Philippa Kaur

Price, D. T. et al. Anticipating the consequences of climate change for Canada’s boreal forest ecosystems. Environ. Rev. 21, 322–365 (2013).Article 

Google Scholar 
Wang, Y., Hogg, H. E., Price, T. D., Edwards, J. & Williamson, T. Past and projected future changes in moisture conditions in the Canadian boreal forest. Forestry Chron. 90, 678–691 (2014).Article 

Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Chang. Biol. 25, 1922–1940 (2019).ADS 
MathSciNet 
Article 

Google Scholar 
Lu, P., Parker, W. C., Colombo, S. J. & Skeates, D. A. Temperature-induced growing season drought threatens survival and height growth of white spruce in southern Ontario, Canada. Forest Ecol. Manag. 448, 355–363 (2019).Article 

Google Scholar 
Giorgi, F., Raffaele, F. & Coppola, E. The response of precipitation characteristics to global warming from climate projections. Earth Syst. Dyn. 10, 73–89 (2019).ADS 
Article 

Google Scholar 
Sherwood, S. & Fu, Q. A drier future? Science 343, 737–739 (2014).ADS 
CAS 
Article 

Google Scholar 
Seager, R. et al. Dynamical and thermodynamical causes of large-scale changes in the hydrological cycle over North America in response to global warming. J. Clim. 27, 7921–7948 (2014).ADS 
Article 

Google Scholar 
Tam, B. Y. et al. CMIP5 drought projections in Canada based on the Standardized Precipitation Evapotranspiration Index. Can. Water Resour. J. 44, 90–107 (2019).Article 

Google Scholar 
Wu, Z., Dijkstra, P., Koch, G. W., Peñuelas, J. & Hungate, B. A. Responses of terrestrial ecosystems to temperature and precipitation change: a meta-analysis of experimental manipulation. Glob. Chang. Biol. 17, 927–942 (2011).ADS 
Article 

Google Scholar 
Zhao, J., Hartmann, H., Trumbore, S., Ziegler, W. & Zhang, Y. High temperature causes negative whole-plant carbon balance under mild drought. New Phytol. 200, 330–339 (2013).CAS 
Article 

Google Scholar 
Reich, P. B. et al. Effects of climate warming on photosynthesis in boreal tree species depend on soil moisture. Nature 562, 263–267 (2018).ADS 
CAS 
Article 

Google Scholar 
Hansen, W. D. & Turner, M. G. Origins of abrupt change? Postfire subalpine conifer regeneration declines nonlinearly with warming and drying. Ecol. Monogr. 89, e01340 (2019).Article 

Google Scholar 
Girardin, M. P. et al. No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization. Proc. Natl Acad. Sci. USA 113, E8406–E8414 (2016).CAS 
Article 

Google Scholar 
Sulla-Menashe, D., Woodcock, C. E. & Friedl, M. A. Canadian boreal forest greening and browning trends: an analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers. Environ. Res. Lett. 13, 014007 (2018).ADS 
Article 

Google Scholar 
Peng, C. et al. A drought-induced pervasive increase in tree mortality across Canada’s boreal forests. Nat. Clim. Chang. 1, 467–471 (2011).ADS 
Article 

Google Scholar 
Ma, Z. et al. Regional drought-induced reduction in the biomass carbon sink of Canada’s boreal forests. Proc. Natl Acad. Sci. USA 109, 2423–2427 (2012).ADS 
CAS 
Article 

Google Scholar 
Ju, J. & Masek, J. G. The vegetation greenness trend in Canada and US Alaska from 1984–2012 Landsat data. Remote Sens. Environ. 176, 1–16 (2016).ADS 
Article 

Google Scholar 
D’Orangeville, L. et al. Beneficial effects of climate warming on boreal tree growth may be transitory. Nat. Commun. 9, 3213 (2018).ADS 
Article 

Google Scholar 
Johnstone, J. F. et al. Changing disturbance regimes, ecological memory and forest resilience. Front. Ecol. Environ. 14, 369–378 (2016).Article 

Google Scholar 
Rodgers, V. L., Smith, N. G., Hoeppner, S. S. & Dukes, J. S. Warming increases the sensitivity of seedling growth capacity to rainfall in six temperate deciduous tree species. AoB Plants 10, ply003 (2018).Article 

Google Scholar 
Moyes, A. B., Castanha, C., Germino, M. J. & Kueppers, L. M. Warming and the dependence of limber pine (Pinus flexilis) establishment on summer soil moisture within and above its current elevation range. Oecologia 171, 271–282 (2013).ADS 
Article 

Google Scholar 
Balducci, L. et al. How do drought and warming influence survival and wood traits of Picea mariana saplings? J. Exp. Bot. 66, 377–389 (2015).CAS 
Article 

Google Scholar 
Reich, P. B. et al. Geographic range predicts photosynthetic and growth response to warming in co-occurring tree species. Nat. Clim. Chang. 5, 148–152 (2015).ADS 
CAS 
Article 

Google Scholar 
Coursolle, C. et al. Moving towards carbon neutrality: CO2 exchange of a black spruce forest ecosystem during the first 10 years of recovery after harvest. Can. J. Forest Res. 42, 1908–1918 (2012).CAS 
Article 

Google Scholar 
Khomik, M., Williams, C. A., Vanderhoof, M. K., MacLean, R. G. & Dillen, S. Y. On the causes of rising gross ecosystem productivity in a regenerating clearcut environment: leaf area vs. species composition. Tree Physiol. 34, 686–700 (2014).Article 

Google Scholar 
Engelbrecht, B. et al. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447, 80–82 (2007).ADS 
CAS 
Article 

Google Scholar 
Friedman, S. K. & Reich, P. B. Regional legacies of logging: departure from presettlement forest conditions in northern Minnesota. Ecol. Appl. 15, 726–744 (2005).Article 

Google Scholar 
Burrill, E. A. et al. The Forest Inventory and Analysis Database: Database Description and User Guide Version 9.0.1 for Phase 2 https://www.fia.fs.fed.us/library/database-documentation/ (Forest Service, US Department of Agriculture, 2022).Cumming, S. G. et al. A gap analysis of tree species representation in the protected areas of the Canadian boreal forest: applying a new assemblage of digital Forest Resource Inventory data. Can. J. Forest Res. 45, 163–173 (2015).Article 

Google Scholar 
Brook, B. W., Ellis, E. C., Perring, M. P., Mackay, A. W. & Blomqvist, L. Does the terrestrial biosphere have planetary tipping points? Trends Ecol. Evol. 28, 396–401 (2013).Article 

Google Scholar 
Reyer, C. P. O. et al. Forest resilience and tipping points at different spatio-temporal scales: approaches and challenges. J. Ecol. 103, 5–15 (2015).ADS 
Article 

Google Scholar 
Stralberg, D. et al. Climate‐change refugia in boreal North America: what, where, and for how long? Front. Ecol. Environ. 18, 261–270 (2020).Article 

Google Scholar 
Etterson, J. R., Cornett, M. W., White, M. A. & Kavajecz, L. C. Assisted migration across fixed seed zones detects adaptation lags in two major North American tree species. Ecol. Appl. 30, e02092 (2020).Article 

Google Scholar 
Solarik, K. A., Cazelles, K., Messier, C., Bergeron, Y. & Gravel, D. Priority effects will impede range shifts of temperate tree species into the boreal forest. J. Ecol. 108, 1155–1173 (2020).Article 

Google Scholar 
Stefanski, A., Bermudez, R., Sendall, K. M., Montgomery, R. A. & Reich, P. B. Surprising lack of sensitivity of biochemical limitation of photosynthesis of nine tree species to open‐air experimental warming and reduced rainfall in a southern boreal forest. Glob. Chang. Biol. 26, 746–759 (2020).ADS 
Article 

Google Scholar 
Perala, D. A. How endemic injuries affect early growth of aspen suckers. Can. J. Forest Res. 14, 755–762 (1984).Article 

Google Scholar 
Buckman, R. E. Effects of prescribed burning on hazel in Minnesota. Ecology 45, 626–629 (1964).Article 

Google Scholar 
Harvey, B. D. & Bergeron, Y. Site patterns of natural regeneration following clear-cutting in northwestern Quebec. Can. J. Forest Res. 19, 1458–1469 (1989).Article 

Google Scholar 
Harris, I. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).Article 

Google Scholar 
Peters, M. P., Prasad, A. M., Matthews, S. N. & Iverson, L. R. Climate Change Tree Atlas, Version 4 https://www.nrs.fs.fed.us/atlas (Northern Research Station and Northern Institute of Applied Climate Science, US Forest Service, 2020)Niinemets, Ü. & Valladares, F. Tolerance to shade, drought, and waterlogging of temperate Northern Hemisphere trees and shrubs. Ecol. Monogr. 76, 521–547 (2006).Article 

Google Scholar  More

Department of Animal Biology, Faculdade de Ciências, Universidade de Lisboa, Lisbon, PortugalCatarina Frazão SantosMARE–Marine and Environmental Sciences Center / ARNET–Aquatic Research Network, University of Lisbon, Lisbon, PortugalCatarina Frazão Santos & Carina Vieira da SilvaEnvironmental Economics Knowledge Center, NOVA-SBE, Carcavelos, PortugalCatarina Frazão Santos & Carina Vieira da SilvaSound Seas, Bethesda, MD, USATundi AgardyWorldFish, Batu Maung, Penang, MalaysiaEdward H. AllisonThe Peopled Seas Initiative, Vancouver, CanadaNathan J. BennettEqualSea Lab, University of Santiago de Compostela, A Coruña, SpainNathan J. Bennett & Sebastián VillasanteEnvironmental Sustainability Research Centre, Brock University, St. Catharines, ON, CanadaJessica L. BlytheMarine and Environmental Sciences Center, University of the Azores – FCT, Ponta Delgada, PortugalHelena CaladoHopkins Marine Station, Stanford University, Stanford, CA, USALarry B. Crowder & Elena GissiARC Centre of Excellence for Coral Reef Studies, Townsville, AustraliaJon C. DayQueen’s University Belfast, Belfast, Northern Ireland, UKWesley FlanneryNational Research Council, Institute of Marine Sciences, Venice, ItalyElena GissiInternational Union for Conservation of Nature and World Commission on Protected Areas, Cambridge, MA, USAKristina M. GjerdeMiddlebury Institute of International Studies at Monterey, Monterey, MA, USAKristina M. GjerdeThe University of the West Indies, St. Augustine Campus, St. Augustine, Trinidad and TobagoJudith F. GobinPermanent Mission of the Federated States of Micronesia to the United Nations, New York, USAClement Yow MulalapDuke University Marine Laboratory, Duke University, Durham, NC, USAMichael OrbachCentre for Marine Socioecology, University of Tasmania, Hobart, AustraliaGretta PeclInstitute for Marine and Antarctic Studies, University of Tasmania, Hobart, AustraliaGretta PeclFederal University of Santa Catarina, Florianópolis, SC, BrazilMarinez SchererCenter for Island Sustainability and Sea Grant, University of Guam, Mangilao, USAAustin J. SheltonSchool of Geography and the Environment, University of Oxford, Oxford, UKLisa Wedding More

White and black spruce are the dominant conifers at Arctic treelines and the boreal forest–tundra ecotone more generally in North America, with white spruce dominating on better drained sites. White spruce reaches its northwestern-most limit in Alaska, USA, at 68.1º N, 163.2º W. For comparison, the northeastern range extent of the species26 is Labrador, Canada, at 57.9º N, 62.5º W (ref. 12), giving an east–west range of >100º in longitude. Of the approximately 6,500-km-long northern boundary of white spruce in North America, 10–15% is located in Alaska’s Brooks Range, where white spruce is the dominant treeline tree.Study areaThe 1,000-km Brooks Range is a high-latitude mountain range dividing Arctic tundra from boreal forest in Alaska. The mountains and nearby lowlands are notable for their wilderness character, protected as a near-contiguous conservation area of >150,000 km2. In the east between the Arctic Ocean’s Beaufort Sea and the uppermost Yukon River basin, the range is cold and dry, reaching 2,736 m above sea level. The south slope of the eastern Brooks Range is included in Alaska’s Northeast Interior climate division, where precipitation is among the lowest in the state51. Descending to the Chukchi Sea in the west, the range is included in Alaska’s West Coast climate division, where precipitation is the highest in northern Alaska51.The Noatak and Kobuk rivers flow in their entirety above the Arctic Circle, draining the western Brooks Range. Both rivers empty into the Chukchi Sea near Kotzebue, Alaska (Fig. 1a). The Baird Mountains of the southwestern Brooks Range separate the Kobuk from the Noatak basin, and the De Long Mountains of the northwestern Brooks Range separate the Noatak from the river basins of the North Slope and from the Wulik basin, located northwest of the Noatak basin. The lower basins of the Noatak and Kobuk rivers are included in the West Coast climate division, with greater precipitation, warmer winters and cooler summers than in the Central Interior climate division and greater precipitation and warmer temperatures than in the North Slope climate division51. The upper basin of the 700-km Noatak River lies at the intersection of all three climate divisions, which warmed from 1949 to 2012; December–January precipitation increased from 1949 to 2012 in the West Coast climate division, as did North Slope winter precipitation from 1980 to 2012 (ref. 52).The Noatak River basin is entirely protected within federal conservation units. Its vegetation includes dwarf, low and tall shrub tundra communities that cover about 60% of the 33,000 km2 basin53. Tussock sedge tundra covers another 30%, and wetlands and barrens cover most of the remainder. The main valley and tributaries along the lowest 200 km of the Noatak River support stands of white spruce, typically associated with a deeper active layer or an absence of permafrost. The treelines bounding these forests have long been identified as the northwest range extent of white spruce26.The upper Noatak basin, a 500-km reach, is underlain by extensive continuous permafrost54. It has been considered empty of spruce since US Geological Survey (USGS) geologist Philip Smith explored the Kobuk, Alatna and Noatak rivers by canoe in 1911 (ref. 55). The adjacent Kobuk and Alatna river basins support boreal forests of black and white spruce, paper birch and aspen along much of their lengths. By surveying transects at and beyond hydrological divides separating the Noatak, Wulik, Kobuk and Alatna river basins, as well as further east in the Brooks Range (Fig. 1a), and informed by very high-resolution satellite scenes (Fig. 1b and Supplementary Figs. 1–13), we documented the locations of over 7,000 individual spruce colonists (Extended Data Fig. 1b–d and Supplementary Figs. 1–3). Overall, we traversed 22° of longitude (141–163° W) in the field, mostly along the treeline from Canada to the Chukchi Sea, locating dozens of populations of colonizing spruce (Fig. 1a) above alpine and beyond Arctic treelines (see ‘Regional extent of colonization’).The primary AOI (Fig. 1a) included the USGS Hydrological Unit Code (HUC) 10 watersheds Kaluich, Cutler, Amakomanak and Imelyak located in the HUC 8 Upper Noatak Subbasin. However, we also documented (longitude, latitude, distance from established treeline) fast-growing, healthy spruce well beyond established treelines within six additional western Arctic watersheds, each separated by over 30 km in the western Brooks Range and 80–200 km distant from the AOI. These populations are within the far upper reaches of the Noatak basin (Lucky Six Creek, 67.594° N, 154.858° W; Kugrak River, 67.428° N, 155.723° W; Ipnelivuk River, 67.552° N, 156.293° W; upper Wrench Creek, 68.251° N, 162.617° W); 25 km northwest of the nearest established treeline and outside the Noatak basin in the Wulik River valley (68.120° N, 163.219° W); and along the Chukchi Sea coast (67.041° N, 163.114° W). We also note that, in the central Brooks Range, humans have actively or inadvertently disseminated spruce seeds and juveniles on the North Slope, with individual white spruce germinating and surviving there for at least 20 years37,56.Patterns of expansionDigitizing spruce shadowsWe used cloud-free Maxar Digital Globe WorldView-1 and WorldView-2 satellite scenes (WV; https://evwhs.digitalglobe.com/myDigitalGlobe/login) of snow-covered landscapes from three missions in early spring 2018, a near-record year for snow depth in northwest Alaska (Fig. 1b, Extended Data Table 1 and Supplementary Figs. 1–13). Ground sample distances of 0.47–0.5 m, a root-mean-squared error of 3.91–3.94 m and off-nadir angles of 5–25º with low sun-elevation angles of 18–27º provided clear images from which to digitize the lengths of individual spruce shadows and identify their locations (Supplementary Information sections 1.2 and 1.3). One technician (S. Taylor), supervised in quality assurance and quality control (QAQC) by R.J.D., digitized 5,986 shadows (densities in Extended Data Fig. 1b, locations in Supplementary Fig. 1) on GEP using WV images as super-overlays. The technician identified all spruce shadows across the imported image tiles and then digitized them as line segments from base to shadow tip.The super-overlays degraded the imagery somewhat, making small tree shadows more difficult to distinguish from snowdrift, rock or shrub shadows (Supplementary Figs. 5 and 6). We suspect that many trees in the height class of 2–3 m were missed. These line segments, saved as .kml files, were imported into R (v.4.1.1)57 using the sf package58, where the length of each line segment was calculated and the coordinates of the shadow’s base were identified. The line segment lengths were used to estimate tree heights, and the coordinates were used in nearest-neighbour calculations and extractions of gridded data values. We estimated snow depth at 2.5–3 m because geolocated trees measured as ≤2.5 m in the field (see below) did not appear on imagery. We observed some trees taller than 2.5 m with no visible shadows on imagery, possibly buried in deeper snow or growing in shadows cast by terrain at the time of image capture. Thus, our estimates of adult populations may be underestimates, although there were also errors of commission where shrub shadows were mistakenly classified as spruce (see following).Digitizing and field validationTo estimate identification accuracy (Supplementary Information sections 1.2 and 1.3) among the 1,971 digitized shadows used for population reconstruction (enclosed by red rectangles in Supplementary Figs. 1–4), we visited 157 shadow locations first identified on imagery (8% of the 1,971) and located in the field with the built-in GNSS of late-model Apple iPhones (models 12 Pro Max, 12 Pro and second-generation SE) with positional accuracy in the open landscapes estimated at 3 m. At these 157 locations, 11 shadows were cast by very tall willows (7%). Of the 146 shadows confirmed as trees, 2 were dead (1%) and 1 had a recently broken top with green foliage on the ground. We added the length of the broken top to the standing height measured with a laser range-finder. Trees that were collinear in the solar azimuth at image capture contributed to errors of omission. The tree standing to solar azimuth obscured others as overlapping shadows fell in line, generating both errors of omission and an overestimate of the height of the first tree in the series. Six trees shadowed in three instances by what we identified on imagery as single shadows fell in this category. An additional three trees were missed during digitizing, also going unnoticed during QAQC, and were discovered in the field when matching shadows with trees. Supplementary Information section 1.3 provides details and a confusion matrix.In summary, 157 trees were expected from digitized shadows and 155 were found in the field. Applying the accuracy of the count overall suggests that 1,945 trees would better estimate the reconstructed population. Across the AOI, the total adult count of 5,988 shadows may represent 5,910 trees. Moreover, in so far as our estimates of ages based on tree heights are predictive, perhaps 2% of the ‘trees’ in our reconstruction are not a single tree casting a long shadow, but 2–3 younger, collinear trees. Thus, our estimate of past populations may be slightly biased to older trees, implying that the population growth rate may be slightly higher than estimated. However, the slightly fewer trees than shadows would suggest that the growth rate is lower. The relative size of these errors appears minor, and we did not incorporate them into the analysis, which seems to us robust and perhaps conservative in adult abundance estimates owing to image degradation with GEP super-overlays and other errors of omission. This study would have benefited from less image degradation using dedicated geographic information system (GIS) or image software. However, the low cost, simplicity and convenience of GEP was appealing for the large-scale digitizing.Returning from the field with individual tree data, R.J.D. displayed digitized shadow points together with field points on GEP, visually matching each field point to the nearest shadow, conditional on relative congruence between shadow size and tree height. This required care in clumps of trees with varying heights (example in Supplementary Information sections 1.2–1.3). The relative patterning of field points compared with shadows and the lengths of shadows compared with tree heights in these cases provided some measure of confidence in attribution.We made field expeditions to six study areas within the extent of the WV imagery we used for digitizing, three within the ‘simulated population area’ rectangle in Extended Data Fig. 1a (red rectangle in Supplementary Figs. 1–4) and three study areas further east (Extended Data Fig. 1c and Supplementary Fig. 2). Among-area variability was apparent in snow depth, terrain slope relative to the solar azimuth at the time of image capture and the solar-elevation angle itself because of the timing of image capture. The variability was identified, calculated and applied on the basis of geographic variability in the heights of trees casting shadows and from the slope and intercept of a mixed-model linear regression of field-measured height on digitized shadow length (see below).Field surveysWe validated species and heights of spruce casting shadows within the AOI along 403 km of ground transects. Our sampling did not appear spatially biased when compared with imagery as measured by proximity to a remote fixed-wing-aircraft landing site. Four field campaigns focused on three objectives in watersheds that were within or adjacent to the Noatak basin but did not have established treelines visible on WV growing season scenes: (1) to locate and document colonists at the geographic range boundary of white spruce; (2) to verify the locations of a sample of trees suggested by imagery in the AOI; and (3) to collect ecological measurements germane to white spruce range expansion. For adults (trees ≥2.5 m), datasets included height above ground (n = 340), diameter at breast height (DBH (~1.4 m); n = 296), CAG (n = 17), foliar nutrient content (n = 17), basal increment cores taken ≤20 cm above the ground (n = 140), tall shrub abundance within 5 m of sampled adults (n = 246), counts of juveniles within 5 m of sampled adults (n = 250), abundance class of cones (n = 339) and status of adults (live, n = 340; dead, n = 8). Of the dead adults, seven of eight were standing and largely without bark, with a median height of 4.1 m. The fallen dead tree was 6.2 m long with a DBH of 13.4 cm; all bark and limbs to fine branches remained. Only one dead adult, 4.1 m tall with a DBH of 4 cm, showed signs of decomposition with shelf fungus on the stem and decomposed limbs on the ground. Five juveniles ≥1.5 m tall had been stripped of their bark and all but their uppermost branches by apparently either porcupine (Erethizon dorsatum) or snowshoe hare (Lepus americanus). Anecdotally, we recorded other signs and possible causes of damage such as wind, bear (Ursus arctos), caribou (Rangifer tarandus) or struggling growth such as layering, stunted krummholz or clonal reproduction, although these growth forms were nearly totally absent.Field measurements for n = 770 juveniles located in the AOI and presented here included overall height, height above ground of bud scars representing 2015–2020 height (n = 302), damage and status. We used these measures to estimate age to increment core of adults (Supplementary Information section 2) and the RGR of juveniles (Supplementary Information section 3).Range expansion analysesDigitized established treelines (DETs) used here were downloaded as CTM_Treeline.kml from https://arcticdata.io/catalog/view/doi:10.18739/A2280506H. Ref. 34 describes drawing DETs on very high-resolution satellite imagery such as WV and Quick Bird. We clipped DETs to the four USGS HUC 10 watersheds within the HUC 8 Middle Kobuk subbasin and adjacent to the AOI (see ‘Environmental conditions’ below). The coordinates of the vertices for the clipped DETs provided the 3,366 locations of established treelines.We used the rdist.earth() function in the R package fields59 to identify the nearest neighbouring mapped adult and juvenile colonists in the AOI and DET vertices in adjacent Kobuk watersheds (Supplementary Information sections 1.8 and 1.9). Using the coordinates of nearest neighbours, we calculated differences in latitude as latitudinal displacement. Displacement north equalled the product of latitudinal displacement and 111.32 km, the distance between 67º and 68º N along 157.6891º W, which splits the AOI. Displacement in elevation was found by extracting from Interferometric Synthetic Aperture Radar (IFSAR) Alaska 5-m digital elevation models (DEMs) the elevation of DET vertices, mapped adults and mapped juveniles using the extract() function in the raster R package60 and then subtracting the elevation of the nearest neighbours from focal adults and juveniles. When geolocated adults or juveniles had estimated establishment years (see ‘Individual growth’ below), we calculated movement rates as the difference between the establishment year of an aged tree and the establishment year of the oldest tree sampled (1901, year of founding) as the denominator and displacement (difference in metres above sea level, kilometres or degrees of latitude) as the numerator (Supplementary Information sections 1.19–1.21). To time the progression of spruce away from DETs, we also binned establishment year by decade as decadal class, identifying within each decadal class the maximum displacement in kilometres north of and elevation in metres above (or below) nearest neighbours.Population growthFrom the 5,986 spruce shadow lengths within the AOI (Extended Data Fig. 1b and Supplementary Fig. 1) that we digitized from snow-covered scenes of DigitalGlobe WV imagery (Extended Data Table 1), we identified a sample of shadows stratified by length and cast by spruce that we located with GNSS-equipped late-model iPhones. We measured the height of n = 260 trees using a laser range-finder (LTI TruPulse 200) and/or a smartphone app (Arboreal Tree on iPhone 12 Pro and Pro Max with laser scanners) and collected n = 122 basal cores from individuals ≥2.5 m in height, then matched to shadows on imagery as described above (see ‘Digitizing and field validation’). Using the relationship between height and shadow length and the probability distribution of establishment year for the 122 cored trees identified within five height classes (Extended Data Fig. 2b), we simulated population growth within two contiguous sub-watersheds (the 135 km2 ‘simulated population area’in Extended Data Fig. 1a; western portion in Extended Data Fig. 2a; red rectangles in Supplementary Figs. 1–4; details in Supplementary Information section 4). These sub-watersheds contained n = 1,971 shadows cast on 26 March 2018. We treated these shadows as single spruce but recognize that they include as many as 138 willows (7%) and calculate an additional 118 (6%) spruce missed either by digitizing omission or by collinearity (Supplementary Information sections 1.2 and 1.3). Incorporating these errors together would not change the outcome of the simulations enough to change the doubling time of the population by more than a few percent.Estimates of tree height from shadow lengthOn a flat landscape covered uniformly in snow, the total height H of a tree equals snow depth S added to the product of shadow length L on the snow surface and the tangent of solar-elevation angle 𝛼, as H = S + Ltan(𝛼). However, because both the relative solar elevation and snow depth vary with terrain, we used a linear mixed-effects model (lmer() in the lme4 R package61) of height on shadow length (random factor of sample area with six levels), interpreting the fixed-effects intercept as the average snow depth (mean ± s.e. = 2.84 ± 0.14 m, t = 20.29) and the regression coefficient as the average tangent of solar elevation relative to the terrain slope (0.27 ± 0.04 m m−1, t = 6.96; details in Supplementary Information sections 4.1 and 4.2).Using these fixed-effects estimates and the random-effects covariance matrix, we applied Monte Carlo sampling to estimate the 1,971 heights with each run of the simulation, thereby propagating the error in height estimates. These 1,971 heights were then binned into five height classes with 0.5-m intervals from 4–5.5 m and with ≥1-m intervals from 3–4 m and 5.5–7 m (details in Supplementary Information sections 4.3 and 4.4). Height classes deduced from the shadow measurements were in some cases only 0.5 m in width. Because the mean snow depth (the intercept in the mixed-effects model) differed by more than this from one part of the study area to another (BobWoods, GaiaHill and BuffaloDrifts in Supplementary Information sections 4.1 and 4.2), this approach may have introduced systematic misclassification between locations. While applying a Monte Carlo model with coefficients drawn randomly using the mvrnorm() function from the MASS package in R with the random-effects covariance matrix was meant to alleviate this, we also ran the simulation with three uniform height classes with a wider interval (1.3-m width, for classes of 3–4.3 m, 4.3–5.6 m and 5.6–7 m).Estimating population-scale establishment yearWe estimated establishment years for each of the 1,971 trees (Supplementary Information sections 4.3 and 4.4). We did so by using the establishment yeardistributions by height class as Gaussian kernel densities for the 122 aged adults binned into the five height classes defined above (Extended Data Fig. 2b). Kernel density estimates were constructed using the function density() in R with options bw = “SJ” as the smoothing bandwidth, n = 107 as the number of consecutive establishment years, from = 1897 as the earliest year and to = 2004 as the latest year. For each of the 1,971 estimated heights binned into height classes, an establishment year was drawn (with replacement) from the corresponding kernel density distribution. We interpreted the total number of individuals in each establishment year as ‘recruitment by year’ into the population of survivors that we had digitized on the 2018 imagery. Sorting and cumulatively summing recruitment by year gave what we interpreted as population size (N) for each year (t) for trees that survived to 2018. Resampling in this manner for 1,000 runs, each time fitting exponential growth equation N(t) = N0ek(t – 1900) using nls() in R and then averaging the population RGR, provided population doubling time as ln(2) divided by mean k. The simulation was run again using three height classes, each of 1.3 m in width. The resulting mean doubling time was unchanged, but variability increased (Supplementary Information section 4.6).Individual growthCurrent annual growth and foliar chemistryIn autumn 2019, we collected current-year lateral branch tips on the west and east sides of each sampled spruce (n1 = 17 adult colonists and n2 = 457 adults at established treelines) at 1.4 m above the ground. Current annual branch growth was measured on 2–6 branches per spruce from the previous year’s bud scar to the tip of the branch. The number of samples varied, ensuring sufficient mass for foliar chemical analysis. Established treelines were sampled for adult foliage in 12 watersheds of the Noatak, Kobuk and Koyukuk river basins where we have ongoing experiments. At these sites, we used a replicated nested plot-based design (Extended Data Table 3). Colonist foliage sample locations (n = 8) in the upper Noatak basin were widespread across three watersheds. At each location, except the upper Noatak where 1–3 spruce per location were sampled, we sampled n = 5 white spruce separated by ≥10  m at a DBH of 8–12 cm. Needles from each branch tip were pooled by individual, dried for 48 h at 60 °C and weighed. Needles of individuals were pooled by treeline location after grinding to powder using a steel ball mill grinder (Mini-Beadbeater, Biospec Products) and subsampled for chemical analysis. Foliar N and 15N isotope were analysed for one subsample run on an Elemental Combustion Analyzer (Costech, 4010) coupled to an isotope ratio mass spectrometer (Delta Plus XP, Thermo Fisher Scientific) at the University of Alaska Anchorage Environment and Natural Resources Institute Stable Isotope Laboratory. Foliar P was measured for another subsample by the Pennsylvania State College Analytical Services Lab using the acid digestion method and analysed by inductively coupled plasma emission spectroscopy62.Juvenile RGRSeveral results presented here depend on juvenile vertical height growth during 2015–2020, which we assumed followed h(t) = h2015e(RGR t), where h(t) is height above ground for year t after 2015, h2015 is the height above ground in 2015 and RGR is the relative growth rate (Supplementary Information section 3). We used juvenile RGR in three contexts: (1) as a means of estimating establishment year in juveniles (Supplementary Information section 3.3); (2) as a metric of growth for comparison between colonist and established treeline juveniles (Supplementary Information section 6); and (3) to estimate the establishment year of cored trees (see second paragraph in ‘Dendrochronology’ below and Supplementary Information section 2).To estimate the RGR for each of 505 juveniles (n1 = 300 juveniles from m1 = 4 colonist populations and n2 = 205 juveniles from m2 = 14 established treelines; Extended Data Table 2), we measured the heights above ground (h) of the six uppermost bud scars in 2020, representing height increments in 2016–2020, the five consecutive years with the warmest mean daily July air temperature on record for Kotzebue. RGR in each juvenile was calculated as the regression slope of ln(h(t)) against t (mean R2 = 0.99 for 300 colonist regressions and 0.98 for 271 established treeline regressions; Supplementary Information section 3.4).To estimate the establishment year of juveniles, we used RGR to back-calculate T, the years required for an individual colonist to grow from 2 cm to h2015, as T = ln(h2015/2)/RGR. By subtracting T from 2020, we estimated the establishment year of each juvenile (Supplementary Information section 3.3).RGR values for colonist and established treeline juveniles (Extended Data Table 2) were compared using a linear mixed-effects model with field site (m = 24) as a random intercept, ln(RGR) as the dependent variable, ln(h2015) as a covariate to capture allometric growth and population (colonist or established treeline) as the fixed factor of interest (Supplementary Information section 6). Using the lmer() function of the lme4 package61 in R with REML = F, we found that the Akaike information criterion (AIC) for the interaction model was lower than that for the corresponding additive one (∆AIC = 22, likelihood ratio test χ2 = 24, degrees of freedom = 1, P 1 km beyond the established treeline, we recorded the location, age classes and presence of cones when possible. In watersheds of the uppermost Noatak basin and the Wulik basin, we also recorded both the total height of juveniles and the height above ground of the sixth bud scar from the tip to estimate RGR and so estimate age. We encountered three watersheds with tree island krummholz >1 km beyond the treeline but do not include these as colonist populations because clonal growth can be very old9,10,11,12,13,14,15,16,17,18,19. Of the 34 watersheds in which we encountered colonist populations >1 km beyond established treelines, 4 watersheds were located between 141° and 149.7° W (eastern Brooks Range), 21 watersheds were located between 149.7° and 156.3° W (central Brooks Range) and 9 watersheds were located between 156.3° and 163.3° W (western Brooks Range). Watersheds west of 150.5° W with colonists are shown in Fig. 1a.In 2021, R.J.D. led a field expedition to a small watershed in the Koyukuk basin (Arrigetch Creek, 67.439° N, 154.090° W). The watershed had been purposefully surveyed for juvenile white spruce above and beyond the treeline during 1978–1980 when seven juveniles 11–112 cm tall (six seedlings More

Amelung, W. et al. Towards a global-scale soil climate mitigation strategy. Nat. Commun. 11, 5427 (2020).ADS 
CAS 
Article 

Google Scholar 
Blouin, M. et al. A review of earthworm impact on soil function and ecosystem services. Eur. J. Soil Sci. 64(2), 161–182. https://doi.org/10.1111/ejss.12025 (2013).Article 

Google Scholar 
Deckmyn, G. et al. KEYLINK: Towards a more integrative soil representation for inclusion in ecosystem scale models I. Review and model concept. PeerJ 8, 9750. https://doi.org/10.7717/peerj.9750 (2020).Article 

Google Scholar 
Phillips, H. R. P. et al. Global distribution of earthworm diversity. Science 366, 6464. https://doi.org/10.1126/science.aax4851 (2019).CAS 
Article 

Google Scholar 
Bertrand, M. et al. Earthworm services for cropping systems. A review. Agron. Sustain. Dev. 35, 553–567 (2015).CAS 
Article 

Google Scholar 
Angst, G. et al. Earthworms act as biochemical reactors to convert labile plant compounds into stabilized soil microbial necromass. Commun. Biol. 2, UNSP 441 (2019).Article 

Google Scholar 
Bohlen, P. J. & Edwards, C. A. Earthworm effects on N dynamics and soil respiration in microcosms receiving organic and inorganic nutrients. Soil Biol. Biochem. 27, 341–348 (1995).CAS 
Article 

Google Scholar 
Bossuyt, H., Six, J. & Hendrix, P. F. Protection of soil carbon by microaggregates within earthworm casts. Soil Biol. Biochem. 37, 251–258 (2005).CAS 
Article 

Google Scholar 
Lubbers, I. M. et al. Greenhouse-gas emissions from soils increased by earthworms. Nat. Clim. Change 3, 187–194 (2013).ADS 
CAS 
Article 

Google Scholar 
Huang, W., Gonzalez, G. & Zou, X. M. Earthworm abundance and functional group diversity regulate plant litter decay and soil organic carbon level: A global meta-analysis. Appl. Soil Ecol. 150, 103473. https://doi.org/10.1016/j.apsoil.2019.103473 (2020).Article 

Google Scholar 
Kruck, S., Joschko, M., Schultz-Sternberg, R., Kroschewski, B. & Tessmann, J. A classification scheme for earthworm populations (Lumbricidae) in cultivated agricultural soils in Brandenburg, Germany. J. Plan Nutr. Soil Sci. 169, 651–660 (2006).Article 

Google Scholar 
Westernacher, E. & Raff, O. Orientation behaviour of earthworms (Lumbricidae) toward different crops. Biol. Fertil. Soils 3, 131–133 (1987).
Google Scholar 
Coppens, F., Garnier, P., Degryze, S., Merckx, R. & Recous, S. Soil moisture, carbon and nitrogen dynamics following incorporation versus surface application of labelled residues in soil columns. Eur. J. Soil Sci. 57, 894–905 (2006).CAS 
Article 

Google Scholar 
Angers, D. A. & Recous, S. Decomposition of wheat straw and rye residues as affected by particle size. Plant Soil 189, 197–203 (1997).CAS 
Article 

Google Scholar 
Iqbal, A., Garnier, P., Lashermes, G. & Recous, S. A new equation to simulate the contact between soil and maize residues of different sizes during their decomposition. Biol. Fertil. Soils 50, 645–655 (2014).CAS 
Article 

Google Scholar 
Šimek, M. & Pižl, V. Soil CO2 flux affected by Aporrectodea caliginosa earthworms. Cent. Eur. J. Biol. 5, 364–370 (2010).
Google Scholar 
Potthoff, M., Joergensenb, R. G. & Woltersc, V. Short-term effects of earthworm activity and straw amendment on the microbial C and N turnover in a remoistened arable soil after summer drought. Soil Biol. Biochem. 33, 583–591 (2001).CAS 
Article 

Google Scholar 
Bernard, L. et al. Endogeic earthworms shape bacterial functional communities and affect organic matter mineralization in a tropical soil. ISME J. 6, 213–122 (2012).CAS 
Article 

Google Scholar 
Borken, W., Gründel, S. & Beese, F. Potential contribution of Lumbricus terrestris L. to carbon dioxide, methane and nitrous oxide fluxes from a forest soil. Biol. Fertil. Soils 32, 142–148 (2000).CAS 
Article 

Google Scholar 
Martin, A. Short-term and long-term effects of the endogeic earthworm Millsonia anomala (Omodeo) (Megascolecidae, Oligochaeta) of tropical savannas, on soil organic matter. Biol. Fertil. Soils 11, 234–238 (1991).Article 

Google Scholar 
Moreau-Valancogne, P., Bertrand, M., Holmstrup, M. & Roger-Estrade, J. Integration of thermal time and hydrotime models to describe the development and growth of temperate earthworms. Soil Biol. Biochem. 63, 50–60. https://doi.org/10.1016/j.soilbio.2013.03.022 (2013).CAS 
Article 

Google Scholar 
Lubbers, I. M., van Groenigen, K. J., Brussaard, L. & van Groenigen, J. W. Reduced greenhouse gas mitigation potential of no-tillage soils through earthworm activity. Sci. Rep. 5, 13787 (2015).ADS 
Article 

Google Scholar 
Joschko, M. et al. Spatial analysis of earthworm biodiversity at the regional scale. Agric. Ecosyst. Environ. 112, 367–380 (2006).Article 

Google Scholar 
Kanianska, R., Jad’ud’ova, J., Makovnikova, J. & Kizekova, M. Assessment of relationships between earthworms and soil abiotic and biotic factors as a tool in sustainable agricultural. Sustainability 8, 906 (2016).Article 

Google Scholar 
Chertov, O. et al. Romul_Hum model of soil organic matter formation coupled with soil biota activity. III Parameterisation of earthworm activity. Ecol. Model. 345, 140–149 (2017).CAS 
Article 

Google Scholar 
Pelosi, C., Bertrand, M., Makowski, D. & Roger-Estrade, J. WORMDYN: A model of Lumbricus terrestris population dynamics in agricultural fields. Ecol. Model. 218, 219–234 (2008).Article 

Google Scholar 
Fisk, M. C., Fahey, T. J., Groffman, P. M. & Bohlen, P. J. Earthworm invasion, fine-root distributions, and soil respiration in north temperate forests. Ecosystems 7, 55–62 (2004).Article 

Google Scholar 
Rizhiya, E. et al. Earthworm activity as a determinant for N2O emission from crop residue. Soil Biol. Biochem. 39, 2058–2069 (2007).CAS 
Article 

Google Scholar 
Snyder, B. A., Boots, B. & Hendrix, P. F. Competition between invasive earthworms (Amynthas corticis, Megascolecidae) and native north American millipedes (Pseudopolydesmus erasus, Polydesmidae): Effects on carbon cycling and soil structure. Soil Biol. Biochem. 41, 1442–1449 (2009).CAS 
Article 

Google Scholar 
Chapuis-Lardy, L. et al. Effect of the endogeic earthworm Pontoscolex corethrurus on the microbial structure and activity related to CO2 and N2O fluxes from a tropical soil (Madagascar). Appl. Soil Ecol. 45, 201–208 (2010).Article 

Google Scholar 
Bertora, C., van Vliet, P. C. J., Hummelink, E. W. J. & van Groenigen, J. W. Do earthworms increase N2O emissions in ploughed grassland?. Soil Biol. Biochem. 39, 632–640 (2007).CAS 
Article 

Google Scholar 
Binet, F., Fayolle, L. & Pussard, M. Significance of earthworms in stimulating soil microbial activity. Biol. Fertil. Soils 27, 79–84 (1998).Article 

Google Scholar 
Butenschoen, O. et al. Endogeic earthworms alter carbon translocation by fungi at the soil–litter interface. Soil Biol. Biochem. 39, 2854–2864 (2007).CAS 
Article 

Google Scholar 
Cortez, J., Hameed, R. & Bouche, M. B. C-transfer and N-transfer in soil with or without earthworms fed with C-14 labelled and N-15 labelled wheat straw. Soil Biol. Biochem. 21, 491–497 (1989).Article 

Google Scholar 
Marhan, S., Langel, R., Kandeler, E. & Scheu, S. Use of stable isotopes (13C) for studying the mobilisation of old soil organic carbon by endogeic earthworms (Lumbricidae). Eur. J. Soil Biol. 43, S201–S208 (2007).CAS 
Article 

Google Scholar 
Scheu, S. Effects of litter (beech and stinging nettle) and earthworms (Octolasion lacteum) on carbon and nutrient cycling in beech forests on a basalt-limestone gradient: A laboratory experiment. Biol. Fertil. Soils 24, 384–393 (1997).CAS 
Article 

Google Scholar 
Wolters, V. & Schaefer, M. Effects of burrowing by the earthworm Aporrectodea caliginosa (Savigny) on beech litter decomposition in an agricultural and in a forest soil. Geoderma 56, 627–632 (1993).ADS 
Article 

Google Scholar  More

Arlinghaus, R., Tillner, R. & Bork, M. Explaining participation rates in recreational fishing across industrialised countries. Fisheries Management and Ecology 22, 45–55 (2015).Article 

Google Scholar 
Cooke, S. J. & Cowx, I. G. The Role of Recreational Fishing in Global Fish Crises. BioScience 54, 857 (2004).Article 

Google Scholar 
World Bank. Hidden harvest: The global contribution of capture fisheries (World Bank, Washington, DC), Report 66469-GLB (2012).Nyboer, E. A. et al. Overturning stereotypes: the fuzzy boundary between recreation and subsistence in inland fisheries. Fish and Fisheries https://doi.org/10.1111/faf.12688 (2022).Article 

Google Scholar 
Gupta, N. et al. Catch-and-release angling as a management tool for freshwater fish conservation in India. Oryx 50, 250–256 (2016).Article 

Google Scholar 
Bower, S. D. et al. Knowledge Gaps and Management Priorities for Recreational Fisheries in the Developing World. Reviews in Fisheries Science & Aquaculture 1–18, https://doi.org/10.1080/23308249.2020.1770689 (2020).FAO. The State of World Fisheries and Aquaculture – 2016 (SOFIA). Rome, Italy (2016).Golden, C. D. et al. Aquatic foods to nourish nations. Nature https://doi.org/10.1038/s41586-021-03917-1 (2021).Article 
PubMed 

Google Scholar 
Cooke, S. J. et al. The nexus of fun and nutrition: Recreational fishing is also about food. Fish and Fisheries 19, 201–224 (2018).Article 

Google Scholar 
Joosse, S., Hensle, L., Boonstra, W. J., Ponzelar, C. & Olsson, J. Fishing in the city for food—a paradigmatic case of sustainability in urban blue space. npj Urban Sustain 1, 41, https://doi.org/10.1038/s42949-021-00043-9 (2021).Article 

Google Scholar 
Fluet-Chouinard, E., Funge-Smith, S. & McIntyre, P. B. Global hidden harvest of freshwater fish revealed by household surveys. Proceedings of the National Academy of Sciences 115, 7623–7628 (2018).CAS 
Article 

Google Scholar 
FAO. The State of World Fisheries and Aquaculture – 2020 (SOFIA). Rome, Italy. (2020).IPBES. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (Version 1). Zenodo https://doi.org/10.5281/zenodo.3831674 (2019).Arlinghaus, R. et al. Global Participation in and Public Attitudes Toward Recreational Fishing: International Perspectives and Developments. Reviews in Fisheries Science & Aquaculture 29, 58–95 (2021).Article 

Google Scholar 
Chan, N. “Large Ocean States”: Sovereignty, Small Islands, and Marine Protected Areas in Global Oceans Governance. Global Governance: A Review of Multilateralism and International Organizations 24, 537–555 (2018).Article 

Google Scholar 
Arlinghaus, R. & Cooke, S. J. Recreational Fisheries: Socioeconomic Importance, Conservation Issues and Management Challenges. in Recreational Hunting, Conservation and Rural Livelihoods (eds. Dickson, B., Hutton, J. & Adams, W. M.) 39–58, https://doi.org/10.1002/9781444303179.ch3 (Wiley-Blackwell, 2009).Arlinghaus, R. et al. Opinion: Governing the recreational dimension of global fisheries. Proceedings of the National Academy of Sciences 116, 5209–5213 (2019).CAS 
Article 

Google Scholar 
Cisneros-Montemayor, A. M. & Sumaila, U. R. A global estimate of benefits from ecosystem-based marine recreation: potential impacts and implications for management. Journal of Bioeconomics 12, 245–268 (2010).Article 

Google Scholar 
Czarkowski, T., Wołos, A. & Kapusta, A. Socio-economic portrait of Polish anglers and its implications for recreational fisheries management in freshwater bodies. Aquatic Living Resources 19, 14, https://doi.org/10.1051/alr/2021018 (2021).Article 

Google Scholar 
Dill, W. A. Inland Fisheries of Europe. Italy: Food and Agriculture Organization of the United Nations. (1993).Baigún, C., Oldani, N., Madirolas, A. & Colombo, G. A. Assessment of Fish Yield in Patagonian Lakes (Argentina): Development and Application of Empirical Models. Transactions of the American Fisheries Society 136, 846–857 (2007).Article 

Google Scholar 
Vigliano, P. H., Bechara, J., & Quiros, R. Allocation policies and its implications for recreational fisheries management in inland waters of Argentina. Sharing the Fish ‘06, 210 (2006).Henry, G. W., & Lyle, J. M. National recreational and indigenous fishing survey. (2003).Murphy J. J. et al. Survey of recreational fishing in NSW, 2019/20 – Key Results. Fisheries Final Report Series No. 161. Department of Primary Industries, New South Wales. 180 pp. (2022).Aas, Øystein, ed. Global challenges in recreational fisheries. (John Wiley & Sons, 2008).DoF. Yearbook of Fisheries Statistics of Bangladesh, 2017-18. Fisheries Resources Survey System (FRSS), Department of Fisheries. Bangladesh: Ministry of Fisheries. 35: p. 129 (2018).Mozumder, M., Uddin, M., Schneider, P., Islam, M. & Shamsuzzaman, M. Fisheries-Based Ecotourism in Bangladesh: Potentials and Challenges. Resources 7, 61 (2018).Article 

Google Scholar 
Craig, John F., ed. Freshwater fisheries ecology. (John Wiley & Sons, 2016).Barkhuizen, L. M., Weyl, O. L. F. & Van As, J. G. An assessment of recreational bank angling in the Free State Province, South Africa, using licence sale and tournament data. WSA 43, 442 (2017).Article 

Google Scholar 
Treer, T. & Kubatov, I. The co-existence of recreational and artisanal fisheries in the central parts of the Danube and Sava rivers. Croatian Journal of Fisheries 75(3), 116–127 (2017).
Google Scholar 
Freire, K. M. F., Machado, M. L. & Crepaldi, D. Overview of Inland Recreational Fisheries in Brazil. Fisheries 37, 484–494 (2012).Article 

Google Scholar 
Freire, K. M. F. et al. Brazilian recreational fisheries: current status, challenges and future direction. Fish Manag Ecol 23, 276–290, https://doi.org/10.1111/fme.12171 (2016).Article 

Google Scholar 
Fisheries and Oceans Canada. Survey of Recreational Fishing in Canada, 2015. 26 (2019).Arismendi, I. & Nahuelhual, L. Non-native Salmon and Trout Recreational Fishing in Lake Llanquihue, Southern Chile: Economic Benefits and Management Implications. Reviews in Fisheries Science 15, 311–325 (2007).Article 

Google Scholar 
Lyach, R., & Čech, M. Differences in fish harvest, fishing effort, and angling guard activities between urban and natural fishing grounds. Urban Ecosystems, 1–13 (2019).Lyach, R. The effect of fishing effort, fish stocking, and population density of overwintering cormorants on the harvest and recapture rates of three rheophilic fish species in central Europe. Fisheries Research 223, 105440 (2020).Article 

Google Scholar 
Lyach, R. The effect of a large-scale angling restriction in minimum angling size on harvest rates, recapture rates, and average body weight of harvested common carps Cyprinus carpio. Fisheries Research 223, 105438 (2020).Article 

Google Scholar 
Lyach, R. & Remr, J. Changes in recreational catfish Silurus glanis harvest rates between years 1986–2017 in Central Europe. Journal of Applied Ichthyology 35(5), 1094:1104 (2019).Article 

Google Scholar 
Lyach, R. & Remr, J. Does harvest of the European grayling, Thymallus thymallus (Actinopterygii: Salmoniformes: Salmonidae), change over time with different intensity of fish stocking and fishing effort? Acta Ichthyol. Piscat. 50(1), 53–62 (2019).Article 

Google Scholar 
Lyach, R. & Remr, J. The effects of environmental factors and fisheries management on recreational catches of perch Perca fluviatilis in the Czech Republic. Aquatic Living Resources 32, 15, https://doi.org/10.1051/alr/2019013 (2019).Article 

Google Scholar 
Rasmussen, G. & Geertz‐Hansen, P. Fisheries management in inland and coastal waters in Denmark from 1987 to 1999. Fisheries Management and Ecology 8(4‐5), 311–322 (2001).
Google Scholar 
Armulik, T. & Sirp, S. Estonian Fishery 2018. (2019).Welcomme, R. Review of the State of the World Fishery Resources: Inland Fisheries. FAO Fisheries and Aquaculture Circular No. 942, Rev. 2. Rome, FAO. 97 pp. (2011).West Greenland Commission, 2020 Report on the Salmon Fishery in Greenland. 8 (2020).Guðbergsson, G. Catch statistics for Atlantic salmon, Arctic char and brown trout in Icelandic rivers and lakes 2013. Institute of Freshwater Fisheries, Iceland Report VMST/14045 (2014).Inland Fisheries Ireland. Wild Salmon and Sea Trout Statistics Report. IFI/2020/1-4513 (2019).Vycius, J. & Radzevicius, A. Fishery and Fishculture Challenges in Lithuania. International Journal of Water Resources Development 25(1), 81–94, https://doi.org/10.1080/07900620802576240 (2009).Article 

Google Scholar 
Bacal, P., Jeleapov, A., Burduja, V. D., & Moroz, I. State and use of lakes from central region of the Republic of Moldova. Present Environment and Sustainable Development, (2), 141–156 (2019).Moroccan Ministry of Fisheries, Annual Report of Fisheries and Fish Farming in Inland Waters, Season 2020/2021 (2021).Centre for Fisheries Research. Recreational fisheries in the Netherlands: Analyses of the 2017 screening survey and the 2016–2017 logbook survey. CVO report: 18.025 (2019).Dedual, M. & Rohan, M. Long‐term trends in the catch characteristics of rainbow trout Oncorhynchus mykiss, in a self‐sustained recreational fishery, Tongariro River, New Zealand. Fisheries Management and Ecology 23(3-4), 234–242 (2016).Article 

Google Scholar 
Unwin, M.J. Angler usage of New Zealand lake and river fisheries. National Institute of Water and Atmospheric Research (2016).Ipinmoroti, M. O. & Ayanboye, O. Biological and socioeconomic viability of recreational fisheries of two Nigerian lakes. IIFET 2012 Tanzania Proceedings (2012).Amaral, S., Ferreira, M.T., Cravo, M.T. Resultado do ‘Inquérito aos Pescadores Desportivos de Áquas Intenores” realizado pela Direcção Geral das Florestas em 1998 a 1999. Pesca Desportivos em Albufeiras do Centro e Sul de Portugal: Contribuição para a reduçao da eutrofização. Instituto Superior de Agronomia. Autoridade Florestal Nacional. Lisboa: III.1-III.53. (2010).Povž, M., Šumer, S. & Leiner, S. Sport fishing catch as an indicator of population size of the Danube roach Rutilus pigus virgo in Slovenia (Cyprinidae). Italian Journal of Zoology 65(S1), 545–548 (1998).Article 

Google Scholar 
Embke, H. S., Beard, T. D., Lynch, A. J. & Vander Zanden, M. J. Fishing for Food: Quantifying Recreational Fisheries Harvest in Wisconsin Lakes. Fisheries fsh.10486, https://doi.org/10.1002/fsh.10486 (2020).Karimov, B. et al. Inland capture fisheries and aquaculture in the Republic of Uzbekistan: current status and planning. FAO Fisheries and Aquaculture Circular. No. 1030/1. Rome, FAO. 124 p. (2009).Magqina, T., Nhiwatiwa, T., Dalu, M. T., Mhlanga, L. & Dalu, T. Challenges and possible impacts of artisanal and recreational fisheries on tigerfish Hydrocynus vittatus Castelnau 1861 populations in Lake Kariba, Zimbabwe. Scientific African 10, e00613 (2020).Article 

Google Scholar 
Embke, H. S. Global dataset of species-specific inland recreational fisheries harvest for consumption. U.S. Geological Survey https://doi.org/10.5066/P9904C3R (2022).Amano, T., González-Varo, J. P. & Sutherland, W. J. Languages are still a major barrier to global science. PLoS biology 14(12), e2000933 (2016).Article 

Google Scholar 
Cooke, S. J. et al. Recreational fisheries in inland waters. In J. F. Craig (Ed.) Freshwater Fisheries Ecology. John Wiley and Sons Ltd. (2016). More

Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40. https://doi.org/10.1126/science.281.5374.237CAS 
Article 
PubMed 

Google Scholar 
Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive Earth’s biogeochemical cycles. Science. 2008;320:1034–9. https://doi.org/10.1126/science.1153213CAS 
Article 
PubMed 

Google Scholar 
Gattuso J-P, Magnan A, Billé R, Cheung WWL, Howes EL, Joos F, et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science. 2015;349:aac4722. https://doi.org/10.1126/science.aac4722Steinacher M, Joos F, Frölicher TL, Bopp L, Cadule P, Cocco V, et al. Projected 21st century decrease in marine productivity: a multi-model analysis. Biogeosciences. 2010;7:979–1005. https://doi.org/10.5194/bg-7-979-2010CAS 
Article 

Google Scholar 
Henson SA, Cael BB, Allen SR, Dutkiewicz S. Future phytoplankton diversity in a changing climate. Nat Commun. 2021;12:5372. https://doi.org/10.1038/s41467-021-25699-wCAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Thomas MK, Kremer CT, Klausmeier CA, Litchman E. A global pattern of thermal adaptation in marine phytoplankton. Science. 2012;338:1085–8. https://doi.org/10.1126/science.1224836CAS 
Article 
PubMed 

Google Scholar 
Collins S, Boyd PW, Doblin MA. Evolution, microbes, and changing ocean conditions. Annu Rev Mar Sci. 2020;12:181–208. https://doi.org/10.1146/annurev-marine-010318-095311Article 

Google Scholar 
Schaum CE, Buckling A, Smirnoff N, Studholme DJ, Yvon-Durocher G. Environmental fluctuations accelerate molecular evolution of thermal tolerance in a marine diatom. Nat Commun. 2018;9:1719. https://doi.org/10.1038/s41467-018-03906-5CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lohbeck KT, Riebesell U, Reusch TBH. Adaptive evolution of a key phytoplankton species to ocean acidification. Nat Geosci. 2012;5:346–51. https://doi.org/10.1038/ngeo1441CAS 
Article 

Google Scholar 
Jin P, Gao K, Beardall J. Evolutionary responses of a coccolithophorid Gephyrocapsa oceanica to ocean acidification. Evolution. 2013;67:1869–78. https://doi.org/10.1111/evo.12112CAS 
Article 
PubMed 

Google Scholar 
Schlüter L, Lohbeck KT, Gutowska MA, Gröger JP, Riebesell U, Reusch TBH. Adaptation of a globally important coccolithophore to ocean warming and acidification. Nat Clim Change. 2014;4:1024–30. https://doi.org/10.1038/nclimate2379CAS 
Article 

Google Scholar 
Listmann L, LeRoch M, Schlüter L, Thomas MK, Reusch TBH. Swift thermal reaction norm evolution in a key marine phytoplankton species. Evol Appl. 2016;9:1156–64. https://doi.org/10.1111/eva.12362Article 
PubMed 
PubMed Central 

Google Scholar 
Zhong J, Guo Y, Liang Z, Huang Q, Lu H, Pan J, et al. Adaptation of a marine diatom to ocean acidification and warming reveals constraints and trade-offs. Sci Total Environ. 2021;771:145167. https://doi.org/10.1016/j.scitotenv.2021.145167CAS 
Article 
PubMed 

Google Scholar 
Brennan GL, Colegrave N, Collins S. Evolutionary consequences of multidriver environmental change in an aquatic primary producer. Proc Natl Acad Sci USA. 2017;114:9930–5. https://doi.org/10.1073/pnas.1703375114CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang S, Wu Y, Lin L, Wang D. Molecular insights into the circadian clock in marine diatoms. Acta Oceano Sin. 2022;41:1–12. https://doi.org/10.1007/s13131-021-1962-4Article 

Google Scholar 
Nagelkerken I, Connell SD. Global alteration of ocean ecosystem functioning due to increasing human CO2 emissions. Proc Natl Acad Sci USA. 2015;112:13272–7. https://doi.org/10.1073/pnas.1510856112CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Boyd PW, Collins S, Dupont S, Fabricius K, Gattuso JP, Havenhand J, et al. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change-a review. Glob Change Biol. 2018;24:2239–61. https://doi.org/10.1111/gcb.14102Article 

Google Scholar 
Matsuda Y, Nakajima K, Tachibana M. Recent progresses on the genetic basis of the regulation of CO2 acquisition systems in response to CO2 concentration. Photosynth Res. 2011;109:191–203. https://doi.org/10.1007/s11120-011-9623-7CAS 
Article 
PubMed 

Google Scholar 
Ohno N, Inoue T, Yamashiki R, Nakajima K, Kitahara Y, Ishibashi M, et al. CO2-cAMP-responsive cis-elements targeted by a transcription factor with CREB/ATF-like basic zipper domain in the marine diatom Phaeodactylum tricornutum. Plant Physiol. 2012;158:499–513. https://doi.org/10.1104/pp.111.190249CAS 
Article 
PubMed 

Google Scholar 
Hennon GMM, Ashworth J, Groussman RD, Berthiaume C, Morales RL, Baliga NS, et al. Diatom acclimation to elevated CO2 via cAMP signalling and coordinated gene expression. Nat Clim Change. 2015;5:761–5. https://doi.org/10.1038/nclimate2683CAS 
Article 

Google Scholar 
Toseland A, Daines SJ, Clark JR, Kirkham A, Strauss J, Uhlig C, et al. The impact of temperature on marine phytoplankton resource allocation and metabolism. Nat Clim Change. 2013;3:979–84. https://doi.org/10.1038/nclimate1989CAS 
Article 

Google Scholar 
Gao K, Beardall J, Häder DP, Hall-Spencer JM, Gao G, Hutchins DA. Effects of ocean acidification on marine photosynthetic organisms under the concurrent influences of warming, UV radiation, and deoxygenation. Front Mar Sci. 2019;6:322. https://doi.org/10.3389/fmars.2019.00322Article 

Google Scholar 
Tu L, Su P, Zhang Z, Gao L, Wang J, Hu T, et al. Genome of Tripterygium wilfordii and identification of cytochrome P450 involved in triptolide biosynthesis. Nat Commun. 2020;11:971. https://doi.org/10.1038/s41467-020-14776-1CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Treves H, Siemiatkowska B, Luzarowska U, Murik O, Fernandez-Pozo N, Moraes TA, et al. Multi-omics reveals mechanisms of total resistance to extreme illumination of a desert alga. Nat Plants. 2020;6:1031–43. https://doi.org/10.1038/s41477-020-0729-9CAS 
Article 
PubMed 

Google Scholar 
Van den Bergh B, Swings T, Fauvart M, Michels J. Experimental design, population dynamics, and diversity in microbial experimental evolution. Microbiol Mol Biol Rev. 2018;82:e00008–18.PubMed 
PubMed Central 

Google Scholar 
Elena SF, Lenski RE. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat Rev Genet. 2003;4:457–69. https://doi.org/10.1038/nrg1088CAS 
Article 
PubMed 

Google Scholar 
Colegrave N, Collins S. Experimental evolution: experimental evolution and evolvability. Heredity. 2008;100:464–70. https://doi.org/10.1038/sj.hdy.6801095CAS 
Article 
PubMed 

Google Scholar 
Jin P, Ji Y, Huang Q, Li P, Pan J, Lu H, et al. A reduction in metabolism explains the trade‐offs associated with the long‐term adaptation of phytoplankton to high CO2 concentrations. N Phytol. 2022;233:2155–67. https://doi.org/10.1111/nph.17917CAS 
Article 

Google Scholar 
Flombaum P, Gallegos JL, Gordillo RA, Rincón J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci USA. 2013;110:9824–9. https://doi.org/10.1073/pnas.1307701110CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hutchins DA, Walworth NG, Webb EA, Saito MA, Moran D, Mcllvin MR, et al. Irreversibly increased nitrogen fixation in Trichodesmium experimentally adapted to elevated carbon dioxide. Nat Commun. 2015;6:8155. https://doi.org/10.1038/ncomms9155Article 
PubMed 

Google Scholar 
Padfield D, Yvon-Durocher G, Buckling A, Jennings S, Yvon-Durocher G. Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecol Lett. 2016;19:133–42.Article 

Google Scholar 
Coles VJ, Stukel MR, Brooks MT, Burd A, Crump BC, Moran MA, et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science. 2017;358:1149–54. https://doi.org/10.1126/science.aan5712CAS 
Article 
PubMed 

Google Scholar 
Linnen CR, Kingsley EP, Jensen JD, Hoekstra HE. On the origin and spread of an adaptive allele in deer mice. Science. 2009;325:1095–8. https://doi.org/10.1126/science.1175826CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Van’t Hof AE, Campagne P, Rigden DJ, Yung CJ, Lingley J, Quail MA, et al. The industrial melanism mutation in British peppered moths is a transposable element. Nature. 2016;534:102–5. https://doi.org/10.1038/nature17951CAS 
Article 
PubMed 

Google Scholar 
Bitter MC, Kapsenberg L, Gattuso JP, Pfister CA. Standing genetic variation fuels rapid adaptation to ocean acidification. Nat Commun. 2019;10:5821. https://doi.org/10.1038/s41467-019-13767-1CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lai YT, Yeung CK, Omland KE, Pang EL, Hao Y, Liao BY, et al. Standing genetic variation as the predominant source for adaptation of a songbird. Proc Natl Acad Sci USA. 2019;116:2152–7. https://doi.org/10.1073/pnas.1813597116Armbrust EV. The life of diatoms in the world’s oceans. Nature. 2009;459:185–92. https://doi.org/10.1038/nature08057CAS 
Article 
PubMed 

Google Scholar 
Rastogi A, Vieira FRJ, Deton-Cabanillas AF, Veluchamy A, Cantrel C, Wang G, et al. A genomics approach reveals the global genetic polymorphism, structure, and functional diversity of ten accessions of the marine model diatom Phaeodactylum tricornutum. ISME J. 2020;14:347–63. https://doi.org/10.1038/s41396-019-0528-3Article 
PubMed 

Google Scholar 
Jin P, Agustí S. Fast adaptation of tropical diatoms to increased warming with trade-offs. Sci Rep. 2018;8:17771. https://doi.org/10.1038/s41598-018-36091-yCAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Barton S, Jenkins J, Buckling A, Schaum CE, Smirnoff N, Raven JA, et al. Evolutionary temperature compensation of carbon fixation in marine phytoplankton. Ecol Lett. 2020;23:722–33.Article 

Google Scholar 
Guillard RR, Ryther JH. Studies of marine planktonic diatoms: I. Cyclotella nana Hustedt, and Detonula confervacea (Cleve) Gran. Can J Microbiol. 1962;8:229–39. https://doi.org/10.1139/m62-029CAS 
Article 
PubMed 

Google Scholar 
Huysman MJ, Martens C, Vandepoele K, Gillard J, Rayko E, Heijde M, et al. Genome-wide analysis of the diatom cell cycle unveils a novel type of cyclins involved in environmental signaling. Genome Biol. 2010;11:R17. https://doi.org/10.1186/gb-2010-11-2-r17CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
IPCC. Summary for policymakers. In: Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, et al. editors. Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Switzerland: IPCC; 2021.Jiang H, Gao K. Effects of lowering temperature during culture on the production of polyunsaturated fatty acids in the marine diatom Phaeodactylum tricornutum (Bacillariophyceae). J Phycol. 2004;40:651–4. https://doi.org/10.1111/j.1529-8817.2004.03112.xCAS 
Article 

Google Scholar 
Pérez EB, Pina IC, Rodríguez LP. Kinetic model for growth of Phaeodactylum tricornutum in intensive culture photobioreactor. Biochem Eng J. 2008;40:520–5. https://doi.org/10.1016/j.bej.2008.02.007CAS 
Article 

Google Scholar 
Boyd PW, Rynearson TA, Armstrong EA, Fu F, Hayashi K, Hu Z, et al. Marine phytoplankton temperature versus growth responses from polar to tropical waters-outcome of a scientific community-wide study. PLoS One. 2013;8:e63091 https://doi.org/10.1371/journal.pone.0063091CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zeng X, Jin P, Jiang Y, Yang H, Zhong J, Liang Z, et al. Light alters the responses of two marine diatoms to increased warming. Mar Environ Res. 2020;154:104871. https://doi.org/10.1016/j.marenvres.2019.104871CAS 
Article 
PubMed 

Google Scholar 
Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–i890. https://doi.org/10.1093/bioinformatics/bty560CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bowler C, Allen AE, Badger JH, Grimwood J, Jabbari K, Kuo A, et al. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature. 2008;456:239–44.CAS 
Article 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60. https://doi.org/10.1093/bioinformatics/btp324CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164. https://doi.org/10.1093/nar/gkq603CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9. https://doi.org/10.1038/nmeth.1923CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12:357–60. https://doi.org/10.1038/nmeth.3317CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33:290–5. https://doi.org/10.1038/nbt.3122CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 2016;11:1650–67. https://doi.org/10.1038/nprot.2016.095CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. https://doi.org/10.1186/s13059-014-0550-8CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gifford RM. Plant respiration in productivity models: conceptualisation, representation and issues for global terrestrial carbon-cycle research. Funct Plant Biol. 2003;30:171–86. https://doi.org/10.1071/FP02083Article 
PubMed 

Google Scholar 
Jassby AD, Platt T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol Oceanogr. 1976;21:540–7. https://doi.org/10.4319/lo.1976.21.4.0540CAS 
Article 

Google Scholar  More

Short-term experimentsPotential toxic and cryoprotection effects of different CPA combinationsFocusing on toxicity bioassays (Figs. 1A, 2A), although there were certain CPA combinations that yielded significant abnormality percentages compared to controls, in general the CPA combinations did not yield any significant toxic effect. The use of Milli-Q Water instead of FSW did not enhance normal larval development after CPA exposure, neither did the addition of PVP at the concentrations tested, even in combination with trehalose (TRE) (p  > 0.05). In fact, the highest concentrations of PVP used in this experiment (9 and 12%) yielded significant abnormal development on exposed trochophores (Fig. 1A) (p  More

German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig, Leipzig, GermanyStephanie D. Jurburg, Nico Eisenhauer, François Buscot, Antonis Chatzinotas, Narendrakumar M. Chaudhari, Anna Heintz-Buschart, Kirsten Küsel & Rine C. ReubenInstitute of Biology, Leipzig University, Leipzig, GermanyStephanie D. Jurburg, Nico Eisenhauer, Antonis Chatzinotas & Rine C. ReubenDepartment of Environmental Microbiology, Helmholtz Centre for Environmental Research–UFZ, Leipzig, GermanyStephanie D. Jurburg, Antonis Chatzinotas, Rene Kallies, Susann Müller & Ulisses Nunes da RochaDepartment of Soil Ecology, Helmholtz Centre for Environmental Research–UFZ, Halle, GermanyFrançois Buscot & Anna Heintz-BuschartAquatic Geomicrobiology, Institute of Biodiversity, Friedrich Schiller University, Jena, GermanyNarendrakumar M. Chaudhari & Kirsten KüselSwammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the NetherlandsAnna Heintz-BuschartKellogg Biological Station, Michigan State University, Hickory Corners, MI, USAElena LitchmanEcology, Evolution and Behavior Program, Michigan State University, East Lansing, MI, USAElena LitchmanDepartment of Global Ecology, Carnegie Institution for Science, Stanford, CA, USAElena LitchmanHawkesbury Institute for the Environment, Western Sydney University, Richmond, New South Wales, AustraliaCatriona A. Macdonald & Brajesh K. SinghLeibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Jena, GermanyGianni PanagiotouThe State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Kowloon, Hong Kong SAR, ChinaGianni PanagiotouDepartment of Medicine, The University of Hong Kong, Kowloon, Hong Kong SAR, ChinaGianni PanagiotouInstitut für Biologie, Freie Universität Berlin, Berlin, GermanyMatthias C. RilligBerlin-Brandenburg Institute of Advanced Biodiversity Research, Berlin, GermanyMatthias C. RilligGlobal Centre for Land-Based Innovation, Western Sydney University, Penrith, New South Wales, AustraliaBrajesh K. SinghB.K.S. conceived the idea in consultation with N.E. and S.J., and led the discussion which was attended by all authors. S.J. and B.K.S. wrote the manuscript and all contributed to refine it. More