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

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

Coleman, D. J. et al. Limnol. Oceanogr. Lett. 7, 140–149 (2022).Article 

Google Scholar 
Noyce, G. L. et al. https://doi.org/10.1038/s41561-022-01070-6 (2022).Kirwan, M. L. & Megonigal, J. P. Nature 504, 53–60 (2013).Article 

Google Scholar 
Morris, J. T., Sundareshwar, P. V., Nietch, C. T., Kjerve, B. & Cahoon, D. R. Ecology 83, 2869–2877 (2002).Article 

Google Scholar 
Noyce, G. L., Kirwan, M. L., Rich, R. L. & Megonigal, J. P. Proc. Natl Acad. Sci. 116, 21623–21628 (2019).Article 

Google Scholar 
Langley, J. A., McKee, K. L., Cahoon, D. R., Cherry, J. A. & Megonigal, J. P. Proc. Natl Acad. Sci. 106, 182–6186 (2009).Article 

Google Scholar 
Dean, J. F. et al. Rev. Geophys. 56, 207–250 (2018).Article 

Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge University Press, 2021).Lin, Y. et al. Water Res. 205, 117682 (2021).Article 

Google Scholar 
Zakharova, L., Meyer, K. M. & Seifan, M. Ecol. Modell. 407, 108703 (2019).Article 

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

Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).Article 

Google Scholar 
Pey, B. et al. Current use of and future needs for soil invertebrate functional traits in community ecology. Basic Appl Ecol 15, 194–206 (2014).Article 

Google Scholar 
Vandewalle, M. et al. Functional traits as indicators of biodiversity response to land use changes across ecosystems and organisms. Biodivers Conserv 19, 2921–2947 (2010).Article 

Google Scholar 
Kim, J. H. et al. Structural implications of traditional agricultural landscapes on the functional diversity of birds near the Korean Demilitarized Zone. Ecol Evol 10, 12973–12982 (2020).Article 

Google Scholar 
McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol Evol 21, 178–185 (2006).Article 

Google Scholar 
Mouillot, D., Graham, N. A. J., Villéger, S., Mason, N. W. H. & Bellwood, D. R. A functional approach reveals community responses to disturbances. Trends Ecol Evol 28, 167–177 (2013).Article 

Google Scholar 
Hevia, V. et al. Trait-based approaches to analyze links between the drivers of change and ecosystem services: Synthesizing existing evidence and future challenges. Ecol Evol 7, 831–844 (2017).Article 

Google Scholar 
Hood, R. R. et al. Pelagic functional group modeling: Progress, challenges and prospects. Deep Sea Res II: Top Stud Oceanogr 53, 459–512 (2006).Article 
ADS 

Google Scholar 
Fonseca, C. R. & Ganade, G. Species functional redundancy, random extinctions and the stability of ecosystems. J Ecol 89, 118–125 (2001).Article 

Google Scholar 
Rosenfeld, J. S. Functional redundancy in ecology and conservation. Oikos 98, 156–162 (2002).Article 

Google Scholar 
Mason, N. W. H., Mouillot, D., Lee, W. G. & Wilson, J. B. Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111, 112–118 (2005).Article 

Google Scholar 
Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).Article 

Google Scholar 
Mouillot, D., Mason, N. W. H. & Wilson, J. B. Is the abundance of species determined by their functional traits? A new method with a test using plant communities. Oecologia 152, 729–737 (2007).Article 
ADS 

Google Scholar 
Mouchet, M. A., Villéger, S., Mason, N. W. H. & Mouillot, D. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules. Funct Ecol 24, 867–876 (2010).Article 

Google Scholar 
Hocking, D. J. & Babbitt, K. J. Amphibian contributions to ecosystem services. Herpetol Conserv Biol 9, 1–17 (2014).
Google Scholar 
Valencia-Aguilar, A., Cortés-Gómez, A. M. & Ruiz-Agudelo, C. A. Ecosystem services provided by amphibians and reptiles in Neotropical ecosystems. Int J Biodivers Sci Ecosyst Serv Manag 9, 257–272 (2013).Article 

Google Scholar 
Cortéz-Gómez, A. M. M., Ruiz-Agudelo, C. A., Valencia-Aguilar, A. & Ladle, R. J. Ecological functions of neotropical amphibians and reptiles: a review. Univ Sci (Bogota) 20, 229–245 (2015).
Google Scholar 
Jeon, J. Y., Jung, J., Suk, H. Y., Lee, H. & Min, M.-S. The Asian plethodontid salamander preserves historical genetic imprints of recent northern expansion. Sci Rep 11, 9193 (2021).Article 
ADS 
CAS 

Google Scholar 
Zhang, H., Yan, J., Zhang, G. & Zhou, K. Phylogeography and demographic history of Chinese black-spotted frog populations (Pelophylax nigromaculata): Evidence for independent refugia expansion and secondary contact. BMC Evol Biol 8, 1–16 (2008).Article 
CAS 

Google Scholar 
Lee, S.-J. et al. Phylogeography of the Asian lesser white-toothed shrew, Crocidura shantungensis, in East Asia: role of the Korean Peninsula as refugium for small mammals. Genetica 2018 146:2 146, 211–226 (2018).CAS 

Google Scholar 
Min, M.-S. et al. Discovery of the first Asian plethodontid salamander. Nature 435, 87–90 (2005).Article 
ADS 
CAS 

Google Scholar 
Baek, H.-J., Lee, M.-Y., Lee, H. & Min, M.-S. Mitochondrial DNA data unveil highly divergent populations within the Genus Hynobius (Caudata: Hynobiidae) in South Korea. Mol Cells 31, 105–112 (2011).Article 
CAS 

Google Scholar 
Borzée, A. et al. Yellow sea mediated segregation between North East Asian Dryophytes species. PLoS One 15, e0234299 (2020).Article 

Google Scholar 
Borzée, A. & Min, M.-S. Disentangling the impacts of speciation, sympatry and the island effect on the morphology of seven Hynobius sp. salamanders. Animals 11, 187 (2021).Article 

Google Scholar 
Borzée, A. et al. Dwindling in the mountains: Description of a critically endangered and microendemic Onychodactylus species (Amphibia, Hynobiidae) from the Korean Peninsula. Zool Res 43, 750–755 (2022).Article 

Google Scholar 
Suk, H. Y. et al. Phylogenetic structure and ancestry of Korean clawed salamander, Onychodactylus koreanus (Caudata: Hynobiidae). Mitochondrial DNA A DNA Mapp Seq Anal 29, 650–658 (2018).CAS 

Google Scholar 
Kim, C., Kang, J. & Kim, M. Status and development of national ecosystem survey in Korea. J Environ Impact Assess 22, 725–738 (2013).Article 

Google Scholar 
Kim, D.-I. et al. Prediction of present and future distribution of the Schlegel’s Japanese gecko (Gekko japonicus) using MaxEnt modeling. J Ecol Environ 44, 1–8 (2020).
Google Scholar 
Clarke, K. R., Gorley, R. N., Somerfield, P. J. & Warwick, R. M. Change in marine communities: an approach to statistical analysis and interpretation. (PRIMER-E: Plymouth, 2014).Borzée, A. & Jang, Y. Policy Recommendation for the conservation of the Suweon Treefrog (Dryophytes suweonensis) in the Republic of Korea. Front Environ Sci 0, 39 (2019).Article 

Google Scholar 
Chung, M. Y. et al. The Korean Baekdudaegan Mountains: A glacial refugium and a biodiversity hotspot that needs to be conserved. Front Genet 9, 489 (2018).Article 

Google Scholar 
Lee, J., Jang, H.-J. & Suh, J.-H. Ecological Guide Book of Herpetofauna in Korea. (National Institutite of Environmental Research, 2011).Lee, J. & Park, D. The Encyclopedia of Korean Amphibians. (Econature, 2016).Kim, J.-B. & Song, J. Y. Korean Herpetofauna. (worldscience, 2010).Strauß, A., Reeve, E., Randrianiaina, R.-D., Vences, M. & Glos, J. The world’s richest tadpole communities show functional redundancy and low functional diversity: ecological data on Madagascar’s stream-dwelling amphibian larvae. BMC Ecol 10, 1–10 (2010).Article 

Google Scholar 
Shim, Y.-J. et al. Site selection of narrow-mouth frog (Kaloula borealis) habitat restoration using habitat suitability index. Journal of the Korean Society of Environmental Restoration. Technology 18, 33–44 (2015).
Google Scholar 
Jeong, S., Seo, C., Yoon, J., Lee, D. K. & Park, J. A study on riparian habitats for amphibians using habitat suitability mode. J Environ Impact Assess 24, 175–189 (2015).Article 

Google Scholar 
Jang, H.-J., Kim, D.-I. & Chang, M.-H. Distribution of reptiles in South Korea – Based on the 3rd National Ecosystem Survey -. Korean Journal of Herpetology 7, 30–35 (2016).
Google Scholar 
Tsianou, M. A. & Kallimanis, A. S. Geographical patterns and environmental drivers of functional diversity and trait space of amphibians of Europe. Ecol Res 35, 123–138 (2020).Article 

Google Scholar 
Chergui, B., Pleguezuelos, J. M., Fahd, S. & Santos, X. Modelling functional response of reptiles to fire in two Mediterranean forest types. Sci Total Environ 732, 139205 (2020).Article 
ADS 
CAS 

Google Scholar 
Nelson, N. & Piovia-Scott, J. Using environmental niche models to elucidate drivers of the American bullfrog invasion in California. Biol Invasions 24, 1767–1783 (2022).Article 

Google Scholar 
Liu, X. et al. Diet and prey selection of the Invasive American bullfrog (Lithobates catesbeianus) in southwestern China. Asian Herpetol Res 6, 34–44 (2015).
Google Scholar 
Borzée, A., Kwon, S., Koo, K. S. & Jang, Y. Policy recommendation on the restriction on amphibian trade toward the Republic of Korea. Front Environ Sci 8, 129 (2020).Article 

Google Scholar 
Kim, H. W., Adhikari, P., Chang, M. H. & Seo, C. Potential distribution of amphibians with different habitat characteristics in response to climate change in South Korea. Animals 11, 2185 (2021).Article 

Google Scholar 
Borzée, A. et al. Climate change-based models predict range shifts in the distribution of the only Asian plethodontid salamander: Karsenia koreana. Sci Rep 9, 1–9 (2019).Article 

Google Scholar 
Shin, Y., Min, M. & Borzée, A. Driven to the edge: Species distribution modeling of a Clawed Salamander (Hynobiidae: Onychodactylus koreanus) predicts range shifts and drastic decrease of suitable habitats in response to climate change. Ecol Evol 11, 14669–14688 (2021).Article 

Google Scholar 
Park, I. et al. Past, present, and future predictions on the suitable habitat of the Slender racer (Orientocoluber spinalis) using species distribution models. Ecol Evol 12, e9169 (2022).Article 

Google Scholar 
Berriozabal-Islas, C., Badillo-Saldaña, L. M., Ramírez-Bautista, A. & Moreno, C. E. Effects of habitat disturbance on lizard functional diversity in a tropical dry forest of the Pacific Coast of Mexico. Trop Conserv Sci 10, 1940082917704972 (2017).
Google Scholar 
Petchey, O. L., O’Gorman, E. J. & Flynn, D. F. B. A functional guide to functional diversity measures. in Biodiversity, Ecosystem Functioning, & Human Wellbeing (eds. Naeem, S., Bunker, D., Hector, A., Loreau, M. & Perrings, C.) 49–59 (Oxford University Press, 2009).Tsianou, M. A. & Kallimanis, A. S. Different species traits produce diverse spatial functional diversity patterns of amphibians. Biodivers Conserv 25, 117–132 (2016).Article 

Google Scholar 
Tsianou, M. A. & Kallimanis, A. S. Trait selection matters! A study on European amphibian functional diversity patterns. Ecol Res 34, 225–234 (2019).Article 

Google Scholar 
Campos, F. S. et al. Ecological trait evolution in amphibian phylogenetic relationships. Ethol Ecol Evol 31, 526–543 (2019).Article 

Google Scholar 
Lourenço-de-Moraes, R. et al. Functional traits explain amphibian distribution in the Brazilian Atlantic Forest. J Biogeogr 47, 275–287 (2020).Article 

Google Scholar 
Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C. & Costa, G. C. AmphiBIO, a global database for amphibian ecological traits. Sci Data 4, 1–7 (2017).Article 

Google Scholar 
Jeon, J. Y. et al. Resolving the taxonomic equivocacy and the population genetic structure of Rana uenoi – insights into dispersal and demographic history. Salamandra 57, 529–540 (2021).
Google Scholar 
Othman, S. N. et al. From Gondwana to the Yellow Sea, evolutionary diversifications of true toads Bufo sp. in the Eastern Palearctic and a revisit of species boundaries for Asian lineages. Elife 11, e70494 (2022).Article 

Google Scholar 
Chang, M.-H., Song, J.-Y. & Koo, K.-S. The status of distribution for native freshwater turtles in Korea, with remarks on taxonomic position. Korean J Environ Biol 30, 151–155 (2012).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria http://www.r-project.org/ (2013).Charrad, M., Ghazzali, N., Boiteau, V. & Niknafs, A. NbClust: An R package for determining the relevant number of clusters in a data set. J Stat Softw 61, 1–36 (2014).Article 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5–7. https://cran.r-project.org/package=vegan (2020).Zizka, A., Antonelli, A. & Silvestro, D. Sampbias, a method for quantifying geographic sampling biases in species distribution data. Ecography 44, 25–32 (2021).Article 

Google Scholar 
Magurran, A. E. Measuring biological diversity. (John Wiley & Sons, Ltd, 2013).Laliberté, E., Legendre, P. & Shipley, B. FD: measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0–12.1 (2014).Crego, R. D., Stabach, J. A. & Connette, G. Implementation of species distribution models in Google Earth Engine. Divers Distrib 28, 904–916 (2022).Article 

Google Scholar 
Evans, J. S., Murphy, M. A., Holden, Z. A. & Cushman, S. A. in Predictive Species and Habitat Modeling in Landscape Ecology: Concepts and Applications (eds. Drew, C. A., Wiersma, Y. F. & Huettmann, F.) Modeling Species Distribution and Change Using Random Forest (Springer New York, 2011).Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many? Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).Article 

Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25, 1965–1978 (2005).Article 

Google Scholar 
Jeon, J., Kim, J. H. & Lee, D. K. Data for: Functional group analyses of herpetofauna in South Korea using a large dataset. Figshare https://doi.org/10.6084/m9.figshare.21755345.v1 (2022). 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

Naturalist Alfred Russel Wallace went on an expedition to Amazonas state in Brazil in 1848–52.Credit: Mondadori Portfolio via Getty

Best known for formulating the theory of evolution by natural selection, independently of Charles Darwin, Alfred Russel Wallace is an appealing if enigmatic figure. The appeal stems in part from his underdog status: poor and self-educated, Wallace had none of Darwin’s social and financial advantages. The enigma comes from his keen embrace of a range of eccentric non-scientific causes, including spiritualism, phrenology and anti-vaccination (for smallpox).Scientists do not like their scientific heroes to bear the taint of irrational thinking. Wallace’s enthusiasms have therefore contributed to him becoming marginalized in the history of evolutionary thought. Most people know about Darwin and the HMS Beagle. But what about Wallace and the Helen?The Helen story is worth revisiting because it shows Wallace at his resolute best. Despite numerous disastrous career setbacks — of which the Helen episode was the most severe — he persevered and eventually succeeded as a scientist.More than 150 years after Wallace’s experience on the Helen, doing science continues to be hard and can be disappointing. Wallace’s misadventure provides both perspective and an object lesson in how to navigate setbacks. His response to problems showcases his most inspiring traits: his commitment to science, his almost superhuman resilience and his refusal to mire himself in self-pity.Tropical explorationsIn his first job as a land surveyor, Wallace developed an interest in the plants he encountered as he tramped across the countryside. Then, in 1844 at the age of 21, he met Henry Walter Bates, who would later discover ‘Batesian mimicry’ (whereby members of a palatable prey species gain protection by mimicking an unpalatable one).Bates, two years Wallace’s junior, had a fixation with beetles, and he catalysed Wallace’s transformation from hobbyist naturalist to serious collector. Wallace’s new-found focus on beetles transcended mere entomological stamp-collecting; he developed an interest in some of the great scientific questions of the time. He was particularly inspired by the anonymously published Vestiges of the Natural History of Creation (1844) by Robert Chambers, which put forward a vision of a transmutational process, with life progressing from simple to complex.Without money or connections, Wallace and Bates aspired to careers in science at a time when the field was the preserve of the moneyed elite. They would have to fund their scientific explorations by collecting and selling specimens. After a hasty choice of destination — tropical South America — and a crash course in collecting methods, Wallace, aged 25, and Bates, aged 23, arrived in Belém, Brazil, in May 1848 (see ‘Doggedly determined’).
Doggedly determined

Alfred Russel Wallace tends to be unjustly relegated to a footnote in the Charles Darwin story. He was, in fact, a pioneering biologist who refused to let disadvantage or disaster prevent him from pursuing his scientific dreams.
January 1823: Alfred Russel Wallace is born in Usk in Wales.
May 1848: Wallace and Henry Walter Bates arrive in Belém, Brazil.
July 1852: Wallace boards the Helen, which catches fire three weeks later while at sea.
October 1852: Wallace reaches Deal, England, aboard the Jordeson.
March 1854: Wallace leaves Southampton for southeast Asia.
September 1855: Wallace’s first evolutionary paper describing his ‘Sarawak Law’ is published.
May 1856: Citing the Sarawak Law paper, geologist Charles Lyell alerts Darwin to the possibility that Wallace is developing ideas similar to Darwin’s.
February 1858: Wallace sends his paper on natural selection to Darwin from Ternate in the Maluku islands (Moluccas), Indonesia.
July 1858: The joint Darwin–Wallace paper is presented at the Linnean Society in London.
November 1859: Darwin’s On the Origin of Species is published.
March 1862: Wallace returns from southeast Asia.
November 1913: Wallace dies in Broadstone, England.

The two split up early on, with Wallace concentrating on the Amazon River’s northern tributary, the Rio Negro, and Bates on the southern fork, the Solimões.Collecting was challenging. The Amazon’s ubiquitous ants often deprived science of hard-won specimens. Crucial collecting materials also disappeared: Wallace once recovered from a bout of fever to discover that local people had drunk the cachaça (a Brazilian rum) he’d been using to pickle specimens. Transport was a constant headache, with travel upstream past rapids requiring unwieldy portages of canoes and cargo. And thanks to his collecting, the cargo became ever more voluminous and unwieldy.Wallace and Bates sporadically sent back shipments of material to their agent in London, Samuel Stevens, who publicized their adventures in scientific journals and sold their specimens, taking a 20% commission.
Escaping Darwin’s shadow: how Alfred Russel Wallace inspires Indigenous researchers
Wallace’s journeys on the Rio Negro and its tributaries took him into areas that had not yet been visited by Europeans. He saw (and collected) an extraordinary array of species, many of them new to science. He had a chance to observe and collect artefacts from several Indigenous groups with little or no previous contact with Europeans. As he travelled, Wallace capitalized on his surveying skills to map the terrain. But the remoteness took its toll. He made an “inward vow never to travel again in such wild, unpeopled districts without some civilised companion or attendant”1.Wallace was frequently ill, on one occasion nearly lethally so. His younger brother came out to join him as an assistant in 1849 but died of yellow fever two years later in Belém, on his way back to England. Wallace learnt that his brother was sick but had to wait many anxious months before news of his death made it upriver.In 1852, after four years of exploring and collecting, it was time for Wallace himself to head home. He envisaged a triumphant return. He would complement his collections of preserved organisms with a menagerie of living ones. Mr Wallace’s biological wonders would surely be the toast of scientific London.On 12 July in Belém, Wallace boarded the Helen, a freighter ship bound for London. The trip across the continent to Belém had not gone smoothly. The authorities in Manaus, Brazil, had had to be persuaded to release some of his earlier shipments meant for London, which they had impounded, making the final haul aboard the Helen even larger. But now all that remained was the long voyage back across the Atlantic. Wallace, who shared Captain Turner’s cabin, was the only passenger.Disaster strikesThree weeks into the voyage, Captain Turner interrupted Wallace’s morning routine to tell him that the ship was on fire.Friction caused by the rocking of the ship had ignited poorly stowed cargo. Attempts to intervene were counterproductive — removing the hold covers merely oxygenated the fire — and soon the ship became what Wallace later called “a most magnificent conflagration”1.Captain Turner gave the order to abandon ship, and the scramble to prepare two small wooden boats began. Having been stored on deck in the tropical sunshine, both boats leaked badly. The cook had to find corks to plug their hulls.Before he left the ship, Wallace “went down into the cabin, now suffocatingly hot and full of smoke, to see what was worth saving”1. He retrieved his “watch and a small tin box containing some shirts and a couple of old note-books, with some drawings of plants and animals, and scrambled up with them on deck”1. He tried to lower himself on a rope into one of the small boats, but fever-weakened, he ended up sliding down the rope, stripping the skin off his hands.

Some of Alfred Russel Wallace’s sketches were salvaged from the fire aboard the Helen on his return journey from South America in 1852.Credit: The Natural History Museum/Alamy

With fine weather, the best hope of rescue lay in other ships seeing the fire. The two boats duly circled the burning wreck for the next 24 hours, meaning that Wallace got to witness every moment of the tragedy. The animals he had brought with him on the long river journey across the continent, now free from their cages, sought refuge on the one part of the ship still untouched by the flames, the bowsprit. Wallace watched as the monkeys, parrots and more — his pets as well as his best hope of impressing London’s scientific elite — were incinerated.The hoped-for rescue did not immediately materialize, and Captain Turner turned the two open boats towards Bermuda, 1,100 kilometres away to the northwest.As the days ticked by, the situation became increasingly desperate. Water ran low and the tropical sun left Wallace’s “hands and face very much blistered”1. Wallace nevertheless remained upbeat, later recalling that during one night, he “saw several meteors, and in fact could not be in a better position for observing them, than lying on [his] back in a small boat in the middle of the Atlantic”1.Finally, ten days into the ordeal, salvation appeared on the horizon in the form of the Jordeson, a creaking and already overladen cargo ship bound for London.With the immediate crisis past, the magnitude of what had happened started to sink in. In a letter2 written aboard the Jordeson to botanist Richard Spruce (see go.nature.com/3prhbdk), Wallace tallied his catastrophic losses — “almost all the reward of my four years of privation & danger was lost” — and concluded with characteristic understatement, “I have some need of philosophic resignation to bear my fate with patience and equanimity.”
Evolution’s red-hot radical
The Jordeson finally limped into Deal, England, on 1 October 1852. Wallace had been at sea for 80 days. His outward voyage with Bates had taken only 29 days.Wallace added a PS to his letter to Spruce. First there was immediate exhilaration about the return — “Such a dinner! Oh! beef steaks & damson tart”. But then came thoughts about the future: “Fifty times since I left Pará [Belém] have I vowed if I once reached England never to trust myself more on the ocean.” Even then, he noted that “good resolutions soon fade”.Stevens had thoughtfully taken out insurance. So Wallace had £200 (US$980 at the time) — a fraction of his collections’ actual value — to cover his costs for a year in London while he tried to salvage what he could from the disaster and make future plans.He rushed out two books, one a travelogue, the other a more technical account of the palm trees of the Amazon. Neither did well — 250 copies remained unsold a decade later from the travel book’s print run of 750. But he was getting his name out there. Stevens, too, had a done a good job of publicizing Wallace’s discoveries while Wallace had been away.Perhaps most crucially, the positive response of the UK Royal Geographical Society to his mapping work of the Rio Negro yielded a free steamship ticket to Singapore.In March 1854, less than 18 months since the Jordeson’s bedraggled arrival at Deal, Wallace departed from Southampton in England for what he would call the “central and controlling incident”2 of his life.Eight more years of perilous travel awaited. So, too, did the discoveries of what came to be known as Wallace’s Line (a boundary between the Asian and Australasian biogeographic regions) and of the theory of evolution by natural selection3,4.The scientific acclaim that greeted Wallace’s return from southeast Asia in 1862 was a just reward both for his contributions and for that phenomenal doggedness — his determination, despite everything, to be a scientist. More

Material characterization resultsTo investigate the structural composition of NMC-2, XRD analysis plots were performed. Figure 1a shows the XRD pattern of the NMC-2 composite before adsorption. The XRD pattern shows the corresponding strong and narrow peaks, from which it can be seen that the peaks of broad diffraction NMC-2 can correspond to the standard cards of Fe, C, Fe7C3, Fe2C, and FeC, indicating that the synthesized adsorbent is an iron-carbon composite. It can be indicated that mesoporous nitrogen-doped composites were formed during the carbonization process. During the experiments, it was found that the materials are magnetic, probably because of the presence of Fe, FeC, Fe7C3, Fe2C. Due to the magnetic properties of this type of material, rapid separation and recovery can be obtained under the conditions of an applied magnetic field, which allows easy separation of the adsorbent and metal ions from the wastewater15.Figure 1XRD and nitrogen adsorption and desorption tests on materials: (a) XRD pattern of NMC-2 adsorbent before adsorption, (b) pore size distribution of NMC-2, (c) nitrogen adsorption–desorption curve of NMC-2 adsorbent.Full size imageFrom the adsorption–desorption curves of adsorbent N2 in Fig. 1b, it can be seen that the NMC-2 isotherm belongs to the class IV curve, and the appearance of H3-type hysteresis loops is observed at the medium pressure end, and H3 is commonly found in aggregates with laminar structure, producing slit mesoporous or macroporous materials, which indicates that N2 condenses and accumulates in the pore channels, and these phenomena prove that NMC-2 is a porous material16. Figure 1c shows the pore size distribution of the adsorbent NMC-2 obtained according to the BJH calculation method, from which it can be seen that the pore size distribution is not uniform in the range, and most of them are concentrated below 20 nm, while according to Table 1, the specific surface area of the original sample of Fenton sludge and fly ash is 124.08 m2/g and 3.79 m2/g, respectively, and the specific surface area of NMC-2 is 228.65 m2/g. The Fenton The pore volume of the original samples of Fenton sludge and fly ash were 0.18 cm3/g and 0.006 cm3/g respectively, while the pore volume of NMC-2 was 0.24 cm3/g. The pore diameters of the original sample of Fenton sludge and fly ash were 5.72 nm and 6.70 nm respectively, while the pore diameter of NMC-2 was 4.22 nm. The above data indicated that the synthetic materials have increased the specific surface area and pore volume compared with the original samples, indicating that the doping of nitrogen can increase the specific surface area of the material. Since the pore size of mesoporous materials is 2–50 nm, NMC-2 is a porous material with main mesopores. Thanks to the large specific surface area provided by the mesopores, the material has a large number of active sites, and in addition, the mesopores can store more Cr(VI)16, which contributes to efficient removal.Table 1 Total pore-specific surface area, pore volume, and pore size of BJH adsorption and accumulation of Fenton sludge, fly ash and NMC-2.Full size tableThe morphological analysis of the material surface using SEM can see the surface structure and the pore structure of NMC-2. And Fig. 2a–d shows the swept electron microscope image of NMC-2. Figure 2a shows that the surface of the material is not smooth, and there are more lint-like fiber structures. The fibers in Fig. 2b are loosely and irregularly arranged, which may be due to the irregular morphology caused by the small particles of the NMC-2 sample. As shown in Fig. 2c and Fig. 2a there are more pores generated on the surface of NMC-2, which may be due to the addition of K2CO3 to urea and, Fenton sludge solution to generate CO217.Figure 2SEM, TEM and EDS testing of materials: (a–d) SEM image of NMC-2 adsorbent, (e) TEM image of NMC-2; (g–i) TEM-EDS spectrum of NMC-2, (j) TEM-EDS spectra of NMC-2 obtained from.Full size imageThese pores can provide many active sites, which is consistent with the results derived in Fig. 1, where NMC-2 is a mesoporous-dominated porous material, and also demonstrates that the addition of urea can provide a nitrogen source for the material, providing abundant active sites. Figure 2j depicts the TEM of NMC-2. the TEM images show that the synthesized NMC-2 has a folded structure with a surface covered by a carbon film, and the HRTEM (Fig. 2e) also confirms this result with a lattice spacing of 0.13, 0.15, 0.20, 0.23, 0.24, and 0.25 nm, corresponding to the (4 5 2) and (1 0 2) of C, the (2 0 1) of FeC) surface, the (2 1 0) surface of Fe7C3, the (5 3 1) surface of Fe2C, and the (2 0 1) surface of FeC, which also confirms the synthesis of the above substances. The corresponding EDS spectra of the dark field diagram NMC-2 were obtained from Fig. 2j, and the EDS spectra proved the presence of various elements: carbon (C) (Fig. 2f) from fly ash, iron (Fe) (Fig. 2g) from Fenton sludge, nitrogen (N) (Fig. 2h) from urea, and the presence of (O) (Fig. 2i), further confirming the successful preparation of NMC-2.The type of functional groups and chemical bonding on the surface of the material can be analyzed by IR spectrogram analysis. Figure 3b shows the FTIR image of NMC-2 adsorbent 3440 cm−1 wide and strong absorption peak is due to the stretching vibration of –OH, there is a large amount of –OH present on the surface of the material; the peak appearing at 1640 cm−1 is –COOH. Characterization reveals that the –OH absorption peak is wider18,19. In addition, the absorptions at 1390 cm−1 and 1000 cm−1 were attributed to the bending of –OH vibrations of alcohols and phenol and the stretching vibration of C–O20. The above results indicate that the surface of NMC-2 contains a large number of oxygen-containing functional groups, and these functional groups can provide many active sites for the removal of Cr(VI). It was also found that the weak peaks corresponding to 573 cm−1 and 550 cm−1 were attributed to Fe–O groups21. The stretching of Fe–O may be due to the oxidation of loaded Fe0 and Fe2+ to Fe3+22. Figure 3a shows the Fenton sludge and fly ash FTIR images. It can be seen from the figure that the surfaces of Fenton sludge and fly ash contain a large number of oxygen-containing functional groups, the surface functional groups of the two raw materials are more abundant, and the functional groups of NMC-2 around 3441 cm−1, 1632 cm−1, and 1400 cm−1 are not significantly different from those of the raw materials, and the C–H stretching vibration peaks of NMC-2 around 873 cm−1 and 698 cm−1 is not obvious, which may be because the material the C–H bond on the surface of the raw material was oxidized to C–O in the process of synthesis.Figure 3FTIR testing of materials: (a) FTIR image of Fenton sludge, fly ash, (b) Ftir image of NMC-2 adsorbent.Full size imageCr(VI) adsorption experimentSelection of adsorbentTo select the best adsorbent, Cr(VI) adsorption tests were performed on four adsorbents. Figure 4a shows the effect of Fenton sludge and the urea addition on the adsorption efficiency. The Cr(VI) removal rates of the four adsorbents were ranked from low to high: MC-1  More