Ecology

Department of Zoology, University of Cambridge, Cambridge, UKLynn V. DicksSchool of Biological Sciences, University of East Anglia, Norwich, UKLynn V. DicksCentre for Agri-Environmental Research, School of Agriculture, Policy and Development, Reading University, Reading, UKTom D. Breeze, Deepa Senapathi & Simon G. PottsIPBES Secretariat, Bonn, GermanyHien T. NgoInstitute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing, ChinaJiandong AnInstituto de Investigaciones en Biodiversidad y Medioambiente (INIBIOMA), Universidad Nacional del Comahue‐CONICET, Río Negro, ArgentinaMarcelo A. AizenDepartment of Zoology, University of Calcutta, Kolkata, IndiaParthiba BasuCenter for Transdisciplinary and Sustainability Sciences, IPB University, Jalan Pajajaran, IndonesiaDamayanti BuchoriDepartment of Plant Protection, IPB University, Bogor, IndonesiaDamayanti BuchoriFacultad de Ciencias Exactas, Físicas y Naturales, Universidad de Córdoba, Córdoba, ArgentinaLeonardo GalettoInstituto Multidisciplinario de Biología Vegetal, CONICET-UNC, Córdoba, ArgentinaLeonardo GalettoInstituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural, Universidad Nacional de Río Negro, Río Negro, ArgentinaLucas A. GaribaldiInstituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural, Consejo Nacional de Investigaciones Científicas y Técnicas, Río Negro, ArgentinaLucas A. GaribaldiWorld Agroforestry Centre, Nairobi, KenyaBarbara Gemmill-HerrenPrescott College, Prescott, AZ, USABarbara Gemmill-HerrenThe New Zealand Institute for Plant & Food Research Limited, Lincoln, New ZealandBrad G. HowlettBiosciences Institute, University of Sao Paulo, São Paulo, BrazilVera L. Imperatriz-FonsecaCentre for Functional Biodiversity, School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South AfricaSteven D. JohnsonInstitute of Ecology and Botany, Centre for Ecological Research, Vácrátót, HungaryAnikó Kovács-HostyánszkiSchool of Applied Biosciences, Kyungpook National University, Daegu, KoreaYong Jung KwonInternational Centre of Insect Physiology and Ecology (icipe), Nairobi, KenyaH. Michael G. LattorffNaga Women’s Union, Manipur, IndiaThingreipi LungharwoSouth African National Biodiversity Institute (SANBI), Kirstenbosch Research Centre, Claremont, South AfricaColleen L. SeymourDepartment of Biological Sciences, FitzPatrick Institute, University of Cape Town, Rondebosch, South AfricaColleen L. SeymourAgroécologie, AgroSup Dijon, INRAE, University of Bourgogne Franche-Comté, Dijon, FranceAdam J. Vanbergen More

Independent reference samplesThe authentic, independent strawberry (Fragaria × ananassa) reference samples used for model validation in this study were provided by Agroisolab GmbH (Jülich, Germany). The samples were collected either directly by the company or on their behalf through authorized sample collectors between 2007 and 2017. The primary purpose of such authentic reference samples is the direct comparison between their stable isotope compositions (oxygen, hydrogen, carbon, nitrogen, or sulfur) to those of samples of suspect origin. Accompanying metadata for each reference sample included information about the geographic origin as community name, postal code, or location coordinates, and information about the month and year the strawberry sample was picked. In total, we used δ18O values from 154 reference samples. Most samples were collected in the UK, Germany, Sweden, and Finland (Fig. 1). All reference samples were grown on open strawberry-fields rather than artificial greenhouse conditions. All berry samples were collected from cultivated, non-endangered plant species (“garden strawberry”), and the research conducted complies with all relevant institutional, the corresponding national, and also international guidelines and legislation.After collection in the field, samples were stored in airtight containers and shipped directly to Agroisolab, where they were stored frozen prior to analysis. In order to analyze the oxygen stable isotope composition of the organic strawberry tissue, the lipids were solvent-extracted with dichloromethane for a at least 4 h, using a Soxhlet extractor. The remaining samples were dried and milled to a fine powder. 1.5 mg of the powder was weighed into silver capsules. The silver capsules were equilibrated for at least 12 h in a desiccator with a fixed relative humidity of 11.3%. After a further vacuum drying the samples were measured via high-temperature furnace (Hekatech, Wegberg, Germany) in combination with an Isotope-ratio mass spectrometer (IRMS) Horizon (NU Instruments, Wrexham, UK). The pyrolysis temperature was 1530 °C and the pyrolysis tube consisted of covalent-bound SiC (Agroisolab patented). The reproducibility of the measurement was better than 0.6 ‰.Oxygen isotope model calculationPlant physiological stable isotope models simulate the oxygen isotopic composition of leaf water or organic compounds synthesized therein as δ18O values in per mil (‰), where δ18O = (18O/16O)sample/(18O/16O)VSMOW − 1, and VSMOW is Vienna Standard Mean Ocean Water as defined by the VSMOW-Standard Light Antarctic Precipitation (SLAP) scale. The Craig-Gordon model57, which was developed to mathematically describe the isotopic enrichment of standing water bodies during evaporation and later modified for plants, is the basis for modelling plant water δ18O values23,58. Plant source water is the baseline for the model, which is the precipitation-derived soil water that plants take up through their roots without isotope fractionation51,59,60. The 18O enrichment of water within leaves is described by the following equation (Eq. 1)36,61:$$ Delta^{18} {text{O}}_{{text{e leaf}}} = left( {1 +upvarepsilon ^{ + } } right)left[ {left( {1 + {upvarepsilon }_{{text{k}}} } right)left( {1 – {text{e}}_{{text{a}}} /{text{e}}_{{text{i}}} } right) + {text{e}}_{{text{a}}} /{text{e}}_{{text{i}}} (1 + Delta^{18} {text{O}}_{{{text{Vapor}}}} )} right]{-}1 $$
(1)

where Δ18Oe_leaf is the oxygen isotopic enrichment above source of water at the evaporative site in leaves, ε+ is the equilibrium fractionation between liquid water and water vapor, εk is the kinetic fractionation associated with the diffusion through the stomata and the boundary layer. ea/ei is the ratio of ambient vapor pressure in the atmosphere to intercellular vapor pressure in the leaf. Δ18OVapor is the isotopic composition of the ambient vapor above source water, which in this study is assumed to be in equilibrium with the source water (Δ18OV = − ε+)62,63. This assumption can be used, if the atmosphere is well mixed, and plants’ source water derives from recent precipitation events. For crops, growing in the temperate climate of the mid latitudes this is usually the case, especially over the long time periods (several weeks) over which strawberries grow. If such a model is applied in other climatic zones (e.g. tropics), this assumption should, however, be reevaluated64. The equilibrium fractionation factor (ε+)65,66 and kinetic fractionation factor (εk)67 can be calculated with the following equations (Eqs. 2 and 3):$$upvarepsilon ^{ + } = left[ {exp left( {frac{1.137}{{left( {273 + T} right)^{2} }}*10^{3} – frac{0.4156}{{273 – T}} – 2.0667*10^{ – 3} } right) – 1} right]*1000 $$
(2)

where T is the leaf temperature in degrees Celsius. In our calculations, leaf temperature was set to 90% of the monthly mean air temperature, which describes a realistic leaf-energy balance scenario for well-watered crops68,69, and also yielded the best model performance with respect to the reference data. As leaf to air temperature differences have a strong influence on leaf water δ18O values, this assumption needs to be independently tested in future applications. For example, changing leaf temperature from 20 °C to 22 °C at a constant air temperature of 20 °C and a source water δ18O value of -10 ‰ will affect leaf water δ18O values by + 1.4 ‰.$$upvarepsilon _{{text{k}}} = frac{{28{text{r}}_{{text{s}}} + 19{text{r}}_{{text{b}}} }}{{{text{r}}_{{text{s}}} + {text{r}}_{{text{b}}} }} $$
(3)

where rs is the stomatal resistance and rb is the boundary layer resistance in m2s/mmol, which is the inverse of the stomatal and boundary layer conductance. For our model calculations, we consistently used stomatal conductance values of 0.4 mol/m2s, stomatal resistance values of 1 m2s/mol70.The Craig-Gordon model predicted leaf water values are often enriched in 18O relative to measured bulk leaf water δ18O values26,27. This is because the model describes the δ18O values of water at the site of evaporation while measurements typically give bulk leaf water δ18O values36,71. The two-pool modification to the Craig-Gordon model corrects for this effect by separating bulk leaf water into a pool of evaporatively enriched water at the site of evaporation (δ18Oe_leaf derived from the Craig-Gordon model, Eq. 1) and a pool of unenriched plant source water (δ18Osource water)25. δ18Oe_leaf is calculated as follows:$$updelta ^{18} {text{O}}_{{{text{e}}_{text{leaf}}}} = , (Delta^{18} {text{O}}_{{{text{e}}_{text{leaf}}}} +updelta ^{18} {text{O}}_{{text{source water}}} ) , + , (Delta^{18} {text{O}}_{{{text{e}}_{text{leaf}}}} * ,updelta ^{18} {text{O}}_{{text{source water}}} )/1000) $$
(4)
In the two-pool modified Craig-Gordon model (Eq. 5), the proportion of unenriched source water is described as fxylem36.$$updelta ^{18} {text{O}}_{{text{leaf water}}} = , left( {1 , {-}f_{xylem} } right) , * ,updelta ^{18} {text{O}}_{{{text{e}}_{text{leaf}}}} + , left( {f_{xylem} * ,updelta ^{18} {text{O}}_{{text{source water}}} } right) $$
(5)
Values for fxylem in leaf water generally range from 0.10 to 0.3336,37,38,39,40 but higher values have also been observed72. For strawberry plants, leaf water fxylem values were recently shown to vary between 0.24 and 0.3428.Organic molecules in leaves generally reflect the δ18O values of the bulk leaf water plus additional isotopic effects occurring during the assimilation of carbohydrates and post-photosynthetic processes21,22,34. The fractionations occur when carbonyl-group oxygen exchanges with leaf tissue water during the primary assimilation of carbohydrates (trioses and hexoses)42. This process causes 18O enrichment, described as εwc42, and has been determined to be ~  + 27 ‰21,22,73.During the synthesis of cellulose from primary assimilates, sucrose molecules are broken down to glucose and re-joined, allowing some of the carbonyl group oxygen to further exchange with water in the developing cell. The isotopic fractionation (εwc) during this process is assumed to be the same as in the carbonyl oxygen exchange during primary carbohydrate assimilation (~ + 27 ‰)41,42. During the formation of cellulose, the δ18O values of the primary assimilates are thus partially modified by the water in the developing cell33. Equation (6) describes this process34$$updelta ^{18} {text{O}}_{{{text{cellulose}}}} = {text{ p}}_{{text{x}}} {text{p}}_{{{text{ex}}}} *left( {updelta ^{18} {text{O}}_{{text{source water}}} + ,upvarepsilon _{{{text{wc}}}} } right) , + , left( {1 , – {text{p}}_{{text{x}}} {text{p}}_{{{text{ex}}}} } right) , * , left( {updelta ^{18} {text{O}}_{{text{leaf water}}} + ,upvarepsilon _{{{text{wc}}}} } right) $$
(6)

where δ18Ocellulose is the oxygen isotopic composition of cellulose, pex is the fraction of carbonyl oxygen in cellulose that exchanges with the medium water during synthesis, and px is the proportion of unenriched source water in the bulk water of the cell where cellulose is synthesized33. Bulk water in developing cells where cellulose is synthesized, i.e. in the leaf growth-and-differentiation zone, has been found to primarily reflect the isotope composition of source water43. Therefore, px in Eq. 6 is likely larger than fxylem in Eq. (5). For practical reasons, the parameters px or pex are typically not determined individually, but as the combined parameter pxpex45. For cellulose in leaves of grasses, crops, and trees pxpex has been found to range from 0.25 to 0.5445,46,47,48,49.In this study as in many applied examples where plant δ18O values are used for origin analysis we attempt to simulate the δ18O values of dried bulk tissue. Bulk dried plant tissue (δ18Obulk) contains in addition to carbohydrates compounds such as lignin, lipids, and proteins, which can be 18O-depleted compared to carbohydrates50. Since this needs to be accounted for in the model, we included the parameter c into the model. As pxpex and c cannot be determined separately they are used as a combined model parameter in our approach pxpexc:$$updelta ^{18} {text{O}}_{{{text{bulk}}}} = {text{ p}}_{{text{x}}} {text{p}}_{{{text{ex}}}} {text{c}}*left( {updelta ^{18} {text{O}}_{{text{source water}}} + ,upvarepsilon _{{{text{wc}}}} } right) , + , left( {1 – {text{ p}}_{{text{x}}} {text{p}}_{{{text{ex}}}} {text{c}}} right) , * , left( {updelta ^{18} {text{O}}_{{text{leaf water}}} + ,upvarepsilon _{{{text{wc}}}} } right) $$
(7)
Bulk dried tissue δ18O values of strawberries in Cueni et al. (in review) did not differ statistically from pure cellulose δ18O values in strawberries. Consequently, pxpex and pxpexc are identical for strawberries and ranges from 0.41 to 0.51. This approach allows the calculation of bulk dried tissue δ18O values without the knowledge of cellulose δ18O values, which is the case for the data set used in this study, and contrasts to the approach by Barbour & Farquhar (2000), where bulk dried tissue δ18O values are assessed by an offset (εcp) to the cellulose δ18O values.Model parameter selectionTo find the best values of the key model parameters for the prediction of strawberry bulk dried tissue δ18O values, we used different combinations of the values for the parameters. Specifically, we compared average parameter values from the literature that were derived from leaves and parameter values that were specifically derived for berries (Cueni et al. in review) to test if a leaf-level parameterization of the model is sufficient or if a berry-specific parameterization is necessary for producing satisfying model prediction. These values were either (i) fxylem and pxpex values reported in literature for leaf water and cellulose from various species that were averaged, (ii) values averaged for leaves (fxylem) and berries (pxpexc) of berry producing plants, or (iii) values for leaves (fxylem) and berries (pxpexc) specifically obtained for strawberry plants. For the general leaf-derived parameter values we used mean literature values originally obtained for leaf water and leaf cellulose δ18O for different species and averaged these values (0.22 for fxylem36,37,38,39,40 and 0.40 for pxpex45,46,47,48,49) (Table 1). For berries (average of the values of raspberries and strawberries) the mean leaf-derived fxylem value was 0.26 and the value for pxpexc was determined to be 0.46 (Table 1) (data derived from Cueni et al. in review). For strawberry plants, the leaf-derived fxylem value we used was 0.30, and the value for bulk dried tissue (pxpexc) was determined to be 0.46 (Table 1) (data derived from Cueni et al. in review). Since the pxpexc values of different berry species did not differ, this resulted in a total of six different model input parameter combinations.Table 1 Values of the model parameters (fxylem and pxpex/pxpexc) used for the simulations of strawberry bulk dried tissue δ18O values.Full size tableEnvironmental model input data selectionIn order to apply the strawberry parametrized bulk dried tissue oxygen model on a spatial scale, spatially gridded climate and precipitation isotope data layers were used as model inputs. The accurate simulation of geographically distinct δ18O values, however, requires the use of the most appropriate and best available input variables. We therefore tested the importance of the temporal averaging and lead time of the input data relative to the picking date of the berry. We defined these collectively as the “integration time” of the input data. Climate of the growing season46,53, and precipitation δ18O values of rain-events prior and during the growing season51,54,55 have been shown to shape plant tissue water and organic compound δ18O values. The major objective of our study was thus a careful evaluation of the most appropriate type and integration time of model input variables needed for this kind of model simulation. Moreover, to find the best data source provided, we also used several different spatial climate and precipitation isotope datasets in our evaluations (Table 2).Table 2 (a) Table showing the different climatic (air temperature and vapor pressure) and isotope (precipitation and vapor δ18O) data products, (b) as well as the different integration times and names used in the study used to simulate strawberry bulk dried tissue δ18O values.Full size tableTwo precipitation isotope data products were compared (Table 2): (1) The mean monthly precipitation δ18O grids by Bowen (2020), which are updated versions of the grids produced by Bowen and Revenaugh (2003) and Bowen et al. (2005) (Online Isotopes in Precipitation Calculator, OIPC Version 3.2). They provide global grids of monthly long-term mean precipitation isotope values. The resolution of these global grids is 5’. (2) Precipitation isotope predictions from Piso.AI (Version 1.01)32. This source provides values for individual months and years based on station coordinates32. Both data sets were on the one hand used for the precipitation δ18O input data of the model, and also to extrapolate the vapor δ18O values from sets (see model description above), which we treated as two individual, independent input data sets.For the climatic drivers of the model (air temperature and vapor pressure), we used the gridded data products from the Climatic Research Unit (CRU) (TS Version 4.04)29 and the E-OBS gridded dataset by the European Climate Assessment & Dataset (Version 22.0e)30 (Table 2). The CRU dataset provided global gridded monthly mean air temperature, and mean vapor pressure with a resolution of 0.5°. The E-OBS dataset included European daily mean air temperature and relative humidity gridded data, with a resolution of 0.1 arc-degrees. We calculated monthly mean air temperature and relative humidity grid layers, on the basis of these daily mean air temperature and relative humidity grids, respectively.Fruit tissue formation takes place over a period of several weeks leading up to picking date46,53. This results in a lead time between the date that best represents the mean climate conditions, and source water and vapor stable isotope signal influencing the isotope signal during tissue formation, and the picking date. As integration time of the input data, we therefore investigated lead times of 1, 2, 3, and 4 months, as well as the three months leading up to the picking date (Table 2). Moreover we also used more general European strawberry growing season averages76, independent of either the sampling month (yearly May to July mean) or the sampling year (2007 to 2017 May to July mean) (Table 2). Precipitation isotope data means were calculated as amount-weighted averages using CRU mean monthly precipitation data. This means that the long term mean precipitation δ18O values taken from OIPC were weighted by yearly specific CRU monthly precipitation totals for the case of the three months or growing season averages for individual years, and by average monthly precipitation totals (May, June, and July) from 2007 to 2017 for the long-term growing season calculation. The same assessment was also made using precipitation values from Piso.AI.Validation of model with reference samplesUsing the plant physiological model described above, we calculated the strawberry bulk dried tissue δ18O values for the location and the growing time of each authentic reference sample. For the model input data, we tested variable combinations using each of the eight integration times described in Table 2, along with all combinations of the data sources outlined in Table 2. This resulted in a total of 65,536 combinations of input variables per model parameter combination (fxylem and pxpex/pxpexc, Table 1), yielding model results to be evaluated against the measured reference samples. Our approach can be described with the following equation:$$updelta ^{18} {text{O}},{text{plant }} = fleft( {{text{air}},{text{temp}}left( {text{s,t}} right),{text{ relative}},{text{humidity}}left( {text{s,t}} right), updelta ^{18} {text{O}},{text{precip}}.left( {text{s,t}} right),updelta ^{18} {text{O}},{text{vapor }}left( {text{s,t}} right)} right) $$
(8)

where δ18O plant is the simulated δ18O value of the strawberry, s is the data product for the specified input variable (Table 2), and t is the integration time of the specified variable (Table 2).For the crucial model parameters fxylem and pxpex/pxpexc we on the one hand used the values proposed for leaves by literature (for pxpex), and on the other hand average of the values of raspberries and strawberries and strawberry-specific values determined from Cueni et al. (in review) (for pxpexc). In all calculations an εwc value of + 27 ‰ was used. To calculate mean monthly relative humidity values from the provided CRU vapor pressure data, site specific elevation was extracted from the ETOPO1 digital elevation model77, and used to calculate the approximate atmospheric pressure. These values were then used in combination with air temperature to calculate the saturation vapor pressure after Buck (1981), in order to assess relative humidity (relative humidity = vapor pressure/saturation vapor pressure). The R-script of the model is available on “figshare”, find the URL in the data availability statement.Statistical analysesStatistical analyses were done using the statistical package R version 3.5.379. The relationships of the range δ18O values observed with latitude, and between CRU and E-OBS mean air temperature were compared with a linear regression model, and with an alpha level that was set to α = 0.05. The results of the 65,536 models for each of the six physiological parameter combinations were compared with the measured δ18O bulk dried tissue values of the authentic reference samples (n = 154) by calculation of the root mean squared error (RMSE).Calculation of prediction mapsPrediction maps showing the regions of possible origin of a sample with unknown provenance are the product that is of interest in the food forensic industry. We calculated the prediction maps shown in Fig. 5 for three example δ18O values of strawberries collected in July 2017: (i) + 20 ‰ representing a mean Finish/Swedish sample, (ii) + 24.5 ‰ representing a mean German sample, and (iii) + 27 ‰ representing a mean southern European sample.The prediction maps were calculated in a two-step approach. First, we calculated a map of the expected strawberry bulk dried tissue δ18O values of berries grown in July 2017. For this we used the average berry model input parameters (fxylem and pxpexc, Table 1), and the best fitting model input data and integration time combination, which we assessed beforehand (Fig. 2). We thus used CRU mean air temperature and vapor pressure from June 2017, precipitation δ18O values from OIPC as an average from April, May and June, and vapor δ18O values calculated from OIPC precipitation δ18O values from April. Since using spatial maps as model input data, this calculation resulted in a mapped model result. In a second step, we calculated the prediction maps. For this we first subtracted the δ18O value of the bulk dried tissue of the sample strawberry from the mapped result of the best berry-specific model. This was done for each pixel value of the map. This resulted in a map showing the difference of the sample δ18O value and the predicted map δ18O value for each pixel of the map. The places (pixels) that are predicted to have the same δ18O value as the sample strawberry thus are represented by a value of zero. Based on the prediction error of the best berry-specific model (RMSE = 0.96 ‰), the one sigma (68%) and two sigma (95%) confidence intervals around the areas showing no difference to the δ18O value of the suspected sample could be assessed. This means that the bigger the difference between the simulated δ18O value and the sample value, the lower the probability of provenance of the sample. In other words, a difference between the sample’s δ18O value and the predicted δ18O value of 0 ‰ to ± 0.96 ‰ equals a possible provenance of at least 68% (one sigma), and a difference between ± 0.96 ‰ and ± 1.92 ‰ reflects a possible provenance between 68 and 27% (two sigma). Regions on the map with bigger differences than ± 1.92 ‰ represent regions of possible provenance, lower than 5% (bigger than two sigma). More

1.Costanza, R. et al. Twenty years of ecosystem services: how far have we come and how far do we still need to go?. Ecosyst. Serv. 28, 1–16 (2017).Article 

Google Scholar 
2.Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).ADS 
CAS 
Article 

Google Scholar 
3.Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).PubMed 
Article 

Google Scholar 
4.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.De Vos, J. M., Joppa, L. N., Gittleman, J. L., Stephens, P. R. & Pimm, S. L. Estimating the normal background rate of species extinction. Conserv. Biol. 29, 452–462 (2015).PubMed 
Article 

Google Scholar 
6.Humphreys, A. M., Govaerts, R., Ficinski, S. Z., Nic Lughadha, E. & Vorontsova, M. S. Global dataset shows geography and life form predict modern plant extinction and rediscovery. Nat. Ecol. Evol. 3, 1043–1047 (2019).PubMed 
Article 

Google Scholar 
7.Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
8.Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
9.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Isbell, F., Tilman, D., Polasky, S. & Loreau, M. The biodiversity-dependent ecosystem service debt. Ecol. Lett. 18, 119–134 (2015).PubMed 
Article 

Google Scholar 
11.Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).ADS 
PubMed 
Article 
CAS 

Google Scholar 
12.Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).ADS 
Article 

Google Scholar 
13.Reilly, J. R. et al. Crop production in the USA is frequently limited by a lack of pollinators. Proc. R. Soc. B. 287, 20200922 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity-ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

Google Scholar 
15.Kaiser‐Bunbury, C. N., Muff, S., Memmott, J., Müller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).PubMed 
Article 

Google Scholar 
16.Hautier, Y. et al. Eutrophication weakens stabilizing effects of diversity in natural grasslands. Nature 508, 521–525 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
17.Duncan, C., Thompson, J. R. & Pettorelli, N. The quest for a mechanistic understanding of biodiversity–ecosystem services relationships. Proc. R. Soc. B 282, 20151348 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).PubMed 
Article 

Google Scholar 
19.Dee, L. E. et al. Operationalizing network theory for ecosystem service assessments. Trends Ecol. Evol. 32, 118–130 (2017).PubMed 
Article 

Google Scholar 
20.Mastrángelo, M. E. et al. Key knowledge gaps to achieve global sustainability goals. Nat. Sustain. 2, 1115–1121 (2019).Article 

Google Scholar 
21.Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
22.Mace, G. M., Hails, R. S., Cryle, P., Harlow, J. & Clarke, S. J. Towards a risk register for natural capital. J. Appl. Ecol. 52, 641–653 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1072–1085 (2016).Article 

Google Scholar 
24.Keyes, A. A., McLaughlin, J. P., Barner, A. K. & Dee, L. E. An ecological network approach to predict ecosystem service vulnerability to species losses. Nat. Commun. 12, 1586 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. USA 96, 1463–1468 (1999).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Pillar, V. D. et al. Functional redundancy and stability in plant communities. J. Veg. Sci. 24, 963–974 (2013).Article 

Google Scholar 
27.Feit, B., Blüthgen, N., Traugott, M. & Jonsson, M. Resilience of ecosystem processes: a new approach shows that functional redundancy of biological control services is reduced by landscape simplification. Ecol. Lett. 22, 1568–1577 (2019).PubMed 
Article 

Google Scholar 
28.Salski, A. Ecological applications of fuzzy logic. Pages 3–14 in Ecological Informatics (ed. Recknagel, F.) (Springer, 2003).29.Ehrlich, P. R. & Mooney, H. A. Extinction, substitution, and ecosystem services. Bioscience 33, 248–254 (1983).Article 

Google Scholar 
30.Winfree, R. & Kremen, C. Are ecosystem services stabilized by differences among species? A test using crop pollination. Proc. R. Soc. B 276, 229–237 (2009).PubMed 
Article 

Google Scholar 
31.Díaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl Acad. Sci. USA 104, 20684 (2007).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Petchey, O. L. & Gaston, K. J. Extinction and the loss of functional diversity. Proc. R. Soc. B 269, 1721–1727 (2002).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Petchey, O. L., Hector, A. & Gaston, K. J. How do different measures of functional diversity perform? Ecology 85, 847–857 (2004).Article 

Google Scholar 
34.Maseyk, F. J. F., Demeter, L., Csergő, A. M. & Buckley, Y. M. Effect of management on natural capital stocks underlying ecosystem service provision: a ‘provider group’ approach. Biodivers. Conserv. 26, 3289–3305 (2017).Article 

Google Scholar 
35.Schröter, M. et al. Assumptions in ecosystem service assessments: increasing transparency for conservation. Ambio 50, 289–300 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Dee, L. E. et al. When do ecosystem services depend on rare species? Trends Ecol. Evol. 34, 746–758 (2019).PubMed 
Article 

Google Scholar 
37.Des Roches, S., Pendleton, L. H., Shapiro, B. & Palkovacs, E. P. Conserving intraspecific variation for nature’s contributions to people. Nat. Ecol. Evol. 5, 574–582 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Lafuite, A.-S., de Mazancourt, C. & Loreau, M. Delayed behavioural shifts undermine the sustainability of social–ecological systems. Proc. R. Soc. B 284, 20171192 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change 26, 152–158 (2014).Article 

Google Scholar 
40.Wyborn, C. et al. Imagining transformative biodiversity futures. Nat. Sustain. 3, 670–672 (2020).Article 

Google Scholar 
41.Bodin, Ö. et al. Improving network approaches to the study of complex social–ecological interdependencies. Nat. Sustain. 2, 551–559 (2019).Article 

Google Scholar 
42.Palumbi, S. R. et al. Managing for ocean biodiversity to sustain marine ecosystem services. Front. Ecol. Environ. 7, 204–211 (2009).Article 

Google Scholar 
43.Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).PubMed 
Article 

Google Scholar 
44.White, L., O’Connor, N. E., Yang, Q., Emmerson, M. C. & Donohue, I. Individual species provide multifaceted contributions to the stability of ecosystems. Nat. Ecol. Evol. 4, 1594–1601 (2020).PubMed 
Article 

Google Scholar 
45.Mace, G. M. et al. Quantification of extinction risk: IUCN’s system for classifying threatened species. Conserv. Biol. 22, 1424–1442 (2008).PubMed 
Article 

Google Scholar 
46.Moreno-Mateos, D. et al. The long-term restoration of ecosystem complexity. Nat. Ecol. Evol. 4, 676–685 (2020).PubMed 
Article 

Google Scholar 
47.Winfree, R. et al. Species turnover promotes the importance of bee diversity for crop pollination at regional scales. Science 359, 791–793 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
48.Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. B 271, 2605–2611 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
49.Purvis, A., Agapow, P. M., Gittleman, J. L. & Mace, G. M. Nonrandom extinction and the loss of evolutionary history. Science 288, 328–330 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
50.Gross, K. & Cardinale, B. J. The functional consequences of random vs. ordered species extinctions. Ecol. Lett. 8, 409–418 (2005).Article 

Google Scholar 
51.Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Ross, S. R. P.-J. et al. Code from: Universal scaling of robustness of ecosystem services to species loss (Version V0.4.2-beta). zenodo https://doi.org/10.5281/zenodo.4749405 (2021). More

Spatial databaseWe collected species occurrences from the most accurate and available source of data for each taxonomic group. For mammals, birds, reptiles and amphibians, we used the IUCN spatial database to assign realm identity for each species15. By doing this, we assigned a realm for 5489 mammal species, 10,787 bird species, 5489 reptile species and 5833 amphibian species. Since IUCN spatial database does not cover all species, we completed our database with two additional sources of species occurrences: (1) the WWF WildFinder species database23, except for mammals where we used the latest version of the species distribution provided by ref. 24. If (1) was not available, we used (2) the global biodiversity information facility (GBIF). Using WWF WildFinder, we assigned a realm for 1634 bird species, 7378 reptile species and 2006 amphibian species. 437 mammal species were assigned using ref. 24. From GBIF, we downloaded all the records belonging to the four classes of animals (Mammals50, Aves51, Reptiles52 and Amphibians53). Before using the spatial data, we cleaned the dataset following a cleaning procedure that was similar to but more conservative than other currently available methods (e.g. CoordinatesCleaner, BDCleaner54). First, records were screened, and only those with (1) coordinates; (2) a taxonomic rank of “species” were kept. From this list, we filtered out the records with clearly false locality coordinates (e.g. latitude equal to longitude, both latitude and longitude equal to 0, and longitude/latitude outside the possible range (i.e. −180; 180 for longitude and −90; 90 for latitude)). Those are the most common errors encountered with GBIF occurrence data55. In addition, we removed the records from living specimens (i.e. from zoos, botanical gardens), conserved specimens (i.e. museums), and unknown sources. We also excluded the species with less than 50 records within each realm as a low number of records can be due to misidentifications, which might have strong effects on our analyses. We finally refined the dataset by overlaying the occurrences within the six biogeographic realms (see below) and dropping the species that fall outside of the polygons. This spatial overlay process was conducted using the ‘sp’ library56 in R. The number of species for which realm was assigned using GBIF was 1 ( More

1.Marles, R. Mineral nutrient composition of vegetables, fruits and grains: The context of reports of apparent historical declines. J. Food Compos. Anal. 56, 93–103 (2017).CAS 
Article 

Google Scholar 
2.Davis, D. Declines in iron content of foods. Br. J. Nutr. 109, 2111 (2013).CAS 
Article 

Google Scholar 
3.Davis, D. Commentary on: “Historical variation in the mineral composition of edible horticultural products” (White, P. J and Broadley, M.R (2005) Journal of Horticultural Science & Biotechnology, 80, 660-667). J. Horticult. Sci. Biotechnol. 81(3), 553–554 (2006).Article 

Google Scholar 
4.Broadley, M. R., Mead, A. & White, P. J. Replay to Davis (2006) Commentary. J. Horticult. Sci. Technol. 81(3), 554–555 (2006).
Google Scholar 
5.Teklic, T., Loncaric, Z., Kovacevic, V. & Singh, B. R. Metallic trace elements in cereal grain—a review: How much metal do we eat?. Food Energy Secur. 2(2), 81–95 (2013).Article 

Google Scholar 
6.Ranum, P., Peña-Rosas, J. P. & Garcia-Casal, M. N. Global maize production, utilization and consumption. Ann N Y Acad Sci. 1312, 105–112 (2014).ADS 
Article 

Google Scholar 
7.Vidal Elgueta, A., Hinojosa, L. F., Pérez, M. F., Peralta, G. & Rodríguez, M. U. Genetic and phenotypic diversity in 2000 years old maize (Zea mays L.) samples from the Tarapacá region, Atacama Desert, Chile. PLoS ONE 14(1), e0210369. https://doi.org/10.1371/journal.pone.0210369 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Fan, M. S., Fairweather, S., Polton, P., Dunham, S. & Mcrath, S. Evidence of decreasing mineral density in wheat grain over the last 160 years. J. Trace Elem. Med. Biol. 22, 315–324 (2008).CAS 
Article 

Google Scholar 
9.McGrath, S. The effects of increasing yields on the macro- and microelement concentrations and offtakes in the grain of winter wheat. J. Sci. Food Agric. 36, 1073–1083 (1985).CAS 
Article 

Google Scholar 
10.De Fries, R., Fanzo, J., Remans, R., Palm, C. & Wood, S. Metrics for land-scarce agriculture Nutrient content must be better integrated into planning. Science 349(6245), 238–240 (2015).ADS 
Article 

Google Scholar 
11.Roschzttardtz, H., Conéjéro, G., Curie, C. & Mari, S. Identification of the endotermal vacuole as the iron storage compartment in the arabidopsis embryo. Plant Physiol. 151, 1329–1338 (2009).CAS 
Article 

Google Scholar 
12.Zang, J. et al. Maize YSL2 is required for iron distribution and development in kernels. J. Exp. Bot. 71, 5896–5910 (2020).CAS 
Article 

Google Scholar 
13.Roschzttardtz, H. et al. Plant cell nucleolus as a hot spot for iron. J. Biol. Chem. 286, 27863–27866 (2011).CAS 
Article 

Google Scholar 
14.Ibeas, M., Grant-Grant, S., Navarro, N., Perez, F. & Roschzttardtz, H. Dynamic subcellular localization of iron during embryo development in Brassicaceae seeds. Front. Plant Sci. 8, 2186 (2017).Article 

Google Scholar 
15.Santana-Sagredo, F. et al. ‘White gold’ guano fertilizer drove agricultural intensification in the Atacama Desert from AD 1000. Nat. Plants. 7, 152–158 (2021).CAS 
Article 

Google Scholar 
16.García, M. et al. Alimentos, tecnologías vegetales y paleoambiente en las aldeas formativas de la pampa del Tamarugal (ca. 900 a.C.–800 d.C.). Estudios Atacameños. 47, 33–58 (2014).Article 

Google Scholar 
17.Santana-Sagredo, F., Uribe, M., Herrera, M. J., Retamal, R. & Flores, S. Brief communication: Dietary practices in ancient populations from northern chile during the transition to agriculture (Tarapaca Region, 1000 BC-AD 900). Am. J. Phys. Anthropol. 158(4), 751–758 (2014).Article 

Google Scholar 
18.Santoro, C. M. et al. Continuities and discontinuities in the socio-environmental systems of the Atacama Desert during the last 13,000 years. J. Anthropol. Archaeol. 46, 28–39 (2017).Article 

Google Scholar 
19.Roschzttardtz, H., Conejero, G., Curie, C. & Mari, S. Identification of the endodermal vacuole as the iron storage compartment in the arabidopsis embryo. Plant Physiol. 151, 1329–1338 (2009).CAS 
Article 

Google Scholar 
20.Ibeas, M. et al. The diverse iron distribution in Eudicotyledoneae seeds: From Arabidopsis to Quinoa. Front. Plant Sci. 15, 1985 (2019).Article 

Google Scholar 
21.Davis, D., Epp, M. & Riordan, H. Changes in USDA food composition data for 43 Garden crops, 1950 to 1990. J. Am. Coll. Nutr. 23(6), 669–682 (2004).CAS 
Article 

Google Scholar 
22.White, P. J. & Broadley, M. R. Historical variation in the mineral composition of edible horticultural products. J. Horticult. Sci. Biotrchnol. 80(6), 660–667 (2005).Article 

Google Scholar 
23.Bronk Ramsey, C. Methods for summarizing radiocarbon datasets. Radiocarbon 59(2), 1809–1833 (2017).Article 

Google Scholar 
24.Hogg, A. G. et al. SHCal13 southern hemisphere calibration, 0–50,000 years cal BP. Radiocarbon 55(4), 1889–1903 (2013).CAS 
Article 

Google Scholar 
25.Gao, F., Robe, K., Bettembourg, M., Navarro, N., Rofidal, V., Santoni, V., Gaymard, F., Vignols, F., Roschzttardtz, H., Izquierdo, E., & Dubos, C. The transcription factor bHLH121 interacts with bHLH105 (ILR3) and its closest homologs to regulate iron homeostasis in arabidopsis. Plant Cell, 32, 508–524 (2020).CAS 
Article 

Google Scholar  More

1.Solomon, S. et al. (eds.) IPCC: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2007).2.Ganopolski, A. & Calov, R. The role of orbital forcing, carbon dioxide and regolith in 100 kyr glacial cycles. Clim. Past 7, 1415–1425, https://doi.org/10.5194/cp-7-1415-2011 (2011).Article 

Google Scholar 
3.Timmermann, A. et al. Modeling Obliquity and CO2 Effects on Southern Hemisphere Climate during the Past 408 ka. J. Climate 27, 1863–1875, https://doi.org/10.1175/JCLI-D-13-00311.1 (2013).ADS 
Article 

Google Scholar 
4.Claussen, M. et al. Earth system models of intermediate complexity: closing the gap in the spectrum of climate system models. Climate Dynamics 18, 579–586, https://doi.org/10.1007/s00382-001-0200-1 (2002).ADS 
Article 

Google Scholar 
5.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, 1965–1978, https://doi.org/10.1002/joc.1276 (2005).ADS 
Article 

Google Scholar 
6.Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Scientific Data 5, 180254, https://doi.org/10.1038/sdata.2018.254 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Lima-Ribeiro, M. S. et al. EcoClimate: a database of climate data from multiple models for past, present, and future for macroecologists and biogeographers. Biodiversity Informatics 10, https://doi.org/10.17161/bi.v10i0.4955 (2015).8.Fordham, D. A. et al. PaleoView: a tool for generating continuous climate projections spanning the last 21 000 years at regional and global scales. Ecography 40, 1348–1358, https://doi.org/10.1111/ecog.03031 (2017).Article 

Google Scholar 
9.Valdes, P. J. et al. The BRIDGE HadCM3 family of climate models: HadCM3@Bristol v1.0. Geosci. Model Dev. 10, 3715–3743, https://doi.org/10.5194/gmd-10-3715-2017 (2017).ADS 
CAS 
Article 

Google Scholar 
10.Armstrong, E., Hopcroft, P. O. & Valdes, P. J. A simulated Northern Hemisphere terrestrial climate dataset for the past 60,000 years. Sci Data 6, 1–16, https://doi.org/10.1038/s41597-019-0277-1 (2019).Article 

Google Scholar 
11.Beyer, R. M., Krapp, M. & Manica, A. High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years. Scientific Data 7, 236, https://doi.org/10.1038/s41597-020-0552-1 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
12.Beyer, R., Krapp, M. & Manica, A. An empirical evaluation of bias correction methods for palaeoclimate simulations. Climate of the Past 16, 1493–1508, https://doi.org/10.5194/cp-16-1493-2020 (2020).ADS 
Article 

Google Scholar 
13.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7, 1–18, https://doi.org/10.1038/s41597-020-0453-3 (2020).Article 

Google Scholar 
14.O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geological Survey Data Series 691 (2012).15.Kaplan, J. O. et al. Climate change and Arctic ecosystems: 2. Modeling, paleodata-model comparisons, and future projections. J. Geophys. Res. 108, 8171, https://doi.org/10.1029/2002JD002559 (2003).Article 

Google Scholar 
16.Singarayer, J. S. & Valdes, P. J. High-latitude climate sensitivity to ice-sheet forcing over the last 120kyr. Quaternary Science Reviews 29, 43–55, https://doi.org/10.1016/j.quascirev.2009.10.011 (2010).ADS 
Article 

Google Scholar 
17.Davies-Barnard, T., Ridgwell, A., Singarayer, J. & Valdes, P. Quantifying the influence of the terrestrial biosphere on glacial–interglacial climate dynamics. Clim. Past 13, 1381–1401, https://doi.org/10.5194/cp-13-1381-2017 (2017).Article 

Google Scholar 
18.Bereiter, B. et al. Revision of the EPICA Dome C CO2 record from 800 to 600 kyr before present. Geophys. Res. Lett. 42, 2014GL061957, https://doi.org/10.1002/2014GL061957 (2015).Article 

Google Scholar 
19.Berger, A. & Loutre, M. F. Insolation values for the climate of the last 10 million years. Quaternary Science Reviews 10, 297–317, https://doi.org/10.1016/0277-3791(91)90033-Q (1991).ADS 
Article 

Google Scholar 
20.Spratt, R. M. & Lisiecki, L. E. A Late Pleistocene sea level stack. Clim. Past 12, 1079–1092, https://doi.org/10.5194/cp-12-1079-2016 (2016).Article 

Google Scholar 
21.Peltier, W. R., Argus, D. F. & Drummond, R. Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_c (VM5a) model. Journal of Geophysical Research: Solid Earth 120, 450–487, https://doi.org/10.1002/2014JB011176 (2014).ADS 
Article 

Google Scholar 
22.Amante, C. & Eakins, B. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. National Geophysical Data Center, NOAA NOAA Technical Memorandum NESDIS NGDC-24, https://doi.org/10.7289/V5C8276M (2009).Article 

Google Scholar 
23.Lehner, B. & Dőll, P. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296, 1–22, https://doi.org/10.1016/j.jhydrol.2004.03.028 (2004).ADS 
Article 

Google Scholar 
24.Araya-Melo, P. A., Crucifix, M. & Bounceur, N. Global sensitivity analysis of the Indian monsoon during the Pleistocene. Clim. Past 11, 45–61, https://doi.org/10.5194/cp-11-45-2015 (2015).Article 

Google Scholar 
25.Lord, N. S. et al. Emulation of long-term changes in global climate: application to the late Pliocene and future. Climate of the Past 13, 1539–1571, https://doi.org/10.5194/cp-13-1539-2017 (2017).Article 

Google Scholar 
26.Hoyt, D. V. Percent of Possible Sunshine and the Total Cloud Cover. Monthly Weather Review 105, 648–652, 10.1175/1520-0493(1977)1052.0.CO;2 (1977).27.Berger, A. L. Long-term variations of daily insolation and Quaternary climatic changes. J. Atm. Sci. 35, 2362–2367 (1978).ADS 
Article 

Google Scholar 
28.Krapp, M. Terrestrial climate of the last 800,000 years, Open Science Framework, https://doi.org/10.17605/OSF.IO/8N43X (2021).29.Herzschuh, U. et al. Glacial legacies on interglacial vegetation at the Pliocene-Pleistocene transition in NE Asia. Nature Communications 7, 11967, https://doi.org/10.1038/ncomms11967 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
30.Schulzweida, U. CDO User Guide. Zenodo https://doi.org/10.5281/zenodo.3539275 (2019).31.Kőster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522, https://doi.org/10.1093/bioinformatics/bts480 (2012).CAS 
Article 
PubMed 

Google Scholar 
32.Seabold, S. & Perktold, J. Statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference (2010).33.Hunter, J. D. Matplotlib: A 2D graphics environment. Computing In Science & Engineering 9, 90–95, https://doi.org/10.1109/MCSE.2007.55 (2007).ADS 
Article 

Google Scholar 
34.Met Office. Cartopy: a cartographic python library with a Matplotlib interface (2010–2015).35.Flyamer, I. et al. Phlya/adjustText: 0.8 beta. Zenodo https://doi.org/10.5281/zenodo.3924114 (2020).36.Whitaker, J. et al. Unidata/netcdf4-python: version 1.5.5 release. Zenodo https://doi.org/10.5281/zenodo.4308773 (2020).37.Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Reback, J. et al. pandas-dev/pandas: Pandas 1.0.3. Zenodo https://doi.org/10.5281/zenodo.3715232 (2020).39.McKinney, W. Data Structures for Statistical Computing in Python. In Walt, S. v. d. & Millman, J. (eds.) Proceedings of the 9th Python in Science Conference, 56–61, https://doi.org/10.25080/Majora-92bf1922-00a (2010).40.Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Walt, Svd et al. scikit-image: image processing in Python. PeerJ 2, e453, https://doi.org/10.7717/peerj.453 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
42.da Costa-Luis, C. et al. tqdm: A fast, Extensible Progress Bar for Python and CLI. Zenodo https://doi.org/10.5281/zenodo.4531988 (2021).43.Past Interglacials Working Group of PAGES. Interglacials of the last 800,000 years. Rev. Geophys. 54, 2015RG000482, https://doi.org/10.1002/2015RG000482 (2016).44.Schaefer, G. et al. Planktic foraminiferal and sea surface temperature record during the last 1 Myr across the Subtropical Front, Southwest Pacific. Marine Micropaleontology 54, 191–212, https://doi.org/10.1016/j.marmicro.2004.12.001 (2005).ADS 
Article 

Google Scholar 
45.Ruddiman, W. F., Raymo, M. E., Martinson, D. G., Clement, B. M. & Backman, J. Pleistocene evolution: Northern hemisphere ice sheets and North Atlantic Ocean. Paleoceanography and Paleoclimatology 4, 353–412, https://doi.org/10.1029/PA004i004p00353 (1989).Article 

Google Scholar 
46.Nürnberg, D., Müller, A. & Schneider, R. R. Paleo-sea surface temperature calculations in the equatorial east Atlantic from Mg/Ca ratios in planktic foraminifera: A comparison to sea surface temperature estimates from U37K’, oxygen isotopes, and foraminiferal transfer function. Paleoceanography and Paleoclimatology 15, 124–134, https://doi.org/10.1029/1999PA000370 (2000).Article 

Google Scholar 
47.Horikawa, K., Murayama, M., Minagawa, M., Kato, Y. & Sagawa, T. Latitudinal and downcore (0–750 ka) changes in n-alkane chain lengths in the eastern equatorial Pacific. Quaternary Research 73, 573–582, https://doi.org/10.1016/j.yqres.2010.01.001 (2010).ADS 
CAS 
Article 

Google Scholar 
48.Martrat, B. et al. Four Climate Cycles of Recurring Deep and Surface Water Destabilizations on the Iberian Margin. Science 317, 502–507, https://doi.org/10.1126/science.1139994 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
49.Rincón-Martínez, D. & Leduc, G. Sea surface temperature calculated from alkenones for the last 285 ka with high-reolution Holocene of sediment core MD02-2529, Panama Basin. PANGAEA https://doi.org/10.1594/PANGAEA.777473 (2012).50.Rodrigues, T., Voelker, A. H. L., Grimalt, J. O., Abrantes, F. & Naughton, F. Iberian Margin sea surface temperature during MIS 15 to 9 (580–300 ka): Glacial suborbital variability versus interglacial stability. Paleoceanography 26, PA1204, https://doi.org/10.1029/2010PA001927 (2011).ADS 
Article 

Google Scholar 
51.Hayward, B. W. et al. Planktic foraminifera-based sea-surface temperature record in the Tasman Sea and history of the Subtropical Front around New Zealand, over the last one million years. Marine Micropaleontology 82–83, 13–27, https://doi.org/10.1016/j.marmicro.2011.10.003 (2012).ADS 
Article 

Google Scholar 
52.Russon, T. et al. Inter-hemispheric asymmetry in the early Pleistocene Pacific warm pool. Geophysical Research Letters 37, https://doi.org/10.1029/2010GL043191 (2010).53.Bard, E., Rostek, F. & Sonzogni, C. Interhemispheric synchrony of the last deglaciation inferred from alkenone palaeothermometry. Nature 385, 707–710, https://doi.org/10.1038/385707a0 (1997).ADS 
CAS 
Article 

Google Scholar 
54.Rostek, F. et al. Reconstructing sea surface temperature and salinity using $delta{18}O$ and alkenone records. Nature 364, 319, https://doi.org/10.1038/364319a0 (1993).ADS 
CAS 
Article 

Google Scholar 
55.Caley, T. et al. High-latitude obliquity as a dominant forcing in the Agulhas current system. Clim. Past 7, 1285–1296, https://doi.org/10.5194/cp-7-1285-2011 (2011).Article 

Google Scholar 
56.Pahnke, K., Zahn, R., Elderfield, H. & Schulz, M. 340,000-Year Centennial-Scale Marine Record of Southern Hemisphere Climatic Oscillation. Science 301, 948–952, https://doi.org/10.1126/science.1084451 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
57.Garidel–Thoron, T. d. et al. A multiproxy assessment of the western equatorial Pacific hydrography during the last 30 kyr. Paleoceanography 22, https://doi.org/10.1029/2006PA001269 (2005).58.Liu, Z., Altabet, M. A. & Herbert, T. D. Glacial-interglacial modulation of eastern tropical North Pacific denitrification over the last 1.8-Myr. Geophysical Research Letters 32, https://doi.org/10.1029/2005GL024439 (2005).59.Yamamoto, M., Yamamuro, M. & Tanaka, Y. The California current system during the last 136,000 years: response of the North Pacific High to precessional forcing. Quaternary Science Reviews 26, 405–414, https://doi.org/10.1016/j.quascirev.2006.07.014 (2007).ADS 
Article 

Google Scholar 
60.Herbert, T. D. Collapse of the California Current During Glacial Maxima Linked to Climate Change on Land. Science 293, 71–76, https://doi.org/10.1126/science.1059209 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
61.Schefuβ, E., Damsté, J. S. S. & Jansen, J. H. F. Forcing of tropical Atlantic sea surface temperatures during the mid-Pleistocene transition. Paleoceanography 19, https://doi.org/10.1029/2003PA000892 (2004).62.Etourneau, J., Martinez, P., Blanz, T. & Schneider, R. Pliocene–Pleistocene variability of upwelling activity, productivity, and nutrient cycling in the Benguela region. Geology 37, 871–874, https://doi.org/10.1130/G25733A.1 (2009).ADS 
CAS 
Article 

Google Scholar 
63.McClymont, E. L., Rosell-Melé, A., Giraudeau, J., Pierre, C. & Lloyd, J. M. Alkenone and coccolith records of the mid-Pleistocene in the south-east Atlantic: Implications for the U37K’ index and South African climate. Quaternary Science Reviews 24, 1559–1572, https://doi.org/10.1016/j.quascirev.2004.06.024 (2005).ADS 
Article 

Google Scholar 
64.Martínez–Garcia, A. et al. Links between iron supply, marine productivity, sea surface temperature, and CO2 over the last 1.1 Ma. Paleoceanography 24, https://doi.org/10.1029/2008PA001657 (2009).65.Crundwell, M., Scott, G., Naish, T. & Carter, L. Glacial–interglacial ocean climate variability from planktonic foraminifera during the Mid-Pleistocene transition in the temperate Southwest Pacific, ODP Site 1123. Palaeogeography, Palaeoclimatology, Palaeoecology 260, 202–229, https://doi.org/10.1016/j.palaeo.2007.08.023 (2008).ADS 
Article 

Google Scholar 
66.Hayward, B. W. et al. The effect of submerged plateaux on Pleistocene gyral circulation and sea-surface temperatures in the Southwest Pacific. Global and Planetary Change 63, 309–316, https://doi.org/10.1016/j.gloplacha.2008.07.003 (2008).ADS 
Article 

Google Scholar 
67.Li, L. et al. A 4-Ma record of thermal evolution in the tropical western Pacific and its implications on climate change. Earth and Planetary Science Letters 309, 10–20, https://doi.org/10.1016/j.epsl.2011.04.016 (2011).ADS 
CAS 
Article 

Google Scholar 
68.Herbert, T. D., Peterson, L. C., Lawrence, K. T. & Liu, Z. Tropical Ocean Temperatures Over the Past 3.5 Million Years. Science 328, 1530–1534, https://doi.org/10.1126/science.1185435 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
69.Nürnberg, D. & Groeneveld, J. Pleistocene variability of the Subtropical Convergence at East Tasman Plateau: Evidence from planktonic foraminiferal Mg/Ca (ODP Site 1172 A). Geochemistry, Geophysics, Geosystems 7, https://doi.org/10.1029/2005GC000984 (2006).70.Dyez, K. A., Ravelo, A. C. & Mix, A. C. Evaluating drivers of Pleistocene eastern tropical Pacific sea surface temperature. Paleoceanography 31, 2015PA002873, https://doi.org/10.1002/2015PA002873 (2016).Article 

Google Scholar 
71.Alonso-Garcia, M. et al. Ocean circulation, ice sheet growth and interhemispheric coupling of millennial climate variability during the mid-Pleistocene (ca 800–400ka). Quaternary Science Reviews 30, 3234–3247, https://doi.org/10.1016/j.quascirev.2011.08.005 (2011).ADS 
Article 

Google Scholar 
72.Medina-Elizalde, M. & W Lea, D. The Mid-Pleistocene Transition in the Tropical Pacific. Science 310, 1009–12, https://doi.org/10.1126/science.1115933 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
73.Liu, Z. Pleistocene climate evolution in the eastern Pacific and implications for the orbital theory of climate change. Ph.D., Brown University, United States – Rhode Island (2004).74.Dyez, K. A. & Ravelo, A. C. Late Pleistocene tropical Pacific temperature sensitivity to radiative greenhouse gas forcing. Geological Society of America 41, 23–26, https://doi.org/10.1130/G33425.1 (2013).CAS 
Article 

Google Scholar 
75.Martínez-Garcia, A., Rosell-Melé, A., McClymont, E. L., Gersonde, R. & Haug, G. H. Subpolar Link to the Emergence of the Modern Equatorial Pacific Cold Tongue. Science 328, 1550–1553, https://doi.org/10.1126/science.1184480 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
76.Lawrence, K. T., Herbert, T. D., Brown, C. M., Raymo, M. E. & Haywood, A. M. High-amplitude variations in North Atlantic sea surface temperature during the early Pliocene warm period. Paleoceanography 24, PA2218, https://doi.org/10.1029/2008PA001669 (2009).ADS 
Article 

Google Scholar 
77.Schmidt, M. W., Vautravers, M. J. & Spero, H. J. Western Caribbean sea surface temperatures during the late Quaternary. Geochemistry Geophysics Geosystems 7, https://doi.org/10.1029/2005GC000957 (2006).78.Ho, S. L. et al. Sea surface temperature variability in the Pacific sector of the Southern Ocean over the past 700 kyr. Paleoceanography 27, https://doi.org/10.1029/2012PA002317 (2012).79.Tierney, J. E., deMenocal, P. B. & Zander, P. D. A climatic context for the out-of-Africa migration. The Geological Society of America 45, 1023–1026, https://doi.org/10.1130/G39457.1 (2017).Article 

Google Scholar 
80.Beck, J. W. et al. A 550,000-year record of East Asian monsoon rainfall from 10Be in loess. Science 360, 877–881, https://doi.org/10.1126/science.aam5825 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
81.Kathayat, G. et al. Indian monsoon variability on millennial-orbital timescales. Scientific Reports 6, 24374, https://doi.org/10.1038/srep24374 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
82.Guo, Z. T., Berger, A., Yin, Q. Z. & Qin, L. Strong asymmetry of hemispheric climates during MIS-13 inferred from correlating China loess and Antarctica ice records. Clim. Past 5, 21–31, https://doi.org/10.5194/cp-5-21-2009 (2009).Article 

Google Scholar 
83.Carolin, S. A. et al. Northern Borneo stalagmite records reveal West Pacific hydroclimate across MIS 5 and 6. Earth and Planetary Science Letters 439, 182–193, https://doi.org/10.1016/j.epsl.2016.01.028 (2016).ADS 
CAS 
Article 

Google Scholar 
84.Waldmann, N., Torfstein, A. & Stein, M. Northward intrusions of low- and mid-latitude storms across the Saharo-Arabian belt during past interglacials. Geology 38, 567–570, https://doi.org/10.1130/G30654.1 (2010).ADS 
CAS 
Article 

Google Scholar 
85.Landwehr, J. M., Sharp, W. D., Coplen, T. B., Ludwig, K. R. & Winograd, I. J. The Chronology for the δ18O Record from Devils Hole, Nevada, Extended Into the Mid-Holocene. Tech. Rep., US Geological Survey (2011).86.Stoykova, D. A., Shopov, Y. Y., Garbeva, D., Tsankov, L. T. & Yonge, C. J. Origin of the climatic cycles from orbital to sub-annual scales. Journal of Atmospheric and Solar-Terrestrial Physics 70, 293–302, https://doi.org/10.1016/j.jastp.2007.08.018 (2008).ADS 
Article 

Google Scholar 
87.Jouzel, J. et al. Orbital and Millennial Antarctic Climate Variability over the Past 800,000 Years. Science 317, 793–796, https://doi.org/10.1126/science.1141038 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
88.Cheng, H. et al. The climatic cyclicity in semiarid-arid central Asia over the past 500,000 years. Geophysical Research Letters 39, https://doi.org/10.1029/2011GL050202 (2012).89.Prokopenko, A. A., Hinnov, L. A., Williams, D. F. & Kuzmin, M. I. Orbital forcing of continental climate during the Pleistocene: a complete astronomically tuned climatic record from Lake Baikal, SE Siberia. Quaternary Science Reviews 25, 3431–3457, https://doi.org/10.1016/j.quascirev.2006.10.002 (2006).ADS 
Article 

Google Scholar 
90.Melles, M. et al. 2.8 Million Years of Arctic Climate Change from Lake El’gygytgyn, NE Russia. Science 337, 315–320, https://doi.org/10.1126/science.1222135 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
91.Vaks, A., Bar-Matthews, M., Matthews, A., Ayalon, A. & Frumkin, A. Middle-Late Quaternary paleoclimate of northern margins of the Saharan-Arabian Desert: reconstruction from speleothems of Negev Desert, Israel. Quaternary Science Reviews 29, 2647–2662, https://doi.org/10.1016/j.quascirev.2010.06.014 (2010).ADS 
Article 

Google Scholar 
92.Bar-Matthews, M., Ayalon, A., Gilmour, M., Matthews, A. & Hawkesworth, C. J. Sea–land oxygen isotopic relationships from planktonic foraminifera and speleothems in the Eastern Mediterranean region and their implication for paleorainfall during interglacial intervals. Geochimica et Cosmochimica Acta 67, 3181–3199, https://doi.org/10.1016/S0016-7037(02)01031-1 (2003).ADS 
CAS 
Article 

Google Scholar 
93.Cheng, H. et al. The Asian monsoon over the past 640,000 years and ice age terminations. Nature 534, 640–646, https://doi.org/10.1038/nature18591 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
94.Tzedakis, P. C., Hooghiemstra, H. & Pälike, H. The last 1.35 million years at Tenaghi Philippon: revised chronostratigraphy and long-term vegetation trends. Quaternary Science Reviews 25, 3416–3430, https://doi.org/10.1016/j.quascirev.2006.09.002 (2006).ADS 
Article 

Google Scholar 
95.Vaks, A. et al. Paleoclimate and location of the border between Mediterranean climate region and the Saharo–Arabian Desert as revealed by speleothems from the northern Negev Desert, Israel. Earth and Planetary Science Letters 249, 384–399, https://doi.org/10.1016/j.epsl.2006.07.009 (2006).ADS 
CAS 
Article 

Google Scholar 
96.K. Thomas, E. et al. Heterodynes dominate precipitation isotopes in the East Asian monsoon region, reflecting interaction of multiple climate factors. Earth and Planetary Science Letters 455, https://doi.org/10.1016/j.epsl.2016.09.044 (2016).97.Hao, Q. et al. Delayed build-up of Arctic ice sheets during 400,000-year minima in insolation variability. Nature 490, 393–396, https://doi.org/10.1038/nature11493 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar  More

1.Connell, J. H. & Slatyer, R. O. Mechanisms of succession in natural communities and their role in community stability and organization. Am. Naturalist 111, 1119–1144 (1977).Article 

Google Scholar 
2.Shulman, M. J. et al. Priority effects in the recruitment of juvenile coral reef fishes. Ecology 64, 1508–1513 (1983).Article 

Google Scholar 
3.Alford, R. A. & Wilbur, H. M. Priority effects in experimental pond communities: competition between Bufo and Rana. Ecology 66, 1097–1105 (1985).Article 

Google Scholar 
4.Grman, E. & Suding, K. N. Within-year soil legacies contribute to strong priority effects of exotics on native California grassland communities. Restor. Ecol. 18, 664–670 (2010).Article 

Google Scholar 
5.Almany, G. R. Priority effects in coral reef fish communities. Ecology 84, 1920–1935 (2003).Article 

Google Scholar 
6.Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015). This study defines mechanisms by which early-arriving species affect late-arriving species (niche pre-emption and niche modification) and describes how and when they are expected to influence community assembly outcomes.Article 

Google Scholar 
7.Mariotte, P. et al. Plant-soil feedback: bridging natural and agricultural sciences. Trends Ecol. Evol. 33, 129–142 (2018).PubMed 
Article 

Google Scholar 
8.Suding, K. N., Gross, K. L. & Houseman, G. R. Alternative states and positive feedbacks in restoration ecology. Trends Ecol. Evol. 19, 46–53 (2004).PubMed 
Article 

Google Scholar 
9.Sprockett, D., Fukami, T. & Relman, D. A. Role of priority effects in the early-life assembly of the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 15, 197–205 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Chng, K. R. et al. Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut. Nat. Ecol. Evol. 4, 1256–1267 (2020).PubMed 
Article 

Google Scholar 
11.Lee, S. M. et al. Bacterial colonization factors control specificity and stability of the gut microbiota. Nature 501, 426–429 (2013). Uncovered the molecular mechanism underlying priority effects between strains of Bacteroides in the mouse gut microbiota.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Martínez, I. et al. Experimental evaluation of the importance of colonization history in early-life gut microbiota assembly. eLife 7, e36521 (2018). Inoculated mice with donor communities at different time points; the mature communities most resembled whichever donor community was inoculated first.PubMed 
PubMed Central 
Article 

Google Scholar 
13.Furman, O. et al. Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics. Nat. Commun. 11, 1904 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Cheong, J. Z. A. et al. Priority effects dictate community structure and alter virulence of fungal-bacterial biofilms. ISME J. https://doi.org/10.1038/s41396-021-00901-5 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Seybold, H. et al. A fungal pathogen induces systemic susceptibility and systemic shifts in wheat metabolome and microbiome composition. Nat. Commun. 11, 1910 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat Ecol. Evol. 3, 1445–1454 (2019). This study experimentally manipulated the assembly sequence of strains in a complex synthetic community in the plant phyllosphere.PubMed 
PubMed Central 
Article 

Google Scholar 
17.Halliday, F. W. et al. Facilitative priority effects drive parasite assembly under coinfection. Nat. Ecol. Evol. 4, 1510–1521 (2020).PubMed 
Article 

Google Scholar 
18.Peay, K. G., Belisle, M. & Fukami, T. Phylogenetic relatedness predicts priority effects in nectar yeast communities. Proc. Biol. Sci. 279, 749–758 (2012).PubMed 

Google Scholar 
19.Wei, Z. et al. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat. Commun. 6, 8413 (2015). Showed that priority effects between commensal and pathogenic bacteria in the plant rhizosphere can be predicted based on overlap in resource consumption in vitro.CAS 
PubMed 
Article 

Google Scholar 
20.Kennedy, P. G., Peay, K. G. & Bruns, T. D. Root tip competition among ectomycorrhizal fungi: Are priority effects a rule or an exception? Ecology 90, 2098–2107 (2009).PubMed 
Article 

Google Scholar 
21.Fukami, T. et al. Assembly history dictates ecosystem functioning: evidence from wood decomposer communities. Ecol. Lett. 13, 675–684 (2010).PubMed 
Article 

Google Scholar 
22.Enke, T. N. et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr. Biol. 29, 1528–1535 (2019).CAS 
PubMed 
Article 

Google Scholar 
23.Svoboda, P., Lindström, E. S., Ahmed Osman, O. & Langenheder, S. Dispersal timing determines the importance of priority effects in bacterial communities. ISME J. 12, 644–646 (2018). Demonstrated that the strength of priority effects in an aquatic community was a product of how well each community was adapted to the habitat and the amount of time between their dispersal events.PubMed 
Article 

Google Scholar 
24.Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant-microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).CAS 
PubMed 
Article 

Google Scholar 
25.Shreiner, A. B., Kao, J. Y. & Young, V. B. The gut microbiome in health and disease. Curr. Opin. Gastroenterol. 31, 69–75 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Long, Z. T. & Karel, I. Resource specialization determines whether history influences community structure. Oikos 96, 62–69 (2002).Article 

Google Scholar 
27.Tan, J., Pu, Z., Ryberg, W. A. & Jiang, L. Species phylogenetic relatedness, priority effects, and ecosystem functioning. Ecology 93, 1164–1172 (2012).PubMed 
Article 

Google Scholar 
28.Maignien, L., DeForce, E. A., Chafee, M. E., Eren, A. M. & Simmons, S. L. Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. mBio 5, e00682–13 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl Med. 8, 343ra81 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Tilman, D. Resource competition between plankton algae: an experimental and theoretical approach. Ecology 58, 338–348 (1977).CAS 
Article 

Google Scholar 
31.Tucker, C. M. & Fukami, T. Environmental variability counteracts priority effects to facilitate species coexistence: evidence from nectar microbes. Proc. Biol. Sci. 281, 20132637 (2014).PubMed 
PubMed Central 

Google Scholar 
32.Poza-Carrion, C., Suslow, T. & Lindow, S. Resident bacteria on leaves enhance survival of immigrant cells of Salmonella enterica. Phytopathology 103, 341–351 (2013).PubMed 
Article 

Google Scholar 
33.Monier, J.-M. & Lindow, S. E. Aggregates of resident bacteria facilitate survival of immigrant bacteria on leaf surfaces. Microb. Ecol. 49, 343–352 (2005).PubMed 
Article 

Google Scholar 
34.Piccardi, P., Vessman, B. & Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl Acad. Sci. USA 116, 15979–15984 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Potnis, N. et al. Xanthomonas perforans colonization influences Salmonella enterica in the tomato phyllosphere. Appl. Environ. Microbiol. 80, 3173–3180 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.Zhang, Y., Kastman, E. K., Guasto, J. S. & Wolfe, B. E. Fungal networks shape dynamics of bacterial dispersal and community assembly in cheese rind microbiomes. Nat. Commun. 9, 336 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Chang, P. V. Chemical mechanisms of colonization resistance by the gut microbial metabolome. ACS Chem. Biol. 15, 1119–1126 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Borton, M. A. et al. Chemical and pathogen-induced inflammation disrupt the murine intestinal microbiome. Microbiome 5, 47 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Snelders, N. C. et al. Microbiome manipulation by a soil-borne fungal plant pathogen using effector proteins. Nat. Plants 6, 1365–1374 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Foster, J. L. & Fogleman, J. C. Bacterial succession in necrotic tissue of agria cactus (Stenocereus gummosus). Appl. Environ. Microbiol. 60, 619–625 (1994).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.O’Keeffe, K. R., Halliday, F. W., Jones, C. D., Carbone, I. & Mitchell, C. E. Parasites, niche modification, and the host microbiome: a field survey of multiple parasites. Mol. Ecol. 30, 2404–2416 (2021).PubMed 
Article 

Google Scholar 
42.Joo, J. et al. Bacteriophage-mediated competition in Bordetella bacteria. Proc. Biol. Sci. 273, 1843–1848 (2006).PubMed 
PubMed Central 

Google Scholar 
43.Fernández, L., Rodríguez, A. & García, P. Phage or foe: an insight into the impact of viral predation on microbial communities. ISME J. 12, 1171–1179 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Sweere, J. M. et al. Bacteriophage trigger antiviral immunity and prevent clearance of bacterial infection. Science 363, eaat9691 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Veiga, P. et al. Bifidobacterium animalis subsp. lactis fermented milk product reduces inflammation by altering a niche for colitogenic microbes. Proc. Natl Acad. Sci. USA 107, 18132–18137 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Topisirovic, L. et al. Potential of lactic acid bacteria isolated from specific natural niches in food production and preservation. Int. J. Food Microbiol. 112, 230–235 (2006).CAS 
PubMed 
Article 

Google Scholar 
47.De Vuyst, L. & Leroy, F. Bacteriocins from lactic acid bacteria: production, purification, and food applications. J. Mol. Microbiol. Biotechnol. 13, 194–199 (2007).PubMed 
Article 
CAS 

Google Scholar 
48.ten Cate, J. M. Biofilms, a new approach to the microbiology of dental plaque. Odontology 94, 1–9 (2006).PubMed 
Article 

Google Scholar 
49.Gibbons, S. M., Kearney, S. M., Smillie, C. S. & Alm, E. J. Two dynamic regimes in the human gut microbiome. PLoS Comput. Biol. 13, e1005364 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. Proc. Natl Acad. Sci. USA 111, 13757–13762 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).PubMed 
Article 

Google Scholar 
52.The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).PubMed Central 
Article 
CAS 
PubMed 

Google Scholar 
53.Zhang, Q.-G. & Zhang, D.-Y. Colonization sequence influences selection and complementarity effects on biomass production in experimental algal microcosms. Oikos 116, 1748–1758 (2007).Article 

Google Scholar 
54.Dickie, I. A., Fukami, T., Wilkie, J. P., Allen, R. B. & Buchanan, P. K. Do assembly history effects attenuate from species to ecosystem properties? A field test with wood-inhabiting fungi. Ecol. Lett. 15, 133–141 (2012).PubMed 
Article 

Google Scholar 
55.Bittleston, L. S., Gralka, M., Leventhal, G. E., Mizrahi, I. & Cordero, O. X. Context-dependent dynamics lead to the assembly of functionally distinct microbial communities. Nat. Commun. 11, 1440 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Boyle, J. A., Simonsen, A. K., Frederickson, M. E. & Stinchcombe, J. R. Priority effects alter interaction outcomes in a legume-rhizobium mutualism. Proc. Biol. Sci. 288, 20202753 (2021).PubMed 
PubMed Central 

Google Scholar 
57.Fukami, T. & Morin, P. J. Productivity–biodiversity relationships depend on the history of community assembly. Nature 424, 423–426 (2003).CAS 
PubMed 
Article 

Google Scholar 
58.Medini, D., Donati, C., Tettelin, H., Masignani, V. & Rappuoli, R. The microbial pan-genome. Curr. Opin. Genet. Dev. 15, 589–594 (2005).CAS 
PubMed 
Article 

Google Scholar 
59.Wagg, C., Schlaeppi, K., Banerjee, S., Juramae, E. E. & van der Heijden, M. G. A. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat. Commun. 10, 4841 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
60.Rummens, K., De Meester, L. & Souffreau, C. Inoculation history affects community composition in experimental freshwater bacterioplankton communities. Environ. Microbiol. 20, 1120–1133 (2018).PubMed 
Article 

Google Scholar 
61.Steen, A. D. et al. High proportions of bacteria and archaea across most biomes remain uncultured. ISME J. 13, 3126–3130 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
62.Imachi, H. et al. Isolation of an archaeon at the prokaryote–eukaryote interface. Nature 577, 519–525 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.D’Onofrio, A. et al. Siderophores from neighboring organisms promote the growth of uncultured bacteria. Chem. Biol. 17, 254–264 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.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). This study identified features of the resident microbiome (bacterial taxa and genes) that predicted variation in the persistence of a probiotic among subjects in a clinical trial.PubMed 
Article 
CAS 

Google Scholar 
65.Christian, N., Herre, E. A., Mejia, L. C. & Clay, K. Exposure to the leaf litter microbiome of healthy adults protects seedlings from pathogen damage. Proc. Biol. Sci. 284, 20170641 (2017).PubMed 
PubMed Central 

Google Scholar 
66.Alavi, S. et al. Interpersonal gut microbiome variation drives susceptibility and resistance to cholera infection. Cell 181, 1533–1546 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Hiscox, J. et al. Priority effects during fungal community establishment in beech wood. ISME J. 9, 2246–2260 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
68.Losos, J. B. Contingency and determinism in replicated adaptive radiations of island lizards. Science 279, 2115–2118 (1998).CAS 
PubMed 
Article 

Google Scholar 
69.Glitzenstein, J. S., Harcombe, P. A. & Streng, D. R. Disturbance, succession, and maintenance of species diversity in an east texas forest. Ecol. Monogr. 56, 243–258 (1986).Article 

Google Scholar 
70.Dominguez-Bello, M. G. et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc. Natl Acad. Sci. USA 107, 11971–11975 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
71.Bäckhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 852 (2015).PubMed 
Article 
CAS 

Google Scholar 
72.Edwards, J. A. et al. Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice. PLoS Biol. 16, e2003862 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
73.Chappell, C. R. & Fukami, T. Nectar yeasts: a natural microcosm for ecology. Yeast 35, 417–423 (2018).CAS 
PubMed 
Article 

Google Scholar 
74.Loeuille, N. & Leibold, M. A. Evolution in metacommunities: on the relative importance of species sorting and monopolization in structuring communities. Am. Nat. 171, 788–799 (2008).PubMed 
Article 

Google Scholar 
75.Vallespir Lowery, N. & Ursell, T. Structured environments fundamentally alter dynamics and stability of ecological communities. Proc. Natl Acad. Sci. USA 116, 379–388 (2019).PubMed 
Article 
CAS 

Google Scholar 
76.Wittmann, M. J. & Fukami, T. Eco-evolutionary buffering: rapid evolution facilitates regional species coexistence despite local priority effects. Am. Nat. 191, E171–E184 (2018).PubMed 
Article 

Google Scholar 
77.Eitam, A., Blaustein, L. & Mangel, M. Density and intercohort priority effects on larval Salamandra salamandra in temporary pools. Oecologia 146, 36–42 (2005).PubMed 
Article 

Google Scholar 
78.Woody, S. T., Ives, A. R., Nordheim, E. V. & Andrews, J. H. Dispersal, density dependence, and population dynamics of a fungal microbe on leaf surfaces. Ecology 88, 1513–1524 (2007).PubMed 
Article 

Google Scholar 
79.Wein, T. et al. Carrying capacity and colonization dynamics of Curvibacter in the hydra host habitat. Front. Microbiol. 9, 443 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
80.Remus-Emsermann, M. N. P. et al. Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environ. Microbiol. 16, 2329–2340 (2014).CAS 
PubMed 
Article 

Google Scholar 
81.Tewksbury, J. J. & Lloyd, J. D. Positive interactions under nurse-plants: spatial scale, stress gradients and benefactor size. Oecologia 127, 425–434 (2001).PubMed 
Article 

Google Scholar 
82.Monier, J.-M. & Lindow, S. E. Differential survival of solitary and aggregated bacterial cells promotes aggregate formation on leaf surfaces. Proc. Natl Acad. Sci. USA 100, 15977–15982 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.LaSarre, B., McCully, A. L., Lennon, J. T. & McKinlay, J. B. Microbial mutualism dynamics governed by dose-dependent toxicity of cross-fed nutrients. ISME J. 11, 337–348 (2017).CAS 
PubMed 
Article 

Google Scholar 
84.McCully, A. L., LaSarre, B. & McKinlay, J. B. Growth-independent cross-feeding modifies boundaries for coexistence in a bacterial mutualism. Environ. Microbiol. 19, 3538–3550 (2017).CAS 
PubMed 
Article 

Google Scholar 
85.Nuñez, M. A., Horton, T. R. & Simberloff, D. Lack of belowground mutualisms hinders Pinaceae invasions. Ecology 90, 2352–2359 (2009).PubMed 
Article 

Google Scholar 
86.Fürst, U. et al. Perception of Agrobacterium tumefaciens flagellin by FLS2XL confers resistance to crown gall disease. Nat. Plants 6, 22–27 (2020).PubMed 
Article 
CAS 

Google Scholar 
87.Lu, P., Bian, G., Pan, X. & Xi, Z. Wolbachia induces density-dependent inhibition to dengue virus in mosquito cells. PLoS Negl. Trop. Dis. 6, e1754 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
88.Vannette, R. L. & Fukami, T. Historical contingency in species interactions: towards niche-based predictions. Ecol. Lett. 17, 115–124 (2014).PubMed 
Article 

Google Scholar 
89.Onoda, Y. et al. Trade-off between light interception efficiency and light use efficiency: implications for species coexistence in one-sided light competition. J. Ecol. 102, 167–175 (2014).Article 

Google Scholar 
90.Burson, A., Stomp, M., Greenwell, E., Grosse, J. & Huisman, J. Competition for nutrients and light: testing advances in resource competition with a natural phytoplankton community. Ecology 99, 1108–1118 (2018).PubMed 
Article 

Google Scholar 
91.Malerba, M. E., Palacios, M. M., Palacios Delgado, Y. M., Beardall, J. & Marshall, D. J. Cell size, photosynthesis and the package effect: an artificial selection approach. N. Phytol. 219, 449–461 (2018).CAS 
Article 

Google Scholar 
92.Hajishengallis, G. et al. Low-abundance biofilm species orchestrates inflammatory periodontal disease through the commensal microbiota and complement. Cell Host Microbe 10, 497–506 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
93.Herren, C. M. & McMahon, K. D. Keystone taxa predict compositional change in microbial communities. Environ. Microbiol. 20, 2207–2217 (2018).PubMed 
Article 

Google Scholar 
94.Battin, T. J., Kaplan, L. A., Newbold, J. D., Cheng, X. & Hansen, C. Effects of current velocity on the nascent architecture of stream microbial biofilms. Appl. Environ. Microbiol. 69, 5443–5452 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Tecon, R., Ebrahimi, A., Kleyer, H., Erev Levi, S. & Or, D. Cell-to-cell bacterial interactions promoted by drier conditions on soil surfaces. Proc. Natl Acad. Sci. USA 115, 9791–9796 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.van der Wal, A., Tecon, R., Kreft, J.-U., Mooij, W. M. & Leveau, J. H. J. Explaining bacterial dispersion on leaf surfaces with an individual-based model (PHYLLOSIM). PLoS ONE 8, e75633 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
97.Pande, S. et al. Privatization of cooperative benefits stabilizes mutualistic cross-feeding interactions in spatially structured environments. ISME J. 10, 1413–1423 (2016).PubMed 
Article 

Google Scholar 
98.Momeni, B., Waite, A. J. & Shou, W. Spatial self-organization favors heterotypic cooperation over cheating. eLife 2, e00960 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
99.Hol, F. J. H., Galajda, P., Woolthuis, R. G., Dekker, C. & Keymer, J. E. The idiosyncrasy of spatial structure in bacterial competition. BMC Res. Notes 8, 245 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
100.Dal Co, A., van Vliet, S., Kiviet, D. J., Schlegel, S. & Ackermann, M. Short-range interactions govern the dynamics and functions of microbial communities. Nat. Ecol. Evol. 4, 366–375 (2020).PubMed 
Article 

Google Scholar 
101.Dang, A. T. & Marsland, B. J. Microbes, metabolites, and the gut–lung axis. Mucosal Immunol. 12, 843–850 (2019).CAS 
PubMed 
Article 

Google Scholar 
102.Morella, N. M., Zhang, X. & Koskella, B. Tomato seed-associated bacteria confer protection of seedlings against foliar disease caused by Pseudomonas syringae. Phytobiomes J. 3, 177–190 (2019).Article 

Google Scholar 
103.Scharschmidt, T. C. et al. A wave of regulatory t cells into neonatal skin mediates tolerance to commensal microbes. Immunity 43, 1011–1021 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
104.Sadd, B. M., Kleinlogel, Y., Schmid-Hempel, R. & Schmid-Hempel, P. Trans-generational immune priming in a social insect. Biol. Lett. 1, 386–388 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
105.Zhou, J. & Ning, D. Stochastic community assembly: does it matter in microbial ecology? Microbiol. Mol. Biol. Rev. https://doi.org/10.1128/MMBR.00002-17 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
106.Rillig, M. C. et al. Interchange of entire communities: microbial community coalescence. Trends Ecol. Evol. 30, 470–476 (2015).PubMed 
Article 

Google Scholar 
107.Meadow, J. F., Bateman, A. C., Herkert, K. M., O’Connor, T. K. & Green, J. L. Significant changes in the skin microbiome mediated by the sport of roller derby. PeerJ 1, e53 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
108.Vannette, R. L. The floral microbiome: plant, pollinator, and microbial perspectives. Annu. Rev. Ecol. Evol. Syst. 51, 363–386 (2020).Article 

Google Scholar 
109.Pachiadaki, M. G. et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell 179, 1623–1635 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
110.Watrous, J. D. & Dorrestein, P. C. Imaging mass spectrometry in microbiology. Nat. Rev. Microbiol. 9, 683–694 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
111.Hungate, B. A. et al. Quantitative microbial ecology through stable isotope probing. Appl. Environ. Microbiol. 81, 7570–7581 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
112.Tropini, C., Earle, K. A., Huang, K. C. & Sonnenburg, J. L. The gut microbiome: connecting spatial organization to function. Cell Host Microbe 21, 433–442 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
113.Garud, N. R., Good, B. H., Hallatschek, O. & Pollard, K. S. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biol. 17, e3000102 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
114.Braga, L. P. P. et al. Impact of phages on soil bacterial communities and nitrogen availability under different assembly scenarios. Microbiome 8, 52 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
115.Rao, C. et al. Multi-kingdom ecological drivers of microbiota assembly in preterm infants. Nature 591, 633–638 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
116.Schluter, D., Price, T. D. & Grant, P. R. Ecological character displacement in Darwin’s finches. Science 227, 1056–1059 (1985).CAS 
PubMed 
Article 

Google Scholar 
117.Zee, P. C. & Fukami, T. Priority effects are weakened by a short, but not long, history of sympatric evolution. Proc. R. Soc. Lond. B Biol. Sci. 285, 20171722 (2018).
Google Scholar 
118.Gensollen, T., Iyer, S. S., Kasper, D. L. & Blumberg, R. S. How colonization by microbiota in early life shapes the immune system. Science 352, 539–544 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
119.Suez, J. et al. Post-antibiotic gut mucosal microbiome reconstitution is impaired by probiotics and improved by autologous FMT. Cell 174, 1406–1423 (2018).CAS 
PubMed 
Article 

Google Scholar 
120.Urban, M. C. & De Meester, L. Community monopolization: local adaptation enhances priority effects in an evolving metacommunity. Proc. R. Soc. Lond. B Biol. Sci. 276, 4129–4138 (2009).
Google Scholar 
121.De Meester, L., Vanoverbeke, J., Kilsdonk, L. J. & Urban, M. C. Evolving perspectives on monopolization and priority effects. Trends Ecol. Evol. 31, 136–146 (2016). This study describes how evolutionary changes in early-arriving strains or species can limit colonization by later-arriving strains or species.PubMed 
Article 

Google Scholar 
122.Madi, N., Vos, M., Murall, C. L., Legendre, P. & Shapiro, B. J. Does diversity beget diversity in microbiomes? eLife 9, e58999 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
123.Castledine, M., Padfield, D. & Buckling, A. Experimental (co)evolution in a multi-species microbial community results in local maladaptation. Ecol. Lett. 23, 1673–1681 (2020).PubMed 
Article 

Google Scholar 
124.von Gillhaussen, P. et al. Priority effects of time of arrival of plant functional groups override sowing interval or density effects: a grassland experiment. PLoS ONE 9, e86906 (2014).Article 
CAS 

Google Scholar 
125.Ferrero, A. F. Effect of compaction simulating cattle trampling on soil physical characteristics in woodland. Soil. Tillage Res. 19, 319–329 (1991).Article 

Google Scholar 
126.Maron, J. L. & Jefferies, R. L. Bush lupine mortality, altered resource availability, and alternative vegetation states. Ecology 80, 443–454 (1999).Article 

Google Scholar 
127.Eng, T. et al. Iron supplementation eliminates antagonistic interactions between root-associated bacteria. Front. Microbiol. 11, 1742 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
128.Gong, B.-Q. et al. Cross-microbial protection via priming a conserved immune co-receptor through juxtamembrane phosphorylation in plants. Cell Host Microbe 26, 810–822 (2019).CAS 
PubMed 
Article 

Google Scholar 
129.Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
130.Lindemann, J. Competition between ice nucleation-active wild type and ice nucleation-deficient deletion mutant strains of Pseudomonas syringae and P. fluorescens biovar I and biological control of frost injury on strawberry blossoms. Phytopathology 77, 882 (1987). This study showed that the effects of delivery mode on the assembly of the cow rumen microbiome extend beyond initial exposure to different microbiota and they continue to affect bacterial species that arrive throughout the first few years of life.Article 

Google Scholar 
131.Guittar, J., Shade, A. & Litchman, E. Trait-based community assembly and succession of the infant gut microbiome. Nat. Commun. 10, 512 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
132.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
133.Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
134.Nyholm, S. V. & McFall-Ngai, M. The winnowing: establishing the squid-Vibrio symbiosis. Nat. Rev. Microbiol. 2, 632–642 (2004).CAS 
PubMed 
Article 

Google Scholar 
135.O’Hanlon, D. E., Moench, T. R. & Cone, R. A. Vaginal pH and microbicidal lactic acid when lactobacilli dominate the microbiota. PLoS ONE 8, e80074 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
136.Pantel, J. H., Duvivier, C. & Meester, L. D. Rapid local adaptation mediates zooplankton community assembly in experimental mesocosms. Ecol. Lett. 18, 992–1000 (2015).PubMed 
Article 

Google Scholar 
137.Fukami, T., Beaumont, H. J. E., Zhang, X.-X. & Rainey, P. B. Immigration history controls diversification in experimental adaptive radiation. Nature 446, 436–439 (2007).CAS 
PubMed 
Article 

Google Scholar 
138.Rigby, M. C., Hechinger, R. F. & Stevens, L. Why should parasite resistance be costly? Trends Parasitol. 18, 116–120 (2002).PubMed 
Article 

Google Scholar 
139.Koskella, B. Phage-mediated selection on microbiota of a long-lived host. Curr. Biol. 23, 1256–1260 (2013).CAS 
PubMed 
Article 

Google Scholar  More

1.Blaser, M. J. & Falkow, S. What are the consequences of the disappearing human microbiota? Nat. Rev. Microbiol. 7, 887–894 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Friesen, M. L. et al. Microbially mediated plant functional traits. Annu. Rev. Ecol. Evol. Syst. 42, 23–46 (2011).Article 

Google Scholar 
3.McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl Acad. Sci. 110, 3229–3236 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Douglas, A. E. Symbiosis as a general principle in eukaryotic evolution. Cold Spring Harb. Perspect. Biol. 6, a016113 (2014).5.Stappenbeck, T. S. & Virgin, H. W. Accounting for reciprocal host-microbiome interactions in experimental science. Nature 534, 191–199 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Moran, N. A. & Sloan, D. B. The hologenome concept: helpful or hollow? PLoS Biol. 13, e1002311 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
7.Douglas, A. E. & Werren, J. H. Holes in the hologenome: why host-microbe symbioses are not holobionts. MBio 7, e02099 (2016).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
9.Morimoto, J. & Baltrus, D. A. The extended genotype: to what extent? A comment on Carthey et al. Trends Ecol. Evol. 34, 186–187 (2019).10.Scheuring, I. & Yu, D. W. How to assemble a beneficial microbiome in three easy steps. Ecol. Lett. 15, 1300–1307 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Dawkins, R. The Extended Phenotype: The Long Reach of the Gene. (Oxford University Press, USA, 1982).12.Whitham, T. G. et al. A framework for community and ecosystem genetics: from genes to ecosystems. Nat. Rev. Genet. 7, 510–523 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Mueller, U. G. & Sachs, J. L. Engineering Microbiomes to Improve Plant and Animal Health. Trends Microbiol 23, 606–617 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: the hologenome theory of evolution. FEMS Microbiol. Rev. 32, 723–735 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Bordenstein, S. R. & Theis, K. R. Host biology in light of the microbiome: ten principles of holobionts and hologenomes. PLoS Biol. 13, e1002226 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
16.Alberdi, A., Aizpurua, O., Bohmann, K., Zepeda-Mendoza, M. L. & Gilbert, M. T. P. Do vertebrate gut metagenomes confer rapid ecological adaptation? Trends Ecol. Evol. 31, 689–699 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Shapira, M. Gut Microbiotas and host evolution: scaling up symbiosis. Trends Ecol. Evol. 31, 539–549 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Carrier, T. J. & Reitzel, A. M. The hologenome across environments and the implications of a host-associated microbial repertoire. Front. Microbiol. 8, 802 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Hurst, G. D. D. Extended genomes: symbiosis and evolution. Interface Focus 7, 20170001 (2017).20.Sudakaran, S., Kost, C. & Kaltenpoth, M. Symbiont acquisition and replacement as a source of ecological innovation. Trends Microbiol 25, 375–390 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Carthey, A. J. R., Gillings, M. R. & Blumstein, D. T. The extended genotype: microbially mediated olfactory communication. Trends Ecol. Evol. 33, 885–894 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Rosenberg, E. & Zilber-Rosenberg, I. The hologenome concept of evolution after 10 years. Microbiome 6, 78 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Futuyma, D. J. & Moreno, G. The evolution of ecological specialization. Annu. Rev. Ecol. Syst. 19, 207–233 (1988).Article 

Google Scholar 
24.Piersma, T. & Drent, J. Phenotypic flexibility and the evolution of organismal design. Trends Ecol. Evol. 18, 228–233 (2003).Article 

Google Scholar 
25.Lande, R. Natural selection and random genetic drift in phenotypic. Evolution. Evolution 30, 314–334 (1976).PubMed 
Article 
PubMed Central 

Google Scholar 
26.West-Eberhard, M. J. Phenotypic plasticity and the origins of diversity. Annu. Rev. Ecol. Syst. 20, 249–278 (1989).Article 

Google Scholar 
27.Ghalambor, C. K., McKay, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).Article 

Google Scholar 
28.Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Metcalf, C. J. E. & Koskella, B. Protective microbiomes can limit the evolution of host pathogen defense. Evol. Lett. 3, 534–543 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Rosenberg, E., Koren, O., Reshef, L., Efrony, R. & Zilber-Rosenberg, I. The role of microorganisms in coral health, disease and evolution. Nat. Rev. Microbiol. 5, 355–362 (2007).CAS 
PubMed 
Article 

Google Scholar 
31.Theis, K. R. et al. Getting the hologenome concept right: an eco-evolutionary framework for hosts and their microbiomes. mSystems 1, e00028 (2016).32.Funkhouser, L. J. & Bordenstein, S. R. Mom knows best: the universality of maternal microbial transmission. PLoS Biol. 11, e1001631 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Salem, H., Florez, L., Gerardo, N. & Kaltenpoth, M. An out-of-body experience: the extracellular dimension for the transmission of mutualistic bacteria in insects. Proc. R. Soc. Lond. B. 282, 20142957 (2015).34.Vacher, C. et al. The phyllosphere: microbial jungle at the plant–climate interface. Annu. Rev. Ecol. Evol. Syst. 47, 1–24 (2016).Article 

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

Google Scholar 
36.Grieneisen, L. E. et al. Genes, geology and germs: gut microbiota across a primate hybrid zone are explained by site soil properties, not host species. Proc. R. Soc. B: Biol. Sci. 286, 20190431 (2019).Article 

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

Google Scholar 
38.Sprockett, D., Fukami, T. & Relman, D. A. Role of priority effects in the early-life assembly of the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 15, 197–205 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Uhr, G. T., Dohnalová, L. & Thaiss, C. A. The dimension of time in host-microbiome interactions. mSystems 4, e00216–18 (2019).40.van Vliet, S. & Doebeli, M. The role of multilevel selection in host microbiome evolution. Proc. Natl Acad. Sci. U.S.A. 116, 20591–20597 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
41.Benson, A. K. The gut microbiome—an emerging complex trait. Nat. Genet. 48, 1301 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.van Opstal, E. J. & Bordenstein, S. R. Rethinking heritability of the microbiome. Science 349, 1172–1173 (2015).ADS 
PubMed 
Article 

Google Scholar 
43.Beilsmith, K. et al. Genome-wide association studies on the phyllosphere microbiome: embracing complexity in host–microbe interactions. Plant J. 97, 164–181 (2019).44.Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe 19, 731–743 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Early, A. M., Shanmugarajah, N., Buchon, N. & Clark, A. G. Drosophila genotype influences commensal bacterial levels. PLoS ONE 12, e0170332 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
47.Walters, W. A. et al. Large-scale replicated field study of maize rhizosphere identifies heritable microbes. Proc. Natl Acad. Sci. U. S. A. 115, 7368–7373 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Camarinha-Silva, A. et al. Host genome influence on gut microbial composition and microbial prediction of complex traits in pigs. Genetics 206, 1637–1644 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Difford, G. F. et al. Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows. PLoS Genet 14, e1007580 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
50.Koga, R., Meng, X.-Y., Tsuchida, T. & Fukatsu, T. Cellular mechanism for selective vertical transmission of an obligate insect symbiont at the bacteriocyte–embryo interface. Proc. Natl Acad. Sci. U.S.A. 109, E1230–E1237 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Nyholm, S. V. & McFall-Ngai, M. The winnowing: establishing the squid–vibrio symbiosis. Nat. Rev. Microbiol. 2, 632–642 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Kaltenpoth, M., Göttler, W., Herzner, G. & Strohm, E. Symbiotic bacteria protect wasp larvae from fungal infestation. Curr. Biol. 15, 475–479 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Clark, R. I. et al. Distinct shifts in microbiota composition during Drosophila aging impair intestinal function and drive mortality. Cell Rep. 12, 1656–1667 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Franzosa, E. A. et al. Identifying personal microbiomes using metagenomic codes. Proc. Natl Acad. Sci. U.S.A. 112, E2930–E2938 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Lokmer, A. et al. Spatial and temporal dynamics of pacific oyster hemolymph microbiota across multiple scales. Front. Microbiol. 7, 1367 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
56.Elena, S. F. & Lenski, R. E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Lapierre, P. & Gogarten, J. P. Estimating the size of the bacterial pan-genome. Trends Genet 25, 107–110 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Koonin, E. V. & Wolf, Y. I. Evolution of microbes and viruses: a paradigm shift in evolutionary biology? Front. Cell. Infect. Microbiol. 2, 119 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
59.Ferreiro, A., Crook, N., Gasparrini, A. J. & Dantas, G. Multiscale evolutionary dynamics of host-associated microbiomes. Cell 172, 1216–1227 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Kikuchi, Y., Hosokawa, T. & Fukatsu, T. Specific developmental window for establishment of an insect-microbe gut symbiosis. Appl. Environ. Microbiol. 77, 4075–4081 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Kikuchi, Y. et al. Symbiont-mediated insecticide resistance. Proc. Natl Acad. Sci. U.S.A. 109, 8618–8622 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Itoh, H. et al. Infection dynamics of insecticide-degrading symbionts from soil to insects in response to insecticide spraying. ISME J. 12, 909–920 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Kohl, K. D. & Dearing, M. D. The woodrat gut microbiota as an experimental system for understanding microbial metabolism of dietary toxins. Front. Microbiol. 7, 1165 (2016).PubMed 
PubMed Central 

Google Scholar 
64.Kohl, K. D., Weiss, R. B., Cox, J., Dale, C. & Dearing, M. D. Gut microbes of mammalian herbivores facilitate intake of plant toxins. Ecol. Lett. 17, 1238–1246 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Miller, A. W., Kohl, K. D. & Dearing, M. D. The gastrointestinal tract of the white-throated Woodrat (Neotoma albigula) harbors distinct consortia of oxalate-degrading bacteria. Appl. Environ. Microbiol. 80, 1595–1601 (2014).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
66.Miller, A. W., Oakeson, K. F., Dale, C. & Dearing, M. D. Effect of dietary oxalate on the gut microbiota of the mammalian herbivore Neotoma albigula. Appl. Environ. Microbiol. 82, 2669–2675 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Kohl, K. D., Stengel, A. & Dearing, M. D. Inoculation of tannin-degrading bacteria into novel hosts increases performance on tannin-rich diets. Environ. Microbiol. 18, 1720–1729 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Kohl, K. D. & Dearing, M. D. Experience matters: prior exposure to plant toxins enhances diversity of gut microbes in herbivores. Ecol. Lett. 15, 1008–1015 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
69.Redman, R. S., Sheehan, K. B., Stout, R. G., Rodriguez, R. J. & Henson, J. M. Thermotolerance generated by plant/fungal symbiosis. Science 298, 1581 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Rodriguez, R. J. et al. Stress tolerance in plants via habitat-adapted symbiosis. ISME J. 2, 404–416 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
71.Miller, E. T., Svanbäck, R. & Bohannan, B. J. M. Microbiomes as Metacommunities: Understanding Host-Associated Microbes through Metacommunity Ecology. Trends Ecol. Evol. 33, 926–935 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
72.Newell, P. D. & Douglas, A. E. Interspecies interactions determine the impact of the gut microbiota on nutrient allocation in Drosophila melanogaster. Appl. Environ. Microbiol. 80, 788–796 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Keebaugh, E. S., Yamada, R., Obadia, B., Ludington, W. B. & Ja, W. W. Microbial quantity impacts drosophila nutrition, development, and lifespan. Science 4, 247–259 (2018).CAS 

Google Scholar 
74.Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl Acad. Sci. U.S.A. 115, E11951–E11960 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Mushegian, A. A., Walser, J.-C., Sullam, K. E. & Ebert, D. The microbiota of diapause: How host-microbe associations are formed after dormancy in an aquatic crustacean. J. Anim. Ecol. 87, 400–413 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
76.Panke-Buisse, K., Poole, A. C., Goodrich, J. K., Ley, R. E. & Kao-Kniffin, J. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J. 9, 980–989 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Rolig, A. S., Parthasarathy, R., Burns, A. R., Bohannan, B. J. M. & Guillemin, K. Individual members of the microbiota disproportionately modulate host innate immune responses. Cell Host Microbe 18, 613–620 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Webster, N. S. & Reusch, T. B. H. Microbial contributions to the persistence of coral reefs. ISME J. 11, 2167–2174 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
79.Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Ainsworth, T. D., Thurber, R. V. & Gates, R. D. The future of coral reefs: a microbial perspective. Trends Ecol. Evol. 25, 233–240 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
81.Sommer, F. et al. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep. 14, 1655–1661 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
82.Metcalf, C. J. E., Henry, L. P., Rebolleda-Gómez, M. & Koskella, B. Why evolve reliance on the microbiome for timing of ontogeny? MBio 10, e01496-19 (2019).83.Gilbert, S. F., Bosch, T. C. G. & Ledón-Rettig, C. Eco-Evo-Devo: developmental symbiosis and developmental plasticity as evolutionary agents. Nat. Rev. Genet. 16, 611–622 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
84.Philippi, T. & Seger, J. Hedging one’s evolutionary bets, revisited. Trends Ecol. Evol. 4, 41–44 (1989).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Storelli, G. et al. Lactobacillus plantarum promotes Drosophila systemic growth by modulating hormonal signals through TOR-dependent nutrient sensing. Cell Metab. 14, 403–414 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Bruijning, M., Henry, L. P., Forsberg, S. K. G., Metcalf, C. J. E. & Ayroles, J. F. When the microbiome defines the host phenotype: selection on vertical transmission in varying environments. bioRxiv 2020.09.02.280040 (2020) https://doi.org/10.1101/2020.09.02.280040.87.Boone, C. K. et al. Bacteria associated with a tree-killing insect reduce concentrations of plant defense compounds. J. Chem. Ecol. 39, 1003–1006 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
88.Berasategui, A. et al. Gut microbiota of the pine weevil degrades conifer diterpenes and increases insect fitness. Mol. Ecol. 26, 4099–4110 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
89.Ceja-Navarro, J. A. et al. Gut microbiota mediate caffeine detoxification in the primary insect pest of coffee. Nat. Commun. 6, 7618 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Berasategui, A. et al. The gut microbiota of the pine weevil is similar across Europe and resembles that of other conifer-feeding beetles. Mol. Ecol. 25, 4014–4031 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Sachs, J. L., Skophammer, R. G. & Regus, J. U. Evolutionary transitions in bacterial symbiosis. Proc. Natl Acad. Sci. U.S.A 108, 10800–10807 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
92.Ohbayashi, T. et al. Insect’s intestinal organ for symbiont sorting. Proc. Natl Acad. Sci. U.S.A 112, E5179–E5188 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Itoh, H. et al. Host–symbiont specificity determined by microbe–microbe competition in an insect gut. Proc. Natl Acad. Sci. U.S.A. 116, 22673–22682 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Bennett, G. M. & Moran, N. A. Heritable symbiosis: the advantages and perils of an evolutionary rabbit hole. Proc. Natl Acad. Sci. 112, 10169–10176 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
95.Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–422 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Klassen, J. L. Defining microbiome function. Nat. Microbiol 3, 864–869 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
97.Lau, J. A. & Lennon, J. T. Rapid responses of soil microorganisms improve plant fitness in novel environments. Proc. Natl Acad. Sci. U.S.A. 109, 14058–14062 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
98.Xu, L. et al. Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria. Proc. Natl Acad. Sci. U.S.A 115, E4284–E4293 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Raymann, K., Bobay, L.-M. & Moran, N. A. Antibiotics reduce genetic diversity of core species in the honeybee gut microbiome. Mol. Ecol. 27, 2057–2066 (2018).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
102.Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
103.Pollock, J., Glendinning, L., Wisedchanwet, T. & Watson, M. The madness of microbiome: attempting to find consensus ‘best practice’ for 16S microbiome studies. Appl. Environ. Microbiol. 84, e02627–17 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
104.Roth-Schulze, A. J. et al. Functional biogeography and host specificity of bacterial communities associated with the Marine Green Alga Ulva spp. Mol. Ecol. 27, 1952–1965 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
105.Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
106.Meaden, S., Metcalf, C. J. E. & Koskella, B. The effects of host age and spatial location on bacterial community composition in the English Oak tree (Quercus robur). Environ. Microbiol. Rep. 8, 649–658 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
107.Lambais, M. R., Barrera, S. E., Santos, E. C., Crowley, D. E. & Jumpponen, A. Phyllosphere metaproteomes of trees from the Brazilian atlantic forest show high levels of functional redundancy. Microb. Ecol. 73, 123–134 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
108.Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
109.Oh, J. et al. Temporal stability of the human skin microbiome. Cell 165, 854–866 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
110.Garud, N. R., Good, B. H., Hallatschek, O. & Pollard, K. S. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biol. 17, e3000102 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
111.Asnicar, F. et al. Studying vertical microbiome transmission from mothers to infants by strain-level metagenomic profiling. mSystems 2, e00164–16 (2017).112.Yassour, M. et al. Strain-level analysis of mother-to-child bacterial transmission during the first few months of life. Cell Host Microbe 24, 146–154.e4 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
113.Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
114.Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
115.Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
116.Jorth, P. et al. Metatranscriptomics of the human oral microbiome during health and disease. MBio 5, e01012–e01014 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
117.Bashiardes, S., Zilberman-Schapira, G. & Elinav, E. Use of metatranscriptomics in microbiome research. Bioinform. Biol. Insights 10, 19–25 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
118.Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. U.S.A. 111, E2329–E2338 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
119.Abu-Ali, G. S. et al. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat. Microbiol. 3, 356–366 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
120.Hoang, K. L., Morran, L. T. & Gerardo, N. M. Experimental evolution as an underutilized tool for studying beneficial animal–microbe interactions. Front. Microbiol. 7, 1444 (2016).121.Martino, M. E. et al. Bacterial adaptation to the host’s diet is a key evolutionary force shaping drosophila-lactobacillus symbiosis. Cell Host Microbe 24, 109–119.e6 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
122.Schlötterer, C., Kofler, R., Versace, E., Tobler, R. & Franssen, S. U. Combining experimental evolution with next-generation sequencing: a powerful tool to study adaptation from standing genetic variation. Heredity 114, 431–440 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
123.Henry, L. P. & Ayroles, J. F. Meta-analysis suggests the microbiome responds to Evolve and Resequence experiments in Drosophila melanogaster. BMC Microbiol 21, 108 (2021).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
124.Wagner, M. R. et al. Natural soil microbes alter flowering phenology and the intensity of selection on flowering time in a wild Arabidopsis relative. Ecol. Lett. 17, 717–726 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
125.Hendry, A. P. Eco-evolutionary Dynamics. (Princeton University Press, 2017).126.Bang, C. et al. Metaorganisms in extreme environments: do microbes play a role in organismal adaptation? Zoology 127, 1–19 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
127.Hoyt, J. R. et al. Bacteria isolated from bats inhibit the growth of Pseudogymnoascus destructans, the causative agent of white-nose syndrome. PLoS ONE 10, e0121329 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
128.Cheng, T. L. et al. Efficacy of a probiotic bacterium to treat bats affected by the disease white-nose syndrome. J. Appl. Ecol. 54, 701–708 (2016).Article 

Google Scholar 
129.Woodhams, D. C., Bletz, M., Kueneman, J. & McKenzie, V. Managing amphibian disease with skin microbiota. Trends Microbiol. 24, 161–164 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
130.Weiss, B. & Aksoy, S. Microbiome influences on insect host vector competence. Trends Parasitol. 27, 514–522 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
131.Zitvogel, L., Ma, Y., Raoult, D., Kroemer, G. & Gajewski, T. F. The microbiome in cancer immunotherapy: diagnostic tools and therapeutic strategies. Science 359, 1366–1370 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
132.Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).PubMed 
Article 
PubMed Central 

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

Google Scholar 
134.Christian, N., Whitaker, B. K. & Clay, K. Microbiomes: unifying animal and plant systems through the lens of community ecology theory. Front. Microbiol. 6, 869 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
135.Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: a call for the consideration of host-associated microbiota in wildlife management practices. Proc. Biol. Sci. 286, 20182448 (2019).PubMed 
PubMed Central 

Google Scholar 
136.Mueller, E. A., Wisnoski, N. I., Peralta, A. L. & Lennon, J. T. Microbial rescue effects: how microbiomes can save hosts from extinction. Funct. Ecol. 34, 2055-2064 (2020).137.Hird, S. M. Evolutionary biology needs wild microbiomes. Front. Microbiol. 8, 725 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
138.Wade, M. J. The co-evolutionary genetics of ecological communities. Nat. Rev. Genet. 8, 185–195 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
139.Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P. & Fierer, N. Caterpillars lack a resident gut microbiome. Proc. Natl Acad. Sci. 114, 9641–9646 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
140.Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. 366, fnz117 (2019).141.Heath, K. D. & Stinchcombe, J. R. Explaining mutualism variation: a new evolutionary paradox? Evolution 68, 309–317 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
142.Sandoval-Motta, S., Aldana, M., Martínez-Romero, E. & Frank, A. The human microbiome and the missing heritability problem. Front. Genet. 8, 80 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
143.Wallace, R. J. et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5, eaav8391 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
144.Vollmar, S. et al. The gut microbial architecture of efficiency traits in the domestic poultry model species japanese quail (Coturnix japonica) assessed by mixed linear models. G3 10, 2553–2562 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
145.Hoffmann, A. A., Sgrò, C. M. & Kristensen, T. N. Revisiting adaptive potential, population size, and conservation. Trends Ecol. Evol. 32, 506–517 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
146.Bruijning, M., Metcalf, C. J. E., Jongejans, E. & Ayroles, J. F. The evolution of variance control. Trends Ecol. Evol. 35, 22–33 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
147.Douglas, G. M., Bielawski, J. P. & Langille, M. G. I. Re-evaluating the relationship between missing heritability and the microbiome. Microbiome 8, 87 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
148.Johannes, F., Colot, V. & Jansen, R. C. Epigenome dynamics: a quantitative genetics perspective. Nat. Rev. Genet. 9, 883–890 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
149.Slatkin, M. Epigenetic inheritance and the missing heritability problem. Genetics 182, 845–850 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
150.Hernando-Herraez, I., Garcia-Perez, R., Sharp, A. J. & Marques-Bonet, T. DNA methylation: insights into human evolution. PLoS Genet 11, e1005661 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
151.Pujol, B. et al. The missing response to selection in the wild. Trends Ecol. Evol. 33, 337–346 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
152.Shaw, R. G. From the past to the future: considering the value and limits of evolutionary prediction. Am. Nat. 193, 1–10 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
153.Garud, N. R., Good, B. H., Hallatschek, O. & Pollard, K. S. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biol.17, e3000102 (2019).154.Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 27, 626–638 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
155.Spor, A., Koren, O. & Ley, R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev. Microbiol. 9, 279–290 (2011).CAS 
PubMed 
Article 

Google Scholar 
156.Guo, Y. et al. Networks underpinning symbiosis revealed through cross-species eQTL mapping. Genetics 206, 2175–2184 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
157.Kohl, K. D. An introductory ‘how-to’ guide for incorporating microbiome research into integrative and comparative biology. Integr. Comp. Biol. 57, 674–681 (2017).PubMed 
Article 

Google Scholar 
158.Marchesi, J. R. & Ravel, J. The vocabulary of microbiome research: a proposal. Microbiome 3, 31 (2015).PubMed 
Article 
PubMed Central 

Google Scholar  More