Philippa Kaur

Further details about radiocarbon and thermal analysis, isotopic partitioning procedures and quantification of their uncertainty, and statistical analyses can be found in Supplementary Methods.Study soils, experimental design and soil samplingWe selected three soil types: eutric cambisol, chromic vertisol and silandic andosol70. The three soil profiles studied were found in long-term semi-natural grasslands located relatively close to each other ( More

Hoegh-Guldberg O, Smith JG. The effect of sudden changes in temperature, light, and salinity on the population density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper) and Seriatopora hysterix (Dana). J Exp Mar Biol Ecol. 1989;129:279–303.Article 

Google Scholar 
Glynn PW. Coral reef bleaching: ecological perspectives. Coral Reefs. 1993;12:1–17.Article 

Google Scholar 
Berkelmans R, van Oppen MJH. The role of zooxanthellae in the thermal tolerance of corals: a “nugget of hope” for coral reefs in an era of climate change. Proc R Soc B: Biol Sci. 2006;273:2305–12.Article 

Google Scholar 
Cunning R, Gillette P, Capo T, Galvez K, Baker AC. Growth tradeoffs associated with thermotolerant symbionts in the coral Pocillopora damicornis are lost in warmer oceans. Coral Reefs. 2015;34:155–60.Article 

Google Scholar 
Scharfenstein HJ, Chan WY, Buerger P, Humphrey C, van Oppen MJH. Evidence for de novo acquisition of microalgal symbionts by bleached adult corals. ISME J. 2022;16:1676–9.Article 

Google Scholar 
Goulet TL. Most corals may not change their symbionts. Mar Ecol Prog Ser. 2006;321:1–7.Article 

Google Scholar 
Jones A, Berkelmans R. Potential costs of acclimatization to a warmer climate: growth of a reef coral with heat tolerant vs. sensitive symbiont types. PLoS ONE. 2010;5:e10437.Article 

Google Scholar 
van Oppen MJH, Oliver JK, Putnam HM, Gates RD. Building coral reef resilience through assisted evolution. Proc R Soc B: Biol Sci. 2015;112:2307–13.
Google Scholar 
Buerger P, Alvarez C, Coppin CW, Pearce SL, Chakravarti LJ, Oakeshott JG, et al. Heat-evolved microalgal symbionts increase coral bleaching tolerance. Sci Adv. 2020;6:eaba2498.Kuffner IB, Toth LT. A geological perspective on the degradation and conservation of western Atlantic coral reefs. Conserv Biol: J Soc Conserv Biol. 2016;30:706–15.Article 

Google Scholar 
Young CN, Schopmeyer SA, Lirman D. A review of reef restoration and Coral propagation using the threatened genus Acropora in the Caribbean and western Atlantic. Bull Mar Sci. 2012;88:1075–98.Article 

Google Scholar 
Reich HG, Kitchen SA, Stankiewicz KH, Devlin-Durante M, Fogarty ND, Baums IB. Genomic variation of an endosymbiotic dinoflagellate (Symbiodinium fitti) among closely related coral hosts. Mol Ecol. 2021;30:3500–14.Article 

Google Scholar 
Baums IB, Devlin-Durante MK, Lajeunesse TC. New insights into the dynamics between reef corals and their associated dinoflagellate endosymbionts from population genetic studies. Mol Ecol. 2014;23:4203–15.Article 

Google Scholar 
Gantt SE, Keister E, Manfroy A, Merck D, Fitt W, Muller E, et al. Wild and nursery-raised corals: comparative physiology of two framework coral species. Coral Reefs. (In Press).Hume BCC, Smith EG, Ziegler M, Hugh J, Warrington M, Burt J, et al. SymPortal: a novel analytical framework and platform for coral algal symbiont next-generation sequencing ITS2 profiling. Mol Ecol Resour. 2019;19:1063–80.Article 

Google Scholar 
Randall CJ, Negri AP, Quigley KM, Foster T, Ricardo GF, Webster NS, et al. Sexual production of corals for reef restoration in the Anthropocene. Mar Ecol Prog Ser. 2020;635:203–32.Article 

Google Scholar 
Bay LK, Cumbo VR, Abrego D, Kool JT, Ainsworth TD, Willis BL. Infection dynamics vary between Symbiodinium types and cell surface treatments during establishment of endosymbiosis with coral larvae. Diversity. 2011;3:356–74.Article 

Google Scholar 
Abrego D, van Oppen MJH, Willis BL. Highly infectious symbiont dominates initial uptake in coral juveniles. Mol Ecol. 2009;18:3518–31.Article 

Google Scholar 
Cunning R, Silverstein RN, Baker AC. Investigating the causes and consequences of symbiont shuffling in a multi-partner reef coral symbiosis under environmental change. Proc R Soc B: Biol Sci. 2015;282:20141725.Chamberland VF, Petersen D, Latijnhouwers KRW, Snowden S, Mueller B, Vermeij MJA. Four-year-old Caribbean Acropora colonies reared from field-collected gametes are sexually mature. Bull Mar Sci. 2016;92:263–4.Silverstein RN, Correa AMS, Baker AC. Specificity is rarely absolute in coral–algal symbiosis: implications for coral response to climate change. Proc R Soc B: Biol Sci. 2012;279:2609–18.Article 

Google Scholar  More

Tularemia affects animal welfare, human health, and the environment and is thus better approached from a one-health perspective27. Several studies in the Northern hemisphere28, and more recently in Australia15,16, have provided a vital research track in the epidemiology of this disease. In contrast, studies in Africa are too limited and scarce. The aim of this study was to investigate the presence of tularemia in wild leporids collected in Northern Algeria. These animals are highly susceptible to F. tularensis infection and considered sentinel hosts for surveillance of tularemia. The strategy we used to detect F. tularensis in leporids mainly used molecular, histological and immunohistochemical analyzes of tissues taken from animals found dead or hunted. To the best of our knowledge, detection of F. tularensis by PCR or culture has not been previously reported in wild leporidae in Algeria or other African countries.Animal tissue samples were tested using three qPCR assays of variable sensitivity and specificity. The Type B-qPCR test targets a specific junction between ISFtu2 and a flanking 3′ region, which is considered specific for F. tularensis subsp. holarctica26, the only tularemia agent found in Europe and Asia. The Tul4-qPCR assay targets a simple copy gene encoding a surface protein, which can be found in the genome of all F. tularensis subspecies causing tularemia and that of the aquatic bacterium F. novicida. Because F. novicida has never been isolated from lagomorphs or other animal species, and very rarely from human29, a positive Tul4 qPCR for the studied tissue samples likely indicated the presence of F. tularensis DNA. The ISFtu2 qPCR is considered highly sensitive because multiple copies of this insertion sequence are found in the F. tularensis genome. However, it lacks specificity because ISFtu2 is also found in many other Francisella species25.Two animals were considered “probable” tularemia cases because some of their samples were positive for the three qPCR tests. Ten animals were considered “possible” tularemia cases because their samples were positive for the ISFtu2 and Tul4 qPCRs but not the Type B qPCR. Finally 19 leporids were “uncertain” cases because only samples positive for the ISFtu2 qPCR were found. For the remaining 43 animals, all the tested samples were negative for the three qPCRs. Overall, we detected F. tularensis DNA-positive samples in 12/74 (16.21%) leporids, which strongly suggest that tularemia is present in the lagomorph population of the study area. The positive Type B qPCR tests in two animals suggested that F. tularensis subsp. holarctica could be the involved subspecies. We did not confirm these data by isolating F. tularensis from the studied leporids. However, the isolation of this pathogen from human or animal samples is tedious and has a low sensitivity13. Moreover, most of our samples were not appropriate for F. tularensis culture because of their long-term preservation in ethanol 70° or 10% formalin. Further study using fresh (non-fixed) tissue samples from dead leporids collected in the same study area is needed to definitively confirm the presence of tularemia in these animals and characterize the F. tularensis subspecies and genotypes involved.Although PCR is usually more sensitive than culture for detecting F. tularensis, it also has some limitations. Firstly, the DNA extraction from organs preserved in ethanol for several months was difficult although easier for spleen than for liver samples. Some tissue samples could be lysed only after overnight incubation with proteinase K. Secondly, tissue samples contained PCR inhibitors as demonstrated by better DNA amplification from some samples after their dilution in PCR grade water. To reduce the effect of PCR inhibitors, organ samples with negative qPCR were retested using Bovine Serum Albumin (BSA) and the Real-time PCR system TaqMan (Applied Biosystems, Munich, Germany)30. Finally, DNA regions to be amplified were optimized to obtain high sensitivity and specificity of qPCR tests.IHC detection of F. tularensis in formalin-fixed tissue can be helpful for tularemia diagnosis31,32. For one possible tularemia case, F. tularensis could be detected on immunohistochemical (IHC) examination of a liver sample using a specific anti-F. tularensis antibody. The intensity and localization of positive staining were comparable to those previously recorded for other animals32,33. IHC did not provide interpretable findings for four other tested specimens. Such negative results might be explained by an inhomogeneous distribution of infectious foci in the involved organs as well as a low bacterial inoculum in infected tissues. This has been previously demonstrated in tularemia granulomatous lesions in cell types like epithelial cells of the kidney, testis, and epididymis, hepatocytes, and bronchiolar epithelial cells31. Besides, IHC is a delicate technology whose results are highly dependent on the quality and fixation time of the organ tissues34. IHC analysis of dead animal tissues remains challenging, especially in case of tissue necrosis34.In our limited case series we found a F. tularensis infection prevalence in leporids of 2.7% (2/74) for probable tularemia cases and 16.2% (12/74) when considering both probable and posible cases. We cannot make a guess about the prevalence of tularemia because our series is not representative of the general lagomorph population in the study area. In Germany, F. tularensis DNA was detected in 1.1% of European Brown hares and 2.4% of wild rabbits collected between 2009 and 201435. Higher infection rates were reported in the same country, including 11.8% (100/848 animals) in hares collcted in the North Rhine-Westphalia region36 and 30% (55/179) in brown hares collected between 2010 and 2016 in Baden-Wuerttemberg37. In Hungary, the prevalence of tularemia in hares was evaluated at 4.9–5.3%38. In Portugal, prevalences of 4.3% and 6.3% were reported in brown hares and wild rabbits, respectively39. However, the comparison of the reported tularemia prevalences in leporids is irrelevant because studies involved different animal species and geographic areas, and used different methods for F. tularensis detection.Two possibilities could explain the lack of detection of tularemia in Algeria before this study. The first hypothesis is that this disease was not searched for in previous years, while it could have been present in this country for decades. The second hypothesis is that tularemia was recently imported in Algeria. Migratory birds may have been involved in the long-distance spread of F. tularensis40. These hosts can be infested by ectoparasites such as ticks which are the primary vectors of tularemia41,42. They can also spread the bacteria in the hydro-telluric environment through their secretions and feces18,43,44. An alternative possibility is that F. tularensis-infected animals (especially game animals) have been imported in Algeria from endemic countries. Whatever the mode of introduction of tularemia in Algeria, the dissemination of this disease over time might have been facilitated by the ability of F. tularensis to infect multiple hosts and its better survival in a cool environment45, which characterizes Northern Algeria climate. The emergence or re-emergence of tularemia in other countries has been related to climate change, human-mediated movement of infected animals, and wartime resulting in a significant rise of F. tularensis infections in the rodent populations39,46.In our study, infected animals were collected throughout 4 years, although more frequently in autumn. Probable and possible tularemia cases were mainly collected during the hunting season (i.e., September, October, November, and December). Animals could not be collected in February because of heavy rains and in May and June because it corresponds to female leporids’ lactation period. In most endemic countries, tularemia cases are typically more frequent in late spring, the summer months, and early autumn37,47,48,49,50. Occasionally, fatal tularemia cases in hares have been predominantly reported during the cold season11,51. The climatic conditions can affect tularemia outbreaks in animals, depending on the reservoir involved and the predominant modes of infection52.We detected tularemia more frequently in female than in male hares, and the reverse was true for wild rabbits. The prevalence of tularemia in male or female lagomorphs varies between studies. In Sweden, Morener et al.50 reported a tularemia case series only involving male hares. In the same country, Borg et al.50 observed an overrepresentation of females in the epizootic of 1967. They suggested that, compared to males, females had a higher risk of exposure to infected mosquitoes or were more vulnerable to tularemia because they were pregnant or had just given birth to a litter50. Tularemia was found in a few juveline leporids, which might be explained by a shorter exposure time to F. tularensis, a higher death rates due to higher susceptibility to F. tularensis infection or easier predation by their natural enemies, or more frequent hunting of adults compared to the juveniles53.Tularemia is usually more frequently detected in leporids found dead than in hunted animals. As an example, a German study reported a higher prevalence of tularemia in hares found dead (2.9%) than in hunted ones (0.7%)35. In our study, most qPCR-positive animals were hunted. Our study might not be representative of the prevalence of tularemia in either population because most collected animals had been hunted.The incubation period and clinical presentation of tularemia in leporids vary according to the species considered. Tularemia is typically an acute disease in mountain hares (Lepus timidus) in Scandinavia and has a chronic pattern in European brown hares (Lepus europaeus) in Central Europe50. The incubation time and clinical presentation of tularemia can be different in Cape hares (Lepus capensis). Wild rabbits are less sensitive to F. tularensis infection than hares31,39,54. An extended incubation period and chronic evolution of tularemia would facilitate the detection of F. tularensis in infected animals. In our study, a similar tularemia prevalence was found in the Cape hares and wild rabbits, which might reflect exposure to a same biotope area and environmental reservoirs of F. tularensis.The pathological lesions of tularemiia in leporids can vary according to the F. tularensis strain involved, the mode and route of infection, and the susceptibility and immune status of the host32,50. In the European brown hares, granulomas with central necrosis have been reported in the lungs and kidneys and occasionally in the liver, spleen, bone marrow, and lymph nodes50. In contrast, only acute necrosis in the liver, spleen, bone marrow, and lymph nodes have been found in Lepus timudus hares in Sweden50. The lesions in the Japanese hare (Lepus brachyurus angustidens) are comparable to those of Lepus timidus, except for cutaneous, lung, brain, and adrenal gland lesions32. In the European rabbit, Oryctolagus cuniculus, tularemia is not associated with identifiable macroscopic tissue lesions39,55. To our knowledge, no reports describing post-mortem lesions in Cape hares with tularemia are available. In this study, similar lesions were found in hares and wild rabbits except necrotic foci only observed in some wild rabbit organs (such as liver, lungs, kidney, ovary). Most animals had pathological lesions of pneumonia, gastritis and enteritis. Kidney lesions and adrenal glands enlargment were oberved. Necrotic lesions were occasionally found in the lungs, liver, spleen and ovary and hemorrhages in the lungs, liver, and intestines.Tularemia is an arthropod-born disease in most endemic areas14,22,28. In our study, 50% of positive leporids were infested by known tularemia vectors such as ticks (Ixodes ricinus56,57, Rhipicephalus sanguineus39), fleas (Spillopsylus cuniculi58), and lice of lagomorphs (Haemodipsus lepori and Haemodipsus setoni59,60). Ticks are the most significant arthropod vectors of tularemia61. Ticks are frequently involved in the transmission of tularemia in North America, including Dermacentor andersoni, D. variabilis, and Amblyomma americanum57,62,63. In Europe, tick-borne tularemia represents 13% to 26% of human cases57,64. The involved species include D. marginatus, D. reticulatus, I. ricinus, R. sanguineus, and Haemaphysalis concinna65,66. Further research on wild leporid sucking arthropods is needed to confirm the presence and clarify the ecology of F. tularensis in Algeria.Our study reports for the first time the detection of F. tularensis DNA in leporids from Northern Algeria. The markers most in favor of tularemia in the animals studied are the positivity of qPCR tests, in particular, the “type B” qPCR test which amplifies a specific DNA sequence of F. tularensis subsp. holarctica, and a positive immunohistological examination in one animal. Further investigation is needed to confirm our results by the isolation of this pathogen from animal samples and determine the F. tularensis subspecies and genotypes involved. This would allow the characterization of the F. tularensis subspecies and genotypes present in Algeria. Furthermore, our findings push us in future studies to seek tularemia in the Algerian human population. To achieve this, interdisciplinary or trans-disciplinary collaborative efforts underpinned by the One Health concept will be necessary. More

All statistical analyses were performed through R version 4.1.050. Besides the explicitly mentioned packages, the R packages cowplot51, data.table52, dplyr53, ggplot254, itsadug55, purrr56, raster57, sf58, sfheaders59, tibble60 and tidyr61 were key for data handling, data analysis, and plotting. Posterior distributions were summarised through means and credible intervals (CIs). CIs are the highest density intervals, calculated through the package bayestestR62. To summarise multiple posterior distributions, 5000 Monte Carlo simulations were used.Study regionThe study included data from the whole of Switzerland. As an observation unit for records, we chose 1 × 1 km squares (henceforth squares). Switzerland covers 41,285 km2, spanning a large gradient in elevation, climate and land use. It can be divided into five coarse biogeographic regions based on floristic and faunistic distributions and on institutional borders of municipalities63 (Fig. 1b). The Jura is a mountainous but agricultural landscape in the northwest (~4200 km2, 300–1600 m asl; annual mean temperature: ~9.4 °C, annual precipitation: ~1100 mm (gridded climate data here and in the following from MeteoSwiss (https://www.meteoswiss.admin.ch), average 1980–2020, at sites ~500 m asl.)). The Jura is separated from the Alps by the Plateau, which is the lowland region spanning from the southwest to the northeast (~11,300 km2, 250–1400 m asl, mostly below 1000 m asl; ~9.5 °C, ~1100 mm). It is the most densely populated region with most intensive agricultural use. For the Alps, three regions can be distinguished. The Northern Alps (~10,700 km2, 350–4000 m asl; ~9.2 °C, ~1400 mm), which entail the area from the lower Prealps, which are rather densely populated and largely used agriculturally, up to the northern alpine mountain range. The Central Alps (~11,300 km2, 450–4600 m asl; ~9.5 °C, ~800 mm) comprise of the highest mountain ranges in Switzerland and the inner alpine valleys characterised by more continental climate (i.e., lower precipitation). Intensive agricultural land use is concentrated in the lower elevations and agriculture in higher elevations is mostly restricted to grassland areas used for summer grazing. The Southern Alps (~3800 km2, 200–3800 m asl; ~10.4 °C, 1700 mm) range from the southern alpine mountain range down to the lowest elevations of Switzerland and are clearly distinguished from the other regions climatically, as they are influenced by Mediterranean climate, resulting in, e.g., milder winters. Besides differences between biogeographic regions, climate, land use and changes therein vary greatly between different elevations (Supplementary Fig. S9). To account for these differences, we split the regions in two elevation classes at the level of squares. All squares with a mean elevation of less than 1000 m asl were assigned to the low elevation, whereas squares above 1000 m asl were assigned to the high elevation (no squares in the Plateau fell in the high elevation). This resulted in nine bioclimatic zones (Fig. 1b), for which separate species trends were estimated in the subsequent analyses. The threshold of 1000 m asl enabled a meaningful distinction based on the studied drivers (climate and land-use change) and was also determined by the availability of records data (high coverage in all nine bioclimatic zones).Species detection dataWe extracted records of butterflies (refers here to Papilionoidea as well as Zygaenidae moths), grasshoppers (refers here to all Orthoptera) and dragonflies (refers here to all Odonata) from the database curated by info fauna (The Swiss Faunistic Records Centre; metadata available from the GBIF database at https://doi.org/10.15468/atyl1j, https://doi.org/10.15468/bcthst, https://doi.org/10.15468/fcxtjg). This database unites faunistic records made in Switzerland from various sources including both records by private persons and from projects such as research projects, Red-List inventories or checks of revitalisation measures. Only records with a sufficient precision, both temporally (day of recording) and spatially (place of recording known to the precision of 1 km2 or less), were used for analyses. Besides temporal and spatial information, information on the observer and the project (if any) was obtained for each record. All records made by a person/project on a day in a square were attributed to one visit, which was later used as replication unit to model the observation process (see below).We included records from the focal time range 1980–2020. Additionally, we included records from 1970–1979 for butterflies in occupancy-detection models to increase the robustness of mean occupancy estimates. We excluded the mean occupancy estimates for these additional years from further analyses to cover the same period for all groups. Prior to analyses, following the approach in ref. 26, we excluded observations of non-adult stages and observations from squares that only were visited in 1 year of the studied period, because these would not contain any information on change between years64. This resulted in 18,018 squares (15,248 for butterflies, 9870 for grasshoppers, 5188 for dragonflies) and 1,448,134 records (879,207 butterflies, 272,863 grasshoppers, 296,064 dragonflies) that we included in the analyses (Supplementary Fig. S2). The three datasets for the different groups were treated separately for occupancy-detection modelling, following the same procedures for all three groups. To determine detections and non-detections for each species and visit, which could then be used for occupancy-detection modelling, we only included visits that (a) did not originate from a project, which had a restricted taxonomic focus not including the focal species, (b) were not below the 5% quantile or above the 95% quantile of the day of the year at which the focal species has been recorded26 and (c) were from a bioclimatic zone, from which the focal species was recorded at least once.Occupancy-detection modelsWe used occupancy-detection models65,66 to estimate annual mean occupancy of squares for the whole of Switzerland and for the nine bioclimatic zones for each species (i.e., mean number of squares occupied by a species), mostly following the approach in ref. 26. We fitted a separate model for each species, based on different datasets for the three groups. We included only species that were recorded in any square in at least 25% of all analysed years. Occupancy-detection models are hierarchical models in which two interconnected processes are modelled jointly, one of which describes occurrence probability (ecological process; used to infer mean occupancy), whereas the other describes detection probability (observation process)65. The two processes are modelled through logistic regression models. The occupancy model estimates occurrence probability for all square and year combinations, whereas the observation model estimates the probability that a species has been detected by an observer during a visit. More formally, each square i in the year t has the latent occupancy status zi,t, which may be either 1 (present) or 0 (absent). zi,t depends on the occurrence probability ψi,t as follows$${z}_{i,t}sim {{{mbox{Bern}}}}left({psi }_{i,t}right)$$
(1)
The occupancy status is linked to the detection/non-detection data yi,t,j at square i in year t at visit j as$${y}_{i,t,, j}{{|}}{z}_{i,t}sim {mathrm {Bern}}({z}_{i,t}{p}_{i,t,j})$$
(2)
where pi,t,j is the detection probability.The regression model for occurrence probability (occupancy model) looked as follows$${{mbox{logit}}}({psi }_{i,t})={mu }_{o}+{beta }_{o1}{{{{{rm{elevatio}}}}}}{{{{{{rm{n}}}}}}}_{i}+{beta }_{o2}{{{{{rm{elevatio}}}}}}{{{{{{rm{n}}}}}}}_{i}^{2}+{alpha }_{o1,i}+{alpha }_{o2,i}+{gamma }_{r(i),t}$$
(3)
with μo being the global intercept, elevationi being the scaled elevation above sea level and αo1,i, αo2,i and γr(i),t being the random effects for fine biogeographic region (12 levels, Supplementary Fig. S10; these were again defined based on floristic and faunistic distributions and followed institutional borders63), square and year. The random effects for fine biogeographic region and square were modelled as follows:$${alpha }_{o1}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{o1}right)$$
(4)
and$${alpha }_{o2}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{o2}right)$$
(5)
The random effect of the year was implemented with separate random walks per zone following ref. 67, which allowed the effect to vary between the nine bioclimatic zones, while accounting for dependencies among consecutive years. Conceptually, in random walks, the effect of 1 year is dependent on the previous year’s effect, resulting in trajectories with less sudden changes between consecutive years. This was implemented as follows:$${gamma }_{r,t}sim left{begin{array}{c}{{{{{rm{Normal}}}}}}left(0,{1.5}^{2}right){{{{rm{for}}}}},t=1\ {{{{{rm{Normal}}}}}}left({gamma }_{r,t-1},{sigma }_{gamma r}^{2}right){{{{rm{for}}}}},t , > ,1end{array}right.$$
(6)
with$${sigma }_{gamma r}sim {{mbox{Cauchy}}}left(0,1right)$$
(7)
The regression model for detection probability (observation model) looked as follows$${{{{rm{logit}}}}}({p}_{i,t,j}) =, {mu }_{d}+{beta }_{d1}{{{{{rm{yda}}}}}}{{{{{{rm{y}}}}}}}_{j}+{beta }_{d2}{{{{{rm{yda}}}}}}{{{{{{rm{y}}}}}}}_{j}^{2}+{beta }_{d3}{{{{{rm{shortlis}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d4}{{{{{rm{longlis}}}}}}{{{{{{rm{t}}}}}}}_{j} \ quad+ {beta }_{d5}{{{{{rm{exper}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d6}{{{{{rm{projec}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d7}{{{{{rm{targeted}}}}}}_{{{{{rm{projec}}}}}}{{{{{{rm{t}}}}}}}_{j} \ quad+ {beta }_{d8}{{{{{rm{redlis}}}}}}{{{{{{rm{t}}}}}}}_{j}+{alpha }_{d1,t}$$
(8)
where μd is the global intercept, ydayj is the scaled day of the year of visit j, shortlistj and longlistj are dummies of a three-level factor denoting the number of species recorded during the visit (1; 2–3; >3), and expertj, projectj, targeted_projectj and redlistj are dummies of a five-level factor denoting the source of the data. The source might either be a common naturalist observation (reference level), an observation by an expert naturalist, an observation made during a not further specified project, an observation made in a project targeted at the focal species or an observation made during a Red-List inventory. An expert naturalist was defined as an observer that contributed a significant number of records, which was defined as the upper 2.5% quantile of all observers arranged by their total number of records, and that made at least one visit with an exceptionally long species list, which was defined as a visit in the upper 2.5% quantile of all visits arranged by the number of records. The proportions of records originating from these different sources changed across years, but change was not unidirectional and differed among the investigated groups (Supplementary Fig. S11), such that accounting for data source in the model should suffice to yield reliable estimates of occupancy trends. αd1,t is a random effect for year, which was modelled as$${alpha }_{d1}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{d1}right)$$
(9)
The occupancy and observation models were fitted jointly in Stan through the interface CmdStanR68. Four Markov chain Monte Carlo chains with 2000 iterations each, including 1000 warm-up iterations, were used. Priors of the model parameters are specified in Supplementary Table S5. For the prior distribution of global intercepts, a standard deviation of 1.5 was chosen to not overweight the extreme values on the probability scale. To ensure that chains mixed well, Rhat statistics for annual mean occupancy estimates were calculated through the package rstan69. For Switzerland-wide annual estimates (n = 18,140), 98.0% of values met the standard threshold of 1.1 (99.9% of values More

Laboratory cultureOchrosphaera neapolitana (RCC1357) was precultured in K/2 medium without Tris buffer8 using artificial seawater (ASW) supplemented with NaHCO3 and HCl to yield an initial DIC of 2050 µM. In triplicate, 1-L bottles were filled with 150 mL of seawater medium with air in the bottle headspace and inoculated with a mid-log phase preculture at an initial cell concentration of 104 cells mL−1. Cultures were grown at 18 °C under a warm white LED light at 100 ± 20 µE on a 16h-light/8h-dark cycle. Bottles were orbitally shaken at 60 rpm to keep cells in suspension. Cell growth was monitored with a Multisizer 4e particle counter and sizer (Beckman Coulter). At ~1.4 × 105 cells mL−1, cells were diluted up to 300 mL to 2–3 × 104 cells mL−1 and harvested after 2 days of more exponential growth up to 7.9 ± 0.6 × 104 cells mL−1. More detailed culture results are listed in the Supplementary Note 1.Immediately after harvesting, pH was measured using a pH probe calibrated with Mettler Toledo NBS standards (it should be noted here that high ionic strength calibration standards would be optimal for pH measurement of liquids like seawater). There was a carbonate system shift during the batch culture and more details are shown in Supplementary Fig. S1. Cells in 50 mL were pelleted by centrifuging at ~1650 × g for 5 min. Seawater supernatant was analyzed for DIC and δ13CDIC by injecting 3.5 mL into an Apollo analyzer and injecting 1 mL into He-flushed glass vials containing H3PO4 for the Gas Bench.For seawater DIC, an Apollo SciTech DIC-C13 Analyzer coupled to a Picarro CO2 analyzer was calibrated with in-house NaHCO3 standards dissolved in deionized water at different known concentrations and δ13C values from −4.66 to −7.94‰. δ13CDIC in media were measured with a Gas Bench II with an autosampler (CTC Analytics AG, Switzerland) coupled to ConFlow IV Interface and a Delta V Plus mass spectrometer (Thermo Fischer Scientific). Pelleted cells were snap-frozen with N2 (l) and stored at −80 °C. For PIC analysis, pellet was resuspended in 1 mL methanol and vortexed. After centrifugation, the methanol phase with extracted organics was removed and the pellet containing the coccoliths was dried at 60 °C overnight. About 300 mg of dried coccolith powder were placed in air-tight glass vials, flushed with He and reacted with five drops of phosphoric acid at 70 °C. PIC δ13C and δ18O were measured by the same Gas Bench system. The system and abovementioned in-house standards were calibrated using international standards NBS 18 (δ13C = −5.01‰, δ18O = +23.00‰) and NBS 19 (δ13C = +1.95‰, δ18O = +2.2‰). The analytical error for DIC concentration and δ13C is More

Wang, Z. & Yuan, J. Typhoon numerical simulation and visualization for disaster risk assessment. Bull. Surv. Mapp. 108–110, 132 (2015).ADS 

Google Scholar 
Tang, J., Xu, L., Li, Y., He, Y. & Cui, S. Assessing the damage caused by typhoon on urban green space ecosystme service based on UAV remote sensing. J. Nat. Disasters 27, 153–161 (2018).
Google Scholar 
Tian, Y., Zhou, W., Qian, Y., Zheng, Z. & Pan, X. The influence of Typhoon Mangkhut on urban green space and biomass in Shenzhen, China. Acta Ecol. Sin. 40, 2589–2598 (2020).
Google Scholar 
Lin, G. Major anti-wind and alkali-resisting landscape plants of south China’s seaside region. For. Invertory Plan. 29, 78–81 (2004).
Google Scholar 
Lin, T. C., Hogan, J. A. & Chang, C. T. Tropical cyclone ecology: a scale-link perspective. Trends Ecol. Evol. 35, 594–604 (2020).Article 

Google Scholar 
Lin, H. et al. Risk assessments of trees and urban spaces under attacks of typhoons: a survey and statistical study in Guangzhou, China. IOP Conf. Ser. Earth Environ. Sci. 588, 032055 (2020).Article 

Google Scholar 
Loehle, C. Biomechanical constraints on tree architecture. Trees Struct. Funct. 30, 2061–2070 (2016).Article 

Google Scholar 
Zhu, J., Matsuzaki, T. & Sakioka, K. Wind speeds within a single crown of Japanese black pine (Pinus thunbergii Parl.). For. Ecol. Manag. 135, 19–31 (2000).Article 

Google Scholar 
Ver Planck, N. R. & MacFarlane, D. W. Branch mass allocation increases wind throw risk for Fagus grandifolia. For. Int. J. For. Res. 92, 490–499 (2019).
Google Scholar 
Dunham, R. A. & Cameron, A. D. Crown, stem and wood properties of wind-damaged and undamaged Sitka spruce. For. Ecol. Manag. 135, 73–81 (2000).Article 

Google Scholar 
Burcham, D. C., Autio, W. R., Modarres-Sadeghi, Y. & Kane, B. After pruning, wind-induced bending moments and vibration decrease more on reduced than raised Senegal mahogany (Khaya senegalensis). Urban For. Urban Green. 61, 127100 (2021).Article 

Google Scholar 
Gilman, E. F., Masters, F. & Grabosky, J. C. Pruning affects tree movement in hurricane force wind. Arboric. Urban For. 34, 20–28 (2008).Article 

Google Scholar 
Päätalo, M. L., Peltola, H. & Kellomäki, S. Modelling the risk of snow damage to forests under short-term snow loading. For. Ecol. Manag. 116, 51–70 (1999).Article 

Google Scholar 
North, E., Johnson, G., Murphy, R., Giblin, C. & Rendahl, A. Boulevard tree failures during wind loading events. Arboric. Urban For. 45, 259–269 (2019).
Google Scholar 
Angelou, N., Dellwik, E. & Mann, J. Wind load estimation on an open-grown European oak tree. For. Int. J. For. Res. 92, 381–392 (2019).
Google Scholar 
Kontogianni, A., Tsitsoni, T. & Goudelis, G. An index based on silvicultural knowledge for tree stability assessment and improved ecological function in urban ecosystems. Ecol. Eng. 37, 914–919 (2011).Article 

Google Scholar 
He, D. & Li, Z. Wind tunnel test on wind- induced responses of roadside trees. J. Nat. Disasters 28, 44–53 (2019).
Google Scholar 
Cao, J., Tamura, Y. & Yoshida, A. Wind tunnel study on aerodynamic characteristics of shrubby specimens of three tree species. Urban For. Urban Green. 11, 465–476 (2012).Article 

Google Scholar 
Zhang, W., Kang, L., Zhang, Q., Li, C. & Zou, X. Speed upwind and downwind of a single plant. J. Beijing Norm. Univ. Nat. Sci. 56, 573–581 (2020).
Google Scholar 
Cheng, H. et al. Wind tunnel study of airflow recovery on the lee side of single plants. Agric. For. Meteorol. 263, 362–372 (2018).Article 
ADS 

Google Scholar 
Ma, S. et al. Experimental research of viscous flow around a Nitraria tangutorum boscage. Res. Soil Water Conserv. 13, 147–149 (2006).ADS 

Google Scholar 
Rahman, M. et al. Disentangling the role of competition, light interception, and functional traits in tree growth rate variation in South Asian tropical moist forests. For. Ecol. Manag. 483, 118908 (2021).Article 

Google Scholar 
Forrester, D. I., Benneter, A., Bouriaud, O. & Bauhus, J. Diversity and competition influence tree allometric relationships—Developing functions for mixed-species forests. J. Ecol. 105, 761–774 (2017).Article 

Google Scholar 
Coombes, A., Martin, J. & Slater, D. Defining the allometry of stem and crown diameter of urban trees. Urban For. Urban Green. 44, 1–15 (2019).Article 

Google Scholar 
Stoffel, M. Mechanical stability and growth performance of trees. (2009).Lento, M., Thijs, D., Jonas, A., Dominique, D. & Jan, C. Comparative study of flow field and drag coefficient of model and small natural trees in a wind tunnel. Urban For. Urban Green. 35, 230–239 (2018).Article 

Google Scholar 
West, G. B., Brown, J. H. & Enquist, B. J. A general model for the structure and allometry of plant vascular systems. Nature 400, 664–667 (1999).Article 
ADS 
CAS 

Google Scholar 
Enquist, B. J. Cope’s rule and the evolution of long-distance transport in vascular plants: Allometric scaling, biomass partitioning and optimization. Plant Cell Environ. 26, 151–161 (2003).Article 

Google Scholar 
Bentley, L. P. et al. An empirical assessment of tree branching networks and implications for plant allometric scaling models. Ecol. Lett. 16, 1069–1078 (2013).Article 

Google Scholar 
Eloy, C., Fournier, M., Lacointe, A. & Moulia, B. Wind loads and competition for light sculpt trees into self-similar structures. Nat. Commun. 8, 1–11 (2017).Article 
CAS 

Google Scholar 
Lin, M. Y. & Khlystov, A. Investigation of ultrafine particle deposition to vegetation branches in a wind tunnel. Aerosol Sci. Technol. 46, 465–472 (2012).Article 
ADS 
CAS 

Google Scholar 
Ji, W. & Zhao, B. A wind tunnel study on the effect of trees on PM2.5 distribution around buildings. J. Hazard. Mater. 346, 36–41 (2018).Article 
CAS 

Google Scholar 
Hui, K. K. W. et al. Unveiling falling urban trees before and during Typhoon Higos (2020): Empirical case study of potential structural failure using tilt sensor. Forests 13, 359 (2022).Article 

Google Scholar 
Liao, S., Huang, M., Lou, W., Lin, W. & Xiao, Z. Numerical simulation of a urban wind field under the influence of typhoon “Mangkhut”. Acta Aerodyn. Sin. 39, 107–116 (2021).
Google Scholar 
Gardiner, B., Berry, P. & Moulia, B. Review: Wind impacts on plant growth, mechanics and damage. Plant Sci. 245, 94–118 (2016).Article 
CAS 

Google Scholar 
Lüttge, U. & Buckeridge, M. Trees: structure and function and the challenges of urbanization. Trees Struct. Funct. https://doi.org/10.1007/s00468-020-01964-1 (2020).Article 

Google Scholar 
Hwang, H. M., Fiala, M. J., Park, D. & Wade, T. L. Review of pollutants in urban road dust and stormwater runoff: part 1. Heavy metals released from vehicles. Int. J. Urban Sci. 20, 334–360 (2016).Article 

Google Scholar 
Kuitert, W. The nature of urban Seoul: Potential vegetation derived from the soil map. Int. J. Urban Sci. 17, 95–108 (2013).Article 

Google Scholar 
Stubbs, C. J., Cook, D. D. & Niklas, K. J. A general review of the biomechanics of root anchorage. J. Exp. Bot. 70, 3439–3451 (2019).Article 
CAS 

Google Scholar 
Sterck, F. J. & Bongers, F. Ontogenetic changes in size, allometry, and mechanical design of tropical rain forest trees. Am. J. Bot. 85, 266–272 (1998).Article 
CAS 

Google Scholar 
Sim, V. & Jung, W. Wind fragility for urban street tree in Korea. J. Wetl. Res. 21, 298–304 (2019).
Google Scholar 
Ueda, M. & Shibata, E. Why do trees decline or dieback after a strong wind? Water status of Hinoki cypress standing after a typhoon. Tree Physiol. 24, 701–706 (2004).Article 

Google Scholar 
Jalkanen, A. & Mattila, U. Logistic regression models for wind and snow damage in northern Finland based on the National Forest Inventory data. For. Ecol. Manag. 135, 315–330 (2000).Article 

Google Scholar 
Hale, S. E., Gardiner, B. A., Wellpott, A., Nicoll, B. C. & Achim, A. Wind loading of trees: Influence of tree size and competition. Eur. J. For. Res. 131, 203–217 (2012).Article 

Google Scholar 
Olson, M. E., Aguirre-Hernández, R. & Rosell, J. A. Universal foliage-stem scaling across environments and species in dicot trees: Plasticity, biomechanics and Corner’s Rules. Ecol. Lett. 12, 210–219 (2009).Article 

Google Scholar 
Blanchard, E. et al. Contrasted allometries between stem diameter, crown area, and tree height in five tropical biogeographic areas. Trees Struct. Funct. 30, 1953–1968 (2016).Article 

Google Scholar 
King, D. A., Davies, S. J., Tan, S. & Noor, N. S. M. The role of wood density and stem support costs in the growth and mortality of tropical trees. J. Ecol. 94, 670–680 (2006).Article 

Google Scholar 
Petty, J. A. & Worrell, R. Stability of coniferous tree stems in relation to damage by snow. Forestry 54, 115–128 (1981).Article 

Google Scholar 
Gilman, E. F. & Lilly, S. Tree pruning (International Society of Arboriculture, 2002).
Google Scholar 
Zhang, J., Gou, Z., Zhang, F. & Shutter, L. A study of tree crown characteristics and their cooling effects in a subtropical city of Australia. Ecol. Eng. 158, 106027 (2020).Article 

Google Scholar 
Marchi, L. et al. State of the art on the use of trees as supports and anchors in forest operations. Forests 9, 467 (2018).Article 

Google Scholar  More

Clemente, J. C., Ursell, L. K., Parfrey, L. W. & Knight, R. The impact of the gut microbiota on human health: An integrative view. Cell 148, 1258–1270 (2012).Article 

Google Scholar 
Cryan, J. F. et al. The microbiota-gut-brain axis. Physiol. Rev. 99, 1877–2013 (2019).Article 

Google Scholar 
Parfrey, L. W., Walters, W. A. & Knight, R. Microbial eukaryotes in the human microbiome: Ecology, evolution, and future directions. Front. Microbiol. 2, 1–6 (2011).Article 

Google Scholar 
Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).Article 

Google Scholar 
Björk, J. R., Dasari, M., Grieneisen, L. & Archie, E. A. Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research. Am. J. Primatol. 81, 1–23 (2019).Article 

Google Scholar 
Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. M. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science 336, 1255–1262 (2012).Article 
ADS 

Google Scholar 
Miller, E. T., Svanbäck, R. & Bohannan, B. J. M. Microbiomes as metacommunities: Understanding host-associated microbes through metacommunity ecology. Trends Ecol. Evol. 33, 926–935 (2018).Article 

Google Scholar 
McKenney, E. A., Koelle, K., Dunn, R. R. & Yoder, A. D. The ecosystem services of animal microbiomes. Mol. Ecol. 27, 2164–2172 (2018).Article 

Google Scholar 
Koskella, B., Hall, L. J. & Metcalf, C. J. E. The microbiome beyond the horizon of ecological and evolutionary theory. Nat. Ecol. Evol. 1, 1606–1615 (2017).Article 

Google Scholar 
Sarkar, A. et al. Microbial transmission in animal social networks and the social microbiome. Nat. Ecol. Evol. 4, 1020–1035 (2020).Article 

Google Scholar 
Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).Article 
ADS 

Google Scholar 
Degnan, P. H. et al. Factors associated with the diversification of the gut microbial communities within chimpanzees from Gombe National Park. Proc. Natl. Acad. Sci. 109, 13034–13039 (2012).Article 
ADS 

Google Scholar 
Bennett, G. et al. Host age, social group, and habitat type influence the gut microbiota of wild ring-tailed lemurs (Lemur catta). Am. J. Primatol. 78, 883–892 (2016).Article 

Google Scholar 
Amato, K. R. et al. Patterns in gut microbiota similarity associated with degree of sociality among sex classes of a neotropical primate. Microb. Ecol. 74, 250–258 (2017).Article 

Google Scholar 
Raulo, A. et al. Social behaviour and gut microbiota in red-bellied lemurs (Eulemur rubriventer): In search of the role of immunity in the evolution of sociality. J. Anim. Ecol. 87, 388–399 (2017).Article 

Google Scholar 
Springer, A. et al. Patterns of seasonality and group membership characterize the gut microbiota in a longitudinal study of wild Verreaux’s sifakas (Propithecus verreauxi). Ecol. Evol. 7, 5732–5745 (2017).Article 

Google Scholar 
Tung, J. et al. Social networks predict gut microbiome composition in wild baboons. Elife 2015, 1–18 (2015).
Google Scholar 
Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997 (2016).Article 
ADS 

Google Scholar 
Perofsky, A. C., Lewis, R. J., Abondano, L. A., Di Fiore, A. & Meyers, L. A. Hierarchical social networks shape gut microbial composition in wild Verreaux’s sifaka. Proc. R. Soc. B Biol. Sci. 284, 20172274 (2017).Article 

Google Scholar 
Raulo, A. et al. Social networks strongly predict the gut microbiota of wild mice. ISME J. 15, 2601–2613 (2021).Article 

Google Scholar 
Arrieta, M. C., Stiemsma, L. T., Amenyogbe, N., Brown, E. & Finlay, B. The intestinal microbiome in early life: Health and disease. Front. Immunol. 5, 1–18 (2014).Article 

Google Scholar 
Ren, T., Grieneisen, L. E., Alberts, S. C., Archie, E. A. & Wu, M. Development, diet and dynamism: Longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environ. Microbiol. 18, 1312–1325 (2016).Article 

Google Scholar 
Jagsi, R. et al. Seasonal cycling in the gut microbiome of the Hadza Hunter-Gatherers of Tanzania. Science 357, 802–806 (2017).Article 

Google Scholar 
Hicks, A. L. et al. Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nat. Commun. 9, 1786 (2018).Article 
ADS 

Google Scholar 
Murillo, T., Schneider, D., Fichtel, C. & Daniel, R. Dietary shifts and social interactions drive temporal fluctuations of the gut microbiome from wild redfronted lemurs. ISME Commun. 2, 3 (2022).Article 

Google Scholar 
Laforest-Lapointe, I. & Arrieta, M.-C. Microbial eukaryotes: A missing link in gut microbiome studies. mSystems 3, e00201-17 (2018).Article 

Google Scholar 
Mann, A. E. et al. Biodiversity of protists and nematodes in the wild nonhuman primate gut. ISME J. 14, 609–622 (2020).Article 

Google Scholar 
Vlčková, K. et al. Relationships between gastrointestinal parasite infections and the fecal microbiome in free-ranging western lowland gorillas. Front. Microbiol. 9, 1–12 (2018).Article 

Google Scholar 
Renelies-Hamilton, J. et al. Exploring interactions between Blastocystis sp., Strongyloides spp. and the gut microbiomes of wild chimpanzees in Senegal. Infect. Genet. Evol. 74, 104010 (2019).Article 

Google Scholar 
Martínez-Mota, R., Righini, N., Mallott, E. K., Gillespie, T. R. & Amato, K. R. The relationship between pinworm (Trypanoxyuris) infection and gut bacteria in wild black howler monkeys (Alouatta pigra). Am. J. Primatol. 83, e23330 (2021).Article 

Google Scholar 
Pereira, M. E., Kaufman, R., Kappeler, P. M. & Overdoff, D. J. Female dominance does not characterize all of the lemuridae. Folia Primatol. 55, 96–103 (1990).Article 

Google Scholar 
Ostner, J. & Kappeler, P. M. Central males instead of multiple pairs in redfronted lemurs, Eulemur fulvus rufus (Primates, Lemuridae)?. Anim. Behav. 58, 1069–1078 (1999).Article 

Google Scholar 
Kappeler, P. M. & Fichtel, C. A 15-year perspective on the social organization and life history of sifaka in Kirindy Forest. In Long-Term Field Studies of Primates 101–121 (Springer, 2012).Chapter 

Google Scholar 
Koch, F., Ganzhorn, J. U., Rothman, J. M., Chapman, C. A. & Fichtel, C. Sex and seasonal differences in diet and nutrient intake in Verreaux’s sifakas (Propithecus verreauxi). Am. J. Primatol. 79, 1–10 (2017).Article 

Google Scholar 
Scholz, F. & Kappeler, P. M. Effects of seasonal water scarcity on the ranging behavior of Eulemur fulvus rufus. Int. J. Primatol. 25, 599–613 (2004).Article 

Google Scholar 
Amoroso, C. R., Kappeler, P. M., Fichtel, C. & Nunn, C. L. Water availability impacts habitat use by red-fronted lemurs (Eulemur rufifrons): An experimental and observational study. Int. J. Primatol. 41, 61–80 (2020).Article 

Google Scholar 
Clough, D., Heistermann, M. & Kappeler, P. M. Host intrinsic determinants and potential consequences of parasite infection in free-ranging red-fronted lemurs (Eulemur fulvus rufus). Am. J. Phys. Anthropol. 142, 441–452 (2010).Article 

Google Scholar 
Ostner, J., Kappeler, P. & Heistermann, M. Androgen and glucocorticoid levels reflect seasonally occurring social challenges in male redfronted lemurs (Eulemur fulvus rufus). Behav. Ecol. Sociobiol. 62, 627–638 (2008).Article 

Google Scholar 
Heistermann, M., Palme, R. & Ganswindt, A. Comparison of different enzymeimmunoassays for assessment of adrenocortical activity in primates based on fecal analysis. Am. J. Primatol. 68, 257–273 (2006).Article 

Google Scholar 
Kappeler, P. M. & Fichtel, C. Female reproductive competition in Eulemur rufifrons: Eviction and reproductive restraint in a plurally breeding Malagasy primate. Mol. Ecol. 21, 685–698 (2012).Article 

Google Scholar 
Ostner, J., Kappeler, P. M. & Heistermann, M. Seasonal variation and social correlates of androgen excretion in male redfronted lemurs (Eulemur fulvus rufus). Behav. Ecol. Sociobiol. 52, 485–495 (2002).Article 

Google Scholar 
Clough, D. Gastro-intestinal parasites of red-fronted lemurs in Kirindy Forest, western Madagascar. J. Parasitol. 96, 245–251 (2010).Article 

Google Scholar 
Gogarten, J. F. et al. Metabarcoding of eukaryotic parasite communities describes diverse parasite assemblages spanning the primate phylogeny. Mol. Ecol. Resour. 20, 204–215 (2020).Article 

Google Scholar 
Barton, R. A. Allogrooming as mutualism in diurnal lemurs. Primates 28, 539–542 (1987).Article 

Google Scholar 
Noguera, J. C., Aira, M., Pérez-Losada, M., Domínguez, J. & Velando, A. Glucocorticoids modulate gastrointestinal microbiome in a wild bird. R. Soc. Open Sci. 5, 171743 (2018).Article 
ADS 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, 1–11 (2013).Article 

Google Scholar 
Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).Article 

Google Scholar 
Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).Article 

Google Scholar 
Guillou, L. et al. The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, 597–604 (2013).Article 

Google Scholar 
Gao, X., Lin, H., Revanna, K. & Dong, Q. A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy. BMC Bioinform. 18, 1–10 (2017).Article 

Google Scholar 
Reitmeier, S. et al. Handling of spurious sequences affects the outcome of high-throughput 16S rRNA gene amplicon profiling. ISME Commun. 1, 1–12 (2021).Article 

Google Scholar 
Shutt, K., Setchell, J. M. & Heistermann, M. Non-invasive monitoring of physiological stress in the Western lowland gorilla (Gorilla gorilla gorilla): Validation of a fecal glucocorticoid assay and methods for practical application in the field. Gen. Comp. Endocrinol. 179, 167–177 (2012).Article 

Google Scholar 
Hämäläinen, A., Heistermann, M., Fenosoa, Z. S. E. & Kraus, C. Evaluating capture stress in wild gray mouse lemurs via repeated fecal sampling: Method validation and the influence of prior experience and handling protocols on stress responses. Gen. Comp. Endocrinol. 195, 68–79 (2014).Article 

Google Scholar 
Rudolph, K., Fichtel, C., Heistermann, M. & Kappeler, P. M. Dynamics and determinants of glucocorticoid metabolite concentrations in wild Verreaux’s sifakas. Horm. Behav. 124, 104760 (2020).Article 

Google Scholar 
Heitlinger, E., Ferreira, S. C. M., Thierer, D., Hofer, H. & East, M. L. The intestinal eukaryotic and bacterial biome of spotted hyenas: The impact of social status and age on diversity and composition. Front. Cell Infect. Microbiol. 7, 262 (2017).Article 

Google Scholar 
Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J. Mem. Lang. 68, 255–278 (2013).Article 

Google Scholar 
Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17, 1–27 (2021).Article 

Google Scholar 
De Cáceres, M., Legendre, P. & Moretti, M. Improving indicator species analysis by combining groups of sites. Oikos 119, 1674–1684 (2010).Article 

Google Scholar 
Silk, J., Cheney, D. & Seyfarth, R. A practical guide to the study of social relationships. Evol. Anthropol. 22, 213–225 (2013).Article 

Google Scholar 
Ostner, J., Nunn, C. L. & Schülke, O. Female reproductive synchrony predicts skewed paternity across primates. Behav. Ecol. 19, 1150–1158 (2008).Article 

Google Scholar 
Bailey, M. T. et al. Exposure to a social stressor alters the structure of the intestinal microbiota: Implications for stressor-induced immunomodulation. Brain Behav. Immun. 25, 397–407 (2011).Article 

Google Scholar 
Bailey, M. T. et al. Stressor exposure disrupts commensal microbial populations in the intestines and leads to increased colonization by Citrobacter rodentium. Infect. Immun. 78, 1509–1519 (2010).Article 

Google Scholar 
Stothart, M. R. et al. Stress and the microbiome: Linking glucocorticoids to bacterial community dynamics in wild red squirrels. Biol. Lett. 12, 20150875 (2016).Article 

Google Scholar 
Vlčková, K. et al. Impact of stress on the gut microbiome of free-ranging western lowland gorillas. Microbiol 164, 40–44 (2018).Article 

Google Scholar 
Chu, H. & Mazmanian, S. K. Innate immune recognition of the microbiota promotes host-microbial symbiosis. Nat. Immunol. 14, 668–675 (2013).Article 

Google Scholar 
Zheng, D., Liwinski, T. & Elinav, E. Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492–506 (2020).Article 

Google Scholar 
Ley, R. E. Prevotella in the gut: Choose carefully. Nat. Rev. Gastroenterol. Hepatol. 13, 69–70 (2016).Article 

Google Scholar 
Manara, S. et al. Microbial genomes from non-human primate gut metagenomes expand the primate-associated bacterial tree of life with over 1000 novel species. Genome Biol. 20, 299 (2019).Article 

Google Scholar 
Round, J. L. & Mazmanian, S. K. The gut microbiota shapes intestinal immune responses during health and disease. Nat. Rev. Immunol. 9, 313–323 (2009).Article 

Google Scholar 
Maltz, R. M. et al. Prolonged restraint stressor exposure in outbred CD-1 mice impacts microbiota, colonic inflammation, and short chain fatty acids. PLoS ONE 13, 1–19 (2018).Article 

Google Scholar 
Ostner, J. & Heistermann, M. Endocrine characterization of female reproductive status in wild redfronted lemurs (Eulemur fulvus rufus). Gen. Comp. Endocrinol. 131, 274–283 (2003).Article 

Google Scholar 
Peckre, L. R., Defolie, C., Kappeler, P. M. & Fichtel, C. Potential self-medication using millipede secretions in red-fronted lemurs: Combining anointment and ingestion for a joint action against gastrointestinal parasites?. Primates 59, 483–494 (2018).Article 

Google Scholar 
Jenkins, T. P. et al. Infections by human gastrointestinal helminths are associated with changes in faecal microbiota diversity and composition. PLoS ONE 12, 1–18 (2017).Article 

Google Scholar 
Rosa, B. A. et al. Differential human gut microbiome assemblages during soil-transmitted helminth infections in Indonesia and Liberia. Microbiome 6, 1–19 (2018).Article 

Google Scholar 
Reynolds, L. A., Finlay, B. B. & Maizels, R. M. Cohabitation in the intestine: Interactions among helminth parasites, bacterial microbiota, and host immunity. J. Immunol. 195, 4059–4066 (2015).Article 

Google Scholar 
Toro-Londono, M. A., Bedoya-Urrego, K., Garcia-Montoya, G. M., Galvan-Diaz, A. L. & Alzate, J. F. Intestinal parasitic infection alters bacterial gut microbiota in children. PeerJ 2019, 1–24 (2019).
Google Scholar 
Vacca, M. et al. The controversial role of human gut Lachnospiraceae. Microorganisms 8, 1–25 (2020).Article 

Google Scholar 
Wei, Z. et al. The effects of non-fiber carbohydrate content and forage type on rumen microbiome of dairy cows. Animals 11, 1–17 (2021).Article 

Google Scholar 
Kaakoush, N. O. Insights into the role of Erysipelotrichaceae in the human host. Front. Cell Infect. Microbiol. 5, 1–4 (2015).Article 

Google Scholar 
Ricaboni, D. et al. ‘Colidextribacter massiliensis’ gen. nov., sp. nov., isolated from human right colon. New Microbes New Infect. 17, 27–29 (2017).Article 

Google Scholar 
Qin, P. et al. Characterization a novel butyric acid-producing bacterium Collinsella aerofaciens subsp. shenzhenensis subsp. nov. Microorganisms 7, 78 (2019).Article 

Google Scholar 
Wei, Y. et al. Commensal bacteria impact a protozoan’s integration into the murine gut microbiota in a dietary nutrient-dependent manner. Appl. Environ. Microbiol. 86, e00303-20 (2020).Article 
ADS 

Google Scholar 
Perofsky, A. C., Ancel Meyers, L., Abondano, L. A., Di Fiore, A. & Lewis, R. J. Social groups constrain the spatiotemporal dynamics of wild sifaka gut microbiomes. Mol. Ecol. 30, 6759–6775 (2021).Article 

Google Scholar 
Pyritz, L., Kappeler, P. M. & Fichtel, C. Coordination of group movements in wild red-fronted lemurs (Eulemur rufifrons): Processes and influence of ecological and reproductive seasonality. Int. J. Primatol. 32, 1325–1347 (2011).Article 

Google Scholar 
Amato, K. R. et al. Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J. 7, 1344–1353 (2013).Article 

Google Scholar 
Hippe, H., Hagelstein, A., Kramer, I., Swiderski, J. & Stackebrandt, E. Phylogenetic analysis of Formivibrio citricus, Propionivibrio dicarboxylicus, Anaerobiospirillum thomasii, Succinirnonas amylolytica and Succinivibrio dextrinosolvens and proposal of Succinivibrionaceae fam. nov. Int. J. Syst. Evol. Microbiol. 49, 779–782 (1999).Article 

Google Scholar 
Grieneisen, L. E., Livermore, J., Alberts, S., Tung, J. & Archie, E. A. Group living and male dispersal predict the core gut microbiome in wild baboons. Integr. Comp. Biol. 57, 770–785 (2017).Article 

Google Scholar 
Amoroso, C. R., Kappeler, P. M., Fichtel, C. & Nunn, C. L. Fecal contamination, parasite risk, and waterhole use by wild animals in a dry deciduous forest. Behav. Ecol. Sociobiol. 73, 1–11 (2019).Article 

Google Scholar 
Vandeputte, D. et al. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut 65, 57–62 (2016).Article 

Google Scholar 
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).Article 
ADS 

Google Scholar 
Sonnenburg, J. L. & Bäckhed, F. Diet-microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).Article 
ADS 

Google Scholar 
Zmora, N., Suez, J. & Elinav, E. You are what you eat: Diet, health and the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 16, 25–56 (2018).
Google Scholar 
Ortmann, S., Bradley, B. J., Stolter, C. & Ganzhorn, J. U. Estimating the quality and composition of wild animal diets—a critical survey of methods. In Feeding Ecology in Apes and Other Primates. Ecological, Physical, and Behavioral Aspects (eds Hohmann, G. et al.) 395–418 (Cambridge University Press, 2006).
Google Scholar  More

Keenan, R. J. et al. Dynamics of global forest area: results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manage. 352, 9–20 (2015).Article 

Google Scholar 
Arneth, A. et al. in Special Report on Climate Change and Land (eds Shukla, P. R. et al.) Ch. 1 (IPCC, 2019).Piao, S. et al. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, GB3018 (2007).Article 

Google Scholar 
Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).Article 

Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 

Google Scholar 
Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).Article 

Google Scholar 
Chen, J. M. et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10, 4259 (2019).Article 

Google Scholar 
Myneni, R. B. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).Article 

Google Scholar 
Filipchuk, A. et al. Russian forests: a new approach to the assessment of carbon stocks and sequestration capacity. Environ. Dev. 26, 68–75 (2018).Article 

Google Scholar 
Goodale, C. L. et al. Forest carbon sinks in the Northern Hemisphere. Ecol. Appl. 12, 891–899 (2002).Article 

Google Scholar 
Tchebakova, N. M. et al. Energy and mass exchange and the productivity of main Siberian ecosystems (from eddy covariance measurements). 2. Carbon exchange and productivity. Biol. Bull. 42, 579–588 (2015).Article 

Google Scholar 
Vaganov, E. A. et al. Forests and swamps of Siberia in the global carbon cycle. Contemp. Probl. Ecol. 1, 168–182 (2008).Article 

Google Scholar 
Schepaschenko, D. et al. Russian forest sequesters substantially more carbon than previously reported. Sci. Rep. 11, 12825 (2021).Article 

Google Scholar 
Shvidenko, A. & Schepaschenko, D. Climate change and wildfires in Russia. Contemp. Probl. Ecol. 6, 683–692 (2013).Article 

Google Scholar 
Bradshaw, C. J. A. & Warkentin, I. G. Global estimates of boreal forest carbon stocks and flux. Glob. Planet. Change 128, 24–30 (2015).Article 

Google Scholar 
Curtis, P. G. et al. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).Article 

Google Scholar 
Sukhinin, A. I. et al. AVHRR-based mapping of fires in Russia: new products for fire management and carbon cycle studies. Remote Sens. Environ. 93, 546–564 (2004).Article 

Google Scholar 
Soja, A. J. et al. Climate-induced boreal forest change: predictions versus current observations. Glob. Planet. Change 56, 274–296 (2007).Article 

Google Scholar 
Dolman, A. J. et al. An estimate of the terrestrial carbon budget of Russia using inventory-based, eddy covariance and inversion methods. Biogeosciences 9, 5323–5340 (2012).Article 

Google Scholar 
Schaphoff, S. et al. Tamm review: Observed and projected climate change impacts on Russia’s forests and its carbon balance. For. Ecol. Manage. 361, 432–444 (2016).Article 

Google Scholar 
de Jong, R. et al. Trend changes in global greening and browning: contribution of short-term trends to longer-term change. Glob. Change Biol. 18, 642–655 (2012).Article 

Google Scholar 
Buermann, W. et al. Recent shift in Eurasian boreal forest greening response may be associated with warmer and drier summers. Geophys. Res. Lett. 41, 1995–2002 (2014).Article 

Google Scholar 
Rödig, E. et al. Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory. Glob. Ecol. Biogeogr. 26, 1292–1302 (2017).Article 

Google Scholar 
Quegan, S. et al. Estimating the carbon balance of central Siberia using a landscape-ecosystem approach, atmospheric inversion and dynamic global vegetation models. Glob. Change Biol. 17, 351–365 (2011).Article 

Google Scholar 
Gurney, K. R. et al. Interannual variations in continental-scale net carbon exchange and sensitivity to observing networks estimated from atmospheric CO2 inversions for the period 1980 to 2005. Glob. Biogeochem. Cycles 22, GB3025 (2008).Article 

Google Scholar 
Stephens, B. B. et al. Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2. Science 316, 1732–1735 (2007).Article 

Google Scholar 
Leskinen, P. et al. Russian Forests and Climate Change: What Science Can Tell Us 11 (EFI, 2020); https://doi.org/10.36333/wsctu11Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).Article 

Google Scholar 
Stow, D. A. et al. Remote sensing of vegetation and land-cover change in Arctic tundra ecosystems. Remote Sens. Environ. 89, 281–308 (2004).Article 

Google Scholar 
Karlsen, S. R. et al. A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high Arctic plant productivity. Environ. Res. Lett. 13, 025011 (2018).Article 

Google Scholar 
Ding, Z. et al. Nearly half of global vegetated area experienced inconsistent vegetation growth in terms of greenness, cover, and productivity. Earths Future 8, e2020EF001618 (2020).Article 

Google Scholar 
Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 

Google Scholar 
Giglio, L. et al. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).Article 

Google Scholar 
Blunden, J. & Arndt, D. S. State of the climate in 2015. Bull. Am. Meteorol. Soc. 97, Si–S275 (2016).Article 

Google Scholar 
Bastos, A. et al. Was the extreme Northern Hemisphere greening in 2015 predictable? Environ. Res. Lett. 12, 044016 (2017).Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).Article 

Google Scholar 
Kukavskaya, E. A. et al. Biomass dynamics of central Siberian Scots pine forests following surface fires of varying severity. Int. J. Wildland Fire 23, 872–886 (2014).Article 

Google Scholar 
Gauthier, S. et al. Boreal forest health and global change. Science 349, 819 (2015).Article 

Google Scholar 
Harris, N. L. et al. Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573 (2012).Article 

Google Scholar 
Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).Article 

Google Scholar 
Rogers, B. M. et al. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nat. Geosci. 8, 228–234 (2015).Article 

Google Scholar 
Shvetsov, E. G. et al. Assessment of post-fire vegetation recovery in southern Siberia using remote sensing observations. Environ. Res. Lett. 14, 055001 (2019).Article 

Google Scholar 
Wang, J. A. et al. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nat. Clim. Change 11, 435–441 (2021).Article 

Google Scholar 
Xu, L. et al. Changes in global terrestrial live biomass over the 21st century. Sci. Adv. 7, eabe9829 (2021).Article 

Google Scholar 
Shuman, J. K. et al. Forest forecasting with vegetation models across Russia. Can. J. For. Res. 45, 175–184 (2014).Article 

Google Scholar 
Flannigan, M. et al. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob. Change Biol. 15, 549–560 (2009).Article 

Google Scholar 
Yuan, W. et al. Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome. Nat. Commun. 5, 4270 (2014).Article 

Google Scholar 
Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change 11, 234–240 (2021).Article 

Google Scholar 
Larjavaara, M. et al. Post-fire carbon and nitrogen accumulation and succession in Central Siberia. Sci. Rep. 7, 12776 (2017).Article 

Google Scholar 
Berner, L. T. et al. Cajander larch (Larix cajanderi) biomass distribution, fire regime and post-fire recovery in northeastern Siberia. Biogeosciences 9, 3943–3959 (2012).Article 

Google Scholar 
Myneni, R. et al. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid v.006 (LAADS DAAC, 2015).Houghton, R. A. et al. Mapping Russian forest biomass with data from satellites and forest inventories. Environ. Res. Lett. 2, 045032 (2007).Article 

Google Scholar 
DiMiceli, C. et al. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000–2014, Collection 5 Percent Tree Cover v.6 (University of Maryland, 2017).Simard, M. et al. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. 116, G04021 (2011).
Google Scholar 
Broxton, P. et al. A global land cover climatology using MODIS data. J. Appl. Meteorol. Climatol. 53, 1593–1605 (2014).Article 

Google Scholar 
Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).Article 

Google Scholar 
Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data. 13, 3927–3950 (2021).Article 

Google Scholar 
Carreiras, J. M. B. et al. Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions. Remote Sens. Environ. 196, 154–162 (2017).Article 

Google Scholar 
Penman, J. et al. Good Practice Guidance for Land Use, Land-Use Change and Forestry (IGES, 2013).Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).Article 

Google Scholar 
Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).Article 

Google Scholar 
Fernandez-Moran, R. et al. SMOS-IC: an alternative SMOS soil moisture and vegetation optical depth product. Remote Sens. 9, 457 (2017).Article 

Google Scholar 
Wigneron, J.-P. et al. SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives. Remote Sens. Environ. 254, 112238 (2021).Article 

Google Scholar 
Mitchard, E. T. A. et al. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Glob. Ecol. Biogeogr. 23, 935–946 (2014).Article 

Google Scholar 
Mitchard, E. T. A. et al. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps. Carbon Balance Manage. 8, 10 (2013).Article 

Google Scholar 
Harmon, M. E. et al. Release of coarse woody detritus-related carbon: a synthesis across forest biomes. Carbon Balance Manage. 15, 1 (2020).Article 

Google Scholar 
Bartalev, S. A. & Stytsenko, F. V. Assessment of forest-stand destruction by fires based on remote-sensing data on the seasonal distribution of burned areas. Contemp. Probl. Ecol. 14, 711–716 (2021).Article 

Google Scholar 
van Wees, D. et al. The role of fire in global forest loss dynamics. Glob. Change Biol. 27, 2377–2391 (2021).Article 

Google Scholar 
Vicente‐Serrano, S. M. et al. A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J. Clim. 23, 1696–1718 (2010).Article 

Google Scholar 
Schepaschenko, D. et al. A new hybrid land cover dataset for Russia: a methodology for integrating statistics, remote sensing and in situ information. J. Land Use Sci. 6, 245–259 (2011).Article 

Google Scholar 
Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations. Earth Syst. Sci. Data. 9, 791–808 (2017).Article 

Google Scholar 
Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).Article 

Google Scholar 
De Grandpré, L. et al. Long-term post-fire changes in the northeastern boreal forest of Quebec. J. Veg. Sci. 11, 791–800 (2000).Article 

Google Scholar  More