Philippa Kaur

Polis, G. A., Anderson, W. B. & Holt, R. D. Toward an integration of landscape and food web ecology: the dynamics of spatially subsidized food webs. Annu. Rev. Ecol. Evol. Syst. 28, 289–316 (1997).Article 

Google Scholar 
Doughty, C. E. et al. Global nutrient transport in a world of giants. Proc. Natl Acad. Sci. USA 113, 868–873 (2016).Article 
CAS 
PubMed 

Google Scholar 
Burpee, B. T. & Saros, J. E. Cross-ecosystem nutrient subsidies in Arctic and alpine lakes: implications of global change for remote lakes. Environ. Sci. 22, 1166–1189 (2020).CAS 

Google Scholar 
Gallardo, B., Clavero, M., Sánchez, M. I. & Vilà, M. Global ecological impacts of invasive species in aquatic ecosystems. Glob. Change Biol. 22, 151–163 (2016).Article 

Google Scholar 
Justino, D. G., Maruyama, P. K. & Oliveira, P. E. Floral resource availability and hummingbird territorial behaviour on a Neotropical savanna shrub. J. Ornithol. 153, 189–197 (2012).Article 

Google Scholar 
Van Overveld, T. et al. Food predictability and social status drive individual resource specializations in a territorial vulture. Sci. Rep. 8, 15155 (2018).Gunn, R. L., Hartley, I. R., Algar, A. C., Nadiarti, N. & Keith, S. A. Variation in the behaviour of an obligate corallivore is influenced by resource availability. Behav. Ecol. Sociobiol. https://doi.org/10.1007/s00265-022-03132-6 (2022).Keith, S. A. et al. Synchronous behavioural shifts in reef fishes linked to mass coral bleaching. Nat. Clim. Change 8, 986–991 (2018).Article 

Google Scholar 
Davies, N. B. & Hartley, I. R. Food patchiness, territory overlap and social systems: an experiment with dunnocks Prunella modularis. J. Anim. Ecol. 65, 837–846 (1996).Article 

Google Scholar 
Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Delarue, P. E. M., Kerr, S. E. & Rymer, T. L. Habitat complexity, environmental change and personality: a tropical perspective. Behav. Process. 120, 101–110 (2015).Stimson, J. The role of the territory in the ecology of the intertidal limpet Lottia gigantea (Gray). Ecology 54, 1020–1030 (1973).Article 

Google Scholar 
Sells, S. N. & Mitchell, M. S. The economics of territory selection. Ecol. Modell. 438, 109329 (2020).Article 

Google Scholar 
Graf, P. M., Mayer, M., Zedrosser, A., Hackländer, K. & Rosell, F. Territory size and age explain movement patterns in the Eurasian beaver. Mamm. Biol. 81, 587–594 (2016).Article 

Google Scholar 
Simon, C. The influence of food abundance on territory size in the Iguanid lizard Sceloporus jarrovi. Ecology 56, 993–998 (1975).Article 

Google Scholar 
Ippi, S., Cerón, G., Alvarez, L. M., Aráoz, R. & Blendinger, P. G. Relationships among territory size, body size, and food availability in a specialist river duck. Emu 118, 293–303 (2018).Article 

Google Scholar 
Berumen, M. L. & Pratchett, M. S. Effects of resource availability on the competitive behaviour of butterflyfishes (Chaetodontidae). In Proc. 10th International Coral Reef Symposium 644–650 (ReefBase, 2006); http://reefbase.org/resource_center/publication/icrs.aspx?icrs=ICRS10Brown, J. L. The evolution of diversity in avian territorial systems. Wilson Bull. 76, 160–169 (1964).
Google Scholar 
Peiman, K. S. & Robinson, B. W. Ecology and evolution of resource-related heterospecific aggression. Q. Rev. Biol. 85, 133–158 (2010).Article 
PubMed 

Google Scholar 
Grant, J. W. A., Girard, I. L., Breau, C. & Weir, L. K. Influence of food abundance on competitive aggression in juvenile convict cichlids. Anim. Behav. 63, 323–330 (2002).Article 

Google Scholar 
Duda, M. P. et al. Long-term changes in terrestrial vegetation linked to shifts in a colonial seabird population. Ecosystems 23, 1643–1656 (2020).Article 
CAS 

Google Scholar 
Graham, N. A. J. et al. Seabirds enhance coral reef productivity and functioning in the absence of invasive rats. Nature 559, 250–253 (2018).Article 
CAS 
PubMed 

Google Scholar 
Jones, H. P. et al. Severity of the effects of invasive rats on seabirds: a global review. Conserv. Biol. 22, 16–26 (2008).Article 
PubMed 

Google Scholar 
Honig, S. E. & Mahoney, B. Evidence of seabird guano enrichment on a coral reef in Oahu, Hawaii. Mar. Biol. 163, 22 (2016).Benkwitt, C. E., Gunn, R. L., Le Corre, M., Carr, P. & Graham, N. A. J. Rat eradication restores nutrient subsidies from seabirds across terrestrial and marine ecosystems. Curr. Biol. 31, 2704–2711.e4 (2021).Article 
CAS 
PubMed 

Google Scholar 
Savage, C. Seabird nutrients are assimilated by corals and enhance coral growth rates. Sci. Rep. 9, 4284 (2019).Benkwitt, C. E., Wilson, S. K. & Graham, N. A. J. Seabird nutrient subsidies alter patterns of algal abundance and fish biomass on coral reefs following a bleaching event. Glob. Change Biol. 25, 2619–2632 (2019).Article 

Google Scholar 
Benkwitt, C. E., Taylor, B. M., Meekan, M. G. & Graham, N. A. J. Natural nutrient subsidies alter demographic rates in a functionally important coral-reef fish. Sci. Rep. 11, 12575 (2021).Benkwitt, C. E., Wilson, S. K. & Graham, N. A. J. Biodiversity increases ecosystem functions despite multiple stressors on coral reefs. Nat. Ecol. Evol. 4, 919–926 (2020).Article 
PubMed 

Google Scholar 
Robles, H. & Martin, K. Resource quantity and quality determine the inter-specific associations between ecosystem engineers and resource users in a cavity-nest web. PLoS ONE 8, e74694 (2013).Catano, L. B., Gunn, B. K., Kelley, M. C. & Burkepile, D. E. Predation risk, resource quality, and reef structural complexity shape territoriality in a coral reef herbivore. PLoS ONE 10, e0118764 (2015).Wilcox, K. A., Wagner, M. A. & Reynolds, J. D. Salmon subsidies predict territory size and habitat selection of an avian insectivore. PLoS ONE 16, e0254314 (2021).Frost, S. K. & Frost, P. G. H. Territoriality and changes in resource use by sunbirds at Leonotis leonurus (Labiatae). Oecologia 45, 109–116 (1980).Maynard Smith, J. Evolution and the Theory of Games (Cambridge Univ. Press, 1982).Book 

Google Scholar 
Dochtermann, N. A., Schwab, T., Anderson Berdal, M., Dalos, J. & Royauté, R. The heritability of behavior: a meta-analysis. J. Hered. 110, 403–410 (2019).Article 
PubMed 

Google Scholar 
Sheppard, C. R. C. et al. Reefs and islands of the Chagos Archipelago, Indian Ocean: why it is the world’s largest no-take marine protected area. Aquat. Conserv. 22, 232–261 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Soeparno, Y. N., Shibuno, T. & Yamaoka, K. Relationship between pelagic larval duration and abundance of tropical fishes on temperate coasts of Japan. J. Fish. Biol. 80, 346–357 (2012).Article 
CAS 
PubMed 

Google Scholar 
Green, A. L. et al. Larval dispersal and movement patterns of coral reef fishes, and implications for marine reserve network design. Biol. Rev. 90, 1215–1247 (2015).Article 
PubMed 

Google Scholar 
Dall, S. R. X., Houston, A. I. & McNamara, J. M. The behavioural ecology of personality: consistent individual differences from an adaptive perspective. Ecol. Lett. 7, 734–739 (2004).Article 

Google Scholar 
Klumpp, D., McKinnon, D. & Daniel, P. Damselfish territories: zones of high productivity on coral reefs. Mar. Ecol. Prog. Ser. 40, 41–51 (1987).Article 

Google Scholar 
Carr, P. et al. Status and phenology of breeding seabirds and a review of important bird and biodiversity areas in the British Indian Ocean Territory. Bird Conserv. Int. 31, 14–34 (2020).Article 

Google Scholar 
Hoey, A. S. & Bellwood, D. R. Damselfish territories as a refuge for macroalgae on coral reefs. Coral Reefs 29, 107–118 (2010).Article 

Google Scholar 
Samways, M. J. Breakdown of butterflyfish (Chaetodontidae) territories associated with the onset of a mass coral bleaching event. Aquat. Conserv. 15, 101–107 (2005).Article 

Google Scholar 
Morgan, I. E. & Kramer, D. L. Determinants of social organization in a coral reef fish, the blue tang, Acanthurus coeruleus. Environ. Biol. Fishes 72, 443–453 (2005).Article 

Google Scholar 
Ceccarelli, D. M. Modification of benthic communities by territorial damselfish: a multi-species comparison. Coral Reefs 26, 853–866 (2007).Article 

Google Scholar 
Gochfeld, D. J. Territorial damselfishes facilitate survival of corals by providing an associational defense against predators. Mar. Ecol. Prog. Ser. 398, 137–148 (2010).Article 

Google Scholar 
Gordon, T. A. C., Cowburn, B. & Sluka, R. D. Defended territories of an aggressive damselfish contain lower juvenile coral density than adjacent non-defended areas on Kenyan lagoon patch reefs. Coral Reefs 34, 13–16 (2015).Article 

Google Scholar 
Hays, G. C. et al. A review of a decade of lessons from one of the world’s largest MPAs: conservation gains and key challenges. Mar. Biol. 167, 159–167 (2020).Article 

Google Scholar 
Nanninga, G. B., Côté, I. M., Beldade, R. & Mills, S. C. Behavioural acclimation to cameras and observers in coral reef fishes. Ethology 123, 705–711 (2017).Article 

Google Scholar 
Polunin, N. V. C. & Klumpp, D. W. Ecological correlates of foraging periodicity in herbivorous reef fishes of the Coral Sea. J. Exp. Mar. Biol. Ecol. 126, 1–20 (1989).Article 

Google Scholar 
Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).Article 

Google Scholar 
Paola, V. D., Vullioud, P., Demarta, L., Alwany, M. A. & Ros, A. F. H. Factors affecting interspecific aggression in a year-round territorial species, the jewel damselfish. Ethology 118, 721–732 (2012).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).Bürkner, P. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Soft. 80, 1–28 (2017).Article 

Google Scholar 
RStan: the R interface to Stan. R package version 2.21.5 (Stan Development Team, 2022).Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Soft. 33, 1–22 (2010).Article 

Google Scholar 
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).Article 

Google Scholar 
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B. & Burkner, P. C. Rank-normalization, folding, and localization: an improved (formula presented) for assessing convergence of MCMC (with Discussion). Bayesian Anal. 16, 667–718 (2021).Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).Article 

Google Scholar  More

FAO. Handbook on crop statistics: improving methods for measuring crop area, production and yield. (FAO, Rome, Italy, 2018).FAO. Land use statistics and indicators: global, regional and county trends 1990-2019. FAOSTAT Anal. Brief Ser. No 28 (2021).Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 1–10 (2021) https://doi.org/10.1038/s43016-021-00429-zFAO. A system of integrated agricultural censuses and surveys. (FAO, 2005).FAO. Land use statistics and indicators. Global, regional and country trends, 2000–2020. (FAO, Rome, Italy, 2022).Loveland, T. R. et al. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens. 21, 1303–1330 (2000).Article 

Google Scholar 
Zanaga, D. et al. ESA WorldCover 10 m 2020 v100. (2021) https://doi.org/10.5281/zenodo.5571936Cochran, W. G. Sampling techniques. (Wiley, 1977).Stehman, S. V. Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. Int. J. Remote Sens. 35, 4923–4939 (2014).Article 

Google Scholar 
Tsujino, R., Kaijisa, T. & Yumoto, T. Causes and history of forest loss in Cambodia. Int. For. Rev. 21, 372–384 (2019).
Google Scholar 
Hu, Q. et al. Global cropland intensification surpassed expansion between 2000 and 2010: A spatio-temporal analysis based on GlobeLand30. Sci. Total Environ. 746, 141035 (2020).Grainger, A. Difficulties in tracking the long-term global trend in tropical forest area. Proc. Natl Acad. Sci. 105, 818–823 (2008).Article 
ADS 
CAS 

Google Scholar 
FAO. FAOSTAT. https://www.fao.org/faostat/en/#home (2021). More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Yin, J. et al. Future socio-ecosystem productivity threatened by compound drought– heatwave events. Nat. Sustain. https://doi.org/10.1038/10.1038/s41893-022-01024-1 (2023). More

Study populationThe study is based on data from mothers and children participating in a longitudinal Southwest Finland cohort, Steps to Healthy development of Children (the STEPS Study) (described in detail in Lagström et al.31). The STEPS study is an ongoing population-based and multidisciplinary study that investigates children’s physical, psychological and social development, starting from pregnancy and continuing until adolescence. All Finnish- and Swedish-speaking mothers delivering a child between 1 January, 2008 and 31 March, 2010 in the Hospital District of Southwest Finland formed the cohort population (in total, 9811 mothers and their 9936 children). Altogether, 1797 mothers with 1805 neonates volunteered as participants for the intensive follow-up group of the STEPS Study. Mothers were recruited by midwives either during the first trimester of pregnancy while visiting maternity health care clinics, or after delivery on the maternity wards of Turku University Hospital or Salo Regional Hospital, or by a letter mailed to the mothers. The participating mothers differ slightly from the whole cohort population in some background characteristics (being older, with first-born child and higher socioeconomic status)31. The ethics committee of the Hospital District of Southwest Finland has approved the STEPS Study (2/2007) and all methods were performed in accordance with relevant guidelines and regulations. Written informed consent was obtained from all the participants and, for children, from one parent for study participation. Subjects have been and are free to withdraw from the study at any time without any specific reason. The STEPS Study have the appropriate government authorization to the use of the National birth register (THL/974/5.05.00/2017).Breastmilk collection and HMO analysisMothers from the STEPS Study were asked to collect breastmilk samples when the infant was approximately 3 months old. In total, 812 of the 1797 mothers (45%) provided a breastmilk sample. There were only slight differences in maternal and child characteristics between the participants providing breastmilk samples and the total STEPS Study cohort40. Altogether, 795 breastmilk samples were included in this study (excluding the duplicate observations and the 2nd born twins, samples with technical unclarity or insufficient sample quantity, one breastmilk sample collected notably later than the other samples, at infant age of 14.5 months (range for the other breastmilk samples: 0.6–6.07 months), one sample with missing information on the date of collection and six mothers missing data on residential green environment) (Supplementary Fig. 2). Mothers received written instructions for the collection of breastmilk samples: samples were collected by manual expression in the morning from one single breast, first milking a few drops to waste before collecting the actual sample (~ 10 ml) into a plastic container (pre-feed sample). The samples were stored in the fridge and the mothers brought the samples to the research center or the samples were collected from their homes on the day of sampling. All samples were frozen and stored at − 70 °C until analysis.High Performance Liquid Chromatography (HPLC) was used to identify HMOs in breastmilk as previously described40,57,58 at the University of California, San Diego (methods described in detail in Berger et al.58). Milk samples were spiked with raffinose (a non-HMO carbohydrate) as an internal standard to allow absolute quantification. HMOs were extracted by high-throughput solid-phase extraction, fluorescently labelled, and measured using HPLC with fluorescent detection (HPLC-FLD)58. Absolute concentrations for the 19 HMOs were calculated based on standard retention times and corrected for internal standard recovery. Quantified HMOs included: 2′-fucosyllactose (2′FL), 3-fucosyllactose (3FL), lacto-N-neotetraose (LNnT), 3′-sialyllactose (3′SL), difucosyllactose (DFlac), 6′-sialyllactose (6′SL), lacto-N-tetraose (LNT), lacto-Nfucopentaose (LNFP) I, LNFP II, LNFP III, sialyl-LNT (LST) b, LSTc, difucosyllacto-LNT (DFLNT), lacto-N-hexaose (LNH), disialyllacto-N-tetraose (DSLNT), fucosyllacto-Nhexaose (FLNH), difucosyllacto-N-hexaose (DFLNH), fucodisialyllacto-lacto-N-hexaose (FDSLNH) and disialyllacto-N-hexaose (DSLNH). HMOs were also summed up to seven groups based on structural features: small HMOs (2′FL, 3FL, 3′SL, 6′SL, and DFLac), type 1 HMOs (LNT, LNFP I, LNFP II, LSTb, DSLNT), type 2 HMOs (LNnT, LNFP III, LSTc), α-1-2-fucosylated HMOs (2’FL, LNFP I), terminal α-2-6-sialylated HMOs (6′SL, LSTc), internal α-2-6-sialylated HMOs (DSLNT, LSTb), terminal α-2-3-sialylated HMOs (3′SL, DSLNT). The total concentration of HMOs was calculated as the sum of the 19 oligosaccharides. HMO-bound fucose and HMO-bound sialic acid were calculated on a molar basis. The proportion of each HMO comprising the total HMO concentration was also calculated. HMO Simpson’s diversity was calculated as Simpson’s Reciprocal Index 1/D, which is the reciprocal sum of the square of the relative abundance of each of the measured 19 HMOs57,59. The higher the diversity value, the more heterogenous is the HMO composition in the sample.Properties of the residential green environmentThe selected residential green environment variables measure the properties of the green environments surrounding the homes of the participants and do not include any measures of the house characteristics, indoor environment or the actual use of green spaces by the participants. The residential green environment variables were selected due to their previously observed associations with residential microbiota and health33,34,35. The variables of the residential green environments were derived from multispectral satellite images series, with a 30 m × 30 m of spatial resolution (Landsat TM 5, National Aeronautics and Space Administration—NASA) and land cover data (CORINE). We used Landsat TM images obtained over the summertime (June–August, greenest months in Finland), to minimize the seasonal variation of living vegetation and cloud cover as well as to better identify vegetation areas and maximise the contrast in our estimated exposure. In each selected Landsat TM 5 images, the cloud was masked out, and the Normalized Difference Vegetation Index (NDVI)36 was calculated. The final NDVI map was the mean of NDVI images collected over three consecutive years (2008–2010), to make an NDVI map with non-missing values due to cloud cover for the study area. NDVI map measures the vegetation cover, vitality and density. The NDVI can get values ranging from − 1 to 1 where values below zero represent water surfaces, values close to zero indicate areas with low intensity of living vegetation and values close to one indicate high abundance of living vegetation. For the analyses, areas covered by water were removed and the value ranged from 0 to 1, to prevent negative values for underestimating the greenness values of the residential area like in some prior studies60. We assumed that summertime NDVI identified the green space and vegetation density well, but greenness intensity might vary seasonally.Second, we used calculated indicators related to the diversity and naturalness of the land cover from CORINE Land Cover data sets of the year 201261. The 12 land cover types include: (1) Residential area, (2) Industrial/commercial area, (3) Transport network, (4) Sport/leisure, (5) Agriculture, (6) Broad-leave forest, (7) Coniferous forest, (8) Mixed forest, (9) Shrub/grassland, (10) Bare surface, (11) Wetland, and (12) Water bodies. From this information, we calculated two vegetation cover indexes. The Vegetation Cover Diversity Index (Simpson’s Diversity Index of Vegetation Cover, VCDI)37, only includes vegetation classes from CORINE land cover types (categories 5–9 and 11). VCDI approaches 1 as the number of different vegetation classes increases and the proportional distribution of area among the land use classes becomes more equitable. Furthermore, because we were particularly interested in the natural vegetation cover in the residential area, we calculated the area-weighted Naturalness Index (NI)38. This is an integrated indicator used to measure the human impact and degree of all human interventions on ecological components. The index is based on CORINE Land Cover data but reclassified to 15 classes. Residential areas have been divided to two classes: Continuous residential area (High density buildings) and Discontinuous residential area (Low density, mostly individual houses area). Agricultural area has also been divided to two classes: Agricultural area (Cropland) and Pasture as well as class 9 (Shrub/grassland) has been separated to Woodland and Natural grassland. Assignment of CORINE Land Cover classes to degrees of naturalness has been made based on Walz and Stein 201438. The area-weighted NI range from 1 to 7, where low values represent low human impact (≤ 3 = Natural), medium values moderate human impact (4–5 = Semi-natural) and high values strong human impact (6–7 = Non-Natural). To ease the interpretation of results and to correspond to the same direction than the other residential green environment variables, we have reverse-scaled the NI values, so that higher values illustrate more natural residential areas.Background factorsAs genetics is strongly linked to HMO composition, maternal secretor status was determined by high abundance (secretor) or near absence (non-secretor) of the HMO 2’FL in the breastmilk samples. Mothers with active secretor (Se) genes and FUT2 enzyme produce high amounts of α-1-2-fucosylated HMOs such as 2′-fucosyllactose (2′FL), whereas in the breastmilk of non-secretor mothers these HMOs are almost absent. Beyond genetics, other maternal and infant characteristics may influence HMO composition. So far, several associations have been reported, including lactation stage, maternal pre-pregnancy BMI, maternal age, parity, maternal diet, mode of delivery, infant gestational age and infant sex22,40. Information on the potential confounding factors, child sex, birth weight, maternal age at birth, number of previous births, marital status, maternal occupational status, smoking during pregnancy (before and during pregnancy), maternal pre-pregnancy BMI, mode of delivery, duration of pregnancy and maternal diseases [including both maternal disorders predominantly related to pregnancy such as pre-eclampsia and gestational diabetes and chronic diseases (diseases of the nervous, circulatory, respiratory, digestive, musculoskeletal and genitourinary systems, cancer and mental and behavioral disorders, according to ICD-10 codes, i.e. WHO International Classification of Diseases Tenth Revision)], were obtained from Medical Birth Registers. Self-administered questionnaires upon recruitment provided information on family net income and maternal education level. Those who had no professional training or a maximum of an intermediate level of vocational training (secondary education) were classified as “basic”. Those who had studied at a University of Applied Sciences or higher (tertiary education) were classified as “advanced”. The advanced level included any academic degree (bachelor’s, master’s, licentiate or doctoral degree). Maternal diet quality was assessed in late pregnancy using the Index of Diet Quality (IDQ62) which measures adherence to health promoting diet and nutrition recommendations. The IDQ score was used in its original form by setting the statistically defined cut-off value at 10, with scores below 10 points indicating unhealthy diets and non-adherence and scores of 10–15 points indicating a health-promoting diet and adherence dietary guidelines. Lactation time postpartum (child age) and season were received from the recorded breastmilk collection dates. Lactation status (exclusive/partial/unknown breastfeeding) at the time of breastmilk collection were gathered from follow-up diaries. From partially breastfeeding mothers (n = 277) 253 had started formula feeding and 28 solids at the time of milk collection. Last, a summary z score representing socio-economic disadvantage in the residential area was obtained from Statistics Finland grid database for the year 2009 and is based on the proportion of adults with low level of education, the unemployment rate, and proportion of people living in rented housing at each participant’s residential area55.Statistical analysesTo harmonize the residential green environment variables we calculated the mean values for NDVI, VCDI and NI in 750 × 750 m squares (and 250 × 250 m) around participant homes in a Geographical Information System (QGIS, www.qgis.org). The same grid sizes were used to calculate residential socioeconomic disadvantage in the residential area55 at the time of child birth. The geographical coordinates (latitude/longitude) of the cohort participants’ home address (795 mothers) were obtained from the Population Register Centre at the time of their child birth.The background characteristics of the mothers and children are given as means and standard deviations (SD) for continuous variables and percentages for categorical variables. Due to non-normal distribution, natural logarithmic transformation was performed for all HMO variables (19 individual components, sum of HMOs, HMO-bound sialic acid, HMO-bound fucose and HMO groups (all in nmol/mL)) except for HMO diversity. Associations between each background factor and HMO diversity and 19 individual HMO components were analysed with univariate generalized linear models to identify factors independently associated with HMO composition. All factors demonstrating a significant association (p  More

Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 4621 (2020).Article 
ADS 
CAS 

Google Scholar 
Elmendorf, S. C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nat. Clim. Change 2, 453–457 (2012).Article 
ADS 

Google Scholar 
Overland, J. E. et al. Surface air temperature. In Arctic Report Card: Update for 2019 (eds Richter-Menge, J. et al.) (U.S. National Park Service, 2020).
Google Scholar 
Post, E., Steinman, B. A. & Mann, M. E. Acceleration of phenological advance and warming with latitude over the past century. Sci. Rep. 8, 3927 (2018).Article 
ADS 

Google Scholar 
Diepstraten, R. A. E., Jessen, T. D., Fauvelle, C. M. D. & Musiani, M. M. Does climate change and plant phenology research neglect the Arctic tundra?. Ecosphere 9, e02362 (2018).Article 

Google Scholar 
Flynn, D. F. B. & Wolkovich, E. M. Temperature and photoperiod drive spring phenology across all species in a temperate forest community. New Phytol. 219, 1353–1362 (2018).Article 
CAS 

Google Scholar 
Billings, W. D. & Bliss, L. C. An alpine snowbank environment and its effects on vegetation, plant development, and productivity. Ecology 40, 388–397 (1959).Article 

Google Scholar 
Billings, W. D. & Mooney, H. A. The ecology of arctic and alpine plants. Biol. Rev. 43, 481–529 (1968).Article 

Google Scholar 
Sørensen, T. Temperature relations and phenology of the northeast Greenland flowering plants. Meddr Gronland 1–305 (1941).Barrett, R. T. & Hollister, R. D. Arctic plants are capable of sustained responses to long-term warming. Polar Res. 35, 25405 (2016).Article 

Google Scholar 
Julitta, T. et al. Using digital camera images to analyse snowmelt and phenology of a subalpine grassland. Agric. For. Meteorol. 198–199, 116–125 (2014).Article 
ADS 

Google Scholar 
Petraglia, A. et al. Responses of flowering phenology of snowbed plants to an experimentally imposed extreme advanced snowmelt. Plant Ecol. 215, 759–768 (2014).Article 

Google Scholar 
Semenchuk, P. R. et al. High Arctic plant phenology is determined by snowmelt patterns but duration of phenological periods is fixed: An example of periodicity. Environ. Res. Lett. 11, 125006 (2016).Article 
ADS 

Google Scholar 
Hollister, R. D., Webber, P. J. & Bay, C. Plant response to temperature in northern Alaska: Implications for predicting vegetation change. Ecology 86, 1562–1570 (2005).Article 

Google Scholar 
Oberbauer, S. et al. Phenological response of tundra plants to background climate variation tested using the International Tundra Experiment. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120481 (2013).Article 
CAS 

Google Scholar 
Tieszen, L. L. Photosynthesis in the principal Barrow, Alaska, species: A summary of field and laboratory responses. In Vegetation and Production Ecology of an Alaskan Arctic Tundra (ed. Tieszen, L. L.) 241–268 (Springer, 1978).Chapter 

Google Scholar 
Körner, Ch. CO2 exchange in the alpine sedge Carex curvula as influenced by canopy structure, light and temperature. Oecologia 53, 98–104 (1982).Article 
ADS 

Google Scholar 
Tieszen, L. L. Photosynthesis and respiration in arctic tundra grasses: Field light intensity and temperature responses. Arct. Alp. Res. 5, 239–251 (1973).Article 
CAS 

Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).Article 

Google Scholar 
Marchand, F. L., Mertens, S., Kockelbergh, F., Beyens, L. & Nijs, I. Performance of high arctic tundra plants improved during but deteriorated after exposure to a simulated extreme temperature event. Glob. Change Biol. 11, 2078–2089 (2005).Article 
ADS 

Google Scholar 
Yan, W. An equation for modelling the temperature response of plants using only the cardinal temperatures. Ann. Bot. 84, 607–614 (1999).Article 

Google Scholar 
Zhou, G. & Wang, Q. A new nonlinear method for calculating growing degree days. Sci. Rep. 8, 10149 (2018).Article 
ADS 

Google Scholar 
Kramer, K. Selecting a model to predict the onset of growth of Fagus sylvatica. J. Appl. Ecol. 31, 172 (1994).Article 

Google Scholar 
Nakano, Y., Higuchi, Y., Sumitomo, K. & Hisamatsu, T. Flowering retardation by high temperature in chrysanthemums: Involvement of FLOWERING LOCUS T-like 3 gene repression. J. Exp. Bot. 64, 909–920 (2013).Article 
CAS 

Google Scholar 
del Olmo, I., Poza-Viejo, L., Piñeiro, M., Jarillo, J. A. & Crevillén, P. High ambient temperature leads to reduced FT expression and delayed flowering in Brassica rapa via a mechanism associated with H2A.Z dynamics. Plant J. 100, 343–356 (2019).Article 

Google Scholar 
Wolkovich, E. M. et al. Warming experiments underpredict plant phenological responses to climate change. Nature 485, 494 (2012).Article 
ADS 
CAS 

Google Scholar 
Hollister, R. D. et al. A review of open top chamber (OTC) performance across the ITEX Network. Arct. Sci. https://doi.org/10.1139/AS-2022-0030 (2022).Article 

Google Scholar 
Bütikofer, L. et al. The problem of scale in predicting biological responses to climate. Glob. Change Biol. 26, 6657–6666 (2020).Article 
ADS 

Google Scholar 
Gu, S. Growing degree hours—A simple, accurate, and precise protocol to approximate growing heat summation for grapevines. Int. J. Biometeorol. 60, 1123–1134 (2016).Article 
ADS 
CAS 

Google Scholar 
Roltsch, W. J., Zalom, F. G., Strawn, A. J., Strand, J. F. & Pitcairn, M. J. Evaluation of several degree-day estimation methods in California climates. Int. J. Biometeorol. 42, 169–176 (1999).Article 
ADS 

Google Scholar 
Richardson, A. D. et al. Ecosystem warming extends vegetation activity but heightens vulnerability to cold temperatures. Nature 560, 368–371 (2018).Article 
ADS 
CAS 

Google Scholar 
Ettinger, A. K., Buonaiuto, D. M., Chamberlain, C. J., Morales-Castilla, I. & Wolkovich, E. M. Spatial and temporal shifts in photoperiod with climate change. New Phytol. 230, 462–474 (2021).Article 
CAS 

Google Scholar 
Seyednasrollah, B., Swenson, J. J., Domec, J.-C. & Clark, J. S. Leaf phenology paradox: Why warming matters most where it is already warm. Remote Sens. Environ. 209, 446–455 (2018).Article 
ADS 

Google Scholar 
Breshears, D. D. et al. Underappreciated plant vulnerabilities to heat waves. New Phytol. 231, 32–39 (2021).Article 

Google Scholar 
Chaudhry, S. & Sidhu, G. P. S. Climate change regulated abiotic stress mechanisms in plants: A comprehensive review. Plant Cell Rep. 41, 1–31 (2022).Article 
CAS 

Google Scholar 
Sun, X. et al. Global diurnal temperature range (DTR) changes since 1901. Clim. Dyn. 52, 3343–3356 (2019).Article 

Google Scholar 
Ballinger, T. J. NOAA Arctic Report Card 2021: Surface Air Temperature. https://doi.org/10.25923/53XD-9K68 (2021).Jagadish, S. V. K., Way, D. A. & Sharkey, T. D. Plant heat stress: Concepts directing future research. Plant Cell Environ. 44, 1992–2005 (2021).Article 
CAS 

Google Scholar 
Gilmore, E. C. Jr. & Rogers, J. S. Heat units as a method of measuring maturity in corn. Agron. J. 50, 611–615 (1958).Article 

Google Scholar 
Sánchez, B., Rasmussen, A. & Porter, J. R. Temperatures and the growth and development of maize and rice: A review. Glob. Change Biol. 20, 408–417 (2014).Article 
ADS 

Google Scholar 
Molitor, D., Junk, J., Evers, D., Hoffmann, L. & Beyer, M. A high-resolution cumulative degree day-based model to simulate phenological development of grapevine. Am. J. Enol. Vitic. 65, 72–80 (2014).Article 

Google Scholar 
CaraDonna, P. J., Iler, A. M. & Inouye, D. W. Shifts in flowering phenology reshape a subalpine plant community. Proc. Natl. Acad. Sci. 111, 4916–4921 (2014).Article 
ADS 
CAS 

Google Scholar 
Inouye, B. D., Ehrlén, J. & Underwood, N. Phenology as a process rather than an event: From individual reaction norms to community metrics. Ecol. Monogr. 89, e01352 (2019).Article 

Google Scholar 
Miles, W. T. S. et al. Quantifying full phenological event distributions reveals simultaneous advances, temporal stability and delays in spring and autumn migration timing in long-distance migratory birds. Glob. Change Biol. 23, 1400–1414 (2017).Article 
ADS 

Google Scholar 
Moussus, J.-P., Julliard, R. & Jiguet, F. Featuring 10 phenological estimators using simulated data. Methods Ecol. Evol. 1, 140–150 (2010).Article 

Google Scholar 
Dowle, M. & Srinivasan, A. data.table: Extension of ‘data.frame’ (2019).Auguie, B. egg: Extensions for ‘ggplot2’: Custom Geom, Custom Themes, Plot Alignment, Labelled Panels, Symmetric Scales, and Fixed Panel Size (2019).Wood, S. & Scheipl, F. gamm4: Generalized Additive Mixed Models using ‘mgcv’ and ‘lme4’ (2020).Auguie, B. gridExtra: Miscellaneous Functions for ‘Grid’ Graphics (2017).Hamner, B. & Frasco, M. Metrics: Evaluation Metrics for Machine Learning (2018).Gilli, M., Maringer, D. & Schumann, E. Numerical Methods and Optimization in Finance (Elsevier/Academic Press, 2019).MATH 

Google Scholar 
Garnier, S. viridis: Default Color Maps from ‘matplotlib’ (2018).Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 
ADS 

Google Scholar  More

Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).Article 

Google Scholar 
Land Use Statistics and Indicators. Global, Regional and Country Trends 2000–2020 FAOSTAT Analytical Brief Series No 48 https://www.fao.org/food-agriculture-statistics/data-release/data-release-detail/en/c/1599856/ (FAO, 2022).FAO. Land Statistics. Global, Regional and Country Trends, 1990–2018 FAOSTAT Analytical Brief Series No. 15 https://www.fao.org/3/cb2860en/cb2860en.pdf (FAO, 2021).Summary for policymakers in: Special Report on Climate Change and Land (eds Shukla, P. R. et al.) https://www.ipcc.ch/site/assets/uploads/sites/4/2020/02/SPM_Updated-Jan20.pdf (WMO, in the press).Sustainable Development Goals Indicator 2.4.1 (FAO, accessed); https://www.fao.org/sustainable-development-goals/indicators/241/en/Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K. 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IGES, 2006).Grassi, G. et al. Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting. Earth Syst. Sci. Data 14, 4643–4666 (2022).Article 
ADS 

Google Scholar 
Tubiello, F. N. et al. Measuring Progress Towards Sustainable Agriculture FAO Statistical Working Papers Series No. 21–24 https://www.fao.org/3/cb4549en/cb4549en.pdf (FAO, 2021).Conchedda, G. & Tubiello, F. N. Drainage of organic soils and GHG emissions: validation with country data. Earth Syst. Sci. Data 12, 3113–3137 (2020).Article 
ADS 

Google Scholar 
Hanson, C., Mazur, E., Stolle, F., Davis, C. & Searchinger, T. 5 takeaways on cropland expansion and what it means for people and the planet. WRI Insights https://www.wri.org/insights/cropland-expansion-impacts-people-planet (2022).Potapov, P. et al. The Global 2000–-2020 land cover and land use change dataset derived from the Landsat archive: first results. Front. Remote Sens. 3, 856903 (2022).Article 

Google Scholar 
Hansen, M. C. et al. Global land use extent and dispersion within natural land cover using Landsat data. Environ. Res. Lett. 17, 034050 (2022).Article 
ADS 

Google Scholar 
Tubiello, F. N. et al. Carbon emissions and removals from forests: new estimates, 1990–2020. Earth Syst. Sci. Data. 13, 1681–1691 (2021).Article 
ADS 

Google Scholar  More

Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021).Article 
CAS 
PubMed 

Google Scholar 
Schmidt, T. S. B., Raes, J. & Bork, P. The human gut microbiome: from association to modulation. Cell 172, 1198–1215 (2018).Article 
CAS 
PubMed 

Google Scholar 
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).Article 
CAS 
PubMed 

Google Scholar 
Tap, J. et al. Gut microbiota richness promotes its stability upon increased dietary fibre intake in healthy adults. Environ. Microbiol. 17, 4954–4964 (2015).Article 
CAS 
PubMed 

Google Scholar 
Smits, S. A. et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 357, 802–806 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
De Filippo, C. et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc. Natl Acad. Sci. USA 107, 14691–14696 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Morrison, K. E., Jašarević, E., Howard, C. D. & Bale, T. L. It’s the fiber, not the fat: significant effects of dietary challenge on the gut microbiome. Microbiome 8, 15 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Maslowski, K. M. & Mackay, C. R. Diet, gut microbiota and immune responses. Nat. Immunol. 12, 5–9 (2011).Article 
CAS 
PubMed 

Google Scholar 
Reynolds, A. et al. Carbohydrate quality and human health: a series of systematic reviews and meta-analyses. Lancet 393, 434–445 (2019).Article 
CAS 
PubMed 

Google Scholar 
Slavin, J. Fiber and prebiotics: mechanisms and health benefits. Nutrients 5, 1417–1435 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Desai, M. S. et al. A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility. Cell 167, 1339–1353.e21 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Makki, K., Deehan, E. C., Walter, J. & Bäckhed, F. The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe 23, 705–715 (2018).Article 
CAS 
PubMed 

Google Scholar 
Cantu-Jungles, T. M. et al. Dietary fiber hierarchical specificity: the missing link for predictable and strong shifts in gut bacterial communities. mBio 12, e01028-21 (2022).
Google Scholar 
Murga-Garrido, S. M. et al. Gut microbiome variation modulates the effects of dietary fiber on host metabolism. Microbiome 9, 117 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cantu-Jungles, T. M. & Hamaker, B. R. New view on dietary fiber selection for predictable shifts in gut microbiota. mBio 11, e02179-19 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Singh, V. et al. Dysregulated microbial fermentation of soluble fiber induces cholestatic liver cancer. Cell 175, 679–694.e22 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Terrapon, N., Lombard, V., Gilbert, H. J. & Henrissat, B. Automatic prediction of polysaccharide utilization loci in Bacteroidetes species. Bioinformatics 31, 647–655 (2015).Article 
CAS 
PubMed 

Google Scholar 
Terrapon, N. et al. PULDB: the expanded database of Polysaccharide Utilization Loci. Nucleic Acids Res. 46, D677–D683 (2018).Article 
CAS 
PubMed 

Google Scholar 
Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495 (2014).Article 
CAS 
PubMed 

Google Scholar 
Kouzuma, A., Kato, S. & Watanabe, K. Microbial interspecies interactions: recent findings in syntrophic consortia. Front. Microbiol. 6, 477 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).Article 
CAS 
PubMed 

Google Scholar 
Rakoff-Nahoum, S., Coyne, M. J. & Comstock, L. E. An ecological network of polysaccharide utilization among human intestinal symbionts. Curr. Biol. 24, 40–49 (2014).Article 
CAS 
PubMed 

Google Scholar 
Luis, A. S. et al. Dietary pectic glycans are degraded by coordinated enzyme pathways in human colonic Bacteroides. Nat. Microbiol. 3, 210–219 (2018).Article 
CAS 
PubMed 

Google Scholar 
Cartmell, A. et al. A surface endogalactanase in Bacteroides thetaiotaomicron confers keystone status for arabinogalactan degradation. Nat. Microbiol. 3, 1314–1326 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rakoff-Nahoum, S., Foster, K. R. & Comstock, L. E. The evolution of cooperation within the gut microbiota. Nature 533, 255–259 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pichler, M. J. et al. Butyrate producing colonic Clostridiales metabolise human milk oligosaccharides and cross feed on mucin via conserved pathways. Nat. Commun. 11, 3285 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rogowski, A. et al. Glycan complexity dictates microbial resource allocation in the large intestine. Nat. Commun. 6, 7481 (2015).Article 
CAS 
PubMed 

Google Scholar 
Feng, J. et al. Polysaccharide utilization loci in Bacteroides determine population fitness and community-level interactions. Cell Host Microbe https://doi.org/10.1016/j.chom.2021.12.006 (2022).Pollak, S. et al. Public good exploitation in natural bacterioplankton communities. Sci. Adv. 7, eabi4717 (2022).Article 

Google Scholar 
Cuskin, F. et al. Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism. Nature 517, 165–169 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Patnode, M. L. et al. Interspecies competition impacts targeted manipulation of human gut bacteria by fiber-derived glycans. Cell 179, 59–73.e13 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Walter, J., Maldonado-Gómez, M. X. & Martínez, I. To engraft or not to engraft: an ecological framework for gut microbiome modulation with live microbes. Curr. Opin. Biotechnol. 49, 129–139 (2018).Article 
CAS 
PubMed 

Google Scholar 
Jernberg, C., Löfmark, S., Edlund, C. & Jansson, J. K. Long-term ecological impacts of antibiotic administration on the human intestinal microbiota. ISME J. 1, 56–66 (2007).Article 
CAS 
PubMed 

Google Scholar 
Dethlefsen, L., Huse, S., Sogin, M. L. & Relman, D. A. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol. 6, e280 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Becattini, S., Taur, Y. & Pamer, E. G. Antibiotic-induced changes in the intestinal microbiota and disease. Trends Mol. Med. 22, 458–478 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shade, A. et al. Fundamentals of microbial community resistance and resilience. Front. Microbiol. 3, 417 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).Article 
CAS 
PubMed 

Google Scholar 
Stone, L. The stability of mutualism. Nat. Commun. 11, 2648 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).Article 
PubMed 

Google Scholar 
Butler, S. & O’Dwyer, J. P. Stability criteria for complex microbial communities. Nat. Commun. 9, 2970 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, W. & Stevens, M. H. H. Fluctuating resource availability increases invasibility in microbial microcosms. Oikos 121, 435–441 (2012).Article 

Google Scholar 
Nobuhiko, K. et al. Regulated virulence controls the ability of a pathogen to compete with the gut microbiota. Science 336, 1325–1329 (2012).Article 

Google Scholar 
Maltby, R., Leatham-Jensen, M. P., Gibson, T., Cohen, P. S. & Conway, T. Nutritional basis for colonization resistance by human commensal Escherichia coli strains HS and Nissle 1917 against E. coli O157:H7 in the mouse intestine. PLoS ONE 8, e53957 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Leatham, M. P. et al. Precolonized human commensal Escherichia coli strains serve as a barrier to E. coli O157:H7 growth in the streptomycin-treated mouse intestine. Infect. Immun. 77, 2876–2886 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Clark, R. L. et al. Design of synthetic human gut microbiome assembly and butyrate production. Nat. Commun. 12, 3254 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hromada, S. et al. Negative interactions determine Clostridioides difficile growth in synthetic human gut communities. Mol. Syst. Biol. 17, e10355 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
MacArthur, R. Species packing and competitive equilibrium for many species. Theor. Popul. Biol. 1, 1–11 (1970).Article 
CAS 
PubMed 

Google Scholar 
Ndeh, D. et al. Complex pectin metabolism by gut bacteria reveals novel catalytic functions. Nature 544, 65–70 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Grondin, J. M., Tamura, K., Déjean, G., Abbott, D. W. & Brumer, H. Polysaccharide utilization loci: fueling microbial communities. J. Bacteriol. 199, e00860-16 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Haiser, H. J. et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295–298 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Devendran, S. et al. Clostridium scindens ATCC 35704: integration of nutritional requirements, the complete genome sequence, and global transcriptional responses to bile acids. Appl. Environ. Microbiol. 85, e00052-19 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rey, F. E. et al. Metabolic niche of a prominent sulfate-reducing human gut bacterium. Proc. Natl Acad. Sci. USA 110, 13582–13587 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kaoutari, A. E., Armougom, F., Gordon, J. I., Raoult, D. & Henrissat, B. The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nat. Rev. Microbiol. 11, 497–504 (2013).Article 
PubMed 

Google Scholar 
Pereira, F. C. & Berry, D. Microbial nutrient niches in the gut. Environ. Microbiol. 19, 1366–1378 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Despres, J. et al. Xylan degradation by the human gut Bacteroides xylanisolvens XB1A(T) involves two distinct gene clusters that are linked at the transcriptional level. BMC Genomics 17, 326 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Déjean, G. et al. Synergy between cell surface glycosidases and glycan-binding proteins dictates the utilization of specific beta(1,3)-glucans by human gut bacteroides. mBio 11, e00095-20 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hamaker, B. R. & Tuncil, Y. E. A perspective on the complexity of dietary fiber structures and their potential effect on the gut microbiota. J. Mol. Biol. 426, 3838–3850 (2014).Article 
CAS 
PubMed 

Google Scholar 
Bishop, C. M. Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer, 2006).Wasserman, L. All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) (Springer, 2003).Willing, B. P., Russell, S. L. & Finlay, B. B. Shifting the balance: antibiotic effects on host–microbiota mutualism. Nat. Rev. Microbiol. 9, 233–243 (2011).Article 
CAS 
PubMed 

Google Scholar 
Panda, S. et al. Short-term effect of antibiotics on human gut microbiota. PLoS ONE 9, e95476 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Ng, K. M. et al. Recovery of the gut microbiota after antibiotics depends on host diet, community context, and environmental reservoirs. Cell Host Microbe 26, 650–665.e4 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Van der Waaij, D., Berghuis-de Vries, J. M. & Lekkerkerk-van der Wees, J. E. C. Colonization resistance of the digestive tract in conventional and antibiotic-treated mice. J. Hygiene 69, 405–411 (1971).Article 

Google Scholar 
Freter, R. In vivo and in vitro antagonism of intestinal bacteria against Shigella flexneri. II. The inhibitory mechanism. J. Infect. Dis. 110, 38–46 (1962).Article 
CAS 
PubMed 

Google Scholar 
Maldonado-Gómez, M. X. et al. Stable engraftment of Bifidobacterium longum AH1206 in the human gut depends on individualized features of the resident microbiome. Cell Host Microbe 20, 515–526 (2016).Article 
PubMed 

Google Scholar 
Sorbara, M. T. & Pamer, E. G. Interbacterial mechanisms of colonization resistance and the strategies pathogens use to overcome them. Mucosal Immunol. 12, 1–9 (2019).Article 
CAS 
PubMed 

Google Scholar 
Litvak, Y. & Bäumler, A. J. The founder hypothesis: a basis for microbiota resistance, diversity in taxa carriage, and colonization resistance against pathogens. PLoS Pathog. 15, e1007563 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jenior, M. L., Leslie, J. L., Young, V. B. & Schloss, P. D. Clostridium difficile colonizes alternative nutrient niches during infection across distinct murine gut microbiomes. mSystems 2, e00063-17 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Momose, Y., Hirayama, K. & Itoh, K. Competition for proline between indigenous Escherichia coli and E. coli O157:H7 in gnotobiotic mice associated with infant intestinal microbiota and its contribution to the colonization resistance against E. coli O157:H7. Antonie van Leeuwenhoek 94, 165–171 (2008).Article 
CAS 
PubMed 

Google Scholar 
Fabich, A. J. et al. Comparison of carbon nutrition for pathogenic and commensal Escherichia coli strains in the mouse intestine. Infect. Immun. 76, 1143–1152 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shepherd, E. S., DeLoache, W. C., Pruss, K. M., Whitaker, W. R. & Sonnenburg, J. L. An exclusive metabolic niche enables strain engraftment in the gut microbiota. Nature 557, 434–438 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jenior, M. L., Leslie, J. L., Young, V. B. & Schloss, P. D. Clostridium difficilealters the structure and metabolism of distinct cecal microbiomes during initial infection to promote sustained colonization. mSphere 3, e00261-18 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, S., Tan, J., Yang, X., Ma, C. & Jiang, L. Niche and fitness differences determine invasion success and impact in laboratory bacterial communities. ISME J. 13, 402–412 (2019).Article 
PubMed 

Google Scholar 
Deng, Y.-J. & Wang, S. Y. Synergistic growth in bacteria depends on substrate complexity. J. Microbiol. 54, 23–30 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Deng, Y.-J. & Wang, S. Y. Complex carbohydrates reduce the frequency of antagonistic interactions among bacteria degrading cellulose and xylan. FEMS Microbiol. Lett. 364, fnx019 (2017).Article 
PubMed Central 

Google Scholar 
Wu, F. et al. Modulation of microbial community dynamics by spatial partitioning. Nat. Chem. Biol. 18, 394–402 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Åström, K. J. & Murray, R. Feedback Systems. An Introduction for Scientists and Engineers (Princeton Univ. Press, 2008).Hammarlund, S. P. & Harcombe, W. R. Refining the stress gradient hypothesis in a microbial community. Proc. Natl Acad. Sci. USA 116, 15760–15762 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pacheco, A. R., Osborne, M. L. & Segrè, D. Non-additive microbial community responses to environmental complexity. Nat. Commun. 12, 2365 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dal Bello, M., Lee, H., Goyal, A. & Gore, J. Resource–diversity relationships in bacterial communities reflect the network structure of microbial metabolism. Nat. Ecol. Evol. 5, 1424–1434 (2021).Article 
PubMed 

Google Scholar 
Magnúsdóttir, S. et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81–89 (2017).Article 
PubMed 

Google Scholar 
Baranwal, M. et al. Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics. eLife 11, e73870 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Palleja, A. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 3, 1255–1265 (2018).Article 
CAS 
PubMed 

Google Scholar 
Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108, 4554–4561 (2011).Article 
CAS 
PubMed 

Google Scholar 
Ramirez, J. et al. Antibiotics as major disruptors of gut microbiota. Front. Cell. Infect. Microbiol. 10, 572912 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).Article 
CAS 
PubMed 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Raue, A. et al. Lessons learned from quantitative dynamical modeling in systems biology. PLoS ONE 8, e74335 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Babtie, A. C., Kirk, P. & Stumpf, M. P. H. Topological sensitivity analysis for systems biology. Proc. Natl Acad. Sci. USA 111, 18507–18512 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Munsky, B., Hlavacek, W. S. & Tsimring, L. S. Quantitative Biology. Theory, Computational Methods, and Models (MIT Press, 2018).Ashyraliyev, M., Fomekong-Nanfack, Y., Kaandorp, J. A. & Blom, J. G. Systems biology: parameter estimation for biochemical models. FEBS J. 276, 886–902 (2009).Article 
CAS 
PubMed 

Google Scholar 
Ravcheev, D. A., Godzik, A., Osterman, A. L. & Rodionov, D. A. Polysaccharides utilization in human gut bacterium Bacteroides thetaiotaomicron: comparative genomics reconstruction of metabolic and regulatory networks. BMC Genomics 14, 873 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Salyers, A. A., Vercelloitti, J. R., West, S. E. & Wilkins, T. D. Fermentation of mucin and plant polysaccharides by strains of Bacteroides from the human colon. Appl. Environ. Microbiol. 33, 319–322 (1977).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sun, X., Liu, Y., Jiang, P., Song, S. & Ai, C. Interaction of sulfated polysaccharides with intestinal Bacteroidales plays an important role in its biological activities. Int. J. Biol. Macromol. 168, 496–506 (2021).Article 
CAS 
PubMed 

Google Scholar 
Respondek, F. et al. Short-chain fructo-oligosaccharides modulate intestinal microbiota and metabolic parameters of humanized gnotobiotic diet induced obesity mice. PLoS ONE 8, e71026 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schwiertz, A. et al. Anaerostipes caccae gen. nov., sp. nov., a new saccharolytic, acetate-utilising, butyrate-producing bacterium from human faeces. Syst. Appl. Microbiol. 25, 46–51 (2002).Article 
CAS 
PubMed 

Google Scholar 
Benítez-Páez, A., Moreno, F. J., Sanz, M. L. & Sanz, Y. Genome structure of the symbiont Bifidobacterium pseudocatenulatum CECT 7765 and gene expression profiling in response to lactulose-derived oligosaccharides. Front. Microbiol. 7, 624 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Bernalier, A., Willems, A., Leclerc, M., Rochet, V. & Collins, M. D. Ruminococcus hydrogenotrophicus sp. nov., a new H2/CO2-utilizing acetogenic bacterium isolated from human feces. Arch. Microbiol. 166, 176–183 (1996).Article 
CAS 
PubMed 

Google Scholar 
Moshfegh, A. J., Friday, J. E., Goldman, J. P. & Ahuja, J. K. C. Presence of inulin and oligofructose in the diets of Americans. J. Nutr. 129, 1407S–1411S (1999).Article 
CAS 
PubMed 

Google Scholar 
Sonnenburg, E. D. et al. Specificity of polysaccharide use in intestinal bacteroides species determines diet-induced microbiota alterations. Cell 141, 1241–1252 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Devillé, C., Damas, J., Forget, P., Dandrifosse, G. & Peulen, O. Laminarin in the dietary fibre concept. J. Sci. Food Agric. 84, 1030–1038 (2004).Article 

Google Scholar 
Selvendran, R. R. The plant cell wall as a source of dietary fiber: chemistry and structure. Am. J. Clin. Nutr. 39, 320–337 (1984).Article 
CAS 
PubMed 

Google Scholar  More

Sample collection and postmortem evaluationBald eagle carcasses, and/or oropharyngeal and cloacal swabs were collected in the field and submitted to the Southeastern Cooperative Wildlife Disease Study Research and Diagnostic Service. In some cases, live bald eagles were found moribund and transported to wildlife rehabilitation clinics and either died in transit or soon after arrival. Carcasses underwent postmortem evaluation, including gross and histopathology. Tissue samples [heart, brain, kidney, spleen, lung, adrenal gland, pancreas, liver, small and large intestine, and cloacal bursa (if present)] were fixed in 10% neutral buffered formalin and routinely processed for histopathology23 at the Athens Veterinary Diagnostic Laboratory. Histopathology was assessed by a board-certified veterinary pathologist.Additional bald eagle and waterfowl species mortality dataData on wild bird deaths attributed to highly pathogenic influenza A viruses were retrieved from the U.S. Department of Agriculture, Animal and Plant Health Inspection Service website, at: https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/animal-disease-information/avian/avian-influenza/hpai-2022/2022-hpai-wild-birds. These data are publicly available and include state, county, date detected, and species of individual birds that tested positive for HP IAV.ImmunohistochemistryImmunohistochemistry (IHC) for avian influenza virus was performed in select cases on brain, pancreas, spleen, liver, and/or adrenal gland at the Athens Veterinary Diagnostic Laboratory. IHC was performed on an automated stainer (Nemesis 3600, Biocare Medical). Polyclonal antiserum against influenza A virus was used as the primary antibody (ab155877, Abcam), diluted 1:3000, and incubated for 60 min at 37 °C with agent-positive control. Antigen retrieval was with Target Retrieval Solution (S2367, Dako) pH (10x) at 110 °C for 15 min. Enzyme blockage was via 3% H2O2 for 20 min (H324-500, Fisher Scientific); protein blockage was with Universal Blocking Reagent (10x) Power Block diluted at 1:10 for 5 min (HK085-5 K, BioGenex); link was by biotinylated rabbit anti-goat (BA-5000, Vector) at a 1:100 dilution for 10 min with 4 + streptavidin alkaline phosphatase label for 10 min (AP605H, BioCare Medical). Staining was with warp red chromogen kit for 5 min (WR8065, BioCare Medical). Known influenza A-virus positive control tissues were tested alongside each case.Polymerase chain reactionOropharyngeal and cloacal swabs from bald eagle carcasses were pooled for each individual eagle and tested by real-time reverse transcription polymerase chain reaction (rRT-PCR). Briefly, swabs samples were extracted with the KingFisher magnetic particle processer using the MagMAX-96 AI/ND Viral RNA isolation Kit (Ambion/Applied Biosystems, Foster City, CA) following a modified MagMAX-S protocol24. Resultant nucleic acids were screened against primers specific for H5 IAV in rRT-PCR; samples that yielded a cycle threshold value  More