Ecology

Johannesson, K., Smolarz, K., Grahn, M. & André, C. The future of baltic sea populations: Local extinction or evolutionary rescue?. Ambio 40, 179–190 (2011).Article 
CAS 

Google Scholar 
Reusch, T. B. H. et al. The Baltic Sea as a time machine for the future coastal ocean. Sci. Adv. 4, eaar8195 (2018).Article 
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Google Scholar 
Kahru, M. & Elmgren, R. Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea. Biogeosciences 11, 3619–3633 (2014).Article 
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Google Scholar 
Kahru, M., Elmgren, R. & Savchuk, O. P. Changing seasonality of the Baltic Sea. Biogeosciences 13, 1009–1018 (2016).Article 
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Google Scholar 
Hjerne, O., Hajdu, S., Larsson, U., Downing, A. S. & Winder, M. Climate driven changes in timing, composition and magnitude of the Baltic Sea phytoplankton spring bloom. Front. Mar. Sci. 6, 482 (2019).Article 

Google Scholar 
Bianchi, T. S. et al. Cyanobacterial blooms in the Baltic Sea: Natural or human-induced?. Limnol. Oceanogr. 45, 716–726 (2000).Article 
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CAS 

Google Scholar 
Poutanen, E.-L. & Nikkilä, K. Carotenoid pigments as tracers of cyanobacterial blooms in recent and post-glacial sediments of the Baltic Sea. Ambio 30, 179–183 (2001).Article 
CAS 

Google Scholar 
Andersson, A., Höglander, H., Karlsson, C. & Huseby, S. Key role of phosphorus and nitrogen in regulating cyanobacterial community composition in the northern Baltic Sea. Estuar. Coast. Shelf Sci. 164, 161–171 (2015).Article 
CAS 

Google Scholar 
Olofsson, M., Suikkanen, S., Kobos, J., Wasmund, N. & Karlson, B. Basin-specific changes in filamentous cyanobacteria community composition across four decades in the Baltic Sea. Harmful Algae 91, 101685 (2020).Article 
CAS 

Google Scholar 
Rolff, C. & Elfwing, T. Increasing nitrogen limitation in the Bothnian Sea, potentially caused by inflow of phosphate-rich water from the Baltic Proper. Ambio 44, 601–611 (2015).Article 
CAS 

Google Scholar 
Eriksson Wiklund, A.-K., Dahlgren, K., Sundelin, B. & Andersson, A. Effects of warming and shifts of pelagic food web structure on benthic productivity in a coastal marine system. Mar. Ecol. Prog. Ser. 396, 13–25 (2009).Article 
ADS 

Google Scholar 
Wikner, J. & Andersson, A. Increased freshwater discharge shifts the trophic balance in the coastal zone of the northern Baltic Sea. Glob. Change Biol. 18, 2509–2519 (2012).Article 
ADS 

Google Scholar 
Gulati, R. D. & Demott, W. R. The role of food quality for zooplankton: remarks on the state-of-the-art, perspectives and priorities. Freshw. Biol. 38, 16 (1997).Article 

Google Scholar 
Martin-Creuzburg, D., von Elert, E. & Hoffmann, K. H. Nutritional constraints at the cyanobacteria- Daphnia magna interface: The role of sterols. Limnol. Oceanogr. 53, 456–468 (2008).Article 
ADS 

Google Scholar 
Hedberg, P., Albert, S., Nascimento, F. J. A. & Winder, M. Effects of changing phytoplankton species composition on carbon and nitrogen uptake in benthic invertebrates. Limnol. Oceanogr. 66, 469–480 (2021).Article 
ADS 
CAS 

Google Scholar 
Gorokhova, E. Toxic cyanobacteria Nodularia spumigena in the diet of Baltic mysids: Evidence from molecular diet analysis. Harmful Algae 8, 264–272 (2009).Article 
CAS 

Google Scholar 
Karlson, A. M. L., Gorokhova, E. & Elmgren, R. Nitrogen fixed by cyanobacteria is utilized by deposit-feeders. PLoS ONE 9, e104460 (2014).Article 
ADS 

Google Scholar 
Karlson, A. M. L. et al. Nitrogen fixation by cyanobacteria stimulates production in Baltic food webs. Ambio 44, 413–426 (2015).Article 
CAS 

Google Scholar 
Lesutienė, J., Bukaveckas, P. A., Gasiūnaitė, Z. R., Pilkaitytė, R. & Razinkovas-Baziukas, A. Tracing the isotopic signal of a cyanobacteria bloom through the food web of a Baltic Sea coastal lagoon. Estuar. Coast. Shelf Sci. 138, 47–56 (2014).Article 
ADS 

Google Scholar 
Rolff, C. Seasonal variation in d13C and d15N of size-fractionated plankton at a coastal station in the northern Baltic proper. Mar. Ecol. Prog. Ser. 203, 47–65 (2000).Article 
ADS 
CAS 

Google Scholar 
Koski, M., Engström, J. & Viitasalo, M. Reproduction and survival of the calanoid copepod Eurytemora affinis fed with toxic and non-toxic cyanobacteria. Mar. Ecol. Prog. Ser. 186, 187–197 (1999).Article 
ADS 

Google Scholar 
Koski, M. et al. Calanoid copepods feed and produce eggs in the presence of toxic cyanobacteria Nodularia spumigena. Limnol. Oceanogr. 47, 878–885 (2002).Article 
ADS 

Google Scholar 
Schmidt, K. & Jónasdóttir, S. Nutritional quality of two cyanobacteria: How rich is ‘poor’ food?. Mar. Ecol. Prog. Ser. 151, 1–10 (1997).Article 
ADS 

Google Scholar 
Kankaanpää, H., Vuorinen, P. J., Sipiä, V. & Keinänen, M. Acute effects and bioaccumulation of nodularin in sea trout (Salmo trutta m. trutta L.) exposed orally to Nodularia spumigena under laboratory conditions. Aquat. Toxicol. 61, 155–168 (2002).Article 

Google Scholar 
Persson, K.-J., Bergström, K., Mazur-Marzec, H. & Legrand, C. Differential tolerance to cyanobacterial exposure between geographically distinct populations of Perca fluviatilis. Toxicon 76, 178–186 (2013).Article 
CAS 

Google Scholar 
Monserrat, J. M., Yunes, J. O. S. & Bianchini, A. Effects of Anabaena Spiroides (cyanobacteria) aqueous extracts on the acetylcholinesteraseactivity of aquatic species. Environ. Toxicol. Chem. 20, 1228–1235 (2001).Article 
CAS 

Google Scholar 
Lehtonen, K. K. et al. Accumulation of nodularin-like compounds from the cyanobacterium Nodularia spumigena and changes in acetylcholinesterase activity in the clam Macoma balthica during short-term laboratory exposure. Aquat. Toxicol. 64, 461–476 (2003).Article 
CAS 

Google Scholar 
Fulton, M. H. & Key, P. B. Acetylcholinesterase inhibition in esturai fish and invertebrates as an indicator of organophoshorus insecticide exposure and effects. Environ. Toxicol. Chem. 20, 37–45 (2001).Article 
CAS 

Google Scholar 
DeMott, W. R., Zhang, Q.-X. & Carmichael, W. W. Effects of toxic cyanobacteria and purified toxins on the survival and feeding of a copepod and three species of Daphnia. Limnol. Oceanogr. 36, 1346–1357 (1991).Article 
ADS 
CAS 

Google Scholar 
Hogfors, H. et al. Bloom-forming cyanobacteria support copepod reproduction and development in the Baltic Sea. PLoS ONE 9, e112692 (2014).Article 
ADS 

Google Scholar 
Motwani, N. H., Duberg, J., Svedén, J. B. & Gorokhova, E. Grazing on cyanobacteria and transfer of diazotrophic nitrogen to zooplankton in the Baltic Sea: Cyanobacteria blooms support zooplankton growth. Limnol. Oceanogr. 63, 672–686 (2018).Article 
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Google Scholar 
Gorokhova, E., El-Shehawy, R., Lehtiniemi, M. & Garbaras, A. How copepods can eat toxins without getting sick: Gut bacteria help zooplankton to feed in cyanobacteria blooms. Front. Microbiol. 11, 589816 (2021).Article 

Google Scholar 
Elmgren, R. Structure and dynamics of Baltic benthos communities, with particular reference to the relationship between macro- and meiofauna. Kieler Meeresforsch. Sonderh. 4, 1–22 (1978).
Google Scholar 
Laine, A. O. Distribution of soft-bottom macrofauna in the deep open Baltic Sea in relation to environmental variability. Estuar. Coast. Shelf Sci. 57, 87–97 (2003).Article 
ADS 
CAS 

Google Scholar 
Hill, C., Quigley, M. A., Cavaletto, J. F. & Gordon, W. Seasonal changes in lipid content and composition in the benthic amphipods Monoporeia afinis and Pontoporeia femorata. Limnol. Oceanogr. 37, 1280–1289 (1992).Article 
ADS 
CAS 

Google Scholar 
Lehtonen, K. K. Ecophysiology of the benthic amphipod Monoporeia affinis in an open-sea area of the northern Baltic Sea: Seasonal variations in body composition, with bioenergetic considerations. Mar. Ecol. Prog. Ser. 143, 87–98 (1996).Article 
ADS 

Google Scholar 
Karlson, A. M. L., Nascimento, F. J. A. & Elmgren, R. Incorporation and burial of carbon from settling cyanobacterial blooms by deposit-feeding macrofauna. Limnol. Oceanogr. 53, 2754–2758 (2008).Article 
ADS 

Google Scholar 
Karlson, A. M. L. & Mozūraitis, R. Deposit-feeders accumulate the cyanobacterial toxin nodularin. Harmful Algae 12, 77–81 (2011).Article 
CAS 

Google Scholar 
Savage, C. Tracing the influence of sewage nitrogen in a coastal ecosystem using stable nitrogen isotopes. Ambio 34, 145–150 (2005).Article 

Google Scholar 
Newsome, S. D., Del Rio, C. M., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5, 429–436 (2007).Article 

Google Scholar 
Layman, C. A., Arrington, D. A., Montaña, C. G. & Post, D. M. Can stable isotope ratio provide for community-wide mesures of trophic structure?. Ecology 88, 42–48 (2007).Article 

Google Scholar 
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable isotope Bayesian ellipses in R: Bayesian isotopic niche metrics. J. Anim. Ecol. 80, 595–602 (2011).Article 

Google Scholar 
Blomqvist, S. & Lundgren, L. A benthic sled for sampling soft bottoms. Helgol. Meeresunters. 50, 453–456 (1996).Article 

Google Scholar 
Karlson, A. M. L., Nascimento, F. J. A., Näslund, J. & Elmgren, R. Higher diversity of deposit-feeding macrofauna enhances phytodetritus processing. Ecology 91, 1414–1423 (2010).Article 

Google Scholar 
Mazur-Marzec, H., Tymińska, A., Szafranek, J. & Pliński, M. Accumulation of nodularin in sediments, mussels, and fish from the Gulf of Gdańsk, southern Baltic Sea. Environ. Toxicol. 22, 101–111 (2007).Article 
ADS 
CAS 

Google Scholar 
van de Bund, W., Ólafsson, E., Modig, H. & Elmgren, R. Effects of the coexisting Baltic amphipods Monoporeia affinis and Pontoporeia femorata on the fate of a simulated spring diatom bloom. Mar. Ecol. Prog. Ser. 212, 107–115 (2001).Article 
ADS 

Google Scholar 
Larsson, U., Hobro, R. & Wulff, F. Dynamics of a Phytoplankton Spring Bloom in a Coastal Area of the Northern Baltic Proper (University of Stockholm, 1986).
Google Scholar 
Heiskanen, A.-S. Factors Governing Sedimentation and Pelagic Nutrient Cycles in the Northern Baltic Sea: = Sedimentaatioon ja Ravinteiden Kiertoon Vaikuttavat Tekijät Pohjoisen Ltämeren Ulapaekosysteemissä (Finnish Environment Institute, 1998).
Google Scholar 
Nadon, M.-O. & Himmelman, J. H. Stable isotopes in subtidal food webs: Have enriched carbon ratios in benthic consumers been misinterpreted?. Limnol. Oceanogr. 51, 2828–2836 (2006).Article 
ADS 
CAS 

Google Scholar 
Gorokhova, E. Shifts in rotifer life history in response to stable isotope enrichment: Testing theories of isotope effects on organismal growth. Methods Ecol. Evol. 9, 269–277 (2017).Article 

Google Scholar 
Karlson, A. M. L., Reutgard, M., Garbaras, A. & Gorokhova, E. Isotopic niche reflects stress-induced variability in physiological status. R. Soc. Open Sci. 5, 171398 (2018).Article 
ADS 

Google Scholar 
del Rio, C. M., Wolf, N., Carleton, S. A. & Gannes, L. Z. Isotopic ecology 10 years after a call for more laboratory experiments. Biol. Rev. 84, 91–111 (2009).Article 

Google Scholar 
Ledesma, M., Gorokhova, E., Holmstrand, H., Garbaras, A. & Karlson, A. M. L. Nitrogen isotope composition of amino acids reveals trophic partitioning in two sympatric amphipods. Ecol. Evol. 10, 10773–10784 (2020).Article 

Google Scholar 
Bocquené, G. & Galgani, F. Biological Effects of Contaminants: Cholinesterase Inhibitation by Organophosphate and Carbamate Compounds (ICES Techniques in Marine Environmental Science (TIMES). Report., 1998). https://doi.org/10.17895/ices.pub.5048.
Book 

Google Scholar 
Ellman, G. L., Courtney, K. D., Andres, V. & Featherstone, R. M. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem. Pharmacol. 7, 88–95 (1961).Article 
CAS 

Google Scholar 
Jarek, S. mvnormtest: Normality test for multivariate variables. (2012). R package version 0.1-9. https://CRAN.R-project.org/package=mvnormtestR Core Team. R: A Language and Environment for Statistical Computing. (2021).Nascimento, F. J. A., Karlson, A. M. L., Näslund, J. & Gorokhova, E. Settling cyanobacterial blooms do not improve growth conditions for soft bottom meiofauna. J. Exp. Mar. Biol. Ecol. 368, 138–146 (2009).Article 

Google Scholar 
Roche-Mayzaud, O., Mayzaud, P. & Biggs, D. Medium-term acclimation of feeding and of digestive and metabolic enzyme activity in the neritic copepod Acartia clause. I. Evidence from laboratory experiments. Mar. Ecol. Prog. Ser. 69, 25–40 (1991).Article 
ADS 
CAS 

Google Scholar 
Stuart, V., Head, E. J. H. & Mann, K. H. Seasonal changes in the digestive enzyme levels of the amphipod Corophium volutator (Pallas) in relation to diet. J. Exp. Mar. Biol. Ecol. 88, 243–256 (1985).Article 
CAS 

Google Scholar 
Schwarzenberger, A., Ilić, M. & Von Elert, E. Daphnia populations are similar but not identical in tolerance to different protease inhibitors. Harmful Algae 106, 102062 (2021).Article 
CAS 

Google Scholar 
Schwarzenberger, A. & Fink, P. Gene expression and activity of digestive enzymes of Daphnia pulex in response to food quality differences. Comp. Biochem. Physiol. B 218, 23–29 (2018).Article 
CAS 

Google Scholar 
Sipiä, V. O. et al. Bioaccumulation and detoxication of nodularin in tissues of flounder (Platichthys flesus), mussels (Mytilus edulis, Dreissena polymorpha), and clams (Macoma balthica) from the Northern Baltic Sea. Ecotoxicol. Environ. Saf. 53, 305–311 (2002).Article 

Google Scholar 
Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).Article 
MathSciNet 

Google Scholar 
MacArthur, R. H. & Pianka, E. R. On optimal use of a patchy environment. Am. Nat. 100, 603–609 (1966).Article 

Google Scholar 
Wiklund, A.-K.E., Sundelin, B. & Rosa, R. Population decline of amphipod Monoporeia affinis in Northern Europe: Consequence of food shortage and competition?. J. Exp. Mar. Biol. Ecol. 367, 81–90 (2008).Article 

Google Scholar 
Leonardsson, K., Sörlin, T., Samberg, H. & Sorlin, T. Does Pontoporeia affinis (Amphipoda) optimize age at reproduction in the Gulf of Bothnia?. Oikos 52, 328 (1988).Article 

Google Scholar 
Eriksson Wiklund, A.-K. & Andersson, A. Benthic competition and population dynamics of Monoporeia affinis and Marenzelleria sp. in the northern Baltic Sea. Estuar. Coast. Shelf Sci. 144, 46–53 (2014).Article 
ADS 

Google Scholar 
Karlson, A. M. L. et al. Linking consumer physiological status to food-web structure and prey food value in the Baltic Sea. Ambio 49, 391–406 (2020).Article 
CAS 

Google Scholar 
Olofsson, M. Nitrogen fixation estimates for the Baltic Sea indicate high rates for the previously overlooked Bothnian Sea. Ambio https://doi.org/10.1007/s13280-020-01331-x (2021).Article 

Google Scholar  More

Aerial imagesWe use publicly available aerial images of Rwanda at 0.25 × 0.25 m2 resolution, collected in June–August of 2008 and 2009. The images were acquired from 3,000 m altitude above ground level, originally with a mean ground resolution of 0.22 × 0.22 m2 pixel size then resampled to 0.25 × 0.25 m2, using a Vexcel UltraCam-X aerial digital photography camera34. The images exhibit a red, green and blue band stored under 8 bit unsigned integer format. The aerial images cover 96% of the country and the remaining 4% was filled with satellite images from WorldView-2, Ikonos, Spot and QuickBird satellite sensors which are part of the publicly available dataset.Environmental dataWe use locally available climate data: mean annual rainfall, mean annual temperature and elevation data (10 × 10 m2 resolution) to assess relationships between tree density, crown cover and environmental gradients. We also use land cover data to extract the spatial extent of plantations, forest, farmland, and urban and built-up areas for our landscape stratification. Climate data were obtained from the Rwanda Meteorological Agency as daily records from 1971 to 2017. The national forest map was manually created in 2012 using on-screen digitizing techniques over the 2008 aerial images35. A forest was defined as ‘a group of trees higher than 7 m and a tree cover of more than 10% or trees able to reach these thresholds in situ on a land of about 0.25 ha or more’51. A shrub was defined as ‘a group of perennial trees smaller than 7 m at maturity and a canopy cover of more than 10% on a land of about 0.25 ha or more’. The forest dataset was composed of 105,690 forest polygons, classified as either natural forest (closed natural forest, degraded natural forest, bamboo stand, wooded savanna and shrubland) or ‘forest plantations’ (Eucalyptus spp., eucalyptus; Pinus spp., pine; Callitris spp., callitris; Cupressus spp., cypress; Acacia mearnsii, black wattle; Acacia melanoxylon, melanoxylon; Grevillea robusta, grevillea; Maesopsis eminii, maesopsis; Alnus acuminata, alnus; Jacaranda mimosifolia, jacaranda; mixed species, mixed; and others) (Extended Data Fig. 7i). We separate shrubland from natural forest and merged it with savanna into the class ‘savannas and shrublands’. We further separated tree plantations and grouped them into Eucalyptus and non-Eucalyptus plantations. Then, a farmland map was acquired from the Rwanda Land Management and Use Authority (RLMUA)52 and overlaid with the 2012 forest cover map as a reference to clean the overlapping parts, under an assumption that the overlap is due to land use dynamics. Finally, a layer marking urban and built-up areas was acquired from RLMUA as well and the same preprocessing step as done for farmlands was applied. The combination of the land cover datasets resulted in our stratification scheme with six classes: natural forests, savannas and shrublands, Eucalyptus plantations, non-Eucalyptus plantations, farmland and urban and built-up.Mapping of individual trees using deep learningWe used the open-source framework developed by ref. 17 to map individual tree crowns. The framework uses a deep neural network based on the U-Net architecture53,54. We trained the network using 97,574 manually delineated tree crowns spread over 103 areas/bounding boxes representing the full range of biogeographical conditions found across Rwanda. To cope with the challenge of separating touching tree crowns, we used a higher weight for boundary areas between crowns, as suggested in refs. 17,53. Crown sizes in the predictions were found to be 27% smaller as compared to the manual delineations within the 103 training areas, due to the applied boundary weight that emphasizes gaps between tree crowns. Therefore, to calculate the real canopy cover, we extended each predicted tree crown by 27% and dissolved the touching crowns into continuous features. We counted single tree crowns for each hectare presented here as tree density and the percentage of each hectare covered by the extended tree crowns as canopy cover.We developed a postprocessing method that separates clumped tree crowns and fills any gap inside a single crown (Extended Data Fig. 2). Our postprocessing method, which we refer to as detect centre and relabel (DCR), determines the crown centres in the model predictions assuming that tree crowns have a round shape and then relabels the model predictions on the basis of weighted distances to the identified crown centres. First, DCR performs a distance transform, computing for each pixel the Euclidean distance to the nearest pixel predicted as background. Let the transformed image be distance-transformed (DT). Then an m × m maximum filter is applied to DT, where m depends on the size of the smallest object to be separated. We store all pixels for which the original DT value is the same before and after max-filtering. These pixels are the instance centres as they are furthest away from the boundary and have the highest distance values within the area defined by m. In the case of several connected instance centres in regions where multiple connected pixels have the same distance from the background, only a single instance centre is kept. Finally, each pixel x predicted as a crown in the original image is assigned to its nearest instance centre, where the distance function penalizes background pixels on the connecting line between the instance centre and x.Allometry for biomass and carbon stock estimationGenerally, allometric equations define a statistical relationship between structural properties of a tree and its biomass55,56. In our case, we assume a relationship between the crown area and aboveground biomass (AGB), which varies between biomes36. Since destructive AGB measurements are rare, we established biome-specific relationships between crown diameter (CD) derived from the crown area (CD = 2√(crown area/π)) and stem diameter at breast height (DBH) (equations (3) and (6)). DBH has been shown to be highly correlated with AGB36,37,38,39,40. We then used established relationships from literature to derive AGB from DBH for savannas and shrublands (equation (4)), tree plantations (equation (5)) and natural forests (equation (7)). AGB was predicted for each tree and summed for 1 ha grids to derive AGB in the unit Mg per ha. Values were multiplied by 0.47 (refs. 57,58) to derive aboveground carbon (AGC). Summed numbers over land cover classes are considered as carbon stocks. The bias as reported here was calculated following the approach from ref. 36 reporting the relative systematic error in per cent:$$mathrm {bias} = frac{1}{N}mathop {sum}limits_{i = 1}^N {frac{{(Y_{mathrm {obs}} – Y_{mathrm {pred}})}}{{Y_{mathrm {obs}}}}}times 100$$
(1)
The error for the evaluation with NFI data was defined by:$$mathrm{bias} = frac{{left| {mathop {sum}nolimits_N {(Y_{mathrm{obs}} – Y_{mathrm{pred}})} } right|}}{{left| {mathop {sum}nolimits_N {Y_{mathrm{obs}}} } right|}}$$
(2)
For trees outside natural forests, we used the database from ref. 36 including 10,591 field-measured trees from woodlands and savanna plus 952 samples from agroforestry landscapes in Kenya37 to establish a linear relationship between CD and DBH (Extended Data Fig. 3a). The Kenyan dataset is compatible with the trees in Rwanda. To ensure compatibility, the Kenya data contained open-grown trees most of which are of the same families or genus as in Rwanda grown under the same conditions, the latter factor shown to be important for generalizing37.A major axis regression (average of four runs each 50% of the data) led to equation (3):$${{{mathrm{DBH}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{cm}}}} = – 4.665 + 5.102 times {{{mathrm{CD}}}}$$
(3)
Equation (3) showed a reasonable performance with a very low bias (average of four runs on the 50% not used to establish the equation (3)): r² = 0.71; slope = 0.95; root mean square error (RMSE) = 6.2 cm; relative RMSE (rRMSE) = 42%; bias = 1%). We tested equation (3) on an independent dataset from Kenya consisting of 93 trees where AGB was destructively measured (Fig. 3b). The Kenyan database provides an uncommon opportunity to use destructive samples in which the carbon mass is not estimated indirectly and the relationship between crown area and carbon is direct: we do not need to invoke a second allometry to derive the dependent variable. All trees were open-grown trees in the same growing conditions as the agricultural areas of Rwanda. On these 93 trees, DBH can be predicted reasonably well from CD using equation (3) (r² = 0.84; slope = 0.86; RMSE = 8 cm; rRMSE = 25%; bias = 6%). We then applied an allometric equation from literature37 established for non-forest trees in East Africa to estimate AGB from DBHpredicted and compared the predicted AGB with the destructively measured AGB (r² = 0.81; RMSE = 511 kg; rRMSE = 55%; bias = 25%) showing an acceptable performance (Extended Data Fig. 3c) but indicating a systematic bias, which will be further tested with biome-specific field data (next section). We apply equation (4) to estimate AGB for trees outside forests in Rwanda in savannas and shrublands:$${{{mathrm{AGB}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{kg}}}} = 0.091 times {{mathrm{DBH}}_{{mathrm{predicted}}}}^{2.472}$$
(4)
Given the different structure of trees in farmlands, urban and built-up areas and plantations as compared to trees in natural forests and in natural non-forest areas, we used a different equation for trees in these areas. It was established in Rwanda using destructive samples from tree plantations39:$${{{mathrm{AGB}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{kg}}}} = 0.202 times {{mathrm{DBH}}_{{mathrm{predicted}}}}^{2.447}$$
(5)
A different CD–DBH relationship was established for natural forests. Here, we conducted a field campaign in December 2021 sampling 793 overstory trees in Rwanda’s protected natural forest. We measured both CD and DBH and established a logarithmic major axis regression model with a Baskerville correction59 between the two variables to predict DBH from CD (Extended Data Fig. 3d). We did four runs each using 50% of the data to establish equation (6) (average of the four runs) and the other 50% to test the performance also averaged over the four runs (r² = 0.71; slope = 0.99; RMSE = 13 cm; rRMSE = 45%; bias = 19%). Note that CD is extended by 27% to account for underestimations of touching crowns in dense forests (see previous section):$$begin{array}{l}{mathrm{DBH}}_{{mathrm{predicted}}},{mathrm{in}},{mathrm{cm}} = left({mathrm{exp}}left(1.154 + 1.248 times {mathrm{ln}}({mathrm{CD}} times 1.27) right)right.\left. times left({mathrm{exp}}(0.3315^2/2) right) right)end{array}$$
(6)
We then used a state-of-the-art allometric equation established for tropical forests38 to predict AGB from DBH for natural forests in Rwanda:$$begin{array}{l}{{{mathrm{AGB}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{kg}}}} = {{{mathrm{exp}}}}Big[ {1.803 – 0.976{{{E}}} + 0.976,{{{mathrm{ln}}}}left( rho right)}\+ 2.673;{{{mathrm{ln}}}}left( {{{{mathrm{DBH}}}}} right) – 0.0299left[ {{{{mathrm{ln}}}}left( {{{mathrm{DBH}}}} right)} right]^2 Big]end{array}$$
(7)
where E measures the environmental stress38 (a gridded layer is accessible via https://chave.ups-tlse.fr/pantropical_allometry.htm) and ρ is the wood density. Here, we used a fixed number (0.54), which is the average wood density for 6,161 trees from ref. 40, weighted according to the abundance of the species in the plots. The relative error was calculated by the quadratic mean of the intraplot and interplot variations, which is 18.2% (Extended Data Table 1b). No destructive AGB measurements were found that showed a similar CD–DBH relationship as we measured during the field trip in Rwanda’s forest. We could thus not evaluate the performance for natural forests at tree level but had to rely on plot-level comparisons (next section).Evaluation and uncertainties of the allometryBiomass estimations without direct measurements of height or DBH inevitably include a relatively high level of uncertainty at tree level38,60. Uncertainty does not only originate from the CD to DBH conversion but also the equation converting DBH to AGB. As shown in the previous section, no strong systematic bias could be detected for the CD to DBH conversion but the evaluation of the CD-based AGB prediction with an independent dataset from destructively measured AGB revealed a bias of 25%. However, this comparison (Extended Data Fig. 3c) may not be representative for an entire country having a variety of landscapes and tree species, so a systematic propagation is unlikely. We also did not have sufficient field data to evaluate the conversions in natural forests. Here, we used data from 15 natural forest plots with 6,161 trees published by ref. 40 and ref. 41 and directly compared the summed biomass of the trees we predicted over their plots. The median measured biomass for the plots is 121 MgC ha−1 and we predict a median biomass of 81 MgC ha−1 (plot-based rRMSE = 54%; bias = 11%; bias on summed plots = 26%). The overall underestimation by our prediction is not necessarily a model bias but may be partly explained by the contribution of the understory trees, which cannot be captured by aerial images. Interestingly, our C stock estimates are in the same range of magnitude as global biomass products43,44,45,61 (Extended Data Fig. 4), indicating that overstory tree-level carbon stock assessments are possible from optical very high resolution images, even in tropical forests. Several global products overestimated biomass for non-forest areas like savannas or croplands, which is probably because they are calibrated in denser forests. The most recent products of ref. 42 and ref. 61 are much closer to the estimates from our results and the NFI. This is also seen in the grid-based correlation matrix where ref. 42 correlates best with our map, followed by ref. 61.We further use NFI data from 2014 to measure the uncertainty of the final carbon stock estimates and evaluate if systematic differences between AGB predictions and field assessments can be found for different land cover classes (Extended Data Table 1). For the NFI data, a total of 373 plots with 2,415 trees were measured and species-specific allometric equations applied62. To identify systematic errors at landscape scale, we extracted averaged values for areas around the plots from our predictions and calculated statistics on averages over all plots. Interestingly, our predictions for farmlands only show a bias of 5.9%: we estimate on average 2.46 MgC ha−1 and the inventories measure 2.37 MgC ha−1 on their 150 plots. For savanna and shrublands, we estimate 4.16 MgC ha−1 while inventories measure 3.31 MgC ha−1 (bias = 18.9%). For plantations, we estimate lower values (8.16 compared to 16.79 MgC ha−1; bias = 52.6%). To calculate the total uncertainty on country-wide C stock estimates, we weighted the bias from the different classes according to their relative area. We estimate a total uncertainty on the carbon stock predictions of 16.9% at the national scale (Extended Data Table 1).We found a very low bias for estimated C density in farmlands (5.9% bias) which make up most of the areas outside natural forests in Rwanda (Extended Data Table 1, Extended Data Fig. 6). The high bias for plantations can be explained by three factors: large bare areas considered part of plantations by the manual delineation of plantation areas (Extended Data Fig. 1); regular harvesting and continual thinning which keep many plantation trees young and small; and the fact that our aerial images are from 2008 while plantation trees have grown until 2014 with a few new NFI plots initiated after 2008. The bias in savannas and shrublands can be explained by the following factors: the presence of multistemed trees with large crowns such as Acacia spp. and Ficus spp. among others; the fact that a crown-based method overestimates C stocks of shrubs with a small height; and presence of shrub trees with both small height and small (multiple) stems. If tree-level based carbon stock assessments derived from crown diameter as presented here should become standard to complement national inventories, a database with sufficient samples to evaluate for systematic errors needs to be established for each biome and inventory and satellite/aerial image-based methods need to be further harmonized.To further quantify the error propagation of the CD to DBH conversion for our application, we established four equations each randomly using 50% of the dataset and predicted the carbon stock for each tree in Rwanda with each equation. We did this separately for natural forests and trees outside natural forests. We calculated the rRMSE between the aggregated carbon stocks for each hectare. We averaged the rRMSE for each land cover class and show that the uncertainty for all classes does not exceed 5% (Extended Data Table 2a).Evaluation and uncertainties of tree crown mappingWe created an independent test dataset, which was never seen during training and was also not used to optimize hyperparameters. The test set consists of 6,591 manually labelled trees located in 15 random 1 ha plots (Extended Data Fig. 5). Thanks to the size of the country, the plots represent all rainfall zones and three major landscapes of the country. The plot-level comparison yielded very high correlations between the predictions and the labels and is shown in Extended Data Fig. 5. We also calculated a confusion matrix showing an overall per pixel accuracy of 96.2%, a true positive rate of 79.6% and a false positive rate of 6.8% (Extended Data Table 2b). Trees outside natural forests are easy to spot and count for the human eye, so we have confidence in the plot-based evaluation. However, it is often challenging in natural forests. Here, we used again the field measurements from 15 plots with 6,161 trees40,41. We find that we underestimate the total tree count by 22.6%, which may, at least partly, be explained by understory trees hidden by overstory trees and which are, therefore, not visible in our images. New field campaigns are needed to better understand and calibrate our results and possibly correct for systematic bias.Application and evaluation beyond RwandaWe acquired 83 Skysat scenes at 80 cm for Tanzania, Burundi, Uganda, Rwanda and Kenya. The model trained on the 25 cm resolution aerial images of Rwanda from 2008 was directly applied on the Skysat images. Forest and non-forest areas were manually delineated to decide which allometric equation to use for the carbon stock conversion. We randomly selected 150 1 × 1 km2 patches and aggregated the predicted carbon density per patch and compared the results with previously published maps42,43,44,45. Results show that the model can directly be applied to comparable landscapes on different datasets. Note, however, that accurate carbon stock predictions need local adjustments with field data. We then tested the tree crown model transferability on aerial images from California (NAIP; 60 cm) and France (20 cm) and found that the model delivers realistic results without any local training or calibration (Extended Data Figure 8).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

All methods were performed in accordance with the relevant guidelines and regulations.Study siteThe laboratory experiments on regeneration and wash resistance were conducted at the KCMUCo-PAMVERC Insecticide Testing Facility; while experimental hut study was carried out at Harusini, the facility’s field site located at Mabogini village (S03˚22.764’ E03˚720.793’), adjacent to Lower Moshi rice irrigation scheme in north-eastern Tanzania. The dominant vector at this site is An. arabiensis with moderate level of resistance to pyrethroids conferred by both oxidase and esterase activities32. In this study, pyrethroid-resistant laboratory reared An. gambiae Muleba-Kis mosquitoes were released into the huts for the release-recapture experiment.Test systemsNon-blood fed, 2–5 day old females of susceptible An. gambiae s.s. Kisumu strain and pyrethroid resistant An. gambiae s.s Muleba-Kis strain were used for the evaluation of efficacy in the laboratory (phase I). The Muleba-Kis strain has been colonized for more than 8 years and it is resistant to permethrin with fixed L1014S kdr frequency and metabolic resistance through increased oxidase activity has also been reported21. Only An. gambiae s.s Muleba-Kis were used in release-recapture experiments. The Kisumu strain is fully susceptible to insecticides and free of any detectable insecticide resistance mechanisms. The strain originated from Kisumu, Kenya and has been colonized for many years in laboratory. At the KCMUCo-PAMVERC Moshi insectary, the adult Kisumu strain mosquitoes are reared at a temperature of 24–27 °C, 75 ± 10% relative humidity (RH) and maintained under a dark:light regime of 12:12 h. The Muleba-Kis mosquitoes used for the release-recapture experiments were reared in the field insectary under ambient temperature and relative humidity and treated as previously explained21. The susceptibility status of these colonies is checked every three months using WHO susceptibility test33 and, CDC bottle bioassay test34. The colonies are regularly genotyped for kdr mutations using TaqMan assays35. To maintain the resistance of Muleba-Kis, larvae are frequently selected with alpha-cypermethrin.Regeneration timeTo determine the regeneration time of the insecticide-treated blankets, blankets were cut into 25 × 25 cm pieces and tested before washing and then washed and dried three times consecutively following WHO recommended procedures for LLINs36. The pieces were then re-tested after one, two, three, six and seven days post-washing using WHO cylinders against susceptible An. gambiae s.s (Kisumu).Graphs for 24-h mortality and 60 min knock down (KD) correlating to insecticide bioavailability, as measured by 3 min exposure in cylinder bioassays, were established before and after washing blanket pieces three times consecutively in a day, and tested within a maximum of seven days post-washing. The time in days required to reach initial mortality or 60 min KD plateau is the period required for full regeneration of insecticide-treated blanket.Wash resistanceWHO cylinder bioassays36 were used to assess the wash resistance for the blanket pieces washed 0, 5, 10, 15 and 20 times at the intervals equivalent to the regeneration time. Four pieces cut from 4 permethrin and 4 untreated blankets were used as positive and negative control respectively, against 4 pieces cut from 4 PBO–permethrin blankets.Bioassay proceduresFive, non-blood fed, 2–5 day old An. gambiae Kisumu or An. gambiae Muleba-Kis mosquitoes were exposed for 3 min or 30 min to blanket pieces in WHO cylinder. Bioassays were carried out at 27 ± 2 °C and 75 ± 10% RH. Knock-down was scored after 60 min post-exposure and mortality after 24 h. Fifty mosquitoes (5 mosquitoes per cylinder) were used on each 25 × 25 cm piece of blanket sample. After exposure, the mosquitoes were held for 24 h with access to 10% glucose solution in the paper cups covered with a net material. Mosquitoes exposed to untreated blanket were referred as a negative control.WHO tunnel test methodBlanket pieces which recorded ≤ 80% mortality in cylinder bioassay were tested in the tunnel assay using WHO guidelines. The tunnel was made of an acrylic square cylinder (25 cm in height, 25 cm in width, and 60 cm in length) divided into two sections using a blanket-covered frame fitted into a slot across the tunnel. During the assays a guinea pig was held in a small wooden cage (as a bait) in one of the sections and 50, non-blood fed, female An. gambiae Kisumu or An. gambiae Muleba-Kis aged 5–8 days were released in the other section at dusk and left overnight (13 h) for experimentation at 27 ± 2 °C and 75 ± 10% RH. The blanket surface was deliberately holed (nine 1-cm holes) to allow mosquitoes to contact the blanket material and penetrate to the baited chamber. Treated blankets were tested concurrently together with an untreated blanket. Scoring for the numbers of mosquitoes found alive or dead, fed or unfed, in each section were done in the morning. Mosquitoes found alive were removed and held in paper cups with labels corresponding to each tunnel sections under controlled conditions (25–27 °C and 75–85% RH) and fed on 10% glucose solution to monitor for delayed mortality post exposurely. Outcomes recorded were: mosquito penetration, blood feeding and mortality.Washing of blankets and whole nets for hut trialBlankets and whole nets were separately washed following WHOPES guidelines. In brief, each blanket/net was washed in Savon de Marseilles soap solution (2 g/L) for 10 min: 3 min stirring, 4 min soaking, then another 3 min stirring. This was followed by 2 rinse cycles of the same duration with water only. The water pH was 6 for all washes. The mean water hardness was within the WHOPES limit of ≤ 89 ppm. All nets used in the experimental hut study were cut with holes (4 cm × 4 cm) to simulate the conditions of a torn net. While nets were washed 20 times as per guidelines, blankets were only washed 10 times. To simulate a situation in emergence situations where washing is less frequent due to water scarcity30,31.Experimental hut trial:experimental hut designExperimental hut study was done in Lower Moshi using typical East African experimental huts design as described in the WHOPES35. Huts were constructed with brick walls and featured with cement plaster on the inside and a ceiling board, a metal iron sheet roof, open eaves with window and veranda traps on each side and window traps. Slight modifications from the original structure were made by installing metal eave baffles on two sides. The baffles allow mosquito entry but prevent exits. The window traps were used to collect mosquitoes that tend to exit the huts.Test item labelling, washing and perforatingBoth blankets and LLINs for the trial were distinctively labelled with fabric labels that withstand washes. For wash resistance, the blankets and nets were separately washed according to a protocol adapted from the standard WHO washing procedure36 at the interval equivalent to the regeneration time established in the laboratory for blanket and LLIN respectively. Before testing in the experimental huts, all nets were deliberately holed i.e. 30 holes measuring 4 × 4 cm were made in each net, 9 holes in each of the long side panels, and 6 holes at each short side (head- and foot-side panels) to enhance blood-feeding on the control arm.Test items packagingEach blanket and net were sealed in a plastic bag and then packed in the large plastic container. Each container was labelled for a single treatment to avoid cross contamination between test items.Experimental hut decontaminationA cone assay with 10 susceptible mosquitoes was performed on one wall per hut to rule out any contamination of the wall surface. Only huts with 24 h mortality of susceptible mosquitoes  More

La Sorte, F. A., Johnston, A. & Ault, T. R. Global trends in the frequency and duration of temperature extremes. Clim. Change 166, 1–2 (2021).Article 
ADS 

Google Scholar 
Hansen, J., Sato, M., Ruedy, R., Lo, K. & Medina-Elizade, M. Global temperature change. Proc. Natl. Acad. Sci. U.S.A. 103(39), 14288–14293 (2006).Article 
ADS 
CAS 

Google Scholar 
Borchert, R., Robertson, K., Schwartz, M. D. & Williams-Linera, G. Phenology of temperate trees in tropical climates. Int. J. Biometeorol. 50, 57–65 (2005).Article 
ADS 

Google Scholar 
Misra, G., Sarah, A. & Menzel, A. Ground and satellite phenology in alpine forests are becoming more heterogeneous across higher elevations with warming. Agric. For. Meteorol. 303, 108383 (2021).Article 
ADS 

Google Scholar 
Zuo, Z., Xiao, D. & Qiong, H. Role of the warming trend in global land surface air temperature variations. Sci. China Earth Sci. 6, 866–871 (2021).Article 
ADS 

Google Scholar 
Ling, Y. et al. Assessing the accuracy of forest phenological extraction from sentinel-1 C-band backscatter measurements in deciduous and coniferous forests. Remote Sens. 14(3), 674 (2022).Article 
ADS 

Google Scholar 
Zhang, H., Yuan, W., Liu, S., Dong, W. & Fu, Y. Sensitivity of flowering phenology to changing temperature in China. J. Geophys. Res. Biogeosci. 120(8), 1658–1665 (2015).Article 

Google Scholar 
Cho, J. G. et al. Apple phenology occurs earlier across South Korea with higher temperatures and increased precipitation. Int. J. Biometeorol. 65, 265–276 (2020).Article 

Google Scholar 
Li, C. et al. Response of vegetation phenology to the interaction of temperature and precipitation changes in Qilian mountains. Remote Sens. 14(5), 1248 (2022).Article 
ADS 

Google Scholar 
Berra, E. F. & Gaulton, R. Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics. For. Ecol. Manage. 480, 118663 (2021).Article 

Google Scholar 
Zhang, Y. & Li, M. A new method for monitoring start of season (SOS) of forest based on multisource remote sensing. Int. J. Appl. Earth Obs. Geoinf. 104, 102556 (2021).
Google Scholar 
Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84(3), 471–475 (2003).Article 
ADS 

Google Scholar 
Thapa, S., Garcia Millan, V. E. & Eklundh, L. Assessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) remote sensing. Remote Sens. 13, 1597 (2021).Article 
ADS 

Google Scholar 
Bórnez, K., Descals, A., Verger, A. & Peñuelas, J. Land surface phenology from VEGETATION and PROBA-V data: Assessment over deciduous forests. Int. J. Appl. Earth Observ. Geoinf. 84, 101974 (2020).
Google Scholar 
Yu, L., Yan, Z. & Zhang, S. Forest phenology shifts in response to climate change over China–Mongolia–Russia international economic corridor. Forests 11, 757 (2020).Article 

Google Scholar 
Lara, C. et al. Climatic regulation of vegetation phenology in protected areas along Western South America. Remote Sens. 13, 2590 (2021).Article 
ADS 

Google Scholar 
Silveira, E. M. O. et al. Forest phenoclusters for Argentina based on vegetation phenology and climate. Ecol. Appl. 32, 2526 (2022).Article 

Google Scholar 
Tatalovich, Z., Wilson, J. P. & Cockburn, M. A comparison of thiessen polygon, kriging, and spline models of potential UV exposure. Cartogr. Geogr. Inf. Sci. 33, 217–231 (2006).Article 

Google Scholar 
Choubin, B. et al. Spatiotemporal dynamics assessment of snow cover to infer snowline elevation mobility in the mountainous regions. Cold Reg. Sci. Technol. 167, 102870 (2019).Article 

Google Scholar 
Rojas, R., Flexas, J. & Coopman, R. E. Particularities of the highest elevation treeline in the world: Polylepis tarapacana Phil. as a model to study ecophysiological adaptations to extreme environments. Flora 292, 152076 (2022).Article 

Google Scholar 
Du, J. et al. Interacting effects of temperature and precipitation on climatic sensitivity of spring vegetation green-up in arid mountains of China. Agric. For. Meteorol. 269–270, 71–77 (2019).Article 
ADS 

Google Scholar 
Du, J. et al. Daily minimum temperature and precipitation control on spring phenology in arid-mountain ecosystems in China. Int. J. Climatol. 40, 2568–2579 (2020).Article 

Google Scholar 
He, Z. et al. Impacts of recent climate extremes on spring phenology in arid-mountain ecosystems in China. Agric. For. Meteorol. 260–261, 31–40 (2018).Article 
ADS 

Google Scholar 
He, Z. et al. Assessing temperature sensitivity of subalpine shrub phenology in semi-arid mountain regions of China. Agric. For. Meteorol. 213, 42–52 (2015).Article 
ADS 

Google Scholar 
Mu, C., Lu, H., Wang, B., Bao, X. & Cui, W. Short-term effects of harvesting on carbon storage of boreal Larix gmelinii–Carex schmidtii forested wetlands in Daxing’anling, northeast China. For. Ecol. Manage. 293, 140–148 (2013).Article 

Google Scholar 
Hu, T. et al. Effects of fire on soil respiration and its components in a Dahurian larch (Larix gmelinii) forest in northeast China: Implications for forest ecosystem carbon cycling. Geoderma 402, 115273 (2021).Article 
ADS 
CAS 

Google Scholar 
Nyikadzino, B., Chitakira, M. & Muchuru, S. Rainfall and runoff trend analysis in the Limpopo river basin using the Mann Kendall statistic. Phys. Chem. Earth 117, 102870 (2020).Article 

Google Scholar 
Gocic, M. & Trajkovic, S. Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Glob. Planet. Change 100, 172–182 (2013).Article 
ADS 

Google Scholar 
Fang, Y. et al. Changing contribution rate of heavy rainfall to the rainy season precipitation in Northeast China and its possible causes. Atmos. Res. 197, 437–445 (2017).Article 

Google Scholar 
Piao, S. et al. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Glob. Change Biol. 17, 3228–3239 (2011).Article 
ADS 

Google Scholar 
Ahas, R., Aasa, A., Menzel, A., Fedotova, V. G. & Scheifinger, H. Changes in European spring phenology. Int. J. Climatol. 22, 1727–1738 (2002).Article 

Google Scholar 
Liang, L., Henebry, G. M., Liu, L., Zhang, X. & Hsu, L. C. Trends in land surface phenology across the conterminous United States (1982–2016) analyzed by NEON domains. Ecol. Appl. 31, e02323 (2021).Article 

Google Scholar 
Fu, Y. H. et al. Decreasing control of precipitation on grassland spring phenology in temperate China. Glob. Ecol. Biogeogr. 30, 490–499 (2020).Article 

Google Scholar 
Aze, T. Unraveling ecological signals from a global warming event of the past. Proc. Natl. Acad. Sci. U.S.A. 119, e2201495119 (2022).Article 

Google Scholar 
Menzel, A., Estrella, N. & Testka, A. Temperature response rates from long-term phenological records. Climate Res. 30, 21–28 (2005).Article 
ADS 

Google Scholar 
Wang, H., Liu, D., Lin, H., Montenegro, A. & Zhu, X. NDVI and vegetation phenology dynamics under the influence of sunshine duration on the Tibetan plateau. Int. J. Climatol. 35, 687–698 (2015).Article 

Google Scholar 
Lesica, P. & Kittelson, P. M. Precipitation and temperature are associated with advanced flowering phenology in a semi-arid grassland. J. Arid Environ. 74, 1013–1017 (2010).Article 
ADS 

Google Scholar 
Shen, M., Piao, S., Cong, N., Zhang, G. & Jassens, I. A. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Glob. Change Biol. 21, 3647–3656 (2015).Article 
ADS 

Google Scholar 
Li, Z. et al. Spatio-temporal responses of cropland phenophases to climate change in Northeast China. J. Geog. Sci. 22, 29–45 (2012).Article 
CAS 

Google Scholar 
Badeck, F. W. et al. Responses of spring phenolgy to climate change. New Phytol. 162, 295–309 (2004).Article 

Google Scholar 
Peng, H., Xia, H., Chen, H., Zhi, P. & Xu, Z. Spatial variation characteristics of vegetation phenology and its influencing factors in the subtropical monsoon climate region of southern China. PLoS ONE 16, e0250825 (2021).Article 
CAS 

Google Scholar 
Zhang, J. et al. NIRv and SIF better estimate phenology than NDVI and EVI: Effects of spring and autumn phenology on ecosystem production of planted forests. Agric. For. Meteorol. 315, 108819 (2022).Article 
ADS 

Google Scholar 
Yu, X., Zhuang, D., Hou, X. & Chen, H. Forest phenological patterns of Northeast China inferred from MODIS data. J. Geog. Sci. 15, 239–246 (2005).Article 

Google Scholar 
Chen, X. & Xu, L. Phenological responses of Ulmus pumila (Siberian Elm) to climate change in the temperate zone of China. Int. J. Biometeorol. 56, 695–706 (2012).Article 
ADS 

Google Scholar 
Ma, X., Bai, H., He, Y. & Li, S. The vegetation RSP of Qinling Mountains based on the NDVI and the response of temperature to it. Appl. Mech. Mater. 700, 394–399 (2014).Article 

Google Scholar  More

Cantwell-Jones, A. et al. Global plant diversity as a reservoir of micronutrients for humanity. Nat. Plants https://doi.org/10.1038/s41477-022-01100-6 (2022).Swenson, N. G. Phylogenetic imputation of plant functional trait databases. Ecography 37, 105–110 (2014).Article 

Google Scholar 
Swenson, N. G. et al. Phylogeny and the prediction of tree functional diversity across novel continental settings. Glob. Ecol. Biogeogr. 26, 553–562 (2017).Article 

Google Scholar 
Molina-Venegas, R. et al. Assessing among-lineage variability in phylogenetic imputation of functional trait datasets. Ecography 41, 1740–1749 (2018).Article 

Google Scholar 
Guénard, G., Legendre, P. & Peres-Neto, P. Phylogenetic eigenvector maps: a framework to model and predict species traits. Methods Ecol. Evol. 4, 1120–1131 (2013).Article 

Google Scholar 
Guénard, G., Ohe, P. C., von der, Walker, S. C., Lek, S. & Legendre, P. Using phylogenetic information and chemical properties to predict species tolerances to pesticides. Proc. R. Soc. B 281, 20133239 (2014).Article 

Google Scholar 
Ezekiel, M. Methods of Correlation Analysis (John Wiley and Sons, 1930).Johnson, T. F., Isaac, N. J. B., Paviolo, A. & González-Suárez, M. Handling missing values in trait data. Glob. Ecol. Biogeogr. 30, 51–62 (2021).Article 

Google Scholar 
Debastiani, V. J., Bastazini, V. A. G. & Pillar, V. D. Using phylogenetic information to impute missing functional trait values in ecological databases. Ecol. Inform. 63, 101315 (2021).Article 

Google Scholar 
Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods Ecol. Evol. 8, 22–27 (2017).Article 

Google Scholar  More

Newton, I. The Migration Ecology of Birds (Academic Press, USA, 2010).
Google Scholar 
Somveille, M., Rodrigues, A. S. L. & Manica, A. Why do birds migrate? A macroecological perspective. Glob. Ecol. Biogeogr. 24(6), 664–674 (2015).Article 

Google Scholar 
Hahn, S., Bauer, S. & Liechti, F. The natural link between Europe and Africa – 2.1 billion birds on migration. Oikos 118(4), 624–626 (2009).Article 

Google Scholar 
DeLuca, W. V. et al. Transoceanic migration by a 12 g songbird. Biol. Let. 11(4), 20141045 (2015).Article 

Google Scholar 
Deppe, J. L. et al. Fat, weather, and date affect migratory songbirds’ departure decisions, routes, and time it takes to cross the Gulf of Mexico. Proc. Natl. Acad. Sci. USA 112(46), E6331–E6338 (2015).Article 
CAS 

Google Scholar 
Sutherland, W. J. The heritability of migration. Nature 334, 471–472 (1988).Article 
ADS 

Google Scholar 
Alerstam, T. & Lindström, Å. Optimal bird migration: the relative importance of time, energy, and safety. In Bird Migration 331–351 (Springer, 1990).Chapter 

Google Scholar 
Thorup, K. Vagrancy of yellow-browed warbler Phylloscopus inornatus and Pallas’s Warbler Ph. proregulusin north-west Europe: misorientation on great circles. Ring. Migr. 19(1), 7–12 (1998).Article 

Google Scholar 
del Hoyo, J., Elliott, A. & Christie, D. Handbook of the Birds of the World (Lynx Edicions, 2008).
Google Scholar 
Rabøl, J. Reversed migration as the cause of westward vagrancy by four Phylloscopus warblers. British Birds 62, 89–92 (1969).
Google Scholar 
Thorup, K. Reverse migration as a cause of vagrancy: capsule reverse migration in autumn does not occur to the same degree in all species of migrants, but is related to migratory direction. Bird Study 51(3), 228–238 (2004).Article 

Google Scholar 
BirdLife International and Handbook of the Birds of the World, Bird species distribution maps of the world. Version 6.0. Available at http://datazone.birdlife.org/species/requestdis. (2016).R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. (2017).Thorup, K. et al. Orientation of vagrant birds on the Faroe Islands in the Atlantic Ocean. J. Ornithol. 153(4), 1261–1265 (2012).Article 

Google Scholar 
Able, K. The concepts and terminology of bird navigation. J. Avian. Biol. 32(2), 174–183 (2001).Article 

Google Scholar 
Griffin, D. R. & Hock, R. J. Experiments on bird navigation. Science 107(2779), 347–349 (1948).Article 
ADS 
CAS 

Google Scholar 
Kishkinev, D. Sensory mechanisms of long-distance navigation in birds: a recent advance in the context of previous studies. J. Ornithol. 156(S1), 145–161 (2015).Article 

Google Scholar 
Thorup, K. et al. Juvenile songbirds compensate for displacement to oceanic islands during autumn migration. PLoS One 6(3), e17903 (2011).Article 
ADS 
CAS 

Google Scholar 
Wingfield, J. & Sapolsky, R. Reproduction and resistance to stress: when and how. J. Neuroendocrinol. 15(8), 711–724 (2003).Article 
CAS 

Google Scholar 
Sapolsky, R. M., Romero, L. M. & Munck, A. U. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr. Rev. 21(1), 55–89 (2000).CAS 

Google Scholar 
Jenni, L. & Jenni-Eiermann, S. Fuel supply and metabolic constraints in migrating birds. J. Avian Biol. 29(4), 521–528 (1998).Article 

Google Scholar 
Casagrande, S. et al. Dietary antioxidants attenuate the endocrine stress response during long-duration flight of a migratory bird. Proc. Biol. Sci. 2020(287), 20200744 (1929).
Google Scholar 
Gwinner, E. et al. Corticosterone levels of passerine birds during migratory flight. Naturwissenschaften 79(6), 276–278 (1992).Article 
ADS 
CAS 

Google Scholar 
Jenni, L. et al. Regulation of protein breakdown and adrenocortical response to stress in birds during migratory flight. Am. J. Physiol. Regul. Integr. Comp. Physiol. 278(5), R1182–R1189 (2000).Article 
CAS 

Google Scholar 
Holberton, R. L., Boswell, T. & Hunter, M. J. Circulating prolactin and corticosterone concentrations during the development of migratory condition in the Dark-eyed Junco Junco hyemalis. Gen. Comp. Endocrinol. 155(3), 641–649 (2008).Article 
CAS 

Google Scholar 
Ramenofsky, M., J. Moffat, and G. Bentley, Corticosterone and migratory behaviour of captive white-crowned sparrows. In International proceedings of ICA-CPB, Pressures of Life: Molecules to Migration. Masai, Mara Game Reserve, p. 575–82 (2008).Eikenaar, C., Klinner, T. & Stowe, M. Corticosterone predicts nocturnal restlessness in a long-distance migrant. Horm. Behav. 66(2), 324–329 (2014).Article 
CAS 

Google Scholar 
Ramenofsky, M. Fat storage and fat metabolism in relation to migration. In Bird Migration 214–231 (Springer, 1990).Chapter 

Google Scholar 
Eikenaar, C., Fritzsch, A. & Bairlein, F. Corticosterone and migratory fueling in Northern wheatears facing different barrier crossings. Gen. Comp. Endocrinol. 186, 181–186 (2013).Article 
CAS 

Google Scholar 
Landys, M. M., Ramenofsky, M. & Wingfield, J. C. Actions of glucocorticoids at a seasonal baseline as compared to stress-related levels in the regulation of periodic life processes. Gen. Comp. Endocrinol. 148(2), 132–149 (2006).Article 
CAS 

Google Scholar 
Romero, L. M. & Reed, J. M. Collecting baseline corticosterone samples in the field: Is under 3 min good enough?. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 140(1), 73–79 (2005).Article 

Google Scholar 
Wingfield, J. C., Kelley, J. P. & Angelier, F. What are extreme environmental conditions and how do organisms cope with them?. Curr. Zool. 57(3), 363–374 (2011).Article 

Google Scholar 
Wingfield, J. C. & Hunt, K. E. Arctic spring: hormone–behavior interactions in a severe environment. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 132(1), 275–286 (2002).Article 

Google Scholar 
Hammer, S. et al. Færøsk Trækfugleatlas: the Faroese bird migration atlas. Fróðskapur spf. (2014).DeSante, D. Vagrants: when orientation or navigation goes wrong. Point Reyes Bird Observ. Newsl. 61, 12–16 (1983).
Google Scholar 
Wingfield, J. C. et al. A mechanistic approach to understanding range shifts in a changing world: What makes a pioneer?. Gen. Comp. Endocrinol. 222, 44–53 (2015).Article 
CAS 

Google Scholar 
Cramp, S. Handbook of the Birds of Europe, the Middle east and North Africa: Birds of the western Palearctic (University Press, 1988).
Google Scholar 
Svensson, L., Identification guide to European passerines. L. Svensson. (1992).Helbig, A. J. & Seibold, I. Molecular phylogeny of Palearctic-African Acrocephalus and Hippolais warblers (Aves: Sylviidae). Mol. Phylogenet. Evol. 11(2), 246–260 (1999).Article 
CAS 

Google Scholar 
Baker, K. Identification of Siberian and other forms of lesser whitethroat. Brit. Birds 81, 382–390 (1988).
Google Scholar 
Olsson, U. et al. New insights into the intricate taxonomy and phylogeny of the Sylvia curruca complex. Mol. Phylogenet. Evol. 67(1), 72–85 (2013).Article 

Google Scholar 
Tsvey, A., Loshchagina, J. & Naidenko, S. Migratory species show distinct patterns in corticosterone levels during spring and autumn migrations. Anim. Migr. 6(1), 4–18 (2019).Article 

Google Scholar 
Owen, J. C. Collecting, processing, and storing avian blood: a review. J. Field Ornithol. 82(4), 339–354 (2011).Article 

Google Scholar 
Pettersson, J. & Hasselquist, D. Fat deposition and migration capacity of robins Erithacus rebecula and goldcrests Regulus regulus at Ottenby Sweden. Ring Migr. 6(2), 66–76 (1985).Article 

Google Scholar 
Bairlein, F. et al. European-African Songbird Migration Network: Manual of Field Methods (Wilhelmshaven, 1995).
Google Scholar 
Wingfield, J. C., Vleck, C. M. & Moore, M. C. Seasonal changes of the adrenocortical response to stress in birds of the Sonoran Desert. J. Exp. Zool. A Comp. Exp. Biol. 264(4), 419–428 (1992).Article 
CAS 

Google Scholar 
SAS Institute, SAS for windows, version 9.4. (2014).Cook, R. D. Detection of influential observation in linear regression. Technometrics 19(1), 15–18 (1977).MathSciNet 
MATH 

Google Scholar 
Rawlings, J. O., Pantula, S. G. & Dickey, D. A. Applied Regression Analysis: A Research Tool (Springer Science & Business Media, 2001).MATH 

Google Scholar 
Grubbs, F. E. Procedures for detecting outlying observations in samples. Technometrics 11(1), 1–21 (1969).Article 

Google Scholar 
Wingfield, J. C. & Kitaysky, A. S. Endocrine responses to unpredictable environmental events: stress or anti-stress hormones?. Integr. Comp. Biol. 42(3), 600–609 (2002).Article 
CAS 

Google Scholar 
Angelier, F. & Wingfield, J. C. Importance of the glucocorticoid stress response in a changing world: theory, hypotheses and perspectives. Gen. Comp. Endocrinol. 190, 118–128 (2013).Article 
CAS 

Google Scholar 
Ralph, C. J. Disorientation and possible fate of young passerine coastal migrants. Bird-Banding 49(3), 237–247 (1978).Article 

Google Scholar 
Atwell, J. W. et al. Boldness behavior and stress physiology in a novel urban environment suggest rapid correlated evolutionary adaptation. Behav. Ecol. 23(5), 960–969 (2012).Article 

Google Scholar 
Krause, J. S. et al. Breeding on the leading edge of a northward range expansion: differences in morphology and the stress response in the arctic Gambel’s white-crowned sparrow. Oecologia 180(1), 33–44 (2016).Article 
ADS 

Google Scholar 
Falsone, K., Jenni-Eiermann, S. & Jenni, L. Corticosterone in migrating songbirds during endurance flight. Horm. Behav. 56(5), 548–556 (2009).Article 
CAS 

Google Scholar 
Long, J. A. & Holberton, R. L. Corticosterone secretion, energetic condition, and a test of the migration modulation hypothesis in the hermit thrush (Catharus Guttatus), a short-distance migrant. Auk 121(4), 1094 (2004).Article 

Google Scholar 
Romero, L. M., Ramenofsky, M. & Wingfield, J. C. Season and migration alters the corticosterone response to capture and handling in an Arctic migrant, the white-crowned sparrow (Zonotrichia leucophrys gambelii). Comp. Biochem. Physiol. C Pharmacol. Toxicol. Endocrinol. 116(2), 171–177 (1997).Article 
CAS 

Google Scholar 
Schwabl, H. Individual variation of the acute adrenocortical response to stress in the white-throated sparrow. Zool.-Anal. Complex Syst. 99(2), 113–120 (1995).CAS 

Google Scholar 
Wingfield, J. et al. Environmental stress, field endocrinology, and conservation biology. In Behavioral approaches to conservation in the wild 95–131 (Cambridge University Press, 1997).
Google Scholar 
Wingfield, J. C., Suydam, R. & Hunt, K. The adrenocortical responses to stress in snow buntings (Plectrophenax nivalis) and Lapland longspurs (Calcarius lapponicus) at Barrow, Alaska. Comp. Biochem. Physiol. C: Pharmacol. Toxicol. Endocrinol. 108(3), 299–306 (1994).
Google Scholar 
Krause, J. S. et al. Weathering the storm: Do arctic blizzards cause repeatable changes in stress physiology and body condition in breeding songbirds?. Gen. Comp. Endocrinol. 267, 183–192 (2018).Article 
CAS 

Google Scholar 
Krause, J. S. et al. The effect of extreme spring weather on body condition and stress physiology in Lapland longspurs and white-crowned sparrows breeding in the Arctic. Gen. Comp. Endocrinol. 237, 10–18 (2016).Article 
CAS 

Google Scholar 
Romero, L. M., Reed, J. M. & Wingfield, J. C. Effects of weather on corticosterone responses in wild free-living passerine birds. Gen. Comp. Endocrinol. 118(1), 113–122 (2000).Article 
CAS 

Google Scholar 
Wingfield, J. C., Moore, M. C. & Farner, D. S. Endocrine responses to inclement weather in naturally breeding populations of white-crowned sparrows (Zonotrichia leucophrys pugetensis). Auk 100(1), 56–62 (1983).Article 

Google Scholar 
Schwabl, H., Bairlein, F. & Gwinner, E. Basal and stress-induced corticosterone levels of garden warblers, Sylvia borin, during migration. J. Comp. Physiol. B. 161(6), 576–580 (1991).Article 
CAS 

Google Scholar 
Wingfield, J. C. et al. Ecological bases of hormone—behavior interactions: the “emergency life history stage”. Am. Zool. 38(1), 191–206 (1998).Article 
CAS 

Google Scholar 
Silverin, B., Arvidsson, B. & Wingfield, J. The adrenocortical responses to stress in breeding willow warblers Phylloscopus trochilus in Sweden: effects of latitude and gender. Funct. Ecol. 11(3), 376–384 (1997).Article 

Google Scholar 
Krause, J. S. et al. Effects of short-term fasting on stress physiology, body condition, and locomotor activity in wintering male white-crowned sparrows. Physiol. Behav. 177, 282–290 (2017).Article 
CAS 

Google Scholar 
Fokidis, H. B. et al. Effects of captivity and body condition on plasma corticosterone, locomotor behavior, and plasma metabolites in curve-billed thrashers. Physiol. Biochem. Zool. 84(6), 595–606 (2011).Article 
CAS 

Google Scholar 
Buttemer, W. A., Astheimer, L. B. & Wingfield, J. C. The effect of corticosterone on standard metabolic rates of small passerine birds. J. Comp. Physiol. B. 161(4), 427–431 (1991).Article 
CAS 

Google Scholar 
Snell, K. R. S. Physiology of avian migratory processes, in Center for Macroecology, Evolution and Climate. University of Copenhagen. (2018).Krause, J. S. et al. The stress response is attenuated during inclement weather in parental, but not in pre-parental, Lapland longspurs (Calcarius lapponicus) breeding in the Low Arctic. Horm. Behav. 83, 68–74 (2016).Article 
CAS 

Google Scholar 
Wingfield, J. C. et al. How birds cope physiologically and behaviourally with extreme climatic events. Philos. Trans. R. Soc. London Ser. B Biol. Sci. 372(1723), 20160140 (2017).Article 

Google Scholar 
Walker, J. J. et al. Rapid intra-adrenal feedback regulation of glucocorticoid synthesis. J. R. Soc. London Interface 12(102), 20140875 (2015).Article 
MathSciNet 
CAS 

Google Scholar 
Holberton, R. L., Parrish, J. D. & Wingfield, J. C. Modulation of the adrenocortical stress response in Neotropical migrants during autumn migration. Auk 113(3), 558–564 (1996).Article 

Google Scholar 
Cornelius, J. M. et al. Contributions of endocrinology to the migration life history of birds. Gen. Comp. Endocrinol. 190, 47–60 (2013).Article 
CAS 

Google Scholar 
Landys-Ciannelli, M. M. et al. Baseline and stress-induced plasma corticosterone during long-distance migration in the bar-tailed godwit Limosa lapponica. Physiol. Biochem. Zool. 75(1), 101–110 (2002).Article 
CAS 

Google Scholar 
Jenni-Eiermann, S. et al. Are birds stressed during long-term flights? A wind-tunnel study on circulating corticosterone in the red knot. Gen. Comp. Endocrinol. 164(2–3), 101–106 (2009).Article 
CAS 

Google Scholar  More

Deng, Y., Li, X., Shi, F. & Hu, X. Woody plant encroachment enhanced global vegetation greening and ecosystem water-use efficiency. Glob. Ecol. Biogeogr. 30, 2337–2353 (2021).Article 

Google Scholar 
Brandt, J., Haynes, M., Kuemmerle, T., Waller, D. & Radeloff, V. Regime shift on the roof of the world: alpine meadows converting to shrublands in the southern Himalayas. Biol. Conserv. 158, 116–127 (2013).Article 

Google Scholar 
García Criado, M., Myers-Smith, I. H., Bjorkman, A. D., Lehmann, C. E. R. & Stevens, N. Woody plant encroachment intensifies under climate change across tundra and savanna biomes. Glob. Ecol. Biogeogr. 29, 925–943 (2020).Article 

Google Scholar 
van Auken, O. Causes and consequences of woody plant encroachment into western North American grasslands. J. Environ. Manage. 90, 2931–2942 (2009).Article 
CAS 

Google Scholar 
Bond, W. J., Midgley, G. F. & Woodward, F. I. The importance of low atmospheric CO2 and fire in promoting the spread of grasslands and savannas. Glob. Chang. Biol. 9, 973–982 (2010).Article 

Google Scholar 
D’Odorico, P., Okin, G. S. & Bestelmeyer, B. T. A synthetic review of feedbacks and drivers of shrub encroachment in arid grasslands. Ecohydrology 5, 520–530 (2012).Article 

Google Scholar 
Kulmatiski, A. & Beard, K. H. Woody plant encroachment facilitated by increased precipitation intensity. Nat. Clim. Change 3, 833–837 (2013).Article 
CAS 

Google Scholar 
Eldridge, D. J. & Soliveres, S. Are shrubs really a sign of declining ecosystem function? Disentangling the myths and truths of woody encroachment in Australia. Aust. J. Bot. 62, 594–608 (2015).Article 

Google Scholar 
Domine, F., Barrere, M. & Morin, S. The growth of shrubs on high Arctic tundra at Bylot Island: impact on snow physical properties and permafrost thermal regime. Biogeosciences 13, 6471–6486 (2016).Article 

Google Scholar 
Maestre, F. T., Callaway, R. M., Valladares, F. & Lortie, C. J. Refining the stress-gradient hypothesis for competition and facilitation in plant communities. J. Ecol. 97, 199–205 (2009).Article 

Google Scholar 
Eldridge, D. J. et al. Impacts of shrub encroachment on ecosystem structure and functioning: towards a global synthesis. Ecol. Lett. 14, 709–722 (2011).Article 

Google Scholar 
Archer, S. R. & Predick, K. I. An ecosystem services perspective on brush management: research priorities for competing land-use objectives. J. Ecol. 102, 1394–1407 (2014).Article 

Google Scholar 
Eldridge, D. J. & Ding, J. Remove or retain: ecosystem effects of woody encroachment and removal are linked to plant structural and functional traits. N. Phytol. 229, 2637–2646 (2020).Article 

Google Scholar 
Albrecht, M. A., Becknell, R. E. & Long, Q. Habitat change in insular grasslands: woody encroachment alters the population dynamics of a rare ecotonal plant. Biol. Conserv. 196, 93–102 (2016).Article 

Google Scholar 
Stanton, R. A. et al. Shrub encroachment and vertebrate diversity: a global meta-analysis. Glob. Ecol. Biogeogr. 27, 368–379 (2017).Article 

Google Scholar 
Archer, S. R. et al. in Rangeland Systems: Processes, Management and Challenges (ed. Briske, D.) 25–84 (Springer, 2017).Anadón, J. D., Sala, O. E., Turner, B. L. & Bennett, E. M. Effect of woody-plant encroachment on livestock production in North and South America. Proc. Natl Acad. Sci. USA 111, 12948–12953 (2014).Article 

Google Scholar 
Maestre, F. T. et al. Structure and functioning of dryland ecosystems in a changing world. Annu. Rev. Eco. Evol. Syst. 47, 215–237 (2016).Article 

Google Scholar 
Teague, W. et al. Sustainable management strategies for mesquite rangeland: the Waggoner Kite project. Rangelands 19, 4–9 (1997).
Google Scholar 
Hamilton, W. T., McGinty, A., Ueckert, D. N., Hanselka, C. W. & Lee, M. R. Brush Management: Past, Present, Future (A&M Univ. Press, 2004).Bestelmeyer, B. T. et al. The grassland–shrubland regime shift in the southwestern United States: misconceptions and their implications for management. BioScience 68, 678–690 (2018).Article 

Google Scholar 
Ding, J. & Eldridge, D. J. Contrasting global effects of woody plant removal on ecosystem structure, function and composition. Perspect. Plant Ecol. Evol. Syst. 39, 125460 (2019).Article 

Google Scholar 
Huxman, T. E. et al. Ecohydrological implication of woody plant encroachment. Ecology 86, 308–319 (2005).Article 

Google Scholar 
Schmutz, E. M., Cable, D. R. & Warwick, J. J. Effect of shrub removal on the vegetation of a semidesert grass-shrub range. Rangel. Ecol. Manag. 12, 34–37 (1959).Article 

Google Scholar 
Noble, J. C. & Walker, P. Integrated shrub management in semi-arid woodlands of eastern Australia: a systems-based decision support model. Agric. Syst. 88, 332–359 (2006).Article 

Google Scholar 
Eldridge, D. J. et al. The pervasive and multifaceted influence of biocrusts on water in the world’s drylands. Glob. Chang. Biol. 26, 6003–6014 (2020).Article 

Google Scholar 
Bestelmeyer, B. T., Goolsby, D. P. & Archer, S. R. Spatial perspectives in state-and-transition models: a missing link to land management. J. Appl. Ecol. 48, 746–757 (2011).Article 

Google Scholar 
Riginos, C. & Young, T. P. Positive and negative effects of grass, cattle, and wild herbivores on Acacia saplings in an East African savanna. Oecologia 153, 985–995 (2007).Article 

Google Scholar 
Soliveres, S. et al. Plant diversity and ecosystem multifunctionality peak at intermediate levels of woody cover in global drylands. Glob. Ecol. Biogeogr. 23, 1408–1416 (2014).Article 

Google Scholar 
Soliveres, S. & Eldridge, D. J. Do changes in grazing pressure and the degree of shrub encroachment alter the effects of individual shrubs on understorey plant communities and soil function? Funct. Ecol. 28, 530–537 (2013).Article 

Google Scholar 
Maestre, F. T., Bowker, M. A., Puche, M., Hinojosa, M. B. & Escudero, A. Shrub encroachment can reverse desertification in semi-arid Mediterranean grasslands. Ecol. Lett. 12, 930–941 (2010).Article 

Google Scholar 
Abreu, R. C. R., Durigan, G., Melo, A. C. G., Pilon, N. A. L. & Hoffmann, W. A. Facilitation by isolated trees triggers woody encroachment and a biome shift at the savanna-forest transition. J. Appl. Ecol. 58, 2650–2660 (2021).Article 

Google Scholar 
North, M., Oakley, B., Fiegener, R. & Barbour, G. M. Influence of light and soil moisture on Sierran mixed-conifer understory communities. Plant Ecol. 177, 13–24 (2005).Article 

Google Scholar 
Muvengwi, J., Mbiba, M., Jimu, L., Mureva, A. & Dodzo, B. An assessment of the effectiveness of cut and ring barking as a method for control of invasive Acacia mearnsii in Nyanga National Park, Zimbabwe. For. Ecol. Manag. 427, 1–6 (2018).Article 

Google Scholar 
Abella, S. R. & Chiquoine, L. P. The good with the bad: when ecological restoration facilitates native and non-native species. Restor. Ecol. 27, 343–351 (2019).Article 

Google Scholar 
Bestelmeyer, B., Ward, J., Herrick, E. J. & Tugel, A. J. Fragmentation effects on soil aggregate stability in a patchy arid grassland. Rangel. Ecol. Manag. 59, 406–415 (2006).Article 

Google Scholar 
Okin, G. S., Gillette, D. A. & Herrick, J. E. Multi-scale controls on and consequences of aeolian processes in landscape change in arid and semi-arid environments. J. Arid. Environ. 65, 253–275 (2006).Article 

Google Scholar 
Hu, X., Li, X. Y., Zhao, Y., Gao, Z. & Zhao, S. J. Changes in soil microbial community during shrub encroachment process in the Inner Mongolia grassland of northern China. Catena 202, 105230 (2021).Article 
CAS 

Google Scholar 
D’Odorico, P. et al. Positive feedback between microclimate and shrub encroachment in the northern Chihuahuan desert. Ecosphere 1, 1–11 (2010).Article 

Google Scholar 
Eldridge, D. J., Soliveres, S., Bowker, M. A. & Val, J. Grazing dampens the positive effects of shrub encroachment on ecosystem functions in a semi‐arid woodland. J. Appl. Ecol. 50, 1028–1038 (2013).Article 

Google Scholar 
Daryanto, S., Eldridge, D. J. & Throop, H. L. Managing semi-arid woodlands for carbon storage: grazing and shrub effects on above- and belowground carbon. Agric. Ecosyst. Environ. 169, 1–11 (2013).Article 

Google Scholar 
Paynter, Q. & Flanagan, G. J. Integrating herbicide and mechanical control treatments with fire and biological control to manage an invasive wetland shrub, Mimosa pigra. J. Appl. Ecol. 41, 615–629 (2004).Article 

Google Scholar 
Throop, H. L., Reichmann, L. G., Sala, O. E. & Archer, S. R. Response of dominant grass and shrub species to water manipulation: an ecophysiological basis for shrub invasion in a Chihuahuan Desert grassland. Oecologia 169, 373–383 (2012).Article 

Google Scholar 
Brantley, S. T. & Young, D. R. Shifts in litterfall and dominant nitrogen sources after expansion of shrub thickets. Oecologia 155, 337–345 (2008).Article 

Google Scholar 
Ding, J. & Eldridge, D. J. The fertile island effect varies with aridity and plant patch type across an extensive continental gradient. Plant Soil 459, 173–183 (2020).Article 

Google Scholar 
Mihoč, M. et al. Soil under nurse plants is always better than outside: a survey on soil amelioration by a complete guild of nurse plants across a long environmental gradient. Plant Soil 408, 31–41 (2016).Article 

Google Scholar 
Ochoa-Hueso, R. et al. Soil fungal abundance and plant functional traits drive fertile island formation in global drylands. J. Ecol. 106, 242–253 (2018).Article 
CAS 

Google Scholar 
Soliveres, S., Eldridge, D. J., Hemmings, F. & Maestre, F. T. Nurse plant effects on plant species richness in drylands: the role of grazing, rainfall and species specificity. Perspect. Plant Ecol. Evol. Syst. 14, 402–410 (2012).Article 

Google Scholar 
Schlesinger, W. et al. Biological feedbacks in global desertification. Science 147, 1043–1048 (1990).Article 

Google Scholar 
Ding, J. & Eldridge, D. J. Climate and plants regulate the spatial variation in soil multifunctionality across a climatic gradient. Catena 201, 105233 (2021).Article 
CAS 

Google Scholar 
Ding, J., Travers, S. K., Delgado-Baquerizo, M. & Eldridge, D. J. Multiple trade-offs regulate the effects of woody plant removal on biodiversity and ecosystem functions in global rangelands. Glob. Chang. Biol. 26, 709–720 (2020).Article 

Google Scholar 
De Soyza, A. G., Whitford, W. G., Martinez-Meza, E. & Van Zee, J. W. Variation in creosotebush (Larrea tridentata) canopy morphology in relation to habitat, soil fertility and associated annual plant communities. Am. Nat. 137, 13–26 (1997).Article 

Google Scholar 
Breemen, N. V. Nutrient cycling strategies. Plant Soil 168, 321–326 (1995).Li, J., Gilhooly, W. P. III., Okin, G. S. & Blackwell, J. III. Abiotic processes are insufficient for fertile island development: a 10-year artificial shrub experiment in a desert grassland. Geophys. Res. Lett. 44, 2245–2253 (2017).Article 

Google Scholar 
Ward, D. et al. Large shrubs increase soil nutrients in a semi-arid savanna. Geoderma 310, 153–162 (2018).Article 
CAS 

Google Scholar 
Miwa, C. Persistence of Western Juniper Resource Islands following Canopy Removal. MSc thesis, Oregon State Univ. (2007).Zhou, L. et al. Shrub-encroachment induced alterations in input chemistry and soil microbial community affect topsoil organic carbon in an Inner Mongolian grassland. Biogeochemistry 136, 311–324 (2017).Article 
CAS 

Google Scholar 
Kwok, A. B. C. & Eldridge, D. J. The influence of shrub species and fine-scale plant density on arthropods in a semiarid shrubland. Rangel. J. 38, 381–389 (2016).Article 

Google Scholar 
Young, J. A., Evans, R. A. & Rimbey, C. Weed control and revegetation following western juniper (Juniperus occidentalis) control. Weed Sci. 33, 513–517 (1985).Article 

Google Scholar 
Wiedemann, H. T. & Kelly, P. J. Turpentine (Eremophila sturtii) control by mechanical uprooting. Rangel. J. 23, 173–181 (2001).Article 

Google Scholar 
Bowker, M. A., Belnap, J., Chaudhary, V. B. & Johnson, N. C. Revisiting classic water erosion models in drylands: the strong impact of biological soil crusts. Soil Biol. Biochem. 40, 2309–2316 (2008).Article 
CAS 

Google Scholar 
Ding, J. & Eldridge, D. J. Biotic and abiotic effects on biocrust cover vary with microsite along an extensive aridity gradient. Plant Soil 450, 429–441 (2020).Article 
CAS 

Google Scholar 
Blaum, N., Seymour, C., Rossmanith, E., Schwager, M. & Jeltsch, F. Changes in arthropod diversity along a land use driven gradient of shrub cover in savanna rangelands: identification of suitable indicators. Biodivers. Conserv. 18, 1187–1199 (2009).Article 

Google Scholar 
Eldridge, D. J., Poore, A., Ruiz-Colmenero, M., Letnic, M. & Soliveres, S. Ecosystem structure, function and composition in rangelands are negatively affected by livestock grazing. Ecol. Appl. 26, 1273–1283 (2016).Article 

Google Scholar 
Maestre, F. T. & Cortina, J. Insights into ecosystem composition and function in a sequence of degraded semiarid steppes. Restor. Ecol. 12, 494–502 (2004).Article 

Google Scholar 
Nakagawa, S. in Ecological Statistics: Contemporary Theory and Application (eds Fox, G. A. et al.) Ch. 4 (Oxford Univ. Press, 2015).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).Article 

Google Scholar 
Tavşanoğlu, Ç. & Pausas, J. G. A functional trait database for mediterranean basin plants. Sci. Data 5, 180135 (2018).Article 

Google Scholar 
The PLANTS Database (USDA, 2019); https://plants.usda.gov/Kattge, J. et al. TRY—a global database of plant traits. Glob. Chang. Biol. 17, 2905–2935 (2011).Article 

Google Scholar 
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

Google Scholar 
Mallen-Cooper, M. et al. Global synthesis reveals strong multifaceted effects of eucalypts on soils. Glob. Ecol. Biogeogr. 31, 1667–1678 (2022).Article 

Google Scholar 
Chen, X., Chen, H. Y. & Chang, S. X. Meta-analysis shows that plant mixtures increase soil phosphorus availability and plant productivity in diverse ecosystems. Nat. Ecol. Evol. 6, 1112–1121 (2022).Article 

Google Scholar 
Noble, D. W. A., Lagisz, M., O’dea, R. E. & Nakagawa, S. Nonindependence and sensitivity analyses in ecological and evolutionary meta-analyses. Mol. Ecol. 26, 2410–2425 (2017).Article 

Google Scholar 
Nakagawa, S. & Santos, E. Methodological issues and advances in biological meta-analysis. Ecol. Evol. 26, 1253–1274 (2012).Article 

Google Scholar 
Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge Univ. Press, 2006).Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar 
Archer, E. rfPermute v2.1.1 (R Foundation for Statistical Computing, 2010).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).Stefan, V. & Levin, S. plotbiomes: plot Whittaker biomes with ggplot2 (R package version 0009001, 2021).Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R. J. 5, 144–161 (2013).Article 

Google Scholar 
R Core Team. MOSR connections (R Foundation for Statistical Computing, 2013). More

Baker, A. C., Glynn, P. W. & Riegl, B. Climate change and coral reef bleaching: an ecological assessment of long-term impacts, recovery trends and future outlook. Estuar. Coast Shelf Sci. 80, 435–471 (2008).Article 

Google Scholar 
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).Article 
CAS 

Google Scholar 
Normille, D. El Niño’s warmth devastating reefs worldwide. Science 352, 2015–2016 (2016).
Google Scholar 
Morikawa, M. K. & Palumbi, S. R. Using naturally occurring climate resilient corals to construct bleaching-resistant nurseries. Proc Natl Acad Sci USA 116, 10586–10591 (2019).Safaie, A. et al. High frequency temperature variability reduces the risk of coral bleaching. Nat. Commun. 9, 1671 (2018).Article 

Google Scholar 
Thomas, L. et al. Mechanisms of thermal tolerance in Reef-building corals across a fine-grained environmental mosaic: lessons from Ofu. Am. Samoa. Front Mar. Sci. 4, 434 (2018).Article 

Google Scholar 
Kenkel, C. D., Meyer, E. & Matz, M. V. Gene expression under chronic heat stress in populations of the mustard hill coral (Porites astreoides) from different thermal environments. Mol. Ecol. 22, 4322–4334 (2013).Article 
CAS 

Google Scholar 
Gomulkiewicz, R. & Holt, R. D. When does evolution by natural selection prevent extinction? Evolution 49, 201–207 (1995).
Google Scholar 
Bruno, J. F., Siddon, C. E., Witman, J. D., Colin, P. L. & Toscano, M. A. El Niño related coral bleaching in Palau, western Caroline Islands. Coral Reefs 20, 127–136 (2001).Article 

Google Scholar 
Golbuu, Y. et al. Palau’s coral reefs show differential habitat recovery following the 1998-bleaching event. Coral Reefs 26, 319–332 (2007).Article 

Google Scholar 
van Woesik, R. et al. Climate-change refugia in the sheltered bays of Palau: analogs of future reefs. Ecol. Evol. 2, 2474–2484 (2012).Article 

Google Scholar 
Barkley, H. C. & Cohen, A. L. Skeletal records of community-level bleaching in Porites corals from Palau. Coral Reefs 35, 1407–1417 (2016).Article 

Google Scholar 
Gouezo, M. et al. Drivers of recovery and reassembly of coral reef communities. Proc. R. Soc. B Biol. Sci. 286, 20182908 (2019).Shamberger, K. E. F. et al. Diverse coral communities in naturally acidified waters of a Western Pacific reef. Geophys. Res. Lett. 41, 499–504 (2014).Article 

Google Scholar 
Barkley, H. C. et al. Changes in coral reef communities across a natural gradient in seawater pH. Sci. Adv. 1, e1500328 (2015).Article 

Google Scholar 
Fabricius, K. E., Mieog, J. C., Colin, P. L., Idip, D. & van Oppen, M. J. H. Identity and diversity of coral endosymbionts (zooxanthellae) from three Palauan reefs with contrasting bleaching, temperature and shading histories. Mol. Ecol. 13, 2445–2458 (2004).Article 
CAS 

Google Scholar 
Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S. & Hoegh-Guldberg, O. Ocean acidification causes bleaching and productivity loss in coral reef builders. Proc. Natl Acad. Sci. USA 105, 17442–17446 (2008).Article 
CAS 

Google Scholar 
Gibbin, E. M., Putnam, H. M., Gates, R. D., Nitschke, M. R. & Davy, S. K. Species-specific differences in thermal tolerance may define susceptibility to intracellular acidosis in reef corals. Mar. Biol. 162, 717–723 (2015).Article 
CAS 

Google Scholar 
Boulay, J. N., Hellberg, M. E., Cortés, J. & Baums, I. B. Unrecognized coral species diversity masks differences in functional ecology. Proc. R. Soc. B Biol. Sci. 281, 20131580 (2013).Baums, I. B., Boulay, J. N., Polato, N. R. & Hellberg, M. E. No gene flow across the Eastern Pacific Barrier in the reef-building coral Porites lobata. Mol. Ecol. 21, 5418–5433 (2012).Article 

Google Scholar 
Forsman, Z. H., Wellington, G. M., Fox, G. E. & Toonen, R. J. Clues to unraveling the coral species problem: Distinguishing species from geographic variation in Porites across the Pacific with molecular markers and microskeletal traits. PeerJ 3, e751 (2015).Article 

Google Scholar 
Levas, S. J., Grottoli, A. G., Hughes, A., Osburn, C. L. & Matsui, Y. Physiological and biogeochemical traits of bleaching and recovery in the mounding species of coral Porites lobata: implications for resilience in mounding corals. PLoS ONE 8, e63267 (2013).Article 
CAS 

Google Scholar 
Linsley, B. K. et al. Coral carbon isotope sensitivity to growth rate and water depth with Paleo-sea level implications. Nat. Commun. 10, 1–9 (2019).
Google Scholar 
Peyrot-Clausade, M., Hutchings, P. & Richard, G. Temporal variations of macroborers in massive Porites lobata on Moorea, French Polynesia. Coral Reefs 11, 161–166 (1992).Article 

Google Scholar 
Nanami, A. & Nishihira, M. Microhabitat association and temporal stability in reef fish assemblages on massive Porites microatolls. Ichthyol. Res. 51, 165–171 (2004).Article 

Google Scholar 
Cantin, N. E. & Lough, J. M. Surviving coral bleaching events: porites growth anomalies on the Great Barrier Reef. PLoS ONE 9, e88720 (2014).Article 

Google Scholar 
Carilli, J. E., Norris, R. D., Black, B., Walsh, S. M. & Mcfield, M. Century-scale records of coral growth rates indicate that local stressors reduce coral thermal tolerance threshold. Glob. Chang Biol. 16, 1247–1257 (2010).Article 

Google Scholar 
Cantin, N. E., Cohen, A. L., Karnauskas, K. B., Tarrant, A. M. & McCorkle, D. C. Ocean warming slows coral growth in the central Red Sea. Science 329, 322–325 (2010).Article 
CAS 

Google Scholar 
Lough, J. M. & Cooper, T. F. New insights from coral growth band studies in an era of rapid environmental change. Earth Sci. Rev. 108, 170–184 (2011).Article 
CAS 

Google Scholar 
Mollica, N. R. N. et al. Skeletal records of bleaching reveal different thermal thresholds of Pacific coral reef assemblages. Coral Reefs 38, 743–757 (2019).Article 

Google Scholar 
Barkley, H. C. et al. Repeat bleaching of a central Pacific coral reef over the past six decades (1960–2016). Commun. Biol. 1, 177 (2018).DeCarlo, T. M. & Cohen, A. L. Dissepiments, density bands and signatures of thermal stress in Porites skeletons. Coral Reefs 36, 749–761 (2017).Article 

Google Scholar 
DeCarlo, T. M. et al. Acclimatization of massive reef-building corals to consecutive heatwaves. Proc. R. Soc. B 286, 20190235 (2019).DeCarlo, T. M. The past century of coral bleaching in the Saudi Arabian central Red Sea. PeerJ 8, e10200 (2020).Article 

Google Scholar 
Silverstein, R. N., Cunning, R. & Baker, A. C. Change in algal symbiont communities after bleaching, not prior heat exposure, increases heat tolerance of reef corals. Glob. Chang Biol. 21, 236–249 (2015).Article 

Google Scholar 
Fabricius, K. E. Effects of irradiance, flow, and colony pigmentation on the temperature microenvironment around corals: Implications for coral bleaching? Limnol. Oceanogr. 51, 30–37 (2006).Article 

Google Scholar 
Edmunds, P. J., Putnam, H. M. & Gates, R. D. Photophysiological consequences of vertical stratification of Symbiodinium in tissue of the coral Porites lutea. Biol. Bull. 223, 226–235 (2012).Article 
CAS 

Google Scholar 
Smith, L. W., Wirshing, H., Baker, A. C. & Birkeland, C. Environmental versus genetic influences on growth rates of the corals Pocillopora eydouxi and Porites lobata. Pac. Sci. 62, 57–69 (2008).Article 

Google Scholar 
Kenkel, C. D. & Bay, L. K. Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition. PeerJ 2018, e6047 (2018).Article 

Google Scholar 
Sunde, J., Yıldırım, Y., Tibblin, P. & Forsman, A. Comparing the performance of microsatellites and RADseq in population genetic studies: analysis of data for Pike (Esox lucius) and a synthesis of previous studies. Front. Genet. 11, 218 (2020).Article 

Google Scholar 
Barkley, H. C., Cohen, A. L., McCorkle, D. C. & Golbuu, Y. Mechanisms and thresholds for pH tolerance in Palau corals. J. Exp. Mar. Biol. Ecol. 489, 7–14 (2017).Article 
CAS 

Google Scholar 
Mollica, N. R. et al. Ocean acidification affects coral growth by reducing skeletal density. Proc. Natl Acad. Sci. USA 115, 1754–1759 (2018).Article 
CAS 

Google Scholar 
DeCarlo, T. M. et al. Coral macrobioerosion is accelerated by ocean acidification and nutrients. Geology 43, 7–10 (2014).Article 

Google Scholar 
Manzello, D. P. et al. Role of host genetics and heat-tolerant algal symbionts in sustaining populations of the endangered coral Orbicella faveolata in the Florida Keys with ocean warming. Glob. Chang Biol. 25, 1016–1031 (2019).Article 

Google Scholar 
Rippe, J. P., Dixon, G., Fuller, Z. L., Liao, Y. & Matz, M. Environmental specialization and cryptic genetic divergence in two massive coral species from the Florida Keys Reef Tract. Mol. Ecol. 1–17 https://doi.org/10.1111/mec.15931 (2021).Schoepf, V. et al. Thermally variable, macrotidal Reef habitats promote rapid recovery from mass coral bleaching. Front. Mar. Sci. 7, 245 (2020).Article 

Google Scholar 
Dixon, G. B. et al. Genomic determinants of coral heat tolerance across latitudes. Science 348, 1460–1462 (2015).Article 
CAS 

Google Scholar 
Baums, I. B. et al. Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic. Ecol. Appl. 29, 1–23 (2019).Article 

Google Scholar 
Gosselin, L. A. & Qian, P.-Y. Juvenile mortality in benthic marine invertebrates. Mar. Ecol. Prog. Ser. 146, 265–282 (1997).Article 

Google Scholar 
Gouezo, M. et al. Modelled larval supply predicts coral population recovery potential following disturbance. Mar. Ecol. Prog. Ser. 661, 127–145 (2021).Golbuu, Y., Gouezo, M., Kurihara, H., Rehm, L. & Wolanski, E. Long-term isolation and local adaptation in Palau’s Nikko Bay help corals thrive in acidic waters. Coral Reefs 35, 909–918 (2016).Article 

Google Scholar 
Golbuu, Y. et al. Predicting coral recruitment in Palau’s complex reef archipelago. PLoS ONE 7, e50998 (2012).Article 
CAS 

Google Scholar 
Barshis, D. J., Birkeland, C., Toonen, R. J., Gates, R. D. & Stillman, J. H. High-frequency temperature variability mirrors fixed differences in thermal limits of the massive coral Porites lobata (Dana, 1846). J. Exp. Biol. jeb.188581 https://doi.org/10.1242/jeb.188581 (2018).Shamberger, K. E. F., Lentz, S. J. & Cohen, A. L. Low and variable ecosystem calcification in a coral reef lagoon under natural acidification. Limnol. Oceanogr. https://doi.org/10.1002/lno.10662 (2017).Cacciapaglia, C. & van Woesik, R. Climate-change refugia: shading reef corals by turbidity. Glob. Chang Biol. 22, 1145–1154 (2016).Article 

Google Scholar 
Anthony, K. R. Enhanced energy status of corals on coastal, high-turbidity reefs. Mar. Ecol. Prog. Ser. 319, 111–116 (2006).Article 

Google Scholar 
Houlbrèque, F. & Ferrier-Pagès, C. Heterotrophy in tropical scleractinian corals. Biol. Rev. Camb. Philos. Soc. 84, 1–17 (2009).Article 

Google Scholar 
Aichelman, H. E. et al. Heterotrophy mitigates the response of the temperate coral Oculina arbuscula to temperature stress. Ecol. Evol. 6, 6758–6769 (2016).Article 

Google Scholar 
Gómez‐Corrales, M. & Prada, C. Cryptic lineages respond differently to coral bleaching. Mol. Ecol. 0, 1–9 (2020).
Google Scholar 
Fifer, J. E., Yasuda, N., Yamakita, T., Bove, C. B. & Davies, S. W. Genetic divergence and range expansion in a western North Pacific coral. Sci. Total Environ. 152423 https://doi.org/10.1016/J.SCITOTENV.2021.152423 (2021).Euclide, P. T. et al. Attack of the PCR clones: rates of clonality have little effect on RAD-seq genotype calls. Mol. Ecol. Resour. 20, 66–78 (2020).Article 
CAS 

Google Scholar 
Noonan, S. H. C., DiPerna, S., Hoogenboom, M. O. & Fabricius, K. E. Effects of variable daily light integrals and elevated CO2 on the adult and juvenile performance of two Acropora corals. Mar. Biol. 169, 1–15 (2022).Article 

Google Scholar 
Martins, C. P. P. et al. Growth response of reef-building corals to ocean acidification is mediated by interplay of taxon-specific physiological parameters. Front. Mar. Sci. 0, 879 (2022).
Google Scholar 
Bairos-Novak, K. R., Hoogenboom, M. O., van Oppen, M. J. H. & Connolly, S. R. Coral adaptation to climate change: meta-analysis reveals high heritability across multiple traits. Glob. Chang. Biol. 27, 5694–5710 (2021).Article 
CAS 

Google Scholar 
Kenkel, C. D., Setta, S. P. & Matz, M. V. Heritable differences in fitness-related traits among populations of the mustard hill coral, Porites astreoides. Heredity 115, 509–516 (2015).Article 
CAS 

Google Scholar 
Dziedzic, K. E., Elder, H., Tavalire, H. & Meyer, E. Heritable variation in bleaching responses and its functional genomic basis in reef-building corals (Orbicella faveolata). Mol. Ecol. 28, 2238–2253 (2019).Article 

Google Scholar 
Quigley, K. M., Bay, L. K. & Oppen, M. J. H. Genome‐wide SNP analysis reveals an increase in adaptive genetic variation through selective breeding of coral. Mol. Ecol. 2176–2188 https://doi.org/10.1111/mec.15482 (2020).Veron, J. E. N. Corals of the World (Australian Institute of Marine Science, 2000).Polato, N. R., Concepcion, G. T., Toonen, R. J. & Baums, I. B. Isolation by distance across the Hawaiian Archipelago in the reef-building coral Porites lobata. Mol. Ecol. 19, 4661–4677 (2010).Article 
CAS 

Google Scholar 
Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: an analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).Article 

Google Scholar 
Puritz, J. B., Hollenbeck, C. M. & Gold, J. R. dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms. PeerJ 2, e431 (2014).Article 

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 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv (2013).Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. ArXiv (2012).Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).Article 
CAS 

Google Scholar 
Kopelman, N. M., Mayzel, J., Jakobsson, M. & Rosenberg, N. A. CLUMPAK: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).Article 
CAS 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
CAS 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).Article 
CAS 

Google Scholar 
Puechmaille, S. J. The program STRUCTURE does not reliably recover the correct population structure when sampling is uneven: subsampling and new estimators alleviate the problem. Mol. Ecol. Resour. 16, 608–627 (2016).Article 

Google Scholar 
Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).Article 
CAS 

Google Scholar 
Goudet, J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Molecular Ecology Notes. 5, 184–186 (2005).Zeileis, A. & Grothendieck, G. zoo: S3 infrastructure for regular and irregular time series. J. Stat. Softw. 14, 1–27 (2005).Ryan, J. A. & Ulrich, J. M. xts: eXtensible Time Series. Package at https://cran.r-project.org/package=xts (2018).LaJeunesse, T. C. Diversity and community structure of symbiotic dinoflagellates from Caribbean coral reefs. Mar. Biol. 141, 387–400 (2002).Article 

Google Scholar 
LaJeunesse, T. C. & Trench, R. K. Biogeography of two species of Symbiodinium (Freudenthal) inhabiting the intertidal sea anemone Anthopleura elegantissima (Brandt). Biol. Bull. 199, 126–134 (2000).Article 
CAS 

Google Scholar  More