Philippa Kaur

1.
United Nations, Department of Economic and Social Affairs, Population Division. World Urban. Prospect. 2018 Highlights (2019).
2.
Sebastián-González, E., Dalsgaard, B., Sandel, B. & Guimarães, P. R. Macroecological trends in nestedness and modularity of seed-dispersal networks: Human impact matters. Glob. Ecol. Biogeogr. 24, 293–303 (2015).
Article  Google Scholar 

3.
Eötvös, C. B., Magura, T. & Lövei, G. L. A meta-analysis indicates reduced predation pressure with increasing urbanization. Landsc. Urban Plan. 180, 54–59 (2018).
Article  Google Scholar 

4.
Anders, J. L. et al. Comparison of the intestinal helminth community of the large Japanese field mouse (Apodemus speciosus) between urban, rural, and natural sites in Hokkaido, Japan. Parasitol. Int. 70, 51–57 (2019).
Article  Google Scholar 

5.
Abrams, P. A. & Rowe, L. The effects of predation on the age and size of maturity of prey. Evolution 50, 1052–1061 (1996).
Article  Google Scholar 

6.
Sheriff, M. J., Krebs, C. J. & Boonstra, R. The ghosts of predators past: Population cycles and the role of maternal programming under fluctuating predation risk. Ecology 91, 2983–2994 (2010).
Article  Google Scholar 

7.
Vollset, K. W., Barlaup, B. T., Skoglund, H., Normann, E. S. & Skilbrei, O. Salmon lice increase the age of returning Atlantic salmon. Biol. Lett. 10, 20140085 (2014).
Article  Google Scholar 

8.
Medina, F. M. et al. A global review of the impacts of invasive cats on island endangered vertebrates. Glob. Change Biol. 17, 3503–3510 (2011).
ADS  Article  Google Scholar 

9.
Devictor, V., Whittaker, R. J. & Beltrame, C. Beyond scarcity: Citizen science programmes as useful tools for conservation biogeography. Divers. Distrib. 16, 354–362 (2010).
Article  Google Scholar 

10.
Hurlbert, A. H. & Liang, Z. Spatiotemporal variation in avian migration phenology: Citizen science reveals effects of climate change. PLoS ONE 7, e31662 (2012).
ADS  CAS  Article  Google Scholar 

11.
Li, E. et al. An urban biodiversity assessment framework that combines an urban habitat classification scheme and citizen science data. Front. Ecol. Evol. 7, 277 (2019).
ADS  CAS  Article  Google Scholar 

12.
Berger, J. Fear, human shields and the redistribution of prey and predators in protected areas. Biol. Lett. 3, 620–623 (2007).
Article  Google Scholar 

13.
Geffroy, B., Samia, D. S. M., Bessa, E. & Blumstein, D. T. How nature-based tourism might increase prey vulnerability to predators. Trends Ecol. Evol. 30, 755–765 (2015).
Article  Google Scholar 

14.
Loss, S. R., Will, T. & Marra, P. P. The impact of free-ranging domestic cats on wildlife of the United States. Nat. Commun. 4, 1396 (2013).
ADS  Article  Google Scholar 

15.
Tablado, Z. & Jenni, L. Determinants of uncertainty in wildlife responses to human disturbance. Biol. Rev. https://doi.org/10.1111/brv.12224 (2015).
Article  PubMed  Google Scholar 

16.
Becker, M. & Buchholz, S. The sand lizard moves downtown—habitat analogues for an endangered species in a metropolitan area. Urban Ecosyst. 19, 361–372 (2016).
Article  Google Scholar 

17.
Chavez-Zichinelli, C. A. et al. How stressed are birds in an urbanizing landscape? Relationships between the physiology of birds and three levels of habitat alteration. Condor 115, 84–92 (2013).
Article  Google Scholar 

18.
Jiménez-Peñuela, J., Ferraguti, M., Martínez-de la Puente, J., Soriguer, R. & Figuerola, J. Urbanization and blood parasite infections affect the body condition of wild birds. Sci. Total Environ. 651, 3015–3022 (2019).
ADS  Article  Google Scholar 

19.
Birnie-Gauvin, K., Peiman, K. S., Gallagher, A. J., de Bruijn, R. & Cooke, S. J. Sublethal consequences of urban life for wild vertebrates. Environ. Rev. 24, 416–425 (2016).
Article  Google Scholar 

20.
Møller, A. P. Successful city dwellers: A comparative study of the ecological characteristics of urban birds in the western Palearctic. Oecologia 159, 849–858 (2009).
ADS  Article  Google Scholar 

21.
Bradley, C. A. & Altizer, S. Urbanization and the ecology of wildlife diseases. Trends Ecol. Evol. 22, 95–102 (2007).
Article  Google Scholar 

22.
Lazic, M. M., Carretero, M. A., Zivkovic, U. & Crnobrnja-Isailovic, J. City life has fitness costs: Lower body condition and increased parasite intensity in urban common wall lizards Podarcis muralis. Salamandra 53, 10–17 (2016).
Google Scholar 

23.
Giraudeau, M., Mousel, M., Earl, S. & Mcgraw, K. Parasites in the city: Degree of urbanization predicts poxvirus and coccidian infections in house finches (Haemorhous mexicanus). PLoS ONE 9, e86747 (2014).
ADS  Article  Google Scholar 

24.
Kellner, K. F., Page, L. K., Downey, M. & Mccord, S. E. Effects of urbanization on prevalence of Baylisascaris procyonis in intermediate host populations. J. Wildl. Dis. 48, 1083–1087 (2012).
Article  Google Scholar 

25.
Geue, D. & Partecke, J. Reduced parasite infestation in urban Eurasian blackbirds (Turdus merula): A factor favoring urbanization?. Can. J. Zool. 86, 1419–1425 (2008).
Article  Google Scholar 

26.
Arnold, E. N. Caudal autotomy as a defense. In Biology of the Reptilia (eds. Gans, C. & Huey, R. B.) 235–273 (Liss, 1988).

27.
Higham, T. E., Russell, A. P. & Zani, P. A. Integrative biology of tail autotomy in lizards. Physiol. Biochem. Zool. 86, 603–610 (2013).
Article  Google Scholar 

28.
Itescu, Y., Schwarz, R., Meiri, S. & Pafilis, P. Intraspecific competition, not predation, drives lizard tail loss on islands. J. Anim. Ecol. 86, 66–74 (2017).
Article  Google Scholar 

29.
Bowker, R. W. A Comparative Behavioral Study and Taxonomic Analyses of Gerrhonotine Lizards (Arizona State University, Tempe, 1988).
Google Scholar 

30.
Bateman, P. W. & Fleming, P. A. To cut a long tail short: A review of lizard caudal autotomy studies carried out over the last 20 years. J. Zool. 277, 1–14 (2009).
Article  Google Scholar 

31.
Tyler, R. K., Winchell, K. M. & Revell, L. J. Tails of the city: Caudal autotomy in the tropical lizard, Anolis cristatellus, in urban and natural areas of Puerto Rico. J. Herpetol. 50, 435–441 (2016).
Article  Google Scholar 

32.
Arnold, E. N. Evolutionary aspects of tail shedding in lizards and their relatives. J. Nat. Hist. 18, 127–169 (1984).
Article  Google Scholar 

33.
Bellairs, A. D. A. & Bryant, S. V. Autotomy and regeneration in reptiles. In Biology of the Reptilia (eds. Gans, C. & Billet, F.) 301–410 (Wiley, Hoboken, 1985).

34.
Chapple, D. G. & Swain, R. Inter-populational variation in the cost of autotomy in the metallic skink (Niveoscincus metallicus). J. Zool. 264, 411–418 (2004).
Article  Google Scholar 

35.
Wright, S. A., Lane, R. S. & Clover, J. R. Infestation of the Southern Alligator Lizard (Squamata: Anguidae) by Ixodes pacificus (Acari: Ixodidae) and its susceptibility to Borrelia burgdorferi. J. Med. Entomol. 35, 1044–1049 (1998).
CAS  Article  Google Scholar 

36.
Tanner, D. & Perry, J. Road effects on abundance and fitness of Galápagos lava lizards (Microlophus albemarlensis). J. Environ. Manag. 85, 270–278 (2007).
Article  Google Scholar 

37.
Main, A. R. & Bull, M. The impact of tick parasites on the behaviour of the lizard Tiliqua rugosa. Oecologia 122, 574–581 (2000).
ADS  CAS  Article  Google Scholar 

38.
Stanley, T. R. et al. Changes in capture rates and body size among vertebrate species occupying an insular urban habitat reserve. Conserv. Sci. Pract. 2, e245 (2020).
Google Scholar 

39.
Pellitteri-Rosa, D. et al. Urbanization affects refuge use and habituation to predators in a polymorphic lizard. Anim. Behav. 123, 359–367 (2017).
Article  Google Scholar 

40.
Rebolo-Ifrán, N. et al. Links between fear of humans, stress and survival support a non-random distribution of birds among urban and rural habitats. Sci. Rep. 5, 13723 (2015).
ADS  Article  Google Scholar  More

This paper uses Landsat TM/OLI images from 1987, 1993, 1999, 2005, 2011 and 2017 by the weight vector AdaBoost (WV AdaBoost) multi-classification algorithm extracting LULC data sets, and the spatiotemporal patterns of LULC over these periods were analysed. The spatiotemporal change patterns and driving force of ESV was estimated. The effect of LULC dynamics on the ESV was evaluated. The flow chart is shown in Fig. 1.
Figure 1

The framework of this paper.

Full size image

Study area
Guangzhou is located at 112° 57′ ~ 114° 3′ E, 22° 26′ ~ 23° 56′ N in the southeast part of Guangdong Province in the northern margin of the Pearl River Delta. It has a total land area of 7434.40 km2. The topography is high in the northeast and low in the southwest, with low mountains and hills in the north and plains in the south and a rich geomorphology. The level of urbanization is high, the land use structure is complex and the land use pattern is changing rapidly. Regional public infrastructure, commercial service land and external transportation land is increasing. Export-oriented industrial agglomeration areas are developing continuously, and the characteristics of export-oriented land use are obvious. Guangzhou has a subtropical oceanic monsoon climate, with an annual average temperature of 20–22 °C and an annual rainfall of about 1720 mm. Guangzhou includes eleven districts (Fig. 2a). This paper focuses on the main urban area of Guangzhou as the research object, a decision based on the city government’s overall urban development strategy for Guangzhou (2010–2020), the main urban area of Guangzhou includes five districts—Liwan, Yuexiu, Haizhu, Baiyun and Tianhe (Fig. 2a). In 2017, the per capita GDP will exceed 136,100 yuan for the first time (Fig. 2b). The population density is large, and the intensity of development is extraordinary. The population has increased from 2.73 million in 1987 to 8.05 million in 2017 (Fig. 2c). The industrial structure has been constantly optimized and improved. The proportion of secondary industries decreased from 31.46% in 1987 to 11.28% in 2017, while the proportion of tertiary industries increased from 61.9% in 1987 to 89.61% in 2017.
Figure 2

Location of study area and GDP and population. (a) location, (b) GDP, (c) population. (Software: Arc Map 10.5.0, http://www.esri.com. OriginPro 2017C SR2, https://www.originlab.com/).

Full size image

Data and data processing
Data
The data used include Landsat series images, an administrative zoning map of Guangzhou, a local historical land use map and social and economic statistics from 1987 to 2017. To explore the socio-economic and natural factors driving the ESV change for different land use types, we examined ten factors—the normalized vegetation index (NDVI), year-end population, elevation, slope, distance from roads, distance from railways, land cover types, GDP, secondary industry GDP and investment in fixed assets.
The Landsat images were taken from the United States Geological Survey website (http://glovis.usgs.gov/), and we downloaded cloud-free Landsat 5 TM and Landsat 8 OLI images (path 122, row 44). Images taken in the dry season (October, November and December) were selected because there was less cloud cover, the change in surface reflectance was much smaller than in other seasons and the image quality was higher. The spatial resolution of imaging is 30 m, and the data of Landsat 5 TM (8 December 1987, 24 December 1993, 25 December 1999, 23 November 2005, 19 November 2011) and Landsat 8 OLI (23 October 2017) were collected.
The elevation data are provided by the Japan Aerospace Exploration Agency (http://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm); the horizontal resolution is 30 m, and the elevation accuracy is 5 m. The road and railway data are provided by Openstreetmap (download.geofabrik.de/), and the distance between roads and railways were calculated. The land use types are provided by Tsinghua University (http://data.ess.tsinghua.edu.cn/) with a resolution of 10 m. GDP, secondary industry GDP and investment in fixed assets were provided by Guangzhou Statistics Bureau28.
Considering the size of the study area and the resolution of the data, we used Fishnet in ArcGIS10.5 to establish a 500 × 500 m grid covering the study area. The values of ten factors were extracted to the grid centre points, and 3915 points were obtained.
Remote sensing extraction of LULC types
To obtain high-precision LULC data, the surface features based on the original remote sensing image were enhanced using the index model (the Index-based Built-up Index (IBI)29, the modified normalized difference water index (MNDWI)30 and the soil-adjusted vegetation index (SAVI)). The humidity, brightness and green chroma indices were transformed using the tasselled-cap method23. Using the six indices, six images were calculated and superposed into a new multi-band image in the order of IBI, SAVI, MNDWI, brightness, green chroma and humidity (referred to as the six-index image). This was superposed with the original image (band 1, 2, 3, 4, 5, 7) to create an enhanced 12-band image as the data source for LULC classification. The image stretching function was then used to stretch the composite image to better distinguish the characteristics of land use types. After feature enhancement, the LULC types in the main urban area of Guangzhou could be divided into seven types—forest, water body, grassland, cultivated land, high reflectivity building, low reflectivity building and bare land. Of these, high reflectance buildings and low reflectance buildings are sub-categories of the built-up area category. The spectrum of high reflectivity buildings is similar to that of bare land; therefore, they are classified separately to avoid erroneous classification31. Based on Google Earth and TM/OLI images, the interpretation marks of LULC types were established (Table 1). To obtain pure and representative samples, the K-means method of unsupervised classification was utilized to cluster the pixels of the ISM image into seven unlabelled classes and to eliminate abnormal clustering. We then selected uniform points and drew polygons on them. Each polygon contains more than 100 pixels, and each class contains between five and seven polygons. All polygons were saved in a shape file as a mask file, which is utilized to extract pure pixels as training samples.
Table 1 Interpretation mark of land cover types.
Full size table

Dou and Chen31 suggest using the weight vector AdaBoost (WV AdaBoost) multi-classification algorithm for LULC classification. Compared with AdaBoost, it provides higher classification accuracy and stability. WV AdaBoost includes a C4.5 decision tree, a Naïve Bayes neural network and an artificial neural network. In this study, we use Naïve Bayes-based WV AdaBoost to classify LULC based on Landsat-enhanced images. To obtain a highly accurate LULC classification, it is necessary to post-process the image after classification, overlay the early land use map during the research period, check the incorrectly classified area, and filter and correct some segments. After image classification, the random selection of 200 pixels in each class was checked, and the correctness of the classification and the evaluated accuracy were confirmed.
The average classification accuracy of WV AdaBoost based on Naïve Bayes on the original image is 85.02%, and the kappa coefficient is 0.839. The WV AdaBoost algorithm is based on Naïve Bayes processes for the 12-band images of the new combination (enhanced 12-band image). The average classification accuracy is 88.86%, and the kappa coefficient is 0.871. The classification results still need to be strengthened by post-processing, which achieves good classification accuracy. The final average classification accuracy is 91.97%. The kappa coefficient is 0.907. These classification results agree with the ensuing analysis of LULC.
Methods
Based on Landsat TM/OLI data from 1987 to 2017, a transfer matrix, a land use change index, an ESV evaluation index, a sensitivity model (CS) and a geo-detector (P) were used to analyze the response of ESVs to LULC evolution.
Transfer matrix
The transfer matrix reflects the dynamic process information about mutual transformation LULC types at the beginning (T) and the end (T + 1) of a specified period of time in a certain region (Fig. 3). The general form is:

$${S}_{ij}=left[begin{array}{c}{s}_{11} {s}_{12}dots {s}_{1n}\ {s}_{21} {s}_{22}dots {s}_{2n}\ dots dots dots dots \ { s}_{n1} {s}_{n2}dots {s}_{nn}end{array}right]$$
(1)

where S stands for area, and i,j (i,j = 1,2,…, n) represents LULC types before and after the transfer.
Figure 3

Land use transfer process. T the beginning of land use types, T + 1 the end of land use types; A, B, C, D, E, F: different land use types.

Full size image

The LULC analyzing indices
(1)
Land use dynamics (RS)
Land use dynamics describe the rate and magnitude of LUCC. The general form is:

$$RS=frac{{U}_{b}-{U}_{a}}{{U}_{a}}times frac{1}{T}times 100mathrm{%},$$
(2)

where ({U}_{a}) is the area of a certain land class at the beginning (km2), ({U}_{b}) is the area of a certain land class at the end (km2), and T is the study period.

(2)
Spatial dynamics of land use (RSS)
Spatial dynamics of land use describe the degree of spatial change in a certain land use type. The general form is:

$$RSS=frac{{U}_{in}-{U}_{out}}{{U}_{a}}times frac{1}{T}times 100mathrm{%},$$
(3)

where ({U}_{in}) is the sum area of other types converted to this type in study period T, and ({U}_{out}) is the sum area of a certain type converted to other types in study period T. ({U}_{a}) is the area of a certain type at the beginning.

(3)
Land use change state index (PS)

The land use change state index represents the trend and state of LUCC. The general form is:

$$PS=frac{{U}_{in}-{U}_{out}}{{U}_{in}+{U}_{out}},$$
(4)

where ({U}_{in}) is the sum area of other types converted to this type in study period T, and ({U}_{out}) is the sum area of a certain type converted to other types in study period T.
Calculation of ecosystem service value
In this paper, the improved ecosystem services valuation method based on the unit area value equivalent factor proposed by Xie et al.2 is employed to evaluate ESV. Therefore, the LULC types of the main urban area of Guangzhou were associated to the corresponding representative biomes (Table 2). The most representative biomes used as a proxy for each LULC type are: (1) cropland for cultivated land, (2) tropical forest for forest, (3) grassland for grassland, and (4) water system and wetland for water body. (5) bare land for bare land.
Table 2 Parameters of ESV for different land use types in the main urban area of Guangzhou.
Full size table

The LULC types are not exactly identical with the representative biomes. For example, cultivated land in this study accounts areas used for paddy fields and dry land. Therefore, the ESV index of cultivated land is calculated by weighting the area ratio of paddy field and dry land in the statistical yearbook. The average value of the ESV index of the water system and wetland is adopted for water body. The ESV index of build-up area is 0. the ESV index of land use types is shown in Table 2. The value of the universal equivalent factor ESV (D value) of the improved ESV method is 340.65 thousand yuan/km2.
In this study, the ESV of each land use unit area is based on the research methods of Costanza3 and Xie et al.2. The calculation formula is:

$$ESV=sum {A}_{i}times {VC}_{i}$$
(5)

$${ESV}_{f}=sum {A}_{i}times {VC}_{fi},$$
(6)

where ESV is the total value (yuan) of ecosystem services, ({A}_{i}) (km2) is the area of class i, and VCi is the ESV coefficient (yuan/km· year) corresponding to class i. ESVf is the single ESV, and VCfi is the value coefficient of the single service function.
Sensitivity analysis model
The sensitivity model is used to calculate the response of ESV to the change of value coefficient (VC)14 by adjusting the 50% of the ESV coefficient of each land use type up and down and determining the change in ESV over time and the degree of dependence on the value coefficient. The calculation formula is as follows:

$$CS=frac{{(ESV}_{j}-{ESV}_{i})/{ESV}_{i}}{{(VC}_{jk}-{VC}_{ik})/{VC}_{ik}},$$
(7)

where ESV is the estimated ESV, VC is the value coefficient, i and j are the initial value and adjusted value (50% up and down adjustment) and K is the land use type. When CS ≥ 1, ESV is elastic relative to VC; when CS ≤ 1, ESV is inelastic. The larger the CS value is, the more critical the accuracy of the ESV index.
Grid analysis method
Grid analysis method is used to divided the study area into regular grid matrixes with the same size and no overlap, and takes grid as the research object to express and statistical unit in geospatial space9. It uses regular square grid as ESV’s spatial statistical unit instead of irregular land-use map spots to ensure the capacity invariance within the unit, which not only highlights the spatial distribution characteristics of ESV, but also facilitates the spatial quantitative statistics of ESV.
The premise of calculating ESV spatial differentiation is to determine the size of grid cell. In this study, based on ArcGIS10.5 software, the area of each land use type in four grid units with side length of 0.5 km, 1 km, 2 km and 3 km was extracted respectively, and then compare the degree of area change, namely the coefficient of variation. The grid of this scale is the optimal grid unit size for the spatial differentiation of ESV in the study area. Finally, the grid analysis method is introduced to construct a (0.5 × 0.5) km square grid as a geospatial statistical unit. By using the equal spacing system sampling method, the study area is divided into 2024 square grids that do not overlap each other (0.5 × 0.5) km, and the grid matrix is composed of these grids. Through the intersection operation of grid matrix and land use data of each research year, the area of various land use types in each grid is counted, and the spatial heterogeneity of land use types and ESV is analyzed.
Geo-detector
Combined with GIS spatial superposition technology and set theory, a statistical method proposed by Wang et al.32 can detect spatial heterogeneity and reveal the driving force to identify the interaction between multiple factors. This model is widely used to analyze the influence mechanism of social economic factors and natural environmental factors. The geo-detector consists of four detectors—a differentiation and factor detector, an interaction detector, a risk area detector and an ecological detector. In this paper, factor detection and interaction detection are utilized to detect and analyze the driving force of ESVs in the main urban area of Guangzhou.
(1) Factor detector: Factor detection can identify the explanatory power of each spatial driving factor in landscape type change, and its model is as follows:

$$P=1-frac{1}{{ndelta }^{2}}sum_{i=1}^{m}{n}_{i}{{delta }^{2}}_{i},$$
(8)

where P is the explanatory power index of ESV influencing factors; ni is the number of samples in the secondary area; n is the total number of samples; m is the number of samples in the secondary area; ({delta }^{2}) is the variance in land use type change in the whole area; and ({{delta }^{2}}_{i}) is the variance in land use type in the secondary area. Thus, the model is established, assuming ({{delta }^{2}}_{i}) ≠ 0.
The range of values for P is [0,1]. When P = 0, it shows that the spatial distribution of ESV changes is random. The larger the P value is, the greater the impact of longitudinal driving factors on ESV changes.
(2) Interactive detector: Interaction detection can be used to identify the interaction between different spatial drivers. When detecting the interaction of X1 and X2, the explanatory power of the dependent variable Y will increase or decrease. The evaluation method is used to calculate the q value of two factors, X1 and X2, for Y, respectively, q (X1) and q (X2); to calculate the q value of their interaction, q (X1 ∩ X2); and to compare q (X1), q (X2) and q (X1 ∩ X2). The five results of the interactive detector are given in Table 3.
Table 3 Types of interactions between two covariates.
Full size table More

In this study, we used the black garden ant Lasius niger to investigate the benefits and factors of pleometrosis, the transitory association between founding queens. The monitoring of colonies founded by one or two queens showed that pleometrosis increased and accelerated offspring production. Then, the experimental pairing of L. niger founding queens revealed that in pairs of queens of different fecundity but similar size, the most fecund queen was more likely to survive. Our experiment could not detect a similar effect of size when controlling for fecundity. Finally, we found that queens associated preferentially with less fecund queens.
Our findings of pleometrosis benefiting offspring production are in line with the literature for this, and other ant species3,7,9,10,12,22,23. Interestingly, we only detected these benefits at the colony level, as pleometrosis had either no effect or a negative influence on the per capita offspring production9,12,22. However, colony-level measurements are more relevant in the case of pleometrosis, as the queen that survives the association inherits all the offspring produced during colony foundation. In the field, colonies with a faster, more efficient worker production would have a competitive advantage over neighbouring founding colonies3,4. This is especially true for L. niger, which shows high density of founding colonies that compete for limiting resources and raid the brood of other colonies10. Thus, the competitive advantage provided by pleometrosis likely enhances colony growth and survival.
The increased and faster production of workers in colonies with two queens may stem from a nutritional boost for the larvae. L. niger founding queens do not forage, and produce the first cohort of workers from their own metabolic reserves. Larvae have been observed to cannibalize both viable and non-viable (trophic) eggs24. We found that colonies with two queens produced more eggs, but that this did not translate in them having more larvae. However, more of these larvae became pupae—and ultimately workers. In addition, while the time to produce the first egg and larva did not differ between colonies with one and two queens, the first pupa and worker were produced faster when two queens were present, consistent with a shorter larval stage. We propose that larvae in pleometrotic colonies developed faster and were more likely to reach pupation because they had more eggs that provided nutrients, boosting the development rate of the first workers.
These benefits of pleometrosis are only inherited by the queens that survive, it is thus important to understand the factors that determine queen survival in pleometrotic associations. Although this question has been relatively well studied3,16,17,18,19,20,21, it has remained challenging to disentangle the effects of correlated factors. For example, we found that size, which has been reported to predict queen survival16,19, correlated with fecundity, which would itself be confounded with the parentage of workers in the first cohort produced. To address this issue, we disentangled size and fecundity experimentally, and used foreign workers that developed from pupae collected in field colonies to prevent any potential nepotistic behaviour.
We found that fecundity, but not size, determined queen survival. The finding that, despite being of similar size, more fecund queens are more likely to survive indicates that the outcome of pleometrosis is not the mere consequence of physical dominance. The higher fecundity could reflect a better health condition, which may give the advantage to the more fecund queen in direct fights3,15, or if workers initiate the fights. Natural selection may have favoured workers that skew aggression toward the less fecund queen, both because this queen would be less efficient at building a colony, and because the workers would be more likely to be the offspring of the more fecund queen. The latter would not necessarily involve direct nepotistic behaviours (the workers would not behave according to parentage, but to fecundity), which have remained elusive in social insects in general25,26,27, and in pleometrotic associations in particular16,17,20. Despite regular behavioural observations, we did not observe who initiated aggression in our experiments, and it remains unclear whether the queens and/or the workers are responsible for the onset of fights. Consistently with previous studies16,23, we found that a certain proportion of queen death occurred before worker emergence, suggesting that worker presence is not required for queen execution. Finally, we cannot rule out that the least fecund queens were more likely to die because of a weaker health status, possibly combined with the stress of being associated with another, healthier queen.
Although it has not been directly reported before, our finding that fecundity determines queen survival is consistent with previous reports of weight being associated with queen survival17, more fecund queens being more aggressive28, cuticular hydrocarbon profiles differing between surviving and culled queens21, and between more and less fecund queens28. We could not directly support previous reports of size correlating with survival16,19. This could be because in those studies, size could have been confounded with fecundity, and/or because we lacked the statistical power to detect such effect in our experiment.
Pleometrosis provides clear benefits, but these benefits are only inherited by the surviving queens, and the losing queens pay the great cost of dying without contributing to the next generation. Natural selection should thus favour queens that decide whether or not to join a pleometrotic association based on the relative benefits compared to individual foundation—these may differ across ecological contexts29—and the likelihood of surviving the association. As fecundity appears to determine queen survival in L. niger, queens may have evolved the ability to choose among potential partners according to their fecundity. Our results are consistent with this hypothesis, as queens preferentially associated with partners that would later produce fewer eggs, possibly because they were less fecund, and therefore less healthy and easier to eliminate. This suggests that founding queens may assess the fecundity of potential partners, possibly via their cuticular hydrocarbon profile28. This result further supports our finding that fecundity plays an important role in pleometrotic associations. It is important to note that this difference in egg production could have alternative explanations. First, it could stem from more fecund queens having no interest in forming an association because they are able to start a competitive colony alone. Second, it could be a consequence, rather than a cause, of the outcome of the choice experiment. We cannot rule out that entering an association with another queen and/or leaving this association prematurely at the end of the choice experiment may have been stressful for the chosen queens, and affected their later production of eggs. We could not detect any difference between chosen and not chosen queens in the number of larvae and pupae produced, which are likely influenced by factors other than fecundity (e.g., brood care behaviour). Interestingly, we did not find that queens chose according to size, consistent with our finding that size may not affect which queen survives the pleometrotic association.
Our study informs on the benefits and factors of pleometrosis, and highlights the role of fecundity in the decision to associate with another queen, and in determining which queen survives the association. As such, it contributes to a better understanding of the onset and outcome of pleometrosis, a classic case of cooperation between unrelated animals. More

1.
Dharampal, P. S., Hetherington, M. C. & Steffan, S. A. Microbes make the meal: Oligolectic bees require microbes within their host pollen to thrive. Ecol. Entomol. https://doi.org/10.1111/een.12926 (2020).
Article  Google Scholar 
2.
Sonnenburg, J. L. & Bäckhed, F. Diet-microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Suzuki, T. A. Links between natural variation in the microbiome and host fitness in wild mammals. Integr. Comp. Biol. 57, 756–769 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Zheng, D., Liwinski, T. & Elinav, E. Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492–506 (2020).
PubMed  PubMed Central  Article  Google Scholar 

5.
Kwong, W. K., Mancenido, A. L. & Moran, N. A. Immune system stimulation by the native gut microbiota of honey bees. R. Soc. Open Sci. 4, 170003 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

6.
Bo, T.-B. et al. Coprophagy prevention alters microbiome, metabolism, neurochemistry, and cognitive behavior in a small mammal. ISME J. https://doi.org/10.1038/s41396-020-0711-6 (2020).
Article  PubMed  PubMed Central  Google Scholar 

7.
Sarkar, A. et al. The role of the microbiome in the neurobiology of social behaviour. Biol. Rev. 95, 12603 (2020).
Article  Google Scholar 

8.
Vernier, C. L. et al. The gut microbiome defines social group membership in honey bee colonies. Sci. Adv. 6, 3431 (2020).
ADS  Article  CAS  Google Scholar 

9.
Lemoine, M. M., Engl, T. & Kaltenpoth, M. Microbial symbionts expanding or constraining abiotic niche space in insects. Curr. Opin. Insect Sci. 39, 14–20 (2020).
PubMed  Article  Google Scholar 

10.
Engel, P. et al. The bee microbiome: Impact on bee health and model for evolution and ecology of host-microbe interactions. MBio. https://doi.org/10.1128/mBio.02164-15 (2006).
Article  Google Scholar 

11.
Daisley, B. A., Chmiel, J. A., Pitek, A. P., Thompson, G. J. & Reid, G. Missing microbes in bees: How systematic depletion of key symbionts erodes immunity. Trends Microbiol. https://doi.org/10.1016/j.tim.2020.06.006 (2020).
Article  PubMed  Google Scholar 

12.
Bonilla-Rosso, G. & Engel, P. Functional roles and metabolic niches in the honey bee gut microbiota. Curr. Opin. Microbiol. 43, 69–76 (2018).
CAS  PubMed  Article  Google Scholar 

13.
Zheng, H., Powell, J. E., Steele, M. I., Dietrich, C. & Moran, N. A. Honeybee gut microbiota promotes host weight gain via bacterial metabolism and hormonal signaling. Proc. Natl. Acad. Sci. 114, 4775–4780 (2017).
CAS  PubMed  Article  Google Scholar 

14.
Zheng, H. et al. Metabolism of toxic sugars by strains of the bee gut symbiont Gilliamella apicola. MBio. https://doi.org/10.1128/mBio.01326-16 (2016).
Article  PubMed  PubMed Central  Google Scholar 

15.
Engel, P. & Moran, N. A. Functional and evolutionary insights into the simple yet specific gut microbiota of the honey bee from metagenomic analysis. Gut Microbes 4, 60–65 (2013).
PubMed  PubMed Central  Article  Google Scholar 

16.
Lee, F. J., Rusch, D. B., Stewart, F. J., Mattila, H. R. & Newton, I. L. G. Saccharide breakdown and fermentation by the honey bee gut microbiome. Environ. Microbiol. 17, 796–815 (2015).
CAS  PubMed  Article  Google Scholar 

17.
Anderson, K. E. et al. Hive-stored pollen of honey bees: Many lines of evidence are consistent with pollen preservation, not nutrient conversion. Mol. Ecol. 23, 5904–5917 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Dharampal, P. S., Carlson, C., Currie, C. R. & Steffan, S. A. Pollen-borne microbes shape bee fitness. Proc. R. Soc. B Biol. Sci. 286, 20182894 (2019).
CAS  Article  Google Scholar 

19.
Rothman, J. A., Leger, L., Graystock, P., Russell, K. & McFrederick, Q. S. The bumble bee microbiome increases survival of bees exposed to selenate toxicity. Environ. Microbiol. 21, 1462–2920. https://doi.org/10.1111/1462-2920.14641 (2019).
CAS  Article  Google Scholar 

20.
Wu, Y. et al. Honey bee ( Apis mellifera ) gut microbiota promotes host endogenous detoxification capability via regulation of P450 gene expression in the digestive tract. Microb. Biotechnol. 13, 1201–1212 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Praet, J. et al. Large-scale cultivation of the bumblebee gut microbiota reveals an underestimated bacterial species diversity capable of pathogen inhibition. Environ. Microbiol. 20, 214–227 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Forsgren, E., Olofsson, T. C., Vásquez, A. & Fries, I. Novel lactic acid bacteria inhibiting Paenibacillus larvae in honey bee larvae. Apidologie 41, 99–108 (2010).
Article  Google Scholar 

23.
Cariveau, D. P., Elijah Powell, J., Koch, H., Winfree, R. & Moran, N. A. Variation in gut microbial communities and its association with pathogen infection in wild bumble bees (Bombus). ISME J. 8, 2369–2379 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Raymann, K., Shaffer, Z. & Moran, N. A. Antibiotic exposure perturbs the gut microbiota and elevates mortality in honeybees. PLoS Biol. 15, e2001861 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Schwarz, R. S., Moran, N. A. & Evans, J. D. Early gut colonizers shape parasite susceptibility and microbiota composition in honey bee workers. Proc. Natl. Acad. Sci. 113, 9345–9350 (2016).
CAS  PubMed  Article  Google Scholar 

26.
Maes, P. W., Rodrigues, A. P., Oliver, R., Mott, B. M. & Anderson, K. E. Diet related gut bacterial dysbiosis correlates with impaired development, increased mortality and Nosema disease in the honey bee (Apis mellifera). Mol. Ecol. 25, 5439–5450 (2016).
CAS  PubMed  Article  Google Scholar 

27.
Koch, H. & Schmid-Hempel, P. Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proc. Natl. Acad. Sci. 108, 19288–19292 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

28.
Evans, J. D. & Lopez, D. L. Bacterial probiotics induce an immune response in the honey bee (Hymenoptera: Apidae). J. Econ. Entomol. 97, 752–756 (2004).
CAS  PubMed  Article  Google Scholar 

29.
Emery, O., Schmidt, K. & Engel, P. Immune system stimulation by the gut symbiont Frischella perrara in the honey bee (Apis mellifera). Mol. Ecol. 26, 2576–2590 (2017).
CAS  PubMed  Article  Google Scholar 

30.
Engel, P., Martinson, V. G. & Moran, N. A. Functional diversity within the simple gut microbiota of the honey bee. Proc. Natl. Acad. Sci. 109, 11002–11007 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

31.
Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Micro 14, 374–384 (2016).
CAS  Article  Google Scholar 

32.
McFrederick, Q. S. & Rehan, S. M. Characterization of pollen and bacterial community composition in brood provisions of a small carpenter bee. Mol. Ecol. 25, 2302–2311 (2016).
CAS  PubMed  Article  Google Scholar 

33.
McFrederick, Q. S. et al. Flowers and wild megachilid bees share microbes. Microb. Ecol. 73, 188–200 (2017).
PubMed  Article  Google Scholar 

34.
McFrederick, Q. S. et al. Environment or kin: whence do bees obtain acidophilic bacteria?. Mol. Ecol. 21, 1754–1768 (2012).
PubMed  Article  PubMed Central  Google Scholar 

35.
McFrederick, Q. S., Wcislo, W. T., Hout, M. C. & Mueller, U. G. Host species and developmental stage, but not host social structure, affects bacterial community structure in socially polymorphic bees. FEMS Microbiol. Ecol. 88, 398–406 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Graystock, P., Rehan, S. M. & McFrederick, Q. S. Hunting for healthy microbiomes: Determining the core microbiomes of Ceratina, Megalopta, and Apis bees and how they associate with microbes in bee collected pollen. Conserv. Genet. 18, 701–711 (2017).
Article  Google Scholar 

37.
McFrederick, Q. S. et al. Specificity between lactobacilli and hymenopteran hosts is the exception rather than the rule. Appl. Environ. Microbiol. 79, 1803–1812 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Sanders, J. G. et al. Stability and phylogenetic correlation in gut microbiota: Lessons from ants and apes. Mol. Ecol. 23, 1268–1283 (2014).
PubMed  Article  PubMed Central  Google Scholar 

39.
Kwong, W. K. et al. Dynamic microbiome evolution in social bees. Sci. Adv. 3, 1–17 (2017).
Article  Google Scholar 

40.
Rothman, J. A., Andrikopoulos, C., Cox-Foster, D. & McFrederick, Q. S. Floral and foliar source affect the bee nest microbial community. Microb. Ecol. 78, 506–516 (2019).
PubMed  Article  Google Scholar 

41.
Cohen, H., McFrederick, Q. S. & Philpott, S. M. Environment shapes the microbiome of the blue orchard bee, Osmia lignaria. Microb. Ecol. 80, 897–907 (2020).
PubMed  Article  PubMed Central  Google Scholar 

42.
Muñoz-Colmenero, M. et al. Differences in honey bee bacterial diversity and composition in agricultural and pristine environments—A field study. Apidologie. https://doi.org/10.1007/s13592-020-00779-w (2020).
Article  Google Scholar 

43.
Kapheim, K. M. et al. Caste-specific differences in hindgut microbial communities of honey bees (Apis mellifera). PLoS ONE 10, 1–14 (2015).
Article  CAS  Google Scholar 

44.
Elijah Powell, J., Eiri, D., Moran, N. A. & Rangel, J. Modulation of the honey bee queen microbiota: Effects of early social contact. PLoS ONE 13, 1–14 (2018).
Google Scholar 

45.
Tarpy, D. R., Mattila, H. R. & Newton, I. L. G. Development of the honey bee gut microbiome throughout the queen-rearing process. Appl. Environ. Microbiol. 81, 3182–3191 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Dong, Z. X. et al. Colonization of the gut microbiota of honey bee (Apis mellifera) workers at different developmental stages. Microbiol. Res. 231, 126370 (2020).
CAS  PubMed  Article  Google Scholar 

47.
D’Alvise, P. et al. The impact of winter feed type on intestinal microbiota and parasites in honey bees. Apidologie 49, 252–264 (2018).
Article  CAS  Google Scholar 

48.
Huang, S. K. et al. Influence of feeding type and Nosema ceranae infection on the gut microbiota of Apis cerana workers. mSystems 3, 177–195 (2018).
Article  Google Scholar 

49.
Rothman, J. A., Carroll, M. J., Meikle, W. G., Anderson, K. E. & McFrederick, Q. S. Longitudinal effects of supplemental forage on the Honey Bee (Apis mellifera) microbiota and inter- and intra-colony variability. Microb. Ecol. 76, 814–824 (2018).
CAS  PubMed  Article  Google Scholar 

50.
Zhang, Y. et al. Nosema ceranae infection enhances Bifidobacterium spp. abundances in the honey bee hindgut. Apidologie 50, 353–362 (2019).
Article  Google Scholar 

51.
Danforth, B. N., Minckley, R. L. & Neff, J. L. The Solitary Bees (Princeton University Press, Princeton, 2019).
Google Scholar 

52.
Santos, P. K. F., Arias, M. C. & Kapheim, K. M. Loss of developmental diapause as prerequisite for social evolution in bees. Biol. Lett. 15, 20190398 (2019).
PubMed  PubMed Central  Article  Google Scholar 

53.
Harmon-Threatt, A. Influence of nesting characteristics on health of wild bee communities. Annu. Rev. Entomol. 65, 39–56 (2020).
CAS  PubMed  Article  Google Scholar 

54.
Johansen, C., Mayer, D., Stanford, A. & Kious, C. Alkali Bees: Their Biology and Management for Alfalfa Seed Production in the Pacific Northwest (Publication, Pacific Northwest Cooperative Extension Service, Genesee, 1982).
Google Scholar 

55.
Cane, J. H. A native ground-nesting bee (Nomia melanderi) sustainably managed to pollinate alfalfa across an intensively agricultural landscape. Apidologie 39, 315–323 (2008).
Article  Google Scholar 

56.
Cane, J. H. Pollinating bees (Hymenoptera: Apiformes) of U.S. alfalfa compared for rates of pod and seed set. J. Econ. Entomol. 95, 22–27 (2002).
PubMed  Article  Google Scholar 

57.
Batra, S. W. & Bohart, G. E. Alkali bees: Response of adults to pathogenic fungi in brood cells. Science 165, 607 (1969).
ADS  CAS  PubMed  Article  Google Scholar 

58.
Galbraith, D. A. et al. Investigating the viral ecology of global bee communities with high-throughput metagenomics. Sci. Rep. 8, 1–11 (2018).
CAS  Article  Google Scholar 

59.
Bohart, G. E., Stephen, W. P. & Eppley, E. K. The biology of Heterostylum robustum (Diptera: Bombyliidae), a parasite of the alkali bee. Ann. Entomol. Soc. Am. 53, 425–435 (1960).
Article  Google Scholar 

60.
Johansen, C. A., Mayer, D. F. & Eves, J. D. Biology and management of the alkali bee, Nomia melanderi Cockrell (Hymenoptera: Halictidae).Melanderii Cockrell (Hymenoptera: Halictidae).Melanderi (Washington State Entomology, Pullman, 1978).
Google Scholar 

61.
Johansen, C. A. & Mayer, D. F. Pollinator Protection: A Bee and Pesticide Handbook (Wicwas Press, Kalamazoo, 1990).
Google Scholar 

62.
Stephen, W. P. Solitary bees in North American agriculture: A perspective. In For Nonnative Crops, Whence Pollinators of the Future? (eds Strickler, K. & Cane, J. H.) 41–66 (Entomological Society of America, Annapolis, 2003).
Google Scholar 

63.
Kapheim, K. M. et al. Draft genome assembly and population genetics of an agricultural pollinator, the solitary alkali bee (Halictidae: Nomia melanderi). G3 9, 625–634 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

64.
Batra, S. W. T. Aggression, territoriality, mating and nest aggregation of some solitary bees (Hymenoptera: Halictidae, Megachilidae, Colletidae, Anthophoridae). J. Kansas Entomol. Soc. 51, 547–559 (1978).
Google Scholar 

65.
Mayer, D. F. & Miliczky, E. R. Emergence, male behavior, and mating in the alkali bee, Nomia melanderi Cockerell (Hymenoptera: Halictidae). J. Kansas Entomol. Soc. 71, 61–68 (1998).
Google Scholar 

66.
Kapheim, K. M. & Johnson, M. M. Juvenile hormone, but not nutrition or social cues, affects reproductive maturation in solitary alkali bees (Nomia melanderi). J. Exp. Biol. https://doi.org/10.1242/jeb.162255 (2017).
Article  PubMed  PubMed Central  Google Scholar 

67.
Koch, H. & Schmid-Hempel, P. Bacterial communities in central European bumble bees: Low diversity and high specificity. Microb. Ecol. 62, 121–133 (2011).
PubMed  Article  PubMed Central  Google Scholar 

68.
Martinson, V. G., Moy, J. & Moran, N. A. Establishment of characteristic gut bacteria during development of the honeybee worker. Appl. Environ. Microbiol. 78, 2830–2840 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

69.
Powell, J. E., Martinson, V. G., Urban-Mead, K. & Moran, N. A. Routes of acquisition of the gut microbiota of the honey bee Apis mellifera. Appl. Environ. Microbiol. 80, 7378–7387 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

70.
Kapheim, K. M. & Johnson, M. M. Support for the reproductive ground plan hypothesis in a solitary bee: Links between sucrose response and reproductive status. Proc. R. Soc. B Biol. Sci. 284, 20162406 (2017).
Article  CAS  Google Scholar 

71.
R Core Team. R: A Language and Environment for Statistical Computing (2019).

72.
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. BioRxiv. https://doi.org/10.1101/221499 (2017).
Article  Google Scholar 

74.
Jari Oksanen, F. et al. vegan: Community Ecology Package (2019).

75.
McMurdie, P. J. & Holmes, S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

76.
Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693 (2006).
PubMed  Article  PubMed Central  Google Scholar 

77.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

78.
Lenth, R. emmeans: Estimated Marginal Means, Aka Least-Squares Means (2020).

79.
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 

80.
Lahti, L. & Shetty, S. Microbiome R Package (2012).

81.
Zheng, J. et al. A taxonomic note on the genus Lactobacillus: Description of 23 novel genera, emended description of the genus Lactobacillus beijerinck 1901, and union of Lactobacillaceae and Leuconostocaceae. Int. J. Syst. Evol. Microbiol. 70, 2782–2858 (2020).
CAS  PubMed  Article  Google Scholar 

82.
Rummel, P. S. et al. Maize root and shoot litter quality controls short-term emissions and bacterial community structure of arable soil. Biogeosciences 17, 1181–1198 (2020).
ADS  CAS  Article  Google Scholar 

83.
McFrederick, Q. S., Vuong, H. Q. & Rothman, J. A. Lactobacillus micheneri sp. nov., Lactobacillus timberlakei sp. nov. and Lactobacillus quenuiae sp. nov., lactic acid bacteria isolated from wild bees and flowers. Int. J. Syst. Evol. Microbiol. 68, 1879–1884 (2018).
CAS  PubMed  Article  Google Scholar 

84.
Wittouck, S., Wuyts, S., Meehan, C. J., van Noort, V. & Lebeer, S. A genome-based species taxonomy of the Lactobacillus genus complex. mSystems. https://doi.org/10.1128/mSystems.00264-19 (2019).
Article  PubMed  PubMed Central  Google Scholar 

85.
Engel, P. & Moran, N. A. The gut microbiota of insects—Diversity in structure and function. FEMS Microbiol. Rev. 37, 699–735 (2013).
CAS  PubMed  Article  Google Scholar 

86.
Cane, J. H., Dobson, H. E. M. & Boyer, B. Timing and size of daily pollen meals eaten by adult females of a solitary bee (Nomia melanderi) (Apiformes: Halictidae). Apidologie 48, 17–30 (2016).
Article  CAS  Google Scholar 

87.
Engel, P., Bartlett, K. D. & Moran, N. A. The bacterium Frischella perrara causes scab formation in the gut of its honeybee host. MBio 6, 1–8 (2015).
Article  CAS  Google Scholar 

88.
Martinson, V. G. et al. A simple and distinctive microbiota associated with honey bees and bumble bees. Mol. Ecol. 20, 619–628 (2011).
PubMed  Article  Google Scholar 

89.
Vásquez, A. & Olofsson, T. C. The lactic acid bacteria involved in the production of bee pollen and bee bread. J. Apic. Res. 48, 189–195 (2009).
Article  Google Scholar 

90.
Vuong, H. Q. & McFrederick, Q. S. Comparative genomics of wild bee and flower isolated Lactobacillus reveals potential adaptation to the bee host. Genome Biol. Evol. 11, 2151–2161 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar  More

1.
Sherman, P. W. & Billing, J. Darwinian gastronomy: why we use spices: spices taste good because they are good for us. BioScience 49, 453–463 (1999).
Article  Google Scholar 
2.
Billing, J. & Sherman, P. W. Antimicrobial functions of spices: why some like it hot. Q. Rev. Biol. 73, 3–49 (1998).
CAS  PubMed  Article  Google Scholar 

3.
Galton, F. Comment on ‘On a method of investigating the development of institutions; applied to laws of marriage and descent’ by E. B. Tylor. J. Anthropol. Inst. Gt Br. Irel. 18, 245–272 (1889).
Google Scholar 

4.
Bromham, L., Hua, X., Cardillo, M., Schneemann, H. & Greenhill, S. J. Parasites and politics: why cross-cultural studies must control for relatedness, proximity and covariation. R. Soc. Open Sci. 5, 181100 (2018).
PubMed  PubMed Central  Article  Google Scholar 

5.
Mace, R. & Holden, C. J. A phylogenetic approach to cultural evolution. Trends Ecol. Evol. 20, 116–121 (2005).
PubMed  Article  Google Scholar 

6.
Freckleton, R. P. & Jetz, W. Space versus phylogeny: disentangling phylogenetic and spatial signals in comparative data. Proc. R. Soc. B 276, 21–30 (2008).
Article  Google Scholar 

7.
Hua, X., Greenhill, S. J., Cardillo, M., Schneemann, H. & Bromham, L. The ecological drivers of variation in global language diversity. Nat. Commun. 10, 2047 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

8.
Ohtsubo, Y. Adaptive ingredients against food spoilage in Japanese cuisine. Int. J. Food Sci. Nutr. 60, 677–687 (2009).
PubMed  Article  Google Scholar 

9.
Murray, D. R. & Schaller, M. Historical prevalence of infectious diseases within 230 geopolitical regions: a tool for investigating origins of culture. J. Cross Cult. Psychol. 41, 99–108 (2010).
Article  Google Scholar 

10.
Sherman, P. W. & Hash, G. A. Why vegetable recipes are not very spicy. Evol. Hum. Behav. 22, 147–163 (2001).
PubMed  Article  Google Scholar 

11.
Havelaar, A. H. et al. World Health Organization global estimates and regional comparisons of the burden of foodborne disease in 2010. PLoS Med. 12, e1001923 (2015).
PubMed  PubMed Central  Article  Google Scholar 

12.
Lewnard, J. A., Lo, N. C., Arinaminpathy, N., Frost, I. & Laxminarayan, R. Childhood vaccines and antibiotic use in low- and middle-income countries. Nature 581, 94–99 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
McMichael, A. J. & Beaglehole, R. The changing global context of public health. Lancet 356, 495–499 (2000).
CAS  PubMed  Article  Google Scholar 

14.
Salomon, J. A. et al. Healthy life expectancy for 187 countries, 1990–2010: a systematic analysis for the Global Burden Disease Study 2010. Lancet 380, 2144–2162 (2012).
PubMed  Article  Google Scholar 

15.
Kummu, M. & Varis, O. The world by latitudes: a global analysis of human population, development level and environment across the north–south axis over the past half century. Appl. Geogr. 31, 495–507 (2011).
Article  Google Scholar 

16.
Johnell, O., Borgstrom, F., Jonsson, B. & Kanis, J. Latitude, socioeconomic prosperity, mobile phones and hip fracture risk. Osteoporos. Int. 18, 333–337 (2007).
CAS  PubMed  Article  Google Scholar 

17.
Kanis, J. A. et al. Variations in latitude may or may not explain the worldwide variation in hip fracture incidence. Osteoporos. Int. 23, 2401–2402 (2012).
Article  Google Scholar 

18.
Fisman, D. et al. Geographical variability in the likelihood of bloodstream infections due to Gram-negative bacteria: correlation with proximity to the equator and health care expenditure. PLoS ONE 9, e114548 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

19.
Coccia, M. The effect of country wealth on incidence of breast cancer. Breast Cancer Res. Treat. 141, 225–229 (2013).
PubMed  Article  Google Scholar 

20.
Buchter, B., Dunkel, M. & Li, J. Multiple sclerosis: a disease of affluence? Neuroepidemiology 39, 51–56 (2012).
PubMed  Article  Google Scholar 

21.
Roberts, S. & Winters, J. Linguistic diversity and traffic accidents: Lessons from statistical studies of cultural traits. PLoS ONE 8, e70902 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Guernier, V., Hochberg, M. E. & Guégan, J.-F. Ecology drives the worldwide distribution of human diseases. PLoS Biol. 2, e141 (2004).
PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Hawkins, B. A. et al. Energy, water, and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117 (2003).
Article  Google Scholar 

25.
Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. USA 104, 5925–5930 (2007).
CAS  PubMed  Article  Google Scholar 

26.
Dunn, R. R., Davies, T. J., Harris, N. C. & Gavin, M. C. Global drivers of human pathogen richness and prevalence. Proc. R. Soc. B 277, 2587–2595 (2010).
PubMed  Article  Google Scholar 

27.
Luck, G. W. A review of the relationships between human population density and biodiversity. Biol. Rev. 82, 607–645 (2007).
PubMed  Article  Google Scholar 

28.
Collen, B. et al. Global patterns of freshwater species diversity, threat and endemism. Glob. Ecol. Biogeogr. 23, 40–51 (2014).
PubMed  Article  Google Scholar 

29.
Just, M. G. et al. Global biogeographic regions in a human‐dominated world: the case of human diseases. Ecosphere 5, 1–21 (2014).
Article  Google Scholar 

30.
Morand, S., Owers, K. & Bordes, F. in Confronting Emerging Zoonoses (eds Yamada, A. et al.) 27–41 (Springer, 2014).

31.
Turner, J. Spice: the History of a Temptation (Alfred A. Knopf, 2004).

32.
Kraft, K. H. et al. Multiple lines of evidence for the origin of domesticated chili pepper, Capsicum annuum, in Mexico. Proc. Natl Acad. Sci. USA 111, 6165–6170 (2014).
CAS  PubMed  Article  Google Scholar 

33.
Portnoy, S. in The SAGE Encyclopedia of Food Issues Vol. 1 (ed. Albala, K.) 84–86 (SAGE Publications, 2015).

34.
Jain, A., Rakhi, N. & Bagler, G. Analysis of food pairing in regional cuisines of India. PLoS ONE 10, e0139539 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Zhu, Y.-X. et al. Geography and similarity of regional cuisines in China. PLoS ONE 8, e79161 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

36.
Kline, M. A., Shamsudheen, R. & Broesch, T. Variation is the universal: making cultural evolution work in developmental psychology. Philos. Trans. R. Soc. B 373, 20170059 (2018).
Article  Google Scholar 

37.
Bagler, G. CulinaryDB (Indraprastha Institute of Information Technology Delhi, 2017); https://cosylab.iiitd.edu.in/culinarydb/

38.
Iranshahy, M. & Iranshahi, M. Traditional uses, phytochemistry and pharmacology of asafoetida (Ferula assa-foetida oleo-gum-resin)—a review. J. Ethnopharmacol. 134, 1–10 (2011).
CAS  PubMed  Article  Google Scholar 

39.
Nakamura, Y. et al. Comparison of the glucosinolate–myrosinase systems among daikon (Raphanus sativus, Japanese white radish) varieties. J. Agric. Food Chem. 56, 2702–2707 (2008).
CAS  PubMed  Article  Google Scholar 

40.
Gupta, S. & Abu-Ghannam, N. Recent developments in the application of seaweeds or seaweed extracts as a means for enhancing the safety and quality attributes of foods. Innov. Food Sci. Emerg. Technol. 12, 600–609 (2011).
CAS  Article  Google Scholar 

41.
Devi, K. P., Suganthy, N., Kesika, P. & Pandian, S. K. Bioprotective properties of seaweeds: in vitro evaluation of antioxidant activity and antimicrobial activity against food borne bacteria in relation to polyphenolic content. BMC Complement. Altern. Med. 8, 1 (2008).
Article  CAS  Google Scholar 

42.
Cox, S., Abu-Ghannam, N. & Gupta, S. An assessment of the antioxidant and antimicrobial activity of six species of edible Irish seaweeds. Int. Food Res. J. 17, 205–220 (2010).
CAS  Google Scholar 

43.
Lipkin, A. et al. An antimicrobial peptide Ar-AMP from amaranth (Amaranthus retroflexus L.) seeds. Phytochemistry 66, 2426–2431 (2005).
CAS  PubMed  Article  Google Scholar 

44.
Maiyo, Z., Ngure, R., Matasyoh, J. & Chepkorir, R. Phytochemical constituents and antimicrobial activity of leaf extracts of three Amaranthus plant species. Afr. J. Biotechnol. 9, 3178–3182 (2010).
Google Scholar 

45.
Dan, S. Antibacterial activity of paeonol in vitro. Her. Med. 9, 009 (2012).
Google Scholar 

46.
Uddin, G., Sadat, A. & Siddiqui, B. S. Phytochemical screening, in vitro antioxidant and antimicrobial activities of the crude fractions of Paeonia emodi Wall. Ex Royle. Middle East J. Sci. Res. 17, 367–373 (2013).
Google Scholar 

47.
Joung, Y.-M. et al. Antioxidative and antimicrobial activities of lilium species extracts prepared from different aerial parts. Korean J. Food Sci. Technol. 39, 452–457 (2007).
Google Scholar 

48.
He, J., Chen, L., Heber, D., Shi, W. & Lu, Q.-Y. Antibacterial compounds from Glycyrrhiza uralensis. J. Nat. Prod. 69, 121–124 (2006).
CAS  PubMed  Article  Google Scholar 

49.
Dhingra, V., Pakki, S. R. & Narasu, M. L. Antimicrobial activity of artemisinin and its precursors. Curr. Sci. 78, 709–713 (2000).
CAS  Google Scholar 

50.
Gupta, V. K. et al. Antimicrobial potential of Glycyrrhiza glabra roots. J. Ethnopharmacol. 116, 377–380 (2008).
PubMed  Article  Google Scholar 

51.
Chen, C. et al. Chemical composition and antimicrobial and DPPH scavenging activity of essential oil of Toona sinensis (A. Juss.) Roem from China. BioResources 9, 5262–5278 (2014).
Google Scholar 

52.
Arzanlou, M. & Bohlooli, S. Introducing of green garlic plant as a new source of allicin. Food Chem. 120, 179–183 (2010).
CAS  Article  Google Scholar 

53.
Shittu, L. et al. Antibacterial and antifungal activities of essential oils of crude extracts of Sesame radiatum against some common pathogenic micro-organisms. Iran. J. Pharmacol. Ther. 6, 165–170 (2008).
Google Scholar 

54.
Medina, E., Romero, C., Brenes, M. & de Castro, A. Antimicrobial activity of olive oil, vinegar, and various beverages against foodborne pathogens. J. Food Prot. 70, 1194–1199 (2007).
CAS  PubMed  Article  Google Scholar 

55.
South, A. rworldmap: a new R package for mapping global data. R J. 3, 35–43 (2011).
Article  Google Scholar 

56.
R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org/ (R Foundation for Statistical Computing, 2016).

57.
GADM Maps and Data (GADM, 2012); https://www.gadm.org

58.
Bivand, R. et al. rgeos: interface to geometry engine—open source (GEOS) v.0.3-21 https://cran.r-project.org/package=rgeos (2016).

59.
Bromham, L. Curiously the same: swapping tools between linguistics and evolutionary biology. Biol. Philos. 32, 855–886 (2017).
Article  Google Scholar 

60.
Mace, R. & Pagel, M. The comparative method in anthropology. Curr. Anthropol. 35, 549–564 (1994).
Article  Google Scholar 

61.
Harvey, P. H. & Pagel, M. The Comparative Method in Evolutionary Biology (Oxford Univ. Press, 1991).

62.
Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).
Article  Google Scholar 

63.
Miller, M. A. & Paige, J. C. Other food borne infections. Vet. Clin. North Am. Food Anim. Pract. 14, 71–89 (1998).
CAS  PubMed  Article  Google Scholar 

64.
Fisman, D. N. & Laupland, K. Guess who’s coming to dinner? Emerging foodborne zoonoses. Can. J. Infect. Dis. Med. Microbiol. 21, 8–10 (2010).
PubMed  PubMed Central  Article  Google Scholar 

65.
Sookias, R. B., Passmore, S. & Atkinson, Q. D. Deep cultural ancestry and human development indicators across nation states. R. Soc. Open Sci. 5, 171411 (2018).
PubMed  PubMed Central  Article  Google Scholar 

66.
Johnson, P. C. D., Barry, S. J. E., Ferguson, H. M. & Muller, P. Power analysis for generalized linear mixed models in ecology and evolution. Methods Ecol. Evol. 6, 133–142 (2015).
PubMed  Article  Google Scholar 

67.
O’Hagan, A. Kendall’s Advanced Theory Of Statistics Vol. 2B: Bayesian Inference (Halsted, 1994).

68.
Bonds, M. H., Keenan, D. C., Rohani, P. & Sachs, J. D. Poverty trap formed by the ecology of infectious diseases. Proc. R. Soc. B: 277, 1185–1192 (2010).
Article  Google Scholar  More

1.
Jiggins, F. M. Male-killing Wolbachia and mitochondrial DNA: Selective sweeps, hybrid introgression and parasite population dynamics. Genetics 164, 5–12 (2003).
CAS  PubMed  PubMed Central  Google Scholar 
2.
Poinsot, D., Charlat, S. & Merçot, H. On the mechanism of Wolbachia-induced cytoplasmic incompatibility: Confronting the models with the facts. BioEssays 25, 259–265 (2003).
PubMed  Article  Google Scholar 

3.
Werren, J. H., Baldo, L. & Clark, M. E. Wolbachia: Master manipulators of invertebrate biology. Nat. Rev. Microbiol. 6, 741–751 (2008).
CAS  PubMed  Article  Google Scholar 

4.
Vavre, F., Fleury, F., Lepetit, D., Fouillet, P. & Boulétreau, M. Phylogenetic evidence for horizontal transmission of Wolbachia in host-parasitoid associations. Mol. Biol. Evol. 16, 1711–1723 (1999).
CAS  PubMed  Article  Google Scholar 

5.
Sintupachee, S., Milne, J. R., Poonchaisri, S., Baimai, V. & Kittayapong, P. Closely related Wolbachia strains within the pumpkin arthropod community and the potential for horizontal transmission via the plant. Microb. Ecol. 51, 294–301 (2006).
CAS  PubMed  Article  Google Scholar 

6.
Li, S.-J. et al. Plantmediated horizontal transmission of Wolbachia between whiteflies. ISME J. 11, 1019–1028 (2017).
CAS  PubMed  Article  Google Scholar 

7.
Turelli, M. & Hoffmann, A. A. Rapid spread of an inherited incompatibility factor in California Drosophila. Nature 353, 440 (1991).
ADS  CAS  PubMed  Article  Google Scholar 

8.
Oliveira, D. C. S. G., Raychoudhury, R., Lavrov, D. V. & Werren, J. H. Rapidly evolving mitochondrial genome and directional selection in mitochondrial genes in the parasitic wasp Nasonia (Hymenoptera: Pteromalidae). Mol. Biol. Evol. 25, 2167–2180 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Raychoudhury, R. et al. Phylogeography of Nasonia vitripennis (Hymenoptera) indicates a mitochondrial–Wolbachia sweep in North America. Heredity 104, 318–326 (2010).
CAS  PubMed  Article  Google Scholar 

10.
Telschow, A., Gadau, J., Werren, J. H. & Kobayashi, Y. Genetic incompatibilities between mitochondria and nuclear genes: Effect on gene flow and speciation. Front. Genet. 10, 62 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

11.
Kodandaramaiah, U., Simonsen, T. J., Bromilow, S., Wahlberg, N. & Sperling, F. Deceptive single-locus taxonomy and phylogeography: Wolbachia-associated divergence in mitochondrial DNA is not reflected in morphology and nuclear markers in a butterfly species. Ecol. Evol. 3, 5167–5176 (2013).
PubMed  PubMed Central  Article  Google Scholar 

12.
Ritter, S. et al. Wolbachia infections mimic cryptic speciation in two parasitic butterfly species, Phengaris teleius and P. nausithous (Lepidoptera: Lycaenidae). PLoS ONE 8, e78107 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Whitworth, T. L., Dawson, R. D., Magalon, H. & Baudry, E. DNA barcoding cannot reliably identify species of the blowfly genus Protocalliphora (Diptera: Calliphoridae). Proc. R. Soc. B Biol. Sci. 274, 1731–1739 (2007).
CAS  Article  Google Scholar 

14.
Barton, N. & Bengtsson, B. O. The barrier to genetic exchange between hybridising populations. Heredity 57, 357–376 (1986).
PubMed  Article  Google Scholar 

15.
Vavre, F., Fleury, F., Varaldi, J., Fouillet, P. & Boulétreau, M. Infection polymorphism and cytoplasmic incompatibility in Hymenoptera-Wolbachia associations. Heredity 88, 361–365 (2002).
CAS  PubMed  Article  Google Scholar 

16.
Jaenike, J., Dyer, K. A., Cornish, C. & Minhas, M. S. Asymmetrical reinforcement and Wolbachia infection in Drosophila. PLoS Biol. 4, e325 (2006).
PubMed  PubMed Central  Article  Google Scholar 

17.
Telschow, A., Flor, M., Kobayashi, Y., Hammerstein, P. & Werren, J. H. Wolbachia-induced unidirectional cytoplasmic incompatibility and speciation: Mainland-island model. PLoS ONE 2, e701 (2007).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
Flor, M., Hammerstein, P. & Telschow, A. Wolbachia-induced unidirectional cytoplasmic incompatibility and the stability of infection polymorphism in parapatric host populations. J. Evol. Biol. 20, 696–706 (2007).
CAS  PubMed  Article  Google Scholar 

19.
Graham, R. I. & Wilson, K. Male-killing Wolbachia and mitochondrial selective sweep in a migratory African insect. BMC Evol. Biol. 12, 204 (2012).
PubMed  PubMed Central  Article  Google Scholar 

20.
Ahmed, M. Z., Breinholt, J. W. & Kawahara, A. Y. Evidence for common horizontal transmission of Wolbachia among butterflies and moths. BMC Evol. Biol. 16, 118 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

21.
Salunkhe, R. C., Narkhede, K. P. & Shouche, Y. S. Distribution and evolutionary impact of Wolbachia on butterfly hosts. Indian J. Microbiol. 54, 249 (2014).
PubMed  PubMed Central  Article  Google Scholar 

22.
Talavera, G., Lukhtanov, V. A., Pierce, N. E. & Vila, R. Establishing criteria for higher-level classification using molecular data: The systematics of Polyommatus blue butterflies (Lepidoptera, Lycaenidae). Cladistics 29, 166–192 (2013).
Article  Google Scholar 

23.
Espeland, M. et al. A Comprehensive and dated phylogenomic analysis of butterflies. Curr. Biol. 28, 770-778.e5 (2018).
CAS  PubMed  Article  Google Scholar 

24.
Dincă, V., Lee, K. M., Vila, R. & Mutanen, M. The conundrum of species delimitation: A genomic perspective on a mitogenetically super-variable butterfly. Proc. R. Soc. B Biol. Sci. 286, 20191311 (2019).
Article  CAS  Google Scholar 

25.
Gaunet, A. et al. Two consecutive Wolbachia-mediated mitochondrial introgressions obscure taxonomy in Palearctic swallowtail butterflies (Lepidoptera, Papilionidae). Zool. Scr. 48, 507–519 (2019).
Article  Google Scholar 

26.
Dinca, V., Zakharov, E. V., Hebert, P. D. N. & Vila, R. Complete DNA barcode reference library for a country’s butterfly fauna reveals high performance for temperate Europe. Proc. R. Soc. B Biol. Sci. 278, 347–355 (2011).
Article  Google Scholar 

27.
Ugelvig, L. V., Vila, R., Pierce, N. E. & Nash, D. R. A phylogenetic revision of the Glaucopsyche section (Lepidoptera: Lycaenidae), with special focus on the Phengaris-Maculinea clade. Mol. Phylogenet. Evol. 61, 237–243 (2011).
CAS  PubMed  Article  Google Scholar 

28.
Sañudo-Restrepo, C. P., Dincă, V., Talavera, G. & Vila, R. Biogeography and systematics of Aricia butterflies (Lepidoptera, Lycaenidae). Mol. Phylogenet. Evol. 66, 369–379 (2013).
PubMed  Article  Google Scholar 

29.
Todisco, V. et al. Molecular phylogeny of the Palaearctic butterfly genus Pseudophilotes (Lepidoptera: Lycaenidae) with focus on the Sardinian endemic P. barbagiae. BMC Zool. 3, 4 (2018).
Article  Google Scholar 

30.
Sucháčková Bartoňová, A., Beneš, J., Fric, Z. F. & Konvička, M. Genetic confirmation of Aricia artaxerxes (Fabricius, 1793) (Lepidoptera, Lycaenidae) in the Czech Republic, its conservation significance and biogeographic context. Nota Lepidopterol. 42(2), 163–176 (2019).
Article  Google Scholar 

31.
Kames, P. Die Aufklärung des Differenzierungsgrades und der Phylogenese der beiden Aricia-Arten agestis Den. et Schiff. und artaxerxes Fabr. (allous G.-Hb.) mit Hilfe von Eizuchten und Kreuzungsversuchen (Lep., Lycaenidae). Mitt. Entomol. Ges. Basel, N. F. 26, 7–13, 29–64 (1976).

32.
Korb, S., Faltynek Fric, Z. & Bartonova, A. On the status of Aricia cf. scythissa (Nekrutenko, 1985) (Lepidoptera: Lycaenidae) based on molecular investigations. Euroasian Entomol. J. 14, 237–240 (2015).
Google Scholar 

33.
Wiemers, M., Chazot, N., Wheat, C. W., Schweiger, O. & Wahlberg, N. A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938, 97–124 (2020).
PubMed  PubMed Central  Article  Google Scholar 

34.
Settele, J., Steiner, R., Reinhardt, R., Feldmann, R. & Hermann, G. Schmetterlinge: Die Tagfalter Deutschlands (Ulmer Eugen Verlag, Stuttgart, 2015).
Google Scholar 

35.
Monteiro, A. & Pierce, N. E. Phylogeny of Bicyclus (Lepidoptera: Nymphalidae) inferred from COI, COII, and EF-1alpha gene sequences. Mol. Phylogenet. Evol. 18, 264–281 (2001).
CAS  PubMed  Article  Google Scholar 

36.
Wahlberg, N. & Wheat, C. W. Genomic outposts serve the phylogenomic pioneers: Designing novel nuclear markers for genomic DNA extractions of lepidoptera. Syst. Biol. 57, 231–242 (2008).
CAS  PubMed  Article  Google Scholar 

37.
Braig, H. R., Zhou, W., Dobson, S. L. & O’Neill, S. L. Cloning and characterization of a gene encoding the major surface protein of the bacterial endosymbiont Wolbachia pipientis. J. Bacteriol. 180, 2373–2378 (1998).
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Sahoo, R. K. et al. Evolution of Hypolimnas butterflies (Nymphalidae): Out-of-Africa origin and Wolbachia-mediated introgression. Mol. Phylogenet. Evol. 123, 50–58 (2018).
PubMed  Article  Google Scholar 

39.
Baldo, L. et al. Multilocus sequence typing system for the endosymbiont Wolbachia pipientis. Appl. Environ. Microbiol. 72, 7098–7110 (2006).
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Kearse, M. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
PubMed  PubMed Central  Article  Google Scholar 

41.
Kajtoch, Ł et al. Using host species traits to understand the Wolbachia infection distribution across terrestrial beetles. Sci. Rep. 9, 847 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Ratnasingham, S. & Hebert, P. D. N. bold: The Barcode of Life Data System (http://www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364 (2007).

43.
Clement, M., Posada, D. & Crandall, K. A. TCS: A computer program to estimate gene genealogies. Mol. Ecol. 9, 1657–1659 (2000).
CAS  PubMed  Article  Google Scholar 

44.
Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).
Article  Google Scholar 

45.
Cheng, L., Connor, T. R., Sirén, J., Aanensen, D. M. & Corander, J. Hierarchical and spatially explicit clustering of DNA sequences with BAPS software. Mol. Biol. Evol. 30, 1224–1228 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Tonkin-Hill, G., Lees, J. A., Bentley, S. D., Frost, S. D. W. & Corander, J. RhierBAPS: An R implementation of the population clustering algorithm hierBAPS. Wellcome Open Res. 3 (2018).

47.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/ (2019).

48.
Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).
CAS  Article  Google Scholar 

49.
Dellicour, S. & Mardulyn, P. SPADS 1.0: A toolbox to perform spatial analyses on DNA sequence data sets. Mol. Ecol. Resour. 14, 647–651 (2014).
PubMed  Article  Google Scholar 

50.
Watson, D. F. & Philip, G. M. A Refinement of inverse distance weighted interpolation. Geoprocessing 2, 315–327 (1985).
Google Scholar 

51.
Manni, F., Guérard, E. & Heyer, E. Geographic patterns of (genetic, morphologic, linguistic) variation: How barriers can be detected by using Monmonier’s algorithm. Hum. Biol. 76, 173–190 (2004).
PubMed  Article  Google Scholar 

52.
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, 016 (2018).
Article  Google Scholar 

53.
Posada, D. jModelTest: Phylogenetic model averaging. Mol. Biol. Evol. 25, 1253–1256 (2008).
CAS  PubMed  Article  Google Scholar 

54.
Rambaut, A., Suchard, M. A., Xie, D. & Drummond, A. J. Tracer v1.6. http://tree.bio.ed.ac.uk/software/tracer/. (2014).

55.
Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).
CAS  PubMed  Article  Google Scholar 

56.
Miller, M. A., Pfeiffer, W. & Schwartz, T. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. in 2010 Gateway Computing Environments Workshop (GCE) 1–8 (2010).

57.
Aagaard, K. et al. Phylogenetic relationships in brown argus butterflies (Lepidoptera: Lycaenidae: Aricia) from northwestern Europe. Biol. J. Linn. Soc. 75, 27–37 (2002).
Article  Google Scholar 

58.
Hernández-Roldán, J. L. et al. Integrative analyses unveil speciation linked to host plant shift in Spialia butterflies. Mol. Ecol. 25, 4267–4284 (2016).
PubMed  Article  Google Scholar 

59.
Jiang, W. et al. Wolbachia infection status and genetic structure in natural populations of Polytremis nascens (Lepidoptera: Hesperiidae). Infect. Genet. Evol. 27, 202–211 (2014).
PubMed  Article  Google Scholar 

60.
Mallet, J., Wynne, I. R. & Thomas, C. D. Hybridisation and climate change: Brown argus butterflies in Britain (Polyommatus subgenus Aricia). Insect Conserv. Divers. 4, 192–199 (2011).
Article  Google Scholar 

61.
Gutzwiller, F., Dedeine, F., Kaiser, W., Giron, D. & Lopez-Vaamonde, C. Correlation between the green-island phenotype and Wolbachia infections during the evolutionary diversification of Gracillariidae leaf-mining moths. Ecol. Evol. 5, 4049–4062 (2015).
PubMed  PubMed Central  Article  Google Scholar 

62.
Mascarenhas, R. O., Prezotto, L. F., Perondini, A. L. P., Marino, C. L. & Selivon, D. Wolbachia in guilds of Anastrepha fruit flies (Tephritidae) and parasitoid wasps (Braconidae). Genet. Mol. Biol. 39, 600–610 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Sucháčková Bartoňová, A. et al. Recently lost connectivity in the Western Palaearctic steppes: The case of a scarce specialist butterfly. Conserv. Genet. 21, 561–575 (2020).
Article  Google Scholar 

64.
Schmitt, T. & Varga, Z. Extra-Mediterranean refugia: The rule and not the exception?. Front. Zool. 9, 1–12 (2012).
Article  Google Scholar 

65.
de Lattin, G. Grundriss der Zoogeographie (VEB Gustav Fischer Verlag, Jena, 1967).
Google Scholar 

66.
Hewitt, G. M. Some genetic consequences of ice ages, and their role in divergence and speciation. Biol. J. Linn. Soc. 58, 247–276 (1996).
Article  Google Scholar 

67.
Schmitt, T. Molecular biogeography of Europe: Pleistocene cycles and postglacial trends. Front. Zool. 4, 11 (2007).
PubMed  PubMed Central  Article  Google Scholar 

68.
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: The rear edge matters. Ecol. Lett. 8, 461–467 (2005).
PubMed  Article  Google Scholar 

69.
Heiser, M., Dapporto, L. & Schmitt, T. Coupling impoverishment analysis and partitioning of beta diversity allows a comprehensive description of Odonata biogeography in the Western Mediterranean. Org. Divers. Evol. 14, 203–214 (2014).
Article  Google Scholar 

70.
Vodă, R. et al. Historical and contemporary factors generate unique butterfly communities on islands. Sci. Rep. 6, 28828 (2016).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

71.
Scalercio, S. et al. How long is 3 km for a butterfly? Ecological constraints and functional traits explain high mitochondrial genetic diversity between Sicily and the Italian Peninsula. J. Anim. Ecol. 89, 2013–2026 (2020).
PubMed  Article  Google Scholar 

72.
Descimon, H. & Mallet, J. Bad species. in Ecology and Evolution of European Butterflies (Oxford University Press, Oxford, 2009).

73.
Habel, J. C., Schmitt, T. & Müller, P. The fourth paradigm pattern of post-glacial range expansion of European terrestrial species: The phylogeography of the Marbled White butterfly (Satyrinae, Lepidoptera). J. Biogeogr. 32, 1489–1497 (2005).
Article  Google Scholar 

74.
Varga, Z. Das Prinzip der areal-analytischen Methode in der Zoogeographie und die Faunenelement-Einteilung der europäischen Tagschmetterlinge (Lepidoptera: Diurna). Acta Biol. Debrecina 14, 223–285 (1977).
Google Scholar 

75.
Schmitt, T. & Zimmermann, M. To hybridize or not to hybridize: What separates two genetic lineages of the Chalk-hill Blue Polyommatus coridon (Lycaenidae, Lepidoptera) along their secondary contact zone throughout eastern Central Europe?. J. Zool. Syst. Evol. Res. 50, 106–115 (2012).
Article  Google Scholar 

76.
Janoušek, V. et al. Genome-wide architecture of reproductive isolation in a naturally occurring hybrid zone between Musmusculus musculus and M. m. domesticus. Mol. Ecol. 21, 3032–3047 (2012).
PubMed  Article  Google Scholar 

77.
Nürnberger, B., Lohse, K., Fijarczyk, A., Szymura, J. M. & Blaxter, M. L. Para-allopatry in hybridizing fire-bellied toads (Bombinabombina and B. variegata): Inference from transcriptome-wide coalescence analyses. Evolution 70, 1803–1818 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

78.
Vitali, F. & Schmitt, T. Ecological patterns strongly impact the biogeography of western Palaearctic longhorn beetles (Coleoptera: Cerambycoidea). Org. Divers. Evol. 17, 163–180 (2017).
Article  Google Scholar 

79.
Narita, S., Nomura, M., Kato, Y. & Fukatsu, T. Genetic structure of sibling butterfly species affected by Wolbachia infection sweep: Evolutionary and biogeographical implications. Mol. Ecol. 15, 1095–1108 (2006).
CAS  PubMed  Article  Google Scholar 

80.
Kageyama, S. et al. Feminizing Wolbachia endosymbiont disrupts maternal sex chromosome inheritance in a butterfly species. Evol. Lett. 1, 232–244 (2017).
PubMed  PubMed Central  Article  Google Scholar 

81.
Nosil, P., Harmon, L. J. & Seehausen, O. Ecological explanations for (incomplete) speciation. Trends Ecol. Evol. (Amst.) 24, 145–156 (2009).
Article  Google Scholar 

82.
Talavera, G., Lukhtanov, V. A., Rieppel, L., Pierce, N. E. & Vila, R. In the shadow of phylogenetic uncertainty: The recent diversification of Lysandra butterflies through chromosomal change. Mol. Phylogenet. Evol. 69, 469–478 (2013).
PubMed  Article  Google Scholar 

83.
Kühne, G., Kosuch, J., Hochkirch, A. & Schmitt, T. Extra-Mediterranean glacial refugia in a Mediterranean faunal element: The phylogeography of the chalk-hill blue Polyommatus coridon (Lepidoptera, Lycaenidae). Sci. Rep. 7, srep43533 (2017).
ADS  Article  Google Scholar 

84.
Wiemers, M., Keller, A. & Wolf, M. ITS2 secondary structure improves phylogeny estimation in a radiation of blue butterflies of the subgenus Agrodiaetus (Lepidoptera: Lycaenidae: Polyommatus). BMC Evol. Biol. 9, 300 (2009).
PubMed  PubMed Central  Article  CAS  Google Scholar 

85.
Duron, O. & Hurst, G. D. Arthropods and inherited bacteria: From counting the symbionts to understanding how symbionts count. BMC Biol. 11, 45 (2013).
PubMed  PubMed Central  Article  Google Scholar 

86.
Bailly-Bechet, M. et al. How long does Wolbachia remain on board?. Mol. Biol. Evol. 34, 1183–1193 (2017).
CAS  PubMed  Article  Google Scholar  More

Relative species abundance distribution
In order to evaluate sample quality, we analysed both the Relative Species Abundance distribution (RSA) and the rarefaction curves. For each sample, we compared the RSA derived by shotgun and 16S sequencing. RSA histograms in logarithmic scale show that the distributions obtained by shotgun and 16S have similar shape at phylum level (Fig. 1a, b). In Fig. 1b, the 16S sample is characterized by a more patchy distribution, having identified less phyla. At phylum level, both strategies produce positively skewed samples in the log2-transformed distributions, except for 16S outliers, because none of the phyla is significantly rare (Fig. 2a).
Figure 1

RSA histograms in logarithmic scale (Preston plots 21) of bacterial abundances in one sample selected as anexample (caeca25): (a) genera sampled by shotgun sequencing, (b) genera sampled by 16S rRNA sequencing, (c) phyla sampled by shotgun sequencing and (d) phyla sampled by 16S sequencing.

Full size image

Figure 2

Box plot of the RSA skewness of bacterial communities at (a) phylum level and (b) genus level. Bacterial communities are sampled with (left) shotgun sequencing and (right) 16S sequencing.

Full size image

On the other hand, at genus level, the two strategies display different shapes (Fig. 1c, d, Supplementary Fig. S1, S2, S3, S4). Indeed, the log2-transformed distributions derived by shotgun sequencing generally have a skewness closer to zero compared to those obtained by 16S, i.e. are more symmetrical (Figure 2b): a paired Student’s t-test on the skewness shows a significant difference between them (P = 8·10–6). This indicates that shotgun samples are characterized by a higher sampling size. According to Preston, left-skewed shapes of the RSA can be explained as artefacts of small sample size21,22, since insufficient sampling of the original space produces a truncation of the left tail of the RSA, increasing its skewness.
In shotgun samples, the RSA skewness at genus level is related to the total number of reads (Supplementary Fig. S5): the shotgun samples with the lowest total number of reads have the largest skewness. Specifically, Supplementary Figure S5 shows that shotgun samples cluster in two groups, one characterized by a low number of reads (# reads  500,000, 50/78 samples) and a less skewed RSA.
Noticeably, the high-skewness group includes all 9 samples from 1st day, all 15 crop samples from 14th day and 4 out of 18 crop samples from 35th day. The samples collected at day 1 were very poor in terms of biomass and the crop samples contained more feed residues than caecal samples, making the DNA extraction less efficient both in terms of DNA quantity and quality. For the comparative analysis we removed samples with less than 500,000 reads being characterized by a low quality. This choice was corroborated by the analysis of the rarefaction curves, showing that shotgun samples with less than 500,000 reads do not reach a plateau in terms of identified genera (Supplementary Fig. S6). All the 50 samples included in the comparative analysis have a total number of reads  > 500,000 and a skewness lower than the median of 16S samples, indicating a good sampling depth. Since included samples were characterized by a high microbial load, we are confident to extend the results of the following analyses only to samples with few contaminant DNA and low cross-contaminations. Nonetheless, we have shown that shotgun samples have a RSA similar to 16S samples when a low number of total reads is available, thus hypothesizing that in differential analyses carried on samples with a low microbial load regime, shotgun sequencing could perform similarly to 16S sequencing or even worse.
For a balanced comparison, also 16S samples corresponding to the discarded shotgun samples were removed.
Differential analysis for the experimental conditions
Since in many situations a metagenomic analysis is used to discriminate between different experimental conditions, we compared the results of differential analysis performed on reads obtained by the two strategies. To this aim, we analysed the fold changes of genera abundances between compartments of the GI tract and between sampling times (Fig. 3 for caeca vs crop, Supplementary Fig. S7 for 14th vs. 35th day) common to both sequencing strategies (288 genera for caeca vs crop, and 246 for 14th vs. 45th day). Comparing the genera abundances between caeca and crop, 16S identified 108 statistically significant differences (adjusted P  More

1.
Tajika, E. Climate change during the last 150 million years: Reconstructing from a carbon cycle model. Earth Planet Sci. Lett. 160, 659–707 (1998).
Article  Google Scholar 
2.
Li, H.-L. et al. Large-scale phylogenetic analyses reveal multiple gains of actinorhizal nitrogen-fixing symbioses in angiosperms associated with climate change. Sci. Rep. 5, 14023 (2015).
ADS  PubMed  PubMed Central  Article  Google Scholar 

3.
van Velzen, R., Doyle, J. J. & Geurts, R. A resurrected scenario: Single gain and massive loss of nitrogen-fixing nodulation. Trends Plant Sci. 24, 49–57 (2019).
PubMed  Article  CAS  PubMed Central  Google Scholar 

4.
Griesmann, M. et al. Phylogenomics reveals multiple losses of nitrogen-fixing root nodule symbiosis. Science 361, eaat1743 (2018).
PubMed  Article  CAS  PubMed Central  Google Scholar 

5.
Menge, D. N. L. & Crews, T. E. Can evolutionary constraints explain the rarity of nitrogen-fixing trees in high-latitude forests?. New Phytol. 211, 1195–1201 (2016).
PubMed  Article  PubMed Central  Google Scholar 

6.
Menge, D. N. L. et al. Nitrogen-fixing tree abundance in higher-latitude North America is not constrained by diversity. Ecol. Lett. 20, 842–851 (2017).
ADS  PubMed  Article  PubMed Central  Google Scholar 

7.
Tedersoo, L. et al. Global database of plants with root-symbiotic nitrogen fixation: NodDB. J. Veg. Sci. 29, 560–568 (2018).
Article  Google Scholar 

8.
Swensen, S. M. & Benson, D. R. Evolution of actinorhizal host plants and Frankia endosymbionts. In Nitrogen-fixing Actinorhizal Symbioses, 73–104 (Springer Netherlands, 2008).

9.
Houlton, B. Z., Wang, Y.-P., Vitousek, P. M. & Field, C. B. A unifying framework for dinitrogen fixation in the terrestrial biosphere. Nature 454, 327–330 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Luo, Y. et al. Progressive nitrogen limitation of ecosystem responses to rising atmospheric carbon dioxide. Bioscience 54, 731 (2004).
Article  Google Scholar 

11.
Cheng, W., Inubushi, K., Yagi, K., Sakai, H. & Kobayashi, K. Effects of elevated carbon dioxide concentration on biological nitrogen fixation, nitrogen mineralization and carbon decomposition in submerged rice soil. Biol. Fertil. Soils. 34, 7–13 (2001).
CAS  Article  Google Scholar 

12.
Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2004).
Article  Google Scholar 

13.
Pawlowski, K. & Newton, W. E. Nitrogen-fixing Actinorhizal Symbioses. (Springer Netherlands, 2008). https://doi.org/10.1007/978-1-4020-3547-0.

14.
Dentener, F. et al. Nitrogen and sulfur deposition on regional and global scales: A multimodel evaluation. Glob. Biogeochem. Cycles. 20, GB4003 (2006).
ADS  Article  CAS  Google Scholar 

15.
Vitousek, P. M. et al. Human alteration of the global nitrogen cycle: Sources and consequences. Ecol. Appl. 7, 737–750 (1997).
Google Scholar 

16.
Benjamin, W. S. et al. Spatially robust estimates of biological nitrogen (N) fixation imply substantial human alteration of the tropical N cycle. PNAS 111, 8101–8106 (2014).
Article  CAS  Google Scholar 

17.
Li, X. et al. Seasonal and spatial variations of bulk nitrogen deposition and the impacts on the carbon cycle in the arid/semiarid grassland of inner Mongolia, China. PLoS ONE 10, e0144689 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
Lamarque, J. F. et al. Assessing future nitrogen deposition and carbon cycle feedback using a multimodel approach: Analysis of nitrogen deposition. J. Geophys. Res. Atmos. 110, D19303 (2005).
ADS  Article  Google Scholar 

19.
Tian, D. & Niu, S. A global analysis of soil acidification caused by nitrogen addition. Environ. Res. Lett. 10, 24019 (2015).
Article  CAS  Google Scholar 

20.
Binkley, D. & Högberg, P. Tamm review: Revisiting the influence of nitrogen deposition on Swedish forests. For. Ecol. Manag. 368, 222–239 (2016).
Article  Google Scholar 

21.
Lee, J. A. Unintentional experiments with terrestrial ecosystems: Ecological effects of sulphur and nitrogen pollutants. J. Ecol. 86, 1–12 (1998).
CAS  Article  Google Scholar 

22.
Southon, G. E., Field, C., Caporn, S. J. M., Britton, A. J. & Power, S. A. Nitrogen deposition reduces plant diversity and alters ecosystem functioning: Field-scale evidence from a nationwide survey of UK heathlands. PLoS ONE 8, e59031 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Vitousek, P. M., Menge, D. N. L., Reed, S. C. & Cleveland, C. C. Biological nitrogen fixation: Rates, patterns and ecological controls in terrestrial ecosystems. Philos. Trans. R. Soc. B. 368, 20130119 (2013).
Article  CAS  Google Scholar 

24.
Skogen, K. A., Holsinger, K. E. & Cardon, Z. G. Nitrogen deposition, competition and the decline of a regionally threatened legume, Desmodium cuspidatum. Oecologia 165, 261–269 (2011).
ADS  PubMed  Article  PubMed Central  Google Scholar 

25.
Salvagiotti, F. et al. Growth and nitrogen fixation in high-yielding soybean: Impact of nitrogen fertilization. Agron J. 101, 958–970 (2009).
CAS  Article  Google Scholar 

26.
Markham, J. H. & Zekveld, C. Nitrogen fixation makes biomass allocation to roots independent of soil nitrogen supply. Can. J. Bot. 85, 787–793 (2007).
CAS  Article  Google Scholar 

27.
Coley, P. D. Possible effects of climate change on plant/herbivore interactions in moist tropical forests. Clim. Change. 39, 455–472 (1998).
Article  Google Scholar 

28.
Coley, P. D., Massa, M., Lovelock, C. E. & Winter, K. Effects of elevated CO2 on foliar chemistry of saplings of nine species of tropical tree. Oecologia 133, 62–69 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Roger, M. G., Damian, J. B. & Jason, L. L. The effects of elevated [CO2] on the C:N and C:P mass ratios of plant tissues. Plant Soil 224, 1–14 (2000).
Article  Google Scholar 

30.
McLauchlan, K. K., Williams, J. J., Craine, J. M. & Jeffers, E. S. Changes in global nitrogen cycling during the Holocene epoch. Nature 495, 352–355 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Hossain, M. A., Ishimine, Y., Akamine, H. & Kuramochi, H. Effect of nitrogen fertilizer application on growth, biomass production and N-uptake of torpedograss (Panicum repens L.). Weed Biol. Manag. 4, 86–94 (2004).
CAS  Article  Google Scholar 

32.
Thomas, R. B., Bashkin, M. A. & Richter, D. D. Nitrogen inhibition of nodulation and N2 fixation of a tropical N2-fixing tree (Gliricidia sepium) grown in elevated atmospheric CO2. New Phytol. 145, 233–243 (2000).
CAS  Article  Google Scholar 

33.
Dordas, C. A. & Sioulas, C. Safflower yield, chlorophyll content, photosynthesis, and water use efficiency response to nitrogen fertilization under rainfed conditions. Ind. Crops Prod. 27, 75–85 (2008).
CAS  Article  Google Scholar 

34.
Xu, D. et al. Interactive effects of nitrogen and silicon addition on growth of five common plant species and structure of plant community in alpine meadow. CATENA 169, 80–89 (2018).
CAS  Article  Google Scholar 

35.
Roy, A. & Bousquet, J. The evolution of the actinorhizal symbiosis through phylogenetic analysis of host plants. Acta Bot. Gall 143, 635–650 (1996).
Article  Google Scholar 

36.
Swensen, S. M. The evolution of actinorhizal symbioses: Evidence for multiple origins of the symbiotic association. Am. J. Bot. 83, 1503–1512 (1996).
Article  Google Scholar 

37.
van Velzen, R. et al. Comparative genomics of the nonlegume Parasponia reveals insights into evolution of nitrogen-fixing rhizobium symbioses. Proc. Natl. Acad. Sci. 115, E4700–E4709 (2018).
PubMed  Article  CAS  PubMed Central  Google Scholar 

38.
Rogers, A., Ainsworth, E. A. & Leakey, A. D. B. Will elevated carbon dioxide concentration amplify the benefits of nitrogen fixation in legumes?. Plant Physiol. 151, 1009–1016 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
DeLuca, T. H., Zackrisson, O., Gundale, M. J. & Nilsson, M. C. Ecosystem feedbacks and nitrogen fixation in boreal forests. Science 320, 1181 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Zheng, M., Zhou, Z., Luo, Y., Zhao, P. & Mo, J. Global pattern and controls of biological nitrogen fixation under nutrient enrichment: A meta-analysis. Glob. Change Biol. 25, 3018–3030 (2019).
ADS  Article  Google Scholar 

41.
Fisher, J. B. et al. Carbon cost of plant nitrogen acquisition: A mechanistic, globally applicable model of plant nitrogen uptake, retranslocation, and fixation. Glob. Biogeochem. Cycles. 24, GB1014 (2010).
ADS  Article  CAS  Google Scholar 

42.
Gentili, F., Wall, L. G. & Huss-Danell, K. Effects of phosphorus and nitrogen on nodulation are seen already at the stage of early cortical cell divisions in Alnus incana. Ann. Bot. 98, 309–315 (2006).
PubMed  PubMed Central  Article  Google Scholar 

43.
Chen, H. & Markham, J. Using microcontrollers and sensors to build an inexpensive CO2 control system for growth chambers. Appl. Plant Sci. 8, e11393 (2020).
PubMed  PubMed Central  Article  Google Scholar 

44.
Werner, G. D. A., Cornwell, W. K., Sprent, J. I., Kattge, J. & Kiers, E. T. A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nat. Commun. 5, 4087 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Chen, H., Renault, S. & Markham, J. The effect of Frankia and multiple ectomycorrhizal fungil species on Alnus growing in low fertility soil. Symbiosis. 80, 207–215 (2020).
CAS  Article  Google Scholar 

46.
Noridge, N. A. & Benson, D. R. Isolation and nitrogen-fixing activity of Frankia sp. strain CpI1 vesicles. J. Bacteriol. 166, 301–305 (1986).
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Markham, J. H. Does Dryas integrifolia fix nitrogen?. Botany. 87, 1106–1109 (2009).
CAS  Article  Google Scholar  More