Philippa Kaur

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

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

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

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

Ceballos, G. et al. Accelerated modern human-induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).Article 

Google Scholar 
Dereniowska, M. & Meinard, Y. The unknownness of biodiversity: its value and ethical significance for conservation action. Biol. Conserv. 260, 109199 (2021).Article 

Google Scholar 
Maron, M. et al. Towards a threat assessment framework for ecosystem services. Trends Ecol. Evol. 32, 240–248 (2017).Article 

Google Scholar 
Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).Article 
CAS 

Google Scholar 
Taborsky, B. et al. Towards an evolutionary theory of stress responses. Trends Ecol. Evol. 36, 39–48 (2021).Article 

Google Scholar 
van de Leemput, I. A., Dakos, V., Scheffer, M. & van Nes, E. H. Slow recovery from local disturbances as an indicator for loss of ecosystem resilience. Ecosystems 21, 141–152 (2018).Article 

Google Scholar 
Fagan, W. F. & Holmes, E. E. Quantifying the extinction vortex. Ecol. Lett. 9, 51–60 (2005).
Google Scholar 
Williams, N. F., McRae, L., Freeman, R., Capdevila, P. & Clements, C. F. Scaling the extinction vortex: body size as a predictor of population dynamics close to extinction events. Ecol. Evol. 11, 7069–7079 (2021).Article 

Google Scholar 
Clements, C. F. & Ozgul, A. Indicators of transitions in biological systems. Ecol. Lett. 21, 905–919 (2018).Article 

Google Scholar 
Shaffer, M. L. in Challenges in the Conservation of Biological Resources (eds. Decker, D. J., Krasny, M. E., Goff, G. R., Smith, C. R. & Gross, D. W.) 107–118 (Routledge, 2019).Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).Article 
CAS 

Google Scholar 
Gardner, T. A. et al. The cost-effectiveness of biodiversity surveys in tropical forests. Ecol. Lett. 11, 139–150 (2008).Article 

Google Scholar 
Coulson, T., Mace, G. M., Hudson, E. & Possingham, H. The use and abuse of population viability analysis. Trends Ecol. Evol. 16, 219–221 (2001).Article 
CAS 

Google Scholar 
Clements, C. F., Drake, J. M., Griffiths, J. I. & Ozgul, A. Factors influencing the detectability of early warning signals of population collapse. Am. Nat. 186, 50–58 (2015).Article 

Google Scholar 
Patterson, A. C., Strang, A. G. & Abbott, K. C. When and where we can expect to see early warning signals in multispecies systems approaching tipping points: insights from theory. Am. Nat. 198, E12–E26 (2021).Article 

Google Scholar 
Vinton, A. C., Gascoigne, S. J. L., Sepil, I. & Salguero-Gómez, R. Plasticity’s role in adaptive evolution depends on environmental change components. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2022.08.008 (2022).Levin, S. A. The problem of pattern and scale in ecology: the Robert H. MacArthur Award lecture. Ecology 73, 1943–1967 (1992).Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).Article 
CAS 

Google Scholar 
Haberle, I., Marn, N., Geček, S. & Klanjšček, T. Dynamic energy budget of endemic and critically endangered bivalve Pinna nobilis: a mechanistic model for informed conservation. Ecol. Model. 434, 109207 (2020).Article 

Google Scholar 
Gislason, H., Daan, N., Rice, J. C. & Pope, J. G. Size, growth, temperature and the natural mortality of marine fish. Fish Fish. 11, 149–158 (2010).Article 

Google Scholar 
Jennings, S. & Blanchard, J. L. Fish abundance with no fishing: predictions based on macroecological theory. J. Anim. Ecol. 73, 632–642 (2004).Article 

Google Scholar 
Valderrama, D. & Fields, K. H. Flawed evidence supporting the metabolic theory of ecology may undermine goals of ecosystem-based fishery management: the case of invasive Indo-Pacific lionfish in the western Atlantic. ICES J. Mar. Sci. 74, 1256–1267 (2017).Article 

Google Scholar 
Marshall, D. J. & McQuaid, C. D. Warming reduces metabolic rate in marine snails: adaptation to fluctuating high temperatures challenges the metabolic theory of ecology. Proc. R. Soc. B 278, 281–288 (2011).Article 

Google Scholar 
Rombouts, I., Beaugrand, G., Ibaňez, F., Chiba, S. & Legendre, L. Marine copepod diversity patterns and the metabolic theory of ecology. Oecologia 166, 349–355 (2011).Article 

Google Scholar 
Allen, A. P. & Gillooly, J. F. The mechanistic basis of the metabolic theory of ecology. Oikos 116, 1073–1077 (2022).Article 

Google Scholar 
Lawton, J. H. From physiology to population dynamics and communities. Funct. Ecol. 5, 155–161 (1991).Article 

Google Scholar 
Ames, E. M. et al. Striving for population-level conservation: integrating physiology across the biological hierarchy. Conserv. Physiol. 8, coaa019 (2020).Article 

Google Scholar 
Berger-Tal, O. et al. Integrating animal behavior and conservation biology: a conceptual framework. Behav. Ecol. 22, 236–239 (2011).Article 

Google Scholar 
Baruah, G., Clements, C. F., Guillaume, F. & Ozgul, A. When do shifts in trait dynamics precede population declines? Am. Nat. 193, 633–644 (2019).Article 

Google Scholar 
Dakos, V. et al. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7, e41010 (2012).Article 
CAS 

Google Scholar 
Ward, R. J., Griffiths, R. A., Wilkinson, J. W. & Cornish, N. Optimising monitoring efforts for secretive snakes: a comparison of occupancy and N-mixture models for assessment of population status. Sci. Rep. 7, 18074 (2017).Article 

Google Scholar 
Thompson, W. Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters (Island Press, 2013).Clements, C. F., Blanchard, J. L., Nash, K. L., Hindell, M. A. & Ozgul, A. Body size shifts and early warning signals precede the historic collapse of whale stocks. Nat. Ecol. Evol. 1, 0188 (2017).Article 

Google Scholar 
Burant, J. B., Park, C., Betini, G. S. & Norris, D. R. Early warning indicators of population collapse in a seasonal environment. J. Anim. Ecol. 90, 1538–1549 (2021).Article 

Google Scholar 
Tuomainen, U. & Candolin, U. Behavioural responses to human-induced environmental change. Biol. Rev. 86, 640–657 (2011).Article 

Google Scholar 
Mazza, V., Dammhahn, M., Lösche, E. & Eccard, J. A. Small mammals in the big city: behavioural adjustments of non-commensal rodents to urban environments. Glob. Change Biol. 26, 6326–6337 (2020).Article 

Google Scholar 
Hendry, A. P., Farrugia, T. J. & Kinnison, M. T. Human influences on rates of phenotypic change in wild animal populations. Mol. Ecol. 17, 20–29 (2008).Article 

Google Scholar 
Speakman, J. R., Król, E. & Johnson, M. S. The functional significance of individual variation in basal metabolic rate. Physiol. Biochem. Zool. 77, 900–915 (2004).Article 

Google Scholar 
Péron, G. et al. Evidence of reduced individual heterogeneity in adult survival of long-lived species. Evolution 70, 2909–2914 (2016).Article 

Google Scholar 
Fleming, A. H., Clark, C. T., Calambokidis, J. & Barlow, J. Humpback whale diets respond to variance in ocean climate and ecosystem conditions in the California Current. Glob. Change Biol. 22, 1214–1224 (2016).Article 

Google Scholar 
Kirkwood, T. B. L., Rose, M. R., Harvey, P. H., Partridge, L. & Southwood, S. R. Evolution of senescence: late survival sacrificed for reproduction. Phil. Trans. R. Soc. Lond. B 332, 15–24 (1991).Article 
CAS 

Google Scholar 
Mallela, A. & Hastings, A. The role of stochasticity in noise-induced tipping point cascades: a master equation approach. Bull. Math. Biol. 83, 53 (2021).Article 

Google Scholar 
Burthe, S. J. et al. Do early warning indicators consistently predict nonlinear change in long-term ecological data? J. Appl. Ecol. 53, 666–676 (2016).Article 

Google Scholar 
Vucetich, J. A. & Waite, T. A. Erosion of heterozygosity in fluctuating populations. Conserv. Biol. 13, 860–868 (1999).Article 

Google Scholar 
Kramer, A. M. & Drake, J. M. Experimental demonstration of population extinction due to a predator-driven Allee effect. J. Anim. Ecol. 79, 633–639 (2010).Article 

Google Scholar 
Oram, E. & Spitze, K. Depth selection by Daphnia pulex in response to Chaoborus kairomone. Freshw. Biol. 58, 409–415 (2013).Article 

Google Scholar 
Trites, A. W. & Donnelly, C. P. The decline of Steller sea lions Eumetopias jubatus in Alaska: a review of the nutritional stress hypothesis. Mammal. Rev. 33, 3–28 (2003).Article 

Google Scholar 
Sibly, R. M., Barker, D., Hone, J. & Pagel, M. On the stability of populations of mammals, birds, fish and insects. Ecol. Lett. 10, 970–976 (2007).Article 

Google Scholar 
Dakos, V. et al. Ecosystem tipping points in an evolving world. Nat. Ecol. Evol. 3, 355–362 (2019).Article 

Google Scholar 
Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).Article 

Google Scholar 
Tanner, R. L. & Dowd, W. W. Inter-individual physiological variation in responses to environmental variation and environmental change: integrating across traits and time. Comp. Biochem. Physiol. A 238, 110577 (2019).Article 
CAS 

Google Scholar 
Patrick, S. C., Martin, J. G. A., Ummenhofer, C. C., Corbeau, A. & Weimerskirch, H. Albatrosses respond adaptively to climate variability by changing variance in a foraging trait. Glob. Change Biol. 27, 4564–4574 (2021).Article 
CAS 

Google Scholar 
Fayet, A. L., Clucas, G. V., Anker‐Nilssen, T., Syposz, M. & Hansen, E. S. Local prey shortages drive foraging costs and breeding success in a declining seabird, the Atlantic puffin. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13442 (2021).Pierce, C. L. Predator avoidance, microhabitat shift, and risk-sensitive foraging in larval dragonflies. Oecologia 77, 81–90 (1988).Article 
CAS 

Google Scholar 
Leibold, M. & Tessier, A. J. Contrasting patterns of body size for Daphnia species that segregate by habitat. Oecologia 86, 342–348 (1991).Article 

Google Scholar 
Charmantier, A. & Gienapp, P. Climate change and timing of avian breeding and migration: evolutionary versus plastic changes. Evol. Appl. 7, 15–28 (2014).Article 

Google Scholar 
Kopp, M. & Matuszewski, S. Rapid evolution of quantitative traits: theoretical perspectives. Evol. Appl. 7, 169–191 (2014).Article 

Google Scholar 
Williams, J. W., Ordonez, A. & Svenning, J.-C. A unifying framework for studying and managing climate-driven rates of ecological change. Nat. Ecol. Evol. 5, 17–26 (2021).Article 

Google Scholar 
Jaureguiberry, P. et al. The direct drivers of recent global anthropogenic biodiversity loss. Sci. Adv. 8, eabm9982 (2022).Article 

Google Scholar 
Chevin, L.-M., Collins, S. & Lefèvre, F. Phenotypic plasticity and evolutionary demographic responses to climate change: taking theory out to the field. Funct. Ecol. 27, 967–979 (2013).Article 

Google Scholar 
Ferriere, R. & Legendre, S. Eco-evolutionary feedbacks, adaptive dynamics and evolutionary rescue theory. Phil. Trans. R. Soc. B 368, 20120081 (2013).Article 

Google Scholar 
Rebecchi, L., Boschetti, C. & Nelson, D. R. Extreme-tolerance mechanisms in meiofaunal organisms: a case study with tardigrades, rotifers and nematodes. Hydrobiologia 847, 2779–2799 (2020).Article 

Google Scholar 
Hansson, B. & Westerberg, L. On the correlation between heterozygosity and fitness in natural populations. Mol. Ecol. 11, 2467–2474 (2002).Article 

Google Scholar 
Mammola, S., Carmona, C. P., Guillerme, T. & Cardoso, P. Concepts and applications in functional diversity. Funct. Ecol. 35, 1869–1885 (2021).Article 
CAS 

Google Scholar 
McClanahan, T. R. et al. Highly variable taxa-specific coral bleaching responses to thermal stresses. Mar. Ecol. Prog. Ser. 648, 135–151 (2020).Article 

Google Scholar 
Reside, A. E. et al. Beyond the model: expert knowledge improves predictions of species’ fates under climate change. Ecol. Appl. 29, e01824 (2019).Article 

Google Scholar 
Desjonquères, C., Gifford, T. & Linke, S. Passive acoustic monitoring as a potential tool to survey animal and ecosystem processes in freshwater environments. Freshw. Biol. 65, 7–19 (2020).Article 

Google Scholar 
Sequeira, A. M. M. et al. A standardisation framework for bio-logging data to advance ecological research and conservation. Methods Ecol. Evol. 12, 996–1007 (2021).Article 

Google Scholar 
Shimada, T. et al. Optimising sample sizes for animal distribution analysis using tracking data. Methods Ecol. Evol. 12, 288–297 (2021).Article 

Google Scholar 
Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 196–205 (2021).Article 

Google Scholar 
Krause, D. J., Hinke, J. T., Perryman, W. L., Goebel, M. E. & LeRoi, D. J. An accurate and adaptable photogrammetric approach for estimating the mass and body condition of pinnipeds using an unmanned aerial system. PLoS ONE 12, e0187465 (2017).Article 

Google Scholar 
Besson, M. et al. Towards the fully automated monitoring of ecological communities. Ecol. Lett. https://doi.org/10.1111/ele.14123 (2022).Article 

Google Scholar 
Cavender-Bares, J. et al. Integrating remote sensing with ecology and evolution to advance biodiversity conservation. Nat. Ecol. Evol. 6, 506–519 (2022).Article 

Google Scholar 
Ingram, D. J., Ferreira, G. B., Jones, K. E. & Mace, G. M. Targeting conservation actions at species threat response thresholds. Trends Ecol. Evol. 36, 216–226 (2021).Article 

Google Scholar 
Keith, S. A. et al. Synchronous behavioural shifts in reef fishes linked to mass coral bleaching. Nat. Clim. Change 8, 986–991 (2018).Article 

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

Google Scholar 
Enquist, B. J. et al. in Advances in Ecological Research Vol. 52 (eds Pawar, S. et al.) 249–318 (Academic Press, 2015).Wei, W. W. S. Multivariate Time Series Analysis and Applications (John Wiley & Sons, 2018).Holmes, E. E., Ward, E. J. & Wills, K. MARSS: multivariate autoregressive state-space models for analyzing time-series data. R J. 4, 11–19 (2012).Article 

Google Scholar 
Zhu, M., Yamakawa, T. & Sakai, T. Combined use of trawl fishery and research vessel survey data in a multivariate autoregressive state-space (MARSS) model to improve the accuracy of abundance index estimates. Fish. Sci. 84, 437–451 (2018).Article 
CAS 

Google Scholar 
Lai, G., Chang, W.-C., Yang, Y. & Liu, H. Modeling long- and short-term temporal patterns with deep neural networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval 95–104, https://doi.org/10.1145/3209978.3210006 (ACM, 2018).Bury, T. M. et al. Deep learning for early warning signals of tipping points. Proc. Natl Acad. Sci. USA 118, e2106140118 (2021).Article 
CAS 

Google Scholar 
Lara-Benítez, P., Carranza-García, M. & Riquelme, J. C. An experimental review on deep learning architectures for time series forecasting. Int. J. Neural Syst. 31, 2130001 (2021).Article 

Google Scholar 
Guo, Q. et al. Application of deep learning in ecological resource research: theories, methods, and challenges. Sci. China Earth Sci. 63, 1457–1474 (2020).Article 

Google Scholar 
Rogers, T. L., Johnson, B. J. & Munch, S. B. Chaos is not rare in natural ecosystems. Nat. Ecol. Evol. 6, 1105–1111 (2022).Article 

Google Scholar 
Samplonius, J. M. et al. Phenological sensitivity to climate change is higher in resident than in migrant bird populations among European cavity breeders. Glob. Change Biol. 24, 3780–3790 (2018).Article 

Google Scholar 
Menzel, A. et al. Climate change fingerprints in recent European plant phenology. Glob. Change Biol. 26, 2599–2612 (2020).Article 

Google Scholar 
Koleček, J., Adamík, P. & Reif, J. Shifts in migration phenology under climate change: temperature vs. abundance effects in birds. Clim. Change 159, 177–194 (2020).Article 

Google Scholar 
Altermatt, F. et al. Big answers from small worlds: a user’s guide for protist microcosms as a model system in ecology and evolution. Methods Ecol. Evol. 6, 218–231 (2015).Article 

Google Scholar 
Beermann, A. J. et al. Multiple-stressor effects on stream macroinvertebrate communities: a mesocosm experiment manipulating salinity, fine sediment and flow velocity. Sci. Total Environ. 610–611, 961–971 (2018).Article 

Google Scholar 
Clements, C. F. & Ozgul, A. Including trait-based early warning signals helps predict population collapse. Nat. Commun. 7, 10984 (2016).Article 
CAS 

Google Scholar 
Jacquet, C. & Altermatt, F. The ghost of disturbance past: long-term effects of pulse disturbances on community biomass and composition. Proc. R. Soc. B 287, 20200678 (2020).Article 

Google Scholar 
Greggor, A. L. et al. Research priorities from animal behaviour for maximising conservation progress. Trends Ecol. Evol. 31, 953–964 (2016).Article 

Google Scholar 
Couvillon, M. J., Schürch, R. & Ratnieks, F. L. W. Waggle dance distances as integrative indicators of seasonal foraging challenges. PLoS ONE 9, e93495 (2014).Article 

Google Scholar 
Hamilton, C. D., Lydersen, C., Ims, R. A. & Kovacs, K. M. Predictions replaced by facts: a keystone species’ behavioural responses to declining Arctic sea-ice. Biol. Lett. 11, 20150803 (2015).Article 

Google Scholar 
Holt, R. E. & Jørgensen, C. Climate change in fish: effects of respiratory constraints on optimal life history and behaviour. Biol. Lett. 11, 20141032 (2015).Article 

Google Scholar 
Gauzens, B. et al. Adaptive foraging behaviour increases vulnerability to climate change. Preprint at https://doi.org/10.1101/2021.05.05.442768 (2021).Lenda, M., Witek, M., Skórka, P., Moroń, D. & Woyciechowski, M. Invasive alien plants affect grassland ant communities, colony size and foraging behaviour. Biol. Invasions 15, 2403–2414 (2013).Article 

Google Scholar 
Hertel, A. G. et al. Don’t poke the bear: using tracking data to quantify behavioural syndromes in elusive wildlife. Anim. Behav. 147, 91–104 (2019).Article 

Google Scholar 
Tini, M. et al. Use of space and dispersal ability of a flagship saproxylic insect: a telemetric study of the stag beetle (Lucanus cervus) in a relict lowland forest. Insect Conserv. Divers. 11, 116–129 (2018).Article 

Google Scholar 
Kunc, H. P. & Schmidt, R. Species sensitivities to a global pollutant: a meta-analysis on acoustic signals in response to anthropogenic noise. Glob. Change Biol. 27, 675–688 (2021).Article 

Google Scholar 
Anestis, A., Lazou, A., Pörtner, H. O. & Michaelidis, B. Behavioral, metabolic, and molecular stress responses of marine bivalve Mytilus galloprovincialis during long-term acclimation at increasing ambient temperature. Am. J. Physiol. 293, R911–R921 (2007).CAS 

Google Scholar 
Pacherres, C. O., Schmidt, G. M. & Richter, C. Autotrophic and heterotrophic responses of the coral Porites lutea to large amplitude internal waves. J. Exp. Biol. 216, 4365–4374 (2013).
Google Scholar 
Ban, S. S., Graham, N. A. J. & Connolly, S. R. Evidence for multiple stressor interactions and effects on coral reefs. Glob. Change Biol. 20, 681–697 (2014).Article 

Google Scholar 
Singh, R., Prathibha, P. & Jain, M. Effect of temperature on life-history traits and mating calls of a field cricket, Acanthogryllus asiaticus. J. Therm. Biol. 93, 102740 (2020).Article 

Google Scholar 
Pellegrini, A. Y., Romeu, B., Ingram, S. N. & Daura-Jorge, F. G. Boat disturbance affects the acoustic behaviour of dolphins engaged in a rare foraging cooperation with fishers. Anim. Conserv. 24, 613–625 (2021).Article 

Google Scholar 
McMahan, M. D. & Grabowski, J. H. Nonconsumptive effects of a range-expanding predator on juvenile lobster (Homarus americanus) population dynamics. Ecosphere 10, e02867 (2019).Article 

Google Scholar 
Vilhunen, S., Hirvonen, H. & Laakkonen, M. V.-M. Less is more: social learning of predator recognition requires a low demonstrator to observer ratio in Arctic charr (Salvelinus alpinus). Behav. Ecol. Sociobiol. 57, 275–282 (2005).Article 

Google Scholar 
Ortega, Z., Mencía, A. & Pérez-Mellado, V. Rapid acquisition of antipredatory responses to new predators by an insular lizard. Behav. Ecol. Sociobiol. 71, 1 (2017).Article 

Google Scholar 
Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Phil. Trans. R. Soc. B 374, 20180174 (2019).Article 

Google Scholar 
Pigeon, G., Ezard, T. H. G., Festa-Bianchet, M., Coltman, D. W. & Pelletier, F. Fluctuating effects of genetic and plastic changes in body mass on population dynamics in a large herbivore. Ecology 98, 2456–2467 (2017).Article 

Google Scholar 
Lomolino, M. V. & Perault, D. R. Body size variation of mammals in a fragmented, temperate rainforest. Conserv. Biol. 21, 1059–1069 (2007).Article 

Google Scholar 
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: a third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).Article 

Google Scholar 
Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).Article 

Google Scholar 
Thoral, E. et al. Changes in foraging mode caused by a decline in prey size have major bioenergetic consequences for a small pelagic fish. J. Anim. Ecol. 90, 2289–2301 (2021).Article 

Google Scholar 
Stirling, I. & Derocher, A. E. Effects of climate warming on polar bears: a review of the evidence. Glob. Change Biol. 18, 2694–2706 (2012).Article 

Google Scholar 
Spanbauer, T. L. et al. Body size distributions signal a regime shift in a lake ecosystem. Proc. R. Soc. B 283, 20160249 (2016).Article 

Google Scholar 
Bjorndal, K. A. et al. Ecological regime shift drives declining growth rates of sea turtles throughout the West Atlantic. Glob. Change Biol. 23, 4556–4568 (2017).Article 

Google Scholar 
Eshun-Wilson, F., Wolf, R., Andersen, T., Hessen, D. O. & Sperfeld, E. UV radiation affects antipredatory defense traits in Daphnia pulex. Ecol. Evol. 10, 14082–14097 (2020).Article 

Google Scholar 
Zhang, H., Hollander, J. & Hansson, L.-A. Bi-directional plasticity: rotifer prey adjust spine length to different predator regimes. Sci. Rep. 7, 10254 (2017).Article 

Google Scholar 
Simbula, G., Vignoli, L., Carretero, M. A. & Kaliontzopoulou, A. Fluctuating asymmetry as biomarker of pesticides exposure in the Italian wall lizards (Podarcis siculus). Zoology 147, 125928 (2021).Article 

Google Scholar 
Leary, R. F. & Allendorf, F. W. Fluctuating asymmetry as an indicator of stress: implications for conservation biology. Trends Ecol. Evol. 4, 214–217 (1989).Article 
CAS 

Google Scholar 
Gavrilchuk, K. et al. Trophic niche partitioning among sympatric baleen whale species following the collapse of groundfish stocks in the Northwest Atlantic. Mar. Ecol. Prog. Ser. 497, 285–301 (2014).Article 

Google Scholar 
Kershaw, J. L. et al. Declining reproductive success in the Gulf of St. Lawrence’s humpback whales (Megaptera novaeangliae) reflects ecosystem shifts on their feeding grounds. Glob. Change Biol. 27, 1027–1041 (2021).Article 
CAS 

Google Scholar 
Rode, K. D., Amstrup, S. C. & Regehr, E. V. Reduced body size and cub recruitment in polar bears associated with sea ice decline. Ecol. Appl. 20, 768–782 (2010).Article 

Google Scholar 
Obbard, M. E. et al. Re-assessing abundance of Southern Hudson Bay polar bears by aerial survey: effects of climate change at the southern edge of the range. Arct. Sci. 4, 634–655 (2018).Article 

Google Scholar 
Hutchings, J. A. The cod that got away. Nature 428, 899–900 (2004).Article 
CAS 

Google Scholar 
Zhang, F. Early warning signals of population productivity regime shifts in global fisheries. Ecol. Indic. 115, 106371 (2020).Article 

Google Scholar 
Fulton, G. R. The Bramble Cay melomys: the first mammalian extinction due to human-induced climate change. Pac. Conserv. Biol. 23, 1–3 (2017).Article 

Google Scholar  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

To calculate total root biomass C colonized by AM and EcM fungi, we developed a workflow that combines multiple publicly available datasets to ultimately link fine root stocks to mycorrhizal colonization estimates (Fig. 1). These estimates were individually derived for 881 different spatial units that were constructed by combining 28 different ecoregions, 15 land cover types and six continents. In a given spatial unit, the relationship between the proportion of AM- and EcM-plants aboveground biomass and the proportion of AM- and EcM-associated root biomass depends on the prevalence of distinct growth forms. Therefore, to increase the accuracy of our estimates, calculations were made separately for woody and herbaceous vegetation and combined in the final step and subsequently mapped. Below we detail the specific methodologies we followed within the workflow and the main assumptions and uncertainties associated.Fig. 1Workflow used to create maps of mycorrhizal fine root biomass carbon. The workflow consists of two main steps: (1) Estimation of total fine root stock capable to form mycorrhizal associations with AM and EcM fungi and (2) estimation of the proportion of fine roots colonized by AM and EcM fungi.Full size imageDefinition of spatial unitsAs a basis for mapping mycorrhizal root abundances at a global scale, we defined spatial units based on a coarse division of Bailey’s ecoregions23 After removing regions of permanent ice and water bodies, we included 28 ecoregions defined according to differences in climatic regimes and elevation (deposited at Dryad-Table S1). A map of Bailey’s ecoregions was provided by the Oak Ridge National Laboratory Distributed Active Archive Center24 at 10 arcmin spatial resolution. Due to potential considerable differences in plant species identities, ecoregions that extended across multiple continents were split for each continent. The continent division was based upon the FAO Global Administrative Unit Layers (http://www.fao.org/geonetwork/srv/en/). Finally, each ecoregion-continent combination was further divided according to differences in land cover types using the 2015 Land Cover Initiative map developed by the European Space Agency at 300 m spatial resolution (https://www.esa-landcover-cci.org/). To ensure reliability, non-natural areas (croplands and urban areas), bare areas and water bodies were discarded (Table 1). In summary, a combination of 28 ecoregions, 15 land cover types and six continents were combined to define a total of 881 different spatial units (deposited at Dryad-Table S2). The use of ecoregion/land cover/continent combination provided a much greater resolution than using a traditional biome classification and allowed to account for human-driven transformations of vegetation, the latter based on the land cover data.Table 1 List of land cover categories within the ESA CCI Land Cover dataset, used to assemble maps of mycorrhizal root biomass.Full size tableMycorrhizal fine root stocksTotal root C stocksEstimation of the total root C stock in each of the spatial units was obtained from the harmonized belowground biomass C density maps of Spawn et al.20. These maps are based on continental-to-global scale remote sensing data of aboveground biomass C density and land cover-specific root-to-shoot relationships to generate matching belowground biomass C maps. This product is the best up-to-date estimation of live root stock available. For subsequent steps in our workflow, we distinguished woody and herbaceous belowground biomass C as provided by Spawn et al.20. As the tundra belowground biomass C map was provided without growth form distinction, it was assessed following a slightly different workflow (see Section 2.2.3 for more details). To match the resolution of other input maps in the workflow, all three belowground biomass C maps were scaled up from the original spatial resolution of 10-arc seconds (approximately 300 m at the equator) to 10 arc‐minutes resolution (approximately 18.5 km at the equator) using the mean location of the raster cells as aggregation criterion.As the root biomass C maps do not distinguish between fine and coarse roots and mycorrhizal fungi colonize only the fine fractions of the roots, we considered the fine root fraction to be 88,5% and 14,1% of the total root biomass for herbaceous and woody plants, respectively. These constants represent the mean value of coarse/fine root mass ratios of herbaceous and woody plants provided by the Fine-Root Ecology Database (FRED) (https://roots.ornl.gov/)25 (deposited at Dryad-Table S3). Due to the non-normality of coarse/fine root mass ratios, mean values were obtained from log-transformed data and then back-transformed for inclusion into the workflow.Finally, the belowground biomass C maps consider the whole root system, but mycorrhizal colonization occurs mainly in the upper 30 cm of the soil18. Therefore, we estimated the total fine root stocks in the upper 30 cm by applying the asymptotic equation of vertical root distribution developed by Gale & Grigal26:$$y=1-{beta }^{d}$$where y is the cumulative root fraction from the soil surface to depth d (cm), and β is the fitted coefficient of extension. β values of trees (β = 0.970), shrubs (β = 0.978) and herbs (β = 0.952) were obtained from Jackson et al.27. A mean value was then calculated for trees and shrubs to obtain a woody vegetation β value of 0.974. As a result, we estimated that 54.6% of the total live root of woody vegetation and 77.1% of herbaceous vegetation is stored in the upper 30 cm of the soil. In combination, this allowed deriving fine root C stocks in the upper 30 cm of woody and herbaceous vegetation.The proportion of root stocks colonized by AM and EcMThe proportion of root stock that forms associations with AM or EcM fungi was obtained from the global maps of aboveground biomass distribution of dominant mycorrhizal types published by Soudzilovskaia et al.14. These maps provide the relative abundance of EcM and AM plants based on information about the biomass of grass, shrub and tree vegetation at 10arcmin resolution. To match with belowground root woody plants biomass data, proportions of AM trees and shrubs underlying the maps of Soudzilovskaia et al.14 were summed up to obtain the proportion of AM woody vegetation. The same was done for EcM trees and shrubs.Our calculations are subjected to the main assumption that, within each growth form, the proportion of aboveground biomass associated with AM and EcM fungi reflects the proportional association of AM and EM fungi to belowground biomass. We tested whether root:shoot ratios were significantly different between AM and EcM woody plants (the number of EcM herbaceous plants is extremely small17). Genera were linked to growth form based on the TRY database (https://www.try-db.org/)19 and the mycorrhizal type association based on the FungalRoots database17. Subsequently, it was tested whether root:shoot ratios of genera from the TRY database (https://www.try-db.org/)19 were significantly different for AM vs EcM woody plants. No statistically significant differences (ANOVA-tests p-value = 0.595) were found (Fig. 2).Fig. 2Mean and standar error of root to shoot ratios of AM and EcM woody plant species.Full size imageEstimation of mycorrhizal fine root stocksWe calculated the total biomass C of fine roots that can potentially be colonized by AM or EcM fungi by multiplying the total woody and herbaceous fine root C biomass in the upper 30 cm of the soil by the proportion of AM and EcM of woody and herbaceous vegetation. In the case of tundra vegetation, fine root C stocks were multiplied by the relative abundance of AM and EcM vegetation without distinction of growth forms (for simplicity, this path was not included in Fig. 1, but can be seen in Fig. 3. As tundra vegetation consists mainly of herbs and small shrubs, the distinction between woody and herbaceous vegetation is not essential in this case.Fig. 3Workflow used to create mycorrhizal fine root biomass C maps specific for tundra areas.Full size imageFinally, we obtained the mean value of mycorrhiza growth form fine root C stocks in each of the defined spatial units. These resulted in six independent estimations: AM woody, AM herbaceous, EcM woody, EcM herbaceous, AM tundra and EcM tundra total fine root biomass C (Fig. 4).Fig. 4Fine root biomass stocks capable to form association with AM (a) and EcM (b) fungi for woody, herbaceous and tundra vegetation. Final AM and EcM stock result from the sum of the growth form individual maps. There were no records of fine root biomass of EcM herbaceous vegetation.Full size imageThe intensity of root colonization by mycorrhizal fungiColonization databaseThe FungalRoot database is the largest up-to-date compilation of intensity of root colonization data, providing 36303 species observations for 14870 plant species. Colonization data was filtered to remove occurrences from non-natural conditions (i.e., from plantations, nurseries, greenhouses, pots, etc.) and data collected outside growing seasons. Records without explicit information about habitat naturalness and growing season were maintained as colonization intensity is generally recorded in the growing season of natural habitats. When the intensity of colonization occurrences was expressed in categorical levels, they were converted to percentages following the transformation methods stated in the original publications. Finally, plant species were distinguished between woody and herbaceous species using the publicly available data from TRY (https://www.trydb.org/)19. As a result, 9905 AM colonization observations of 4494 species and 521 EcM colonization observations of 201 species were used for the final calculations (Fig. 5).Fig. 5Number of AM (a) and EcM (b) herbaceous and woody plant species and total observations obtained from FungalRoot database.Full size imageThe use of the mean of mycorrhizal colonization intensity per plant species is based on two main assumptions:

1)

The intensity of root colonization is a plant trait: It is known that the intensity of mycorrhizal infections of a given plant species varies under different climatic and soil conditions28,29, plant age30 and the identity of colonizing fungal species31. However, Soudzilovskaia et al.9 showed that under natural growth conditions the intraspecific variation of root mycorrhizal colonization is lower than interspecific variation, and is within the range of variations in other plant eco-physiological traits. Moreover, recent literature reported a positive correlation between root morphological traits and mycorrhizal colonization, with a strong phylogenetic signature of these correlations32,33. These findings provide support for the use of mycorrhizal root colonization of plants grown in natural conditions as a species-specific trait.

2)

The percentage of root length or root tips colonized can be translated to the percentage of biomass colonized: intensity of root colonization is generally expressed as the proportion of root length colonized by AM fungi or proportion of root tips colonized by EcM fungi (as EcM infection is restricted to fine root tips). Coupling this data with total root biomass C stocks requires assuming that the proportion of root length or proportion of root tips colonized is equivalent to the proportion of root biomass colonized. While for AM colonization this equivalence can be straightforward, EcM colonization can be more problematic as the number of root tips varies between tree species. However, given that root tips represent the terminal ends of a root network34, the proportion of root tips colonized by EcM fungi can be seen as a measurement of mycorrhizal infection of the root system and translated to biomass independently of the number of root tips of each individual. Yet, it is important to stress that estimations of fine root biomass colonized by AM and EcM as provided in this paper might not be directly comparable.

sPlot databaseThe sPlotOpen database21 holds information about the relative abundance of vascular plant species in 95104 different vegetation plots spanning 114 countries. In addition, sPlotOpen provides three partially overlapping resampled subset of 50000 plots each that has been geographically and environmentally balanced to cover the highest plant species variability while avoiding rare communities. From these three available subsets, we selected the one that maximizes the number of spatial units that have at least one vegetation plot. We further checked if any empty spatial unit could be filled by including sPlot data from other resampling subsets.Plant species in the selected subset were classified as AM and EcM according to genus-based mycorrhizal types assignments, provided in the FungalRoot database17. Plant species that could not be assigned to any mycorrhizal type were excluded. Facultative AM species were not distinguished from obligated AM species, and all were considered AM species. The relative abundance of species with dual colonization was treated as 50% AM and 50% ECM. Plant species were further classified into woody and herbaceous species using the TRY database.Estimation of the intensity of mycorrhizal colonizationThe percentage of AM and EcM root biomass colonized per plant species was spatially upscaled by inferring the relative abundance of AM and EcM plant species in each plot. For each mycorrhizal-growth form and each vegetation plot, the relative abundance of plant species was determined to include only the plant species for which information on the intensity of root colonization was available. Then, a weighted mean intensity of colonization per mycorrhizal-growth form was calculated according to the relative abundance of the species featuring that mycorrhizal-growth form in the vegetation plot. Lastly, the final intensity of colonization per spatial unit was calculated by taking the mean value of colonization across all plots within that spatial unit. These calculations are based on 38127 vegetation plots that hold colonization information, spanning 384 spatial units.The use of vegetation plots as the main entity to estimate the relative abundance of AM and EcM plant species in each spatial unit assumes that the plant species occurrences and their relative abundances in the selected plots are representative of the total spatial unit. This is likely to be true for spatial units that are represented by a high number of plots. However, in those spatial units where the number of plots is low, certain vegetation types or plant species may be misrepresented. We addressed this issue in our uncertainty analysis. Details are provided in the Quality index maps section.Final calculation and maps assemblyThe fraction of total fine root C stocks that is colonized by AM and EcM fungi was estimated by multiplying fine root C stocks by the mean root colonization intensity in each spatial unit. This calculation was made separately for tundra, woody and herbaceous vegetation.To generate raster maps based on the resulting AM and EcM fine root biomass C data, we first created a 10 arcmin raster map of the spatial units. To do this, we overlaid the raster map of Bailey ecoregions (10 arcmin resolution)24, the raster of ESA CCI land cover data at 300 m resolution aggregated to 10 arcmin using a nearest neighbour approach (https://www.esa-landcover-cci.org/) and the FAO polygon map of continents (http://www.fao.org/geonetwork/srv/en/), rasterized at 10 arcmin. Finally, we assigned to each pixel the corresponding biomass of fine root colonized by mycorrhiza, considering the prevailing spatial unit. Those spatial units that remained empty due to lack of vegetation plots or colonization data were filled with the mean value of the ecoregion x continent combination.Quality index mapsAs our workflow comprises many different data sources and the extracted data acts in distinct hierarchical levels (i.e plant species, plots or spatial unit level), providing a unified uncertainty estimation for our maps is particularly challenging. Estimates of mycorrhizal fine root C stocks are related mainly to belowground biomass C density maps and mycorrhizal aboveground biomass maps, which have associated uncertainties maps provided by the original publications. In contrast, estimates of the intensity of root colonization in each spatial unit have been associated with three main sources of uncertainties:

1)

The number of observations in the FungalRoot database. The mean species-level intensity of mycorrhizal colonization in the vegetation plots has been associated with a number of independent observations of root colonization for each plant species. We calculated the mean number of observations of each plant species for each of the vegetation plots and, subsequently the mean number of observations (per plant species) from all vegetation plots in each spatial unit. These spatial unit averaged number of observations ranged from 1 to 14 in AM and from 1 to 26 in EcM. A higher number of observations would indicate that the intraspecific variation in the intensity of colonization is better captured and, therefore, the species-specific colonization estimates are more robust.

2)

The relative plant coverage that was associated with colonization data. From the selected vegetation plots, only a certain proportion of plant species could be associated with the intensity of colonization data in FungalRoot database. The relative abundance of the plant species with colonization data was summed up in each vegetation plot. Then, we calculated the average values for each spatial unit. Mean abundance values ranged from 0.3 to 100% in both AM and EcM spatial units. A high number indicates that the dominant plant species of the vegetation plots have colonization data associated and, consequently, the community-averaged intensity of colonization estimates are more robust.

3)

The number of vegetation plots in each spatial unit. Each of the spatial units differs in the number of plots used to calculate the mean intensity of colonization, ranging from 1 to 1583 and from 1 to 768 plots in AM and EcM estimations, respectively. A higher number of plots is associated with a better representation of the vegetation variability in the spatial units, although this will ultimately depend on plot size and intrinsic heterogeneity (i.e., a big but homogeneous spatial unit may need fewer vegetation plots for a good representation than a small but very heterogeneous spatial unit).

We provide independent quality index maps of the spatial unit average of these three sources of uncertainty. These quality index maps can be used to locate areas where our estimates have higher or lower robustness. More

Figure 1 presents the average monthly energy expenditure at the household level based on USD across the 37 surveyed nations. The households in Singapore expend the most amount of energy, that is, 748 USD each month on average. The energy consumption appears positively associated with the economic development level; for example, households from high-income countries, including France, Italy, Japan and the US, tend to consume more energy than those from low-income countries (e.g., Kazakhstan, Myanmar, and Mongolia). In India, Indonesia, and Vietnam, households with higher income expend more on energy than rural/slum households. For the energy expenditure to household income ratio, strong trends were not found between developing and developed countries. Notably, middle-income countries (e.g., Greece, Chile, Brazil, Egypt) spend a relatively higher share of total income on energy.Figure 1Average monthly energy expenditure at the household level across the 37 surveyed nations. Data source: Original survey.Full size imageThe relationship between subjective well-being and energy consumption expenditure based on the ordered logit, ordered probit, and OLS models is shown in Table 2, panel A. The LR Chi-Square test and Pseudo R-squared for the ordered logistic regression model and the ordered probit model were applied to measure the goodness of the fit, whereas F-statistics and adjusted R-squared were used for the OLS model. For the validation of the measurement of subjective well-being, life satisfaction and happiness measures were used. Importantly, the results from variated regression models are consistent, indicating a positive relationship between household energy consumption expenditure and the improvement of individuals’ subjective well-being. Regarding the model’s goodness of fit, the LR Chi-Square test with ordered logit and probit models, and the F-statistic in the OLS model are all statistically significant at 0.1%, which validates the regression model. As the consistency of the robustness results is derived from different models, the ordered logit model is applied in Table 2 (Panel B).Table 2 Association between energy consumption expenditure and subjective well-being in high- and non-high-income countries.Full size tableWith the control variables being constant, energy consumption expenditure improves subjective well-being, including life satisfaction and happiness. The coefficients for the relationship of energy consumption with life satisfaction and with happiness are 0.018 and 0.008, respectively, and they are statistically significant at the 1% level; in other words, there is increased energy consumption for people who are satisfied with their lives and are happier. This is because electricity, water, gas, or gasoline are indispensable consumption goods in daily life. The results suggest that when policies lead to a reduction in the consumption of these goods at the household level, the life satisfaction of citizens is likely to decrease. When reducing energy consumption at the household level to reduce the emission of greenhouse gases, the conflicts of interest of individuals in these households (given that they derive life satisfaction from energy consumption) pose a challenge to policymakers; therefore, policymakers should devise strategies to improve both citizens’ living standards and environmental preservation.Referring to the criteria developed by the World Bank, the standard classification of high-income nations and non-high-income nations is as follows. Based on the 2017 gross national income (GNI) per capita, the World Bank List of Economies (June 2018) presented the following criteria for nations to be classified as high-income and non-high-income nations, respectively: a GNI per capita of $12,056 or higher, and less than $12,056. According to this standard of classification, in this study, high-income nations comprise Japan, Singapore, Chile, Australia, the United States, Germany, the United Kingdom, France, Spain, Italy, Sweden, Canada, Netherlands, Greece, Hungary, Poland, and the Czech Republic, whereas non-high-income nations comprise Thailand, Malaysia, Indonesia, Vietnam, Philippines, Mexico, Venezuela, Brazil, Colombia, South Africa, India, Myanmar, Kazakhstan, Mongolia, Egypt, Russia, China, Turkey, Romania, and Sri Lanka.Regarding the comparison of high- and non-high-income countries, energy consumption at the household level is more likely to lead to life satisfaction in non-high-income than in high-income countries. In high-income countries, the coefficients for the relationship of energy consumption with life satisfaction and with happiness are 0.010 and 0.003, respectively; these coefficients are 0.035 and 0.015, respectively, among non-high-income countries. Hence, in both high-income and non-high-income countries, an increase in energy consumption leads to an increase in life satisfaction; nonetheless, energy consumption is more crucial for households in non-high-income countries. Compared to the effect of energy consumption on satisfaction in high-income countries and non-high-income countries, individuals living in less urbanized countries appear more satisfied with energy consumption.Table 3 presents the association between life satisfaction and energy consumption expenditure at the household level in each country by estimating Eq. (2) based on the ordered logit model for each country. There is a positive relationship between energy consumption expenditure and life satisfaction in 27 out of the 37 nations. For example, the coefficient of this relationship is 0.062 in Brazil, and is statistically significant at the 1% level. An increase in energy consumption expenditure positively impacts the life satisfaction of households in Brazil, meaning that individuals with greater energy expenditure tend to be satisfied with their lives. Similar results are found in other countries: Canada, Chile, China, Egypt, France, Germany, Greece, India, Indonesia, Italy, and Japan. As life satisfaction is a proxy of well-being, energy consumption is expected to increase when households can afford more energy to obtain higher life satisfaction. These results indicate that most of the developed and developing countries analyzed face a conflict of interest in addressing individuals’ life satisfaction and environment conservation goals; these countries include China and India that are home to large populations that have a positive desire for energy consumption.Table 3 Relationship between energy expenditure and life satisfaction for each country.Full size tableHowever, the association between life satisfaction and energy consumption expenditure at the household level was non-significant across some countries. In Australia, the coefficient of this association is positive but not statistically significant; hence, an increase in energy expenditure is not completely associated with life satisfaction at the household level here. Similar results are found in the Netherlands, Hungary, Sweden, Singapore, Poland, the Czech Republic, and Colombia. In these countries, energy consumption is at an adequate level, and additional energy consumption does not lead to higher life satisfaction. It may be that households consume an adequate amount of energy with their income and energy price.Tables 4, 5, 6, and 7 display the determinant factors of household energy consumption in 37 nations by estimating the energy demand equation for each country using Eq. (3). The key energy consumption metric is the quantity of energy consumed (e.g., kWh) across the targeted households. Since price information is limited, transforming consumption expenditure into a quantity (e.g., kWh) is problematic. As explained earlier, this study adopted the energy demand equation.Table 4 Household socioeconomic and demographic determinants of household energy consumption expenditure I.Full size tableTable 5 Household socioeconomic and demographic determinants of household energy consumption expenditure II.Full size tableTable 6 Household socioeconomic and demographic determinants of household energy consumption expenditure III.Full size tableTable 7 Household socioeconomic and demographic determinants of household energy consumption expenditure IV.Full size tableThere are positive relationships between energy consumption expenditure at the household level and household income across countries. If the coefficients for household income are positive and statistically significant, this means that energy consumption expenditure at the household level would increase with an increase in household income ensuing from economic development in the country, ceteris paribus. The positive coefficients for the association between energy consumption expenditure and household income range from 0.756 (Japan) to 3.613 (the Philippines) in our sample, indicating that an additional 10,000 USD would lead to an additional energy consumption expenditure at the household level of approximately 17.3% (Japan) – 445% (Mongolia). The number is calculated using the magnitude of the coefficient/energy consumption expenditure. The results also show that homeowners tend to consume more energy than renters in Australia, Brazil, Canada, Chile, China, Colombia, Germany, India, Italy, Japan, Malaysia, Mexico, Russia, the United States, and Vietnam. This indicates that if individuals live in their own houses, the household energy consumption expenditure tends to be higher owing to the wealth effect, as energy is a normal consumption good. Overall, the wealth effect on energy consumption expenditure at the household level is increasing in our sample, and with economic development, energy consumption may increase.The following factors are confirmed to reduce energy consumption at the household level: (1) energy-curtailment behavior regarding electricity, (2) higher education, and (3) age. The energy-saving effect is confirmed in households. In Canada, the coefficient of energy-saving behaviors is -0.642, indicating that households consume 12.5% less energy when they adopt both energy curtailment behavior and non-saving groups (64.2/513). The Canadian household average energy consumption is 513 USD. Similar results are seen in Colombia, Germany, India, Indonesia, Italy, Japan, the Netherlands, Poland, Russia, Turkey, the United Kingdom, and the United States. The magnitude of the effect of energy curtailment behavior ranged from 6.4% (Russia) to 32% (India) less energy consumption expenditure. Hence, energy-saving behaviors have a favorable effect on environmentally preferable outcomes. By contrast, households in Indonesia save electricity as they tend to spend more on purchasing energy.Individuals with higher education tend to save energy in 23 out of the 37 nations. For instance, the coefficient for individuals with university-level education is -2.292 and statistically significant at the 1% level. This suggests that households with individuals who have university-level education have less energy consumption expenditure than households with individuals with junior high school or lower levels of education. Similar results are seen in Brazil, Canada, Chile, Colombia, the Czech Republic, France, Germany, Hungary, India, Indonesia, Japan, Malaysia, the Netherlands, the Philippines, Poland, Russia, Singapore, South Africa, Spain, Sweden, Turkey, the United Kingdom, and the United States. Encouraging households to engage in energy curtailment behaviors and higher educational attainment may lead to environment-friendly outcomes.Surprisingly, purchasing energy-saving household products has a limited effect on reducing energy consumption expenditure at the household level. The coefficients for purchasing energy-saving household products are negative, ranging between -0.044 and -0.763, and are statistically significant in Australia, Canada, the Czech Republic, Italy, and Kazakhstan. Hence, the purchase of these products in these five countries decreases energy expenditure from 2.9% (China) to 14% (Australia). However, the relationship between energy consumption expenditure at the household level and purchasing energy-saving household products is non-significant in the other countries. Moreover, in Poland and Turkey, households that purchase these products consume more energy than those that do not. Therefore, purchasing energy-saving household products has a limited contribution to energy saving at the household level.The findings also show that older individuals tend to have lower energy consumption. The coefficients for the age variable are negative and statistically significant in 30 countries (out of 37). The effect of age on energy consumption expenditure ranges between -0.003 and -0.148, indicating that as the average age of individuals increases by one year, their monthly energy consumption expenditure reduces from 0.3–14.8 USD. This may be because older individuals are more likely to live frugally. More