Ecology

Rahman, M. A. et al. Comparing the infiltration potentials of soils beneath the canopies of two contrasting urban tree species. Urban For. Urban Green. 38, 22–32. https://doi.org/10.1016/j.ufug.2018.11.002 (2019).Article 

Google Scholar 
Zölch, T., Henze, L., Keilholz, P. & Pauleit, S. Regulating urban surface runoff through nature-based solutions – An assessment at the micro-scale. Environ. Res. 157, 135–144. https://doi.org/10.1016/j.envres.2017.05.023 (2017).Article 
CAS 

Google Scholar 
Barron, O. V., Barr, A. D. & Donn, M. J. Effect of urbanisation on the water balance of a catchment with shallow groundwater. J. Hydrol. 485, 162–176. https://doi.org/10.1016/j.jhydrol.2012.04.027 (2013).Article 
ADS 

Google Scholar 
Rosenzweig, B. R. et al. The value of urban flood modeling. Earth’s Future 9, e2020EF001739. https://doi.org/10.1029/2020EF001739 (2021).Article 
ADS 

Google Scholar 
Pauleit, S., Fryd, O., Backhaus, A. & Jensen, M. B. In Encyclopedia of Sustainability Science and Technology (ed. Meyers, R. A.) 1–29 (Springer, 2020).
Google Scholar 
Rahman, M. A. et al. Traits of trees for cooling urban heat islands: A meta-analysis. Build. Environ. 170, 106606. https://doi.org/10.1016/j.buildenv.2019.106606 (2020).Article 

Google Scholar 
Ziter, C. D., Pedersen, E. J., Kucharik, C. J. & Turner, M. G. Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proc. Natl. Acad. Sci. USA 116, 7575–7580. https://doi.org/10.1073/pnas.1817561116 (2019).Article 
ADS 
CAS 

Google Scholar 
Waldrop, M. M. News feature: The quest for the sustainable city. Proc. Natl. Acad. Sci. 116, 17134–17138. https://doi.org/10.1073/pnas.1912802116 (2019).Article 
ADS 
CAS 

Google Scholar 
Cleugh, H. A., Bui, E., Simon, D., Xu, J. & Mitchell, V. G. The Impact of Suburban Design on Water Use and Microclimate (2005).Chan, F. K. S. et al. “Sponge City” in China—A breakthrough of planning and flood risk management in the urban context. Land Use Policy 76, 772–778. https://doi.org/10.1016/j.landusepol.2018.03.005 (2018).Article 

Google Scholar 
Morgan, R. P. C. Soil Erosion and Conservation (Wiley, 2005).
Google Scholar 
Xu, C. et al. Surface runoff in urban areas: The role of residential cover and urban growth form. J. Clean. Prod. 262, 121421. https://doi.org/10.1016/j.jclepro.2020.121421 (2020).Article 

Google Scholar 
Ostoić, S. K. & van den Bosch, C. C. K. Exploring global scientific discourses on urban forestry. Urban For. Urban Green. 14, 129–138. https://doi.org/10.1016/j.ufug.2015.01.001 (2015).Article 

Google Scholar 
Rahman, M. A. et al. Tree cooling effects and human thermal comfort under contrasting species and sites. Agric. For. Meteorol. 287, 107947. https://doi.org/10.1016/j.agrformet.2020.107947 (2020).Article 
ADS 

Google Scholar 
Rötzer, T., Rahman, M. A., Moser-Reischl, A., Pauleit, S. & Pretzsch, H. Process based simulation of tree growth and ecosystem services of urban trees under present and future climate conditions. Sci. Total Environ. 676, 651–664. https://doi.org/10.1016/j.scitotenv.2019.04.235 (2019).Article 
ADS 
CAS 

Google Scholar 
Grote, R. et al. Functional traits of urban trees: Air pollution mitigation potential. Front. Ecol. Environ. 14, 543–550. https://doi.org/10.1002/fee.1426 (2016).Article 

Google Scholar 
Pace, R. et al. A single tree model to consistently simulate cooling, shading, and pollution uptake of urban trees. Int. J. Biometeorol. 65, 277–289. https://doi.org/10.1007/s00484-020-02030-8 (2021).Article 
ADS 

Google Scholar 
Kuehler, E., Hathaway, J. & Tirpak, A. Quantifying the benefits of urban forest systems as a component of the green infrastructure stormwater treatment network. Ecohydrology https://doi.org/10.1002/eco.1813 (2017).Article 

Google Scholar 
Rahman, M. A., Moser, A., Gold, A., Rötzer, T. & Pauleit, S. Vertical air temperature gradients under the shade of two contrasting urban tree species during different types of summer days. Sci. Total Environ. 633, 100–111. https://doi.org/10.1016/j.scitotenv.2018.03.168 (2018).Article 
ADS 
CAS 

Google Scholar 
Rahman, M. A., Smith, J. G., Stringer, P. & Ennos, A. R. Effect of rooting conditions on the growth and cooling ability of Pyrus calleryana. Urban For. Urban Green. 10, 185–192. https://doi.org/10.1016/j.ufug.2011.05.003 (2011).Article 

Google Scholar 
Schellekens, J., Scatena, F. N., Bruijnzeel, L. A. & Wickel, A. J. Modelling rainfall interception by a lowland tropical rain forest in northeastern Puerto Rico. J. Hydrol. 225, 168–184. https://doi.org/10.1016/S0022-1694(99)00157-2 (1999).Article 
ADS 

Google Scholar 
Guevara-Escobar, A., González-Sosa, E., Véliz-Chávez, C., Ventura-Ramos, E. & Ramos-Salinas, M. Rainfall interception and distribution patterns of gross precipitation around an isolated Ficus benjamina tree in an urban area. J. Hydrol. 333, 532–541. https://doi.org/10.1016/j.jhydrol.2006.09.017 (2007).Article 
ADS 

Google Scholar 
Xiao, Q. F. & McPherson, E. G. Surface water storage capacity of twenty tree species in Davis, California. J. Environ. Qual. 45, 188–198. https://doi.org/10.2134/jeq2015.02.0092 (2016).Article 
CAS 

Google Scholar 
Xiao, Q. F., McPherson, E. G., Ustin, S. L. & Grismer, M. E. A new approach to modeling tree rainfall interception. J. Geophys. Res. Atmos. 105, 29173–29188. https://doi.org/10.1029/2000jd900343 (2000).Article 
ADS 

Google Scholar 
Carlyle-Moses, D. E. & Gash, J. H. C. In Forest Hydrology and Biogeochemistry: Synthesis of Past Research and Future Directions (eds Levia, D. F. et al.) 407–423 (Springer, 2011).Chapter 

Google Scholar 
Hirano, T. et al. The difference in the short-term runoff characteristic between the coniferous catchment and the deciduous catchment: The effects of storm size on storm generation processes of small forested catchment. J. Jpn. Soc. Hydrol. Water Resour. 22, 24–39. https://doi.org/10.3178/jjshwr.22.24 (2009).Article 

Google Scholar 
Chandler, K. R. & Chappell, N. A. Influence of individual oak (Quercus robur) trees on saturated hydraulic conductivity. For. Ecol. Manage. 256, 1222–1229. https://doi.org/10.1016/j.foreco.2008.06.033 (2008).Article 

Google Scholar 
Stewart, I. D. A systematic review and scientific critique of methodology in modern urban heat island literature. Int. J. Climatol. 31, 200–217. https://doi.org/10.1002/joc.2141 (2011).Article 

Google Scholar 
Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214. https://doi.org/10.1038/sdata.2018.214 (2018).Article 

Google Scholar 
Moreno-de las Heras, M., Nicolau, J. M., Merino-Martín, L. & Wilcox, B. P. Plot-scale effects on runoff and erosion along a slope degradation gradient. Water Resour. Res. 46, W04503. https://doi.org/10.1029/2009WR007875 (2010).Article 
ADS 

Google Scholar 
Wu, L., Peng, M., Qiao, S. & Ma, X.-Y. Effects of rainfall intensity and slope gradient on runoff and sediment yield characteristics of bare loess soil. Environ. Sci. Pollut. Res. 25, 3480–3487. https://doi.org/10.1007/s11356-017-0713-8 (2018).Article 

Google Scholar 
Rutter, A. J., Kershaw, K. A., Robins, P. C. & Morton, A. J. A predictive model of rainfall interception in forests, 1. Derivation of the model from observations in a plantation of Corsican pine. Agric. Meteorol. 9, 367–384. https://doi.org/10.1016/0002-1571(71)90034-3 (1971).Article 

Google Scholar 
Gash, J. H. C. An analytical model of rainfall interception by forests. Q. J. R. Meteorol. Soc. 105, 43–55. https://doi.org/10.1002/qj.49710544304 (1979).Article 
ADS 

Google Scholar 
Véliz-Chávez, C., Mastachi-Loza, C. A., Gonzalez-Sosa, E., Becerril-Pia, R. & Ramos-Salinas, N. M. Canopy storage implications on interception loss modeling. Am. J. Plant Sci. 05, 3032–3048. https://doi.org/10.4236/ajps.2014.520320 (2014).Article 

Google Scholar 
Fan, J., Oestergaard, K. T., Guyot, A. & Lockington, D. A. Measuring and modeling rainfall interception losses by a native Banksia woodland and an exotic pine plantation in subtropical coastal Australia. J. Hydrol. 515, 156–165. https://doi.org/10.1016/j.jhydrol.2014.04.066 (2014).Article 
ADS 

Google Scholar 
Ghimire, C. P., Bruijnzeel, L. A., Lubczynski, M. W. & Bonell, M. Rainfall interception by natural and planted forests in the Middle Mountains of Central Nepal. J. Hydrol. 475, 270–280. https://doi.org/10.1016/j.jhydrol.2012.09.051 (2012).Article 
ADS 

Google Scholar 
Pereira, F. L. et al. Modelling interception loss from evergreen oak Mediterranean savannas: Application of a tree-based modelling approach. Agric. For. Meteorol. 149, 680–688. https://doi.org/10.1016/j.agrformet.2008.10.014 (2009).Article 
ADS 

Google Scholar 
Pypker, T. G., Bond, B. J., Link, T. E., Marks, D. & Unsworth, M. H. The importance of canopy structure in controlling the interception loss of rainfall: Examples from a young and an old-growth Douglas-fir forest. Agric. For. Meteorol. 130, 113–129. https://doi.org/10.1016/j.agrformet.2005.03.003 (2005).Article 
ADS 

Google Scholar 
Ringgaard, R., Herbst, M. & Friborg, T. Partitioning forest evapotranspiration: Interception evaporation and the impact of canopy structure, local and regional advection. J. Hydrol. 517, 677–690. https://doi.org/10.1016/j.jhydrol.2014.06.007 (2014).Article 
ADS 

Google Scholar 
Price, A. G. & Carlyle-Moses, D. E. Measurement and modelling of growing-season canopy water fluxes in a mature mixed deciduous forest stand, southern Ontario, Canada. Agric. For. Meteorol. 119, 69–85. https://doi.org/10.1016/S0168-1923(03)00117-5 (2003).Article 
ADS 

Google Scholar 
Fathizadeh, O., Hosseini, S. M., Zimmermann, A., Keim, R. F. & Darvishi Boloorani, A. Estimating linkages between forest structural variables and rainfall interception parameters in semi-arid deciduous oak forest stands. Sci. Total Environ. 601–602, 1824–1837. https://doi.org/10.1016/j.scitotenv.2017.05.233 (2017).Article 
ADS 
CAS 

Google Scholar 
Livesley, S. J., Baudinette, B. & Glover, D. Rainfall interception and stem flow by eucalypt street trees—the impacts of canopy density and bark type. Urban For. Urban Green. 13, 192–197. https://doi.org/10.1016/j.ufug.2013.09.001 (2014).Article 

Google Scholar 
Xiao, Q. & McPherson, E. G. Rainfall interception by Santa Monica’s municipal urban forest. Urban Ecosyst. 6, 291–302. https://doi.org/10.1023/B:UECO.0000004828.05143.67 (2002).Article 

Google Scholar 
Rohatgi, A. WebPlotDigitizer (4.4), 2020).Team, R. C. (R Foundation for Statistical Computing, 2020).García-Palacios, P., Gross, N., Gaitán, J. & Maestre, F. T. Climate mediates the biodiversity–ecosystem stability relationship globally. PNAS 115, 8400–8405. https://doi.org/10.1073/pnas.1800425115 (2018).Article 
ADS 
CAS 

Google Scholar 
Le Provost, G. et al. Land-use history impacts functional diversity across multiple trophic groups. PNAS 117, 1573–1579. https://doi.org/10.1073/pnas.1910023117 (2020).Article 
ADS 
CAS 

Google Scholar 
El Kateb, H., Zhang, H., Zhang, P. & Mosandl, R. Soil erosion and surface runoff on different vegetation covers and slope gradients: A field experiment in Southern Shaanxi Province, China. CATENA 105, 1–10. https://doi.org/10.1016/j.catena.2012.12.012 (2013).Article 

Google Scholar 
Oliveira, P. T. S. et al. The water balance components of undisturbed tropical woodlands in the Brazilian cerrado. Hydrol. Earth Syst. Sci. 19, 2899–2910. https://doi.org/10.5194/hess-19-2899-2015 (2014).Article 
ADS 

Google Scholar 
Hümann, M. et al. Identification of runoff processes – The impact of different forest types and soil properties on runoff formation and floods. J. Hydrol. 409, 637–649. https://doi.org/10.1016/j.jhydrol.2011.08.067 (2011).Article 
ADS 

Google Scholar 
Sun, D. et al. Soil erosion and water retention varies with plantation type and age. For. Ecol. Manage. 422, 1–10. https://doi.org/10.1016/j.foreco.2018.03.048 (2018).Article 

Google Scholar 
Jost, G., Schume, H., Hager, H., Markart, G. & Kohl, B. A hillslope scale comparison of tree species influence on soil moisture dynamics and runoff processes during intense rainfall. J. Hydrol. 420–421, 112–124. https://doi.org/10.1016/j.jhydrol.2011.11.057 (2012).Article 

Google Scholar 
Sadeghi, S. M. M., Attarod, P., Van Stan, J. T. & Pypker, T. G. The importance of considering rainfall partitioning in afforestation initiatives in semiarid climates: A comparison of common planted tree species in Tehran, Iran. Sci. Total Environ. 568, 845–855. https://doi.org/10.1016/j.scitotenv.2016.06.048 (2016).Article 
ADS 
CAS 

Google Scholar 
Pretzsch, H. et al. Climate change accelerates growth of urban trees in metropolises worldwide. Sci. Rep. https://doi.org/10.1038/s41598-017-14831-w (2017).Article 

Google Scholar 
Rahman, M. A., Moser, A., Rötzer, T. & Pauleit, S. Microclimatic differences and their influence on transpirational cooling of Tilia cordata in two contrasting street canyons in Munich, Germany. Agric. For. Meteorol. 232, 443–456. https://doi.org/10.1016/j.agrformet.2016.10.006 (2017).Article 
ADS 

Google Scholar 
Nytch, C. J., Meléndez-Ackerman, E. J., Pérez, M. E. & Ortiz-Zayas, J. R. Rainfall interception by six urban trees in San Juan, Puerto Rico. Urban Ecosyst. 22, 103–115. https://doi.org/10.1007/s11252-018-0768-4 (2018).Article 

Google Scholar 
Rahman, M. A. et al. Comparative analysis of shade and underlying surfaces on cooling effect. Urban For. Urban Green. 63, 127223. https://doi.org/10.1016/j.ufug.2021.127223 (2021).Article 

Google Scholar 
Chen, L., Zhang, Z. & Ewers, B. E. Urban tree species show the same hydraulic response to vapor pressure deficit across varying tree size and environmental conditions. PLoS One https://doi.org/10.1371/journal.pone.0047882 (2012).Article 

Google Scholar 
Moser-Reischl, A., Rahman, M. A., Pauleit, S., Pretzsch, H. & Rötzer, T. Growth patterns and effects of urban micro-climate on two physiologically contrasting urban tree species. Landsc. Urban Plan. 183, 88–99. https://doi.org/10.1016/j.landurbplan.2018.11.004 (2019).Article 

Google Scholar 
Hao, M. et al. Impacts of changes in vegetation on saturated hydraulic conductivity of soil in subtropical forests. Sci. Rep. 9, 8372. https://doi.org/10.1038/s41598-019-44921-w (2019).Article 
ADS 
CAS 

Google Scholar 
Peters, E. B., McFadden, J. P. & Montgomery, R. A. Biological and environmental controls on tree transpiration in a suburban landscape. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2009jg001266 (2010).Article 

Google Scholar 
Komatsu, H., Kume, T. & Otsuki, K. Increasing annual runoff—broadleaf or coniferous forests?. Hydrol. Process. 25, 302–318. https://doi.org/10.1002/hyp.7898 (2011).Article 
ADS 

Google Scholar 
Li, X. et al. Process-based rainfall interception by small trees in Northern China: The effect of rainfall traits and crown structure characteristics. Agric. For. Meteorol. 218–219, 65–73. https://doi.org/10.1016/j.agrformet.2015.11.017 (2016).Article 
ADS 

Google Scholar 
Lukaszkiewicz, J. & Kosmala, M. Determining the age of streetside trees with diameter at breast height-based multifactorial model. Arboricult. Urban For. 34, 137–143. https://doi.org/10.48044/jauf.2008.018 (2008).Article 

Google Scholar 
Buttle, J. M. & Farnsworth, A. G. Measurement and modeling of canopy water partitioning in a reforested landscape: The Ganaraska Forest, southern Ontario, Canada. J. Hydrol. 466–467, 103–114. https://doi.org/10.1016/j.jhydrol.2012.08.021 (2012).Article 

Google Scholar 
Yang, B., Lee, D. K., Heo, H. K. & Biging, G. The effects of tree characteristics on rainfall interception in urban areas. Landsc. Ecol. Eng. 15, 289–296. https://doi.org/10.1007/s11355-019-00383-w (2019).Article 
CAS 

Google Scholar 
Klamerus-Iwan, A. & Witek, W. Variability in the Wettability and Water Storage Capacity of Common Oak Leaves (Quercus robur L). Water 10, 695. https://doi.org/10.3390/w10060695 (2018).Article 
CAS 

Google Scholar 
Van Stan, J. T., Siegert, C. M., Levia, D. F. & Scheick, C. E. Effects of wind-driven rainfall on stemflow generation between codominant tree species with differing crown characteristics. Agric. For. Meteorol. 151, 1277–1286. https://doi.org/10.1016/j.agrformet.2011.05.008 (2011).Article 
ADS 

Google Scholar 
Selbig, W. R. et al. Quantifying the stormwater runoff volume reduction benefits of urban street tree canopy. Sci. Total Environ. 806, 151296. https://doi.org/10.1016/j.scitotenv.2021.151296 (2022).Article 
ADS 
CAS 

Google Scholar 
Centre for Watershed Protection. Review of the Available Literature and Data on the Runoff and Pollutant Removal Capabilities of Urban Trees (Center for Watershed Protection, 2017).
Google Scholar 
Berland, A. et al. The role of trees in urban stormwater management. Landsc. Urban Plan. 162, 167–177. https://doi.org/10.1016/j.landurbplan.2017.02.017 (2017).Article 

Google Scholar 
Pauleit, S. Urban street tree plantings: Indentifying the key requirements. Proc. Inst. Civ. Eng. Municipal Eng. 156, 43–50. https://doi.org/10.1680/muen.2003.156.1.43 (2003).Article 

Google Scholar 
Weller, M. Tree Inventory Data of Central European Cities—Studies on the Composition and Structure of Urban Tree Populations and Derivation of Ecosystem Services. MSC thesis, Technical University of Munich, Germany (2021). More

Land vulnerability of an area is directly related to the natural as well as anthropogenic activities involved in the geomorphological unit. Being one of the most vulnerable ecosystems, the estuaries and estuarine islands are delicately affected by both ecological processes of the sea and land and have pressures from multiple anthropogenic stressors and global climate change42,43,44. Ecological vulnerability and ecological sensitivity are similar and both originated from the concept of ecotone10,45. The geomorphologic concept of landscape sensitivity was first proposed by Brunsden and Thornes, who argued that the sensitivity indicated the propensity to change and the capacity to absorb the effects of disturbances10,46,47. Landscape sensitivity is studied by many researchers such as Allison and Thomas, Miles et al., Harvey, Knox, Usher, Haara et al., Thomas, Jennings and Yuan Chi8,47,48,49,50,51,52,53,54, through different case studies. Based on their findings Yuan Chi summarized the important characteristics of the landscape sensitivity are: a, the change of the landscape ecosystem; it involves the change likelihood, ratio, and component, as well as the resistance and susceptibility to the change, b, the temporal and spatial scales; which determine the occurrence, degree, and distribution of the change, c, the external disturbances that cause the change; the disturbances included natural and anthropogenic origins with different categories and intensities, and d, the threshold of the landscape sensitivity; it refers to the point of transition for the landscape ecosystem8. The environmental vulnerability of the Munroe Island has been studied based on the characterization of the geomorphological and sociocultural dynamics of the region based on the above characteristics.Bathymetric surveys in Ashtamudi lake and the Kallada riverThe present study shows that the geomorphic processes occurring on the Munroe Island are affected by anthropogenic disturbances in the morpho-dynamics of the Kallada river, Ashtamudi backwaters and associated fluvio-tidal interactions. A detailed bathymetric survey of both water bodies up to the tidal-influenced upper limit of the Kallada river27 was conducted with 200 m spaced grid references (Fig. 5). Bathymetry shows that the deepest point of the Ashtamudi backwater system is in Vellimon lake (13.45 m), the SE extension of Ashtamudi lake. The eastern side of Ashtamudi lake is deeper than the western side of this backwater system. The depth of the backwater decreases towards the estuary, and most parts of the lakebed are exposed here at the mouth of the inlet during the low tide. Compared to Ashtamudi lake, the Kallada river is deeper, and the riverbed area is recorded as the average depth is greater than 13 m. The deepest part of 14.9 m is recorded near Kunnathoor bridge, which is 12 km upstream from Munroe Island. Except for a few spots of hard (resistant) rocks, the river fairly and consistently follows a higher depth throughout its course.Figure 5Bathymetric profile of Ashtamudi lake and adjoining Kallada river (Figure was generated by Arc GIS 10.6).Full size imageOnce the Kallada river supplied very fertile alluvium during its flooding seasons (monsoon/rainy season), and most of this alluvium is deposited in the floodplains of the Munroe Island and the Ashtamudi lake. With a vast river catchment area from elevated lands of Western Ghats and a shorter course of 121 km33,55 and a higher elevation gradient of 12.6 m/km56, the Kallada river has a higher transporting capacity. The eroded surface and mined river/lakebeds at lower courses were replaced by the sediment load supplied by the Kallada river during each flood season until dam construction. During the focus group discussions with residents of the Island, they had described that they were crossing the Kallada river on foot in the 1990s or even earlier during the dry seasons. The construction of the Thenmala reservoir dam in 1980s across the river drastically choked the sediment supply of the Kallada river. In addition, excessive commercial sand mining without any regulation from the riverbeds of Kallada and Ashtamudi waterbodies accelerated the deepening of waterbodies. It increased the erosion of surface and subsurface soils through fluvial and hydraulic action. This, in turn, drastically reduced the deposition of fertile alluvium over the low-lying Munroe Island. The current bathymetry shows that the river channel has deepened its course to 14 m compared to 5–6 m of 1980s. When comparing the bathymetric data of 200127, it is interesting to note that no considerable changes occurred in the bathymetry of Ashtamudi lake over the last two decades.Dams indeed alter aquatic ecology and river hydrology, upstream and downstream, affecting water quality, quantity, breeding grounds and habitation22. The other significant impact of the damming of the Kallada river is the saline water intrusion towards upstream of Ashtamudi lake and the Kallada river. The freshwater discharge is regulated after the construction of the Thenmala reservoir, and the water is being diverted to the reservoir and associated canals. There is a decline in sedimentation over the floodplains and catchment area as a result of the increased tidal effects and associated running water dynamics, which may accelerate the erosion trend of the nearby places.Lithological characterization of the Munroe IslandThe Munroe Island is a riverine delta formation by the Kallada river at the conjunction of river and backwater systems. To understand the micro-geomorphological processes of the study area, the near-surface geology of the Munroe Island had been studied in detail with the help of resistivity meter surveys and borehole datalogs from different locations. As per the current resistivity survey, it is evident that the Munroe Island is formed by recent unconsolidated loose sediments more than 120 m thick succession below ground level (Figs. 6 and 7). The electrical resistivity tomography of identified locations within the deltaic region shows a meagre resistance value to its maximum penetration (Fig. 6), which proves that the sedimentary column with intercalations of sand and carbonaceous clays of varying thickness extends to a depth of 120 m, in turn indicating the process of enormous sedimentation happened during the recent geological period. Loose wet soils of saline nature records a lower resistance value for an electric circuit. The layers formed in the diagram (Fig. 6) represent the seasonal deposition of unconsolidated soils as thin sequence. The Mulachanthara station of the resistivity meter tomography, which is situated at a more stable location of the Island, has a higher resistivity value than the West Pattamthuruth location, which is located at the exact alluvial flood plain.Figure 6Electrical resistivity profiles of Munroe Island.Full size imageFigure 7Geomorphological map showing litho-log of north (Kannamkadu); middle (Konnayil Kadavu); and south (Perumon bridge) locations of Munroe Island (borehole data source: PWD, Govt of Kerala) (Software used: Arc GIS 10.6).Full size imageThe Public Works Department (PWD), Kerala State carried out soil profile studies through Soil Penetrating Test (SPT) borehole drilling method as part of constructing bridges at three different locations up to a depth of 62 m, i.e., one across the Kallada river (north side)57, one across Ashtamudi lake in southern Munroe Island58 and one at the central part of Munroe Island (across a canal)59 (Fig. 7). The hard rock is found only on the southern side of the lake at a depth of 45 m. The litho-log shows that unconsolidated loose sediments of significantly higher thickness occur in the entire Munroe Island (Fig. 7). Anidas Khan et al.60 studied the shear strength and compressibility characteristics of Munroe Island’s soil for two different locations with disturbed and undisturbed samples. They classified the soil of Mundrothuruth into medium compressibility clay (CI) and high compressibility clay (CH) with natural moisture contents of 44.5% and 74%, respectively. The unconfined compressive strengths of the undisturbed and remolded samples for the first location are 34.5 kN/m2 and 22.1 kN/m2, respectively, while they are 13 kN/m2 and 9 kN/m2 respectively for the second location60. Such compressive strength indicates that the soils of Munroe Island are soft or very soft in nature.Land degradation: a morphological analysisTo decrease the impact of the monsoon floods and to distribute the alluvium to the southern part of the island, Canol Munroe, the then Diwan of the Thiruvithamkoor Dynasty, made an artificial man-made canal during the 1820s connecting the Kallada river with the eastern extension of Ashtamudi lake, and this river is known as “Puthanar” (meaning a new river). During the last few decades, (after 1980s) the estuarine island ecosystem of Munroe Island has faced several structural deformities. The natural sedimentation and flooding happening in the Islands were very limited and hence, the normal events happened over the past several decades disturbed and significantly affected the land neutrality. These islands, once known as the region’s rice bowl, now devoid of any paddy cultivation mainly because of the increased soil salinity. According to the Cadastral map prepared by the revenue department (1960s) there were many paddy fields, locally named as Mathirampalli Vayal (Vayal is the local name for paddy field), Thekke Kothapppalam Vayal, Mattil Vayal, Kottuvayal, pallaykattu Vayal, Konnayil Vayal, Vadakke Kundara Vayal, Thachan Vayal, Thekke Kundara Vayal, Kizhakke Oveli Vayal, Thekke Oveli Vayal, Odiyil Vettukattu Vayal, Nedumala Vayal, Madathil Vayal, Karichal Vayal, Moonumukkil Vayal, Arupara Vayal, Kaniyampalli Vayal, Manakkadavu Vayal, Panampu Vayal, Pattamthuruth Vayal etc. The recent satellite images shows that no paddy cultivation exist now, which is further confirmed by the field observations conducted through our study. The annual report published by Gramapanchayat39 indicate that the paddy field of region was reduced from 227 to 8 acres (from 1950 to 1995) and now about in 2 acres only (2018). Most of the paddy fields of northern and northwestern regions are severely affected by land degradation due to erosion, saline water intrusion and flooding and are entirely or partially buried under the backwater system. Figure 8 depicts the morphological degradation of the severely affected areas of Munroe Island from 1989 to 2021 through different satellite images. Some paddy fields are converted into filtration ponds to take the benefit of frequent tidal flooding. The coconut plantations were later introduced in place of paddy fields, and they eventually replaced the paddy fields. However, during the last decades, it has been observed that these coconut plantations are also under threat mainly because of degradation of the soil fertility, which directly bears the quality and quantity of production (Fig. 9).Figure 8Morphological changes in the study area from the satellite images (a) 1989 (aerial photograph); (b) 2000 (Landsat); (c) 2011 (World View—II); (d) 2021 (Sentinel) (the modified maps of (a) is obtained from National remote Sensing Centre (NRSC), Hyderabad, (b) is downloaded from https://earthexplorer.usgs.gov/ (c) is obtained from Digital Globe through NRSC and (d) is downloaded from https://scihub.copernicus.eu/. Figures were generated using Arc GIS 10.6).Full size imageFigure 9Threatened coconut plantations indicating the low productive regime. Photographs taken by Rafeeque MK.Full size imageOver the study area the most affected alluvial plain of the Peringalam and Cheriyakadavu island are taken separately to study the morphological changes over the decades. This area is named Puthan Yekkalpuram (which means new alluvium land), and the north side of the Kallada river (the northward extension in the Mundrothuruth GP) is demarcated as old alluvium land (Pazhaya Yekkalpuram) as per the revenue department’s cadastral map. The study shows that total 38.73 acres of land has lost from the Peringalam and Cheriyakadavu Islands during the last 32 years, which is equivalent to 11.78% and 46.95% of the total geographical area of the Peringalam and Cheriyakadavu Islands, respectively. The land degradation details over the last three decades are given in the Table 2. Many other locations, such as Nenmeni and West Pattamthuruth, are also severely affected by land degradation. However, these areas are landlocked and less affected by running water or floods. Hence, the land degradation experienced is the settling of the topsoil and subsidence of structures such as houses and bridges. The sinking of basements of many houses and even the subsidence of railway platforms are well observed during field visits, indicating the alarming land degradation issues (Figs. 1 and 10) to be addressed its deserving importance. There are also clear indications of the gradual formation of new waterlogged areas in the islands, which may further deteriorate and forms the part of the backwater system which eventually affects total land area of the Munroe Island.Table 2 Land degradation of Peringalam and Cheriyakadavu region for the past 32 years.Full size tableFigure 10Various environmental degradations in Munroe Island. Photographs taken by Rafeeque MK.Full size imageThe island population also shows a negative growth over the years. According to the census report of 201138, the total population of Gramapanchayat has decreased to 9440 person/km2 in 2011 from 10,013 person/km2 of 2001 and 10,010 person/km2 of 1991 census reports. Frequent flooding (especially tidal flooding), the lack of drinking water, and migration in search of a better livelihood are the main reasons for the observed population reduction as revealed through the survey. The high intrusion of saline water into the cultivated land through tidal flooding and the lack of flushing of surface saline soils by monsoon floods (freshwater) decreased agricultural productivity of the area, and hence, now people are more dependent on fishing and backwater activities for their livelihood. Lack of proper transportation to the nearby markets limits their fishing activities to a daily subsistence level. Due to the flooding caused by subsidence/tidal surges and land degradation during the last few decades, more than 500 households have vacated their houses38,39.Tidal Flooding and Estuarine ProcessesIn Mundrothuruth, the major environmental degradation problems where occurring due to tidal flooding and saline water intrusion into the freshwater ecosystem. Mathew et al. studied the tidal and current mechanisms of the Ashtamudi backwater in 200161. They reported that the Kallada river plays a vital role in determining the eastern lake’s circulation pattern. In addition, the increased discharge from the north Chavara canal and the south Kollam canal also influences the local circulation of the Ashtamudi backwater. The current velocity reaches up to 100 cm/s at the estuary entrance, but it rapidly diminishes in the eastern parts, where the speed is generally less than 30 cm/s. One of the critical observations made during the field study, which corroborates with the acquaintance of local people as well, is that the flooding on Munroe Island is not related to the spring tide of the open ocean. The disappearance of the semidiurnal tide in the central lakes occurs due to frictional resistance and the time lags for the tide to travel across the estuary61. At the shorter semidiurnal period of approximately 12 h, the tide is more dissipated than the more extended constituents of 24-h duration. The survey conducted with the island inhabitants also reiterates these views.As per the experience of local inhabitants, tidal flooding in Munroe Island was not frequent in earlier times. The comparison of the bathymetry data collected during 200058 and 2017 (Fig. 5) in and around the regions of Munro Islands shows that there is not much change in bathymetry during the period. Hence, changes in basin geometry are not having a significant role in tidal dynamics in imparting the variations as observed. In addition to the bathymetric survey, the data on tide measurements at four locations corresponding to three seasons were also collected. The tide data measured during the pre-monsoon period is shown in Fig. 11a. The figure shows that the tidal range in the inland area is almost the same even during the spring and neap tides. As discussed earlier, the tidal flooding in Munro Island is not related to spring tide in the ocean, and there may be the influence of specific complicated dynamics in the basin for this flooding that needs to be studied more profoundly. Further the data pertaining to tidal dynamics were inadequate; we established three tide gauges in selected locations in and around Munro Island. From the analysis of tide gauge data, it is found that the signature of anomalous variability in water column height, which is not at all linked to the tidal dynamics.Figure 11(a) Salinity variation of bottom water at selected locations in Kallada river during monsoon and post monsoon. (b) Observed tide during pre-monsoon months.Full size imageThe water quality analysis for three time periods, during the year of the cyclonic storm, Okhi (2017), was conducted to understand river run-up impact on salinity in and around Munroe Island (Fig. 11). The riverbed is lowered below the baseline of erosion, and dense saline water is trapped in the deeps during high tide. This has been confirmed during the bathymetric survey of the Kallada river and Ashtamudi backwaters, which showed a significant increase in water depth, particularly within the river channel. The high-density saline water is trapped in the basins and trenches created in the river channel due to uncontrolled sand mining, which leads to the degradation of the quality of sediments and groundwater in the region. Nevertheless, the samples collected immediately after Okhi (when the dam’s shutter was opened due to heavy rainfall in the catchment area) show that the high runoff replaced the trapped saline water with fresh water. After ten days of the first sampling, the water became saline nature after the closure of the dam’s shutter. This proves that because of dam construction, the river runoff in the Kallada river was reduced significantly, and extensive human interactions especially sand mining activities increased the riverbed deepening and formation of pools beyond the base level of running water.Conservations and management strategiesConsidering the facts discussed above, the Munroe Island may continue to be badly affected unless suitable sustainable management strategies are not evolved. Construction and associated activities, such as the damming of reservoirs, sand mining and landfilling, are indispensable for any nation’s economic and social development. United Nations’s member states have formulated 17-point Sustainable Developmental Goals (SDGs) to better the world sustainably. Local and national governments pertaining to the Munroe Island need to develop a sustainable management plan to protect this Ramsar-listed wetland. The environmental issues of Mundrothuruth can be controlled, and land degradation may be monitored through a well-drafted working plan. All aspects of earth and social sciences may be integrated to draft such a management plan of reverse landscaping. The reverse landscaping (i.e., recalling the degrading landscape to its geomorphic isostatic state) method is a must-considered sustainable solution for land degradation and other environmental issues.The deep courses of Kallada river must be upwarped through a well-planned artificial sedimentation to eradicate the saline banks of deep basins. The sediments deposited in the Thenmala reservoir and the sediments removed through the digging of boat channels may be utilized in a periodic monitoring method. Sand mining from Ashtamudi lake and the Kallada river may be strictly controlled, and the minimum freshwater flow should be ensured. The construction methods practiced in Mundrothuruth are outdated and technically nonexistent. Well-studied engineering methods suitable for an environmentally fragile area must be implemented with a proper understanding of the soil characteristics, such as shear strength and compressibility rate, and hydrodynamics, such as tidal and fluvial actions. Soil fertility must be increased by supplying additional fertile soil and freshwater, at least for a minimum period. The inhabitants’ socioeconomic well-being is strengthened by advancing technology and providing easy access to the market and other social amenities. More

Establishing size-weight relationships for heavily targeted coral species is an important first step towards informing sustainable harvest limits19. Placing coral harvests into an ecological context is a core requirement for implementing a defensible stock assessment strategy, and this need is particularly critical given escalating disturbances and widespread reports of coral loss7,17,25. Using these relationships, managers can now easily sample and calculate biomass per unit area. It is important to point out that all sites sampled in our study represent fished locations, and there is no information available to test whether standing biomass has declined due to sustained coral harvesting at these locations. While these data may now provide a critical baseline for assessing the future effects of ongoing fishing, it is also important to sample at comparable locations where fishing is not permitted or has not occurred (where possible), to test for potential effects of recent and historical harvesting.Biomass per unit area data presented herein highlights the highly patchy abundance and biomass of targeted coral species14, which is evident based on the often vastly different mean and median values (Table 2). Examining biomass per unit area estimates for C. jardinei for example, which returned some of the highest biomass estimates, the 33.75 kg·m−2 maximum estimate from a transect stands as an extreme outlier, with 12 of the 16 other transects being below 0.2 kg·m−2. This indicates the challenges of managing species that occur in patchily distributed concentrations, particularly in a management area the size of the QCF. It is also important to note, these estimates are generated only on transects where the target species occurred, and therefore, should technically not be considered as an overall estimate of standing biomass. While the estimation of size-weight relationships is a step towards a standing biomass estimate, many challenges remain in terms of sampling or reliably predicting the occurrence of these patchily distributed species. Bruckner et al.14 attempted to overcome this management challenge in a major coral fishery region of Indonesia by categorising and sampling corals (in terms of coral numbers) in defined habitat types, and then extrapolating to estimated habitat area based on visual surveys and available data. This approach, utilising size-weight relationship derived biomass per unit area estimates (instead of coral numbers), may be a viable method for the QCF, however much more information is needed to understand the habitat associations (e.g., nearshore to offshore), and environmental gradients that influence the size and abundance of individual corals. Fundamentally, it is also clear that much more data is required to effectively assess the standing biomass of aquarium corals in the very large area of operation available to Australian coral fisheries.These corals are found in a range of environments, and it is important to consider available information on life history if attempting to use coral size-weight relationships to inform management strategies via standing biomass estimation. All corals in this study can be found as free living corals (at least post-settlement) in soft-sediment, inter-reefal habitats, from which they are typically harvested by commercial collectors19. However, only four of the 6 species are colonial (C. jardinei, D. axifuga, E. glabrescens, M. lordhowensis) while the remaining two species (H. cf. australis and T. geoffroyi) are more typically monostomatous or solitary. As indicated in previous work24, if larger colonial corals were to be fragmented during harvesting instead of removed entirely, fishery impacts would likely be lessened24. Given the power relationship between coral maximum diameter and weight, larger corals contribute disproportionately to the total available biomass of each species in a given area. The potential environmental benefit of leaving larger colonies (at least partially) intact is not limited to impacts on standing biomass, as this practice would likely be demographically beneficial given the greater reproductive potential (i.e., fecundity) of larger colonies, which also do not need to overcome barriers to replenishment of populations associated with new recruits (i.e., high mortality during and post-settlement26). This conclusion was drawn largely from data on branching taxa (e.g., Acropora), which are relatively resilient to fragmentation and commonly undergo fragmentation as a result of natural processes27,28,29. D. axifuga can be considered to exhibit a relatively similar branching growth form, however, the growth form of E. glabrescens and C. jardinei changes with size, moving from small discrete polyps to large phaceloid and flabello-meandroid colonies, respectively19. While larger colonies of E. glabrescens and C. jardinei may be relatively resilient to harvesting via fragmentation, the same may not be true for smaller colonies, or species with massive growth forms such as M. lordhowensis. Typically, for each species, the average reported weight was quite low, coinciding with the lower end of the sampled maximum diameter range. For colonial species, the harvested smaller maximum diameters (if fragments) are ideal from an ecological perspective as this will have the least impact possible on standing biomass, and may also leave a potentially mature breeding colony intact. Ultimately, in light of these considerations, the development of uniform and standardised industry-wide harvest guidelines to balance economic and ecological outcomes may be necessary. The development of these guidelines would require consultation with commercial harvesters, as well as considerable additional work in measuring ecological impacts and better understanding the cost of these impacts from an economic perspective. Conversely, if whole colonies are collected, which is necessarily the case for solitary species such as H. cf. australis and T. geoffroyi (and potentially smaller colonies of other species such as E. glabrescens and C. jardinei); smaller colonies may be collected before they reach sexual maturity, hindering their ability to contribute to population replenishment. Therefore, collection of small fragments should be encouraged for colonial species; while for monostomatous species where this is not possible, introduction of a minimum harvest size based on sexual maturity should be considered.Additionally, the need for further consideration of the selectivity of ornamental coral harvest fisheries3,4,30 when assessing standing biomass is evident. Due to various desirable traits, the majority of available biomass may not be targeted by collectors. As emphasised in this study, the focus on smaller corals is indicative of the trend towards collection of most of these species at the lower portion of their size range, at least compared to some of the maximum sizes recorded on transects (e.g., see Tables 1 and 2, section b). However, it is also important to consider that transects were conducted in areas subject to commercial collection and are likely to skew results and prevent clear conclusions relating to size selectivity. Sampling of unfished populations (i.e., any residing outside of permitted fishing zones) and/or spatial and temporal matching of catch data and transect data across a larger sample of operators will be required to properly address industry size selectivity trends. For instance, only 17.5% of C. jardinei corals measured on transects fell within the diameter range represented by data obtained from collectors, with 81.9% of corals measured on transects exceeding this range. If it is viable to collect fragments from larger colonies (which does appear to be the case for some corals such as C. jardinei), then a larger proportion of standing biomass outside of this size range could be targeted by fishers. As an additional consideration, only desirable colour morphs of these corals will be harvested, and due to lack of appropriate data, the prevalence of these morphs remains unclear. H. cf. australis and M. lordhowensis for example often occur in brown colour morphs, which are far less popular in markets where certain aesthetic qualities (e.g., specific, eye-catching colours or combinations of colours) are desired, such as the ornamental aquarium industry. Even without delving into further considerations such as heritability of phenotypic traits, management conclusions drawn from standing biomass estimates may be ineffective in the absence of efforts to account for selectivity in this fishery.The relationship between size and weight was found to differ between all corals, with the exception of C. jardinei and E. glabrescens. There can be some moderate similarity in skeletal structure between these two species, particularly between small colonies, reflecting the similar maximum diameter range of sampling in the current study. Subsequently, inherent physiological constraints may be imposed on corals that prevent the maintenance of growth rates between corals of smaller and larger sizes, for example, as the surface area to volume ratio declines with growth31. In the current study, all corals, with the exception of C. jardinei, showed evidence of allometric growth, as exhibited by an estimated exponent value different to 3. Sample size for C. jardinei was greatly limited, as this species typically forms extensive beds, and are rarely brought to facilities as whole colonies. Therefore, the lack of evidence for allometric growth may reflect higher error for the species coefficient parameter due to the comparatively small sample size for this species. This suggests that mass would not increase consistently with changes in colony size in 3 dimensions31, which seems likely considering the change in exhibited form described for E. glabrescens and C. jardinei previously. In the current context, this indicates that the estimated ‘a’ and ‘b’ constants are likely to vary as the sample range increases, reflecting the changes in the size-weight relationship between smaller and larger samples of these species. Therefore, ideally, these models should incorporate data that reflect the maximum diameter range of the species in the region of application to allow increased accuracy of biomass estimation. To achieve this will require additional fishery-independent sampling, as large colonies are rarely collected whole, though may be collected as fragments depending on the species. Sampling may be challenging for some species given the difficulty of physically collecting and replacing large whole colonies, particularly for inter-reefal species such as M. lordhowensis, which can occur in deep, soft sediment habitat, subject to strong currents. Importantly, obtaining ex situ or in situ growth rate data should be considered a priority for the management of heavily targeted species. This data is likely to be another necessary component (in conjunction with size-weight relationships) of any stock assessment model developed for LPS corals, and may also eliminate the need to collect large sample colonies to improve estimated size-weight relationships.The disproportionate focus on smaller corals (i.e., corals in the current study averaged between 4.28 and 11.48 cm in maximum diameter) is likely to lead to an underestimation of weight in corals at greater diameters when used as inputs for size-weight models. This may explain the apparent minor underestimation observed in some species (e.g., M. micromussa, T. geoffroyi). In the current context, this represents an added level of conservatism with estimates obtained from these equations. While the relationship between size and weight was particularly strong for some species, (mainly D. axifuga and T. geoffroyi), for other species, such as M. lordhowensis, growth curves tended towards underestimation at larger diameter values. As the mass of a coral is reflective of the amount of carbonate skeleton that has been deposited32, the coral skeleton may increase disproportionately to coral diameter if or when corals start growing vertically. For example, in massive corals such as M. lordhowensis, vertical growth (i.e., skeletal thickening) is often very negligible among smaller colonies, with thickening of the coral skeleton only becoming apparent once the coral has reached a threshold size in terms of horizontal planar area. Additional fisheries-independent sampling outside of the relatively narrow size range of harvested colonies will be required to address this source of error in future applications. Ecological context in the form of fishery independent data on stock size and structure is essential for effective management, especially in ensuring that exploitation levels are sustainable and appropriate limits are in place. Coral harvest fisheries offer managers an ecologically and biologically unique challenge, as the implementation of standard fisheries management techniques and frameworks is hampered by their coloniality and unique biology, as well as a general lack of relevant data for assessing standing biomass and population turnover, not to mention the evolving taxonomy of scleractinian corals33. Similarly, fishery-related management challenges such as the extreme selectivity in terms of targeted size-ranges and colour-morphs, plus the potentially vast difference in the impact of various collection strategies (i.e., whole colony collection vs fragmentation during collection) also complicates the application of typical fisheries stock assessment frameworks. The relationships and equations established in the current work offer an important first step for coral fisheries globally by laying the groundwork for a defensible, ecologically sound management strategy through estimation of standing biomass, thus bridging the gap between weight-based quotas and potential environmental impacts of ongoing harvesting. It is important to note that the species selected for the current work do not represent the extent of heavily targeted LPS corals. For example, Fimbriaphyllia ancora (Veron & Pichon, 1980), Fimbriaphyllia paraancora (Veron, 1990), Cycloseris cyclolites (Lamark, 1815), and Acanthophyllia deshayesiana (Michelin, 1850) are examples of other heavily targeted corals of potential environmental concern19, and management would also benefit from the estimation of size-weight relationships for these species. Moving forward, the next challenge for the coral harvest fisheries will be to comprehensively document and track the standing biomass of heavily targeted and highly vulnerable coral stocks, explicitly accounting for fisheries effects and also non-fisheries threats, especially global climate change. More

Koonin, E. V. & Dolja, V. V. Virus world as an evolutionary network of viruses and capsidless selfish elements. Microbiol. Mol. Biol. Rev. 78, 278–303 (2014).Article 
CAS 

Google Scholar 
Pritham, E. J., Putliwala, T. & Feschotte, C. Mavericks, a novel class of giant transposable elements widespread in eukaryotes and related to DNA viruses. Gene 390, 3–17 (2007).Article 
CAS 

Google Scholar 
Kapitonov, V. V. & Jurka, J. Self-synthesizing DNA transposons in eukaryotes. Proc. Natl Acad. Sci. USA 103, 4540–4545 (2006).Article 
CAS 

Google Scholar 
Krupovic, M. & Koonin, E. V. Polintons: a hotbed of eukaryotic virus, transposon and plasmid evolution. Nat. Rev. Microbiol. 13, 105–115 (2015).Article 
CAS 

Google Scholar 
Koonin, E. V., Krupovic, M. & Yutin, N. Evolution of double-stranded DNA viruses of eukaryotes: from bacteriophages to transposons to giant viruses. Ann. N. Y. Acad. Sci. 1341, 10–24 (2015).Article 
CAS 

Google Scholar 
Yutin, N., Raoult, D. & Koonin, E. V. Virophages, polintons, and transpovirons: a complex evolutionary network of diverse selfish genetic elements with different reproduction strategies. Virol. J. 10, 158 (2013).Article 
CAS 

Google Scholar 
Krupovic, M., Bamford, D. H. & Koonin, E. V. Conservation of major and minor jelly-roll capsid proteins in Polinton (Maverick) transposons suggests that they are bona fide viruses. Biol. Direct 9, 6 (2014).Article 

Google Scholar 
Yutin, N., Shevchenko, S., Kapitonov, V., Krupovic, M. & Koonin, E. V. A novel group of diverse Polinton-like viruses discovered by metagenome analysis. BMC Biol. 13, 95 (2015).Article 

Google Scholar 
Bellas, C. M. & Sommaruga, R. Polinton-like viruses are abundant in aquatic ecosystems. Microbiome 9, 13 (2021).Article 
CAS 

Google Scholar 
Pagarete, A., Grébert, T., Stepanova, O., Sandaa, R.-A. & Bratbak, G. Tsv-N1: a novel DNA algal virus that infects Tetraselmis striata. Viruses 7, 3937–3953 (2015).Article 
CAS 

Google Scholar 
Bekliz, M., Colson, P. & La Scola, B. The expanding family of virophages. Viruses 8, 317 (2016).Article 

Google Scholar 
Fischer, M. G. The virophage family Lavidaviridae. Curr. Issues Mol. Biol. https://doi.org/10.21775/cimb.040.001 (2021).Desnues, C. et al. Provirophages and transpovirons as the diverse mobilome of giant viruses. Proc. Natl Acad. Sci. USA 109, 18078–18083 (2012).Article 
CAS 

Google Scholar 
Campos, R. K. et al. Samba virus: a novel mimivirus from a giant rain forest, the Brazilian Amazon. Virol. J. 11, 95 (2014).Article 

Google Scholar 
Gaia, M. et al. Broad spectrum of mimiviridae virophage allows its isolation using a mimivirus reporter. PLoS ONE 8, e61912 (2013).Article 
CAS 

Google Scholar 
Hackl, T., Duponchel, S., Barenhoff, K., Weinmann, A. & Fischer, M. G. Virophages and retrotransposons colonize the genomes of a heterotrophic flagellate. eLife 10, e72674 (2021).Article 
CAS 

Google Scholar 
Yau, S. et al. Virophage control of Antarctic algal host-virus dynamics. Proc. Natl Acad. Sci. USA 108, 6163–6168 (2011).Article 
CAS 

Google Scholar 
Gong, C. et al. Novel virophages discovered in a freshwater lake in China. Front. Microbiol. 7, 5 (2016).Article 

Google Scholar 
Zhou, J. et al. Three novel virophage genomes discovered from Yellowstone Lake metagenomes. J. Virol. 89, 1278–1285 (2014).Article 

Google Scholar 
Yutin, N., Kapitonov, V. V. & Koonin, E. V. A new family of hybrid virophages from an animal gut metagenome. Biol. Direct 10, 19 (2015).Article 

Google Scholar 
Stough, J. M. A. et al. Genome and environmental activity of a Chrysochromulina parva virus and its virophages. Front. Microbiol. 10, 703 (2019).Article 

Google Scholar 
La Scola, B. et al. The virophage as a unique parasite of the giant mimivirus. Nature 455, 100–104 (2008).Article 

Google Scholar 
Fischer, M. G. & Suttle, C. A. A virophage at the origin of large DNA transposons. Science 332, 231–234 (2011).Article 
CAS 

Google Scholar 
Gaia, M. et al. Zamilon, a novel virophage with Mimiviridae host specificity. PLoS ONE 9, e94923 (2014).Article 

Google Scholar 
Mougari, S. et al. Guarani virophage, a new Sputnik-like isolate from a Brazilian lake. Front. Microbiol. 10, 1003 (2019).Article 

Google Scholar 
Sheng, Y., Wu, Z., Xu, S. & Wang, Y. Isolation and identification of a large green alga virus (Chlorella Virus XW01) of Mimiviridae and its virophage (Chlorella Virus Virophage SW01) by using unicellular green algal cultures. J. Virol. 96, e02114–e02121 (2022).Article 

Google Scholar 
Baudoux, A. C. & Brussaard, C. P. D. Characterization of different viruses infecting the marine harmful algal bloom species Phaeocystis globosa. Virology 341, 80–90 (2005).Article 
CAS 

Google Scholar 
Santini, S. et al. Genome of Phaeocystis globosa virus PgV-16T highlights the common ancestry of the largest known DNA viruses infecting eukaryotes. Proc. Natl Acad. Sci. USA 110, 10800–10805 (2013).Article 
CAS 

Google Scholar 
Tarutani, K., Nagasaki, K. & Yamaguchi, M. Virus adsorption process determines virus susceptibility in Heterosigma akashiwo (Raphidophyceae). Aquat. Microb. Ecol. 42, 209–213 (2006).Article 

Google Scholar 
Gann, E. R., Gainer, P. J., Reynolds, T. B. & Wilhelm, S. W. Influence of light on the infection of Aureococcus anophagefferens CCMP 1984 by a ‘giant virus’. PLoS ONE 15, e0226758 (2020).Article 
CAS 

Google Scholar 
Van Etten, J. L., Burbank, D. E., Xia, Y. & Meints, R. H. Growth cycle of a virus, PBCV-1, that infects Chlorella-like algae. Virology 126, 117–125 (1983).Article 

Google Scholar 
Boyer, M. et al. Mimivirus shows dramatic genome reduction after intraamoebal culture. Proc. Natl Acad. Sci. USA 108, 10296–10301 (2011).Article 
CAS 

Google Scholar 
Desnues, C. & Raoult, D. Inside the lifestyle of the virophage. Intervirology 53, 293–303 (2010).Article 
CAS 

Google Scholar 
Sobhy, H., Scola, B. L., Pagnier, I., Raoult, D. & Colson, P. Identification of giant Mimivirus protein functions using RNA interference. Front. Microbiol. 6, 345 (2015).Article 

Google Scholar 
Fischer, M. G. & Hackl, T. Host genome integration and giant virus-induced reactivation of the virophage mavirus. Nature 540, 288–291 (2016).Article 
CAS 

Google Scholar 
Wodarz, D. Evolutionary dynamics of giant viruses and their virophages. Ecol. Evol. 3, 2103–2115 (2013).Article 

Google Scholar 
Farr, G. A., Zhang, L. & Tattersall, P. Parvoviral virions deploy a capsid-tethered lipolytic enzyme to breach the endosomal membrane during cell entry. Proc. Natl Acad. Sci. USA 102, 17148–17153 (2005).Article 
CAS 

Google Scholar 
Suhre, K., Audic, S. & Claverie, J.-M. Mimivirus gene promoters exhibit an unprecedented conservation among all eukaryotes. Proc. Natl Acad. Sci. USA 102, 14689–14693 (2005).Article 
CAS 

Google Scholar 
Legendre, M. et al. mRNA deep sequencing reveals 75 new genes and a complex transcriptional landscape in Mimivirus. Genome Res. 20, 664–674 (2010).Article 
CAS 

Google Scholar 
Smith, D. R., Arrigo, K. R., Alderkamp, A.-C. & Allen, A. E. Massive difference in synonymous substitution rates among mitochondrial, plastid, and nuclear genes of Phaeocystis algae. Mol. Phylogenet. Evol. 71, 36–40 (2014).Article 
CAS 

Google Scholar 
Krupovic, M., Kuhn, J. H. & Fischer, M. G. A classification system for virophages and satellite viruses. Arch. Virol. 161, 233–247 (2016).Article 
CAS 

Google Scholar 
Suplatov, D. A., Besenmatter, W., Svedas, V. K. & Svendsen, A. Bioinformatic analysis of alpha/beta-hydrolase fold enzymes reveals subfamily-specific positions responsible for discrimination of amidase and lipase activities. Protein Eng. Des. Sel. 25, 689–697 (2012).Article 
CAS 

Google Scholar 
Burt, A. & Koufopanou, V. Homing endonuclease genes: the rise and fall and rise again of a selfish element. Curr. Opin. Genet. Dev. 14, 609–615 (2004).Article 
CAS 

Google Scholar 
Sullivan, M. B. DNA extraction of cesium chloride-purified viruses using wizard prep columns. Protocols https://doi.org/10.17504/protocols.io.c26yhd (2016).González-Domínguez, J. & Schmidt, B. ParDRe: faster parallel duplicated reads removal tool for sequencing studies. Bioinformatics 32, 1562–1564 (2016).Article 

Google Scholar 
Guillard, R. R. L. Culture of phytoplankton for feeding marine invertebrates. In Culture of Marine Invertebrate Animals: Proceedings—1st Conference on Culture of Marine Invertebrate Animals Greenport (eds Smith, W. L., & Chanley, M. H.) 29– 60 (Springer, 1975).Cottrell, M. & Suttle, C. Wide-spread occurrence and clonal variation in viruses which cause lysis of a cosmopolitan, eukaryotic marine phytoplankter Micromonas pusilla. Mar. Ecol. Prog. Ser. 78, 1–9 (1991).Article 

Google Scholar 
Krueger, F., James, F., Ewels, P., Afyounian, E. & Schuster-Boeckler, B. FelixKrueger/TrimGalore: v0.6.7 – DOI via Zenodo. https://doi.org/10.5281/zenodo.5127899 (2021).Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).Article 
CAS 

Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).Article 
CAS 

Google Scholar 
Patel, A. et al. Virus and prokaryote enumeration from planktonic aquatic environments by epifluorescence microscopy with SYBR Green I. Nat. Protoc. 2, 269–276 (2007).Article 
CAS 

Google Scholar 
Bolte, S. & Cordelières, F. P. A guided tour into subcellular colocalization analysis in light microscopy. J. Microsc. 224, 213–232 (2006).Article 
CAS 

Google Scholar 
Brussaard, C. P. D. Optimization of procedures for counting viruses by flow cytometry. Appl. Environ. Microbiol. 70, 1506–1513 (2004).Article 
CAS 

Google Scholar 
Kirzner, S., Barak, E. & Lindell, D. Variability in progeny production and virulence of cyanophages determined at the single-cell level. Environ. Microbiol. Rep. 8, 605–613 (2016).Article 

Google Scholar 
Ziv, I. et al. A perturbed ubiquitin landscape distinguishes between ubiquitin in trafficking and in proteolysis. Mol. Cell. Proteomics 10, M111.009753 (2011).HaileMariam, M. et al. S-Trap, an ultrafast sample-preparation approach for shotgun proteomics. J. Proteome Res. 17, 2917–2924 (2018).Article 
CAS 

Google Scholar 
Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007).Article 
CAS 

Google Scholar 
Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).Article 
CAS 

Google Scholar 
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).Article 
CAS 

Google Scholar 
Lechner, M. et al. Proteinortho: detection of (Co-)orthologs in large-scale analysis. BMC Bioinformatics 12, 124 (2011).Article 

Google Scholar 
Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).Article 
CAS 

Google Scholar 
Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction termed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014).Article 
CAS 

Google Scholar 
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).Article 
CAS 

Google Scholar 
O’Connell, J. et al. NxTrim: optimized trimming of Illumina mate pair reads. Bioinformatics 31, 2035–2037 (2015).Article 

Google Scholar 
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 
CAS 

Google Scholar 
Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience 1, 2047-217X-1–18 (2012).Chevreux, B., Wetter, T. & Suhai, S. Genome sequence assembly using trace signals and additional sequence information. In Proc. German Conference on Bioinformatics 45–56 (Fachgruppe Bioinformatik, 1999).Deng, Z. & Delwart, E. ContigExtender: a new approach to improving de novo sequence assembly for viral metagenomics data. BMC Bioinformatics 22, 119 (2021).Article 
CAS 

Google Scholar 
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).Article 

Google Scholar 
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
CAS 

Google Scholar 
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).Article 
CAS 

Google Scholar 
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).Article 
CAS 

Google Scholar 
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).Article 
CAS 

Google Scholar 
Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).Article 
CAS 

Google Scholar 
Barbera, P. et al. EPA-ng: massively parallel evolutionary placement of genetic sequences. Syst. Biol. 68, 365–369 (2019).Article 

Google Scholar 
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).Article 

Google Scholar 
Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).Article 
CAS 

Google Scholar 
Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics 20, 473 (2019).Article 

Google Scholar 
Enright, A. J., Van Dongen, S. & Ouzounis, C. A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–1584 (2002).Article 
CAS 

Google Scholar 
Bolduc, B. et al. vConTACT: an iVirus tool to classify double-stranded DNA viruses that infect Archaea and Bacteria. PeerJ 5, e3243 (2017).Article 

Google Scholar 
Zimmermann, L. et al. A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J. Mol. Biol. 430, 2237–2243 (2018).Article 
CAS 

Google Scholar 
Heger, A. & Holm, L. Rapid automatic detection and alignment of repeats in protein sequences. Proteins 41, 224–237 (2000).Article 
CAS 

Google Scholar 
Chase, E., Desnues, C. & Blanc, G. Integrated viral elements unveil the dual lifestyle of Tetraselmis spp. polinton-like viruses. Virus Evol. 8, veac068 (2022).Egge, E. S., Eikrem, W. & Edvardsen, B. Deep-branching novel lineages and high diversity of haptophytes in the Skagerrak (Norway) uncovered by 454 pyrosequencing. J. Eukaryot. Microbiol. 62, 121–140 (2015).Article 
CAS 

Google Scholar 
Hovde, B. T. et al. Chrysochromulina: genomic assessment and taxonomic diagnosis of the type species for an oleaginous algal clade. Algal Res. 37, 307–319 (2019).Article 

Google Scholar 
Andersen, R. A., Bailey, J. C., Decelle, J. & Probert, I. Phaeocystis rex sp. nov. (Phaeocystales, Prymnesiophyceae): a new solitary species that produces a multilayered scale cell covering. Eur. J. Phycol. 50, 207–222 (2015).Article 

Google Scholar 
Stepanova, O. A. Black Sea algal viruses. Russ. J. Mar. Biol. 42, 123–127 (2016).Article 

Google Scholar 
Alarcón-Schumacher, T., Guajardo-Leiva, S., Antón, J. & Díez, B. Elucidating viral communities during a phytoplankton bloom on the West Antarctic Peninsula. Front. Microbiol. 10, 1014 (2019).Article 

Google Scholar  More

Liphschitz, N., Gophna, R., Hartman, M. & Biger, G. The beginning of olive (Olea europaea) cultivation in the Old World: a reassessment. J. Archaeol. Sci. 18, 441–453 (1991).Article 

Google Scholar 
Blondel, J. & Aronson, J. Biology and Wildlife of the Mediterranean Region (Oxford Univ. Press, 1999).Fall, P. L., Falconer, S. E. & Lines, L. Agricultural intensification and the secondary products revolution along the Jordan Rift. Hum. Ecol. 30, 445–482 (2002).Article 

Google Scholar 
Terral, J.-F. et al. Historical biogeography of olive domestication (Olea europaea L.) as revealed by geometrical morphometry applied to biological and archaeological material. J. Biogeogr. 31, 63–77 (2004).Article 

Google Scholar 
Chartzoulakis, K. Salinity and olive: growth, salt tolerance, photosynthesis and yield. Agric. Water Manag. 78, 108–121 (2005).Article 

Google Scholar 
Vossen, P. Olive oil: history, production, and characteristics of the world’s classic oils. HortScience 42, 1093–1100 (2007).Article 

Google Scholar 
Kaniewski, D. et al. Primary domestication and early uses of the emblematic olive tree: palaeobotanical, historical and molecular evidence from the Middle East. Biol. Rev. 87, 885–899 (2012).Article 

Google Scholar 
Langgut, D. et al. The origin and spread of olive cultivation in the Mediterranean Basin: the fossil pollen evidence. Holocene 29, 902–922 (2019).Article 

Google Scholar 
IPCC. AR5 Synthesis Report: Climate Change 2014 https://www.ipcc.ch/report/ar5/syr/ (IPCC, 2014).IPCC. IPCC WGII Sixth Assessment Report. Cross-Chapter Paper 4: Mediterranean Region https://www.ipcc.ch/report/sixth-assessment-report-working-group-ii/ (IPCC, 2022).Fischer, E. M. & Schär, C. Consistent geographical patterns of changes in high-impact European heatwaves. Nat. Geosci. 3, 398–403 (2010).Article 
CAS 

Google Scholar 
Cramer, W. et al. Climate change and interconnected risks to sustainable development in the Mediterranean. Nat. Clim. Change 8, 972–980 (2018).Article 

Google Scholar 
Santos, J. A., Costa, R. & Fraga, H. Climate change impacts on thermal growing conditions of main fruit species in Portugal. Clim. Change 140, 273–286 (2017).Article 

Google Scholar 
Orlandi, F. et al. Impact of climate change on olive crop production in Italy. Atmosphere 11, 595 (2020).Article 

Google Scholar 
Rodríguez Sousa, A. A., Barandica, J. M., Aguilera, P. A. & Rescia, A. J. Examining potential environmental consequences of climate change and other driving forces on the sustainability of Spanish olive groves under a socio-ecological approach. Agriculture 10, 509 (2020).Article 

Google Scholar 
Besnard, G. et al. The complex history of the olive tree: from Late Quaternary diversification of Mediterranean lineages to primary domestication in the northern Levant. Proc. R. Soc. B 280, 20122833 (2013).Article 
CAS 

Google Scholar 
Besnard, G., Terral, J. F. & Cornille, A. On the origins and domestication of the olive: a review and perspectives. Ann. Bot. 121, 385–403 (2018).Article 

Google Scholar 
Bartolini, G., Prevost, G., Messeri, C., Carignani, C. & Menini, U. G. Olive Germplasm: Cultivars and World-wide Collections (FAO, 1998).Zohary, D. & Spiegel-Roy, P. Beginnings of fruit growing in the Old World. Science 187, 319–327 (1975).Article 
CAS 

Google Scholar 
Terral, J.-F. Wild and cultivated olive (Olea europaea L.): a new approach to an old problem using inorganic analyses of modern wood and archaeological charcoal. Rev. Palaeobot. Palynol. 91, 383–397 (1996).Article 

Google Scholar 
Carrión, Y., Ntinou, M. & Badal, E. Olea europaea L. in the North Mediterranean basin during the Pleniglacial and the Early–Middle Holocene. Quat. Sci. Rev. 29, 952–968 (2010).Article 

Google Scholar 
Zohary, M. Plants of the Bible (Cambridge Univ. Press, 1982).Galili, E., Weinstein-Evron, M. & Zohary, D. Appearance of olives in submerged Neolithic sites along the Carmel Coast. J. Isr. Plant Sci. 22, 95–97 (1989).
Google Scholar 
Galili, E., Stanley, D. J., Sharvit, J. & Weinstein-Evron, M. Evidence for earliest olive-oil production in submerged settlements off the Carmel Coast, Israel. J. Archaeol. Sci. 24, 1141–1150 (1997).Article 

Google Scholar 
Galili, E. et al. Early production of table olives at a mid-7th millennium BP submerged site off the Carmel Coast (Israel). Sci. Rep. 11, 2218 (2021).Article 
CAS 

Google Scholar 
Fraga, H., Pinto, J. G., Viola, F. & Santos, J. A. Climate change projections for olive yields in the Mediterranean Basin. Int. J. Climatol. 40, 769–781 (2020).Article 

Google Scholar 
Ben Zaied, Y. & Zouabi, O. Impacts of climate change on Tunisian olive oil output. Clim. Change 139, 535–549 (2016).Article 

Google Scholar 
Brito, C., Dinis, L. T., Moutinho-Pereire, J. & Correia, C. M. Drought stress effects and olive tree acclimation under a changing climate. Plants 8, 232 (2019).Article 
CAS 

Google Scholar 
Fraga, H., Moriondo, M., Leolini, L. & Santos, J. A. Mediterranean olive orchards under climate change: a review of future impacts and adaptation strategies. Agronomy 11, 56 (2021).Article 

Google Scholar 
Trærup, S. & Stephan, J. Technologies for adaptation to climate change. Examples from the agricultural and water sectors in Lebanon. Clim. Change 131, 435–449 (2015).Article 

Google Scholar 
Chalak, L. et al. Extent of the genetic diversity in Lebanese olive (Olea europaea L.) trees: a mixture of an ancient germplasm with recently introduced varieties. Genet. Resour. Crop. Evol. 62, 621–633 (2015).Article 

Google Scholar 
Bou-Zeid, E. & El-Fadel, M. Climate change and water resources in Lebanon and the Middle East. J. Water Resour. Plan. Manag. 128, 343–355 (2002).Article 

Google Scholar 
Ramadan, H. H., Beighley, R. E. & Ramamurthy, A. S. Sensitivity analysis of climate change impact on the hydrology of the Litani Basin in Lebanon. Int. J. Environ. Pollut. 52, 65–81 (2013).Article 
CAS 

Google Scholar 
Saade, J., Atieh, M., Ghanimeh, S. & Golmohammadi, G. Modeling impact of climate change on surface water availability using SWAT model in a semi-arid basin: case of El Kalb River, Lebanon. Hydrology 8, 134 (2021).Article 

Google Scholar 
Halwani, J. & Halwani, B. in Climate Change in the Mediterranean and Middle Eastern Region (eds Filho, W. L. & Manolas, E.) 395–412 (Springer, 2022).Aubet, M.E. in Nomads of the Mediterranean: Trade and Contact in the Bronze and Iron Ages (eds Gilboa, A. & Yasur-Landau, A.) 14–30 (Brill, 2020).Bikai, P. M. The Pottery of Tyre (Aris & Phillips, 1979).Hajar, L., Khater, C. & Cheddadi, R. Vegetation changes during the late Pleistocene and Holocene in Lebanon: a pollen record from the Bekaa Valley. Holocene 18, 1089–1099 (2008).Article 

Google Scholar 
Hajar, L., Haïdar-Boustani, M., Khater, C. & Cheddadi, R. Environmental changes in Lebanon during the Holocene: man vs. climate impacts. J. Arid. Environ. 74, 746–755 (2010).Article 

Google Scholar 
Cheddadi, R. & Khater, C. Climate change since the last glacial period in Lebanon and the persistence of Mediterranean species. Quat. Sci. Rev. 150, 146–157 (2016).Article 

Google Scholar 
Ozturk, M. et al. An overview of olive cultivation in Turkey: botanical features, eco-physiology and phytochemical aspects. Agronomy 11, 295 (2021).Article 
CAS 

Google Scholar 
Lionello, P., Congedi, L., Reale, M., Scarascia, L. & Tanzarella, A. Sensitivity of typical Mediterranean crops to past and future evolution of seasonal temperature and precipitation in Apulia. Reg. Environ. Change 14, 2025–2038 (2014).Article 

Google Scholar 
Arenas-Castro, S., Gonçalves, J. F., Moreno, M. & Villar, R. Projected climate changes are expected to decrease the suitability and production of olive varieties in southern Spain. Sci. Total Environ. 709, 136161 (2020).Article 
CAS 

Google Scholar 
Mechri, B., Tekaya, M., Hammami, M. & Chehab, H. Effects of drought stress on phenolic accumulation in greenhouse-grown olive trees (Olea europaea). Biochem. Syst. Ecol. 92, 104112 (2020).Article 
CAS 

Google Scholar 
Pedan, V., Popp, M., Rohn, S., Nyfeler, M. & Bongartz, A. Characterization of phenolic compounds and their contribution to sensory properties of olive oil. Molecules 24, 2041 (2019).Article 
CAS 

Google Scholar 
Dias, M. C., Pinto, D. C. G. A., Figueiredo, C., Santos, C. & Silva, A. M. S. Phenolic and lipophilic metabolite adjustments in Olea europaea (olive) trees during drought stress and recovery. Phytochemistry 185, 112695 (2021).Article 
CAS 

Google Scholar 
Peres, F. et al. Phenolic compounds of ‘Galega Vulgar’ and ‘Cobrançosa’ olive oils along early ripening stages. Food Chem. 211, 51–58 (2016).Article 
CAS 

Google Scholar 
Tsimidou, M. Z. in Handbook of Olive Oil: Analysis and Properties (eds Aparicio, R. & Harwood, J.) 311–333 (Springer, 2013).Valente, S. et al. Modulation of phenolic and lipophilic compounds of olive fruits in response to combined drought and heat. Food Chem. 329, 127191 (2020).Article 
CAS 

Google Scholar 
WCRP. World Research Climate Program https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6 (WCRP, 2022).Rallo, L. et al. in Advances in Plant Breeding Strategies: Fruits (eds Al-Khayri, J. et al.) (Springer, 2018).Abou-Saaid, O. et al. Statistical approach to assess chill and heat requirements of olive tree based on flowering date and temperatures data: towards selection of adapted cultivars to global warming. Agronomy 12, 2975 (2022).Article 

Google Scholar 
Faegri, K. & Iversen, I. Textbook of Pollen Analysis 4th edn. (Wiley, 1989).Ferrara, G., Camposeo, S., Palasciano, M. & Godini, A. Production of total and stainable pollen grains in Olea europaea L. Grana 46, 85–90 (2007).Article 

Google Scholar 
Kaniewski, D. et al. Wild or cultivated Olea europaea L. in the eastern Mediterranean during the Middle–Late Holocene? A pollen-numerical approach. Holocene 19, 1039–1047 (2009).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, 2020).Hammer, O. & Harper, D. Paleontological Data Analysis (Blackwell, 2006).Cheddadi, R. et al. Microrefugia, climate change, and conservation of Cedrus atlantica in the Rif Mountains, Morocco. Front. Ecol. Evol. 5, 114 (2017).Article 

Google Scholar 
Kaniewski, D. et al. Cold and dry outbreaks in the eastern Mediterranean 3200 years ago. Geology 47, 933–937 (2019).Article 

Google Scholar 
Kaniewski, D. et al. Recent anthropogenic climate change exceeds the rate and magnitude of natural Holocene variability on the Balearic Islands. Anthropocene 32, 100268 (2020).Article 

Google Scholar 
Kaniewski, D. et al. Coastal submersions in the north-eastern Adriatic during the last 5200 years. Glob. Planet. Change 204, 103570 (2021).Article 

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

Google Scholar 
Akima, H. & Gebhardt, A. Akima: Interpolation of Irregularly and Regularly Spaced Data. R v.0.6-2 (R Foundation for Statistical Computing, 2016).Ooms, J. D., Debroy, S., Wickham, H. & Horner, J. RMySQL: Database Interface and ‘MySQL’ Driver for R. R v.0.10.18 (R Foundation for Statistical Computing, 2019).Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high resolution grids of monthly climatic observations – the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar  More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Rockström, J. et al. A safe operation space for humanity. Nature 461, 472–475 (2009).Article 

Google Scholar 
Chancel, L., Piketty, T., Saez, E. & Zucman, G. World Inequality Report 2022 (Belknap Press, 2022).Ivanova, D. et al. Environmental impact assessment of household consumption. J. Ind. Ecol. 20, 526–536 (2016).Article 
CAS 

Google Scholar 
Steen-Olsen, K., Weinzettel, J., Cranston, G., Ercin, A. E. & Hertwich, E. G. Carbon, land, and water footprint accounts for the European Union: consumption, production, and displacements through international trade. Environ. Sci. Technol. 46, 10883–10891 (2012).Article 
CAS 

Google Scholar 
Tukker, A. et al. Environmental and resource footprints in a global context: Europe’s structural deficit in resource endowments. Glob. Environ. Change 40, 171–181 (2016).Article 

Google Scholar 
Bruckner, B., Hubacek, K., Shan, Y., Zhong, H. & Feng, K. Impacts of poverty alleviation on national and global carbon emissions. Nat. Sustain. 5, 311–320 (2022).Article 

Google Scholar 
Hubacek, K. et al. Global carbon inequality. Energy, Ecol. Environ. 2, 361–369 (2017).Article 

Google Scholar 
Yu, Y., Feng, K. & Hubacek, K. Tele-connecting local consumption to global land use. Glob. Environ. Change 23, 1178–1186 (2013).Article 

Google Scholar 
Wilting, H. C., Schipper, A. M., Bakkenes, M., Meijer, J. R. & Huijbregts, M. A. J. Quantifying biodiversity losses due to human consumption: a global-scale footprint analysis. Environ. Sci. Technol. 51, 3298–3306 (2017).Article 
CAS 

Google Scholar 
Lucas, P. L., Wilting, H. C., Hof, A. F. & Van Vuuren, D. P. Allocating planetary boundaries to large economies: distributional consequences of alternative perspectives on distributive fairness. Glob. Environ. Change 60, 102017 (2020).Article 

Google Scholar 
Beylot, A. et al. Assessing the environmental impacts of EU consumption at macro-scale. J. Clean. Prod. 216, 382–393 (2019).Article 

Google Scholar 
Koslowski, M., Moran, D. D., Tisserant, A., Verones, F. & Wood, R. Quantifying Europe’s biodiversity footprints and the role of urbanization and income. Glob. Sustain. 3, e1 (2020).Lutter, S., Pfister, S., Giljum, S., Wieland, H. & Mutel, C. Spatially explicit assessment of water embodied in European trade: a product-level multi-regional input-output analysis. Glob. Environ. Change 38, 171–182 (2016).Article 

Google Scholar 
Stadler, K. et al. EXIOBASE 3 (3.8.1) [Data set]. Zenodo https://doi.org/10.5281/ZENODO.4588235 (2021).Roadmap to a Resource Efficient Europe (European Commission, 2011).Steinmann, Z. J. N. et al. Headline environmental indicators revisited with the global multi-regional input–output database EXIOBASE. J. Ind. Ecol. 22, 565–573 (2018).Article 

Google Scholar 
Ivanova, D. et al. Mapping the carbon footprint of EU regions. Environ. Res. Lett. 12, 054013 (2017).Wiedmann, T. O. et al. The material footprint of nations. Proc. Natl Acad. Sci. USA 112, 6271–6276 (2015).Article 
CAS 

Google Scholar 
Lenzen, M. et al. Implementing the material footprint to measure progress towards Sustainable Development Goals 8 and 12. Nat. Sustain. 5, 157–166 (2022).Dorninger, C. et al. The effect of industrialization and globalization on domestic land-use: a global resource footprint perspective. Glob. Environ. Change 69, 102311 (2021).Article 

Google Scholar 
Mekonnen, M. M. & Gerbens-Leenes, W. The water footprint of food. Water 12, 12 (2020).Article 

Google Scholar 
Prell, C. & Feng, K. Unequal carbon exchanges: the environmental and economic impacts of iconic U.S. consumption items. J. Ind. Ecol. 20, 537–546 (2016).Article 

Google Scholar 
Prell, C., Feng, K., Sun, L., Geores, M. & Hubacek, K. The economic gains and environmental losses of US consumption: a world-systems and input-output approach. Soc. Forces 93, 405–428 (2014).Article 

Google Scholar 
Prell, C. Wealth and pollution inequalities of global trade: a network and input-output approach. Soc. Sci. J. 53, 111–121 (2016).Article 

Google Scholar 
World Economic Outlook (October 2022) (International Monetary Fund, 2022); https://www.imf.org/external/datamapper/datasets/WEOWilting, H. C., Schipper, A. M., Ivanova, O., Ivanova, D. & Huijbregts, M. A. J. Subnational greenhouse gas and land-based biodiversity footprints in the European Union. J. Ind. Ecol. 25, 79–94 (2021). https://doi.org/10.1111/jiec.13042Cabernard, L. & Pfister, S. A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress. Sci. Total Environ. 755, 142587 (2021).Jakob, M., Ward, H. & Steckel, J. C. Sharing responsibility for trade-related emissions based on economic benefits. Glob. Environ. Chang. 66, 102207 (2021).Article 

Google Scholar 
Wood, R. et al. The structure, drivers and policy implications of the European carbon footprint. Clim. Policy 20, S39–S57 (2020).Article 

Google Scholar 
Wood, R. et al. Growth in environmental footprints and environmental impacts embodied in trade: resource efficiency indicators from EXIOBASE3. J. Ind. Ecol. 22, 553–564 (2018).Article 

Google Scholar 
Hubacek, K., Chen, X., Feng, K., Wiedmann, T. & Shan, Y. Evidence of decoupling consumption-based CO2 emissions from economic growth. Adv. Appl. Energy 4, 100074 (2021).Article 

Google Scholar 
Wiedmann, T. & Lenzen, M. Environmental and social footprints of international trade. Nat. Geosci. 11, 314–321 (2018).Article 
CAS 

Google Scholar 
Dorninger, C. et al. Global patterns of ecologically unequal exchange: Implications for sustainability in the 21st century. Ecol. Econ. 179, 106824 (2021).Article 

Google Scholar 
Hickel, J., Dorninger, C., Wieland, H. & Suwandi, I. Imperialist appropriation in the world economy: drain from the global South through unequal exchange, 1990–2015. Glob. Environ. Change 73, 102467 (2022).Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018).Article 
CAS 

Google Scholar 
Ivanova, D. et al. Quantifying the potential for climate change mitigation of consumption options. Environ. Res. Lett. 15, 093001 (2020).Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).Article 
CAS 

Google Scholar 
Ivanova, D. & Wood, R. The unequal distribution of household carbon footprints in Europe and its link to sustainability. Glob. Sustain. 3, e18 (2020).Hickel, J., O’Neill, D. W., Fanning, A. L. & Zoomkawala, H. National responsibility for ecological breakdown: a fair-shares assessment of resource use, 1970–2017. Lancet Planet. Heal. 6, e342–e349 (2022).Article 

Google Scholar 
Otto, I. M., Kim, K. M., Dubrovsky, N. & Lucht, W. Shift the focus from the super-poor to the super-rich. Nat. Clim. Change 9, 82–84 (2019).Article 

Google Scholar 
Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).Nielsen, K. S., Nicholas, K. A., Creutzig, F., Dietz, T. & Stern, P. C. The role of high-socioeconomic-status people in locking in or rapidly reducing energy-driven greenhouse gas emissions. Nat. Energy 6, 1011–1016 (2021).Article 

Google Scholar 
Jakob, M. Why carbon leakage matters and what can be done against it. One Earth 4, 609–614 (2021).Article 

Google Scholar 
Lave, L. B. Using input–output analysis to estimate economy-wide discharges. Environ. Sci. Technol. 29, 420A–426A (1995).Article 
CAS 

Google Scholar 
Wiedmann, T. A review of recent multi-region input–output models used for consumption-based emission and resource accounting. Ecol. Econ. 69, 211–222 (2009).Article 

Google Scholar 
Ewing, B. R. et al. Integrating ecological and water footprint accounting in a multi-regional input–output framework. Ecol. Indic. 23, 1–8 (2012).Article 

Google Scholar 
Brizga, J., Feng, K. & Hubacek, K. Household carbon footprints in the Baltic States: a global multi-regional input–output analysis from 1995 to 2011. Appl. Energy 189, 780–788 (2017).Hertwich, E. G. & Peters, G. P. Carbon footprint of nations: a global, trade-linked analysis. Environ. Sci. Technol. 43, 6414–6420 (2009).Article 
CAS 

Google Scholar 
Zhong, H., Feng, K., Sun, L., Cheng, L. & Hubacek, K. Household carbon and energy inequality in Latin American and Caribbean countries. J. Environ. Manag. 273, 110979 (2020).Article 

Google Scholar 
Stadler, K. et al. EXIOBASE 3: developing a time series of detailed environmentally extended multi-regional input–output tables. J. Ind. Ecol. 22, 502–515 (2018).Article 

Google Scholar 
Hardadi, G., Buchholz, A. & Pauliuk, S. Implications of the distribution of German household environmental footprints across income groups for integrating environmental and social policy design. J. Ind. Ecol. 25, 95–113 (2021).Zhang, Q. et al. Transboundary health impacts of transported global air pollution and international trade. Nature 543, 705–709 (2017).Article 
CAS 

Google Scholar 
Hoekstra, A. Y., Mekonnen, M. M., Chapagain, A. K., Mathews, R. E. & Richter, B. D. Global monthly water scarcity: blue water footprints versus blue water availability. PLoS ONE 7, e32688 (2012).Article 
CAS 

Google Scholar 
IPCC Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) (Cambridge Univ. Press, 2007).Schmidt, S. et al. Understanding GHG emissions from Swedish consumption—current challenges in reaching the generational goal. J. Clean. Prod. 212, 428–437 (2019).Article 

Google Scholar 
Huijbregts, M. A. J. Priority Assessment of Toxic Substances in the Frame of LCA. Development and Application of the Multi-Media Fate, Exposure and Effect Model USES-LCA (Interfaculty Department of Envrionmental Science, 1999).Huijbregts, M. A. J. Priority Assessment of Toxic Substances in the Frame of LCA. Time Horizon Dependency in Toxicity Potentials Calculated with the Multi-Media Fate, Exposure and Effects Model USES-LCA (Institute for Biodiversity and Ecosystem Dynamics, 2000).International Reference Life Cycle Data System (ILCD) Handbook (Publications Office EU, 2011).Verones, F., Moran, D., Stadler, K., Kanemoto, K. & Wood, R. Resource footprints an d their ecosystem consequences. Sci. Rep. 7, 40743 (2017).Chaudhary, A., Pfister, S. & Hellweg, S. Spatially explicit analysis of biodiversity loss due to global agriculture, pasture and forest land use from a producer and consumer perspective. Environ. Sci. Technol. 50, 3928–3936 (2016).Article 
CAS 

Google Scholar 
Chaudhary, A., Verones, F., De Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species-area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).Article 
CAS 

Google Scholar 
Marquardt, S. G. et al. Consumption-based biodiversity footprints—do different indicators yield different results? Ecol. Indic. 103, 461–470 (2019).Article 

Google Scholar 
World Development Indicators DataBank (World Bank, 2022); https://databank.worldbank.org/source/world-development-indicatorsWorld Population Prospects 2022 (United Nations, 2022); https://population.un.org/wpp/Natural Earth Vector (Natural Earth, 2022); https://www.naturalearthdata.com/Lahti, L., Huovari, J., Kainu, M. & Biecek, P. Retrieval and analysis of eurostat open data with the Eurostat package. R J. 9, 385–392 (2017).Castellani, V., Beylot, A. & Sala, S. Environmental impacts of household consumption in Europe: comparing process-based LCA and environmentally extended input-output analysis. J. Clean. Prod. 240, 117966 (2019).Article 

Google Scholar  More

Essl, F. et al. A conceptual framework for range-expanding species that track human-induced environmental change. BioScience 69, 908–919 (2019).Article 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).Article 

Google Scholar 
Freeman, B. G., Lee-Yaw, J. A., Sunday, J. M. & Hargreaves, A. L. Expanding, shifting and shrinking: the impact of global warming on species’ elevational distributions. Glob. Ecol. Biogeogr. 27, 1268–1276 (2018).Article 

Google Scholar 
van Kleunen, M. et al. Global exchange and accumulation of non-native plants. Nature 525, 100–103 (2015).Article 

Google Scholar 
Alexander, J. M. et al. Lags in the response of mountain plant communities to climate change. Glob. Change Biol. 24, 563–579 (2018).Article 

Google Scholar 
Graae, B. J. et al. Stay or go—how topographic complexity influences alpine plant population and community responses to climate change. Perspect. Plant Ecol. Evol. Syst. 30, 41–50 (2018).Article 

Google Scholar 
Rumpf, S. B., Hülber, K., Zimmermann, N. E. & Dullinger, S. Elevational rear edges shifted at least as much as leading edges over the last century. Glob. Ecol. Biogeogr. 28, 533–543 (2019).Article 

Google Scholar 
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).Article 
CAS 

Google Scholar 
Mamantov, M. A., Gibson-Reinemer, D. K., Linck, E. B. & Sheldon, K. S. Climate-driven range shifts of montane species vary with elevation. Glob. Ecol. Biogeogr. 30, 784–794 (2021).Article 

Google Scholar 
Alexander, J. M. et al. Plant invasions into mountains and alpine ecosystems: current status and future challenges. Alp. Bot. 126, 89–103 (2016).Article 

Google Scholar 
Pauchard, A. et al. Ain’t no mountain high enough: plant invasions reaching new elevations. Front. Ecol. Environ. 7, 479–486 (2009).Article 

Google Scholar 
Alexander, J. M., MIREN Consortium et al. Assembly of nonnative floras along elevational gradients explained by directional ecological filtering. Proc. Natl Acad. Sci. USA 108, 656–661 (2011).Article 
CAS 

Google Scholar 
Seipel, T. et al. Processes at multiple spatial scales determine non-native plant species richness and similarity in mountain regions around the world. Glob. Ecol. Biogeogr. 21, 236–246 (2012).Article 

Google Scholar 
Dainese, M. et al. Human disturbance and upward expansion of plants in a warming climate. Nat. Clim. Change 7, 577–580 (2017).Article 

Google Scholar 
McDougall, K. L. et al. Running off the road: roadside non-native plants invading mountain vegetation. Biol. Invasions 20, 3461–3473 (2018).Article 

Google Scholar 
Petitpierre, B. et al. Will climate change increase the risk of plant invasions into mountains? Ecol. Appl. 26, 530–544 (2016).Article 

Google Scholar 
Lembrechts, J. J. et al. Microclimate variability in alpine ecosystems as stepping stones for non‐native plant establishment above their current elevational limit. Ecography 41, 900–909 (2017).Article 

Google Scholar 
Haider, S. et al. Mountain roads and non-native species modify elevational patterns of plant diversity. Glob. Ecol. Biogeogr. 27, 667–678 (2018).Article 

Google Scholar 
Wolf, A., Zimmerman, N. B., Anderegg, W. R. L., Busby, P. E. & Christensen, J. Altitudinal shifts of the native and introduced flora of California in the context of 20th-century warming. Glob. Ecol. Biogeogr. 25, 418–429 (2016).Article 

Google Scholar 
Seipel, T., Alexander, J. M., Edwards, P. J. & Kueffer, C. Range limits and population dynamics of non-native plants spreading along elevation gradients. Perspect. Plant Ecol. Evol. Syst. 20, 46–55 (2016).Article 

Google Scholar 
Koide, D., Yoshida, K., Daehler, C. C. & Mueller-Dombois, D. An upward elevation shift of native and non-native vascular plants over 40 years on the island of Hawai’i. J. Vegetation Sci. 28, 939–950 (2017).Article 

Google Scholar 
Becker, T., Dietz, H., Billeter, R., Buschmann, H. & Edwards, P. J. Altitudinal distribution of alien plant species in the Swiss Alps. Perspect. Plant Ecol. Evol. Syst. 7, 173–183 (2005).Article 

Google Scholar 
Haider, S. et al. The role of bioclimatic origin, residence time and habitat context in shaping non-native plant distributions along an altitudinal gradient. Biol. Invasions 12, 4003–4018 (2010).Article 

Google Scholar 
Pyšek, P., Jarošík, V., Pergl, J. & Wild, J. Colonization of high altitudes by alien plants over the last two centuries. Proc. Natl Acad. Sci. USA 108, 439–440 (2011).Article 

Google Scholar 
Gottfried, M. et al. Continent-wide response of mountain vegetation to climate change. Nat. Clim. Change 2, 111–115 (2012).Article 

Google Scholar 
Lenoir, J. et al. Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate. Ecography 33, 295–303 (2010).
Google Scholar 
Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T. & Mynsberge, A. R. Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science 331, 324–327 (2011).Article 
CAS 

Google Scholar 
Rumpf, S. B. et al. Range dynamics of mountain plants decrease with elevation. Proc. Natl Acad. Sci. USA 115, 1848–1853 (2018).Article 
CAS 

Google Scholar 
Scherrer, D. & Körner, C. Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J. Biogeogr. 38, 406–416 (2011).Article 

Google Scholar 
Kelly, C. & Price, T. D. Correcting for regression to the mean in behavior and ecology. Am. Nat. 166, 700–707 (2005).Article 

Google Scholar 
Mazalla, L. & Diekmann, M. Regression to the mean in vegetation science. J. Vegetation Sci. 33, e13117 (2022).Article 

Google Scholar 
Colwell, R. K. & Lees, D. C. The mid-domain effect: geometric constraints on the geography of species richness. Trends Ecol. Evol. 15, 70–76 (2000).Article 
CAS 

Google Scholar 
Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322, 258–261 (2008).Article 
CAS 

Google Scholar 
Taheri, S., Naimi, B., Rahbek, C. & Araújo, M. B. Improvements in reports of species redistribution under climate change are required. Sci. Adv. 7, eabe1110 (2021).Article 

Google Scholar 
Haider, S. et al. Think globally, measure locally: the MIREN standardized protocol for monitoring plant species distributions along elevation gradients. Ecol. Evol. 12, e8590 (2022).Article 

Google Scholar 
Jacobsen, D. The dilemma of altitudinal shifts: caught between high temperature and low oxygen. Front. Ecol. Environ. 18, 211–218 (2020).Article 

Google Scholar 
Kueffer, C. et al. in Plant Invasions in Protected Areas Vol. 7 (eds Foxcroft, L. C. et al.) 89–113 (Springer, 2013).Halbritter, A. H., Alexander, J. M., Edwards, P. J. & Billeter, R. How comparable are species distributions along elevational and latitudinal climate gradients? Glob. Ecol. Biogeogr. 22, 1228–1237 (2013).Article 

Google Scholar 
Vitasse, Y. et al. Phenological and elevational shifts of plants, animals and fungi under climate change in the European Alps. Biol. Rev. 96, 1816–1835 (2021).Article 

Google Scholar 
Angert, A. L. et al. Do species’ traits predict recent shifts at expanding range edges? Ecol. Lett. 14, 677–689 (2011).Article 

Google Scholar 
Matteodo, M., Wipf, S., Stöckli, V., Rixen, C. & Vittoz, P. Elevation gradient of successful plant traits for colonizing alpine summits under climate change. Environ. Res. Lett. 8, 024043 (2013).Article 

Google Scholar 
Lembrechts, J. et al. Disturbance is the key to plant invasions in cold environments. Proc. Natl Acad. Sci. USA 113, 14061–14066 (2016).Article 
CAS 

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

Google Scholar 
Pebesma, E. Simple features for R: standardized support for spatial vector data. R J. 10, 439–446 (2018).Article 

Google Scholar 
Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R J. 5, 144–161 (2013). http://journal.r-project.org/archive/2013-1/kahle-wickham.pdfSeipel, T., Haider, S. & MIREN consortium. MIREN survey of plant species in mountains (v2.0). Zenodo https://doi.org/10.5281/zenodo.5529072 (2022). More