olsrr, raster, rgdal, rgeos, sandwich, spatialreg, spdep and vegan, using codes provided in ref. 47.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Gaston, K. J., Jackson, S. F., Cantú-Salazar, L. & Cruz-Piñón, G. The ecological performance of protected areas. Annu. Rev. Ecol. Evol. Syst. 39, 93–113 (2008).
Google Scholar 
2.Staver, A. C., Abraham, J. O., Hempson, G. P., Karp, A. T. & Faith, J. T. The past, present, and future of herbivore impacts on savanna vegetation. J. Ecol. 109, 2804–2822 (2021).
Google Scholar 
3.Bond, W. J. Keystone species. In Biodiversity and Ecosystem Function (eds Schulze, E. D. & Mooney, H. A.) 237–253 (Springer, 1994).
Google Scholar 
4.Cole, M. M. The influence of soils, geomorphology and geology on the distribution of plant communities in savanna ecosystems. In Ecology of Tropical Savannas (eds Huntley, B. J. & Walker, B. H.) 145–174 (Springer, 1982).
Google Scholar 
5.Huntley, B. J. & Walker, B. H. (eds) Ecology of Tropical Savannas Vol. 42 (Springer, 1982).
Google Scholar 
6.Frost, P. et al. Responses of Savannas to Stress and Disturbance: A Proposal for a Collaborative Programme of Research (Biology International 10, 1985).
Google Scholar 
7.Medina, E. & Silva, J. F. Savannas of northern South America: A steady state regulated by water–ire interactions on a background of low nutrient availability. J. Biogeogr. 17, 403–413 (1990).
Google Scholar 
8.Fensham, R. J., Fairfax, R. J. & Archer, S. R. Rainfall, land use and woody vegetation cover change in semi-arid Australian savanna. J. Ecol. 93, 596–606 (2005).
Google Scholar 
9.Mucina, L. & Rutherford, M. C. The Vegetation of South Africa, Lesotho and Swaziland (South African National Biodiversity Institute, 2006).
Google Scholar 
10.Staver, A. C., Botha, J. & Hedin, L. Soils and fire jointly determine vegetation structure in an African savanna. New Phytol. 216, 1151–1160 (2017).CAS 
PubMed 

Google Scholar 
11.Jakubka, D. et al. Effects of climate, habitat and land use on the cover and diversity of the savanna herbaceous layer in Burkina-Faso, West Africa. Folia Geobot. 52, 129–142 (2017).
Google Scholar 
12.Bond, W. J. Open Ecosystems: Ecology and Evolution Beyond the Forest Edge (Oxford University Press, 2019).
Google Scholar 
13.O’Connor, T. G. Composition and population responses of an African savanna grassland to rainfall and grazing. J. Appl. Ecol. 31, 155–171 (1994).
Google Scholar 
14.Walker, B. & Langridge, J. Predicting savanna vegetation structure on the basis of plant available moisture (PAM) and plant available nutrients (PAN): A case study from Australia. J. Biogeogr. 24, 813–825 (1997).
Google Scholar 
15.Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).CAS 
ADS 
PubMed 

Google Scholar 
16.Bucini, G. & Hanan, N. P. A continental-scale analysis of tree cover in African savannas. Glob. Ecol. Biogeogr. 16, 593–605 (2007).
Google Scholar 
17.Venter, F. J., Scholes, R. J. & Eckhardt, H. C. The abiotic template and its associated vegetation pattern. In The Kruger Experience: Ecology and Management of Savanna Heterogeneity (eds du Toit, J. T. et al.) 83–129 (Island Press, Washington, D.C., 2003).18.Valeix, M. et al. Vegetation structure and ungulate abundance over a period of increasing elephant abundance in Hwange National Park, Zimbabwe. J. Trop. Ecol. 23, 87–93 (2007).
Google Scholar 
19.Asner, G. P. et al. Large-scale impacts of herbivores on the structural diversity of African savannas. Proc. Natl Acad. Sci. USA 106, 4947–4952 (2009).CAS 
ADS 
PubMed 
PubMed Central 

Google Scholar 
20.Archibald, S. & Hempson, G. P. Competing consumers: Contrasting the patterns and impacts of fire and mammalian herbivory in Africa. Philos. Trans. R. Soc. B 371, 20150309 (2016).
Google Scholar 
21.Archibald, S., Bond, W. J., Stock, W. D. & Fairbanks, D. H. K. Shaping the landscape: Fire–grazer interactions in an African savanna. Ecol. Appl. 15, 96–109 (2005).
Google Scholar 
22.Staver, A. C. & Bond, W. J. Is there a ‘browse trap’? Dynamics of herbivore impacts on trees and grasses in an African savanna. J. Ecol. 102, 595–602 (2014).
Google Scholar 
23.Jeltsch, F., Weber, G. E. & Grimm, V. Ecological buffering mechanisms in savannas: A unifying theory of long-term tree-grass coexistence. Plant Ecol. 105, 161–171 (2000).
Google Scholar 
24.February, E. C., Higgins, S. I., Bond, W. J. & Swemmer, L. Influence of competition and rainfall manipulation on the growth responses of savanna trees and grasses. Ecology 94, 1155–1164 (2013).PubMed 

Google Scholar 
25.Savadogo, P., Tiveau, D., Sawadogo, L. & Tigabu, M. Herbaceous species responses to long-term effects of prescribed fire, grazing and selective tree cutting in the savanna-woodlands of West Africa. Perspect. Plant Ecol. Evol. Syst. 10, 179–195 (2008).
Google Scholar 
26.Smith, M. D. et al. Long-term effects of fire frequency and season on herbaceous vegetation in savannas of the Kruger National Park, South Africa. J. Plant Ecol. 6, 71–83 (2013).
Google Scholar 
27.Thrash, I. Impact of water provision on herbaceous vegetation in Kruger National Park, South Africa. J. Arid Environ. 38, 437–450 (1998).ADS 

Google Scholar 
28.Staver, A. C., Bond, W. J., Stock, W. D., van Rensburg, S. J. & Waldram, M. S. Browsing and fire interact to suppress tree density in an African savanna. Ecol. Appl. 19, 1909–1919 (2009).PubMed 

Google Scholar 
29.Smit, I. P. J. & Ferreira, S. M. Management intervention affects river-bound spatial dynamics of elephants. Biol. Conserv. 143, 2172–2181 (2010).
Google Scholar 
30.Loarie, S. R., van Aarde, R. J. & Pimm, S. L. Elephant seasonal vegetation preferences across dry and wet savannas. Biol. Conserv. 142, 3099–3107 (2009).
Google Scholar 
31.Young, K. D., Ferreira, S. M. & Van Aarde, R. J. Elephant spatial use in wet and dry savannas of southern Africa. J. Zool. 278, 189–205 (2009).
Google Scholar 
32.Codron, J. et al. Elephant (Loxodonta africana) diets in Kruger National Park, South Africa: spatial and landscape differences. J. Mammal. 87, 27–34 (2006).
Google Scholar 
33.Timberlake, J. Colophospermum mopane: Annotated Bibliography and Review (Zimbabwe Bulletin of Forestry Research No. 11, 1995).
Google Scholar 
34.Pyšek, P. et al. Into the great wide open: Do alien plants spread from rivers to dry savanna in the Kruger National Park?. NeoBiota 60, 61–77 (2020).
Google Scholar 
35.Eckhardt, H. C., van Wilgen, B. W. & Biggs, H. C. Trends in woody vegetation cover in the Kruger National Park, South Africa, between 1940 and 1998. Afr. J. Ecol. 38, 108–115 (2000).
Google Scholar 
36.Groen, T. A. Spatial Matters: How Spatial Patterns and Processes Affect Savanna Dynamics. PhD Thesis, Wageningen University, The Netherlands (2007).37.MacFadyen, S., Hui, C., Verburg, P. H. & Van Teeffelen, A. J. A. Quantifying spatiotemporal drivers of environmental heterogeneity in Kruger National Park, South Africa. Landsc. Ecol. 31, 2013–2029 (2016).
Google Scholar 
38.Munyati, C. & Ratshibvumo, T. Differentiating geological fertility derived vegetation zones in Kruger National Park, SouthAfrica, using Landsat and MODIS imagery. J. Nat. Conserv. 18, 169–179 (2010).
Google Scholar 
39.Colgan, M. S., Asner, G. P., Levick, S. R., Martin, R. E. & Chadwick, O. A. Topo-edaphic controls over woody plant biomass in South African savannas. Biogeosciences 9, 1809–1821 (2012).ADS 

Google Scholar 
40.Walter, H. & Burnett, J. H. Ecology of Tropical and Subtropical Vegetation Vol. 539 (Oliver and Boyd, 1971).
Google Scholar 
41.Scholes, R. J., Bond, W. J. & Eckhardt, H. C. Vegetation dynamics in the Kruger ecosystem. In The Kruger Experience: Ecology and Management of Savanna Heterogeneity (eds du Toit, J. T. et al.) 243–262 (Island Press, 2003).
Google Scholar 
42.Knoop, W. T. & Walker, B. H. Interactions of woody and herbaceous vegetation in southern African savanna. J. Ecol. 73, 235–253 (1985).
Google Scholar 
43.Tilman, D. The resource-ratio hypothesis of plant succession. Am. Nat. 125, 827–852 (1985).
Google Scholar 
44.Scholes, R. J. & Archer, S. R. Tree–grass interactions in savannas. Annu. Rev. Ecol. Syst. 28, 517–544 (1997).
Google Scholar 
45.Muvengwi, J., Davies, A. B., Parrini, F. & Witkowski, E. T. F. Contrasting termite diversity and assemblages on granitic and basaltic African savanna landscapes. Insect. Soc. 65, 25–35 (2018).
Google Scholar 
46.Trollope, W. S. W., Potgieter, A. L. F. & Zambatis, N. Assessing veld condition in the Kruger National Park using key grass species. Koedoe 32, 67–93 (1989).
Google Scholar 
47.Ehleringer, J. R. & Monson, R. K. Evolutionary and ecological aspects of photosynthetic pathway variation. Annu. Rev. Ecol. Syst. 24, 411–439 (1993).
Google Scholar 
48.Chytrý, M., Tichý, L. & Roleček, J. Local and regional patterns of species richness in Central European vegetation types along the pH/calcium gradient. Folia Geobot. 38, 429–442 (2003).
Google Scholar 
49.Owen-Smith, N., Page, B., Teren, G. & Druce, D. J. Megabrowser impacts on woody vegetation in savannas. In Savanna Woody Plants and Large Herbivores (eds Scogings, P. F. & Sankaran, M.) 585–611 (Wiley, 2019).
Google Scholar 
50.Nasseri, N. A., McBrayer, L. D. & Schulte, B. A. The impact of tree modification by African elephant (Loxodonta africana) on herpetofaunal species richness in northern Tanzania. Afr. J. Ecol. 49, 133–140 (2010).
Google Scholar 
51.Asner, G. P. & Levick, S. R. Landscape-scale effects of herbivores on treefall in African savannas. Ecol. Lett. 15, 1211–1217 (2012).PubMed 

Google Scholar 
52.Haynes, G. Elephants (and extinct relatives) as earth-movers and ecosystem engineers. Geomorphology 157, 99–107 (2012).ADS 

Google Scholar 
53.Kruger, L. M., Coetzee, J. A. & Vickers, K. The Impacts of Elephants on Woodlands and Associated Biodiversity (Summary report to South African National Parks, Organization for Tropical Studies, 2007).54.Guy, P. R. The influence of elephants and fire on a Brachystegia julbernardia woodland in Zimbabwe. J. Trop. Ecol. 5, 215–226 (1989).
Google Scholar 
55.Laws, R. M., Parker, I. S. C. & Johnstone, R. C. B. Elephants and Their Habitats: The Ecology of Elephants in North Bunyoro, Uganda (Clarendon Press, 1975).
Google Scholar 
56.Thompson, P. J. The role of elephants, fire and other agents in the decline of Brachystegia woodlands. J. S. Afr. Wildl. Manag. Assoc. 5, 11–18 (1975).
Google Scholar 
57.Barnes, M. E. Effects of large herbivores and fire on the regeneration of Acacia erioloba woodlands in Chobe National Park, Botswana. Afr. J. Ecol. 39, 340–350 (2001).
Google Scholar 
58.Scogings, P. F., Johansson, T., Hjältén, J. & Kruger, J. Responses of woody vegetation to exclusion of large herbivores in semi-arid savannas. Austral Ecol. 37, 56–66 (2012).
Google Scholar 
59.Verweij, R. J. T., Higgins, S. I., Bond, W. J. & February, E. C. Water sourcing by trees in a mesic savanna: Responses to severing deep and shallow roots. Environ. Exp. Bot. 74, 229–236 (2011).
Google Scholar 
60.Smit, I. et al. Effects of fire on woody vegetation structure in African savanna. Ecol. Appl. 20, 1865–1875 (2010).PubMed 

Google Scholar 
61.MacFadyen, S., Hui, C., Verburg, P. H. & Van Teeffelen, A. J. A. Spatiotemporal distribution dynamics of elephants in response to density, rainfall, rivers and fire in Kruger National Park, South Africa. Divers. Distrib. 25, 880–894 (2019).
Google Scholar 
62.O’Connor, T. G., Goodman, P. S. & Clegg, B. A functional hypothesis of the threat of local extirpation of woody plant species by elephant in Africa. Biol. Conserv. 136, 329–345 (2007).
Google Scholar 
63.Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006. NASA EOSDIS Land Processes DAAC. Accessed 2021-06-08 from https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).64.Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. 104, 5925–5930 (2007).CAS 
ADS 
PubMed 
PubMed Central 

Google Scholar 
65.Bohdalková, E., Toszogyova, A., Šímová, I. & Storch, D. Universality in biodiversity patterns: variation in species-temperature and species-productivity relationships reveals a prominent role of productivity in diversity gradients. Ecography 44, 1366–1378 (2021).
Google Scholar 
66.Knight, R. S., Crowe, T. M. & Siegfried, W. R. Distribution and species richness of trees in southern Africa. J. S. Afr. Bot. 48, 455–480 (1982).
Google Scholar 
67.Gaylard, A., Owen-Smith, N. & Redfern, J. Surface water availability: implications for heterogeneity and eco-system processes. In The Kruger Experience: Ecology and Management of Savanna Heterogeneity (eds du Toit, J. T. et al.) 171–188 (Island Press, 2003).
Google Scholar 
68.Venter, F. J. A Classification of Land for Management Planning in the Kruger National Park. PhD Thesis, University of South Africa (1990).69.du Toit, J. T. et al. (eds) The Kruger Experience: Ecology and Management of Savanna Heterogeneity (Island Press, 2003).
Google Scholar 
70.Smit, I. P. J., Smit, C. F., Govender, N., van der Linde, M. & MacFadyen, S. Rainfall, geology and landscape position generate large-scale spatiotemporal fire pattern heterogeneity in an African savanna. Ecography 36, 447–459 (2013).
Google Scholar 
71.Obermeijer, A. A. A preliminary list of plants found in the Kruger National Park. Ann. Transvaal Mus. 17, 185–227 (1937).
Google Scholar 
72.van der Schijff, H. P. ‘n Ekologiese Studie van die Flora van die Nasionale Krugerwildtuin. D.Sc. Thesis, Potchefstroom University (1957).73.van der Schijff, H. P. The affinities of the flora of the Kruger National Park. Kirkia 7, 109–120 (1968).
Google Scholar 
74.Coetzee, B. J. Phytosociology, Vegetation Structure and Landscapes of the Central District. Kruger National Park, South Africa. Dissertationes Botanicae, J. Cramer, Vaduz (1983).75.Gertenbach, W. P. D. Landscapes of the Kruger National Park. Koedoe 26, 9–121 (1983).
Google Scholar 
76.Siebert, F. & Eckhardt, H. C. The vegetation and floristics of the Nkhuhlu exclosures, Kruger National Park. Koedoe 50, 126–144 (2008).
Google Scholar 
77.Siebert, F., Eckhardt, H. C. & Siebert, S. J. The vegetation and floristics of the Letaba exclosures, Kruger National Park, South Africa. Koedoe 52, 1–12 (2010).
Google Scholar 
78.Wigley, B. J., Fritz, H., Coetsee, C. & Bond, W. J. Herbivores shape woody plant communities in the Kruger National Park: Lessons from three long-term exclosures. Koedoe 56, 1–12 (2014).
Google Scholar 
79.Enslin, B. W., Potgieter, A. L. F., Biggs, H. C. & Biggs, R. Long term effects of fire frequency and season on the woody vegetation dynamics of the Sclerocarya birrea/Acacia nigrescens savanna of the Kruger National Park. Koedoe 43, 27–37 (2000).
Google Scholar 
80.Brits, J., Van Rooyen, M. W. & Van Rooyen, N. Ecological impact of large herbivores on the woody vegetation at selected watering points on the eastern basaltic soils in the Kruger National Park. Afr. J. Ecol. 40, 53–60 (2002).
Google Scholar 
81.Todd, S. W. Gradients in vegetation cover, structure and species richness of Nama-Karoo shrublands in relation to distance from livestock watering points. J. Appl. Ecol. 43, 293–304 (2006).
Google Scholar 
82.Foxcroft, L. C., Henderson, L., Nichols, G. R. & Martin, B. W. A revised list of alien plants for the Kruger National Park. Koedoe 46, 21–44 (2003).
Google Scholar 
83.Foxcroft, L. C., Richardson, D. M. & Wilson, J. R. Ornamental plants as invasive aliens: Problems and solutions in Kruger National Park, South Africa. Environ. Manag. 41, 32–51 (2008).ADS 

Google Scholar 
84.Mueller-Dombois, D. & Ellenberg, H. Aims and Methods of Vegetation Ecology (Wiley, 1974).
Google Scholar 
85.van der Maarel, E. Transformation of cover-abundance values in phytosociology and its effects on community similarity. Vegetatio 38, 97–114 (1979).
Google Scholar 
86.Magurran, A. E. Ecological Diversity and Its Measurement (Croom Helm, 1988).
Google Scholar 
87.Pooley, E. Wildflowers of Kwazulu-Natal and the Eastern Region (Natal Flora Publications Trust, 1998).
Google Scholar 
88.Schmidt, E., Lötter, M. & McCleland, W. Trees and Shrubs of Mpumalanga and Kruger National Park (Jacana Media, 2002).
Google Scholar 
89.van der Walt, R. Wild Flowers of the Limpopo Valley (Retha van der Walt, 2009).
Google Scholar 
90.Oudtshoorn, F. V. Guide to Grasses of Southern Africa 3rd edn. (Briza Publications, 2018).
Google Scholar 
91.R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2013).
Google Scholar 
92.Lenth, R. Emmeans: Estimated Marginal Means, aka Least-Square Means. R package version 1.2.1. https://CRAN.R-project.org/package=emmeans (2018).93.Lepš, J. & Šmilauer, P. Multivariate Analysis of Ecological Data Using CANOCO 5 (Cambridge University Press, 2014).MATH 

Google Scholar 
94.Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: A comprehensive framework 562 for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Modell. 196, 483–563 (2006).
Google Scholar 
95.Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using Canoco 5 2nd edn. (Cambridge University Press, 2014).MATH 

Google Scholar  More

1.Bastviken, D. Methane. in Encyclopedia of Inland Waters (ed. Likens, G. E.) 783–805 (Elsevier, 2009). https://doi.org/10.1016/B978-012370626-3.00117-42.Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Thottathil, S. D., Reis, P. C. J., del Giorgio, P. A. & Prairie, Y. T. The extent and regulation of summer methane oxidation in Northern Lakes. J. Geophys. Res. Biogeosciences 123, 3216–3230 (2018).CAS 
ADS 

Google Scholar 
4.Kankaala, P., Taipale, S., Nykänen, H. & Jones, R. I. Oxidation, efflux, and isotopic fractionation of methane during autumnal turnover in a polyhumic, boreal lake. J. Geophys. Res. Biogeosciences 112, 1–7 (2007).
Google Scholar 
5.Kankaala, P., Huotari, J., Peltomaa, E., Saloranta, T. & Ojala, A. Methanotrophic activity in relation to methane efflux and total heterotrophic bacterial production in a stratified, humic, boreal lake. Limnol. Oceanogr. 51, 1195–1204 (2006).CAS 
ADS 

Google Scholar 
6.Bastviken, D., Ejlertsson, J., Sundh, I. & Tranvik, L. Methane as a source of carbon and energy for lake pelagic food webs. Ecology 84, 969–981 (2003).
Google Scholar 
7.Kankaala, P., Lopez Bellido, J., Ojala, A., Tulonen, T. & Jones, R. I. Variable production by different pelagic energy mobilizers in Boreal Lakes. Ecosystems 16, 1152–1164 (2013).CAS 

Google Scholar 
8.Morana, C. et al. Methanotrophy within the water column of a large meromictic tropical lake (Lake Kivu, East Africa). Biogeosciences 12, 2077–2088 (2015).ADS 

Google Scholar 
9.Grey, J. The incredible lightness of being methane-fuelled: stable isotopes reveal alternative energy pathways in aquatic ecosystems and beyond. Front. Ecol. Evol. 4, 1–14 (2016).
Google Scholar 
10.Jones, R. I. & Grey, J. Biogenic methane in freshwater food webs. Freshw. Biol. 56, 213–229 (2011).CAS 

Google Scholar 
11.Kankaala, P., Taipale, S. & Grey, J. Experimental d13C evidence for a contribution of methane to pelagic food webs in lakes. Limnol. Oceanogr. 51, 2821–2827 (2006).CAS 
ADS 

Google Scholar 
12.Guérin, F. & Abril, G. Significance of pelagic aerobic methane oxidation in the methane and carbon budget of a tropical reservoir. J. Geophys. Res. 112, 1–14 (2007).
Google Scholar 
13.Rasilo, T., Hutchins, R. H. S., Ruiz-González, C. & del Giorgio, P. A. Transport and transformation of soil-derived CO2, CH4 and DOC sustain CO2 supersaturation in small boreal streams. Sci. Total Environ. 579, 902–912 (2017).CAS 
PubMed 
ADS 

Google Scholar 
14.Soued, C. & Prairie, Y. T. The carbon footprint of a Malaysian tropical reservoir: measured versus modeled estimates highlight the underestimated key role of downstream processes. Biogeosciences 17, 515–227 (2020).CAS 
ADS 

Google Scholar 
15.Del Giorgio, P. A. & Gasol, J. M. Physiological structure and single-cell activity in marine bacterioplankton. in Microbial Ecology of the Oceans: Second Edition (ed. Kirchman, D. L.) 243–298 (John Wiley & Sons, Inc, 2008). https://doi.org/10.1002/9780470281840.ch816.Reis, P. C. J., Ruiz-González, C., Soued, C., Crevecoeur, S. & Prairie, Y. T. Rapid shifts in methanotrophic bacterial communities mitigate methane emissions from a tropical hydropower reservoir and its downstream river. Sci. Total Environ. 748, 141374 (2020).CAS 
PubMed 
ADS 

Google Scholar 
17.Thottathil, S. D., Reis, P. C. J. & Prairie, Y. T. Methane oxidation kinetics in northern freshwater lakes. Biogeochemistry 143, 105–116 (2019).CAS 

Google Scholar 
18.Milucka, J. et al. Methane oxidation coupled to oxygenic photosynthesis in anoxic waters. ISME J. 9, 1991–2002 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Zigah, P. K. et al. Methane oxidation pathways and associated methanotrophic communities in the water column of a tropical lake. Limnol. Oceanogr. 60, 553–572 (2015).ADS 

Google Scholar 
20.Mayr, M. J. et al. Growth and rapid succession of methanotrophs effectively limit methane release during lake overturn. Commun. Biol. 3, 1–9 (2020).
Google Scholar 
21.Bussmann, I., Rahalkar, M. & Schink, B. Cultivation of methanotrophic bacteria in opposing gradients of methane and oxygen. FEMS Microbiol. Ecol. 56, 331–344 (2006).CAS 
PubMed 

Google Scholar 
22.Kankaala, P., Eller, G. & Jones, R. I. Could bacterivorous zooplankton affect lake pelagic methanotrophic activity? Fundam. Appl. Limnol. / Arch. f.ür. Hydrobiol. 169, 203–209 (2007).
Google Scholar 
23.Khmelenina, V. N. et al. Structural and functional features of methanotrophs from hypersaline and alkaline lakes. Microbiology 79, 472–482 (2010).CAS 

Google Scholar 
24.Westfall, C. S. & Levin, P. A. Bacterial cell size: multifactorial and multifaceted. Annu. Rev. Microbiol. 71, 499–517 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Chien, A. C., Hill, N. S. & Levin, P. A. Cell size control in bacteria. Curr. Biol. 22, R340–R349 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Velimirov, B. Nanobacteria, ultramicrobacteria and starvation forms: a search for the smallest metabolizing bacterium. Microbes Environ. 16, 67–77 (2001).
Google Scholar 
27.Reis, P. C. J., Thottathil, S. D., Ruiz-González, C. & Prairie, Y. T. Niche separation within aerobic methanotrophic bacteria across lakes and its link to methane oxidation rates. Environ. Microbiol. 22, 738–751 (2020).CAS 
PubMed 

Google Scholar 
28.Garcia-Chaves, M. C., Cottrell, M. T., Kirchman, D. L., Ruiz-González, C. & del Giorgio, P. A. Single-cell activity of freshwater aerobic anoxygenic phototrophic bacteria and their contribution to biomass production. Isme J. 10, 1579–1588 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
29.Jürgens, K. & Matz, C. Predation as a shaping force for the phenotypic and genotypic composition of planktonic bacteria. Antonie van Leeuwenhoek. Int. J. Gen. Mol. Microbiol. 81, 413–434 (2002).
Google Scholar 
30.Rautio, M. & Vincent, W. F. Benthic and pelagic food resources for–zooplankton in shallow high-latitude lakes and ponds. Freshw. Biol. 51, 1038–1052 (2006).CAS 

Google Scholar 
31.Rissanen, A. J. et al. Gammaproteobacterial methanotrophs dominate methanotrophy in aerobic and anaerobic layers of boreal lake waters. Aquat. Microb. Ecol. 81, 257–276 (2018).
Google Scholar 
32.Zimmermann, M. et al. Microbial methane oxidation efficiency and robustness during lake overturn. Limnol. Oceanogr. Lett. 6, 320–328 (2021).CAS 

Google Scholar 
33.Puri, A. W. et al. Genetic tools for the industrially promising methanotroph Methylomicrobium buryatense. Appl. Environ. Microbiol. 81, 1775–1781 (2015).PubMed 
PubMed Central 
ADS 

Google Scholar 
34.Strong, P. J., Kalyuzhnaya, M., Silverman, J. & Clarke, W. P. A methanotroph-based biorefinery: potential scenarios for generating multiple products from a single fermentation. Bioresour. Technol. 215, 314–323 (2016).CAS 
PubMed 

Google Scholar 
35.Oswald, K. et al. Aerobic gammaproteobacterial methanotrophs mitigate methane emissions from oxic and anoxic lake waters. Limnol. Oceanogr. 61, S101–S118 (2016).
Google Scholar 
36.Smith, E. M. & Prairie, Y. T. Bacterial metabolism and growth efficiency in lakes: the importance of phosphorus availability. Limnol. Oceanogr. 49, 137–147 (2004).CAS 
ADS 

Google Scholar 
37.Del Giorgio, P. A., Cole, J. J., Caraco, N. F. & Peters, R. H. Linking planktonic biomass and metabolism to net gas fluxes in northern temperate lakes. Ecology 80, 1422–1431 (1999).
Google Scholar 
38.Kellerman, A. M., Dittmar, T., Kothawala, D. N. & Tranvik, L. J. Chemodiversity of dissolved organic matter in lakes driven by climate and hydrology. Nat. Commun. 5, 3804 (2014).CAS 
PubMed 
ADS 

Google Scholar 
39.Sun, L., Perdue, E. M., Meyer, J. L. & Weis, J. Use of elemental composition to predict bioavailability of dissolved organic matter in a Georgia river. Limnol. Oceanogr. 42, 714–721 (1997).CAS 
ADS 

Google Scholar 
40.Kellerman, A. M., Kothawala, D. N., Dittmar, T. & Tranvik, L. J. Persistence of dissolved organic matter in lakes related to its molecular characteristics. Nat. Geosci. 8, 454–457 (2015).CAS 
ADS 

Google Scholar 
41.Guillemette, F. & del Giorgio, P. A. Reconstructing the various facets of dissolved organic carbon bioavailability in freshwater ecosystems. Limnol. Oceanogr. 56, 734–748 (2011).CAS 
ADS 

Google Scholar 
42.Logue, J. B. et al. Experimental insights into the importance of aquatic bacterial community composition to the degradation of dissolved organic matter. ISME J. 10, 533–545 (2016).CAS 
PubMed 

Google Scholar 
43.Salcher, M. M., Posch, T. & Pernthaler, J. In situ substrate preferences of abundant bacterioplankton populations in a prealpine freshwater lake. ISME J. 7, 896–907 (2013).CAS 
PubMed 

Google Scholar 
44.Sobek, S., Tranvik, L. J., Prairie, Y., Kortelainen, P. & Cole, J. J. Patterns and regulation of dissolved organic carbon: an analysis of 7,500 widely distributed lakes. Limnol. Oceanogr. 52, 1208–1219 (2007).CAS 
ADS 

Google Scholar 
45.Kalyuzhnaya, M. G. et al. Highly efficient methane biocatalysis revealed in a methanotrophic bacterium. Nat. Commun. 4, 2785 (2013).CAS 
PubMed 
ADS 

Google Scholar 
46.Oshkin, I. Y. et al. Methane-fed microbial microcosms show differential community dynamics and pinpoint taxa involved in communal response. ISME J. 9, 1119–1129 (2014).PubMed 
PubMed Central 

Google Scholar 
47.Chistoserdova, L. & Kalyuzhnaya, M. G. Current trends in methylotrophy. Trends Microbiol 26, 703–714 (2018).CAS 
PubMed 

Google Scholar 
48.Martinez-Cruz, K. et al. Anaerobic oxidation of methane by aerobic methanotrophs in sub-Arctic lake sediments. Sci. Total Environ. 607–608, 23–31 (2017).PubMed 
ADS 

Google Scholar 
49.Samad, M. S. & Bertilsson, S. Seasonal variation in abundance and diversity of bacterial methanotrophs in five temperate lakes. Front. Microbiol. 8, 1–12 (2017).
Google Scholar 
50.Ricão Canelhas, M., Denfeld, B. A., Weyhenmeyer, G. A., Bastviken, D. & Bertilsson, S. Methane oxidation at the water-ice interface of an ice-covered lake. Limnol. Oceanogr. 61, S78–S90 (2016).ADS 

Google Scholar 
51.Houser, J. N. Water color affects the stratification, surface temperature, heat content, and mean epilimnetic irradiance of small lakes. Can. J. Fish. Aquat. Sci. 63, 2447–2455 (2006).
Google Scholar 
52.Caplanne, S. & Laurion, I. Effect of chromophoric dissolved organic matter on epilimnetic stratification in lakes. Aquat. Sci. 70, 123–133 (2008).CAS 

Google Scholar 
53.Oswald, K. et al. Light-dependent aerobic methane oxidation reduces methane emissions from seasonally stratified lakes. PLoS ONE 10, e0132574 (2015).PubMed 
PubMed Central 

Google Scholar 
54.Savvichev, A. S. et al. Light-dependent methane oxidation is the major process of the methane cycle in the water column of the Bol’shie Khruslomeny Polar Lake. Microbiology 88, 370–374 (2019).CAS 

Google Scholar 
55.Baines, S. B. & Pace, M. L. The production of dissolved organic matter by phytoplankton and its importance to bacteria: patterns across marine and freshwater systems. Limnol. Oceanogr. 36, 1078–1090 (1991).ADS 

Google Scholar 
56.Cole, J. J., Likens, G. E. & Strayer, D. L. Photosynthetically produced dissolved organic carbon: an important carbon source for planktonic bacteria. Limnol. Oceanogr. 27, 1080–1090 (1982).CAS 
ADS 

Google Scholar 
57.Dumestre, J. et al. Influence of light intensity on methanotrophic bacterial activity in Petit Saut reservoir, French Guiana. Appl. Environ. Microbiol. 65, 534–539 (1999).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
58.Murase, J. & Sugimoto, A. Inhibitory effect of light on methane oxidation in the pelagic water column of a mesotrophic lake (Lake Biwa, Japan). Limnol. Oceanogr. 50, 1339–1343 (2005).CAS 
ADS 

Google Scholar 
59.Moran, M. A. & Hodson, R. E. Bacterial production on humic and nonhumic components of dissolved organic carbon. Limnol. Oceanogr. 35, 1744–1756 (1990).CAS 
ADS 

Google Scholar 
60.Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).ADS 

Google Scholar 
61.Roulet, N. & Moore, T. R. Browning the waters. Nature 444, 283–284 (2006).CAS 
PubMed 
ADS 

Google Scholar 
62.Weyhenmeyer, G. A., Prairie, Y. T. & Tranvik, L. J. Browning of boreal freshwaters coupled to carbon-iron interactions along the aquatic continuum. PLoS ONE 9, e88104 (2014).PubMed 
PubMed Central 
ADS 

Google Scholar 
63.O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 42, 10773–10781 (2015).ADS 

Google Scholar 
64.Yamamoto, S., Alcauskas, J. B. & Crozier, T. E. Solubility of methane in distilled water and seawater. J. Chem. Eng. Data 21, 78–80 (1976).CAS 

Google Scholar 
65.Cantin, A., Beisner, B. E., Gunn, J. M., Prairie, Y. T. & Winter, J. G. Effects of thermocline deepening on lake plankton communities. Can. J. Fish. Aquat. Sci. 68, 260–276 (2011).
Google Scholar 
66.Smith, D. C. & Azam, F. A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine. Mar. Microb. Food Webs 6, 107–114 (1992).
Google Scholar 
67.Del Giorgio, P. A., Pace, M. L. & Fischer, D. Relationship of bacterial growth efficiency to spatial variation in bacterial activity in the Hudson River. Aquat. Microb. Ecol. 45, 55–67 (2006).
Google Scholar 
68.Eller, G., Stubner, S. & Frenzel, P. Group-specific 16S rRNA targeted probes for the detection of type I and type II methanotrophs by fluorescence in situ hybridisation. FEMS Microbiol. Lett. 198, 91–97 (2001).CAS 
PubMed 

Google Scholar 
69.Zeder, M. ACME tool3. (2014).70.Callieri, C. et al. Bacteria, Archaea, and Crenarchaeota in the epilimnion and hypolimnion of a deep holo-oligomictic lake. Appl. Environ. Microbiol. 75, 7298–7300 (2009).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
71.Lew, S. & Glińska-Lewczuk, K. Environmental controls on the abundance of methanotrophs and methanogens in peat bog lakes. Sci. Total Environ. 645, 1201–1211 (2018).CAS 
PubMed 
ADS 

Google Scholar 
72.Fagerbakke, K. M., Heldal, M. & Norland, S. Content of carbon, nitrogen, oxygen, sulfur and phosphorus in native aquatic and cultured bacteria. Aquat. Microb. Ecol. 10, 15–27 (1996).
Google Scholar 
73.Read J. S. et al. Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environmental Modelling and Software. 26, 1325–1336 (2011).
Google Scholar 
74.R Core Team. R: a language and environment for statistical computing. (2019). https://www.R-project.org/.75.RStudio Team. RStudio: Integrated Development for R. RStudio, Inc., Boston, MAURL. (2018). http://www.rstudio.com/.76.Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 0.8.3. (2019). https://cran.r-project.org/package=dplyr.77.Sievert, C. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida. (2020).78.Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, New York, 2016). https://ggplot2.tidyverse.org.79.Wilke, C.O. cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R package version 1.0.0. (2019). https://CRAN.R-project.org/package=cowplot.80.Auguie, B. gridExtra: Miscellaneous Functions for ‘Grid’ Graphics. R package version 2.3. (2017). https://CRAN.R-project.org/package=gridExtra.81.Reis, P. C. J., Thottathil, S. D. & Prairie, Y. T. Dataset: the role of methanotrophy in the microbial carbon metabolism of temperate lakes. (1.0.0) [Data set]. Zenodo. (2021). https://doi.org/10.5281/zenodo.5737277. More

1.Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. P Natl Acad. Sci. USA 103, 12115–12120 (2006).CAS 
ADS 

Google Scholar 
2.Pedros-Alio, C. The rare bacterial biosphere. Ann. Rev. Mar. Sci. 4, 449–466 (2012).PubMed 

Google Scholar 
3.Lynch, M. D. J. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).CAS 
PubMed 

Google Scholar 
4.Campbell, B. J., Yu, L. Y., Heidelberg, J. F. & Kirchman, D. L. Activity of abundant and rare bacteria in a coastal ocean. P Natl Acad. Sci. USA 108, 12776–12781 (2011).CAS 
ADS 

Google Scholar 
5.Gobet, A. et al. Diversity and dynamics of rare and of resident bacterial populations in coastal sands. Isme J. 6, 542–553 (2012).PubMed 

Google Scholar 
6.Wilhelm, L. et al. Rare but active taxa contribute to community dynamics of benthic biofilms in glacier-fed streams. Environ. Microbiol. 16, 2514–2524 (2014).CAS 
PubMed 

Google Scholar 
7.Lawson, C. E. et al. Rare taxa have potential to make metabolic contributions in enhanced biological phosphorus removal ecosystems. Environ. Microbiol. 17, 4979–4993 (2015).CAS 
PubMed 

Google Scholar 
8.Newton, R. J. & Shade, A. Lifestyles of rarity: understanding heterotrophic strategies to inform the ecology of the microbial rare biosphere. Aquat. Micro. Ecol. 78, 51–63 (2016).
Google Scholar 
9.Lauro, F. M. et al. The genomic basis of trophic strategy in marine bacteria. P Natl Acad. Sci. USA 106, 15527–15533 (2009).CAS 
ADS 

Google Scholar 
10.Klappenbach, J. A., Dunbar, J. M. & Schmidt, T. M. RRNA operon copy number reflects ecological strategies of bacteria. Appl Environ. Micro. 66, 1328–1333 (2000).CAS 
ADS 

Google Scholar 
11.Roller, B. R. K., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 16160 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Polz, M. F. & Cordero, O. X. Bacterial evolution: genomics of metabolic trade-offs. Nat. Microbiol. 1, 16181 (2016).CAS 
PubMed 

Google Scholar 
13.Giovannoni, S. J. SAR11 bacteria: the most abundant plankton in the oceans. Ann. Rev. Mar. Sci. 9, 231–255 (2017).PubMed 

Google Scholar 
14.Elser, J. J. et al. Biological stoichiometry from genes to ecosystems. Ecol. Lett. 3, 540–550 (2000).
Google Scholar 
15.Hessen, D. O., Elser, J. J., Sterner, R. W. & Urabe, J. Ecological stoichiometry: an elementary approach using basic principles. Limnol. Oceanogr. 58, 2219–2236 (2013).CAS 
ADS 

Google Scholar 
16.Acharya, K., Kyle, M. & Elser, J. J. Biological stoichiometry of Daphnia growth: an ecophysiological test of the growth rate hypothesis. Limnol. Oceanogr. 49, 656–665 (2004).CAS 
ADS 

Google Scholar 
17.Hendrixson, H. A., Sterner, R. W. & Kay, A. D. Elemental stoichiometry of freshwater fishes in relation to phylogeny, allometry and ecology. J. Fish. Biol. 70, 121–140 (2007).
Google Scholar 
18.Matzek, V. & Vitousek, P. M. N: P stoichiometry and protein: RNA ratios in vascular plants: an evaluation of the growth-rate hypothesis. Ecol. Lett. 12, 765–771 (2009).PubMed 

Google Scholar 
19.Ghoul, M. & Mitri, S. The ecology and evolution of microbial competition. Trends Microbiol 24, 833–845 (2016).CAS 
PubMed 

Google Scholar 
20.Laland, K., Matthews, B. & Feldman, M. W. An introduction to niche construction theory. Evol. Ecol. 30, 191–202 (2016).PubMed 
PubMed Central 

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

Google Scholar 
22.Větrovský, T. & Baldrian, P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. Plos ONE 8, e57923 (2013).PubMed 
PubMed Central 
ADS 

Google Scholar 
23.Molenaar, D., van Berlo, R., de Ridder, D. & Teusink, B. Shifts in growth strategies reflect tradeoffs in cellular economics. Mol. Syst. Biol. 5, 323–323 (2009).PubMed 
PubMed Central 

Google Scholar 
24.Nemergut, D. R. et al. Decreases in average bacterial community rRNA operon copy number during succession. Isme J. 10, 1147–1156 (2016).CAS 
PubMed 

Google Scholar 
25.Wu, L. W. et al. Microbial functional trait of rRNA operon copy numbers increases with organic levels in anaerobic digesters. Isme J. 11, 2874–2878 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Wu, L. W. et al. Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nat. Microbiol. 4, 2579–2579 (2019).PubMed 

Google Scholar 
27.Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. K. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res. 43, D593–D598 (2015).CAS 
PubMed 

Google Scholar 
28.Dai, T. et al. Dynamics of coastal bacterial community average ribosomal RNA operon copy number reflect its response and sensitivity to ammonium and phosphate. Environ. Pollut. 260, 113971 (2020).CAS 
PubMed 

Google Scholar 
29.Zhou, J., Deng, Y., Luo, F., He, Z. & Yang, Y. Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. Mbio 2, e00122–00111 (2011).PubMed 
PubMed Central 

Google Scholar 
30.Vellend, M. Conceptual Synthesis in Community Ecology. Q Rev. Biol. 85, 183–206 (2010).PubMed 

Google Scholar 
31.Frey, E. Evolutionary game theory: Theoretical concepts and applications to microbial communities. Phys. A 389, 4265–4298 (2010).MathSciNet 
CAS 
MATH 

Google Scholar 
32.Boeuf, D. et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. P Natl Acad. Sci. USA 116, 11824 (2019).CAS 

Google Scholar 
33.Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
34.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 

Google Scholar 
35.Gorter, F. A., Manhart, M. & Ackermann, M. Understanding the evolution of interspecies interactions in microbial communities. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190256 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Gandhi, S. R., Korolev, K. S. & Gore, J. Cooperation mitigates diversity loss in a spatially expanding microbial population. P Natl Acad. Sci. USA 116, 23582–23587 (2019).CAS 

Google Scholar 
37.Calatayud, J. et al. Positive associations among rare species and their persistence in ecological assemblages. Nat. Ecol. Evol. 4, 40–45 (2020).PubMed 

Google Scholar 
38.Furman, O. et al. Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics. Nat. Commun. 11, 1904 (2020).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
39.Tardy, V. et al. Stability of soil microbial structure and activity depends on microbial diversity. Env. Microbiol. Rep. 6, 173–183 (2014).CAS 

Google Scholar 
40.Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).CAS 
PubMed 
ADS 

Google Scholar 
41.Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063 (2020).PubMed 

Google Scholar 
42.Blasche, S. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat. Microbiol. 6, 196–208 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Chatzinikolaou, E. et al. Spatio-temporal benthic biodiversity patterns and pollution pressure in three Mediterranean touristic ports. Sci. Total Environ. 624, 648–660 (2018).CAS 
PubMed 
ADS 

Google Scholar 
44.Filippini, G. et al. Sediment bacterial communities associated with environmental factors in Intermittently Closed and Open Lakes and Lagoons (ICOLLs). Sci. Total Environ. 693, 133462 (2019).CAS 
PubMed 
ADS 

Google Scholar 
45.Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data 2, 150023 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Huse, S. M. et al. Exploring microbial diversity and taxonomy using SSU rRNA hypervariable tag sequencing. Plos Genet. 4, e1000255 (2008).PubMed 
PubMed Central 

Google Scholar 
47.Salas-González, I. et al. Coordination between microbiota and root endodermis supports plant mineral nutrient homeostasis. Science 371, eabd0695 (2021).PubMed 

Google Scholar 
48.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. P Natl Acad. Sci. USA 108, 4516 (2011).CAS 
ADS 

Google Scholar 
49.Wear, E. K., Wilbanks, E. G., Nelson, C. E. & Carlson, C. A. Primer selection impacts specific population abundances but not community dynamics in a monthly time-series 16S rRNA gene amplicon analysis of coastal marine bacterioplankton. Environ. Microbiol. 20, 2709–2726 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
PubMed 

Google Scholar 
51.Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar 
52.Wu, L. W. et al. Long-term successional dynamics of microbial association networks in anaerobic digestion processes. Water Res. 104, 1–10 (2016).PubMed 

Google Scholar 
53.Galand, P. E., Casamayor, E. O., Kirchman, D. L. & Lovejoy, C. Ecology of the rare microbial biosphere of the Arctic Ocean. P Natl Acad. Sci. USA 106, 22427–22432 (2009).CAS 
ADS 

Google Scholar 
54.Ju, F. & Zhang, T. Bacterial assembly and temporal dynamics in activated sludge of a full-scale municipal wastewater treatment plant. Isme J. 9, 683–695 (2015).CAS 
PubMed 
ADS 

Google Scholar  More

1.Lyson, T. R. et al. Origin of the unique ventilatory apparatus of turtles. Nat. Commun. 5(5211), 1–11. https://doi.org/10.1038/ncomms6211 (2014).CAS 
Article 

Google Scholar 
2.Gans, C. & Hughes, G. The mechanism of lung ventilation in the tortoise Testudo graeca Linné. J. Exp. Biol. 47(1), 1–20 (1967).CAS 
Article 
PubMed 

Google Scholar 
3.Jackson, D. C., Singer, J. H. & Downey, P. T. Oxidative cost of breathing in the turtle Chrysemys picta bellii. Am. J. Physiol. 261, R1325–R1328 (1991).CAS 
PubMed 

Google Scholar 
4.Landberg, T., Mailhot, J. D. & Brainerd, E. L. Lung ventilation during treadmill locomotion in a semi-aquatic turtle, Trachemys scripta. J. Exp. Zool. 311A, 551–562. https://doi.org/10.1002/jez.478 (2009).Article 

Google Scholar 
5.Ruhr, I., Rose, K., Sellers, W., Crossley, D. II. & Codd, J. Turning turtle: Scaling relationships and self-righting ability in Chelydra serpentina. Proc. R. Soc. B. 288, 20210213. https://doi.org/10.1098/rspb.2021.0213 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Pritchard, P. C. H. Encyclopaedia of Turtles (TFH, 1979).
Google Scholar 
7.Carr, A. Handbook of Turtles: The Turtles of the United States, Canada, and Baja California (Cornell University Press, 1952).
Google Scholar 
8.Rivera, G. Ecomorphological variation in shell shape of the freshwater turtle Pseudemys concinna inhabiting different aquatic flow regimes. Int. Comp. Biol. 48(6), 769–787. https://doi.org/10.1093/icb/icn088 (2008).Article 

Google Scholar 
9.McNeill Alexander, R. Gaits of mammals and turtles. J. R. Soc. Jpn. 11(3), 314–319 (1993).Article 

Google Scholar 
10.Zani, P. A. & Kram, R. Low metabolic cost of locomotion in ornate box turtles, Terrapene ornate. J. Exp. Biol. 211, 3671–3676. https://doi.org/10.1242/jeb.019869 (2008).Article 
PubMed 

Google Scholar 
11.Sellers, W. I., Rose, K. A. R., Crossley, D. A. II. & Codd, J. R. Inferring cost of transport from whole-body kinematics in three sympatric turtle species with different locomotor habits. Comp. Biochem. Physiol. A. 247, 110739. https://doi.org/10.1016/j.cbpa.2020.110739 (2020).CAS 
Article 

Google Scholar 
12.Chiari, Y., van der Meijden, A., Caccone, A., Claude, J. & Gilles, B. Self-righting potential and the evolution of shell shape in Galápagos tortoises. Sci. Rep. 7(1), 1–8. https://doi.org/10.1038/s41598-017-15787-7 (2017).CAS 
Article 

Google Scholar 
13.Woledge, R. C. The energetics of tortoise muscle. J. Physiol. 197(3), 685–707 (1968).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Steyermark, A. C. & Spotila, J. R. Body temperature and maternal identity affect snapping turtle (Chelydra serpentina) righting response. Copeia 4, 1050–1057. https://doi.org/10.1643/0045-8511(2001)001[1050:BTAMIA]2.0.CO;2 (2001).Article 

Google Scholar 
15.Rubin, A. M., Blob, R. W. & Mayerl, C. J. Biomechanical factors influencing successful self-righting in the Pleurodire turtle, Emydura subglobosa. J. Exp. Biol. 221, jeb182642. https://doi.org/10.1242/jeb.182642 (2018).Article 
PubMed 

Google Scholar 
16.Penn, D. & Brockmann, H. J. Age-biased stranding and righting in male horseshoe crabs, Limulus polyphemus. Anim. Behav. 49, 1531–1539. https://doi.org/10.1016/003-3472(95)90074-8 (1995).Article 

Google Scholar 
17.Bonnet, X. et al. Sexual dimorphism in steppe tortoises (Testudo horsfieldii): Influence of the environment and sexual selection on body shape and mobility. Biol. J. Linn. Soc. 72, 357–372. https://doi.org/10.1006/bjls.2000.0504 (2001).Article 

Google Scholar 
18.Zuffi, M. A. L. & Corti, C. Aspects of population ecology of Testudo hermanni hermanni from Asinara Island, NW Sardinia (Italy, Western Mediterranean Sea): Preliminary data. Amphib-Reptil. 24, 441–447 (2003).Article 

Google Scholar 
19.Domokos, G. & Várkonyi, P. L. Geometry and self-righting of turtles. Proc. R. Soc. B. 275(1630), 11–17. https://doi.org/10.1098/rspb.2007.1188 (2008).Article 
PubMed 

Google Scholar 
20.Mann, G. K. H., O’Riain, M. J. & Hofmeyr, M. D. Shaping up to fight: Sexual selection influences body shape and size in the fighting tortoise (Chersina angulata). J. Zool. 269, 373–379. https://doi.org/10.1111/j.1469-7998.2006.00079x (2006).Article 

Google Scholar 
21.Golubović, A., Bonnet, X., Djordjević, S., Djurakic, M. & Tomović, L. Variations in righting behavior across Hermann’s tortoise populations. J. Zool. 291, 69–75. https://doi.org/10.1111/jzo.12047 (2013).Article 

Google Scholar 
22.Golubović, A., Andelkovic, M., Arsovski, D., Bonnet, X. & Tomović, L. Locomotor performances reflect habitat constraints in an armoured species. Behav. Ecol. Sociobiol. 71, 93. https://doi.org/10.1007/s00265-017-2318-0 (2017).Article 

Google Scholar 
23.Ashe, V. M. The righting reflex in turtles: A description and comparison. Psychol. Sci. 20, 150–152. https://doi.org/10.3758/BF03335647 (1970).Article 

Google Scholar 
24.Golubović, A., Tomović, L. & Ivanović, A. Geometry of self-righting: The case of Hermann’s tortoises. Zool. Anz. 254, 99–105. https://doi.org/10.1016/j.jcz.2014.12.003 (2015).Article 

Google Scholar 
25.Finkler, M. S. Influence of water availability during hatching on hatchling size, body composition, desiccation tolerance, and terrestrial locomotor performance in the snapping turtle, Chelydra serpentina. Physiol. Biochem. Zool. 72, 714–722. https://doi.org/10.1086/316711 (1999).CAS 
Article 
PubMed 

Google Scholar 
26.Stojadinović, D., Milošević, D. & Crnobrnja-Isailović, J. Righting time versus shell size and shape dimorphism in adult Hermann’s tortoises: Field observations meet theoretical predictions. Anim. Biol. 63(4), 381–396. https://doi.org/10.1163/15707563-00002420 (2013).Article 

Google Scholar 
27.Delmas, V., Baudry, E., Girondot, M. & Prevot-Julliard, A.-C. The righting reflex as a fitness indicator in freshwater turtles. Biol. J. Linn. Soc. 91, 99–109. https://doi.org/10.1111/j.1095-8312/2007.00780.x (2007).Article 

Google Scholar 
28.Burger, J. Behavior of hatchling diamondback terrapins (Malaclemys terrapin) in the field. Copeia 1976, 742. https://doi.org/10.2307/1443457 (1976).Article 

Google Scholar 
29.Landberg, T., Mailhot, J. D. & Brainerd, E. L. Lung ventilation during treadmill locomotion in a terrestrial turtle, Terrapene carolina. J. Exp. Biol. 206, 3391–3404. https://doi.org/10.1242/jeb.00553 (2003).Article 
PubMed 

Google Scholar 
30.Gaunt, A. S. & Gans, C. Mechanics of respiration in the snapping turtle, Chelydra serpentina (Linné). J. Morph. 128, 195–227. https://doi.org/10.1002/jmor.1051280205 (1969).Article 

Google Scholar 
31.Lambertz, M., Böhme, W. & Perry, S. F. The anatomy of the respiratory system in Platysternon megacephalum Gray, 1831 (Testudines: Crytodira) and related species, and its phylogenetic implications. Comp. Biochem. Physiol. 156, 330–336. https://doi.org/10.1016/j.cbpa.2009.12.016 (2010).CAS 
Article 

Google Scholar 
32.de Souza, R. B. B. & Klein, W. The influence of the post-pulmonary septum and submersion on the pulmonary mechanics of Trachemys scripta (Cryptodira: Emydidae). J. Exp. Biol. 224(12), 242386. https://doi.org/10.1242/jeb.242386 (2021).Article 

Google Scholar 
33.Jodice, P. G. R., Epperson, D. M. & Visser, G. H. Daily energy expenditure in free-ranging gopher tortoises (Gopherus polyphemus). Copeia 2006(1), 129–136. https://doi.org/10.1643/0045-8511(2006)006[0129:DEEIFG]2.0.CO;2 (2006).Article 

Google Scholar 
34.Zera, A. J. & Harshman, L. G. The physiology of life history trade-offs in animals. Ann. Rev. Ecol. Syst. 32, 95–126. https://doi.org/10.1146/annurev.ecolsys.32.081501.114006 (2001).Article 

Google Scholar 
35.Shadmehr, R., Huang, H. J. & Ahmed, A. A. A representation of effort in decision-making and motor control. Curr. Biol. 26, 1929–1934. https://doi.org/10.1016/j.cub.2016.05.065 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Shepard, E. L. C. et al. Energy landscapes shapes animal movement ecology. Am. Nat. 182(3), 298–312. https://doi.org/10.1086/671257 (2013).Article 
PubMed 

Google Scholar 
37.Baudinette, R. V., Miller, A. M. & Sarre, M. P. Aquatic and terrestrial locomotory energetics in a toad and a turtle: A search for generalisations among ectotherms. Physiol. Biochem. Zool. 73(6), 672–682. https://doi.org/10.1086/318101 (2000).CAS 
Article 
PubMed 

Google Scholar 
38.Hailey, A. & Coulson, I. M. Measurement of time budgets from continuous observation of thread-trailed tortoises (Kinixys spekii). Herp. J. 9, 15–20 (1999).
Google Scholar 
39.Kram, R. & Taylor, C. R. Energetics of running: A new perspective. Nature 346, 265–267. https://doi.org/10.1038/346265a0 (1990).CAS 
Article 
ADS 
PubMed 

Google Scholar 
40.Taylor, C. R. Relating mechanics and energetics during exercise. Adv. Vet. Sci. Comp. Med. 38A, 181–215 (1994).CAS 
PubMed 

Google Scholar 
41.Cavagna, G. A. & Kaneko, M. Mechanical work and efficiency in level walking and running. J. Physiol. 268(2), 467–481. https://doi.org/10.1113/jphysiol.1977.sp011866 (1977).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
42.Carrier, D. R., Deban, S. M. & Fischbein, T. Locomotor function of the pectoral girdle “muscular sling” in trotting dogs. J. Exp. Biol. 209, 2224–2237. https://doi.org/10.1242/jeb.02236 (2006).Article 
PubMed 

Google Scholar 
43.Heglund, N. C. & Cavagna, G. A. Efficiency of vertebrate locomotory muscles. J. Exp. Biol. 115, 283–292. https://doi.org/10.1242/jeb.115.1.283 (1985).CAS 
Article 
PubMed 

Google Scholar 
44.Barclay, C. J. The basis of difference in thermodynamic efficiency among skeletal muscles. Clin. Exp. Pharm. Physiol. 44(12), 1279–1286. https://doi.org/10.1111/1440-1681.12850 (2017).MathSciNet 
CAS 
Article 

Google Scholar 
45.Nwoye, L. O. & Goldspink, G. Biochemical efficiency and intrinsic shortening speed in selected fast and slow muscles. Experientia 37, 856–857. https://doi.org/10.1007/BF1985678 (1981).CAS 
Article 
PubMed 

Google Scholar 
46.Lambert, M. Temperature, activity and field sighting in the Mediterranean spur-thighed or common garden tortoise Testudo graeca. Biol. Conserv. 21, 39–54. https://doi.org/10.1016/0006-3207(81)90067-7 (1981).Article 

Google Scholar 
47.Tracy, R., Zimmerman, L., Tracy, C., Bradley, K. & Castle, K. Rates of food passage in the digestive tract of young desert tortoises: Effects of body size and diet quality. Chelonian Conserv. Biol. 5(2), 269–273. https://doi.org/10.2744/1071-8443(2006)5[269:ROFPIT]2.0.co;2 (2006).Article 

Google Scholar 
48.Huey, R. & Kingsolver, J. Evolution of thermal sensitivity of ectotherm performance. Trends Ecol. Evol. 4(5), 131–135. https://doi.org/10.1016/0169-5347(89)90211-5 (1989).CAS 
Article 
PubMed 

Google Scholar 
49.Lailvaux, S. & Irschick, D. Effects of temperature and sex on jump performance and biomechanics in the lizard Anolis carolinensis. Funct. Ecol. 21(3), 534–543. https://doi.org/10.1111/j.1365-2435.2007.01263.x (2007).Article 

Google Scholar 
50.Lighton, J. Measuring Metabolic Rates: A Manual for Scientists (Oxford University Press, 2008).Book 

Google Scholar 
51.Brody, S. Bioenergetics and Growth (Reinhold, 1945).
Google Scholar  More